Towards Design of Sustainable Energy Systems in Developing Countries: Centralized and Localized Options
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Berrin Kursun
Graduate Program in Chemical and Biomolecular Engineering
The Ohio State University
2013
Dissertation Committee:
Dr. Bhavik R. Bakshi, Adviser
Dr. David L. Tomasko
Dr. Barbara Wyslouzil
Copyright by
Berrin Kursun
2013
Abstract
Energy use in developing countries is projected to equal and exceed the demand in developed countries in the next five years. Growing concern about environmental problems, depletion and price fluctuation of fossil fuels pushes the efforts for meeting energy demand in an environmentally friendly and sustainable way. Hence, it is essential to design energy systems consisting of centralized and localized options that generate the optimum energy mix to meet this increasing energy demand in a sustainable manner.
In this study, we try to answer the question, “How can the energy demand in
Rampura village be met sustainably?” via two centralized clean coal (CCC) technology and three localized energy technology options analyzed. We perform the analysis of these energy technologies through joint use of donor-side analysis technique emergy analysis
(EA) and user-side analysis technique life cycle assessment (LCA). Sustainability of such an energy combination depends on its reliance on renewable inputs rather than non- renewable or purchased inputs.
CCC technologies are unsustainable energy systems dependent on purchased external inputs almost 100%. However, increased efficiency and significantly lower environmental impacts of CCC technologies can lead to more environmentally benign utilization of coal as an energy source. CCC technologies supply electricity at a lower price compared to the localized energy options investigated.
ii
Localized energy options analyzed include multi-crystalline solar PV, floating drum biogas digester and downdraft biomass gasifier. Solar PV has the lowest water and land use, however, solar electricity has the highest price with a high global warming potential (GWP). Contrary to general opinion, solar electricity is highly non-renewable.
Although solar energy is a 100% renewable natural resource, materials utilized in the production of solar panels are mostly non-renewable purchased inputs causing the low renewability of solar electricity.
Best sustainability results are obtained for full capacity operation in anaerobic digestion and for single fuel mode (SFM) operation in biomass gasification. For both of the processes, cost of electricity reduces 2-3 times if they are operated properly.
However, there is not enough ipomea to run the biomass gasifier in SFM in Rampura, hence optimum operation scheme is ideal dual fuel mode (DFM) operation for the biomass gasifier analyzed.
Emergy analysis of Rampura village and its subsystems reveal that sustainability is not achieved both at the village and in the subsystems levels since they are highly dependent on non-renewable material and energy inputs. To improve the overall sustainability in Rampura, dependency on purchased inputs fodder, fertilizer and diesel, non-renewable cooking fuel wood should be reduced.
In satisfying energy demand in Rampura, biogas cooking and 70% biogas cooking scenarios perform better than electricity options in all of the objectives considered. Other than minimum land and water use objectives, electricity-RM and electricity-GM scenarios overlap and do not have a significant difference in terms of performance.
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Based on these results, the best option to meet the energy demand in Rampura would be to meet all the cooking energy with direct use of biogas. However, 70% biogas cooking scenario may be a more practical option since it both satisfies energy demand in an environmentally benign manner and satisfies the cultural needs of Rampura people.
When 30% of cooking is performed by utilizing improved biomass cook stoves in the traditional way, the biogas potential becomes enough to meet all the remaining energy demand (70% of cooking, lighting and irrigation) in Rampura, hence energy security and reliability are ensured. Furthermore, utilizing biogas for cooking enables more agricultural residues to be available as fodder and eases the pressure on environment due to excessive woody biomass harvesting. Additionally, CH4 emissions from cow dung are avoided via production of biogas while the sanitation improves in the area. The GHG emissions related to cooking with inefficient cook stoves are also significantly mitigated through the use of biogas and improved biomass cook stoves.
Energy demand in developing countries is subject to increase with increasing prosperity and consumerism. This increasing energy demand will necessitate the utilization of centralized energy options even in the rural areas of developing countries in the near future. Utilizing centralized clean coal technologies to meet this demand can ease energy related environmental problems, especially global warming significantly.
And, adopting conscious and renewable energy oriented consumption patterns, avoiding consumption beyond the carrying capacity of these regions can contribute to achieve global level sustainability and ease the environmental burdens and problems in the developing countries.
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Dedicated to Dr. Faik Tanman, my dear father and my guide in life.
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Acknowledgments
I am in debt to many people in achieving and completing my studies at The Ohio State
University. First of all, I would like to express my gratitude to my adviser Dr. Bhavik R.
Bakshi for his constant support and encouragement through the years. Especially, I would like to thank Dr. Bakshi for his precious guidance during our studies in India. Working in
India was an once-in-a-lifetime experience for me. I feel privileged to work with Dr.
Bakshi during these for four years. I would like to present my special thanks to Dr. Jay F.
Martin for sharing his guidance and expertise in emergy analysis of energy modules and
Rampura village, guiding and teaching me almost like a co-adviser. I would like to express my sincere gratitude to Development Alternatives employees, especially Mr.
Manoj Mahata and Ms. Shivani Mathur for their helps and supports during data collection in Rampura and when I perform calculation. I would like to also thank Dr. Liang-Shih
Fan and Dr. Shwetha Ramkumar for providing the process data for clean coal technologies and their support during the study. I would like to thank my committee members Dr. Barbara Wyslouzil and Dr. David Tomasko for their time and support.
I would like to thank Dr. Nathan Cruze for his support and sharing his experiences while
I was writing my thesis. I would like to thank all of my group members for the time and joy we shared over the years.
Most importantly, I would like to thank my family for their love and support throughout my life. It is good to be going back to them. vi
Last but not least, I would like to thank Dr. Faik Tanman, my dear father and guide in life. Nothing I achieved would be possible without his trust in me. I thank him for being the first person in believing my dreams and make me believe that I can realize them.
I sincerely appreciate all these people and feel lucky to have them in my life!
Berrin Kursun
Columbus, OH
May, 2013.
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Vita
June 1999 ...... Adile Mermerci Anatolian High School
June 2003 ...... B.S. Chemical Engineering Istanbul
University, Istanbul, Turkey.
July 2005 ...... M.S. Bioengineering, Yildiz Technical
University, Istanbul, Turkey.
September 2009 to present ...... Graduate Research Associate, Chemical and
Biomolecular Engineering Department,
The Ohio State University
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Publications
Kursun B. Copper Complexes of Polyamides and Their Interactions with Proteins.
Yildiz Technical University, Istanbul. 2005.
Kursun B, Ramkumar S, Bakshi BR, Fan LS. Coal gasification by conventional versus calcium looping process- A life cycle energy, global warming and water assessment. Proc. 6th International Conference on Industrial Ecology. Berkeley, CA
(2011).
Kursun B, Bakshi BR. Sustainability assessment of centralized and decentralized energy solutions. Proc.12th AIChE Meeting Pittsburg, PA (2012).
Fields of Study
Major Field: Chemical and Biomolecular Engineering
ix
Table of Contents
Abstract ...... ii
Acknowledgments...... vi
Vita ...... viii
Publications ...... ix
Fields of Study ...... ix
Table of Contents ...... x
List of Tables ...... xvi
List of Figures ...... xxi
Chapter 1: Introduction ...... 1
1.1 Motivation and Contribution ...... 1
1.2 Project Site Rampura Village and Development Alternatives ...... 8
1.3 Organization of the Dissertation ...... 9
Chapter 2: Centralized and Localized Energy Options...... 12
2.1. Background ...... 12
2.2 Centralized Energy Options ...... 15
2.2.1 Clean Coal Technologies ...... 15
x
2.2.2 CO2 Sequestration and CO2 Capture Techniques ...... 17
2.2.3 Conventional Process ...... 22
2.2.4 Calcium Looping Process (CLP) ...... 25
2.3. Localized Energy Options ...... 29
2.3.1 Solar Photovoltaics (PV) ...... 29
2.3.2 Biogas Digesters ...... 33
2.3.3. Biomass Gasifiers ...... 40
Chapter 3: Methodology ...... 47
3.1 Sustainability ...... 47
3.2.1 Life Cycle Assessment Framework ...... 50
3.2.2 Boundary Selection and Models Utilized in LCA ...... 53
3.2.3 Allocation in LCA ...... 56
3.2.4 Net Energy Analysis ...... 56
3.2.5 Environmental Impacts ...... 57
3.3 Emergy Analysis ...... 58
3.3.1 Fundamentals of Emergy Analysis ...... 58
3.3.2 Emergy Analysis Procedure ...... 61
3.3.3 Emergy Algebra ...... 66
3.3.4 Applications of Emergy Analysis ...... 69
xi
3.4 Joint Use of LCA and Emergy Analysis ...... 70
3.4.1 Underlying assumptions ...... 71
3.4.2 Methods ...... 72
3.4.3 Analysis Results ...... 73
3.4.4 Strengths and Weaknesses ...... 74
3.4.5 Application ...... 75
Chapter 4: Analysis of Centralized Energy Options ...... 78
4.3.1. Life Cycle Assessment ...... 90
4.3.2 Land Use ...... 99
4.3.3 Water Use ...... 100
4.3.4 Global Warming Potential: ...... 102
4.3.5 Energy Return on Investment (EROI) and Energy Utilized ...... 104
4.3.6 Allocation ...... 108
4.3.7 Sensitivity Analysis ...... 110
Chapter 5: Analysis of Localized Energy Options ...... 127
5.1. Multi-crystalline Solar PV Plant ...... 128
5.1.1 Emergy Analysis of Solar PV ...... 129
5.1.2 Solar Life Cycle Assessment ...... 136
5.1.3 Economic Analysis of Solar Electricity Generation ...... 141
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5.2. Biogas Digester ...... 142
5.2.1 Emergy Analysis of Biogas Digester...... 143
5.2.2 Life Cycle Assessment of Electricity Generation via Biogas Digestion ...... 162
5.2.3 Economic Analysis of Electricity Generation from Biogas ...... 167
5.3 Biomass Gasification...... 169
5.3.1 Emergy Analysis of Biomass Gasifier...... 170
5.3.2 Life Cycle Assessment of Electricity Generation via Biomass Gasification . 188
5.3.3 Economic Analysis of Electricity Generation from Producer Gas ...... 193
Chapter 6: Emergy Analysis Results of Rampura Village ...... 196
6.1 Development Alternatives and Rampura Village...... 196
6.2. Village Level Emergy Analysis ...... 199
6.3 Emergy Analysis of Husbandry Sector ...... 207
6.4. Emergy Analysis of Agricultural Sector ...... 211
6.5 Emergy Analysis of Domestic Sector ...... 216
Chapter 7: Supplying Energy in Rampura with Different Energy Options ...... 221
7.1.1 Irrigation Energy Requirements ...... 224
7.1.2 Lighting Energy Requirements ...... 225
7.1.3 Cooking and Heating Energy Requirements ...... 226
7.2.1 Solar Electricity Potential ...... 230
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7.2.2 Biogas and Electricity Potential ...... 231
7.2.3 Biomass Electricity Potential...... 232
7.4.1 Scenarios ...... 235
7.4.2 Scenario 1: All Electrical Energy, Ground Mounted Solar Panels ...... 237
7.4.3 Scenario 2: All Electrical Energy, Rooftop Mounted Solar Panels ...... 244
7.4.4 Scenario 3: All Biogas Energy for Cooking ...... 249
7.4.5 Scenario 4: 70% Biogas and 30% Traditional Biomass Energy for Cooking, 255
7.4.6 Greenhouse Gas (GHG) Mitigation ...... 261
Chapter 8: Conclusions and Future Work ...... 264
8.1 Conclusions ...... 264
8.2 Future Work ...... 273
Appendix A: Supporting Information for Clean Coal Technologies ...... 276
A.1 Assumptions ...... 277
A.1.1 Conventional Process: ...... 277
A.1.2 Calcium Looping Process (CLP): ...... 278
A.2 Raw Data and Calculations: ...... 278
A.2.1 Raw Data ...... 278
A.2.2 Calculation Details for Each Analysis Scale ...... 280
Appendix B: Rampura Village...... 291
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References ...... 294
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List of Tables
Table 3.1: A generic emergy evaluation table...... 64
Table 4.1: Economic data for conventional process: Activities, related sectors and monetary data (NETL, 2008)……………………………………………………………84
Table 4.2: Economic data for CLP: Activities, related sectors and monetary data
(Ramkumar, 2010)...... 85
Table 4.3: Emergy evaluation table for conventional process...... 86
Table 4.4: Emergy evaluation table for CLP ...... 87
Table 4.5: Product transformities, total emergy yield, emergy ratio and indices for conventional and CL processes...... 89
Table 4.6: Equipment, process LCA and economy scale GWP results for CLP and conventional process ...... 103
Table 4.7: Cost of electricity generation via calcium looping process...... 125
Table 4 8: Cost of electricity generation via conventional process...... 126
Table 5.1: Emergy evaluation table for solar PV...... 132
Table 5 2: Transformity, yield, emergy ratio and indices values for solar electricity. ... 132
Table 5.3: Breakdown of solar electricity cost per kWh generated...... 142
Table 5 4: Emergy evaluation table of biogas production phase for current case...... 145
Table 5.5: Transformity, yield and emergy ratio values of biogas production phase for different scenarios considered...... 147
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Table 5.6: Emergy evaluation table of electricity generation from biogas for current case.
...... 154
Table 5.7: Transformity, yield and emergy ratio values of electricity from biogas for different scenarios considered...... 156
Table 5.8: Cost of biogas electricity for different scenarios with changing manure cost.
...... 168
Table 5.9: Emergy evaluation table for producer gas production from ipomea ...... 173
Table 5 10: Transformity, total emergy yield and emergy ratios for producer gas production...... 175
Table 5.11: Emergy evaluation table of electricity generation from producer gas for the current case ...... 181
Table 5.12: Electricity transformity, total emergy yield and emergy ratio values for electricity generation from producer gas...... 183
Table 5.13: Cost of electricity from producer gas under different scenarios considered.
...... 194
Table 6.1: Population of Rampura village...... 197
Table 6.2: Energy use in Rampura village annually...... 197
Table 6.3: Land use in Rampura Village ...... 198
Table 6.4: Agricultural products grown in Rampura and their annual quantity...... 199
Table 6.5: Number of animals available in Rampura and annual milk production...... 199
Table 6.6: Emergy evaluation table for village level emergy analysis of Rampura...... 202
Table 6.7: Emergy ratios and indices for Rampura village ...... 205
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Table 6.8: Sensitivity analysis results for cases where fodder is 20% Re and 40% Re. . 206
Table 6.9: Emergy evaluation table for husbandry sector in Rampura village...... 209
Table 6.10: Emergy indicators for current case of husbandry sector and their variation due to change in renewability of fodder input...... 211
Table 6.11: Emergy evaluation for agricultural sector in Rampura...... 213
Table 6.12: Emergy ratios and indices for agricultural sector in Rampura...... 215
Table 6.13: Sensitivity analysis for agricultural sector in Rampura...... 216
Table 6.14: Emergy evaluation table for domestic sector in Rampura...... 218
Table 6.15: Emergy indicators for current case of domestic sector and their change due to change in renewability of fodder...... 220
Table 7 1: Energy use in Rampura village annually...... 224
Table 7 2: Annual energy demand in Rampura in kWhe equivalents ...... 227
Table 7 3: Annual energy demand in Rampura with direct use of biogas for cooking and heating...... 228
Table 7 4: Annual energy demand in Rampura in kWhe equivalents for direct use of biogas for cooking and heating...... 229
Table 7.5: Biogas and biogas electricity potential in Rampura village...... 231
Table 7.6: Potential of electricity via biomass gasification in Rampura...... 232
Table 7.7: Rampura linear programming problem constants...... 235
Table 7.8: Energy demand and potential in kWhe equivalents in Rampura village...... 236
Table 7.9: Cooking energy use and related GHG emission currently in Rampura...... 262
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Table 7.10: Cooking related GHG emissions using different cook stoves and fuels.
Adapted from Bhattacharya and Salam, 2002...... 262
Table 7.11: GHG emissions from biogas use for cooking in scenario 3...... 263
Table 7:12: GHG emissions from biogas and improved biomass cook stove use for cooking in scenario 4 ...... 263
Table 8.1: Linear programming results for energy combinations to meet the energy demand in Rampura under different scenarios considered. 269
Table A.1: Conventional process raw data: Inputs, outputs and unit prices (NREL, 2008).
...... 279
Table A.2: CLP raw data: Inputs, outputs and unit prices (Ramkumar, 2010)...... 280
Table A.3: Breakdown of equipment scale water make-up in CLP (Ramkumar, 2010). 281
Table A.4: Breakdown of equipment scale water make-up in conventional process
(NETL, 2008)...... 281
Table A.5: Breakdown of equipment scale energy use and electricity production in CLP
(Ramkumar, 2010)...... 282
Table A.6: Breakdown of equipment scale energy use and electricity generation in conventional process (NETL, 2008)...... 283
Table A.7:GWP of conventional process in process LCA scale ...... 285
Table A.8: GWP of CLP at process LCA scale (Ramkumar, 2010 and NREL, 2012). . 286
Table A.9: Energy use of conventional process in process LCA scale (NREL, 2012 and
Ramkumar, 2010)...... 287
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Table A.10: Energy use of CLP at process LCA scale (NREL, 2012 and Ramkumar,
2010)...... 288
Table A:11: Economy scale data for CLP: Life cycle steps, related sectors and monetary data (Ramkumar, 2010 and OSU, 2013)...... 289
Table A 12: Economy scale data for conventional process: Life cycle steps, related sectors and monetary data (NETL, 2008 and OSU, 2013)...... 290
Table B.1: Agricultural crop yields per year in Rampura (Development Alternatives,
2011). 292
Table B.2: Agricultural residue amounts per year in Rampura (Development Alternatives,
2011)...... 292
Table B.3: Fodder consumption and manure production per day by different types of animals (Development Alternatives, 2011)...... 293
Table B.4: Year around irrigation data in Rampura: Ground water and diesel consumption (Development Alternatives, 2011)...... 293
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List of Figures
Figure 1.1: Energy consumption in USA, China and India: History and projections (EIA,
2011)...... 2
Figure 1.2: Geographical location of India in the world and location of Jhansi district in
India...... 9
Figure 2.1: Simplified box diagram of conventional process (NETL, 2008)...... 24
Figure 2 2: CLP reactions (Ramkumar and Fan, 2010)...... 27
Figure 2.3: 8.7 kWp capacity mc-Si solar PV in Rampura village...... 33
Figure 2.4: Different types of biogas digesters: 1. Floating drum, 2. Fixed dome, 3.
Balloon plant (Kossmann et al, 1997(a))...... 35
Figure 2.5: Reaction pathways for anaerobic digestion of manure (Husain, 1997)...... 37
Figure 2.6: Floating drum biogas digester in cowshed in Jhansi and 7.5 VA electricity generator...... 39
Figure 2.7: Different biomass gasifier types (APL (a))...... 41
Figure 2.8: Process steps in biomass gasification (APL(b))...... 43
Figure 2.9: Ready to use ipomea and its storage in Orchha campus of Development
Alternatives...... 45
Figure 3.1: LCA framework and its applications (ISO, 2006)...... 51
Figure 3.2: Linear algebraic representation of input-output model...... 55
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Figure 3.3: Transformation of sun energy in nature (Adapted from Odum, 1996 and Hau and Bakshi, 2004)...... 60
Figure 3.4: Energy systems diagramming symbols (UFCEP, 2013)...... 63
Figure 3.5: Classification of emergy flows to a system which is the basis for calculation of emergy ratios (Ulgiati and Brown, 1998)...... 65
Figure 3.6: Partitioning of total emergy among co-products and splits (Herendeen , 2004).
...... 68
Figure 3.7: Integration of emergy analysis and LCA (Rugani and Benetto, 2012). 76
Figure 4.1: Conventional process simplified flow chart (Ramkumar, 2010)...... 80
Figure 4.2: CLP simplified flow chart (Ramkumar, 2010)...... 81
Figure 4.3: Energy system diagrams for conventional and CL processes...... 83
Figure 4 4: Emergy signature diagram of conventional and CL processes...... 88
Figure 4.5: Process LCA scale boundary for calcium looping process...... 92
Figure 4.6: Process LCA scale boundary for conventional process...... 93
Figure 4.7: Economy scale boundaries for calcium looping and conventional processes. 95
Figure 4.8: Life cycle land uses (m2/kWhe) for CLP and conventional process at economy scale...... 100
Figure 4.9: Equipment scale and economy scale water uses (liter/kWhe) for CLP and conventional processes...... 102
Figure 4.10: Global warming potentials (gCO2 eq. /kWhe) for CLP and conventional process at economy scale...... 103
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Figure 4 11: Global warming potentials (gCO2 eq. /kWhe) for CLP and conventional process at process LCA scale...... 104
Figure 4 12: Energy utilized per kWh for CLP and conventional processes at economy (a) and process LCA (b) scales...... 106
Figure 4 13: Energy return on investment (EROI) results for CLP and conventional processes at process-LCA and economy scales for energy quality corrected and not corrected cases...... 108
Figure 4.14: Product EROIs for CLP and conventional process as a result of monetary allocation at economy and process LCA scales...... 109
Figure 4.15: MJ/kg CO2 values for CLP and conventional process at economy and process scales...... 110
Figure 4.16: Dominating factors in the life cycle of CLP for land use: Amount of coal transported and coal mined at the economy scale...... 111
Figure 4.17: Sensitivity of conventional process to changes in the percentage of GHG
(CO2eq) emitted from the stack at the economy scale...... 112
Figure 4.18: Dominating factors of CLP for water use at the equipment scale: Cooling tower and hydration steam water uses...... 113
Figure 4.19: Dominating factors in the life cycle of CLP for water use at the economy scale: Equipment scale and coal mining water...... 113
Figure 4.20: Dominating factors of conventional process for water use at the equipment scale: Cooling tower, condenser make-up and shift steam water uses...... 114
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Figure 4.21: Dominating factors in the life cycle of conventional for water use at the economy scale: Equipment scale and coal mining water...... 115
Figure 4.22: Dominating factors of conventional process for GWP at the equipment scale: CO2% from stack...... 116
Figure 4.23: Dominating factors in the life cycle of conventional process for GWP at the process LCA scale: CO2% from stack...... 116
Figure 4.24: Dominating factors in the life cycle of conventional process for GWP at the economy scale: CO2 % from stack and coal mining...... 117
Figure 4.25: Dominating factors in the life cycle of CLP for GWP at the process LCA scale: Coal mining and transportation...... 118
Figure 4.26: Dominating factors in the life cycle of CLP for GWP at the economy scale:
Coal mining ...... 118
Figure 4.27: The sensitivity of total energy consumption per kWh electricity generation to the changes in coal mining energy consumption for CL and conventional processes at the process LCA scale...... 120
Figure 4.28: The sensitivity of total energy consumption per kWh electricity generation to the changes in coal mining and equipment production energy consumption for CL and conventional processes at the economy scale...... 121
Figure 4.29: Dominating factors in the life cycle of CLP and conventional process for
EROI at the process LCA scale with or without energy quality consideration...... 122
Figure 4.30: Dominating factors in the life cycle of CLP for EROI at the economy scale with or without energy quality consideration...... 122
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Figure 4.31: Dominating factors in the life cycle of conventional process for EROI at the economy scale with or without energy quality consideration...... 123
Figure 5.1: Energy systems diagram of multi-crystalline solar PV...... 130
Figure 5.2: Emergy signature diagram of multi-crystalline solar plant according to contribution of different life cycle steps considered...... 135
Figure 5.3: Emergy signature diagram of multi-crystalline solar plant according to contribution of material and energy inputs...... 135
Figure 5.4: Life cycle stages considered for LCA of solar PV...... 137
Figure 5.5: Global warming potential (GWP) results for solar PV ...... 138
Figure 5.6: Land use results for solar PV...... 140
Figure 5.7: Water use results for solar PV...... 140
Figure 5.8: Energy system diagram for biogas production phase...... 144
Figure 5.9: Emergy signature diagram of biogas production phase for different scenarios considered...... 146
Figure 5.10: Sensitivity analysis results of % Re for biogas production phase...... 148
Figure 5.11; Sensitivity analysis results of EYR for biogas production phase...... 149
Figure 5.12: Sensitivity analysis results of ELR for biogas production phase...... 150
Figure 5.13: Sensitivity analysis results of EIR for biogas production phase...... 151
Figure 5.14: Sensitivity analysis results of ESI for biogas production phase...... 152
Figure 5.15: Emergy system diagram for electricity generation from biogas...... 153
Figure 5.16: Emergy signature diagram of electricity generation from biogas for different scenarios considered...... 156
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Figure 5.17: Sensitivity analysis results of % Re for electricity generation from biogas.
...... 158
Figure 5.18: Sensitivity analysis results of % EYR for electricity generation from biogas.
...... 159
Figure 5.19: Sensitivity analysis results of ELR for electricity generation from biogas. 160
Figure 5.20: Sensitivity analysis results of EIR for electricity generation from biogas. 161
Figure 5.21: Sensitivity analysis results of ESI for electricity generation from biogas. 162
Figure 5.22: Life cycle steps considered for electricity generation from biogas...... 163
Figure 5.23: Life cycle GWP of electricity generation from biogas...... 164
Figure 5.24: Life cycle land use results of electricity from biogas with different allocation strategies...... 165
Figure 5.25: Life cycle water use results of electricity from biogas with different allocation strategies...... 166
Figure 5.26: Breakdown of electricity from biogas under different scenarios with changing manure cost...... 169
Figure 5. 27: Emergy system diagram of producer gas production from ipomea...... 172
Figure 5.28: Emergy signature diagram for producer gas production from ipomea...... 174
Figure 5.29: % Re values for producer gas production under three scenarios considered.
...... 175
Figure 5.30: EYR values for producer gas production under three scenarios considered.
...... 177
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Figure 5.31: ELR values for producer gas production under three scenarios considered.
...... 177
Figure 5.32: EIR values for producer gas production under three scenarios considered.
...... 178
Figure 5.33: ESI values for producer gas production under three scenarios considered. 178
Figure 5.34: Energy systems diagram of electricity generation from producer gas ...... 179
Figure 5.35: Emergy signature diagram for electricity generation from producer gas. .. 182
Figure 5.36: % Re values for electricity generation from producer gas under three scenarios considered...... 184
Figure 5.37: EYR values for electricity generation from producer gas under three scenarios considered...... 185
Figure 5.38: ELR values for electricity generation from producer gas under three scenarios considered...... 186
Figure 5.39: EIR values for electricity generation from producer gas under three scenarios considered...... 186
Figure 5.40: ESI values for electricity generation from producer gas under three scenarios considered...... 187
Figure 5.41: Life cycle steps considered for electricity generation from producer gas. . 189
Figure 5.42: Contribution of all life cycle steps to total GWP...... 190
Figure 5.43: Net GWP results for electricity generation from producer gas under different scenarios...... 191
Figure 5.44: Life cycle land use results for electricity from producer gas...... 192
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Figure 5.45: Life cycle water use results for electricity from producer gas...... 193
Figure 5.46: Breakdown of cost of electricity from producer gas under different scenarios considered...... 195
Figure 6 1: Energy systems diagram for Rampura village...... 201
Figure 6.2: Emergy signature diagram of Rampura village for village level emergy analysis...... 204
Figure 6.3: Energy system diagram of husbandry sector in Rampura...... 208
Figure 6.4: Emergy signature diagram for husbandry sector in Rampura...... 210
Figure 6.5: Energy system diagram of agricultural sector in Rampura ...... 212
Figure 6.6: Emergy signature diagram belonging to agricultural sector in Rampura. .... 214
Figure 6.7: Energy system diagram of domestic sector in Rampura...... 217
Figure 6.8: Emergy signature diagram for domestic sector in Rampura...... 219
Figure 7.1: A regular cook stove in Rampura...... 222
Figure 7.2: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 1...... 239
Figure 7.3: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 1...... 240
Figure 7.4: Radar diagram for energy technology combination satisfying minimum annual cost objective in scenario 1...... 242
Figure 7.5: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 1...... 243
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Figure 7.6: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 2...... 245
Figure 7.7: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 2...... 246
Figure 7.8: Radar diagram for energy technology combination satisfying minimum annual cost objective in scenario 2...... 247
Figure 7.9: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 2...... 249
Figure 7.10: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 3...... 250
Figure 7.11: Radar diagram for energy technology combination satisfying minimum
GWP objective in scenario 3...... 252
Figure 7.12: Radar diagram for energy technology combination satisfying minimum cost objective in scenario 3...... 253
Figure 7.13: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 3...... 255
Figure 7.14: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 4...... 257
Figure 7.15: Radar diagram for energy technology combination satisfying minimum
GWP objective in scenario 4...... 258
Figure 7.16: Radar diagram for energy technology combination satisfying minimum cost objective in scenario 4...... 259
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Figure 7.17: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 4...... 261
Figure 8 1: Linear programming results for energy combinations satisfying different objectives under different scenarios considered...... 270
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Chapter 1: Introduction
1.1 Motivation and Contribution
Energy use in developing countries is projected to equal and exceed the demand in developed countries, with increasing prosperity, consumerism, and changing social norms
(Kaygusuz, 2011, EIA, 2011 and Buragohain et al, 2010). In India, the energy per capita consumption increased from 567 kWh to 704 kWh during 2005-2010 period (Boragohain et al, 2010). Country population rises 1.6 % annually and is expected to stabilize around
1.72 billion in 2060s. With this trend, the population in India is expected to surpass the population of China (James, 2011). According to the World Energy Outlook, energy demand in China will increase 60% by 2035 compared to 2010 and energy demand in
India will double during this period. Energy demand increase in OECD countries will be only 3% compared 2010 (IEA, 2012). Figure 1.1 obtained from World Energy Outlook
2011 version summarizes these trends. In addition to increasing energy demand, demand for fossil energy will also grow in absolute terms through 2035, and even though their contribution to the energy mix will drop, these fuels will continue to dominate for at least the next few decades (IEA, 2012). While liquid fossil fuels are mainly being used for transportation, coal is a major source for electricity generation worldwide (Fan et al, 2008 and Yang et al, 2008).The increasing energy demand in developing countries is expected to be supplied mainly from fossil fuels, dominantly coal and oil now, and expected to be supplied in greater portions especially from coal in the future (Chandler et al, 2002, BP
1
2009) However, electricity produced at coal-fired power plants is a major component of total “energy related” global greenhouse gas emissions (GHG) (EIA,2012). Hence, employing clean coal technologies implemented with CO2 capture and with improved efficiency can significantly reduce “energy related” environmental impacts (Corti and
Lombardi, 2004 and Hurst et al, 2012).
Figure 1.1: Energy consumption in USA, China and India: History and projections (EIA, 2011).
Beyond the problem of meeting increasing energy demand, growing concern about environmental problems such as air pollution, global warming and depletion of fresh water resources pushes the efforts for meeting this increasing energy demand in an
2 environmentally friendly and sustainable manner. Furthermore, depletion and price fluctuation of fossil fuels mandate finding alternative “clean” technologies to satisfy this energy demand (Alteri and Masera, 1993, Zheng et al, 2010 and Banerjee and Tierney,
2011).
Especially, rural areas of developing countries are more prone to energy poverty.
Although overall rural electrification rate is 82% in India, only 44% of the rural households are connected to the grid. Even if the households are connected to the grid, electricity supply is erratic and unreliable. This lack of energy disables local people in rural areas in earning their income and compels them to economic poverty (Kaygusuz,
2011, Buragohain et al, 2010 and Romijn et al, 2010). Economic poverty pushes rural people to utilize locally available free but inefficient energy sources such as cow dung, biomass residues causing local environmental problems such as indoor smoke further triggering health problems and health care costs (Kaygusuz, 2011).
Given above stated reasons, it is essential to design energy systems consisting of centralized and localized options that generate the optimum energy mix supplying energy requirements in a sustainable manner. In designing sustainable energy systems; social, economic and environmental aspects should to be considered simultaneously.
Furthermore, these systems must be in harmony with the society and biosphere they exist in (Griggs et al, 2013). And, sustainability can be achieved only if these systems are implemented in a way satisfying necessities arising from social, environmental and economic aspects specific to the region in question (Martin et al, 2010). Additionally, complementing centralized government grid and increasing the access to efficient energy
3 sources by utilizing local renewable energy sources is of vital importance and can improve energy reliability and independence in rural settings (Hiremath et al 2007 and
Romijn et al, 2010). Having reliable and sufficient energy in turn can ease poverty burdens (Kaygusuz, 2011 and Zheng et al, 2010). However, incomplete and misleading efforts can be observed in research.
Romijn et al exemplify four energy projects resulted in failure in Tumkur district of Karnataka state in India. They investigate reasons of “failure and success” based on exemplified cases. In case of biogas plant in Pura, feedstock cow dung quantity was not sufficient and when the project implementers cease their support, the plant is shut down.
In Kagganahalli vegetable oil system, collaboration within local people to supply the oil seeds and to maintain the plant failed leading to termination of project. Biogas system in
Mavinakere is employed for pumping water and the system failed due to electrical engine problems, and not being able compete with government funded water supply. Lastly the producer gas systems utilized for house lighting in Hosahalli and Hanumanthanagara failed because electricity fees from villagers could not be collected sufficiently and problems that arose within social groups in the villages led to termination of the project
(Romijn et al, 2010). Consequently, for an energy project to be successful and sustain itself in the long run, social background and environmental specifics of the region, technical and economic factors, availability of feedstock should be considered in design and implementation. Another point is that one type of energy technology is implemented and not all but certain kind of energy needs is aimed to be met in these projects.
4
Introducing one type of energy technology, considering only technical and economic aspects is observed in many studies (Ravindranath et al, 2004 and Kishore et al, 2004).
Employing advanced biomass cook stoves without considering traditional cooking practices, adoption dynamics and with design flaws conclude in failures (Venkatamaran et al, 2010 and Ruiz-Merhado, 2011). Many improved biomass cook stove studies focus only on increasing resource use efficiency and green house gas (GHG) mitigation potential ignoring social and technical aspects (Bhattacharya and Salam, 2002).
Another trend observed is comparison or replacement of a centralized fossil fuel technology by a decentralized renewable technology option by employing one analysis technique. This approach leads to a picture partly unexplored regarding the effects of technologies analyzed (Paoli et al, 2008 and Brown et al, 2012).
Levin and Thomas performed the analysis of “least cost centralized and decentralized” energy options for 150 countries. They considered only economic side of these technologies in their analysis finding that centralized energy technologies are cheapest option for most of the countries excluding many countries in Africa. Electricity generation in Finland with contribution of renewable and non-renewable energy technologies is analyzed by Hayha et al. They considered electricity cost, GHG emissions and “emergy cost” of electricity (Hayha et al, 2011). However, they did not consider resource use such as water and land and to what extent electricity “emergy cost” is renewable and all options they investigated are centralized, high capacity plants.
According to World Energy Outlook, energy trends in the world will be determined by the energy trends in developing countries (IEA, 2012). Following the path developed
5 countries followed for development will not lead to sustainability for these developing countries as can be inferred from the current status in the world today. Based on this fact, the contributions of our study can be stated as follows:
- Exploration of the potential of centralized, high efficiency IGCC clean coal
technologies in developing countries
Coal is being utilized and will continue to be utilized as a major energy source in developing countries. Increased efficiency and significantly lower environmental impacts of clean coal technologies can lead to more environmentally benign utilization of coal as an energy source in developing countries
- Accounting for local specifics of the region via analysis prior to implementation
by adopting a bottom-up approach
Emergy analysis of Rampura village and its interacting subsystems reveals the state of sustainability both in subsystem and village level. How the material and energy exchanges and external inputs affect the self-sufficiency of these systems is also investigated leading to recommendations for improvement. Local energy demand and potential is determined.
- Analysis of centralized and localized energy options by accounting for their
downstream (life cycle impacts) and upstream (ecosystem services) effects by joint
use life cycle assessment (LCA) and emergy analysis
LCA enables us to account for environmental impacts of energy technologies due to emissions and resource use. However, only LCA is not sufficient to account for the work nature invested into these resources for their formation and it cannot evaluate and
6 quantify inputs having no economic value such as sun, rain, labor. By performing emergy analysis of the technology options investigated, role of nature to support these processes can be captured leading to a more holistic picture of the processes and their state of sustainability.
- Adoption of a multi-objective optimization approach to find the optimum energy
combination to meet the energy demand
In our study, we set minimum land use, water use, global warming potential (GWP) objectives as life cycle impacts; minimum cost objective as economic criterion and maximum percent renewability (%Re), emergy yield ratio (EYR) and minimum environmental loading ratio (ELR) objectives as emergy indicators of energy combinations. This approach enables us to explore pros and cons of energy combinations from different perspectives reducing the risk of overlooking important impacts related to energy generation.
- Taking the first step towards developing a framework which can provide a robust
basis for design of sustainable energy systems.
As a whole, this procedure is aimed to be transformed into a framework specific enough to find the optimum energy mix for a given region, but general enough to be applied in a variety of settings and scales. Developing such a framework can provide a robust basis for design of sustainable energy systems in the end.
7
1.2 Project Site Rampura Village and Development Alternatives
In India, we collaborated with Development Alternatives uniting their field experience with our holistic analysis experience. Development Alternatives is a non- governmental organization (NGO) working in area of sustainable development, and capacity building of people for income generation in rural setting (Development
Alternatives, 2013). Development Alternatives` technology disseminating branch TARA
(Technology and Action for Rural Advancement) works in Bundelkhand region in
Central India for over 10 years (TARA, 2013). We chose Rampura village in
Bundelkhand region as our project site because of successful renewable energy and capacity building applications implemented by Development Alternatives (Development
Alternatives, 2013 and TARA, 2013).
Rampura, also contained in Bundelkhand region, is a village in Jhansi district of
Uttar Pradesh, India. Their main income generating activities are agriculture and animal husbandry. The main crops grown are wheat, pulses, groundnut and mustard. There are
117 buffalos, 45 bullocks, 55 cows, 36 calves and 155 goats in the village totally. The literacy rate is 70 %. There are 69 households in the village out of 44 is connected to the
8.7 kWp multi-crystalline silicon solar grid implemented by Development Alternatives.
Solar grid meets the lighting energy needs. Vegetable and milk production are the items for liquid cash flow within the year. In the village there is no dedicated grazing land or reserve forest. According to the season, non-irrigated land is utilized as grazing land.
Bundelkhand is a semiarid region suffering from lack of water and prone to climate change. The area went through a 4-year-long draught making village suffer from
8 the existing climate change problems more severely and underground water level continues to decline (Development Alternatives, 2011). Figure 1.2 presents the geographical location of India in the world and location of Jhansi district which contains project site Rampura village.
Figure 1.2: Geographical location of India in the world and location of Jhansi district in India (Worldatlas, 2013 and Parsis, 2013).
1.3 Organization of the Dissertation
The organization of the rest of this thesis is as follows.
Chapter 2 starts with an overview of centralized and localized energy options.
Then, background information about centralized clean coal technologies analyzed in this
9 study is presented. Background information about solar electricity generation, anaerobic digestion, biomass gasification and the related plant types conclude the chapter.
Chapter 3 includes the background information related to analysis techniques utilized in this study. Background information about life cycle assessment (LCA) methodology, analysis stages, boundary selection and allocation in LCA is given.
Following LCA, background information about emergy analysis and its application is presented. In the last section of this chapter, joint use of LCA and emergy analysis is discussed.
Emergy analysis, LCA and economic assessment results of the clean coal technologies constitutes chapter 4. A sensitivity analysis is also presented revealing effects of dominant factors on LCA results.
Chapter 5 includes emergy analysis, LCA and economic assessment results of the localized energy options. Firstly, results regarding multi-crystalline solar PV analysis are presented followed by the floating drum biogas digester emergy analysis, LCA and economic assessment results. Lastly, the same results for downdraft biomass gasifier are stated.
Emergy analysis of Rampura village and its interacting subsystems is presented in chapter 6. Sensitivity analysis results which are performed to determine the effects of changes in renewability of significant emergy inputs on emergy indicators are also stated.
In chapter 7, energy demand and local energy resource potential in Rampura is discussed. Then, a linear programming (LP) problem is formulated to find the energy combinations meeting the energy demand and satisfying the objectives and constraints
10 set. In section 7.4, results obtained from solution of LP problem under different scenarios are presented. Lastly, greenhouse gas (GHG) mitigation potential under these scenarios is explored.
Chapter 8 includes the conclusions inferred from analysis of the technology options, emergy analysis of Rampura village and its subsystems and the LP problem solved. Based on these results, recommendations are made for satisfying the energy needs in Rampura. Lastly, future research directions are discussed.
Last of all, appendix section includes raw data and calculations regarding clean coal technologies and raw data utilized in emergy analysis of Rampura and its interacting subsystems.
1
11
Chapter 2: Centralized and Localized Energy Options
2.1. Background
There are different terms referring localized energy options in literature such as
“distributed generation” (Alenne and Saari, 2006) and “decentralized generation”
(Watson and Wright, 2011). Rather than being renewable or non-renewable, classifying a technology option as centralized or localized depends on the production capacity of the power plant in question. Alenne classifies energy options having capacity lower than 200 kWe as localized (Alenne and Saari, 2006). However, there is not a consensus about production capacity in classifying energy options as localized. Cardell classifies energy technologies having a production capacity between 500 kW and 1 MW as localized
(Cardell and Tabors, 1997). These definitions also vary according to the intended region of service and energy demand. In the context of our study, to supply the energy demand in a rural setting of a developing country, an energy technology having a production capacity of higher than 100kWe can be classified as centralized and an energy technology having a lower production capacity can be classified as a localized energy option (Alenne and Saari, 2006).
Centralized energy options can be considered as favorable to meet energy demand of areas having high and dense population such as urban areas where constructing transmission and distribution infrastructure is economically feasible (Watson and Wright,
2011). This claim is supported by a study in which least cost centralized and localized
12 energy combinations for 150 countries have been investigated (Levin and Thomas, 2012).
According to results of this study, centralized energy options are least cost options for countries densely populated such as Bangladesh. However, decentralized energy options are found to be more adequate in sparsely populated countries such as Uganda and
Botswana in Africa. Localized energy options do not require high voltage transmission and distribution infrastructure since they are situated close to the consumers (Watson and
Wright, 2011). Furthermore, transmission losses of localized energy options are minimal
(Ackermann et al, 2011).
However, only considering cost of energy is not sufficient given the social and environmental problems world faces today. Complementing centralized government grid and increasing the access to efficient energy sources by utilizing local renewable energy sources is of vital importance and can improve energy reliability and independence in rural settings (Hiremath et al 2007 and Romijn et al, 2010). Having reliable and sufficient energy in turn can ease poverty burdens in developing countries (Kaygusuz, 2011 and
Zheng et al, 2010). Furthermore, localized energy options can be cost effective in remote rural areas where extending government grid is not feasible or even if extended not reliable (Kanase- Patil et al, 2011).
Kanase-Patil et al studied combination of localized renewable energy options with minimized cost objective for decided reliability values (Kanase- Patil et al, 2011). One of the first multi objective energy combination studies has been performed by Ramanathan and Ganesh for urban households in India (Ramanathan and Ganesh, 1994). They considered grid, biogas, fuel wood, diesel and solar electricity options. One significant
13 result of their study is that generating electricity from biogas is not feasible rather biogas should be used directly (Ramanathan and Ganesh, 1993). Electricity generation in
Finland with contribution of renewable and non-renewable energy technologies is analyzed by Hayha et al. They considered electricity cost, GHG emissions and “emergy cost” of electricity (Hayha et al, 2011). However they did not considered resource use such as water and land and to what extent electricity “emergy cost” is renewable and all options they investigated are centralized high capacity plants. The potential of wind, wave, solar PV technology combinations as centralized energy options in Denmark has been investigated by Lund (Lund, 2006). Connolly et al studied the potential of 100% centralized energy combinations in Ireland for electricity and heat production and also for transportation sector. According to their results, meeting energy demand by renewable energy is potentially possible. However, a more intense study should be performed and these technologies should be investigated in more detail (Connolly et al, 2011).
We perform our study to determine the optimum energy technology combination in a developing country context. The centralized energy options considered in our work are two clean coal technologies. Both of the technologies are integrated gasification combined cycle (IGCC) systems implemented with CO2 capture. The first system, conventional process generates 30.3 MWe of electricity and 560 tonnes of H2 per day.
Implementation of CO2 capture reduces conventional process` efficiency by 22 %. The second clean coal technology we investigate is calcium looping process (CLP). This system generated 266 MWe of electricity and 495 tonnes of H2 daily. Implementation
CO2 capture reduces CLP` efficiency by 28 %. The reason of higher efficiency decrease
14 in CLP is that CO2 capture technology utilized in CLP is a chemical CO2 capture technique which consumes more energy than the physical capture technology utilized in conventional process.
The localized energy options we study are a100kW capacity downdraft biomass gasifier operating with local biomass ipomea, a 60 m3 biogas production capacity floating drum biogas digester and a 8.7 kWp multi-crystalline solar PV.
In the following sections of this chapter, background information about centralized energy options studied is introduced, then, background information regarding localized energy options is presented.
2.2 Centralized Energy Options
The process specific background information regarding conventional and CL processes are presented in chapter 4. In this section, a more general introduction is aimed to be given. Background information about clean coal technologies, CO2 capture techniques, chemistry of calcium looping and process structure of conventional process and CLP is introduced.
2.2.1 Clean Coal Technologies
There are four “grand processes” of coal use in the world today. These are coal, combustion, gasification, carbonation and liquefaction processes (Miller, 2011).
In the context of electricity generation from coal, coal combustion and gasification are the processes of interest in our work. In coal combustion, coal is directly combusted in
15 boilers producing heat. This heat is utilized to produce high pressure, high temperature steam which in turn is utilized to create mechanical energy via utilization of steam turbines to generate electricity. In coal gasification, coal is partially oxidized to produce the synthesis gas. Synthesis gas mainly consists of CO and H2 (Miller, 2011). Coal gasification will be explained in more detail while we explain structure of clean coal technologies we study.
Coal is a major source for electricity generation worldwide (Fan et al, 2008 and
Yang et al, 2008). However, electricity generated at coal-fired power plants is a major component of total “energy related” global greenhouse gas emissions (EIA, 2012). Due to this fact, clean coal technologies implemented with CO2 capture and with improved efficiency are developed with the aim of reducing “energy related” environmental impacts (Corti and Lombardi, 2004 and Hurst et al, 2012).
From the stand point of clean coal technologies, amount of CO2 emitted per kWh electricity generated can be reduced in two ways: by increasing the efficiency of coal power plant, thus utilizing less coal for producing the same amount of electricity and by carbon capture (CC) (Rao and Rubin, 2002 and Franco and Diaz, 2009). Integrated gasification combined cycle (IGCC) systems have this efficiency advantage over pulverized coal combustion (PCC) plants even though the former are not widely adopted today except in a few cases (Pehnt and Henkel, 2009).
The thermal efficiency of PCC plants is explained according to Rankine cycle. In
Rankine cycle, thermal efficiency is proportional to the ratio of difference between steam turbine inlet and outlet temperature to steam turbine inlet temperature. In PCC plant
16 efficiency increase is provided by increasing steam temperature and pressure at steam turbine inlet (Miller, 2011). Subcritical, supercritical, ultra-supercritical and fluidized bed PCC plants have evolved which produce steam with higher pressure and temperature, thus creating a more efficient system than conventional PCC plants (Miller, 2011).
IGCC systems have higher thermal efficiency than PCC plants. Brayton cycle explains the operation of gas turbines. Since gas turbines can operate with higher turbine inlet temperatures, they have higher efficiency than steam turbines. IGCC systems combine
Rankine and Brayton cycles. In IGCC plants, the waste heat from gas turbines which still has work performing potential is captured and utilized in steam turbines further to generate power. This combined structure increases the thermal efficiency of IGCC plants significantly (Miller, 2011).
2.2.2 CO2 Sequestration and CO2 Capture Techniques
2.2.2.1 CO2 Sequestration
One strategy proposed for mitigation of GHG emissions is CO2 capture and storage (CCS) or CO2 sequestration. Many CO2 capture technologies are being developed today since CCS is perceived as a cheaper and faster way of CO2 mitigation than CO2 abatement. Before adopting CCS techniques widely, the extent in which CCS applied, which means what amount of CO2 produced should be captured, and sequestered and methods to be used to capture this CO2 are important factors to consider (Keller et al,
2008 and Yang et al, 2008).
17
The extent in which CO2 sequestration to be applied is dependent on many factors. The factors that affect the feasibility of CO2 sequestration are C tax, CO2 leakage rate, discount rate on the present value of investment, avoided CO2 abatement costs, potential maturing of technologies and price reduction due to this maturation (Keller et al,
2008). Keller et al. developed a model to investigate the economic feasibility of CO2 sequestration and found that feasibility of CCS increases with reduced leakage rate, C tax and energy requirement, but with increasing discount rate. Maturation of CO2 sequestration technologies will also affect the feasibility of CCS positively. According to their results, CO2 sequestration can be a feasible way of mitigating CO2 levels in the atmosphere with residence time exceeding one thousand years (Keller et al, 2008). A CO2 sequestration cost of 100 $/ ton C can make CO2 sequestration a feasible CCS technique
(Herzog et al, 2005).
When it comes to reducing CO2 emitted by coal combustion, it can be performed by utilizing many technologies such as absorption, adsorption, membrane separation, chemical looping, mineral carbonation. Captured CO2 by one of these methods can be stored in underground aquifers, rock formations, emptied wells etc. Ocean fertilization and forestation are other means considered for capturing CO2 from the atmosphere but not from point sources (Yang et al, 2008).
CO2 sequestration creates additional energy requirements causing earlier depletion of fossil fuels when the current world energy mix is considered. Furthermore,
the leakage rate of the sequestered CO2 will not be zero (Herzog et al, 2005). With
18 extensive adoption of CCS, some environmental problems may arise because of the above mentioned two issues (Herzog et al, 2005).
CCS induced environmental risks can be classified as follows: Leakage related risks, risks related to changes in geological structure of storage sites, risks related to the chemicals (such as monoethanolamine (MAE)) used for CCS and higher energy demand problems. Leakage of CO2 can change the chemical structure of the surrounding rocks and contaminate the underground water resources. Ocean or deep sea ecosystems are threatened because of the carbonic acid formation in the water and pH change in the environment. These are local effects that CO2 leakage may cause. On global scale, large amounts of CO2 leakage to the atmosphere can cause severe global warming problems. In geological terms, some seismic activities or soil collapses can occur. Due to additional energy use, CO2 sequestration causes creation of extra CO2 and other pollutants which may increase the impacts of the processes such as human toxicity, eutrophication etc.
Furthermore, chemicals used for CCS and their regeneration can worsen these effects as in the case of MEA use (Rao and Rubin, 2002, Franco and Diaz, 2009, Pehnt and Henkel,
2009).
2.2.2.2 CO2 Capture Technologies
One of the CO2 capturing technologies from point sources, amine absorption is a commercially adopted technology today in existing PCC plants to eliminate pollutants from coal flue gas (Rao and Rubin, 2002). However, regenerating monoethanolamine
(MEA) utilized in CO2 capture necessitates large amount of energy which is supplied
19 from steam cycle, thus significantly reducing the overall plant efficiency. Furthermore, significant amount of MEA decomposes and evaporates during regeneration which in turn creates the requirement of significant amounts of MEA makeup (Yang et al, 2008,
Rao and Rubin, 2002). Ammonia emission is caused as a result of decomposition of
MEA and solvent loss via decomposition and evaporation causes amine absorption technology to have economic penalty as well as energy penalty. Besides, high energy requirement and ammonia emissions trigger the search for more environmentally benign technologies than amine absorption for CO2 capture. Thus, reducing energy required, decomposition and evaporation rates are essential for anime absorption to be a viable
option for CO2 capture (Yang et al, 2008, Rao and Rubin, 2002).
When other capturing technologies are examined, adsorption technologies require improvement in their capturing capacity. For mineral carbonation, higher reaction rates and compound stability should be achieved. A membrane technology achieving high CO2 flux and selectivity along with stability can be a feasible CO2 capturing option if related energy and technology issues can be overcome. For this purpose, “mixed-matrix membranes” are promising (Yang et al, 2008).
The conventional process we investigate here utilizes physical absorption
(Selexol) technique and CLP utilizes chemical looping techniques to capture CO2 produced. The captured CO2 is then compressed and sequestration ready CO2 is produced
(NETL, 2008 and Ramkumar et al, 2008). In studying energetic performance and life cycle impacts of these processes, we consider process and life cycle steps up to
20 preparation of sequestration ready CO2 and exclude carbon dioxide storage or sequestration step in our calculations.
Coal power plants are point sources where CO2 can be captured economically. In such systems, CO2 can be captured by three schemes, namely pre-combustion, post- combustion and oxy-fuel technology. Conventional pulverized coal power plants adopt post-combustion CO2 capture technology in which CO2 is separated from flue gas after combustion. Amine absorption is a technology utilized in PCC plants in post-combustion scheme. Here, pollutants other than CO2 are emitted. Because of the extra energy demand of CO2 capture, more coal is utilized in the process with more pollutant formation.
Arising from this fact, effects of pulverized coal combustion implemented with CO2 capture increases in almost all impact categories such as human toxicity, eutrophication, ozone depletion potential and so on( Koornneef et al, 2008, Pehnt ad Henkel, 2009).
In IGCC systems, coal is converted to CO2 and an energy carrier without carbon such as H2. Then, CO2 is captured before combustion of the fuel and stored while the carbon free fuel source can be used for multiple purposes (Pehnt and Henkel, 2009).The two systems which are the subject of our study are IGCC systems which adopt pre- combustion CO2 capture technology. Since all pollutants along with CO2 are separated from the fuel in IGCC systems, an improvement in all impact categories is observed
(Pehnt and Henkel, 2009).
In systems utilizing oxy-fuel technology, oxygen is separated from the nitrogen in air. Fuel is combusted with pure oxygen. Water vapor and CO2 are the main components of fuel gas. The environmental performance of oxy-fuel systems is dependent on the
21 amount of energy for separation of oxygen and capturing of other pollutants along with
CO2. If these factors can be optimized, oxy-fuel systems can have the lowest environmental impacts among other options (Pehnt and Henkel, 2009).
2.2.3 Conventional Process
2.2.3.1 Gasification
In coal gasification, firstly coal is heated and volatile substances are gasified, then carbonized coal reacts with steam or O2 to be partially oxidized. The synthesis gas or producer gas produced mainly consists of CO and H2 (Miller, 2011). Gasification can be represented by the formula 2.1 (Ramkumar et al, 2008).
CxHy + H2O xCO + (y/2+1) H2 (2.1)
In IGCC systems, producer gas is converted to CO2 and an energy carrier without carbon such as H2 via water gas shift reaction (WGSR). WGSR can be represented by the formula 2.2
CO + H2O CO2 + H2 (2.2)
Both coal gasification and WGSR are exothermic reactions which provide the heat for the following reactions in CO2 capture and power generation.
22
2.2.3.2 CO2 Capture in Conventional Process
In conventional process for capturing CO2, physical absorption technique, Selexol is utilized. Selexol is a mixture of dimethyl ethers of polyethylene glycol (DMPEG) with chemical formula CH3O(C2H4O)CH3 (Miller, 2011).This system can operate at room temperature minimizing refrigeration needs. The absorption selectivity of Selexol for H2S and CO2 is 1:9 (Miller, 2011).
In conventional process, a double –stage Selexol unit is employed for sequential removal of H2S and CO2. Clean and cooled producer gas enters the first absorber unit at around
0 39 C. Here, Selexol saturated with CO2 (comes from second absorber) removes H2S from producer gas. Then, solution leaving the bottom of the absorber is regenerated using indirect heat. This stream is then sent to the Claus unit where H2S is oxidized into elemental sulfur (NETL, 2008). In the second, absorber regenerated “lean” Selexol absorbs CO2 selectively and becomes CO2 saturated. CO2 saturated Selexol is again sent back to first absorber. With this cycle, elemental sulfur and sequestration ready CO2 are obtained in conventional process.
2.2.3.3 Conventional Process Description
In conventional process, coal is grinded and mixed with water for slurry preparation. Coal slurry and O2 rich stream coming from air separation unit (ASU) reacts in the gasifier. The resulting producer gas consists “primarily of hydrogen and carbon monoxide, with lesser amounts of water vapor and carbon dioxide, and small amounts of
23 hydrogen sulfide, carbonyl sulfide, methane, argon, hydrogen chloride, and nitrogen”
(NETL, 2008). Producer gas exiting the gasifier is cooled firstly via heat exchangers and the water eliminating the particles in the producer gas. In gas shift reactors, producer gas is converted into H2 and CO2. After removal of mercury, H2 and CO2 are sent to absorbers for removal of CO2. Exit gas of second absorber is sent to pressure swing adsorber (PSA) for purification of H2.Tail gas leaving PSA is utilized in a boiler to generate electricity. CO2 stream leaving Selexol unit is sent to CO2 compressors to become sequestration ready (NETL, 2008). Simplified box diagram of conventional process in figure 2.1 summarizes these process steps.
GAS COOLING FINAL SHIFT MERCURY SELEXOL CO 10 11 BFW HEATING 12 SYNGAS 13 14 2 15 REACTORS REMOVAL UNIT COMPRESSOR STEAM & KNOCKOUT SCRUBBER CO2 PRODUCT
9 18 O QUENCH & WATER TO 16 17 2 SOUR WATER STRIPPER SYNGAS CLEAN SCRUBBER CLAUS GAS 19 PLANT SULFUR 8 20 PRESSURE COAL GEE RADIANT SWING 21 TAIL GAS 5 7 H GASIFIER SLAG ADSORBER 2 RECYCLE PRODUCT 6 TO SELEXOL SLURRY FUEL GASIFIER WATER 22 GAS 4 OXIDANT
ELEVATED STACK 1 PRESSURE 3 AIR 23 BOILER 24 AMBIENT ASU CLAUS PLANT AIR OXIDANT 2
VENT GAS
Figure 2.1: Simplified box diagram of conventional process (NETL, 2008).
24
2.2.4 Calcium Looping Process (CLP)
2.2.4.1 CL Process Description
Up to gasification, all process steps in conventional process also apply to CLP.
There are mainly three stages in the CL process; namely carbonation, calcination and hydration of CaO sorbent for elongated activity. In the carbonation stage, the modified sorbent CaO reacts with CO2, H2S and halides in the synthesis gas produced by gasification of coal. After CO2, H2S and halides are separated from the synthesis gas, spent sorbent is sent to the calciner.
Calcination is the regeneration step of the spent absorbent. In this stage sequestration-ready CO2 is produced and spent absorbent is regenerated. Finally, regenerated absorbent is reactivated in the hydrator and re-fed to the carbonation reactor
(Ramkumar et al, 2008 and Fan et al, 2008). CL process integrates gasification, water gas shift reaction, CO2 and S separation steps into one process step. PSA exit gas is not used as a fuel in electricity production resulting in an emission-free technology. All the CO2 produced in this process can be sequestered or used for making other products.
Separation of sulfur as elemental sulfur rather than H2S increases the amount of H2 produced (Fan et al, 2008). Simplified flow chart of CLP is given in figure 4.2 in chapter
4.
25
2.2.4.2 Chemistry of Calcium Looping and CO2 Capture
CLP system consists of three reactors: carbonator, calciner and hydrator. In carbonator, dehydration of hydrated sorbent (Ca(OH)2), WGSR, carbonation of dehydrated sorbent (CaO) (or CO2 capture), removal of sulfur and halides occur
(Ramkumar and Fan, 2010).
The WGSR and CO2 capture (carbonation reaction) are exothermic reactions. The heat released from carbonation reactor can be used for steam and electricity generation
(Fan et al, 2008, Ramkumar and Fan, 2010). Dehydration reaction in carbonator releases water and reduces the need for steam since temperature in the reactor is between 500-
o 700 C. Removal of CO2 sorbent increases the efficiency of WGSR. By this way, more H2 is produced (Ramkumar et al, 2008). Modified sorbent CaO also reacts with sulfur and halides removing all pollutants simultaneously. Figure 2.2 explains the reactions occurring in different reactors of CLP.
Spent sorbent CaCO3 is regenerated in calcination reactor, calciner. Calcination reaction
o is an endothermic reaction and occurs above 890 C. CaCO3 decomposes into CaO and
CO2 with the effect of heat (Ramkumar and Fan, 2010). Since calcination occurs at high temperatures, sintering takes place reducing CO2 capturing capacity of the sorbent. For this reason regenerated sorbent CaO is sent to hydration reactor for reactivation
(Ramkumar et al, 2008). Hydration process regulates surface characteristics of sorbent and results in elongated activity (Ramkumar and Fan, 2010). Hydration reaction occurs
26 above 600oC, hence, less heating and cooling needed between the calciner and the carbonator (Ramkumar et al, 2008).
Figure 2 2: CLP reactions (Ramkumar and Fan, 2010).
In a recent study by Hurst and his colleagues, life cycle global warming potential
(GWP) assessment of pulverized coal combustion (PCC) processes implemented with
CO2 capture and offshore sequestration is performed. In this study, they compare post- combustion CO2 capture via calcium looping and MEA techniques (Hurst et al, 2012).
27
Electricity is the only product of the processes compared. Study also investigates potential of three different spent sorbent (CaO) disposal options for reduced life cycle emissions. The disposal options considered are landfill disposal, using spent sorbent in cement industry and deep ocean dumping. According to their results, calcium looping has a comparable performance to amine based technology in GWP reduction (229 gCO2 eq.
/kWh versus 225 gCO2eq. / kWh) However, MEA capture requires more energy causing more reduction in overall system efficiency (Hurst et al, 2012). Among the spent sorbent disposal options, using spent sorbent in cement industry and deep ocean dumping are promising. However, there are significant concerns related to environmental effects of deep ocean dumping (Hurst et al, 2012) Despite being the first LCA study for calcium looping CO2 capture, aforementioned study does not evaluate the technologies in terms of their energetic performance and other environmental impacts such as land use and water use.
Different from Hurst et al.’s study which analyzes PCC systems, we perform the life assessment (LCA) of two coal gasification processes (IGCC) implemented with CO2 capture. Life cycle energetic performance, water use, land use, GWPs are calculated and are discussed chapter 4. Furthermore, we perform emergy analysis and economic assessment of these technologies.
For the emerging processes like we study with the main goal of producing fuel grade H2, efficiency is essentially important for the feasibility and industrial adoptability of the process and the CO2 capture technology chosen is a key factor for overall plant efficiency (Cormos et al, 2008). Thus, evaluation of CO2 capture technology employed
28 multi-dimensionally is vital. With our study, a better understanding of IGCC systems implemented with CO2 capture in terms of their energetic performance and resource use other than only green house gas emissions can be obtained.
2.3. Localized Energy Options
2.3.1 Solar Photovoltaics (PV)
Abundant solar energy available to earth, especially in tropical countries, makes solar PV attractive technology for electricity generation (Ecoinvent, 2009). There are different technologies evolving to produce more efficient solar panels to improve somewhat low efficiency of solar PVs (IRENA, 2012a). Types of solar panels change according to the materials utilized or production technology utilized for panel production.
Solar PVs can also be classified according to the installation structure applied (Ecoinvent,
2009).
In this section, we discuss types of solar PVs shortly. Then, production of crystalline silicon cells, basics of PV operation and specifics of multi-crystalline solar PV installed in Rampura village is discussed.
2.3.1.1 Types of Solar PVs
Crystalline silicon (c-Si) PVs are the solar PVs which are firstly commercialized and dominate the market today. C-Si PVs can be classified as single-crystalline, multi- crystalline (mc) and ribbon silicon PVs. Their module efficiency changes between 14 and
19% (IRENA, 2012a). Single-crystalline silicon (sc-Si) PVs being the most efficient, they
29 are followed by mc-Si and ribbon silicon modules (Ecoinvent, 2009). Silicon based PVs constituted 87% of PV sales in 2010 (IRENA, 2012a).
Second generation PVs are thin film technologies. They are composed of layers deposited on top of each other in 1-4 µm thickness (IRENA, 2012a). Amorphous silicon
(a-Si), cadmium telluride (Cd-Te) and copper indium selenide (CIS) are types of thin film technologies (Ecoinvent, 2009). A-Si PVs have a module efficiency of 4-8%. Module efficiency of Cd-Te PVs can go up to 16-17 % and efficiency of CIS modules change between 7-16% (IRENA, 2012a). Development of third generation solar PVs continues.
Organic solar PVs and concentrating PVs are some of the third generation PVs under development (IRENA, 2012a).
According to their installation types, solar PVs can be classified as follows: open ground and flat roof top mounted, façade roof integrated and mounted, slanted roof mounted and integrated (Ecoinvent, 2009). The system we evaluate in Rampura is a 8.7
Wp mc-Si, open ground mounted PV panel operating since 2009.
2.3.1.2 Production and Installation of Silicon Based PVs
Production of a silicon based solar plant starts with purification of SiO2 by reduction with carbon. Coal, charcoal, coke , woodchips are the sources of carbon.
Silicon with 98.5-99.5% purity (metallurgical grade silicon (MG-Si)) is produced as a result of this process (Ranjan et al, 2011) Then, MG-Si is further purified into electronic grade silicon (EG-Si) and solar grade silicon (SG-Si) to form silicon mix for PVs (Ranjan et al, 2011 and Ecoinvent , 2009). Silicon mix contains 85 % SG-Si and 15 % EG-Si.
30
Difference between EG-Si and SG-Si is in their purity. SG-Si contains impurities 0.01 part per million by weight (ppmw) and EG-Si contains impurities 0.0001 ppmw
(Ecoinvent, 2009).
The difference among c-Si PVs is in their crystalline structures. In sc-Si production, silicon crystal growth occurs on one single crystal without any grain boundaries. The mc- Si and ribbon silicon have multiple grain boundaries in their structure. The silicon is then melted and casted as blocks from which the crystalline wafers are cut as layers in certain thicknesses. Wafers are treated with chemicals (NaOH,
HCl) to eliminate the damages on their surface. The wafers are then doped to create p/n junction in the wafers. After parts for electronic connections are added and anti reflection coating is applied, solar cell production is completed. These solar cells are connected in series and parallel to form the solar panels. After mounting of solar panels and electronic connections with inverter and battery bank are completed, solar plant takes form
(Ecoinvent, 2009 and Ranjan et al, 2011).
2.3.1.3 Fundamentals of Silicon Based Solar PV Operation
Silicon is a semiconducting material having band gap of 1.1 eV (electron volt)
(Ranjan et al, 2011 and IREANA, 2012a). Not all of the solar radiation illuminating a solar PV is converted into electricity. Some of incoming radiation is reflected, some of it passes through the void areas and some of it is absorbed. When solar radiation having energy equal to or more than 1.1 eV hits on silicon based PVs, solar electricity is generated. Absorbed solar radiation excites the valance electrons in Si and the free
31 valance electrons of Si accumulate in n-layer of p-n junction and p junction becomes positively charged. If the circuit is closed, electrons flow from n layer to p layer and electric current flows from p- layer to n-layer (Ecoinvent, 2009). By this way direct current (one direction) is generated by the solar PVs. Inverters convert direct current into alternative current (AC) and electricity becomes usable for lighting or in running several appliances (Ranjan et al, 2011).
2.3.1.4 Multi-crystalline Solar PV in Rampura
Figure 2.3 shows the 8.7 kWp capacity, open ground mounted mc-Si solar PV panels in Rampura village. Solar plant is operated in Rampura since January 2009. 44 of the 69 households in Rampura are connected to the solar grid. Solar electricity is utilized for lighting needs of households. Additionally, 13 street lights in the village are electrified by solar PVs. Utilization of solar power in the village resulted in 2000 liter/year kerosene savings which was used for lighting in the village prior to solar electricity (Development Alternatives, 2011).
The lifetime of the mc-Si solar PV plant is 20 years. The solar system consists of
60 PV panels each having 50 cells. The production capacity of each panel or module is
145 Wp. Solar plant has a 67.5 m2 of photo-sensitive area and a total area of 74 m2 after being framed and mounted. This system includes two inverters of 5 kW capacity and a battery bank of 24 batteries, each 2 V for a back-up of 3 days. The electricity generated is distributed to village Rampura via a 0.75 km mini-transmission line (Development
Alternatives, 2011).
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Figure 2.3: 8.7 kWp capacity mc-Si solar PV in Rampura village.
2.3.2 Biogas Digesters
In this section; types of biogas producing digesters, biochemistry of anaerobic digestion and specifics of floating drum biogas digester situated in a cowshed in Jhansi district is introduced. Social and environmental benefits of biogas utilization are also discussed.
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2.3.2.1 Types of Biogas Digesters
In essence, digesters are reactors in which anaerobic digestion takes place and biogas is produced .We use term “biogas digester” referring to a reactor producing biogas via anaerobic digestion. As in all reactors, biogas digesters can be classified as batch and continuous broadly.
Continuous biogas digesters are continuous stirred tank reactors (CSTR).
Continuous reactors are appropriate for high capacity productions and according to the type of the feedstock, CSTRs may not provide appropriate residence time for anaerobic digestion. This situation may cause washout problems (Khanal, 2008). Fed-batch or cyclic batch reactors are generally utilized for anaerobic digestion of animal manure
(cow, pig, poultry) providing appropriate residence time (Keshtkar et al, 2001).
Fed-batch biogas digesters can be classified as fixed dome, floating drum and balloon digesters (Kossmann et al, 1997(a)). Figure 2.4 presents the general structure of these plants.
In essence, balloon pants are polymeric plastic bags such as PVC. Gas inlet and outlet are situated on the plastic skin of the balloon. Balloon plants are cheap and easy to construct, however their lifetimes are comparatively short (Kossmann et al, 1997(a)). Life time of balloon digesters is suggested to be around 5 years (Ciotola et al, 2011).
34
Figure 2.4: Different types of biogas digesters: 1. Floating drum, 2. Fixed dome, 3. Balloon plant (Kossmann et al, 1997(a)).
Fixed dome digesters are composed of a mixing pit for manure and water to be mixed, the digester body, a fixed gas holder, a compensation tank in which slurry is kept and a gas outlet. When gas production starts, slurry is sent to the compensation tank according to the volume of the gas produced. Fixed dome reactors are cheaper to construct than floating drum digesters and do not have moving parts prone to rusting.
However, gas pressure problems may occur due to fixed gas holder. Furthermore, cracks in brick masonry can cause gas leakages. Hence, construction and operation requires expertise. They have lifetime of over 20 years (Kossmann et al, 1997(a) and 1997(b)).
Floating drum biogas digesters have a floating gas holder and an underground digester. Floating drum swims on top of the slurry generally and to obstruct it from toppling, it is attached to a guiding frame. As the amount of biogas in the gas holder
35 changes, gas holder moves up and down in the slurry. By this way, gas pressure is kept constant.
Floating drum (gas holder) is generally made of steel with high material cost and prone to rusting. However, utilizing stainless steel can solve rusting problems. Floating drum digesters are easy to construct and operate with a life time over 20 years
(Kossmann et al, 1997(a) and 1997(b)).
2.3.2.2 Biochemistry of Anaerobic Digestion
Anaerobic digestion encompasses a set of reactions in which breakdown of complex organic compounds occurs in absence of O2. CO2 and CH4 are produced as outputs of this process (Khanal, 2008). Figure 2.5 shows the reaction pathways in anaerobic digestion of animal manure.
In anaerobic digestion, four main types of bacteria function. These are facultative acid forming bacteria (acetogens), hydrogenogens, homoacetogens and anaerobic methane forming bacteria (methanogens) (Husain, 1997). Anaerobic digestion starts with hydrolysis of complex organic compounds by the extracellular enzymes (cellulose, amylase, protease, lipase) of acetogenic bacteria. Acetogenic bacteria then utilize dissolved O2 in the slurry or oxygen bounded in the compounds to convert monomers of organic material into propionate, butyrate and acetate.
36
Figure 2.5: Reaction pathways for anaerobic digestion of manure (Husain, 1997).
Acetogens grow in acidic pHs and by utilizing O2, they create the anaerobic conditions for hydrogenogens and methanogens to function (Khanal, 2008 and Husain,
1997). Following acidogenesis, hydrogenogens convert propionate, butyrate into acetate,
- formate (CHOO ), CO2 and H2. Methanogens can directly transform formate, CO2 and H2 into CH4. On the other hand; formate, CO2 and H2 can be converted into acetate by
37 homoacetogens (Khanal, 2008 and Husain, 1997). 30 % of methane production is performed by methanogens in conversion of formate, CO2 and H2 into CH4 (Husain,
1997). In the last step of anaerobic digestion, all acetate is converted into CO2 and CH4 by methanogens forming 70% of CH4 production (Husain, 1997).
Production and utilization of biogas has multiple benefits such as gaining a renewable fuel, GHG mitigation and improvement in hygienic conditions of the region where production is performed (Keshtkar et al, 2001).
2.3.2.3 Floating Drum Biogas Digester in Jhansi
The floating drum biogas digester we evaluate is shown in figure 2.6. It is of 60 m3 biogas capacity having a life time of 25 years and is situated in a cowshed in Jhansi,
Uttar Pradesh in India. The main body of plant is built using bricks and the floating drum
(gas holder) is made of steel. In a floating drum digester, the pressure of biogas is kept constant as the gas holder moves up and down with changing gas volume (Kalia and
Singh, 1999). For each m3 biogas production capacity, 25 kg of cow manure should be fed to the reactor daily with equal amount of water (Kalia and Singh, 1999).
Since cows are holy in India, scrap cows are not slaughtered, but kept in cowsheds after they are discarded. Our partner Development Alternatives established such a cowshed and utilizes the manure that cows produce to generate electricity from biogas and to supply energy needs for the income generation activities in their center.
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Figure 2.6: Floating drum biogas digester in cowshed in Jhansi and 7.5 VA electricity generator.
Currently, 345 kg cow dung is fed to the digester everyday with the same amount of water producing 8.5 m3 of biogas and slurry which is 50-75 % of the dry weight of cow dung fed (Kalia and Singh, 1999). The slurry produced can be utilized as organic
fertilizer. Biogas is 60-65 % consists of methane, rest being CO2 (Zhou et al, 2010).
Produced biogas is combusted in a 100% biogas operated generator producing 6 kWh of electricity on average daily. However, 850 kg cow dung should be fed to be able to supply all the energy demand of the center which constitutes the ideal case of 20 kWh of electricity generation from 28.5 m3 biogas per day. A third scenario investigated is full capacity case in which 1500 kg wet cow dung is fed to the digester and 60 m3 of biogas can be produced. In this case, 42 kWh of electricity can be generated daily. As can be
39 seen, amount of biogas produced per unit of cow dung increases as the plant is operated closer to full operation mode.
2.3.3. Biomass Gasifiers
In this section; types of biomass gasifiers, chemistry of biomass gasification and specifics of downdraft biomass gasifier situated in Orchha campus of Development
Alternatives is introduced.
2.3.3.1 Biomass Gasifier Types
The biomass gasifiers are classified according to the direction air or O2 moves within the gasifier. Hence, biomass gasifiers are classified as downdraft, updraft and crossdraft gasifiers. In updraft gasifiers, O2 or air is introduced from the bottom and it moves upward. In downdraft gasifiers, air is introduced from the middle of the gasifier and moves downward (Rajvanshi, 1986). In figure 2.7, different types of biomass gasifiers are shown.
In updraft gasifiers, biomass is fed from the top of the gasifier and combustion zone is located at the bottom where air is taken in. Here, dried and pyrolyzed biomass is converted into CO2 and H2O. In reduction zone, CO2 and H2O are reduced into CO and
H2.These combustible gases leave the gasifier from the top. Ash accumulates at the bottom of the gasifier (FAO, 1986). Updraft gasifiers can operate with multiple types of feedstock with low pressure drop and have high equipment efficiency. However, they are
40 very sensitive to the tar and moisture content of the feedstock (FAO, 1986 and Reed and
Das, 1988).
Figure 2.7: Different biomass gasifier types (APL (a)).
In downdraft gasifiers, feedstock is also fed from the top of the gasifier. Air or O2 is introduced to the gasifier from the middle and combustion zone is located after drying and pyrolysis zones. Reduction zone is at the bottom. In downdraft gasifiers, combustible gases are removed from the bottom of the gasifier and biomass and air move in the same direction (co-current). The tar problem in updraft gasifiers is solved in downdraft gasifiers since tar has to pass through the hot charcoal in reduction zone and is broken down. However, the producer gas from downdraft gasifiers has lower heat content and
41 certain size and density feedstock can be utilized for gasification (FAO, 1986 and Reed and Das, 1988).
Crossdraft gasifiers are designed for gasification of charcoal. Because of the high temperatures (1500oC and above) in gasification of charcoal, insulation for material damage is required and this insulation is provided by the feedstock charcoal (FAO,
1986). Crossdraft gasifiers can be feasible to operate even at small scales. However, their ability to breakdown tar is limited (Reed and Das, 1988 and FAO, 1986).
2.3.3.2 Chemistry of Biomass Gasification
Overall, biomass gasification is an incomplete combustion process. There are four regions in a biomass gasifier. These are drying, pyrolysis, combustion and reduction zones (Rajvanshi, 1986). In figure 2.8 reactions and changes taking place in each zone are introduced (APL (b)).
In drying zone, some of the heat of combustion is utilized to evaporate the water contained in the biomass. In essence, pyrolysis is a carbonization reaction. Charcoal and tar is produced in absence of air. Charcoal produced in pyrolysis step is utilized in reduction step as the reducing agent. Combustion supplies all the heat utilized in all other processes. Mixing and reaching high temperatures in combustion is vital for an overall adequate gasification. “Tarry gas” and charcoal is burned or oxidized in this step to produce CO2 and H2O. And in the last step reduction, CO2 and H2O are reduced in to H2 and CO forming the main portion of the producer gas. However, CH4, H2 and H2O are also produced in small amounts (Rajvanshi, 1986 and Reed and Das, 1988).
42
Figure 2.8: Process steps in biomass gasification (APL (b)).
The reactions taking place in combustion and reduction zones are presented in formulae 2.3-2.8.
Combustion Zone
C + O2 CO2 (2.3)
H +1/2 O2 H2O (2.4) 43
Reduction Zone
C + CO2 2CO (2.5)
C + H2O CO + H2 (2.6)
C + 2H2 CH4 (2.7)
CO2 + H2 CO + H2O (2.8)
2.3.3.3 Downdraft Biomass Gasifier in Orchha, India
The biomass gasification system under study is a 100 kW capacity downdraft biomass gasifier with 25 years lifetime and utilizing locally available biomass ipomea.
Ipomea has an energy content of 16 kJ /g on dry basis. (Pandey et al, 2012). Ready to use ipomea has 15% moisture and 10-15 cm in length (Development Alternatives, 2011). In figure 2.9, dried and cut ready to use ipomea feedstock in Orchha is seen.
The synthesis gas produced contains 15-30% CO, 10-20% H2, 2-4% CH4, 5-15%
CO2, 6-8% H2O and the remainder is N2 (Development Alternatives, 2011). In our calculations, the content of producer gas is assumed as 23 % CO, 15 % H2, 3 % CH4,
3 10% CO2, 7 % H2O and 42% N2 by volume. The density of producer gas is 1100 g/m and energy content is 4.7 MJ/m3. The system under study is an air blown gasifier which is the reason of high N2 content and low calorific value of the producer gas. Producer gas is then combusted in a diesel engine in dual fuel mode to generate electricity (Development
Alternatives, 2011).
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Figure 2.9: Ready to use ipomea and its storage in Orchha campus of Development Alternatives.
We consider three operation schemes in the analysis of the biomass gasification technology. Current case represents the current operating scheme in Development
Alternatives campus in Orchha. A diesel engine generates electricity, utilizing producer gas from the gasifier and diesel in dual fuel mode and produces 17420 kWh of electricity utilizing 20295 kg of ipomea and 1665 liters of diesel per year. The second scenario is ideal case operation in dual fuel mode. In this scenario, we assume the gasifier operates with 70% efficiency and 6 hrs per day generating 420 kWh electricity, resulting in 45
153300 kWh of electricity generation per year. In the second scenario, the plant utilizes
184000 kg of ipomea and 15330 liter of diesel per year. 153300 of kWh electricity is also generated in the third scenario considered. However, a natural gas engine is utilized operating with producer gas only in single fuel mode. In this fuel mode, 261000 kg of ipomea is utilized per year.
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Chapter 3: Methodology
3.1 Sustainability
Prior to introducing the background information regarding analysis techniques utilized in this study, it is worthwhile to explain what we refer to by sustainability given our aim of designing sustainable energy systems in a developing country context.
Sustainability is a complex-natured concept. One definition for sustainable development given by World Commission on Environment and Development (WCED) in
1987 is “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987). This definition is vague in terms of foreseeing what the needs of future generations will be and how the current needs will be met without compromising an unknown future (Marshall and
Toffel, 2005).
The vagueness in definition of sustainability is due to lack of unanimity in defining what is regarded as sustainable. To Costanza, sustainability is not a “definition”, but a “prediction” problem. To better objectify the sustainability concept, the “time and space scales” should be specified regarding what is considered as sustainable and unsustainable (Costanza and Patten, 1995). In nature, systems are interconnected within a hierarchical order through energy and material exchanges (Odum, 1996). Entities which are higher in the hierarchy encompass broader time and space scales. For instance, ecosystems have longer lifetimes than the species they contain or species survive longer 47 than an individual of the species. Hence, each entity has an expected life span increasing as the entity ascends higher in the hierarchy. Based on this fact, Costanza defines a sustainable system as “one attains its full expected lifespan within the nested hierarchy of systems within which it is embedded” (Costanza and Patten, 1995). Hence, actions which cause a system survive shorter than its expected life span diminishes that system`s sustainability. Pollutants shortening human life or excessive nitrogen and phosphorous flows released into water resources causing algal blooms affect the functionality of these systems negatively, thus reducing their sustainability (Costanza and Patten, 1995,
Marshall and Toffel, 2005). To achieve sustainability, actions supporting and enhancing the harmony of a system with its life support mechanisms should be adopted (Costanza and Patten, 1995). Resources utilized in a system should not be consumed faster than their renewal rate. Pollutant and toxic material emission rates should not be larger than their assimilation rate in an ecosystem (Marshall and Toffel, 2005).
Engineering efforts to achieve or improve sustainability are focused on increasing resource and energy use (Bakshi, 2011). However, this reductionist approach does not lead to sustainability. Given the complex nature of sustainability, analysis of systems in consideration should be performed multi-dimensionally adopting a holistic approach. The role of ecosystem services in supporting the systems analyzed should be accounted for.
Any system needs external energy or material inputs to sustain themselves. What matters in terms of sustainability is how renewable this energy and material inputs are and what the amount of consumption is within the carrying capacity of surroundings that the analyzed system exists (Szargut, 2005). For that reason the dependence on external
48 inputs, especially to non-renewable inputs, should be aimed to be reduced in analyzes and designs (Bakshi, 2011).The joint use of life cycle assessment (LCA) and emergy analysis
(EA) renders possible to analyze user side and donor side effects related to a process. It also becomes possible to account for the role of ecosystem services by enlarging the analysis boundary from “technosphere (LCA) to ecosystems (EA)” (Rugani and Benetto,
2012).
With these motivations a new “paradigm” or definition for sustainable development is stated by Griggs et al as “Development that meets the needs of the present while safeguarding Earth’s life-support system, on which the welfare of current and future generations depends”. Earlier triple bottom line approach which considers nature, environment and economy as components of sustainability is integrated in this paradigm, earth`s life support system being the most important aspect to sustain since all other system`s existence is supported by earth`s this support system (Griggs et al, 2013).
By this way, the vitality of the carrying capacity of ecosystems or Earth`s life support system is also acknowledged (Rocksrom, 2009).
In designing sustainable human systems; social, economic and environmental aspects should to be considered simultaneously. Furthermore, these systems must be in harmony with the society and biosphere they exist in (Griggs et al, 2013). And, sustainability can be achieved only if these systems are implemented in a way satisfying necessities arising from these aspects (Martin et al, 2010). Conscious consumption patterns relying on renewable resources within the carrying capacity of the planet can lead to the achievement of global sustainability.
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3.2 Life Cycle Assessment (LCA)
LCA is an analysis technique developed to evaluate the environmental impacts of a process or product due to emissions and resource use from “cradle to grave”
(Koornneef et al, 2008). Taking its roots from traditional energy analysis, LCA has evolved into “environmental burden” analysis in 1970s further improving into a “life cycle impact assessment” in 1980s and 1990s (Guinee et al, 2010). Starting from resource extraction, material processing, transportation, use, maintenance and disposal stages and their impacts are considered in LCA (Guinee and Heijungs, 2005).
In development of LCA, 1970-1990 were the “decades of conception”. From
1990 to 2000 has been the decade of “standardization by the efforts of Society of
Environmental Toxicology and Chemistry (SETAC) and International Organization of
Standardization (ISO) to create a framework for LCA. 2000-2010 has been the decade of
“elaboration” in which many support tolls for LCA were developed (Guinee et al, 2010).
3.2.1 Life Cycle Assessment Framework
According to ISO14040 standards, LCA framework consists of four stages. These are goal and scope definition, inventory analysis, impact assessment and interpretation
(ISO, 2006). Figure 3.1 presents the conceptual framework of LCA and its applications.
In goal and scope definition stage, LCA practitioner should define the motivation to perform the LCA and what kinds of outcomes are intended to be obtained. After the intended outcome is defined; system boundary to be analyzed, impact categories to be
50 calculated and functional unit based on which impacts per functional unit are calculated should be defined. The assumptions made for the analysis should also be defined in goal and scope definition stage (Curran, 2012).
Figure 3.1: LCA framework and its applications (ISO, 2006).
Inventory analysis is the “phase of life cycle assessment involving the compilation and quantification of inputs and outputs for a product throughout its life cycle” (ISO, 2006). In this stage, raw data related to the processes are gathered and presented in tables. Raw data includes the amounts of inputs utilized, products produced 51 and emissions released into air, water or soil. Generally, these data represents the amounts per day or per year. By this way, life cycle inventory (LCI) for the processes or products considered is constructed (Curran, 2012 and Guinée and Heijungs, 2005).
However, LCA is a data intensive and complex methodology. Creating life cycle inventory for numerous processes and products requires long time and intensive human effort. Moreover, since these data gathered from multiple sources by multiple people, uncertainties occur in data resulting in a decline in data quality (Guinee and Heijungs,
2005).
According to ISO, impact assessment is the “phase of life cycle assessment aimed at understanding and evaluating the magnitude and significance of potential environmental impacts of a product system throughout the life cycle of the product”. The calculated environmental impacts can be life cycle global warming potential (GWP), acidification potential, land use or water use etc (Guinee and Heijungs, 2005 and ISO,
2006).
Last stage of LCA is interpretation in which the methods to improve the current state of the system analyzed is defined according to the results of inventory analysis and impact assessment. Interpretation stage is defined as the “phase of life cycle assessment in which the findings of either the inventory analysis or impact assessment, or both, are evaluated in relation to the defined goal and scope in order to reach conclusions and recommendations” (ISO, 2006). Based on LCA analysis results and interpretation of these results, recommendations for process improvement for an existing process, favoring one production scheme over the other or policy making decisions can be made.
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3.2.1.1 Application of LCA
LCA can be employed to evaluate the total impacts (attributional LCA) related to a production process or to evaluate the effect of changes (consequential LCA) in an application (Rehl et al, 2012). Furthermore, LCA results can be utilized to determine inefficient process or life cycle steps as we do in clean coal technologies. Here, we determine that the energetic inefficiency of CLP compared to conventional process is because of the calcination energy consumed for regeneration of the sorbent and recommend the efforts to be directed to reduce the energy consumption in this step. Or,
LCA can be utilized to compare two products or processes based on their impacts and to favor one over the other (Xie et al, 2011 and Hurst et al, 2012).
3.2.2 Boundary Selection and Models Utilized in LCA
According to the boundary they account for, LCA can be applied at two scales utilizing two major models. These are process-LCA model and economic input- output
(EIO) model (Urban and Bakshi, 2009). While process-LCA accounts for the process itself and most important life cycle steps as the analysis boundary, EIO model considers the system boundary as a region or a country since it uses economic data belonging to that region or country (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)). Below, pros and cons of these models are discussed.
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3.2.2.1 Process-LCA Model
For LCA at the process scale, the process itself and most important life cycle steps are considered, secondary and tertiary effects are neglected. Hence, system boundary includes the process and significant steps in the supply chain of the process such as resource extraction and transportation and waste disposal. In this model, the process can be analyzed in a network of life cycle steps which are related to each other in sequence and process information is evaluated within this network. Then, lifecycle resource consumptions and emissions can be calculated. Although process-LCA model gives detailed information about the process, it underestimates secondary or tertiary effects (truncation errors) in the process life cycle. The underestimation of these indirect effects can cause large errors, furthermore wrong process data can reflect with greater impact in lifecycle results (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)).
3.2.2.2 EIO Model
The algebraic representation of input-output model is presented in figure 3.2.This model, developed by Leontief, utilizes monetary values of flows (inputs and outputs) belonging to a process and the system boundary is chosen as a region or a country since it uses economic data belonging to that region or country. Considering the system of interest within a network, the flows among the sectors for production of a certain amount of good can be related with this model, which means that total input is equal to the total output from a sector. The algebraic representation of relations among sectors opens many other doors for lifecycle study. Not only monetary flows but also energetic or mass flows
54 among sectors can be related by using input-output model. (Zhang et al, 2010 (a) Cruze,
2013).
Despite its intensiveness, input-output model suffers from high aggregation of data within sectors and errors resulting from this aggregation. The sectors in EIO are defined on the basis of product similarities not on the “similarities of production pathways, resource consumption or emissions” (Cruze, 2013). As a result, the life cycle inventory values given for a sector may not be representative of impacts related to a specific product.
Figure 3.2: Linear algebraic representation of input-output model.
By applying LCA at different scales, how the analysis results vary with changing analysis boundary can be determined. As it is stated, each method (process LCA or economy scale) has its own plusses or minuses. Instead of employing only one method,
55 performing analysis at all scales can give a better insight about the processes analyzed and enable benefiting from strengths of these methods (Urban and Bakshi, 2009). With this motivation, we utilized process scale and economy scale LCA in life cycle assessment of clean coal technologies since EIO data are available for GWP, water and land use impacts for US. However, LCA of localized energy options is performed at process-LCA scale only since EIO data for these impacts are not available for India.
3.2.3 Allocation in LCA
In multi- product processes, allocation is utilized to allot emissions or resources utilized among products in LCA studies. Besides, allocation enables to compare different processes producing the same products based on the impacts allocated to those products.
Process producing the same product with higher impacts becomes less favorable (Cruze,
2013). Allocation can be performed based on market value, mass, energy content or a common property that the products share. Allocation type chosen can change analysis results. Analyses of which results are robust with changing allocation strategy are desirable (Urban and Bakshi, 2009 and Cruze, 2013).
3.2.4 Net Energy Analysis
To evaluate energetic performance of clean coal technologies net energy analysis is utilized within LCA framework. Here, energy utilized in the life cycle of processes is calculated by using net energy analysis technique which only considers the energy originating from fossil fuels utilized in the lifecycle (Cleveland, 2005 and Mulder and
56
Hagens, 2008). Net energy analysis does not consider the energy quality differences among the fossil fuels utilized. In other words, one joule of coal energy is assumed to have the same quality with one joule of gasoline energy. Despite these drawbacks, net energy analysis is a fundamental tool for evaluation of energetic efficiencies of different processes.
Once the energy utilized in the processes is determined, energy return on investment (EROI) which is the ratio of energy obtained from products to processing energy can be calculated. For a process to be energitically profitable EROI should be larger than 1. The higher the EROI of a process, the more efficient the processes will be
(Cleveland, 2005 and Mulder and Hagens, 2008). Hence, based on calculated EROIs two clean coal processes we evaluate can be compared in terms of their energetic performance.
3.2.5 Environmental Impacts
In calculation of life cycle impacts, firstly amounts of inputs to and outputs from the system studied are quantified. After inputs and outputs are quantified for a specified time period (per day or per year); energy consumed, emissions and resources utilized for production of these inputs and for disposal of system wastes can be calculated. The energy consumption, emissions and resources utilized in each life cycle step are then added up to calculate cumulative life cycle impacts. Impacts calculated in a life cycle 57 study can be global warming potential (GWP), eutrophication, acidification, ozone depletion potentials; land use, water use and energy consumption in the life cycle of a product.
- Eutrophication potential is measured in g PO4 eq. / functional unit. Acidification potential is measured in g SO2 eq. / functional unit. Unit of ozone depletion potential is g chloro flouro carbon eq. / functional unit. Land use is expressed as the land area utilized in the life cycle of a product for production of its unit amount. Water volume utilized for unit production represents the life cycle water use. GWP is expressed as g CO2 eq. / functional unit (Ulgiati et al, 2010).
Greenhouse gases having global warming potential are CO2, CH4 and N2O. To convert CH4 and N2O into CO2 equivalents amount of CH4 is multiplied by 21and amount of N2O is multiplied by 310 for 100 years time interval of effects (EPA, 2013).
In our study, we calculate life cycle GWP because of its contribution to climate change.
We also calculate life cycle land and water resource uses due to importance and degradation of these natural resources.
3.3 Emergy Analysis
3.3.1 Fundamentals of Emergy Analysis
Emergy is “the total amount of available energy of one kind that is directly or indirectly required to make a given product or to support a given flow” (Odum, 96).
Therefore, we can employ different emergy types like coal emergy, oil emergy or solar emergy (Ulgiati et al, 2010). We utilize solar equivalent joules (sej) as our emergy unit
58 since solar energy is the driving force for all transformations in the nature and human activities. According to Odum (1996), solar energy is transferred among living things
(producers, primary consumers, predators) or in formation of energy sources. All ecosystems or human created economic systems take part in a network of energy transformations by concentrating and increasing the quality of lower quality energy into higher quality energy and waste heat (Hau, 2005). For instance, green plants convert solar energy into chemical energy via photosynthesis or a higher quality of energy, electricity is generated in coal power plants by combustion or gasification of coal. By this way, biological and economic systems sustain themselves and support each other by creating an “energy hierarchy” (Odum, 1996 and Hau, 2005).
Specific emergy or transformity of a resource or product is the solar emergy required for unit amount of production of that resource or product (Brown and Ulgiati,
2004). Transformity can be in units of sej/J, sej/g or sej/$. The higher a resource in the energy hierarchy, the higher its transformity will be since in energy transformations, the energy is concentrated and its quality is increased. Hence, transformity is accepted as an indicator of energy quality (Odum, 1996 and Hau, 2005). In other words, it takes more solar equivalent joules of energy or environmental work to make a higher quality product.
Figure 3.3 elaborates these energy transformations within ecosystems and economy related systems.
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Figure 3.3: Transformation of sun energy in nature (Adapted from Odum, 1996 and Hau and Bakshi, 2004).
Emergy of each input flow to a system can be calculated by multiplying specific emergy or transformity of the input by its quantity (energetic or material quantity). Then, total emergy input to the system can be calculated simply by adding emergy of all input flows (Odum, 1996 and Pizzigallo et al, 2008).
Utilizing solar emergy as a common denominator to represent emergy of all inputs makes it possible to account for quality differences among the resources (Odum,
1996 and Ulgiati et al, 2010). All other energy inputs to the earth which do not have solar origin such as crustal heat and tidal energy can be converted to solar equivalents by appropriate equivalence factors. Emergy accounts for all the environmental work spent back in time for formation of natural resources, creating an “energy memory”. In short, emergy quantifies environmental support demand by a process or product (Odum, 1996).
In emergy analysis, emergy input flows are classified as locally available renewable (R) and non-renewable (N) inputs and external purchased (P) inputs. With this classification,
60 it becomes possible to calculate emergy ratios as indicators of environmental performance of the process evaluated. These emergy ratios provide information about how much environmental support is required for the product or process in question, system renewability, system efficiency, load of system to environment and dependency of system on external purchased inputs (Odum, 1996 and Ulgiati et al, 2010).
In the following subsection, emergy analysis procedure will be introduced followed by a subsection regarding emergy algebra. Lastly, current improvements and application of emergy analysis will be discussed shortly.
3.3.2 Emergy Analysis Procedure
Emergy analysis is performed at three stages. Firstly, a systems diagram is drawn to identify emergy flows entering and leaving the system and emergy flows among system components. Then, an emergy table is constructed in which all input quantities, transformity values and calculated emergy values for each input are listed. In the last stage of analysis, emergy ratios are calculated according to ratios of the inputs classified as renewable (R), non-renewable (N) and purchased (P) (Odum, 1996, Paoli et al, 2008 and Brown et al, 2012).
3.3.2.1 Energy System Diagrams
Energy system diagram is a pictorial representation of the system under study.
The aim of beginning an emergy analysis with drawing an energy system diagram is to understand and to define energy and material flows entering the system outside the
61 analysis boundary, to determine the material and energy exchange flows and interactions among system components and understand interaction between the system investigated and its surroundings (Brown, 2004 and Odum, 1996). In other words, a pictorial model of the system analyzed is prepared by drawing an energy systems diagram.
Figure 3.4 lists important symbols utilized in drawing energy systems diagrams.
Here, arrows represent the material or energy flows, circles represent external forcing functions such as sun and rain or purchased inputs such as diesel or gasoline. Producer symbol generally represents green plants or items like algae. For instance, agriculture is represented by a producer symbol in energy diagramming of Rampura village. Storage represents any entity that accumulates within the analysis boundary. In energy systems diagramming of Rampura underground water and topsoil storages are represented as storages. Consumers are represented with hexagons and in Rampura livestock and domestic sector are the consumers represented by hexagons. Interaction symbol is utilized to identify interactions and transformations among system components (Odum,
1996 and Brown, 2004).
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Figure 3.4: Energy systems diagramming symbols (UFCEP, 2013).
3.3.2.2 Emergy Evaluation Table
Emergy evaluation tables are kind of accounting sheets in emergy analysis. An inventory of all inputs, outputs, their quantities for a specific time interval, transformity values for each input and calculated emergy values are all listed in an emergy evaluation table (Odum, 1996).
In table 3.1, a generic emergy evaluation table is presented. Total emergy input to the system is calculated by addition of all the emergy inputs and the transformity of each output is calculated by division of dedicated emergy input by the quantity of that output
(Odum, 1996).
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Table 3.1: A generic emergy evaluation table.
Item Data Transformity Solar Emergy Item (name) (flow/time) Units (sej/unit) (sej/time) 1 First item xxxx J/year xxxx Em1 Second 2 item xxxx g/year xxxx Em2 n.. nth ite xxxx J/year xxxx Emn
J/year or O Output xxxx g/year xxxx
3.3.2.3 Calculation of Emergy Ratios
In classifying emergy inputs to a system, free inputs which are provided by the nature such as solar radiation, wind, rain are categorized as renewable inputs. The amount of renewable inputs entering a system is flow-limited, one can not intervene to increase or decrease the amount of these flows. Non-renewable inputs are also local resources.
However, they are not necessarily free. Minerals such as quartz, ground water and topsoil are examples of non-renewable inputs. If the withdrawal rate of these inputs is slower than their regeneration rate by the nature, then these items are classified as renewable inputs. Purchased or feedback emergy flows are external inputs which are neither free nor locally available (Ulgiati and Brown, 1998).
In figure 3.5, classification of emergy inputs and interaction of natural systems with economic human systems are summarized. By classification of emergy inputs which contribute a system, calculation of emergy ratios becomes possible and inferences about system`s environmental performance can be made (Ulgiati et al, 2010).
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Figure 3.5: Classification of emergy flows to a system which is the basis for calculation of emergy ratios (Ulgiati and Brown, 1998).
The total emergy consumption of process or yield (Y) is equal to summation of all emergy inputs to the system (Y). Percent renewability (% Re) is the percentage of renewable emergy inputs to total emergy consumption. Emergy yield ratio (EYR) is the ratio of total emergy(Y) to purchased emergy (P). EYR is an indicator of process efficiency in terms of process` ability to use purchased inputs to produce a product.
Environmental loading ratio (ELR) is the ratio of non-renewable and purchased inputs to renewable inputs. A high ELR value indicates high stress on the environment due to the production process evaluated. Environmental sustainability index (ESI) is the ratio of
EYR to ELR. A high ESI indicates high output with low environmental loading. Lastly, environmental investment ratio (EIR) is ratio of purchased inputs to renewable (R) and non-renewable (N) local inputs. A high EIR value can indicate system fragility due to
65 high dependence to external resources (Odum, 1996, Paoli et al, 2008, Pizzigallo et al,
2008 and Ulgiati et al 2010). Calculation of emergy ratios according to the classification of emergy inputs is presented mathematically below.
3.3.3 Emergy Algebra
3.3.3.1 Co-products and Splits
As discussed in section 3.1.3, allocation in LCA can be performed based on market value, mass, energy content or a common property that the products share for a multi-product process (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)). Emergy analysis avoids allocation for co-products except splits and allocates all the emergy input to all the co-products or partitions total emergy input according to the available energy content of outputs if all the outputs of the system analyzed are known (Hau and Bakshi, 2004).
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The rules in assigning total emergy yield to outputs of a system are known as
“emergy algebra” (Brown and Herendeen, 1996). In emergy analysis, co-products are products which can only be produced jointly and have different purposes of use and properties (Bastionani et al, 2009). For instance, outputs of husbandry sector in Rampura which are animal draft, milk and cow dung are classified as co-products. When, emergy inputs are invested into husbandry sector, cows produce these products and it is not possible to produce these items separately. As a result, milk, animal draft and cow dung are co-products of husbandry sector in our analysis.
On the other hand, splits are products of similar characteristics with similar functionality and they can be produced separately (Odum, 1996 and Bastionani et al,
2009). In emergy analysis of agricultural sector in this work, agricultural products and residues are classified as splits since different crops can be grown independently from each other and they have similar functionality. Here, total emergy is allocated among them according to their available energy content.
Likewise; rain, wind, earth cycle are the co-products of earth processes. As a result, total emergy available to earth in a year is assigned to each of these co-products.
Their transformity is calculated by division of this total emergy by their available energy content (Odum, 2000).
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3.3.3.2 Rules of Emergy Algebra
The first rule of emergy algebra states that “All source emergy is assigned to the process outputs.” meaning the total emergy yield is assigned to each of the co-products
(Brown and Herendeen, 1996).
The second rule of emergy algebra is regarding splits. It states that “When a pathway (co-product) splits, emergy is assigned into each leg based on its percentage of the total energy flow on the pathway.” meaning splits obtain their share of emergy based on their available energy content (Brown and Herendeen, 1996).
Figure 3.6: Partitioning of total emergy among co-products and splits (Herendeen , 2004).
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Figure 3.6 elaborates partitioning of total emergy yield among co-products and splits according to the rules of emergy algebra. Here a total emergy of 500 sej is assigned to each of the co-products. Then, 500 sej is partitioned between two splits according to their energy content. The first leg has an energy content of 7 J and 350 sej is assigned to this leg. Remaining 150 sej is assigned to the second leg.
The third rule of emergy algebra sets the conditions to avoid double counting in emergy analysis. Third rule states that “Emergy cannot be counted twice within a system.
Co-products, when they are reunited, cannot be added to equal to a sum greater than the source emergy from which they are derived.” (Brown and Herendeen, 1996 and
Bastionani et al, 2009).
Hence emergy of splits are additive whereas emergy of co-products are not. In case of co-products the emergy of co-product with maximum magnitude is chosen
(Odum, 2000).
In our analysis, we choose emergy of rain and do not account for emergies of wind or earth cycle in calculating the total emergy input to our systems to avoid double counting since they are co-products of earth processes and rain has the emergy content with maximum magnitude (Odum, 2000).
3.3.4 Applications of Emergy Analysis
Emergy analysis does not have a standardized analysis framework creating problems of inaccuracy and reproducibility. Main area of criticism for emergy analysis is calculation of specific emergy values of inputs. It is claimed that they do not have fixed
69 values and calculated by vague, over simplified models (Rugani and Benetto, 2012 and
Hau and Bakshi, 2004). Bastoniani et al studied the transformity calculation of petroleum fuels and found only 1.7% higher transformity values than values calculated by Odum
(Bastionani et al, 2009). To create a framework and increase the reproducibility of emergy studies, efforts to integrate emergy within LCA framework and adapt emergy algebra to LCA calculation schemes have been made (Rugani and Benetto, 2012 and
Marvuglia et al, 2013). Marvuglia et al also study on a software to calculate emergy of systems and products by utilizing life cycle inventories (Marvuglia et al, 2013).
Emergy analysis can be utilized to evaluate self-sufficiency and dependence of a region on external inputs (Zhang et al, 2007) or it can be utilized to evaluate renewability and sustainability of an energy system (Ciotola et al 2011). Furthermore, emergy analysis can be utilized to compare the relative sustainability and environmental performance of different energy technologies as we did to compare five different energy technology options in this work. Comparison of solar electricity and electricity generated from fossil fuels has also been performed utilizing emergy analysis (Brown et al, 2012 and Paoli et al, 2008).
3.4 Joint Use of LCA and Emergy Analysis
In essence, emergy analysis and life cycle assessment (LCA) are two complementary analysis techniques. One technique can account for the factors the other cannot, so that using them jointly provides a more complete picture of the systems under study (Pizzigallo et al, 2008). Here, comparison of these two techniques will be presented
70 in terms of their underlying assumptions, methods, results obtained and related strengths and weaknesses that these techniques have. Then, we try to explain how they will be used in this research for designing sustainable energy systems.
3.4.1 Underlying assumptions
Comparison of LCA and emergy analysis can be done in terms of their underlying assumptions under three aspects: main considerations of the techniques, system boundary and allocation strategies.
LCA takes into account all life cycle stages starting from resource extraction, transportation, distribution, disposal/ recycle of a product and analyzes related impact and resource use in each stage, in other words, inputs and outputs to the system and their impacts are accounted for in LCA. In that sense, LCA is a “human-side” or user side evaluation technique ignoring the work biosphere done to form the natural resources.
Whereas, emergy analysis accounts for work nature has done to form all the inputs or natural resources to the system analyzed on the common denominator of solar equivalent joules (sej). So, in emergy analysis, solar emergy is assumed to be the driving force for all transformations in nature and human economic activities (Pizzigallo et al, 2010 and
Rugani and Benetto, 2012). Emergy analysis is nature- oriented or donor-side analysis technique, not being able to account for impacts of emissions from the system.
System boundary for LCA is either value-chain of the process analyzed (process-
LCA) or the economy in which that product is produced if LCA framework is integrated with economic input-output models. Economy considered can be at regional, national or
71 global scales. On the other hand, system boundary is the ecosystem which is the production process or product is integrated in for emergy analysis (Pizzigallo et al, 2010 and Rugani and Benetto, 2012). It can be said that application of emergy analysis enlarges analysis boundary from vale chain or economy to ecosystems.
LCA adopts partitioning impacts, inputs or outputs to products according to the mass, energy content or monetary value of the outputs as allocation strategy (Rugani and
Benetto, 2012). Emergy analysis avoids allocation for co-products except splits and allocates all the emergy input to all co-products if all the outputs from the system is not known or partitions emergy input according to the exergy (available energy) content of outputs if all system outputs are known (Hau and Bakshi, 2004).
3.4.2 Methods
Life cycle assessment methodology has a four-stage framework; namely goal and scope definition, life cycle inventory analysis, impact assessment and interpretation
(Guinee and Heijungs, 2005).Whereas, emergy analysis is performed at three stages.
Firstly, a systems diagram is drawn to identify emergy flows of inputs and among system components using energy systems language. Then, an emergy table is constructed in which all input quantities, specific emergy values (emergy needed for formation of per unit of product or input) and emergy values calculated by multiplying specific emergy and input quantities are listed for each of the inputs. In the last stage of analysis, emergy ratios are calculated by which conclusions about the investigated system can be withdrawn (Odum, 1996). Unlike LCA in emergy analysis, inputs to the system can be
72 classified as locally available renewable (R) and non-renewable (N) inputs and external purchased (P) inputs. With this classification, it becomes possible to determine how the system analyzed is dependent on external resources and how self-sufficient (Odum,1996).
3.4.3 Analysis Results
LCA provides results related to the emissions and resource use per functional unit chosen such as grams of product, kwh of power generated vice versa. Results regarding emissions can be life cycle GWP in g of CO2 equivalents per functional unit,
3- eutrophication potential in g PO4 equivalents per functional unit, acidification potential in g SO2 equivalents per functional unit and so on. Results regarding resource use can include life cycle water use (volume/functional unit), land use (area/functional unit), fossil fuel consumption (such as gallons of oil/functional unit or tons of coal/ functional unit) (Guinee and Heijungs, 2005 and Ulgiati et al, 2010). Life cycle energy consumption and a metric called energy return on investment (EROI) can also be among results of a
LCA study.
Emergy analysis results include total emergy consumption and emergy ratios providing information about how much environmental support is required for the product or process in question, system renewability, system efficiency, load of system to environment and dependency of system on external purchased inputs (Odum, 1996 and
Ulgiati et al, 2010). The total emergy consumption of a process is equal to summation of all emergy inputs to the system (Y) and represents extent of natural support invested into the system. Percent renewability (% Re) is the percentage of renewable inputs to total
73 emergy consumption. High % Re is an indicator of an environmentally sustainable system. Emergy yield ratio (EYR) is the ratio of total emergy(Y) to purchased emergy (P) and is an indicator of process efficiency in terms of process` ability to use purchased inputs to produce a product. Environmental loading ratio (ELR) is the ratio of non- renewable and purchased inputs to renewable inputs. A high ELR value indicates high stress on environment due to the production process evaluated. Lastly, environmental investment ratio (EIR) is ratio of purchased inputs to renewable (R) and non-renewable
(N) local inputs. A high EIR value can indicate system fragility due to high dependence of system to external resources (Odum, 1996 and Ulgiati et al, 2010).
As a result, LCA results provide information about emissions and amount of resources consumed, emergy results can provide information about how much environmental work is needed for formation of these resources, thus can account for role of biosphere in human activities.
3.4.4 Strengths and Weaknesses
Emergy analysis and LCA are two techniques which aid and complement each other especially in terms of embracing characteristic of a system when they are utilized jointly (Pizzigallo et al, 2008 and Ulgiati et al, 2010).
LCA is a well standardized analysis technique which can evaluate multiple downstream environmental impacts (multi-criteria analysis technique) due to resource use and emissions. However, it cannot account for the environmental work invested into natural resources for their formation. Since LCA is a human oriented or user-side analysis
74 technique, it cannot evaluate and quantify inputs having no economic value such as sun, rain, labor etc. Moreover, LCA cannot account for energy quality differences among natural resources utilized (Pizzigallo et al, 2008 and Guinee and Heijungs, 2005). Here comes emergy analysis into aid. With the main assumption that solar energy is the driving force for all transformations in nature, it can account for quality differences among all inputs to the system by evaluating them on the common denominator of solar equivalent joules since it takes different amounts of solar available energy for each input to form (Odum, 2005) Emergy analysis is a nature oriented or donor-side thermodynamic analysis technique. Emergy analysis can evaluate natural capital, sources not having monetary or market value. In other words, emergy analysis can quantify environmental work invested in natural resources and can relate natural systems to human economic systems (Hau and Bakshi, 2004). However, emergy analysis cannot address issues regarding user preferences since it cannot evaluate and quantify downstream impacts related to emissions and resource consumption. So, this is the point where LCA aids emergy analysis (Pizzigallo et al, 2008 and Hau and Bakshi, 2004).
3.4.5 Application
To design sustainable human systems; social, economic and environmental aspects should to be considered simultaneously. Furthermore, these systems must be in harmony with the society and biosphere they exist in (Griggs et al, 2013). And, sustainability can be achieved only if these systems are implemented in a way satisfying necessities arising from these aspects (Martin et al, 2010). Here, emergy analysis and
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LCA can capture and evaluate factors related to environmental dimension of sustainability (Pizzigallo et al, 2008 and Rugani and Benetto, 2012). When emergy analysis and LCA are used separately, they cannot take a complete picture for environmental sustainability, since emergy analysis can capture upstream effects and
LCA downstream effects only. However, if they are utilized jointly a decent evaluation of system can be preformed (Pizzigallo et al, 2008 and Ulgiati et al, 2010).
Figure 3.7: Integration of emergy analysis and LCA (Rugani and Benetto, 2012).
The underlying assumptions of LCA and emergy analysis make it possible to analyze effects at value-chain, economy (LCA) or ecosystem (emergy) scales given the system boundaries they consider. Starting from formation of natural resources to extraction, use and disposal related impacts and costs to the nature can be evaluated. And, allocation of these impacts and costs to products can lead understanding weaknesses and 76 strengths of processes in terms of their environmental performance (Pizzigallo et al,
2008, Rugani and Benetto, 2012 and Ulgiati et al, 2010). Figure 3.7 expresses this analysis boundary expansion via integration and joint use of LCA and emergy analysis.
These methodologies can create a basis for a systematic analysis leading to calculation of metrics to evaluate system`s environmental performance and comparison of different products and processes (Pizzigallo et al, 2008, Rugani and Benetto, 2012 and
Ulgiati et al, 2010).
Resulting metrics or methodology outcomes makes it possible to quantify system efficiency, renewability, dependency on external resources, polluting emissions and so on. With this information; inefficient, polluting or over-consuming system elements can be detected and process improvements, impact reductions can be performed via optimization. Or, system options having better environmental performance can be chosen over options having lower environmental performance for implementation (Pizzigallo et al, 2008 and Ulgiati et al, 2010).
By aiding LCA with emergy analysis for factors LCA cannot account for or the reverse, we think that the weaknesses of the analysis methods can be compensated and a close-to-complete picture can be taken in terms of important system factors, their impacts and costs to nature.
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Chapter 4: Analysis of Centralized Energy Options
According to the World Energy Outlook, demand for fossil energy will grow in absolute terms through 2035, and even though their contribution to the energy mix will decrease, these fuels will continue to dominate for at least the next few decades
(International Energy Agency (IEA), 2012). While liquid fossil fuels are mainly being used for transportation, coal is a major source for electricity generation worldwide (Fan et al, 2008). Energy use in developing countries is projected to equal and exceed the demand in developed countries by 2020 (EIA, 2011 and Kaygusuz, 2011). This increasing energy demand in developing countries is expected to be supplied mainly from fossil fuels, dominantly coal and oil now, and expected to be supplied in greater portions especially from coal in the future (Chandler et al, 2002 and British Petroleum, 2009).
Coal is and will continue to be a major energy source for humanity. Adoption of clean coal technologies can reduce energy related negative impacts, especially in developing countries given their tendency to utilize coal in greater portions in their future energy mix.
With this motivation, we evaluate two integrated gasification combined cycle
(IGCC) coal technologies with the main goal of producing fuel grade H2 along with electricity: conventional and calcium looping processes (CLP) implemented with CO2 sequestration. In the analysis, we compare these processes in terms of their environmental, energetic and economic performance. In the following first section, 78 background information about these processes is presented. Then, emergy analysis, LCA and economic assessment results are presented respectively.
Emergy analysis results include percent renewability (%Re), emergy yield ratio
(EYR), environmental loading ratio (ELR), environmental investment ratio (EIR) and environmental sustainability index (ESI). LCA results include global warming potential
(GWP), land use, water use per kWhe equivalents and energy return on investment
(EROI). Additionally, sensitivity analysis is performed to determine the effects of dominant variables on LCA results. In economic assessment, we calculate price of 1 kWhe equivalent energy generation.
4.1 Conventional and Calcium Looping Process
The assessment performed covers two studies: the conventional process studied by the National Energy Technology Laboratory (DOE-NETL) (NETL, 2008); and the calcium looping process (CLP) developed at The Ohio State University (Ramkumar et al., 2008). Each process uses Bituminous Illinois #6 coal. The conventional process shown in figure 4.1 utilizes 5430 tons of coal and produces 560 tons of H2 and 30.3 MWe per day. It employs a two-stage Selexol unit for sequential removal of H2S and CO2. The clean gas is then sent to pressure swing absorber (PSA) for purification of H2. After separation of H2, the remaining gas is used as fuel in a boiler to produce steam to be used in a steam turbine to produce electricity. CO2 produced in this step of the process is emitted resulting in 90 % CO2 sequestration efficiency in the whole process (NETL, 2008
79 and Fan et al., 2008). All inputs to the conventional process are shown in figure 4.1, where outputs from the system are circled.
The CL process shown in figure 4.2 utilizes 8000 tons of coal and produces 495 tons of H2, 266 MWe per day. There are mainly three stages in the CL process, namely carbonation, calcination and hydration of CaO sorbent for elongated activity. In the carbonation stage, the modified sorbent CaO reacts with CO2, H2S and halides in the syngas produced from gasification of coal. After CO2, H2S and halides are separated from the syngas, spent sorbent is sent to the calciner.
Figure 4.1: Conventional process simplified flow chart (Ramkumar, 2010).
Calcination is the regeneration step of the spent absorbent. In this stage sequestration-ready CO2 is produced and spent absorbent is regenerated. Finally,
80 regenerated absorbent is reactivated in the hydrator and re-fed to the carbonation reactor
(Ramkumar et al, 2008 and Fan et al, 2008). CL process integrates gasification, water gas shift reaction, CO2 and S separation steps into one process step. PSA exit gas is not used as a fuel in electricity production resulting in an emission-free technology. All the CO2 produced in this process can be sequestered or used for making other products.
Separation of sulfur as elemental sulfur rather than H2S increases the amount of H2 produced (Fan et all, 2008). Likewise, all inputs to CLP are shown in figure 4.2 and outputs from the system are circled.
Figure 4.2: CLP simplified flow chart (Ramkumar, 2010).
Slag, which is composed of coal ash and unburned coal constituents, forms the solid waste and is dumped back to the mine from where coal is originally supplied in conventional process (NETL, 2008). In CLP, slag is handled likewise. The spent sorbent
81 in CLP is composed of calcium carbonate, sulfate, hydroxide and chloride which cannot be regenerated further. As in case of solid wastes, the spent sorbent is dumped back to the coal mine (Ramkumar, 2010).
4.2. Emergy Analysis of Clean Coal Technologies
For emergy analysis of clean coal technologies, we utilized the Eco-LCA software
(OSU, 2013). Eco-LCA software has money/emergy ratios for all economic sectors of
2002 Producer Price Model. Economic value of each action for coal gasification per year is multiplied by these money/ emergy ratios to calculate emergy flow related to each activity.
The emergy of coal is the emergy for coal to be utilized in a process for engineering purposes (Odum, 1996). In other words, the emergy spent for coal mining and transportation is calculated in this analysis. It is likewise for limestone. Furthermore, emergy spent for power plant construction, production of equipment utilized for gasification, production of chemicals, disposal of wastes from the coal power plants are all calculated. Except water input all the emergy flows are purchased emergy flows.
Water is classified as 100% renewable local input. The energy system diagrams for conventional and CL processes is presented in figure 4.3.
The monetary values in the last column of tables 4.1 and 4.2 are entered to Eco-
LCA software as an input for corresponding sectors listed. For instance, sector 33329A other industrial machinery manufacturing sector is chosen for equipment production activity in ECO-LCA and 35.58 M$/year money flow is entered as an input.
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Figure 4.3: Energy system diagrams for conventional and CL processes.
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Eco-LCA multiplies the emergy/money ratio of sector 33329A with 35.58 M$ and
calculates the emergy flow belonging to equipment production activity.
Table 4.1: Economic data for conventional process: Activities, related sectors and monetary data (NETL, 2008).
Monetary Value Inputs Activity Sector (M$/year) Equipment 33329A Other industrial Production machinery manufacturing 35.58 Power Plant 230102 Non-residential Construction manufacturing structures 16.36 Coal Coal Mining 212100 Coal mining 60.40 Coal Transportation 482000 Rail transportation 30.20 Water 325188 All other basic Treatment Chemical inorganic chemical Chemicals Production manufacturing 0.840 325188 All other basic Chemicals inorganic chemical Active Carbon Production manufacturing 0.040 325188 All other basic Chemical inorganic chemical Shift Catalyst Production manufacturing 0.720 325188 All other basic Selexol Chemicals inorganic chemical Solutions Production manufacturing 0.470 Management of Wastes Waste Water 221300 Water, sewage and Water Disposal other systems 0.950 Ash, Spent Solid Waste 562000 Waste management Sorbent Disposal and remediation services 3.600
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Table 4.2: Economic data for CLP: Activities, related sectors and monetary data (Ramkumar, 2010).
Monetary Value Inputs Activity Sector (M$/year) Equipment 33329A Other industrial Production machinery manufacturing 57.11 Power Plant 230102 Non-residential Construction manufacturing structures 24.25 Coal Coal Mining 212100 Coal mining 88.96 Coal Transportation 482000 Rail transportation 44.48 Limestone 212390 Other non-metallic Limestone Mining mineral mining 5.150 Limestone Transportation 482000 Rail transportation 7.260 Water Treatment Chemical 325188 All other basic inorganic chemicals Production chemical manufacturing 0.840 Chemical 325188 All other basic inorganic Active Carbon Production chemical manufacturing 0.040
Management of Wastes Waste Water 221300 Water, sewage and Water Disposal other systems 0.760 Solid Waste 562000 Waste management and Solid Disposal Disposal remediation services 3.600 Rail Solid Purge Transportation 482000 Rail transportation 4.580
85
The emergy flows calculated for conventional and CL processes are presented in
emergy evaluation tables 4.3 and 4.4. Coal mining is the largest emergy flow for both of
the processes, being 79% in conventional process and 60% in CLP. Limestone mining is
second dominant emergy flow in CLP, 25% of total emergy yield. These results can be
seen in more detail in emergy signature diagram figure 4.4
Table 4.3: Emergy evaluation table for conventional process.
Monetary Value Exergy Transformity Emergy Inputs (M$/year) Activity ( J/year) (Sej/J)(Sej/g) (Sej/year) 35.58 Equipment Production 5.84E+19 16.36 Power Plant Infrastructure 1.69E+19 Coal (100 % F) 60.40 Coal Mining 5.08E+20 30.20 Coal Transportation 2.77E+19 Chemicals (100% F) 7 Chemical Production 2.94E+19 Water Treatment Chemicals(100 % F) 0.84 Active Carbon(100 % F) 0.04 Shift Catalyst(100 % F) 0.72 Selexol Solutions(100 % F) 0.47 Water (100 %R) 0.95 Waste Water Disposal 2.80E+18 Solid Disposal (100%F) 3.28 Solid Waste Disposal 1.70E+18 Outputs Emergy Yield 6.45E+20 Electricity(J) 5.44E+15 1.19E+05 6.45E+20
H2(J) 2.90E+16 2.22E+04 6.45E+20 Carbon Dioxide(g) 4.11E+09 1.57E+11 6.45E+20 Sulfur(g) 4.95E+07 1.30E+13 6.45E+20
86
Table 4.4: Emergy evaluation table for CLP
Monetary Value Exergy Transformity Emergy Inputs (M$/year) Activity ( J/Year) (Sej/J)(Sej/g) (Sej/year) 57.11 Equipment Production 9.55E+19 24.25 Power Plant Infrastructure 2.67E+19 Coal (100 % F) 88.96 Coal Mining 7.68E+20 44.48 Coal Transportation 4.15E+19 Limestone (100 % F) 4.67 Limestone Mining 3.29E+20 7.26 Limestone Transportation 6.80E+18 Chemicals (100 % F) 0.88 1.31E+19 Water Treatment Chemicals (100 % F) 0.84 Chemical Production Active Carbon (100 % F) 0.04 Chemical Production Water (100 % R) 0.63 Waste Water Disposal 2.10E+18 Solid Disposal (100 % F) 3.28 Solid Waste Disposal 1.70E+28 Solid Purge (100 % F) 4.58 Rail Transportation 4.25E+18 Emergy Yield 12.9E+20 Outputs Electricity(J) 1.62E+16 7.95E+04 12.9E+20
H2(J) 2.57E+16 5.03E+04 12.9E+20 Carbon Dioxide(g) 6.87E+09 1.88E+11 12.9E+20
Almost all the emergy flows belonging to clean coal technologies are imported
purchased emergy flows excluding the emergy flow for water. Furthermore, total emergy
yield for CLP is two-fold of conventional process. Additionally, power plant construction
and equipment emergy flows of CLP are larger in absolute terms and their share in total
emergy yield is larger compared to conventional process. To be able to create the
integrated system in CLP, more equipment is utilized, creating a more massive system.
All of these reasons increase the resource and energy consumption in the operation of
CLP system.
87
1.4E+21 Solid Purge 1.2E+21 Solid Disposal Water 1E+21
Chemical Production
8E+20 Limestone Transportation Limestone Mining 6E+20 Sej/year Coal Transportation 4E+20 Coal Mining
2E+20 Power Plant Infrastructure Equipment Production 0 CLP Conventional
Figure 4 4: Emergy signature diagram of conventional and CL processes.
After emergy flows are classified and quantified; total renewable, non-renewable and purchased emergy flows are calculated. Calculation of these emergy flows enables calculation of emergy ratios and indices to evaluate the overall sustainability of two systems compared. Table 4.5 presents the results for product transformities and emergy indicators for CLP and conventional process. Although total emergy yield of CLP is two- fold of conventional process, the transformity of electricity generated in CLP is lower because of significantly higher electricity generation (266 MWe/day vs 30.3 MWe/day) meaning that less environmental support is needed for generation of 1J of electricity in
CL process. On the other hand, transformities of products CO2 and hydrogen in CLP are higher compared to conventional process since more hydrogen and CO2 are produced in 88 conventional process despite its lower total emergy yield. Both CLP and conventional process have very low % renewability due to the dominance of purchased inputs to the systems. The dominance of purchased inputs causes both processes to have low EYR values. The share of purchased emergy inputs in CLP is higher and the share of renewable inputs is lower compared to the conventional process. Because of that reason,
CLP has higher ELR and EIR values compared to conventional process. Having higher
ELR and almost the same EYR, CL process` ESI is one thirds of conventional process.
Although conventional process seems more sustainable compared to CLP, both processes have extremely low ESI values, high ELR, EIR values and low % Re and EYR. All these results confirm that these systems are unsustainable in environmental terms.
Table 4.5: Product transformities, total emergy yield, emergy ratio and indices for conventional and CL processes.
CLP Conventional Total Emergy Yield 1.29E+21 6.45E+20 Transformity of Electricity(Sej/J) 7.95E+04 1.19E+05 Transformity of Hydrogen(Sej/J) 5.03E+04 2.22E+04 Transformity of Carbon Dioxide(Sej/g) 1.88E+11 1.57E+11 %Re 0.16% 0.43% EYR 1.00 1.01 ELR 613 229 EIR 611 229 ESI 0.0016 0.0044
89
4.3 Life Cycle Assessment (LCA) of Clean Coal Technologies
4.3.1. Life Cycle Assessment
Life cycle assessment of the processes is performed at two scales: process- LCA and economy scales. These two scales differ in terms of the analysis boundary they account for as discussed in analysis techniques chapter. Furthermore, equipment scale calculations are performed where data are available. Here, we present how different scales of LCA are applied to our systems. Additionally, conversion of hydrogen energy into electricity equivalents (energy quality accounting) and allocation of energy use at different scales among products within LCA framework is discussed.
The main products of the processes we study are electricity and H2. Only in conventional process, small amount of sulfur is produced. Considering sulfur as a byproduct, we attribute all the environmental impacts to electricity and H2. Likewise, we consider electricity and H2 (products having energy content) in EROI calculations.
In allocation, CO2 produced in the processes is also considered as a product together with sulfur in conventional process to be able allocate energy utilized among all outputs of coal gasification processes examined. In the following sections, detailed explanations are presented.
4.3.1.1 Equipment Scale
The equipment scale considers the boundary of the typical engineering flowsheet.
Thus, only direct inputs, outputs, and products are considered. The data utilized at this
90 scale is the process level data including direct input and output amounts supplied by
NREL (NREL, 2012) and Ohio State University team (Ramkumar, 2010).
The amount of water per kWhe equivalents generated is calculated by simply dividing direct water use amounts by the kWhe generated in the processes. Likewise, GWP of conventional process per unit generation is calculated by dividing GHG emissions from the stack to the electricity equivalent generation of the processes. CLP does not have
GHG emissions as stated in the background information about processes. At this scale,
EROI calculation is not performed since the energy utilized for production of co-products electricity and H2 is supplied from feedstock coal and does not go beyond classical process efficiency calculation (Mulder and Hagens, 2008 and Cleveland, 2005).
4.3.1.2 Process LCA Scale
For LCA at the process scale, the process itself and most important life cycle steps are considered, secondary and tertiary effects are neglected. Hence, system boundary includes the process and significant steps in the supply chain of the process such as resource extraction and transportation and waste disposal. In this model, the process can be analyzed in a network of life cycle steps which are related to each other in sequence and process information is evaluated within this network. Then, lifecycle resource consumptions and emissions can be calculated (Zhang et al, 2010(a) and Zhang et al, 2010(b)).
The boundaries and life cycle steps considered for process LCA scale is given in figures 4.5 and 4.6. Coal mining, transportation of coal to power plant, limestone mining
91 and transportation, solid disposal (ash and other solid wastes) and disposal of spent sorbent which is named as solid purge are the life cycle steps considered for CLP. For conventional process, these steps are coal mining and transportation and solid disposal since limestone is not an input for conventional process.
The data utilized for the life cycle steps considered at process LCA scale analysis is obtained from NREL life cycle assessment database (NREL, 2012). At this scale, only
EROI and GWP calculations are performed due to lack of land and water use data for process LCA scale. Energy utilized and GHG emissions in each life cycle step added up to find the life cycle impacts. Calculation of EROI and GWP per kWhe equivalents generated is explained in calculations section in detail.
Figure 4.5: Process LCA scale boundary for calcium looping process.
92
Figure 4.6: Process LCA scale boundary for conventional process.
4.3.1.3 Economy Scale
For economy scale calculations, economic input output model is utilized. This model considers the system boundary as a region or a country since it uses economic data belonging to that region or country. Developed by Leontief, model uses monetary values of flows (inputs and outputs) belonging to a process.
The system boundary and life cycle steps accounted for are given in figure 4.7. For hybrid economic scale assessment, Eco-LCA software is utilized combined with process data. Monetary values of input and output flows per year are calcuated based on annual utilization amount of inputs and annual production of wastes and products multiplied by their unit costs. All information about inputs, outputs, wastes and unit costs comes from process information (NETL, 2008 and Ramkumar 2010).
93
Eco-LCA utilizes the U.S National Producer Price Model for 2002 including 428 sectors. Using this input-output model, we calculate EROI, land use, water use and GWP including steps of power plant construction, plant equipment production, coal mining and transportation of coal via railway, limestone mining and limestone transportation via railway, solid purge, solid and waste water disposal and production of chemicals used in the process for CLP. We include the same steps except limestone mining and transportation and solid purge for the conventional process. The monetary values for each life cycle step is fed as an input to the Eco-LCA software, energy or resource use or
GHG emissions corresponding to each life cycle step are added up giving the total life cycle energy, resource use and GWP.
In calculations, we assume that the life time of each plant is 20 years, the rail system is used for transportation of coal and limestone from 240 km distance and one third of coal price is spent for transportation (NETL, 2008 and EIA, 2010). These assumptions are based on the study performed by the National Energy Technology
Laboratory (DOE-NETL) with the target of generating electricity not more than 30% price increase by 2030 (NETL, 2008). Main aim is here is to meet the energy demand with reasonable prices while reducing the energy related GHG emissions (NETL, 2008).
Solid disposal is an important issue in terms of pollution created by coal power plants.
The conventional process produces 617 tonnes of solid waste per day and CLP produces around 1500 tonnes of spent sorbent per day in addition to the solid wastes. We assume that these wastes are dumped back to the coal mine from which the coal is supplied via railway transportation.
94
Figure 4.7: Economy scale boundaries for calcium looping and conventional processes.
95
4.3.1.4 Energy Quality Accounting
Different energy sources have different available energy content, in other words, different work performing capacity. For instance, 1 J of oil energy does more work than 1
J of coal. When coal energy is converted into oil energy equivalents, coal energy is multiplied by a conversion factor originating from this different work performing capacity or their efficiency in generation of the same amount of electricity (Cleveland,
1992). With the same mentality, we account for the energy quality difference between co- products electricity and H2. In fuel cells in electric cars, H2 is converted into electricity with an average efficiency of 50% (Thomas, 2009 and EIA, 2008). In other words, to generate 1 J of electrical energy 2 J of H2 energy is required. Hence, we account 1 joule of electrical energy equivalent to 2 joules of H2 energy in our study.
In EROI, calculations co-product electrical energy is multiplied by 2 for energy quality considered cases and is assumed to have the same quality with H2 energy in no energy quality considered cases.
In calculation of per kWhe environmental impacts of land use, water use and
GWP, H2 energy is converted into electrical energy equivalents simply by dividing by 2.
Hence, total energy production in kWhe equivalents is calculated.
By means of energy quality accounting, we are able convert co-product H2 into electricity equivalents and calculate overall EROIs and environmental impacts per kWhe for the processes. Without energy quality accounting, calculating process EROIs and environmental impacts would not be possible for the multi-product processes we study.
As a result, all environmental impacts and EROIs calculated are for the processes.
96
Allocation of energy utilized in the life cycle of processes among the products and calculation of product EROIs and energy consumption per kg of sequestration ready CO2 is explained in allocation section 4.3.1.5.
4.3.1.5 Allocation
The amounts of electricity, H2 produced and CO2 emitted in the processes compared are different. To be able to make a fair comparison, calculating the energy spent to produce unit amounts of these products are essential (Zhang et al, 2010(a) and
Zhang et al, 2010(b)) So that, the energy spent in the life cycle of processes to produce the products at process LCA and economy scales is allocated among the products. Here, we choose monetary value as the basis of our allocation since this value is the common property of products considered and forms the common basis that they can be evaluated together. Allocation based on mass or energy content was not possible since electricity does not have mass in that sense and CO2 does not have energy content. Monetary data
for electricity and H2 comes from the process information (NETL, 2008 and Ramkumar,
2010) and monetary value for CO2 is calculated based on carbon trading market value in
Europe (Analysis of the European CO2 Market, 2012 and X-Rates, 2011).
As a result, we calculate product EROIs for electricity and H2 since they have energy content themselves and MJ of energy spent per kg of CO2 for allocation of spent energy among products.
97
4.3.1.6. Sensitivity Analysis
Sensitivity analysis is performed to see the effects of dominant life cycle steps on life cycle results. By increasing or decreasing related amount of resource or energy use
10%, the changes in overall life cycle results have been determined. In life cycle results section the total ranges originating from joint effect of these dominant factors are presented. Effects of all individual factors are presented in sensitivity analysis results in more details.
4.3.1.7 Calculation of Life Cycle Impacts
In calculation of life cycle impacts, firstly amounts of inputs to and outputs from the system studied are quantified. After inputs and outputs are quantified for a specified time (per day or per year); energy consumed, emissions and resources utilized for production of these inputs and for disposal of system wastes can be calculated.
At process LCA scale, energy utilized (related EROIs) and green house gas (GHG) emissions per kWh electricity (kWhe) equivalents are calculated. Data provided by
National Renewable Energy Laboratory (NREL, 2012) at process LCA scale includes different fossil fuel consumptions and related emissions for per tonne of coal, limestone mining and coal, limestone and solid waste transportation per tonne kilometer (tkm).
When the total quantity of inputs and outputs are multiplied by the multipliers of energies and emissions in each life cycle step and added up in the end, life cycle GWP and energy
98 use as a consequence EROIs at process LCA scale can be calculated (Guinee et al, 2011 and NREL, 2012).
At economy scale, after the quantities of inputs and outputs per year are quantified, monetary values of these entities per year are calculated. The monetary values for each life cycle step are fed as an input to the Eco-LCA software (OSU, 2013). Similar to process LCA scale; life cycle GHG emissions, energy, water and land use results calculated at each life cycle step by this software added up giving the total life cycle energy, resource use and GWP.
4.3.2 Land Use
Conventional process has a-342,240 metric tonnes CO2 eq. higher GWP than CLP in absolute terms and assuming that 1 hectare (10,000 m2) forest land sequesters 6.6 metric tons CO2/year (Rodriguez and Nebra, 2010), we conclude that the real land use by conventional process is almost 6 fold of CLP land use because of the additional land use needed to offset the extra CO2 emitted in the process. For each kWhe equivalents produced, land use is found to be 1.77x10-2 m2 for CLP and 1.35x10-1 m2 for conventional process.
According to life cycle land use results, coal mining and coal transportation are the most land utilizing life cycle steps for CLP and CO2 offsetting for conventional process as can be identified from the stacked bar graph in figure 4.8.
Sensitivity analysis has been performed for these dominant steps and the difference in the amount of land area utilized by two processes is out of the ranges determined in
99 sensitivity analysis (detailed figures are in sensitivity analysis, here cumulative range originating from these factors is given). Hence it can be stated that, CLP utilizes significantly less land area than conventional process for the case where CO2 offsetting is considered. If CO2 offsetting is not considered, CLP process utilizes slightly higher land area per unit of power generation (0.018 m2/kWhe versus 0.014 m2/kWhe).
1.60E-01
1.40E-01 Solid Purge CO2 Offset 1.20E-01 Waste Water
1.00E-01 Chemical Production
Solid Disposal 8.00E-02
Limestone Transportation m2/kWhe 6.00E-02 Limestone Mining Coal Transportation 4.00E-02 Coal Mining 2.00E-02 Power Plant Infrastructure Equipment Production 0.00E+00 CLP Conventional
Figure 4.8: Life cycle land uses (m2/kWhe) for CLP and conventional process at economy scale.
4.3.3 Water Use
At the equipment scale conventional process utilizes 1.85 liter and CLP utilizes
1.1 liter water per kWhe generated. For CLP, the dominating factors are water use in cooling tower and hydration steam at the equipment scale. Water utilization in this scale 100 is more sensitive to changes in the cooling tower water use. Equipment scale dominating factors for conventional process are cooling tower, condenser make-up and shift steam water uses. Like CLP, conventional process water use is more sensitive to utilization in the cooling tower. Reductions in cooling water can reduce equipment scale water use significantly. Shift steam has the same effect as condenser make-up water use on overall equipment scale water utilization. Considering the ranges determined in sensitivity analysis (detailed figures are in sensitivity analysis section, again here cumulative ranges are given), we conclude that conventional process utilizes significantly more water than
CLP in the equipment scale.
CLP consumes slightly more water in total amount in economy scale (1.3x1010 liter versus 1.22x1010 liter), however significantly more electrical energy is generated in
CLP, hence, CLP is superior to conventional process in terms of life cycle water use
(2.21 liter /kWhe versus 2.84 liter/kWhe) for production of unit amount of power
(kWhe).
As can be seen from figure 4.9, most dominant water utilizing steps in the life cycle are equipment scale and coal mining water uses for both CLP and conventional process. Reductions in process water utilization can reduce life cycle water use significantly. Sensitivity analysis results again confirm that CLP utilizes significantly less water compared to conventional process in economy scale (detailed figures are in the sensitivity analysis section, in this text cumulative range for changes are given).
101
3.50E+00
Solid Purge 3.00E+00 Solid Disposal
2.50E+00 Waste Water
Chemicals Production
2.00E+00 Limestone Transportation Limestone Mining 1.50E+00 liter/kWhe Coal Transportation 1.00E+00 Coal Mining Power Plant Infrastructure 5.00E-01 Equipment Production Equipment Scale 0.00E+00 CLP Conventional
Figure 4.9: Equipment scale and economy scale water uses (liter/kWhe) for CLP and conventional processes.
4.3.4 Global Warming Potential:
We performed GWP calculations at equipment, process LCA and economy scales.
These results are summarized in table 4.6. CLP gives significantly better results from global warming potential point of view given the sensitivity analysis results in all scales
(detailed figures are in the sensitivity analysis section, in this text cumulative ranges are given).
As can be seen from the graphs in figures 4.10 and 4.11, equipment scale GWP
(which is in essence the CO2 emitted from stack in conventional process) is the most dominant step for GHG emissions of conventional process both in process LCA and economy scales, followed by coal mining in economy scale only. For CLP, transportation
102 and mining of coal are most GHG emitting life cycle steps both in process LCA and economy scales. At the equipment scale, CLP is emission free in terms of GHG.
Table 4.6: Equipment, process LCA and economy scale GWP results for CLP and conventional process
CLP Conventional CLP Conventional (gCO2eq) (gCO2eq) (gCO2eq./kWhe) (gCO2eq/kWhe) Equip. Scale GWP 0.00E+00 5.17E+11 0 120E+02
Process LCA GWP 2.63E+11 6.93E+11 4.48E+01 1.61E+02
Economy Scale GWP 5.12E+11 8.54E+11 8.69E+01 1.99E+02
2.50E+02 Solid Purge
2.00E+02 Solid Disposal Waste Water
Chemicals Production 1.50E+02 Equipment Scale Limestone Transportation
1.00E+02 Limestone Mining gCO2eq./kWhe Coal Transportation 5.00E+01 Coal Mining Power Plant Infrastructure
0.00E+00 Equipment Production CLP Conventional
Figure 4.10: Global warming potentials (gCO2 eq. /kWhe) for CLP and conventional process at economy scale.
103
200.0
180.0
160.0
140.0
Limestone Transportation 120.0 Limestone Mining 100.0 Equipment Scale
80.0 Coal Transportation gCO2eq/kWhe
60.0 Coal Mining
40.0
20.0
0.0 CLP Conventional
Figure 4 11: Global warming potentials (gCO2 eq. /kWhe) for CLP and conventional process at process LCA scale.
4.3.5 Energy Return on Investment (EROI) and Energy Utilized
Energy analysis of the two processes compared is performed at three scales in terms of energy utilization per kWhe equivalents generated. Energy analysis at the equipment scale reveals that the most energy consuming process unit is ASU air/O2 compressor followed by calcination energy in CLP. The sensitivity of results to changes in calcination energy is highest when energy quality of product electricity is not considered. When energy quality is considered sensitivity to ASU air/O2 compressor energy becomes higher and dominates calcination energy since it utilizes electricity, a higher quality energy source. In conventional process, ASU air/O2 compressor, CO2 compressor and acid gas removal energy consumptions are the most energy consuming 104 steps for equipment scale where the sensitivity to changes is in descending order of ASU compressor, CO2 compressor and acid gas removal. This situation does not change whether energy quality is considered or not.
At the process LCA scale, coal mining and transportation of coal are the most energy consuming life cycle steps for both CLP and conventional process, coal mining being the factor consuming more than 80% of energy utilized. Sensitivity of results to the changes in the amount of coal is highest whether energy quality is considered or not.
Electricity is not utilized in these steps so energy quality is not an issue (detailed figure is in sensitivity analysis section, here cumulative ranges are given).
At the economy scale, coal mining and production of equipment utilized in coal power plant are the most dominant factors for both processes. Again, sensitivity of results is highest to coal mining irrespective of energy quality consideration similar to process
LCA scale (detailed figure is in sensitivity analysis section, cumulative ranges are given in this text).
Another major outcome of evaluation is that more energy is utilized for generation of unit amount of power (1 kWhe) in CLP, the main reason of lower energetic efficiency of CLP. However, the difference in energy use per kWhe generation between
CLP and conventional process is not significant according to sensitivity analysis results as can be seen in figure 4.12.
105
5.00E+05
4.50E+05 Solid Purge 4.00E+05 Waste Water 3.50E+05 Chemicals Production 3.00E+05
Solid Disposal
2.50E+05 Limestone Transportation
J/kWhe Limestone Mining 2.00E+05 Coal Transportation 1.50E+05 Coal Mining 1.00E+05 Power Plant Infrasturucture
5.00E+04 Equipment Production
0.00E+00 CLP Conventional
(a)
3.50E+05
3.00E+05
2.50E+05 Solid Purge
LimestoneTransportation 2.00E+05 Limestone Mining
J/kWhe 1.50E+05 Solid Disposal Coal Transportation 1.00E+05 Coal Mining 5.00E+04
0.00E+00 CLP Conventional
(b)
Figure 4 12: Energy utilized per kWh for CLP and conventional processes at economy (a) and process LCA (b) scales. 106
EROI calculations are performed at process LCA and economy scales. We performed the calculations with and without considering differences in energy quality.
EROIs for conventional process are higher than CLP EROIs irrespective of scale and energy quality consideration which proves that conventional process is more energy efficient process as can be seen in figure 4.13. As can be concluded also from energetic evaluation, lower efficiency of CLP is due to higher amount of inputs needed for unit power generation in CLP.
Sensitivity analysis is performed for the factors defined in energy evaluation as dominant. These are coal mining in process scale and coal mining and equipments production in economy scale. As a general tendency, the sensitivity of EROI results to the changes in these factors reduces when energy quality of product electricity is considered
(detailed figures are in sensitivity analysis section, cumulative ranges are give here).
Overall, sensitivity analysis results of energy consumption shows that CLP consumes more energy per kWhe than conventional process, however the difference in energy consumption per kWhe is within the margin of error when energy quality of product electricity is considered. Hence, we conclude that this difference is not significant in this case. On the other hand, CLP has significantly lower EROIs compared to the conventional process when the energy quality between co-products electricity and
H2 is not considered.
107
35
30
25
20
EROI 15 CLP 10 Conventional
5
0 Energy Qual No Energy Energy Qual No Energy Cor. Qual. Cor. Cor. Qual. Cor. Process LCA Scale EROI Economy Scale EROI
Figure 4 13: Energy return on investment (EROI) results for CLP and conventional processes at process-LCA and economy scales for energy quality corrected and not corrected cases.
4.3.6 Allocation
The amounts of electricity, H2 produced and CO2 emitted in the processes compared are different. To be able to make a fair comparison of the processes, calculating the energy spent to produce unit amounts of these products are essential
(Cleveland, 2005 and Guinée et al, 2011). So that, the energy spent in the processes to produce the products at process LCA and economy scales is allocated among the products. Here, we choose monetary value as the basis of our allocation since this value is the common property of products considered and forms the common basis that they can be evaluated together. Allocation based on mass or energy content was not possible since electricity does not have mass in that sense and CO2 does not have energy content. We calculate product EROIs for electricity and H2 since they have energy content themselves
108 and MJ of energy spent per kg of CO2. In conventional process, elemental sulfur is produced which is also accounted for. We are not considering energy quality in allocation, because energies consumed in process LCA and economy scales do not include electricity. When energy quality is considered for EROI calculation of product electricity, EROI doubles, for other products H2 EROI and for CO2 energy spent per kg of CO2 equivalent remains the same. Since energy utilized in equipment scale all originates from feedstock coal, allocation and product EROI calculation has not been performed for this scale.
Allocation results for economy and process LCA scale energy consumptions reveal that CLP has lower product EROIs for electricity and H2 and higher MJ/ kg values for CO2 at both scales supporting the earlier conclusion that the conventional process is more energy efficient.
45.00 40.00 35.00 30.00
25.00
EROI 20.00 CLP 15.00 Conventional 10.00 5.00 0.00 Process Economy Process Economy LCA Scale Scale LCA Scale Scale Electrcity EROI H2 EROI
Figure 4.14: Product EROIs for CLP and conventional process as a result of monetary allocation at economy and process LCA scales. 109
0.14
0.12
0.10
0.08 CLP 0.06
Conventional MJ/kgCO2 0.04
0.02
0.00 Process LCA Scale Economy Scale
Figure 4.15: MJ/kg CO2 values for CLP and conventional process at economy and process scales.
4.3.7 Sensitivity Analysis
4.3.7.1 Sensitivity Analysis of Land Use
Land use analysis is done at economy scale only. For CLP the most dominant steps in the life cycle were coal transportation and the amount of coal mined in terms of land use.
When the amount of coal transported is increased and decreased by 10 %, we can see that land area utilized per kWh electricity generated has a high sensitivity to changes in amount of coal transported. Whereas the sensitivity against the changes in the amount of coal mined is less compared to the amount of coal transported.
110
1.90E-02
1.80E-02
1.70E-02
1.60E-02
1.50E-02 /kWhe
2 1.40E-02 m 1.30E-02
1.20E-02
1.10E-02 Coal Transportation Coal Mining 1.00E-02
Figure 4.16: Dominating factors in the life cycle of CLP for land use: Amount of coal transported and coal mined at the economy scale.
For conventional process the factor constituting more than 90% of land area utilized is for offsetting of the CO2 emitted from the stack. In conventional process, 90% of the CO2 produced is sequestered and 10 % is emitted from the stack as a result of electricity production. Changing percentage of CO2 has an enormous effect on the largeness of land area utilized by the process, in other words, amount of land area utilized in conventional process is most sensitive to the changes in the percentage of CO2 emitted from the stack.
111
1.60E-01
1.40E-01
1.20E-01
1.00E-01
/kWhe 2
m 8.00E-02
6.00E-02
4.00E-02
Figure 4.17: Sensitivity of conventional process to changes in the percentage of GHG (CO2eq) emitted from the stack at the economy scale.
4.3.7.2 Sensitivity Analysis of Water Use Results
Water use is calculated in equipment and economy scales due to the lack of data in process LCA scale. For CLP, the dominating factors are water use in cooling tower and hydration steam in equipment scale. Water use in this scale is more sensitive to changes in the cooling tower water use. In economy scale, equipment scale water use and utilization in coal mining were dominating factors and reductions in equipment scale water use can reduce life cycle water use significantly.
112
1.20E+00
1.10E+00
1.00E+00
9.00E-01 Cooling Tower
8.00E-01 Hydration Steam liter/kWhe 7.00E-01
6.00E-01
5.00E-01
Figure 4.18: Dominating factors of CLP for water use at the equipment scale: Cooling tower and hydration steam water uses.
2.40E+00
2.20E+00
2.00E+00
1.80E+00 Equipment Scale
1.60E+00 Coal Mining liter/kWhe 1.40E+00
1.20E+00
1.00E+00
Figure 4.19: Dominating factors in the life cycle of CLP for water use at the economy scale: Equipment scale and coal mining water.
113
Equipment scale dominating factors for conventional process are cooling tower, condenser make-up and shift steam water uses. Like CLP, conventional process water use is more sensitive to water utilization in the cooling tower. Reductions in cooling water can reduce equipment scale water use significantly. Shift steam has the same effect as condenser make-up water use on overall equipment scale water use.
2 1.9 1.8
1.7
1.6 Cooling Tower 1.5 Condenser Make-up
1.4 Shift Steam liter/kWhe 1.3 1.2 1.1 1
Figure 4.20: Dominating factors of conventional process for water use at the equipment scale: Cooling tower, condenser make-up and shift steam water uses.
Same factors in CLP at economy scale are dominating for conventional process, too. Parallel to CLP, reductions in equipment scale water use can reduce overall life cycle water use considerably. In both of the processes, equipment scale water use is a more dominant factor on life cycle results than coal mining is at economy scale.
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3.3
3.1
2.9
2.7
2.5 Equipment Scale
2.3 Coal Mining liter/kWhe 2.1
1.9
1.7
1.5
Figure 4.21: Dominating factors in the life cycle of conventional for water use at the economy scale: Equipment scale and coal mining water.
4.3.7.3 Sensitivity Analysis of GWP
Global warming potential calculations have been performed in equipment, process
LCA and economy scales. At the equipment and process LCA scales, dominating factor in GWP of conventional process is CO2 % emitted from stack, and coal mining GHG emissions and CO2 % emitted from stack are dominating factors in economy scale.
GWP in all scales are considerably sensitive to reductions in emissions of GHG from the stack. Additionally, coal mining contributes significantly to life cycle GWP at economy scale. However, CO2 % emitted from stack is still a more dominant factor.
115
140
120
100
80 CO2 from Stack 60
gCO2eq/kWhe 40
20
0
Figure 4.22: Dominating factors of conventional process for GWP at the equipment scale: CO2% from stack.
180
170
160
150
140 CO2 from Stack
130 cCO2eq/kWhe
120
110
100
Figure 4.23: Dominating factors in the life cycle of conventional process for GWP at the process LCA scale: CO2% from stack.
116
220
200
180
CO2 from Stack 160 Coal Mining
gCO2eq/kWhe 140
120
100
Figure 4.24: Dominating factors in the life cycle of conventional process for GWP at the economy scale: CO2 % from stack and coal mining.
For the equipment scale, GWP is 0 since all the CO2 produced in the calcium looping process is sequestered. In process LCA scale, amount of coal mined and the coal transported are dominating factors in the life cycle, coal mining being much more dominant on life cycle results. In economy scale, amount of coal mined is the largest contributor to the total life cycle GWP of CLP.
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55
50
45
Coal Transportation 40 Coal Mining
gCO2eq/kWhe 35
30
25
Figure 4.25: Dominating factors in the life cycle of CLP for GWP at the process LCA scale: Coal mining and transportation.
100
90
80
70 Coal Mining
gCo2eq/kWhe 60
50
40
Figure 4.26: Dominating factors in the life cycle of CLP for GWP at the economy scale: Coal mining
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4.3.7.4 Sensitivity Analysis of EROI and Energy Use
Energy analysis of the two processes compared is performed at three scales in terms of energy utilization and EROI. However, we have not calculated EROI at equipment scale since the energy utilized for production of co-products electricity and H2 is supplied from feedstock coal and does not go beyond classical process efficiency calculation.
At the equipment scale, most energy consuming units are ASU air/O2 compressors followed by calciner in CLP. The sensitivity of results to changes in calcination energy is highest when energy quality of product electricity is not considered.
When energy quality is considered sensitivity to ASU air/O2 compressor energy becomes higher and dominates calcination energy since it utilizes electricity, a higher quality energy source. In conventional process, ASU air/O2 compressor, CO2 compressor and acid gas removal energy consumptions are the most energy consuming steps for equipment scale where the sensitivity to changes is in descending order of ASU compressor, CO2 compressor and acid gas removal. This situation does not change whether energy quality is considered or not.
At process LCA scale, coal mining is the most energy consuming life cycle step for both CLP and conventional process, coal mining being the factor consuming more than 80% of energy utilized. In figure 4.27, the sensitivity of total energy consumption per kWh electricity generation to the changes in coal mining energy consumption for CL and conventional processes is seen.
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3.50E+05
3.00E+05
2.50E+05
2.00E+05 Coal Mining
J/kWhe 1.50E+05
1.00E+05
5.00E+04
0.00E+00 CLP Conventional
Figure 4.27: The sensitivity of total energy consumption per kWh electricity generation to the changes in coal mining energy consumption for CL and conventional processes at the process LCA scale.
At the economy scale, coal mining and production of equipments utilized in coal power plant are the most energy consuming factors for both processes. In figure 4.28, sensitivity of total energy consumption per kWhe to the changes in these factors is seen; coal mining has a higher dominance than equipment production over the total energy consumption per kWhe.
120
5.00E+05 4.50E+05 4.00E+05 3.50E+05
3.00E+05 2.50E+05 CLP
J/kWhe 2.00E+05 Conventional 1.50E+05 1.00E+05 5.00E+04 0.00E+00 Coal Mined Equipment Production
Figure 4.28: The sensitivity of total energy consumption per kWh electricity generation to the changes in coal mining and equipment production energy consumption for CL and conventional processes at the economy scale.
The effects of these dominant factors on EROI results are also analyzed with or without considering differences in energy quality of electricity and hydrogen. At the process LCA scale, sensitivity of results to the changes in the amount of coal mined is highest whether energy quality is considered or not.
At the economy scale, coal mining and production of equipments utilized in coal power plant are the most dominant factors for both processes. Again, sensitivity of results is highest to coal mined irrespective of energy quality consideration similar to process
LCA scale.
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19
18
17 CLP. Ener. Qual Coal Mined 16 CLP. Ener. Qual Equip. 15 Production
EROI 14 CLP. No Ener. Qual Coal Mined 13 CLP. No Ener. Qual Equip 12 Production
11
10
Figure 4.29: Dominating factors in the life cycle of CLP and conventional process for EROI at the process LCA scale with or without energy quality consideration.
19
18
17 CLP. Ener. Qual Coal Mined 16 CLP. Ener. Qual Equip. 15 Production
EROI 14 CLP. No Ener. Qual Coal Mined 13 CLP. No Ener. Qual Equip 12 Production
11
10
Figure 4.30: Dominating factors in the life cycle of CLP for EROI at the economy scale with or without energy quality consideration.
122
22
20 Conventional Ener Qual Coal Mined 18 Conventional Ener Qual Equip Production 16
EROI Conventional No Ener Qual Coal Mined 14 Conventional No Ener Qual Equip Production 12
10
Figure 4.31: Dominating factors in the life cycle of conventional process for EROI at the economy scale with or without energy quality consideration.
4.4 Economic Assessment Results
Total annualized cost of electricity generation in CLP and conventional process encompasses capital and operating costs. Capital cost includes power plant construction and cost of equipment utilized for coal gasification. Plant and equipment costs are annualized by dividing by plant lifetime (20 years).Operating cost includes cost of raw materials and their transportation costs, cost of chemicals used in gasification and cost of waste disposal. The annual cost of these items is calculated by multiplication of annual input quantities by their unit price. For wastes, annual waste quantity is multiplied by unit cost of corresponding disposal activity. Economic data regarding operating and capital cost are obtained from NETL study (NETL, 2008) and The Ohio State University team
(Ramkumar, 2010). The electricity generated in these centralized coal power plants
123 should be transmitted and distributed to the final consumer. The cost data regarding transmission cost in Uttar Pradesh India is obtained from Uttar Pradesh Electricity
Regulatory Commission (UPERC) report for year 2009 (UPERC, 2010). Cost of electricity distribution in India is 0.75 Rs/kWh (IMA, 2013). Addition of total annualized cost of generation per kWhe, distribution and transmission costs per kWhe constitutes the final cost of electricity delivered to the consumer per kWhe.
Tables 4.7 and 4.8 present the economic assessment results for CLP and conventional process. In conventional process, cost of electricity is slightly lower. CLP has higher plant construction and equipment costs and it consumes more resources, hence has a higher operating cost. However, CLP generates unit 1 kWhe power almost at the same price since CLP has higher production capacity than conventional process
(5.89E+09 kWhe/year versus 4.30E+09 kWhe/year).
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Table 4.7: Cost of electricity generation via calcium looping process.
Total Annualized Capital Cost Cost(M$) Cost(M$) Power Plant Construction Cost 485.00 24.25 Equipment Cost 1142.25 57.11
Operating Cost Amount Amount Unit Annualized (Tonnes/day) (tonnes/year) Price($/unit) Cost(M$) Coal 7990 2916350 30.47 88.85 Coal Transportation 7990 2916350 15.23 44.43 Limestone 1699 620135 8.3 5.15 Limestone Transportation 1699 620135 11.7 7.26 Water 17374 6341510 0.10 0.76 Activated C 0.047 17.155 2288 0.04 Water Treatment Chemicals 6.18 2255.7 374 0.84 OUTPUTS Solid Disposal 617 225205 16 3.60 Solid Purge 1569 572685 8 4.58
Total Annualized Cost(M$) 236.87 Annual Elect Generation(kWhe) 5.89E+09 Cost per kWhe($/kWhe) 0.04 Cost per kWhe(Rs/kWhe) 1.94 Transmission Cost(Rs/kWhe) 0.22 Distribution Cost(Rs/kWhe) 0.75 Total (Rs/kWhe) 2.91
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Table 4 8: Cost of electricity generation via conventional process.
Total Annualized Capital Cost Cost(M$) Cost(M$) Power Plant Construction Cost 327.10 16.36 Equipment Cost 711.54 35.58
Operating Cost Amount Amount Unit Price Annualized Inputs (Tonnes/day) (tonnes/year) ($/unit) Cost(M$) Coal 5426 1980490 30.47 60.34 Coal Transportation 5426 1980490 15.23 30.17 Water 21787 7952255 0.10 0.95 Shift Catalyst 0.0018 0.657 1086800 0.71 Selexol Solution 0.043 15.695 29524 0.46 Activated C 0.047 17.155 2288 0.04 Water Treatment Chemicals 6.18 2255.7 374 0.84 OUTPUTS Solid Disposal 617 225205 16 3.60
Total Annualized Cost(M$) 149.06 Annual Elect Generation(kWhe) 4.30E+09 Cost per kWh ($/kWhe) 0.035 Cost per kWh (Rs/kWhe) 1.67 Transmission Cost(Rs/kWhe) 0.22 Distribution Cost(Rs/kWhe) 0.75 Total (Rs/kWhe) 2.64
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Chapter 5: Analysis of Localized Energy Options
The localized energy options analyzed in this work consist of a 8.7 kW capacity multi-crystalline solar power plant, a 60 m3 capacity floating drum biogas digester and a100 kW downdraft biomass gasifier. We evaluated these energy technologies by means of complementary evaluation techniques of emergy analysis (EA) and life cycle assessment (LCA). As described in chapter 3, the donor-side analysis technique EA enables us to evaluate the work nature did to support these technologies to produce their products, in other words we can evaluate the role of ecosystems goods and services in supporting these technologies by utilizing EA (Pizzigallo et al., 2008) .User-side analysis technique LCA enables us to evaluate resource use and emissions as a result of these production activities (Guinée et al, 2011). In other words, we can evaluate up-steam and down-stream effects of these processes by joint utilization of EA and LCA analysis methods (Pizzigallo et al., 2008).
EA results include percent renewability (%Re), emergy yield ratio (EYR), environmental loading ratio (ELR), emergy investment ratio (EIR) and environmental sustainability index (ESI). LCA results include life cycle land use, water use and global warming potential (GWP) results per kWh electrical energy generated. In chapter 3, we discuss these indicators and their outcomes. Furthermore, annualized total cost per kWh electrical energy generated is calculated for each technology option. Combined with
127 economic assessment results, modeling a sustainable energy combination for a given region is aimed in our approach.
In this chapter we present emergy analysis, LCA and economic assessment results of mc-crystalline solar PV, floating drum biogas digester and downdraft biomass gasifier respectively.
5.1. Multi-crystalline Solar PV Plant
The solar system studied is an open ground mounted mc-crystalline silicon photovoltaic (PV) power plant with a-8.7 kWp generation capacity. This system has a life time of 20 years. PV power plant consists of 60 panels, each having 50 solar cells that are connected in series and parallel to form a 67.5 m2 photo-sensitive area and a total area of 74 m2 after being framed and mounted. The PV system captures solar radiation and produces direct current electricity which is then converted to alternate current by the inverter. There are two inverters of 5 kW capacity and a battery bank of 24 batteries, each of 2 V to provide a back-up of 3 days. The electricity generated is distributed to the village Rampura via a 0.75 km mini-transmission line. This system has electrified the village since January 2009. It provides the energy for lighting of the village and for a small flourmill enterprise of power 3 HP. 44 out of 69 households are connected to the solar grid. Utilization of solar power in the village has resulted in 2000 liter/year kerosene savings which was used for lighting in the village prior to solar electricity
(Development Alternatives, 2011).
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5.1.1 Emergy Analysis of Solar PV
In emergy analysis of the solar power plant, we utilized Ecoinvent V2.1 dataset for quantification of material and energy inputs to the system (Ecoinvent 2009). These data are modified according to specifications of our system. The process data regarding the operation of solar PV are obtained from Development Alternatives. The solar radiation data for Jhansi is assumed to be the same for Bhopal, the closest meteorological station to Rampura as 18.65 MJ/m2day (Tyagi, 2009). The system generates around 8350 kWh electrical energy per annum.
Figure 5.1 presents the energy systems diagram drawn for determining energy and material inputs to and outputs from the solar system. Materials and fuels are utilized in the production of solar panels. Gravel is used for mounting of the panels to the ground.
Services include monetary value of maintenance work done by professionals and materials used. Labor represents the work at the time of mounting of solar panels, their wiring and the work for maintenance and operation of solar panels. And, solar radiation is the source of energy for electricity generation.
Emergy analysis performed for the solar PV is given in table 5.1. The first column of this table includes the inputs and classification of each input as renewable, non-renewable or purchased (feedback flows). The units for each input forms the second column of the table. The quantity of each inputs used per annum in the given units are in column three. Transformity values and total emergy flows calculated by multiplication of transformity and annual amount of inputs are given in the following columns. Last column includes the references from which transformity values are obtained.
129
Figure 5.1: Energy systems diagram of multi-crystalline solar PV.
In classifying inputs to a system, free inputs which are provided by nature such as solar radiation, wind, rain are categorized as renewable inputs. The amount of renewable inputs entering a system is flow-limited, one can not intervene to increase or decrease the amount of these flows. Non-renewable inputs are not necessarily free. Minerals such as quartz, ground water and topsoil are examples of non-renewable inputs. If the withdrawal rate of these inputs is slower than their regeneration rate by the nature, then these inputs are classified as renewable inputs. Purchased or feedback inputs are external inputs that a
130 neither free nor locally available (Ulgiati and Brown, 1998). Based on these principles, solar radiation is classified as a 100% renewable input. The inputs purchased from the economy are classified as purchased external inputs (100% F). These include fuels, metals, plastics for packaging and polymers utilized in supporting structures (HDPE) and solar cell surface protection (EVA). Inverter, battery bank and maintenance inputs are expressed in terms of their monetary values which covers both human and material content of these inputs and consequently classified as purchased inputs (100% F). Quartz
(silica sand), the raw material for solar panel production, is classified as a non-renewable local input (100% N). Since labor regarding mounting and maintenance takes place in
Rampura, labor is classified as 20% renewable and 80 % purchased. These percentages are calculated by emergy analysis of Rampura village domestic sector. The related analysis results are presented in table 6.15. The transformity for labor is also obtained from the domestic sector emergy analysis of Rampura. Lastly, wood and water inputs are classified as renewable inputs since the solar panels are produced in Norway and donated to Rampura by Scatec Solar, a Norwegian solar company and water and wood sources are not degrading resources in Norway (Ge et al., 2013).
After classification of each material and energy input to the solar system; total emergy, renewable, non-renewable and purchased emergy flows have been calculated.
With calculation of different emergy flows, calculation of emergy ratio and indices has been possible. Table 5.2 includes the calculated solar electricity transformity, total yield, emergy ratio and index values for the solar power plant.
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Table 5.1: Emergy evaluation table for solar PV.
Amount Transformity Emergy Inputs Units /year (sej/unit) (sej/year) Reference 1.Sun (100% R) J 4.59E+11 1.00E+00 4.59E+11 Odum, 1996 2.Quartz(silica sand)(100% N) g 1.26E+04 1.00E+09 1.26E+13 Paoli, 2008 3.Electricity ( 100% F) J 1.51E+09 1.74E+05 2.63E+14 Odum, 1996 4.Coal( 100% F) g 3.74E+03 2.89E+09 1.08E+13 Brown,2012 5.Petroleum Coke ( 100% F) g 2.33E+03 1.18E+09 2.75E+12 Paoli, 2008 6.Woodchips(100% R) g 6.30E+03 8.79E+08 5.54E+12 Paoli, 2008 7.Charcoal (100% F) g 7.94E+02 3.14E+09 2.49E+12 Paoli,2008 8.Graphite ( 100% F) g 4.67E+02 3.15E+09 1.47E+12 Paoli,2008 9.HCl( 30% in water)(100% F) g 7.29E+03 3.64E+09 2.66E+13 Paoli,2008 10.NaOH(50% in water) (100 %F) g 1.61E+03 1.90E+09 3.05E+12 Paoli,2008 11. PE (100%F) g 2.33E+00 5.87E+09 1.37E+10 Paoli,2008 12.Heat in form of Fuels( 100% F) J 7.56E+08 6.60E+04 4.99E+13 Odum, 1996 13. Water (100% R) g 1.55E+07 7.30E+06 1.13E+14 Paoli, 2008 14.Natural gas (100% F) J 2.96E+07 4.80E+04 1.42E+12 Odum,1996 15.Steel (100% F) g 1.22E+04 4.15E+09 5.07E+13 Paoli, 2008 16.Fuels (100% F) J 3.92E+06 6.60E+04 2.58E+11 Odum, 1996 17.Ag/Al Paste (100% F) g 4.15E+01 1.69E+10 7.02E+11 Paoli, 2008 18.Aliminium ( 100%F) g 1.93E+04 1.27E+10 2.45E+14 Paoli, 2008 19.Tempering Glass (100% F) g 3.74E+04 1.90E+09 7.10E+13 Paoli,2008 20.Copper(100% F) g 2.55E+03 9.90E+10 2.52E+14 Odum, 1996 21.EVA, HDPE, Plastics(100% F) g 1.62E+04 5.87E+09 9.51E+13 Paoli, 2008 22.Gravel (100 % F) g 4.26E+05 1.68E+09 7.15E+14 Pulselli, 2008 23.Labor (20% R,80 % F) J 1.12E+07 1.37E+06 8.24E+13 Self Calc. 24.Inverter ( 100%F) € 5.96E+02 2.22E+12 1.32E+15 Paoli, 2008 25.Maintenance (100% F) € 2.27E+02 2.22E+12 5.04E+14 Paoli, 2008 26. Battery Bank (100% F) € 7.61E+02 2.22E+12 1.69E+15 Paoli, 2008 OUTPUT Yield 5.46E+15 27.Electricity J 3.01E+10 1.81E+05 5.46E+15
The total emergy input to the solar plant is 5.46E+15sej/year. Out of this total emergy
flow, 1.22E+14 sej/year is renewable which equals emergy flows of water, woodchips,
sun and 20% of labor inputs. 1.26E+13 sej/year is the magnitude of non-renewable
132 emergy flow corresponding to quartz emergy flow. The remainder of the inflowing emergy is composed of purchased inputs of which emergy flow equals to 5.32E+15 sej/year. By taking the ratio of these different flows, the emergy ratios and indices in table 5.2 are calculated.
Table 5 2: Transformity, yield, emergy ratio and indices values for solar electricity.
Transformity of Electricity (Sej/J) 1.81E+05 Yield(Sej/year) 5.46E+15 % Re 2.24%
EYR 1.03 ELR 43.7 EIR 39.5 ESI 0.02
As shown in table 5.2, multi-crystalline solar panel has unexpectedly low renewability (2.24%) despite the intuition for solar power as a renewable energy source.
This low renewability (%Re) of the solar power plant is because of the large emergy of purchased inputs needed to manufacture the solar plant. The high environmental loading ratio (ELR) (43.7) of solar pant can also be attributed to the low renewable emergy input to the system. Furthermore, emergy yield ratio which is the ratio of yield to purchased inputs is only 1.03, confirming again the dominance of purchased inputs to the solar plant analyzed. High emergy investment ratio (EIR) emphasizes low performance of system to utilize purchased inputs. And, the solar electricity transformity of our system is 1.81E+5
133 sej/J. Paoli et al. performed emergy analysis of a mono-crystalline solar panel. They found solar electricity transformity as 8.92E+4 sej/J, % Re as 2%, ELR as 48.93 and
EYR as 1.03 for their mono-crystalline solar plant. Our results are highly compatible with their results.
The ecoinvent database presents material and energy inputs separately for each life cycle step considered. Total input of different materials to the solar system is calculated by addition of these material inputs in each life cycle considered. Here, we present emergy analysis results of solar PV according to contribution of each life cycle step and also each material and energy input separately. This approach can allow us to see if life cycle impacts and emergy contribution of each life cycle step are in accordance.
This comparison will be done after presentation of life cycle results in section 5.1.2
(Ecoinvent, 2009).
Figures 5.2 and 5.3 present the emergy analysis results for the solar power plant.
Balance of system components (BOS) which includes battery bank, inverter, mounting structures such as support rack, wires and switches for electronic connections. These inputs constitute more than 50% of emergy inputs to the solar system (figure 5.3). When emergy results of solar plant are examined material wise, battery bank, inverter and gravel which is utilized in mounting of the solar plant to the ground are the largest emergy inputs to the solar system (figure 5.4). From figures 5.2 and 5.3, it is also possible to see the dominance of purchased inputs which is the reason of low renewability and low
EYR of the solar system.
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1.80E+15 1.60E+15 1.40E+15 1.20E+15 1.00E+15 8.00E+14
sej/year 6.00E+14 4.00E+14 2.00E+14 0.00E+00
Figure 5.2: Emergy signature diagram of multi-crystalline solar plant according to contribution of different life cycle steps considered.
1.80E+15 1.60E+15 1.40E+15 1.20E+15 1.00E+15 8.00E+14 6.00E+14 sej/year 4.00E+14 2.00E+14 0.00E+00
Figure 5.3: Emergy signature diagram of multi-crystalline solar plant according to contribution of material and energy inputs.
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5.1.2 Solar Life Cycle Assessment
Figure 5.4 shows all life cycle steps taken into account in the LCA of multi-crystalline solar PV. Production of a silicon based solar plant starts with purification of SiO2 via reduction with carbon. Coal, woodchips, charcoal and coke are the sources of carbon.
Silicon with 98.5-99.5% purity (metallurgical grade silicon (MG-Si)) is produced as a result of this process. Then, MG-Si is further purified into electronic grade silicon (EG-
Si) and solar grade silicon (SG-Si) to form silicon mix for PVs. This silicon mix contains
85 % SG-Si and 15 % EG-Si. Difference between EG-Si and SG-Si is in their purity. SG-
Si contains impurities 0.01 part per million by weight (ppmw) and EG-Si contains impurities 0.0001 ppmw. The multi-crystalline silicon is then melted and cast as blocks from which the multi-crystalline wafers are cut as layer in certain thicknesses. Wafers are treated with chemicals (NaOH, HCl) to eliminate any damages on their surface and are then doped to create p/n junction in the wafer. The production of solar cell is completed after adding parts for electronic connections and applying anti reflection coating. These solar cells are connected to form the solar panels. After mounting of solar panels and electronic connections with inverter and battery bank is completed, solar plant takes form
(Ecoinvent, 2009)
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Figure 5.4: Life cycle stages considered for LCA of solar PV.
LCA of solar PV encompasses evaluation of impacts related to the life cycle steps considered. Balance of system (BOS) components are responsible for a significant portion of environmental impacts related to solar power plants (Hsu et al., 2012). BOS components include battery bank, inverter, mounting structures such as support rack, wires and switches for electronic connections. According to ecoinvent report, BOS related environmental impacts are in the range of 30%-50% related environmental impacts (Ecoinvent, 2009).
137
Figure 5.5 shows the GWP results for electricity generation by multi-crystalline solar plant situated in Rampura village and analyzed by us. If BOS is assumed to contribute 30%, the GWP per kWh is 79 g CO2 eq. If its contribution is 50 %, the GWP per kWh is 111 g CO2 eq. After BOS, purification of MG-Si to purified silicon mix containing EG-Si and SG-Si is the most dominant life cycle step to GWP of the solar plant.
In terms of land use, BOS and panel production from solar cells are most land utilizing life cycle steps. For 30 % contribution of BOS, land use is 5.85E-3 m2/kWh, and for 50% contribution of BOS, land use is 8.19E-3 m2/kWh. The results regarding land use of multi crystalline solar PV is given in figure 5.6.
120.0
100.0 Operation
MG-Si Production 80.0 Wafer Production
60.0 Casting eq/kWh 2 2 Cell Production
gCO 40.0 Panel Production BOS 20.0 Purified Silicon 0.0 BOS 30% BOS 50%
Figure 5.5: Global warming potential (GWP) results for solar PV
138
Figure 5.7 presents the life cycle water use results for the solar plant analyzed.
The most water intensive steps are operation and BOS components in the life cycle of the power plant. For cleaning of solar panels during operation, 20 kg water per year per m2 of panel is assumed to be used (Ecoinvent, 2009). If contribution of BOS is taken as 30%, water use per kWh is 2.05 liter/kWh and if it is taken as 50%, water use is 2.33 liter/kWh.
Life cycle study performed in Ecoinvent report, assumes life time of solar plants they analyzed is 30 years and kWh/kWp efficiency of them is 820 kWh/kWp. Hence, the solar plants analyzed generate 820 kWh of electricity per 1 kWp production capacity on average. With these assumptions, they conclude that CO2 emissions from the solar plants analyzed between 40 and 70 g/kWh generated. However, lifetime of our system is 20 years. If we were to assume the same life time for our plant having an efficiency of 960 kWh/kWp, the life cycle GWP would change between 52 and 74 g CO2 eq./kWh.
As stated in section 5.1.1, BOS components which include inverter, battery bank and supporting structures contain more than 50 % of emergy inputs. Now, it can be seen that BOS has also significant life cycle impact share in a solar photovoltaic plant.
139
9.00E-03
8.00E-03 Casting 7.00E-03 Purified Silicon 6.00E-03 Cell Production 5.00E-03
Wafer Production /kWh
2 4.00E-03
m Operation 3.00E-03 MG-Silicon Production 2.00E-03 BOS 1.00E-03 Panel Production 0.00E+00 BOS 30% BOS 50%
Figure 5.6: Land use results for solar PV.
2.50E+00
2.00E+00 Operation
BOS
1.50E+00 Panel Production Cell Production
1.00E+00 Wafer Production liter/kWh Casting 5.00E-01 Purified Silicon MG-Silicon Production 0.00E+00 BOS 30% BOS 50%
Figure 5.7: Water use results for solar PV.
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5.1.3 Economic Analysis of Solar Electricity Generation
The economic assessment of solar panel is performed based on the data supplied by International Renewable Energy Agency (IRENA, 2012a). The total annualized cost for the solar plant ($2877) which consists of operating cost and annualized capital cost is calculated and this number is divided by annual electricity generation to find the cost of electricity per kWh. As in LCA, life time of the solar plant is 20 years. All the prices are leveled to year 2009, which the solar plant started to operate in the village Rampura.
Capital cost encompasses the panel, panel mounting, inverter, battery bank costs. These costs are divided by the life time of related equipment to be annualized for one year of operation. Panel cost per watt capacity is calculated as $ 1.736 for year 2009 in Europe.
Mounting cost was 6% of the total capital cost. Inverter cost is $ 0.67/W capacity and there are 5 kW inverters with a life time of 10 years. Battery bank price is obtained from
Wholesale Solar and this price is adjusted to year 2009 with present value of money (US
Inflation Calculator, 2013).
Operating cost includes the maintenance cost. This cost is $ 522/ kW capacity.
Multiplied by the capacity of our plant (8.7 kWp) and divided by plant life time (20 years), annualized maintenance cost is calculated.
As a result, the price of solar electricity per kWh is found to be $ 0.34 and its conversion to Rupees is Rs 17 for year 2009 (X-Rates, 2013). These results are presented in table 5.3 altogether.
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Table 5.3: Breakdown of solar electricity cost per kWh generated.
Annual Capital Cost Total Cost($) Cost($/year) Panels 15103 755 Inverter 13386 669 Mounting 3462 173 Battery Bank 16740 1116 48691 Annual Operating Costs Total Cost($) Cost($/year) Maintenance 3267 163 Total 51958 2877 Electricity Generated(kWh/year) 8353 Price($/kWh) 0.34 Price(Rs/kWh 17
5.2. Biogas Digester
The plant we evaluated is a floating drum biogas digester of 60 m3 biogas production capacity. The main body of plant is built using bricks and the floating drum is made of steel. In a floating drum digester, the pressure of biogas is kept constant (Kalia and Singh, 1999). The digester evaluated is situated in a cowshed in Jhansi, Uttar Pradesh in India. Since, cows are holy in India, scrap cows are not slaughtered, but kept in cowsheds after they are discarded. Our partner Development Alternatives established such a cowshed and utilizes the manure from those cows to produce electricity from biogas to supply energy needs for the income generating activities in their center.
Currently, 345 kg cow dung is fed to the digester everyday with the same amount of water producing 8.5 m3 of biogas and slurry which is 50-75 % of the dry weight of
142 cow dung fed by weight (Kalia and Singh, 1999). The slurry produced can be utilized as organic fertilizer. Biogas is 60-65 % consists of methane, rest being CO2 (Zhou et al,
2010). Produced biogas is combusted in a 100% biogas operated generator producing 6 kWh of electricity per day on average. However, 850 kg cow dung should be fed to be able to supply all the energy demand of the center which constitutes the ideal case of 20 kWh of electricity generation from 28.5 m3 biogas daily. A third scenario investigated is full capacity case in which 1500 kg wet cow dung is fed to the digester and 60 m3 of biogas can be produced. In this case, 42 kWh of electricity can be generated daily.
5.2.1 Emergy Analysis of Biogas Digester
Generation of electricity from biogas is a two-phase process. First, cow dung is digested to produce biogas and biogas is then combusted in a natural gas engine to generate electricity. To be able to assess the sustainability of these two phases separately, we performed emergy analysis for production of biogas and generation of electricity from biogas for all cases considered.
5.2.1.1 Biogas Production Phase
Figure 5.8 is the energy system diagram drawn for representing energy and material inputs to and outputs from the biogas production phase. Among these inputs, solar energy is renewable. Water is classified as a non-renewable local input since it is withdrawn faster than it replenished, as indicated by depletion of underground water level in Rampura. As in case of solar panel, labor is categorized as 20% renewable and 80%
143 purchased, the percentages calculated in our study. Steel, PVC piping and fuel are 100% purchased external inputs. Although manure is assumed to be 100% renewable originally, there are non-renewable inputs that cows consume to produce cow dung. So, it is highly optimistic to assume cow dung as100% renewable. For that reason, a sensitivity analysis for the cases where manure is 80% and 60% renewable is done. Also, for brick, the second largest emergy input source to the system, sensitivity analysis is performed where bricks are 10% or 20 % renewable. It can be argued that, there will be renewable inputs or at least partially renewable inputs like human labor for production of bricks. Effects of these changes on emergy ratios and indices will be discussed in presenting sensitivity analysis of emergy indicators.
Figure 5.8: Energy system diagram for biogas production phase. 144
Table 5.4 shows the emergy evaluation table of biogas production phase for current case. For ideal and full capacity cases only quantities of water and manure inputs changes, the quantities of rest of the inputs stay the same. In the last column of emergy evaluation table, references for the transformity values are given. As can be seen be seen from this table, manure and brick are the largest emergy inputs to the system in the current case scenario. Manure contains 58 % and brick contains 33% of total emergy inputs. Share of manure in ideal (77%) and full capacity (88%) cases increases, while share of brick reduces in ideal (18%) and full capacity (11%). These results can be seen in the emergy signature diagram of biogas production phase in figure 5.9 in detail.
Table 5 4: Emergy evaluation table of biogas production phase for current case.
Transformity Emergy Input Items Units Amount (sej/unit) (sej/year) Reference Sun (100% R) J 5.29E+11 1 5.29E+11 Odum, 1996 Manure (100 % R) g 1.04E+08 1.13E+08 1.17E+16 Bastianoni, 2000 Water (100 % N) m3 1.04E+02 2.40E+11 2.48E+13 Brown, 2012 Materials Brick (100 % F) g 1.82E+06 3.68E+09 6.71E+15 Pulselli, 2007 Steel (100 % F) g 3.51E+04 6.97E+09 2.44E+14 Pulselli, 2007 Fossil Fuels- Transportation (100 % F) g 1.86E+06 9.19E+06 1.71E+13 Pulselli, 2008 Labor (20 % R, 80% F) hr 7.50E+02 1.79E+12 1.34E+15 Self Calculation PVC Piping (100 % F) g 5.59E+03 1.14E+10 6.37E+13 Ciotola, 2011 Total 1.19E+16 Output Biogas J 5.77E+10 3.84E+05 2.01E+16 Slurry J 2.85E+11 7.05E+04 2.01E+16
145
After categorization of energy and material inputs to the system, total emergy yield, renewable, non-renewable, purchased emergy flows have been calculated. Then, biogas transformity, system renewability, EYR, ELR, EIR ratios and ESI index are calculated for all three scenarios considered. These results are summarized in table 5.5.
As can be seen in this table, transformity of biogas reduces as the amount of manure fed to the system increases, meaning biogas can be produced more efficiently.
Furthermore, system renewability and emergy yield ratio increases with this efficiency increase. The stress biogas production creates on environment or ELR decreases in accordance with increasing system sustainability (ESI). Likewise, EIR reduces as system operates more efficiently.
6.00E+16 PVC Piping (100 % F) 5.00E+16 Labor (20 % R, 80% F)
4.00E+16 Transportation (100 % F)
3.00E+16 Steel (100 % F)
sej/year Brick (100 % F) 2.00E+16 Water (100 % N) 1.00E+16 Manure (100 % R) Sun (100% R) 0.00E+00 Current Ideal Full Capacity
Figure 5.9: Emergy signature diagram of biogas production phase for different scenarios considered.
146
Table 5.5: Transformity, yield and emergy ratio values of biogas production phase for different scenarios considered.
Current Ideal Full Capacity Biogas Trans(sej/J) 3.48E+05 1.91E+05 1.46E+05 Yield(sej/year) 2.01E+16 3.73E+16 5.93E+16 % Re 60% 78% 86% EYR 2.48 4.59 7.35 ELR 0.68 0.28 0.16 EIR 0.68 0.28 0.16 ESI 3.64 16.3 45.5
5.2.1.2 Sensitivity Analysis for Biogas Production Phase
As stated in section 5.2.1.1, sensitivity analysis for the cases where manure is an
80% renewable-20 % purchased and 60% renewable- 40 % purchased input is performed.
Also, for brick, the second largest emergy input source to the system, sensitivity analysis is performed where bricks are 10% renewable- 90 % purchased and 20% renewable- 80% purchased to determine the effects of these factors on emergy ratios and index calculated.
Figure 5.10 presents the impacts of the changes in these factors on the % Re of the system under three scenarios considered. As can be expected, % Re decreases as the renewability of manure decreases and % Re increases as renewability of input brick increases. Being a larger input of emergy to the system, changes in the renewability of input manure has a higher effect on system renewability.
147
100% 90% 80%
70%
60% Current 50% Ideal 40% Biogas %Re Biogas Full Capacity 30% 20% 10% 0% Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.10: Sensitivity analysis results of % Re for biogas production phase.
The sensitivity of results to the changes in the renewability of input brick decreases from current case to full capacity operation since brick based emergy input is fixed but the total emergy yield of the system increases.
Figure 5.11 presents the impacts of the changes in brick and manure renewability on the EYR of the system for current, ideal and full capacity cases. As renewable portion of manure decreases and purchased portion of it increases. EYR decreases since EYR is the ratio of the total emergy yield to the purchased emergy flows. Although, total emergy yield is constant, portion of purchased emergy flows increases. Based on the same fact, as purchased portion of input brick decreases, EYR of the system for corresponding scenarios increases. EYR results are also more sensitive to the changes in input manure.
148
Figure 5.12 presents the impacts of the changes in renewability of manure and brick inputs on the ELR of the biogas production system under three scenarios considered. ELR is the ratio of sum of purchased and non-renewable emergy inputs to renewable emergy inputs. As a result, system ELR for considered three scenarios increases as renewability of input manure decreases and ELR for these three scenarios decreases as the renewable portion of input brick increases. As in cases of % Re and
EYR, ELR results are also more sensitive to the changes in renewability of manure input.
10.00 9.00 8.00
7.00
6.00 Current 5.00 Ideal 4.00 Biogas EYR Biogas Full Capacity 3.00 2.00 1.00 0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.11: Sensitivity analysis results of EYR for biogas production phase.
149
2.00 1.80 1.60
1.40
1.20 Current 1.00 Ideal
0.80 Biogas ELR Biogas Full Capacity 0.60 0.40 0.20 0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.12: Sensitivity analysis results of ELR for biogas production phase.
Figure 5.13 presents the impacts of the changes in manure and brick renewability on the EIR of the system for current, ideal and full capacity cases. EIR is the ratio of purchased emergy inputs to the sum of non-renewable and renewable emergy inputs. As a result, system EIR for considered three scenarios increases as renewable portion of input manure decreases. EIR for these three scenarios decreases as the renewable portion of input brick increases. EIR results are also more sensitive to the changes in input manure.
150
2.00
1.80
1.60
1.40
1.20 Current 1.00 Ideal
Biogas EIR Biogas 0.80 Full Capacity 0.60
0.40
0.20
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.13: Sensitivity analysis results of EIR for biogas production phase.
Figure 5.14 presents the impacts of the changes in manure and brick renewability on the ESI of the system under three scenarios considered. ESI is the ratio of EMY to
ELR. EMY reduces as the purchased portion of an input to a system increases and ELR increases as renewability of an input to a system decreases. Combination of these two effects is reflected on ESI stronger. As a result, system ESI decreases as renewable portion of manure decreases or its purchased portion increases.ESI increases as renewable portion of input brick increases or its purchased portion decreases. As in all other ratios results of ESI are more sensitive to the changes in input manure.
151
70.00
60.00
50.00
40.00 Current
30.00 Ideal Biogas ESI Biogas Full Capacity 20.00
10.00
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.14: Sensitivity analysis results of ESI for biogas production phase.
Ciotola et al classified animal manure as 32% purchased and 68 % renewable input for their biogas system based on the renewable and purchased input ratio to Costa Rica economy for the year 2000. Emphasizing the necessity of purchased or non-renewable inputs in production of animal manure, they also state the unsuitableness of classifying manure as a 100% renewable input (Ciotola et al, 2011). In accordance with their results, the % Re, EYR and ESI values increase as the renewable portion of manure increases and
ELR and EIR values decrease.
5.2.1.3 Electricity Generation Phase
Figure 5.15 is the energy system diagram drawn for representing energy and material inputs to and outputs from the electricity generation from biogas. Different from 152 biogas production from manure, now an electricity generator operating 100% with biogas is added to the system. Categorization of all other inputs being the same, electricity generator is classified as 100% purchased input. As in case of biogas production, sensitivity analysis for manure renewability is 80% and 60% and for brick renewability is
10% or 20. Effects of these changes on emergy ratios and indices will be discussed in electricity generation sensitivity analysis section 5.2.1.4.
Figure 5.15: Emergy system diagram for electricity generation from biogas.
Table 5.6 is the emergy evaluation table of electricity generation from biogas for current case. For ideal and full capacity cases only quantities of water and manure inputs 153
changes, the quantities of rest of the inputs stay the same. In the last column of emergy
evaluation table, references for the transformity values are given. The emergy money
ratio is utilized here to convert the monetary value of electricity generator in to emergy.
As can be seen be seen from this table, manure and brick are the largest emergy
inputs to the system in the current case scenario. Manure contains 57 % and brick
contains 33% of total emergy inputs. Share of manure in ideal (77%) and full capacity
(85%) cases increases, while share of brick reduces in ideal (18%) and full capacity
(11%). These results can be seen in the emergy signature diagram of biogas production
phase in figure 5.16 in detail.
Table 5.6: Emergy evaluation table of electricity generation from biogas for current case.
Transformity Emergy Input Items Units Amount/Year sej/unit) (sej/year) Reference Sun (100 % R) J 5.29E+11 1.00E+00 5.29E+11 Odum, 1996 Manure (100 % R) g 1.04E+08 1.13E+08 1.17E+16 Bastianoni, 2000 Water ( 100% N) m3 1.04E+02 2.40E+11 2.48E+13 Brown, 2012 Materials Brick (100 % F) g 1.82E+06 3.68E+09 6.71E+15 Pulselli, 2007 Steel (100% F) g 3.51E+04 6.97E+09 2.45E+14 Pulselli, 2007 Fossil Fuels- Transportation(100 % F) g 1.86E+06 9.19E+06 1.71E+13 Pulselli, 2008 Labor (20 % R, 80% F) hr 7.50E+02 1.79E+12 1.34E+15 Self Calculation Generator( 100% F) $ 6.97E+01 2.38E+12 1.66E+14 Ciotola, 2011 PVC Piping (100 % F) g 1.10E+04 1.14E+10 1.25E+14 Ciotola, 2011 Total 2.03E+16 Output Electricity J 6.48E+09 3.14E+06 2.03E+16 Slurry J 2.85E+11 7.05E+04 2.01E+16
154
As before, energy and material inputs to the system are categorized and total emergy yield; renewable, non-renewable, purchased emergy flows have been calculated.
After these quantities have been calculated, electricity transformity, % Re, EYR, ELR,
EIR ratios and ESI index are calculated for all three scenarios considered for electricity generation from biogas. These results are summarized in table 5.7. As can be seen in this table, transformity of electricity from biogas reduces as the amount of manure fed to the system increases, meaning electricity can be generated more efficiently. Furthermore, system renewability and emergy yield ratio increases with this efficiency increase. The stress electricity generation creates on environment or ELR decreases in accordance with increasing system sustainability (ESI). Likewise, EIR reduces as system operates more efficiently or in higher capacity.
When we compare the related results for biogas production and electricity generation, it is seen that they are quite similar. Addition of electricity generator to the system does not change emergy ratios or environmental sustainability index significantly.
The contribution of electricity generator emergy flow to the total emergy yield is less than 1%. For these reason, the emergy analysis results for these two phases do not differ significantly. However, utilization of a higher capacity electricity generator would affect the analysis results more. For instance, a 40 kW generator is utilized in a biogas digester system studied by Ciotola et al. Here, %Re of biogas production reduces from 66% to
52% for electricity generation from biogas.
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6.00E+16 PVC Piping (100 % F) 5.00E+16 Generator( 100% F) Labor (20 % R, 80% F)
4.00E+16
Transportation (100 % F) 3.00E+16
Steel (100 % F) sej/year 2.00E+16 Brick (100 % F) Water (100 % N) 1.00E+16 Manure (100 % R) 0.00E+00 Sun (100% R) Current Ideal Full Capacity
Figure 5.16: Emergy signature diagram of electricity generation from biogas for different scenarios considered.
Table 5.7: Transformity, yield and emergy ratio values of electricity from biogas for different scenarios considered.
Current Ideal Full Capacity Electricity Trans(sej/J) 3.45E+06 1.72E+06 1.30E+06 Yield(sej/year) 2.03E+16 3.75E+16 5.96E+16 % Re 59% 78% 86% EMY 2.44 4.50 7.14 ELR 0.70 0.29 0.17 EIR 0.70 0.29 0.16 ESI 3.49 15.6 43.2
The only additional emergy input for electricity generation from biogas is the purchased emergy input belonging to the electricity generator. Contribution of electricity generator
156 to total emergy yield changes between 0.8 % and 0.3% from current to full capacity operating schemes. Because of low contribution of generator, emergy index and ratios calculated for biogas production and electricity generation phases are the same or slightly different. If a high capacity electricity generator contributing significantly to the total emergy yield was utilized as in case of the analysis performed by Ciotola et al, there would be recognizable differences among the emergy indicators of biogas production and electricity generation phases (Ciotola, 2011).
5.2.1.4 Sensitivity Analysis for Generation of Electricity from Biogas
For electricity generation from biogas, sensitivity analysis is also performed for the cases where manure is an 80% renewable- 20% purchased and 60% renewable-40 % purchased input and brick is a 10% renewable-90% purchased and 20 % renewable-80% purchased input to determine the effects of these factors on emergy ratios and index calculated.
Figure 5.17 presents the impacts of the changes in the renewability these inputs on the % Re of the electricity generation from biogas under three scenarios considered. As can be expected, % Re decreases as the renewability of manure decreases and % Re increases as renewability of input brick increases. Being a larger input of emergy to the system, changes in the renewability of input manure has a higher effect on system % renewability. The sensitivity of %Re results to the changes in the renewability of input brick decreases from current case to full capacity operation similar to biogas generation phase.
157
100% 90% 80%
70% 60% Current 50% Ideal 40% Full Capacity Electricity %Re Electricity 30% 20% 10% 0% Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.17: Sensitivity analysis results of % Re for electricity generation from biogas.
Figure 5.18 presents the impacts of the changes in the renewability of inputs manure and brick on the EMY of the system for current, ideal and full capacity cases. As renewable portion of manure decreases and purchased portion of it increases, the EYR decreases, since EYR is the ratio of the total emergy yield to the purchased emergy flow.
Despite the constant total emergy yield, portion of purchased emergy flows increases.
Based on the same fact, as renewability of input brick increases, EMY of the system for corresponding scenarios increases. EMY results are more sensitive to the changes in renewability of manure.
158
9.00
8.00
7.00
6.00
5.00 Current 4.00 Ideal
Electricity EYR Electricity 3.00 Full Capacity
2.00
1.00
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.18: Sensitivity analysis results of % EYR for electricity generation from biogas.
Figure 5.19 presents the impacts of the changes in inputs manure and brick on the
ELR of the electricity generation from biogas under three scenarios considered. ELR is the ratio of sum of purchased and non-renewable emergy inputs to renewable emergy inputs. As a result, system ELR for considered three scenarios increases as renewability of input manure decreases and ELR for these three scenarios decreases as the renewable portion of input brick increases. As in cases of % Re and EYR, ELR results are also more sensitive to the changes in input manure in electricity generation phase.
159
1.20
1.00
0.80
0.60 Current
Ideal Electricity ELR Electricity 0.40 Full Capacity
0.20
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.19: Sensitivity analysis results of ELR for electricity generation from biogas.
Figure 5.20 presents the impacts of the changes in renewability of inputs manure and brick on the EIR of the system for current, ideal and full capacity cases. EIR is the ratio of purchased inputs to the sum of non-renewable and renewable emergy inputs. As a result, system EIR for considered three scenarios increases as renewable portion of input manure decreases. EIR for these three scenarios decreases as the renewable portion of input brick increases. EIR results for electricity generation from biogas are also more sensitive to the changes in renewability of input manure.
160
2.00
1.80
1.60
1.40
1.20 Current 1.00 Ideal 0.80
Electricity EIR Electricity Full Capacity 0.60
0.40
0.20
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.20: Sensitivity analysis results of EIR for electricity generation from biogas.
Figure 5.21 presents the impacts of the changes in renewability of inputs manure and brick on the ESI of the system under three scenarios considered. As in biogas production phase, reduction of renewable portion of manure decreases ESI and ESI increases as renewable portion of input brick increases in the considered three scenarios.
As in all other ratios, results of ESI are more sensitive to the changes in input manure.
Emergy indicators of both biogas production and electricity generation phases have similar sensitivity to the changes in renewability of brick and manure inputs again due to low contribution of generator to total emergy yield.
161
70.00
60.00
50.00
40.00 Current
30.00 Ideal
Electrcity ESI Electrcity Full Capacity 20.00
10.00
0.00 Manure Manure Manure Brick Brick Brick 100% Re 80% Re 60% Re 100% F 90% F 80% F
Figure 5.21: Sensitivity analysis results of ESI for electricity generation from biogas.
5.2.2 Life Cycle Assessment of Electricity Generation via Biogas Digestion
Figure 5.22 presents the life cycle steps considered in LCA of electricity generation from biogas. Considered life cycle starts with cultivation of fodder for cows to eat. Then, cows produce manure. Per kg of manure, scrap cows in cowshed of
Development Alternatives are fed 0.9 kg fodder. Fodder is mainly wheat straw. In this region, 1800 kg of wheat straw and 1800 kg of wheat are obtained from 1 acre of agricultural land. To allocate land and water utilized between wheat and wheat straw; mass, monetary value based allocation strategies are adopted (Zhang et al, 2010 and
2011). Case where manure is accounted as waste is also considered. Manure is then fed to the biogas digester. There are two products of bio-digestion, slurry and biogas. This
162 slurry or digestate can be utilized as fertilizer reducing chemical fertilizer use. In LCA of the system, emissions and resource use (land and water) are allocated among these products according to their energetic content (Zhang et al, 2010 and 2011). The portion of impacts and resource use allocated to biogas is attributed to electricity generation, since electricity is generated by combustion of biogas. 1% of biogas produced is assumed to leak. In last step of life cycle, produced biogas is combusted to generate electricity by a natural gas engine. This analysis considers the same three cases presented in emergy analysis section 5.2.1.
Figure 5.22: Life cycle steps considered for electricity generation from biogas.
The greenhouse gas (GHG) emissions emitted during biogas production and electricity generation are sequestered by the fodder while it is growing. During
163 photosynthesis wheat captures CO2 and produces the biomass. However in biogas production stage, 1 % of the biogas leaks of which 60% is CO2 and 40% is CH4. CH4 has a GWP 21 times stronger than CO2 (Guinee, 2011). The leaked CH4 is accounted as GHG emissions from biogas generation step. In addition, manure produced by cows can be applied to fields as fertilizer or the slurry from biogas digester can be applied to fields.
This practice change creates reduction in GHG emissions (Jorgesson and Berlund, 2007).
Furthermore, application of slurry reduces the need for chemical fertilizers. And, we take credit for these reductions in evaluation of our system.
Electricity Generation
5.00E+01 Biogas Production
0.00E+00 Chemical Fertilizer -5.00E+01 Replacement Manure Digestion -1.00E+02
gCO2eq/kWh -1.50E+02
-2.00E+02
Figure 5.23: Life cycle GWP of electricity generation from biogas.
Figure 5.23 presents the life cycle GWP results obtained for three scenarios considered. Orange bars represent the net GWP under different scenarios. The electricity
164 generation from biogas has a negative GWP for all cases accounted for. As generation capacity increases, GWP becomes more negative.
Current Case Ideal Case Full Capacity 1.00E+01
1.00E+00
Mass Allocation 1.00E-01
Monetary Allocation
kWh \ 2 Waste No Allocation
m 1.00E-02
1.00E-03
1.00E-04
Figure 5.24: Life cycle land use results of electricity from biogas with different allocation strategies.
Figure 5.24 presents the life cycle land use results with different allocation strategies. When wheat straw is accounted as waste, no land use is allocated to it. Lowest land use results are obtained for this option. In terms of monetary value, wheat straw is cheaper than wheat so less land area is allocated to wheat straw. Lastly, land area allocation between wheat and wheat straw based on mass is one to one. Highest land use result per kWh electricity generated is obtained with mass based allocation since it gives the highest weight to wheat straw among other allocation options.
Secondly, as generation capacity increases, land use per kWh decreases for the three scenarios (current, ideal, full capacity) that were considered. 165
1.00E+03
1.00E+02
Mass Allocation
Monetary Allocation liter/kWh 1.00E+01 Waste No Allocation
1.00E+00 Current Case Ideal Case Full Capacity
Figure 5.25: Life cycle water use results of electricity from biogas with different allocation strategies.
Figure 5.25 presents the life cycle water use results for electricity generation from biogas. As in land use, mass allocation gives highest life cycle water use. Considering wheat straw as waste and allocating no water to it give lowest water use results. In terms of scenarios considered, current case use highest amount of water per kWh generation followed by ideal and full capacity cases.
166
5.2.3 Economic Analysis of Electricity Generation from Biogas
As stated in emergy analysis and LCA sections 5.2.1 and 5.2.2, three cases are also considered for economic assessment of biogas plant and electricity from biogas. The installed biogas digester and electricity generator costs are the capital costs related for electricity generation from biogas. Feedstock manure cost and labor related to maintenance and operation of biogas digester constitutes the operating cost for the biogas electricity generation. In terms of operation of biogas digester three scenarios are considered.
Current case represents the current operation scheme in Development Alternatives cowshed with 345 kg daily wet cow dung input in 300 days of a year. In this operation scheme, 6 kWh of electricity is generated per day. In this scenario, cow dung is free and supplied from the scrap cows in the campus. Electricity generation price with 0.2 Rs/kg collection fee is also calculated for the current case. Ideal case represents the operation scheme with 850 kg wet cow dung feed with daily 20.13 kWh of electricity generation.
Ideal case operation scheme represents the case which produces enough electricity to meet the energy demand in the cowshed. In ideal case, scenarios where cow dung is free, with 0.2 Rs/kg collection fee and at a cost of 0.4 Rs/kg cost are considered. Full capacity operation scenario represents full capacity production capacity of 60 m3 biogas production per day and around 12700 kWh of electricity generation per year. 1500 kg cow dung is fed to the digester per day in 300 days of a year in this scenario. Likewise, cases where cow dung is free, with a collection fee and at a cost of 0.4 Rs/kg are considered in price calculations of biogas electricity.
167
Installed biogas digester cost data is obtained from IRENA for year 2009
(IRENA, 2012b). The installed biogas digester cost is annualized by dividing this cost to
25 years, the life time of the biogas plant. The electricity generator set cost changes from
$500 to $1000 for a 7.5 kW capacity with 10 years life time (Alibaba, 2013). In the analysis, $750 price is assumed for the electricity generator as an average and this price is leveled to year 2009 (1$=48.3 Rs) as in case of solar electricity (X-rates, 2013). Cost of labor (22 Rs/hr) and manure (0.4 Rs/kg) cost data is supplied by Development
Alternatives.
Table 5.8 summarizes the calculated prices of biogas electricity for different scenarios and manure costs. Electricity cost from biogas per kWh is calculated by dividing the total annualized cost in each case to the electricity generated per year in the corresponding scenario considered.
Table 5.8: Cost of biogas electricity for different scenarios with changing manure cost.
Unit Cost of Outputs Current Case Ideal Case Full Capacity Electricity(Rs/kWh) 16.4 14.9 13.2 Electricity with Free Manure (Rs/kWh) 16.4 4.9 2.3 Electricity with Collection Fee (Rs/kWh) 27.9 9.9 7.8
Figure 5.26 shows the breakdown of generation cost for different scenarios considered. As more cow manure is fed to the system and more electricity is generated, the share of capital cost for generation of unit electricity reduces. As a result, the cost of electricity per kWh reduces. For corresponding manure price scenarios, cost of electricity
168 generation reduces with increasing capacity of operation. If manure can be supplied as a free input, cost of electricity reduces dramatically. On the other hand, if manure is purchased at 0.4 Rs/kg cost or collection fee is paid, feedstock cost dominates other costs for generation of electricity from biogas.
30.0 25.0 20.0 15.0
Rs/kWh 10.0 Electrcity Generator 5.0 Biogas Digester 0.0 Maintenance Labor Manure
Figure 5.26: Breakdown of electricity from biogas under different scenarios with changing manure cost.
5.3 Biomass Gasification
The biomass gasification system under study is a 100 kW capacity downdraft biomass gasifier utilizing locally available grass. The synthesis gas produced contains 15-
30% CO, 10-20% H2, 2-4% CH4, 5-15% CO2, 6-8% H2O and the remainder is N2
(Development Alternatives,2011).
In our calculations, the content of producer gas is assumed as 23 % CO, 15 % H2,
3 % CH4, 10% CO2, 7 % H2O and 42% N2 by volume. The density of producer gas is 169
1100 g/m3 and energy content is 4.7 MJ/m3. The system under study is an air blown gasifier which is the reason of high N2 content and low calorific value of the producer gas. Producer gas is then combusted in a diesel engine in dual fuel mode to generate electricity (Development Alternatives, 2011).
We consider three operation schemes in analysis of biomass gasification technology. Current case represents the current operating scheme in Development
Alternatives campus in Orccha. A diesel engine generates electricity, utilizing producer gas from the gasifier and diesel in dual fuel mode and produces 17420 kWh electricity utilizing 20295 kg of ipomea and 1665 liters of diesel per year. Second scenario is ideal case operation in dual fuel mode. In this scenario, we assumed the biogas plant operates with 70% efficiency and 6 hrs per day generating 420 kWh electricity daily, resulting in
153300 kWh of electricity generation per year. In this scenario, the plant utilizes 184000 kg of ipomea and 15330 liter of diesel per year. 153300 kWh of electricity is also generated in third scenario, however utilizing a natural gas engine operating with producer gas only in single fuel mode. In this mode, 261000 kg of ipomea is utilized per year.
5.3.1 Emergy Analysis of Biomass Gasifier
Production of electricity from biomass is composed of two phases. Firstly, producer gas is produced from the biomass via gasification, then, producer gas is combusted by a diesel engine to generate electricity. Emergy analysis of these phases has
170 been performed separately to be able to determine relative sustainability of these energy transformations from ipomea to producer gas and from producer gas to electricity.
5.3.3.1 Producer Gas Production Phase
Figure 5.27 represents the energy systems diagram drawn to determine the inputs and outputs from the biomass gasifier. From these inputs, ipomea is a renewable input since it is a naturally growing local biomass. Water is classified as non-renewable local input since it is withdrawn faster than it replenished. Underground water level is decreasing in Rampura (Development Alternatives, 2011). As in cases of solar panel and biogas digester, labor is categorized as 20% renewable and 80% purchased. Diesel, gasifier materials, 3 HP biomass cutting machine and electricity are 100% purchased external inputs.
Table 5.9 shows the emergy evaluation table for producer gas production phase of current case. There are lower and upper bounds showing the range of change in the evaluation. The sources of this change are ipomea and biomass gasifier materials. Firstly, ipomea is a local biomass which has a growing yield of 8-18 kg/m2.
For that reason, the transformity calculated for ipomea has a range, consequently the emergy flow originating from ipomea changes. Secondly, the installed cost of biomass gasifier changes from Rs 70000 to Rs 85000 per kW of production capacity including the electricity generator cost. In calculations for producer gas production phase, the generator cost is subtracted from these prices. Then, prices excluding generator cost are leveled to
2008 to be able to match with 2008 emergy money ratio of India. As a result, upper and
171 lower bounds have been calculated for total emergy yield. In the last column of emergy evaluation table, the references for the transformities utilized are given.
Figure 5. 27: Emergy system diagram of producer gas production from ipomea.
As can be seen from the emergy evaluation table (5.9), the largest emergy inputs to the system are gasifier materials and labor. The emergy signature diagram for current, ideal case in dual fuel mode (DFM) and ideal case in single fuel mode (SFM) is shown in figure 5.28.
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In current case, emergy flow related to gasifier building materials constitutes
90% of total emergy yield. While the share of labor input increases, the share of gasifier
building materials decreases in ideal DFM and SFM cases. In ideal cases, water and
ipomea become sources of significant emergy flows to the system.
Table 5.9: Emergy evaluation table for producer gas production from ipomea
Transformity Emergy Input Items Units Amount/Year (sej/unit) (sej/year) Reference Ipomea Production Ipomea Up. (100%Re) g 2.03E+07 3.54E+07 7.18E+14 Self Calculation Ipomea Low. (100%Re) g 2.03E+07 1.57E+07 3.19E+14 Self Calculation Cutting Labor (20% Re,80% F) hr 6.56E+02 1.79E+12 1.17E+15 Self Calculation Transportation of Ipomea Fuels(diesel) (100%F) J 6.92E+08 6.58E+04 4.55E+13 Bastianoni,2009 Labor (20% Re,80% F) hr 5.54E+01 1.79E+12 9.91E+13 Self Calculation Preparation of biomass 3 HP Machine (100%F) Rs 1.52E+03 1.59E+11 2.42E+14 Lei, 2012 Cutting Labor (20% Re,80% F) hr 9.23E+01 1.79E+12 1.65E+14 Self Calculation Electricity J 4.13E+08 1.74E+05 7.18E+13 Odum,1996 Gasification Gasifier Materials Up (100%F) Rs 1.81E+05 1.59E+11 2.88E+16 Lei, 2012 Gasifier Materials Low (100%F) Rs 1.40E+05 1.59E+11 2.23E+16 Lei, 2012 Water (100% N) g 1.45E+08 7.30E+06 1.06E+15 Paoli, 2008 Yield Up 3.24E+16 Yield Low 2.55E+16 Outputs Producer Gas( Up) J 2.08E+11 1.56E+05 3.24E+16 Producer Gas( Low) J 2.08E+11 1.22E+05 2.55E+16
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7.00E+16 Water(100% N) 6.00E+16 Gasifier Building Materials
5.00E+16 (100% F)
4.00E+16 Electricity(100% F)
3.00E+16
Sej/year 3 HP Machine(100% F) 2.00E+16 Fuels(diesel)(100% F) 1.00E+16 Labor(20% R,80% F) 0.00E+00 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Ipomea(100% R) Current Case Ideal Case Ideal Case DFM SFM
Figure 5.28: Emergy signature diagram for producer gas production from ipomea.
Following the classification of inputs and calculation of renewable, non renewable and purchased emergy flows, emergy ratios and ESI for the system are determined. Table 5.10 lists these ratios, producer gas transformity and total emergy yield values for the current and the ideal cases considered.
The system renewability is higher for upper bounds due to higher ipomea emergy input. In the upper bound, transformity of ipomea is taken as 3.54E+7 sej/g of ipomea with a yield of 8 kg/m2 which increases the share of renewable ipomea based emergy in total emergy yield. System renewability in dual fuel mode operation in ideal case is also higher compared to current case. Here, the gasifier materials emergy is constant but the ipomea emergy input (renewable) and labor emergy input (20% renewable) increase.
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Table 5 10: Transformity, total emergy yield and emergy ratios for producer gas production.
Current Current Ideal Case Ideal Case Ideal Case Ideal Case Case Case DFM DFM SFM SFM Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Producer gas Transformity (sej/J) 1.22E+05 1.56E+05 2.27E+04 2.80E+04 1.84E+04 2.29E+04 Yield (sej/year) 2.55E+16 3.26E+16 4.29E+16 5.30E+16 4.97E+16 6.13E+16 % Re 2.38% 3.11% 12.8% 17.2% 15.7% 21.1% EYR 1.07 1.07 1.28 1.32 1.30 1.38 ELR 41.0 31.2 6.8 4.81 5.39 3.75 EIR 14.3 14.7 3.59 3.09 3.28 2.66 ESI 0.03 0.03 0.19 0.28 0.24 0.37
25.00%
20.00%
15.00%
10.00%
Producer Gas % Re Gas % Producer 5.00%
0.00% Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.29: % Re values for producer gas production under three scenarios considered.
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In single fuel mode, diesel and lubricant are not used. In addition, higher amount of ipomea and labor is utilized to make up for the diesel in dual fuel mode and to prepare this extra feed stock. As a result % Re in single fuel mode (SFM) is higher compared to dual fuel mode (DFM) operation in ideal case. These results can be seen in figure 5.29.
As the share of purchased inputs in the total emergy yield decrease, EYR which is the ratio of total emergy yield to purchased emergy input flows increases. As a result,
EYR of ideal case is higher than EYR of current case and EYR in SFM is higher than
EYR in DFM. This situation is a consequence of increase in share of renewable inputs to the system and following decline in the share of purchased inputs in the total emergy yield. These results can be seen in figure 5.30.
On the other hand, ELR and EIR values decrease as the portion of renewable emergy flows in the total emergy yield increases. ELR is the ratio of sum of purchased and non-renewable emergy inputs to renewable emergy inputs. EIR is the ratio of purchased inputs to the sum of renewable and non-renewable local inputs. Increase in renewable emergy input decreases these ratios. In other words, EIR and ELR values of current case are highest, whereas these values are lowest for ideal case SFM operation scheme. These results can be seen in figures 5.31 and 5.32.
ESI values increase with reduction in ELR and increase in EYR ratios. Based on this fact, ESI values are highest for ideal case SFM operation and lowest for current case where share of purchased emergy inputs decrease and portion of renewable emergy inputs increase in the total emergy yield.ESI results are presented in figure 5.33.
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1.45 1.4
1.35 1.3 1.25 1.2 1.15
Producer Gas Gas EYR Producer 1.1 1.05 1 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.30: EYR values for producer gas production under three scenarios considered.
45.0 40.0
35.0 30.0 25.0 20.0 15.0
Producer Gas Gas ELR Producer 10.0 5.0 0.0 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.31: ELR values for producer gas production under three scenarios considered.
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16
14
12 10 8 6
Producer Gas Gas EIR Producer 4 2 0 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.32: EIR values for producer gas production under three scenarios considered.
0.4 0.35 0.3 0.25 0.2 0.15
Producer Gas Gas ESI Producer 0.1 0.05 0 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.33: ESI values for producer gas production under three scenarios considered.
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5.3.3.2 Electricity Generation Phase
Figure 5.34 represents the energy systems diagram drawn to determine the inputs and outputs for electricity generation via biomass gasification. Different from producer gas production system; electricity generator, lubricant and diesel are included in the system for DFM operations in current and ideal cases. In SFM operation, only electricity generator is included since diesel and lubricant are not utilized. Categorization of inputs in producer gas production phase is also the same in electricity generation phase. Diesel, lubricant and electricity generator inputs are 100% purchased external inputs to the system considered.
Figure 5.34: Energy systems diagram of electricity generation from producer gas
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Table 5.11 is the emergy evaluation table of electricity generation from producer gas for the current case. There are lower and upper bounds showing the range of change in the evaluation similar to producer gas production phase. The sources of this change are also the same, ipomea and biomass gasifier materials. Likewise, biomass gasifier material cost excluding generator cost is leveled to 2008 to be able to match with 2008 emergy money ratio of India. Additionally, electricity generator cost is leveled to year 2008 and emergy flow originating from electricity generator is calculated. As in producer gas production phase, upper and lower bounds have been calculated for total emergy yield.
Again, in the last column of emergy evaluation table, the references for the transformities utilized are given.
The largest emergy inputs to the system are biomass gasifier materials and generator in the current case. While the share of diesel input increases, the share of gasifier building materials decreases in ideal DFM compared to the current case.
Furthermore, diesel emergy input dominates emergy input originating from gasifier building materials. Since diesel and lubricants are not utilized in SFM operation scheme, labor and gasifier building materials are the largest emergy inputs to the electricity generation from producer gas system. Figure 5.35 presents these results in emergy signature diagram of electricity generation.
Following classification of inputs and calculation of renewable, non renewable and purchased emergy flows, emergy ratios and ESI for the electricity generation system are determined as in producer gas production phase.
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Table 5.11: Emergy evaluation table of electricity generation from producer gas for the current case. Transformity Emergy Input Items Units Amount/year (sej/unit) (sej/year) Reference Ipomea Production Ipomea Up. (100%Re) g 2.03E+07 3.54E+07 7.18E+14 Self Calculation Ipomea Low. (100%Re) g 2.03E+07 1.57E+07 3.19E+14 Self Calculation Cutting Labor (20% Re,80% F) hr 6.56E+02 1.79E+12 1.17E+15 Self Calculation Transportation of Ipomea Fuels(diesel) (100%F) J 6.92E+08 6.58E+04 4.55E+13 Bastianoni,2009 Labor (20% Re,80% F) hr 5.54E+01 1.79E+12 9.91E+13 Self Calculation Preparation of biomass 3 HP Machine (100%F) Rs 1.52E+03 1.59E+11 2.42E+14 Lei, 2012 Cutting Labor (20% Re,80% F) hr 9.23E+01 1.79E+12 1.65E+14 Self Calculation Electricity (100% F) J 4.13E+08 1.74E+05 7.18E+13 Odum, 1996 Gasification Gasifier Materials Up (100%F) Rs 1.81E+05 1.59E+11 2.88E+16 Lei, 2012 Gasifier Materials Low (100%F) Rs 1.40E+05 1.59E+11 2.23E+16 Lei, 2012 Water (100% N) g 1.45E+08 7.30E+06 1.06E+15 Paoli, 2008 Electricity Generation Generator (100% F) Rs 4.98E+04 1.59E+11 7.92E+15 Lei, 2012 Diesel (100% F) J 5.76E+10 1.10E+05 6.34E+15 Lei, 2012 Lubricant (100% F) J 1.18E+10 1.10E+05 1.30E+15 Lefroy,2003 Yield Up 4.79E+16 Yield Low 4.10E+16 Outputs Electricity ( Up) J 6.27E+10 7.64E+05 4.79E+16 Electricity( Low) J 6.27E+10 6.54E+05 4.10E+16
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Figure 5.35: Emergy signature diagram for electricity generation from producer gas.
Table 5.12 shows these ratios, electricity transformity and total emergy yield values for the current and the ideal cases considered. Transformity of electricity from producer gas is lower in the lower bound due to lower total emergy yield invested in the system for corresponding operation schemes. Additionally, the electricity transformity reduces in ideal case DFM compared to the current case since more electricity is generated. Electricity transformity reduces further in ideal case SFM since solely ipomea is utilized in the system as feedstock instead of a higher quality fuel, diesel.
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Table 5.12: Electricity transformity, total emergy yield and emergy ratio values for electricity generation from producer gas.
Current Current Ideal Case Ideal Case Ideal Case Ideal Case Case Case DFM DFM SFM SFM Lower Upper Bound Bound Lower Bound Upper Bound Lower Bound Upper Bound Electricity Transformity(sej/J) 6.54E+05 7.64E+05 2.20E+05 2.39E+05 1.05E+05 1.26E+05 Yield (sej/year) 4.10E+16 4.79E+16 1.22E+17 1.32E+17 5.77E+16 6.93E+16 % Re 1.48% 2.10% 4.52% 6.92% 13.5% 18.7% EYR 1.04 1.05 1.08 1.11 1.25 1.32 ELR 66.7 46.6 21.1 13.4 6.4 4.4 EIR 23.7 22.2 12.0 9.2 4.0 3.1 ESI 0.02 0.02 0.05 0.08 0.20 0.30
System renewability is higher for upper bounds due to higher ipomea emergy
input. System renewability in dual fuel mode operation in ideal case is also higher
compared to current case. Here, the portion of renewable ipomea emergy input and
partially renewable labor emergy input increase. In single fuel mode, diesel and lubricant
are not used. Consequently, higher amount of ipomea and labor are utilized to make up
for the diesel in dual fuel mode and to prepare this extra feedstock. As a result % Re in
single fuel mode (SFM) is higher compared to dual fuel mode (DFM) operation in ideal
case. These results can be seen in figure 5.36.
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Figure 5.36: % Re values for electricity generation from producer gas under three scenarios considered.
As the share of purchased inputs in the total emergy yield decrease, EYR of the system increase. As a result, EYR of ideal case is higher than EYR of current case and
EYR in ideal case SFM is higher than EYR in ideal case DFM. This situation is a consequence of decline in the share of purchased inputs in the total emergy yield. These results can be seen in figure 5.37.
On the other hand, ELR and EIR values decrease as the share of renewable emergy increases in the total emergy yield increases. ELR is the ratio of sum of purchased and non-renewable emergy inputs to renewable emergy inputs. EIR is the ratio of purchased inputs to the sum of renewable and non-renewable local inputs. Increases in renewable emergy inputs decrease these ratios. In other words, EIR and ELR values of
184 current case are highest, whereas these values are lowest for ideal case SFM operation scheme. These results can be seen in figures 5.38 and 5.39.
1.35
1.3
1.25
1.2
1.15
Electricity EYR Electricity 1.1
1.05
1 Lower Upper Lower Upper Lower Upper Bound Bound Bound Bound Bound Bound Current Case Ideal Case DFM Ideal Case SFM
Figure 5.37: EYR values for electricity generation from producer gas under three scenarios considered.
ESI values increase with reduction in ELR and increase in EYR ratios.
Consequently, ESI values are highest for ideal case SFM operation and lowest for current case where share of purchased emergy inputs decrease and portion of renewable emergy inputs increase in the total emergy yield. ESI results are presented in figure 5.40.
185
Figure 5.38: ELR values for electricity generation from producer gas under three scenarios considered.
Figure 5.39: EIR values for electricity generation from producer gas under three scenarios considered.
186
Figure 5.40: ESI values for electricity generation from producer gas under three scenarios considered.
Emergy analysis of producer gas production and electricity generation phases in three different operating schemes is performed. In DFM operation cases for electricity generation, %Re, EYR values decrease and ELR, ERI values increases compared to producer gas production case due to purchased generator, diesel and lubricant emergy contributions in electricity generation. In ideal case SFM operation, less difference is observed between the emergy indicators of producer gas production and electricity generation phases compared to DFM cases. In SFM, diesel and lubricant are not utilized in electricity generation, but more wood is utilized. As a result, electricity generation in
SFM has highest %Re and EYR values among other electricity generation options via biomass gasification.
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5.3.2 Life Cycle Assessment of Electricity Generation via Biomass Gasification
Figure 5.41 presents the life cycle steps considered in the LCA of electricity generation from producer gas. As stated in emergy analysis section, ipomea is a naturally growing local plant around riverbanks and marshy areas. The life cycle of electricity generation from biomass via gasification starts with growth of feedstock ipomea. Ipomea is then collected by villagers and transported to Development Alternatives campus by tractor. Each load of tractor can carry 2000 kg ipomea. However, 25% of green plant is lost during cutting. Wet ipomea is sold to Development Alternatives at a cost of 0.7
Rs/kg. Ipomea is dried to have 15% moisture and to be ready to be used in the gasifier.
The cost of ready to use ipomea is 1.2 Rs/kg. The distance that ipomea is transported is approximately 10 km and 1 liter of diesel is consumed by the tractor for this transportation. 15% wet ipomea is then gasified and producer gas is produced. Producer gas and diesel are co-fired in a diesel engine in dual fuel mode (DFM).
As in case of emergy analysis, three operation schemes are considered in LCA of biomass gasifier and electricity generation from producer gas. These are current case, ideal case in dual fuel (DFM) mode and ideal case in single fuel mode (SFM). Life cycle global warming potential (GWP), land use and water use for these three cases have been calculated.
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Figure 5.41: Life cycle steps considered for electricity generation from producer gas.
Two factors have been taken into account in ipomea growth step for GWP calculations. First is the CO2 sequestered by the ipomea and second is the CO2 sequestered by soil. It is assumed that 50% of dry ipomea weight is carbon and 15 g of carbon per m2 per year is sequestered by soil (Lal, 2004). In transportation stage, emissions caused by consumption of diesel are accounted for. CO2 content of producer gas is the emissions from gasification stage, since it will not be combusted further in electricity generation. In Electricity, generation emissions originating from diesel and combustible portion of producer gas are summed up to find the total life cycle GWP. In ideal SFM, no diesel is utilized, consequently emissions originate from combustion of producer gas only.
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Contribution of all life cycle steps to total GWP is presented in figure 5.42. Net
GWP values for different scenarios considered are given in figure 5.43. There is an upper bound and lower bound range for GWP due to difference in growing yield of ipomea (8-
18 kg/m2).
In DFM operation schemes (current and ideal DFM), there is a positive net GWP mainly due to utilization of diesel together with producer gas (around 200 g CO2 eq/kWh). In SFM operation, electricity generation via biomass gasification creates a carbon sink (from -31 to -47 gCO2 eq./kWh) mainly due to soil carbon sequestration.
Producer gas being a bio-based fuel, emission for its production and emission from its combustion is sequestered by ipomea during growth.
3.00E+03
2.00E+03
1.00E+03 Electrcity Generation 0.00E+00 Biomass Gasification
-1.00E+03 Ipomea Transportation gCO2eq/kWh Soil Carbon Sequestration -2.00E+03 Ipomea CO2 Sequestration -3.00E+03 Lower Upper Lower Upper Lower Upper Current Ideal DFM Ideal SFM
Figure 5.42: Contribution of all life cycle steps to total GWP of electricity generation from producer gas.
190
3.00E+02
2.50E+02
2.00E+02
1.50E+02
1.00E+02
eq/kWh 2
5.00E+01 Net GWP gCO 0.00E+00
-5.00E+01
-1.00E+02 Lower Upper Lower Upper Lower Upper Current Ideal DFM Ideal SFM
Figure 5.43: Net GWP results for electricity generation from producer gas under different scenarios.
Land use has also upper and lower bounds corresponding to different growth yield of ipomea for all cases considered. Main source of land use is the land area utilized for growth of ipomea. The land use during biomass gasification includes the land are biomass gasifier, water tank and biomass storage areas. Electricity generator is situated inside biomass gasifier building so no additional land use is attributed to electricity generation stage. Land utilization during biomass transportation is zero. Land utilization is higher in upper bounds due to lower growing yield of ipomea. Land utilization in SFM is higher than DFM since more feedstock per kWh generation is utilize to replace diesel and larger land area is needed. Additionally, land area utilized per kWh in ideal DFM is slightly higher than current operation scheme. These results are presented in figure 5.44.
Main source of water use in the life cycle is also ipomea growth. However, the water utilized in this step is renewable rain water not irrigation water obtained from 191 underground or fresh water resources. The only other step where water is utilized as cooling water is in biomass gasification step. 10% of the water is assumed to evaporate so that needs to be replaced, rest is cooled and recycled. In transportation and electricity generation steps, there is no water use. As in land use results, the water used per kWh generation is higher in upper bounds due to lower growing yield of ipomea. As a result more rain is needed to grow the amount of ipomea used in corresponding scenarios.
Water use per kWh in DFM operation schemes are almost the same. In SFM, higher amount of water is utilized since more ipomea for kWh generation is used as feedstock.
6.00E-01
5.00E-01
4.00E-01
3.00E-01 Electricity Generation /kWh
2 Biomass Gasification m 2.00E-01 Transportation of Ipomea
1.00E-01 Ipomea Growth
0.00E+00 Lower Upper Lower Upper Lower Upper Current Case Ideal Case Ideal Case DFM SFM
Figure 5.44: Life cycle land use results for electricity from producer gas.
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450.00
400.00
350.00
300.00
250.00 Electricity Generation
200.00 Biomass Gasification
liter/kWh Transportation of Ipomea 150.00 Ipomea Growth 100.00
50.00
0.00 Lower Upper Lower Upper Lower Upper Current Case Ideal Case DFM Ideal Case SFM
Figure 5.45: Life cycle water use results for electricity from producer gas.
5.3.3 Economic Analysis of Electricity Generation from Producer Gas
In economic assessment of biomass gasifier, three scenarios have also been considered. Current case represents the current operating scheme in Development
Alternatives campus in Orchha. A diesel engine generates electricity, utilizing producer gas from the gasifier and diesel in dual fuel mode and produces 17420 kWh electricity utilizing 20295 kg of ipomea and 1665 liters of diesel per year. Second scenario is ideal case operation in dual fuel mode. In this scenario, we assumed the biogas plant operates with 70% efficiency and 6 hrs per day generating 420 kWh electricity, resulting in
153300 kWh of electricity production per year. In this scenario, plant utilizes 184000 kg of ipomea and 15330 liter of diesel per year.153300 kWh of electricity is also generated
193 in third scenario, however utilizing a natural gas engine operating with producer gas only in single fuel mode. In this mode, 261000 kg of ipomea per year is utilized.
The cost data related to capital and operating costs of biomass gasifier is obtained from Development Alternatives. As in case of solar electricity and electricity from biogas, these costs are annualized and total annualized cost is divided by the electricity generated in kWh in a year in each corresponding scenario.
Table 5.13 summarizes the cost of electricity from producer gas for each scenario considered. When generation capacity is increases cost of electricity reduces from 26.1
Rs to 8.8 Rs and when generation is shifted from dual fuel mode to single fuel mode, price of electricity reduces further.
Table 5.13: Cost of electricity from producer gas under different scenarios considered.
Unit Cost of Outputs Current Case Ideal Case DF Ideal Case SF Electricity (Rs/kWh) 26.1 8.8 4.4
Figure 5.46 shows the breakdown of cost for biomass electricity. As in case of electricity generation from biogas, share of capital cost which contains biomass gasifier, electricity generator, and cutting machine costs reduces as production capacity increases.
In current case, cost of biomass gasifier dominates all other contributing factors to the cost of electricity. In ideal case operating in dual fuel mode, diesel cost becomes more significant than other inputs and share of capital costs diminishes. In ideal case operating
194 in single fuel mode, no diesel or lubricant is utilized and feedstock ipomea cost dominates.
30
25 Cutting Machine
20 Electrcity Generator
Biomass Gasifier 15
Lubricant Rs/kWh Diesel 10 Feedstock
5 Feedstock Preparation Labor
0 Current Case Ideal Case DF Ideal Case SF
Figure 5.46: Breakdown of cost of electricity from producer gas under different scenarios considered.
195
Chapter 6: Emergy Analysis Results of Rampura Village
6.1 Development Alternatives and Rampura Village
In India, we collaborated with Development Alternatives uniting their field experience with our holistic analysis experience. Development Alternatives is a non- governmental organization (NGO) working in area of sustainable development, and capacity building of people for income generating activities in rural setting (Development
Alternatives, 2013). Development Alternatives` technology disseminating branch TARA
(Technology and Action for Rural Advancement) works in Bundelkhand region in
Central India (TARA, 2013). We chose a village, Rampura in Bundelkhand region as our project site because of successful renewable energy and capacity building applications implemented by Development Alternatives in that village.
Rampura, also contained in Bundelkhand region, is a village in Jhansi district of
Uttar Pradesh, India. There are 436 people living in the village out of which 179 are males, 147 are females and rest is children. The literacy in the village is 70%. Table 6.1 summarizes the population information of Rampura.
There are 69 households in the village out of 44 are connected to the 8.7 kWp multi-crystalline silicon solar grid implemented by Development Alternatives in collaboration with Scatec Solar (Scatec Solar, 2013). The houses which are connected to the solar grid utilize solar electricity for lighting and those which are not connected to the grid utilize kerosene for the same purpose. Cooking and heating needs are supplied from
196 wood, agricultural residues and cow dung cake. Table 6.2 summarizes the amount of each energy source utilized in the village annually.
Table 6.1: Population of Rampura village.
Total population 436 Males 177 Females 149 Children 110 Literacy Rate 70%
Table 6.2: Energy use in Rampura village annually.
Energy Use In Rampura Amount/year Electricity(kWh) 6935 Kerosene(liter) 1278 Wood(kg) 355875 Agricultural Residues(kg) 100375 Dung Cake(kg) 189800
Total village area is 133.6 hectares. In the village there is no dedicated grazing land or reserve forest. According to the season, non-irrigated land is utilized as grazing land. The land use pattern in Rampura is listed in table 6.3.
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Table 6.3: Land use in Rampura Village
Land Use Area (Hectare) Total Village Area 133.6 Cultivable Land 89.0 Irrigated Land 68.0 Grazing Land - Reserve Forest -
The main income generating activities in the village are agriculture and animal husbandry. The main crops grown are wheat, pulses, groundnut, mustard and different vegetables. The annual amounts of each product grown are given in table 6.4. There are
117 buffalos, 45 bullocks, 55 cows, 36 calves and 155 goats in the village totally.
Bullocks and sometimes buffalos are utilized as draft animals. Cows, goats and buffalos produce milk. While goats open graze and are not fed fodder, other animals are fed fodder consisting mainly of wheat straw. The number of animals available and the amount of milk produced annually is given table 6.5. Vegetables and milk production are the items for liquid cash flow within the year.
Bundelkhand is a semiarid region suffering from lack of water and prone to climate change. The area went through a 4-year-long draught making village suffer from the existing climate change problems more and underground water level continues to decline. The project data are supplied by Development Alternatives and surveys performed with villagers.
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Table 6.4: Agricultural products grown in Rampura and their annual quantity.
Agricultural Products Amount(kg/year) Wheat 1.38E+08 Gram 1.28E+07 Mustard 1.61E+07 Maize 2.12E+07 Urad Dal 1.41E+07 Soya 3.53E+07 Groundnut 1.25E+07 Vegetables 8.25E+07 Agricultural Residues 2.95E+08
Table 6.5: Number of animals available in Rampura and annual milk production.
Buffalo 117 Bullock 45 Cow 55 Calf 36 Goat 155 Milk Yield(kg/year) 2.08E+05
6.2. Village Level Emergy Analysis
In Rampura village, there are three subsystems interacting, systems exchanging materials and energy among themselves. These subsystems are animal husbandry, agriculture sectors and human ecosystem or domestic sector. Human ecosystem provides the labor needed in husbandry for the animals to be fed, barns to be cleaned etc. In agriculture, labor is needed to manage animals or tractors for fields to be ploughed, to
199 apply fertilizers and pesticides and to harvest the crops. Husbandry sector provides the animal draft for the fields to be ploughed. Milk produced by cows, buffalos and goats is an important nutrition source for the people in the village. Furthermore, cow dung dried under the sun is an important energy source for cooking and heating depending on the season. Around 40% of the agricultural residues are fed to cows as fodder and around 34
% of agricultural residues are used for cooking and heating purposes by human in the village.
Figure 6.1 is the energy systems diagram representing these internal interactions among subsystems, external inputs to Rampura village and outputs from the village.
Since emergy analysis is an input-output analysis method, only external inputs to the village and outputs from the village are taken into account for village level emergy analysis (Odum, 1996; Ulgiati and Brown, 1998). Interactions among three subsystems within the village boundary have not considered. However, these interactions among subsystems determine the need for external inputs. For instance, some portion of agricultural residues is fed to animals and less fodder is bought from the market. If agricultural residues were not used as fodder, more fodder would be purchased from the market for animals to be fed. Or if cow dung was not used for cooking and heating, another energy source would be needed. Given the influence of internal interactions within the village on overall village system, these three subsystems are separately analyzed to understand their relative sustainability and their individual effects on the village. Emergy analysis of these subsystems will be presented in the following sections.
200
Figure 6 1: Energy systems diagram for Rampura village.
Sun, rain and wind are the renewable emergy inputs to the system. Fossil fuels, fertilizer, pesticide, inputs related to the solar panel in the village, purchased fodder for animals, kerosene for lighting are purchased external inputs. Wood, ground water and soil erosion (topsoil loss) are local non-renewable emergy inputs contributing to the village system. Table 6.6 presents the emergy evaluation table for village level emergy analysis of Rampura. In the last column of the emergy evaluation table, references for transformity values utilized are given. 201
Table 6.6: Emergy evaluation table for village level emergy analysis of Rampura.
Transformity Emergy Inputs Amount Unit (Sej/Unit) (Sej/year) Reference Sun(land)(100% R) 1.86E+15 J 1 1.86E+15 Odum,2000 Sun(field)(100% R) 4.64E+15 J 1 4.64E+15 Odum,2000 Rain(100% R) 5.61E+12 J 3.06E+04 1.71E+17 Odum,2000 Wind (100% R) 6.56E+08 J 2.52E+03 1.65E+12 Odum,2000 Earth Cycle(100% R) 1.34E+12 J 4.28E+04 5.71E+16 Odum,2000 Topsoil loss (low) (100% N) 3.01E+11 J 1.25E+05 3.76E+16 Pizzigallo,2008 Topsoil loss(Up) (100% N) 6.03E+11 J 1.25E+05 7.54E+16 Pizzigallo,2008 Ground water (100% N) 6.62E+11 g 3.98E+05 2.63E+17 Zhang, 2012 Diesel(Irrigation)(100% F) 4.36E+06 g 2.89E+09 1.26E+16 Bastianoni,2009 Pesticide(100% F) 2.41E+04 g 1.48E+10 3.56E+14 Brandt-Williams, 2002 Fertilizer(100% F) 6.92E+06 g 2.22E+10 1.54E+17 Brandt-Williams, 2002 Tractor Traction(Up)(100% F) 2.53E+07 g 2.89E+09 4.43E+16 Bastianoni,2009 Tractor Traction(Low)(100% F) 1.15E+07 g 2.89E+09 3.32E+16 Bastianoni,2009 Solar Electricity(100%F)(Total) 3.01E+10 j 1.81E+05 5.46E+15 Self calculation Sun 4.59E+11 J 1.00E+00 4.59E+11 Odum,2000 Fossil Fuels and electricity g 5.16E+14 Mounting and Panel Materials g 1.39E+15 Maintenance 261.10 € 2.22E+12 5.80E+14 Paoli, 2008 Maintenance Labor(20%R,80%F) 3.17E+06 J 1.37E+06 4.34E+12 Self Calculation Batter Bank(100%F) 761 € 2.22E+12 1.69E+15 Paoli, 2008 Inverter(100%F) 596 € 2.22E+12 1.32E+15 Paoli, 2008 Kerosene(100%F) 1.02E+06 g 2.88E+09 2.94E+15 Bastionani,2009 Wood(100%N) 3.56E+08 g 6.79E+08 2.42E+17 Pizzigallo, 2008 Ground water(100%N) 3.15E+09 g 3.98E+05 1.25E+15 Zhang, 2012 Fodder(100%F) 1.07E+09 g 2.07E+09 2.21E+18 Zhang, 2007 Ground water(100%N) 3.99E+09 g 3.98E+05 1.59E+15 Zhang,2012 Gasoline(100%F) 4.34E+06 g 2.92E+09 1.27E+16 Bastionani,2009 Yield (Up) 3.20E+18 Yield(low) 3.14E+18
Continued
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Table 6.6 continued
OUTPUTS 1. Agriculture Amount(g) Exergy (J) Wheat 1.03E+08 1.63E+12 Gram 1.02E+07 1.74E+11 Mustard 1.61E+07 3.06E+11 Maize 2.01E+06 3.40E+10 Urad Dal 1.24E+07 1.82E+11 Soya 3.09E+07 5.25E+11 Groundnut 1.19E+07 2.83E+11 Vegetables 5.78E+07 8.60E+11 Agricultural Residues 7.60E+07 1.29E+12 2. Domestic Human Labor - 5.60E+11 Human Feces 3.91E+07 3.13E+11 3. Husbandry Milk 1.45E+08 2.34E+12 Animal Draft - 6.63E+11 Manure 1.34E+09 7.08E+12
Animal draft, manure and milk are husbandry sector outputs. Agricultural crops and residues are the outputs from agriculture. Human ecosystem provides human labor and feces. There is an important point to be emphasized here. The output quantities stated here are the net amounts coming out of the village system. For example, the value given for agricultural residues is equal to total residues minus the residues utilized in domestic and husbandry sectors. As stated earlier, we consider only items leaving from or entering to the village boundary in village level emergy analysis.
As can be seen from emergy evaluation table (table 6.6), the largest emergy input to the village is fodder input for animals followed by ground water. There is an upper and 203 lower limit for total yield calculated originating from topsoil loss and tractor traction.
Topsoil loss for this area is taken as 5-10 tonnes/ha/year (Yedla, 2003) and there is a range for diesel use per season utilized by farmers of Rampura to plough their fields and transport their crops to the market. These results can be seen in emergy signature diagram of Rampura village for village level emergy analysis in figure 6.2
1.00E+19
1.00E+18
1.00E+17
1.00E+16 sej/year
1.00E+15
1.00E+14
Figure 6.2: Emergy signature diagram of Rampura village for village level emergy analysis.
Similar to earlier emergy analyzes presented; renewable, non-renewable and purchased emergy flows are calculated after classification of each input. Calculation of different emergy flows enables calculation of emergy ratio and indices to evaluate the
204 sustainability of the village studied. Table 6.7 presents the values calculated for these emergy indicators in the village level.
Table 6.7: Emergy ratios and indices for Rampura village
Up Low %Re 5.35% 5.44% EYR 1.31 1.29 ELR 17.7 17.4 EIR 3.24 3.41 ESI 0.04 0.04
The village has low % renewability due to the dominance of purchased inputs
(mainly fodder) to the system. Because of the dominance of the purchased inputs emergy yield ratio (EYR) is also a little higher than 1. System has high environmental loading ratio (ELR) and environmental investment ratio (EIR) again due to dominance of purchased external inputs in the total emergy yield. Environmental sustainability index
(ESI) which is the ratio of EYR to ELR is much lower than 1 because of low EYR and high ELR values. From these results it can be concluded that the amount purchased emergy inputs should be reduced to improve the overall sustainability in Rampura village.
Fodder purchased from the market is mainly wheat straw which is an agricultural residue. As it can also be seen in agricultural sector emergy analysis in section 6.4, there
205 are renewable (rain, sun, wind) or partially renewable (human labor, animal draft) inputs for production of agricultural outputs. For that reason, fodder cannot be classified as
100% purchased despite being an imported input. A sensitivity analysis is performed to detect the effect of changes in renewability of fodder input on the village system sustainability and the related emergy indicators. Table 6.8 presents the results of the sensitivity analysis performed.
Table 6.8: Sensitivity analysis results for cases where fodder is 20% Re and 40% Re.
Fodder 100%F Fodder 80% F Dodder 60%F %Re 5.40% 19.5% 33.0% EYR 1.30 1.59 2.04 ELR 17.60 4.24 2.05 EIR 3.33 1.69 0.96 ESI 0.04 0.38 1.00
As renewable portion of fodder emergy input increases, system renewability and
EYR increases significantly. Accordingly, ELR and EIR ratios decrease. With increasing
EYR and declining ELR, ESI increases significantly supporting the conclusion that the share of purchased emergy inputs to the system should be reduced or share of renewable emergy inputs should be increased to improve the overall sustainability of the village system.
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6.3 Emergy Analysis of Husbandry Sector
The first subsystem analyzed is the husbandry sector in Rampura village. Figure
6.3 is the energy systems diagram showing the inputs to and outputs from the husbandry sector in Rampura. Sun, rain and wind are the renewable emergy inputs to the system.
Groundwater is the local non-renewable input since underground water level declines.
Human labor is categorized as 20 % renewable and 80 % purchased input of which calculation is presented in human ecosystem emergy analysis in section 6.5. Likewise agricultural residues are 20 % renewable and 80% purchased input of which calculation is presented in agricultural sector emergy analysis in section 6.4. As in case of village level emergy analysis; fodder and gasoline for the transportation of milk to the market is
100% purchased inputs in the husbandry sector.
Animal draft, manure and milk are the products obtained from the husbandry sector. Outputs of the husbandry sector are co-products which cannot be produced independently. For that reason, the total emergy yield is assigned to all products without allocation according to the emergy algebra rules (Odum, 1996 and Bastianoni, 2009).
Product transformities are calculated by division of total emergy yield by product available energy (exergy) or product mass.
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Figure 6.3: Energy system diagram of husbandry sector in Rampura.
Emergy evaluation table for husbandry sector in table 6.9 shows the emergy flows calculated for all these inputs. References from which transformity values for each input obtained are listed in the last column of the table.
Fodder is the largest emergy input to the husbandry sector followed by agricultural residues used as fodder. Emergy signature diagram in figure 6.4 presents the contribution of each input to husbandry sector in Rampura.
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Table 6.9: Emergy evaluation table for husbandry sector in Rampura village.
Transformity Emergy Inputs Amount Unit (Sej/Unit) (Sej/year) Reference Sun(land)(100%R) 1.86E+15 J 1 1.86E+15 Odum,2000 Sun(field)(100%R) 4.64E+15 J 1 4.64E+15 Odum,2000 Rain(100%R) 5.61E+12 J 3.06E+04 1.71E+17 Odum,2000 Wind(100%R) 6.56E+08 J 2.52E+03 1.65E+12 Odum,2000 Earth Cycle(100%R) 1.34E+12 J 4.28E+04 5.71E+16 Odum,2000 Fodder(100%F) 1.07E+09 g 2.07E+09 2.21E+18 Zhang, 2007 Human Labor(20%R,80%F) 1.72E+11 J 1.37E+06 2.36E+17 Self Calculation Ground water(100%F) 3.99E+09 g 3.98E+05 1.59E+15 Zhang,2012 Ag. Residue Fodder(20%R,80%F) 1.19E+08 g 2.37E+09 2.81E+17 Self Calculation Milk Transportation To Market Gasoline(100%F) 4.34E+06 g 2.92E+09 1.27E+16 Bastionani,2009 Total 2.92E+18 Outputs Milk 2.08E+08 g 1.40E+10 2.92E+18 Animal Draft 8.60E+11 J 3.38E+06 2.92E+18 Manure 1.85E+09 g 1.58E+09 2.92E+18
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1.00E+19
1.00E+18
1.00E+17
sej/year 1.00E+16
1.00E+15
1.00E+14 Fodder Ag Residue Human Rain Gasoline Ground Fodder Labor water
Figure 6.4: Emergy signature diagram for husbandry sector in Rampura.
After categorization of each input and calculation of emergy belonging to each input; renewable, non-renewable and purchased emergy flows are calculated by addition of corresponding emergy flows. Emergy ratios and index are calculated following this step. As in village level emergy analysis, sensitivity analysis is performed to detect the effect of changes in renewability of fodder since it is the input constituting 75 % of all emergy inputs to the husbandry subsystem. Table 6.10 presents the results for emergy indicators for the base case (fodder 100%F) and the results for the sensitivity analysis performed (fodder 20% and 40 % e or 80% and 60% F).
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Table 6.10: Emergy indicators for base case and their variation due to change in renewability of fodder input.
Fodder 100%F Fodder 80%F Fodder 60%F %Re 9.42% 24.6% 39.7% EYR 1.10 1.33 1.66 ELR 9.62 3.07 1.52 EIR 9.56 3.06 1.51 ESI 0.09 0.43 1.09
As can be seen from table 6.10, % renewability and EYR of the husbandry subsystem increase as the renewable portion of fodder input increases. In other words, as the share of purchased input flows decreases, %Re and EYR increase. Despite being lower than village level, husbandry sector has also high ELR value decreasing with increasing fodder renewability. Husbandry EIR is higher than village level EIR mainly due to the higher portion of purchased inputs and lower portion of local non-renewable inputs in the total emergy yield. As ELR, EIR also reduces with increasing fodder renewability. ESI increases due to increasing EYR and decreasing ELR values.
As a result we can conclude that, overall sustainability of husbandry sector increases with increasing share of renewable inputs in total emergy yield.
6.4. Emergy Analysis of Agricultural Sector
Agricultural sector provides food, agricultural residue to be used as fuel for cooking and heating to the people of Rampura and provides around 10% of the fodder 211 animals consume. Some of the agricultural products are consumed by villagers and rest of them is sold in the market in Jhansi. Figure 6.5 shows the energy systems diagram drawn to define the inputs to and outputs from agricultural subsystem in Rampura.
Figure 6.5: Energy system diagram of agricultural sector in Rampura
Here, water and topsoil are non-renewable inputs to agriculture since they are utilized faster than they are replenished by nature. Sun, wind and rain are renewable inputs. Fertilizer, pesticide, fossil fuel (diesel) used for irrigation and tractor traction
(diesel) are imported purchased inputs to the subsystem. As in husbandry, human labor is
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20% renewable and 80 % purchased. Animal draft from husbandry sector is 10 %
renewable and 90 % purchased. For emergy analysis, emergy flow related to each input is
calculated. Figure 6.11 is the emergy evaluation table for agricultural sector in Rampura.
In the last column of this table, references for the transformity values utilized are given.
Table 6.11: Emergy evaluation for agricultural sector in Rampura
Transformity Emergy Inputs Amount Unit (Sej/Unit) (Sej/year) Reference Sun(field)(100% R) 4.64E+15 J 1 4.64E+15 Odum,2000 Rain(100% R) 5.61E+12 J 3.06E+04 1.71E+17 Odum, 2000 Wind(100% R) 6.56E+08 J 2.52E+03 1.65E+12 Odum,2000 Earth Cycle(100% R) 1.34E+12 J 4.28E+04 5.71E+16 Odum,2000 Topsoil loss(Up)(100%N) 3.01E+11 J 1.25E+05 3.76E+16 Odum,2000 Topsoil loss(low)(100%N) 6.03E+11 J 1.25E+05 7.54E+16 Pizzigallo,2008 Ground water(100%N) 6.62E+11 g 3.98E+05 2.63E+17 Pizzigallo,2008 Diesel(Irrigation)(100%F) 4.36E+06 g 2.89E+09 1.26E+16 Zhang, 2012 Pesticide(100%F) 2.41E+04 g 1.48E+10 3.56E+14 Brandt-Williams, 2002 Fertilizer(100%F) 6.92E+06 g 2.22E+10 1.54E+17 Brandt-Williams, 2002 Self Calculation Human Labor(20%R,80%F) 6.88E+11 J 1.37E+06 9.43E+17 Self Calculation Animal Draft(10%R,90%F) 1.98E+11 J 3.38E+06 6.69E+17 Bastianoni, 2009 Tractor Traction(up)(100%F) 1.53E+07 g 2.89E+09 4.43E+16 Bastianoni, 2009 Tractor Traction(Low)(100%F) 1.15E+07 g 2.89E+09 3.32E+16 Y Lower 2.28E+18 Yupper 2.33E+18 OUTPUTS Amount(g) Exergy(J) Trans. Low Trans Up Emergy Low Emergy Up Wheat 1.38E+08 2.18E+12 2.37E+05 2.42E+05 5.16E+17 5.27E+17 Gram 1.28E+07 2.18E+11 2.37E+05 2.42E+05 5.16E+16 5.27E+16 Mustard 1.61E+07 3.06E+11 2.37E+05 2.42E+05 7.25E+16 7.41E+16 Maize 2.12E+07 3.58E+11 2.37E+05 2.42E+05 8.48E+16 8.66E+16 Urad Dal 1.41E+07 2.09E+11 2.37E+05 2.42E+05 4.94E+16 5.05E+16 Soya 3.53E+07 6.00E+11 2.37E+05 2.42E+05 1.42E+17 1.45E+17 Groundnut 1.25E+07 2.98E+11 2.37E+05 2.42E+05 7.05E+16 7.20E+16 Vegetables 8.25E+07 1.23E+12 2.37E+05 2.42E+05 2.91E+17 2.97E+17 Agricultural Residues 2.95E+08 4.25E+12 2.37E+05 2.42E+05 1.01E+18 1.03E+18
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Outputs of agricultural sector are classified as splits since they can be produced independently and serve to meet similar purposes (Bastionani, 2009). According to the emergy algebra rules, the total emergy of a system is allocated among splits according to their available energy (exergy) content. Based on the ranges for tractor traction diesel use and topsoil loss, there is an upper and lower range for total emergy yield calculated.
Consequently, upper and lower transformity values for outputs are calculated.
Human labor and animal draft are the largest emergy inputs to the agricultural system. Groundwater and rain are also important emergy inputs. In Rampura, agriculture is not highly mechanized as can be inferred from the contribution of tractor traction to the total emergy yield. These can be better seen in emergy system diagram presented in figure 6.6.
1.00E+18
1.00E+17
1.00E+16
sej/year 1.00E+15
1.00E+14
1.00E+13
Figure 6.6: Emergy signature diagram belonging to agricultural sector in Rampura.
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When emergy flows are added up according to their type, the total renewable, non-renewable and purchased emery flows for agricultural sector can be determined followed by calculation of emergy ratios and indices. Table 6.12 presents the results obtained for the current case of agricultural sector in Rampura village.
Table 6.12: Emergy ratios and indices for agricultural sector in Rampura.
Low Up % Re 18.7% 18.3% EYR 2.39 2.44 ELR 2.95 3.04 EIR 1.32 1.25 ESI 0.80 0.81
Agriculture in Rampura is more renewable than the village and the husbandry sector. This situation is due to higher portion of renewable emergy flows to the agricultural system. Based on the same reason, agricultural system has higher EYR and lower ELR than the other two systems. Furthermore, agricultural system EIR is lower than husbandry and village level EIR. Consequently, agricultural sector is more sustainable (higher ESI) the husbandry sector and the overall village system.
Although fodder is not a direct input to the agricultural system, renewability of fodder affects the renewability of animal draft. When fodder is 20% renewable, animal draft becomes 25 % renewable. When fodder is 40% renewable, animal draft becomes 40
% renewable. Sensitivity analysis for these cases is performed to see how emergy
215 indicators for agricultural sector changes. Results related to this sensitivity analysis are presented in table 6.13.
As in husbandry sector, increase in renewability of fodder increases agricultural system renewability and EYR. System ELR and EIR reduces. As a consequence of increased EYR and reduced ELR, system ESI increases indicating an improvement in overall system sustainability.
Table 6.13: Sensitivity analysis for agricultural sector in Rampura.
Fodder 100%F Fodder 80%F Fodder 60%F %Re 18.5% 26.0% 30.4% EYR 2.41 2.89 3.31 ELR 3.00 1.84 1.43 EIR 0.81 0.88 0.79 ESI 1.30 1.57 2.32
6.5 Emergy Analysis of Domestic Sector
Domestic sector provides the human labor required in husbandry and agricultural sectors. Human feces can also be a source of organic fertilizer despite not being utilized in the village currently. Figure 6.7 is the energy systems diagram for domestic sector in
Rampura village.
Food crops and agricultural residues are 20% renewable, 80 % purchased inputs classified according to the analysis results of agricultural sector. Milk and manure are
10% renewable, 90% purchased inputs classified according to the analysis results of
216 husbandry sector. Wood and groundwater are non-renewable local inputs which are utilized faster than their replenishment rate by nature. Sun, rain and wind are local renewable inputs. Lastly, kerosene and inputs for solar electricity are purchased emergy inputs contributing to domestic sector.
Figure 6.7: Energy system diagram of domestic sector in Rampura.
As in the other subsystems of Rampura village, emergy flow belonging to each emergy input is calculated. The emergy flow for each input and references for related transformity values are presented in table 6.14. As it can be seen in this table, cow dung and milk are two co-products of husbandry sector. According to the emergy algebra rules,
217 emergy flows for co-products cannot be added but the emergy flow having the maximum value is picked (Odum, 1996 and Basianoni, 2009). For that reason, emergy flow of milk is accounted for in our emergy evaluation. On the other hand, food crops and agricultural residues are the products of agricultural sector. However, their emergy flows can be added up since they are splits not co-products (Bastianoni, 2009).
Table 6.14: Emergy evaluation table for domestic sector in Rampura.
Transformity Emergy Inputs Amount Unit (Sej/Unit) (Sej/year) Reference Sun(land)(100%R) 1.86E+15 J 1 1.86E+15 Odum,2000 Sun(field)(100%R) 4.64E+15 J 1 4.64E+15 Odum, 2000 Rain(100%R) 5.61E+12 J 3.06E+04 1.71E+17 Odum,2000 Wind(100%R) 6.56E+08 J 2.52E+03 1.65E+12 Odum, 2000 Earth Cycle(100%R) 1.34E+12 J 4.28E+04 5.71E+16 Odum,2000
1 .Lighting Solar Electricity(100%F) 3.01E+10 j 1.81E+05 5.46E+15 Self Calculation Kerosene(100%F) 1.02E+06 g 2.88E+09 2.94E+15 Bastionani, 2009 2.Cooking Cow Dung(10%R,90%F) 2.67E+12 J 2.98E+05 7.97E+17 Self Calculation Agricultural Residues (20%R,80%F) 1.00E+08 g 2.37E+09 2.38E+17 Self Calculation Wood(100%N) 3.56E+08 g 6.79E+08 2.42E+17 Pizzigallo, 2008 Ground Water(100%N) 3.15E+09 g 3.98E+05 1.25E+15 Zhang, 2012 Food (20%R,80%F) 6.96E+07 g 2.57E+09 1.79E+17 Self Calculation Milk(10%R,90%F) 6.20E+07 g 1.40E+10 8.74E+17 Self Calculation Total 1.71E+18 Outputs Human Labor 1.25E+12 J 1.37E+06 1.71E+18 Human Feces 3.13E+11 J 5.46E+06 1.71E+18
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The largest emergy input to domestic sector is milk, almost containing 50% of total emergy inputs followed by wood and agricultural residues utilized for cooking and heating purposes. Rain and food crops are other significant emergy inputs to domestic sector. These details are presented in emergy signature diagram of domestic sector in figure 6.8.
1.00E+18 1.00E+17
1.00E+16 1.00E+15 1.00E+14
sej/year 1.00E+13 1.00E+12 1.00E+11
Figure 6.8: Emergy signature diagram for domestic sector in Rampura.
Fodder is not a direct input to domestic sector, however renewability of fodder affects the renewability of milk directly and the renewability of the agricultural residues indirectly. When fodder is 20% renewable, milk becomes 25 % renewable and agricultural residues become 26% renewable. When fodder is 40% renewable, milk is 40
% renewable and agricultural residues are 30% renewable. Sensitivity analysis for these cases is performed to see how emergy indicators for domestic sector changes according
219 to the renewability of fodder. Results related to this emergy analysis are presented in table 6.15.
As in husbandry and agricultural sectors, increase in renewability of fodder increases domestic sector renewability and EYR. System ELR and EIR reduces as a result of reduced share of purchased and increased share of renewable emergy inputs. As a consequence of increased EYR and reduced ELR, system ESI increases indicating an improvement in domestic sector sustainability.
Table 6.15: Emergy indicators for current case of domestic sector and their change due to change in renewability of fodder.
Fodder 100%F Fodder 80%F Fodder 60%F % Re 20.0% 29.6% 43.5% EYR 1.52 1.99 2.43 ELR 3.99 2.18 1.27 EIR 1.92 1.15 0.71 ESI 0.38 0.91 1.90
220
Chapter 7: Supplying Energy in Rampura with Different Energy Options
In preceding chapters, analyses of five different centralized and localized energy options have been presented. In these analyses, sustainability of each energy option has been examined utilizing emergy analysis. Further; water use, land use and GWP of these technologies in their life cycle are calculated. Cost of electricity generation by each of these technologies has also been presented. This multidimensional analysis approach has enabled us to determine the pluses and minuses of these technologies from many perspectives.
Emergy analysis of Rampura village and its subsystems (domestic, agricultural and husbandry sectors) has revealed the state of sustainability in the village and in the interacting subsystems. Via analysis of Rampura, we also determine the different energy needs in the village. Lack of affordable and efficient energy sources creates energy related poverty for people of Rampura, also affecting their social wellbeing negatively.
The main aim in this chapter is to examine different energy technology options to supply the energy needs in Rampura in the most sustainable manner possible. Cost, water use, land use, GWP per kWh electricity utilized and sustainability of each energy technology or combination of different energy technologies are investigated.
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7.1 Energy Demand in Rampura
In reality, there is nothing that can be considered as waste in a rural setting. Every residue or resource which at first sight may be perceived as waste is used for some purpose. People of Rampura utilize most of agricultural residues either as fodder for their animals or as cooking fuel. Likewise, solar dried cow dung is an important energy source for cooking all year around. However, the way these energy sources are utilized is very primitive and inefficient.
Figure 7.1: A regular cook stove in Rampura.
222
The picture in figure 7.1 that is taken in Rampura presents a regular cook stove utilized. Cook stoves are either located in open air in gardens or inside the houses in kitchens. The insulation of these stoves is very poor. If the food is cooked using cow dung, the efficiency of the stove is around 5 % and if the stove is operated with wood or agricultural residues, efficiency is around 10% (Kaygusuz, 2011). In other words, only
5% of the energy in cow dung or 10 % of the energy in wood or agricultural residues is absorbed by the food cooked, rest of the available energy in the fuels is wasted.
Especially, women who cook using these traditional stoves expose pollutants and in general indoor smoke is an important problem in rural areas like Rampura (Alteri and
Masera, 1993 and Zheng et al, 2010). Likewise using kerosene for lighting is not a satisfactory method. It does not provide sufficient light and people in Rampura reported that they have itchy eyes when they use kerosene. With solar electricity use, this situation has improved (Development Alternatives, 2011).
According to the village level data obtained from Development Alternatives, 520 kg of cow dung, 275 kg of agricultural residues, 975 kg of wood per day are utilized for cooking. Additionally, 3.5 liters of kerosene and 19 kWh of electricity are used for lighting per. To irrigate fields, diesel pumps are utilized. For total irrigation hours of
3580, around 4350 liters of diesel is consumed per year in the village. Table 7.1 presents the consumption of each energy source in Rampura annually.
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Table 7 1: Energy use in Rampura village annually.
Energy Use In Rampura Amount/year Electricity (kWh) 6935 Kerosene (liter) 1278 Wood(kg) 355875 Agricultural Residues(kg) 100375 Dung Cake(kg) 189800 Diesel (Irrigation) (liter) 4350
The other energy needs in Rampura include gasoline use for transportation of milk to the market in Jhansi and diesel use in tractors for field ploughing and transportation of agricultural products to the market. However, the technologies which can replace transportation fuels are outside the scope of our research.
For calculating the annual total energy demand in Rampura, we convert all energy needs into electricity equivalents. However, utilizing biogas directly for cooking can be another option. Different options and outcomes of these different applications will be discussed in section 7.4.
7.1.1 Irrigation Energy Requirements
Submersible diesel pumps are used in Rampura to irrigate the fields. These pumps utilize 1.25 liter diesel per hour of pump operation. For around 3480 pump hours at the village level, 4350 liter diesel is utilized annually. According to diesel engine operation data in Rampura, 1 liter diesel is consumed to generate 2.25 kWh of electrical energy 224
(Development Alternatives, 2011). As a result, 9810 kWh electricity is needed to meet the irrigation energy requirements per year in Rampura.
7.1.2 Lighting Energy Requirements
Prior to solar electricity use, kerosene was the only energy source utilized for lighting in Rampura. Currently, 44 of the households out of 69 are connected to the solar grid. Rest of the households (25) still utilizes kerosene for lighting. As stated in table 7.1,
6935 kWh of solar electricity is utilized in Rampura annually. Additionally, 1278 liter ofkerosene (1022 kg) is consumed annually. Kerosene is a mixture of heavy hydrocarbons which has similar chemical and physical characteristics to diesel. Its chemical formula can be approximated as C12H26. Diesel can be converted to electricity with 22.2% efficiency (Development Alternatives, 2011). Assuming, 4.5 J of kerosene can generate 1 J of electricity as in case of diesel, kWh of electricity which can be generated is calculated by the formula 7.1.
Kerosene has energy content of 44 kJ/g and density of 800 g/liter (Bastionani et al, 2009). As a result, 2780 kWh electricity equivalents energy is needed for households which are not connected to the solar grid. Cumulatively, 9715 kWh of electricity is needed in Rampura for lighting purposes.
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7.1.3 Cooking and Heating Energy Requirements
In Rampura, sun dried cow dung (20% moisture), agricultural residues and wood
(15% moisture content) are used for cooking (Beck, 2003 and Zhang et al, 2007).
Moisture content of fuels utilized is important since wet wood or cow dung do not burn properly and produce smoke. In table 7.1 the annual amounts of cow dung, wood and agricultural residues utilized for cooking are presented. As it is earlier stated in this chapter, efficiency of stoves utilized by burning cow dung is 5 % and it is 10 % if agricultural residues or wood are used. If biogas operated cook stoves are utilized, this efficiency raises to 60% (Marchaim, 1992). Electric stoves have an average efficiency of
70% (Kaygusuz, 2011). Improved biomass stoves operating with cow dung, agricultural residues or wood have higher efficiency than traditional cook stoves (Bhattacharya and
Salam, 2002). The efficiency of these improved cook stoves are 19%, 21% and 24 % if they are operated with cow dung, agricultural residues and wood respectively
(Bhattacharya and Salam, 2002 and Panwar et al, 2009).
As discussed in centralized energy options in chapter 4, different energy sources have different available energy contents, in other words, different work performing capacity. For instance, 1 J of oil energy does more work than 1 J of coal. When coal energy is converted into oil energy equivalents, coal energy is multiplied by a conversion factor originating from this different work performing capacity or their efficiency in generating the same amount of electricity (Cleveland, 1992). Based on this fact, the energy quality of cow dung, wood, agricultural residues, biogas and electricity can be
226 evaluated on their effectiveness to cook food or their work performing capacity. When this is done, the equality given below can be assumed for cooking performances of different fuels utilized.
Based on this assumption and utilization amount of each energy source, energy demand for cooking in Rampura is found to be 302500 kWh electricity equivalents per year if all the cooking energy needs are supplied by electricity . Table 7.2 presents the annual electricity needs for all the energy requirements including lighting and irrigation.
Table 7 2: Annual energy demand in Rampura in kWhe equivalents
Cooking (kWhe) 302500 Irrigation(kWhe) 9810 lighting(kWhe) 9715 Total(kWhe) 322025
Another option to meet cooking energy needs would be to use biogas directly without converting it into electricity. The efficiency of stoves operating with biogas or electricity is similar (60% versus 70%). Here, 1.17 J of biogas energy can perform the work 1 J of electrical energy can. On the other hand, if we choose to generate electricity from biogas and use it for cooking, it takes 9 J of biogas energy to generate 1 J of 227 electricity which will be explained in more detail in the following biogas and electricity potential subsection. Consequently, most of the available energy potential in biogas is lost in transformation from biogas to electricity, although these two energy sources can perform similar useful work (cooking) without any conversion. As a result, utilizing biogas directly to meet cooking needs and converting excess biogas into electricity to meet other energy demands can make more sense. If we choose this second option, 1.27
E+12 J of biogas energy would be needed cumulatively for cooking. Then, energy needs in Rampura can be listed as in table 7.3. It is important to be aware here that cooking energy need is in kWh biogas equivalents.
Table 7 3: Annual energy demand in Rampura with direct use of biogas for cooking and heating.
Cooking (kWhbiogas) 352900 Irrigation(kWhe) 9810 lighting(kWhe) 9715
If we convert biogas into electricity equivalents but keep in mind that it is utilized directly as biogas, total energy demand in Rampura can be represented in kWhe as in table 7.4.
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Table 7 4: Annual energy demand in Rampura in kWhe equivalents for direct use of biogas for cooking and heating.
Cooking & Heating(kWhe) 39220 Irrigation(kWhe) 9810 lighting(kWhe) 9715 Total(kWhe) 58745
Remarkable reduction in energy demand with direct use of biogas also supports our insight that utilizing biogas directly to meet cooking needs and converting excess biogas into electricity can make more sense.
In analysis of the different scenarios to meet energy demand in Rampura, modifications into these two basic cases of utilizing biogas directly for cooking or utilizing electricity for all energy needs are investigated as separate scenarios. Pros and cons of each case are discussed in detail in section 7.4.
7.2 Energy Potential in Rampura
The energy needs in Rampura is to be met by the energy generated by the five energy technology options investigated. These are two clean coal technologies
(conventional and CLP), biomass gasification, anaerobic digestion and solar power generation. In this section, we present the potential of local energy resources which can be utilized by localized energy options to generate electricity. The clean coal technologies are centralized high capacity energy technologies of which electricity generation can be transmitted to Rampura.
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7.2.1 Solar Electricity Potential
Rampura is in Bundelkhand region of India and has a semi arid climate with high temperatures except the month of January. The average solar insolation is 18.65
MJ/m2.day (Tyagi, 2009). The magnitude of solar insolation and number of sunny days are significantly higher compared to Northern European countries (Ecoinvent, 2009). In general, being a tropical country, India has a high solar potential.
The solar plant in Rampura is a 8.7 kWp capacity multi-crystalline solar plant with 67.5 m2 photo-sensitive and 74 m2 framed area. A total area of 100 m2 is dedicated to the solar plant including battery bank, inverters, charge controllers and other electronic equipment.
This plant provides around 8350 kWh electricity per year.
Theoretically, all the energy need in Rampura can be met by solar electricity.
However, we limit solar electricity potential to 20000 kWh for ground mounted solar panels option since land is a valuable asset in Rampura. A solar plant providing all the energy demand would occupy around 3800 m2 land area which is an unreasonable number for a village of which major income is agriculture and of which total land area is
133.5 hectares. Rampura village cannot dedicate that large area to solar electricity generation. A plant generating 20000 kWh of electricity per year would cover a land area of around 250 m2 which is manageable.
If the solar panels were to be mounted on the roofs of the 44 houses currently utilizing solar electricity, 40000 kWh of electricity generation would be possible. Around
11 m2 area on each rooftop is occupied by the panels in this case. Mounting solar panels on rooftops alleviates the dedicated land area limitation for solar panels in Rampura.
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Rooftop mounted solar panels constitute a second scenario for energy options considered to meet the energy demand in Rampura village.
7.2.2 Biogas and Electricity Potential
In Rampura, there are 436 individuals from which 3.98E+07 (wet) g of human feces are obtained annually. From 408 different animals, 5.55E+08 g of manure (dry) are obtained annually. Human feces have an energetic content of 8000 J/g on wet basis
(Murphy et al, 1991), and manure has an energetic content of 17613 J/g on dry basis (Fan et al, 1985). Based on these values, the potential of biogas energy and potential of electrical energy from biogas in Rampura village are listed in table 7.5.
Table 7.5: Biogas and biogas electricity potential in Rampura village.
Mass(g/year) Energy Content(J) Biogas(J) Electricity(kWhe) Human Feces (Wet) 3.98E+07 3.18E+11 5.44E+10 1.70E+03 Manure (Dry) 5.55E+08 9.78E+12 1.67E+12 5.22E+04 Total 1.01E+13 1.73E+12 5.38E+04
The conversion factors from manure to biogas and biogas to electricity are based on emergy analysis performed for full capacity case of the biogas digester presented in chapter 5. There is a demand of 1.27E+12 J of biogas in Rampura and a potential of
1.73E+12 J of biogas. After supplying the cooking energy demand, excess biogas
(4.60E+11 J of biogas) can be converted to electricity (1.40E+04 kWh of electricity) to meet other energy requirements.
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7.2.3 Biomass Electricity Potential
In Rampura, 2.95E+08 g of agricultural residues are available annually.
Availability of ipomea changes between 6.50E+07 and 8.00E+07 g of ipomea per year
(Development Alternatives, 2011). On average, agricultural residues have an energy content of 14400 J/g (Zhang et al, 2007) and ipomea has an average energy content of
16000 J/g on dry basis (Pandey et al, 2012). From 6 J of agricultural residue or wood, 1 J of electrical energy can be generated by gasification. Based on these values, the electricity potential in Rampura by gasification is presented in table 7.6. On average,
Rampura has a total potential of 243000 kWh of electricity generation through biomass gasification.
Table 7.6: Potential of electricity through biomass gasification in Rampura.
Energy Availability of Feedstock Mass (g) Content(J) Electricity(kWhe) Agricultural Residues 2.95E+08 4.25E+12 1.97E+05 Ipomea(wood) 6.50E+07 8.84E+11 4.09E+04 Ipomea(wood) 8.00E+07 1.09E+12 5.04E+04 Total up 5.34E+12 2.47E+05 Total low 5.13E+12 2.38E+05
7.3 Meeting Energy Demand in Rampura: Problem formulation
As stated in the beginning of this chapter, our main aim here is to examine different energy technology options providing the energy mix which will meet the energy demand in Rampura in the most sustainable manner possible. In essence, this problem is
232 an optimization problem. More specifically, the problem in question is to solve a linear programming optimization problem to find out the optimum energy mix consisting of centralized and localized energy options for Rampura village. As in all optimization problems, the problem we present is subject to some constraints for realizing the objectives we set to meet a certain amount of energy demand. To solve the linear programming problem proposed, Excel 2007 solver is utilized.
Decision Variables:
Xi = kWh electricity provided by each energy technology option, i = 1,…,5.
X1= kWh electricity provided by conventional clean coal technology,
X2= kWh electricity provided by calcium looping clean coal technology,
X3= kWh electricity provided by biogas digester,
X4= kWh electricity provided by biomass gasification,
X5 = kWh electricity provided by multi crystalline solar PV.
Constants:
2 Yi = Land use in m / kWhe for different technology options, i =1, ..,5.
Zi = Water use in liters/ kWhe for different technology options, i =1,...5.
Wi = GWP in g CO2 equiv. / kWhe for different technology option, i =1,...5.
Mi = Total annualized cost for different technology options in Rupees/kWhe, i =1,...5.
Ti = Transfromity of electricity for different energy technology options in sej/kWhe, i=1,...5.
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Ri= Renewable emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5.
Pi =Purchased emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5.
Ni= Non-renewable emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5
Objective Functions:
1. Minimum Land Use: Min z =
2. Minimum Water Use: Min z=
3. Minimum GWP: Min z =
4. Minimum Total Annualized Cost: Min z =
5. Maximum % Renewability (%Re): Max z =
6. Maximum Emergy Yield Ratio (EYR): Max z =
7. Minimum Environmental Loading Ratio (ELR): Min z =
Subject to
Energy Demand Constraint =
Capacity Constraints:
1. Electricity from biogas ≤ X3 kWhe
2. Electricity from biomass gasification ≤ X4 kWhe
3. Solar electricity ≤ X5 kWhe
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4. Xi ≥ 0
In this analysis for different scenarios are considered. Energy demand and energy potential from local energy options change in each scenario. Energy demand and potential constraint values are presented in section 7.4 for each scenario separately. The values of constants utilized in calculations are presented in table 7.7.
Table 7.7: Rampura linear programming problem constants.
Constants X1 X2 X3 X4 X5 2 Yi (m /kWhe) 1.35E-01 1.77E-02 1.80E+00 2.71E-01 7.02E-03 Zi (liter/kWhe) 2.84E+00 2.21E+00 1.07E+02 2.02E+02 2.19E+00 Wi( CO2eq/kWhe) 1.61E+02 4.42E+01 -1.18E+02 2.38E+02 9.48E+01 Mi (Rs/kWhe) 2.64 2.91 7.80 8.80 17.0 Ti(Sej/kWhe) 4.28E+11 2.86E+11 4.68E+12 8.24E+11 6.52E+11 Ri (Sej/kWhe) 1.84E+09 4.58E+08 2.43E+12 4.77E+10 1.46E+10 Pi (Sej/kWhe) 4.27E+11 2.86E+11 2.26E+12 7.53E+11 6.37E+11 Ni (Sej/kWhe) 0.00E+00 0.00E+00 8.50E+09 2.50E+10 1.51E+09
7.4 Scenario Analysis Results
7.4.1 Scenarios
Four different scenarios are analyzed to meet the energy demand in Rampura.
Cooking energy demand is the largest chunk in the total energy demand. Different applications in cooking have the greatest impact on the overall system and its sustainability. In the first scenario; irrigation, lighting and cooking energy needs are all to be met with electrical energy. Thus, electrical stoves with 70% efficiency are used for cooking. Solar energy potential is limited to 20000 kWhe based on the dedicated land
235 area constraint of the village for ground mounted solar panels. In the second scenario, everything except the solar potential is kept the same. Considering rooftop mounted solar panels option, solar power potential is taken as 40000 kWhe. In the third scenario, all cooking energy is supplied by biogas energy directly. Excess biogas can then be utilized to generate electricity to meet other energy demands (irrigation, lighting). In this scenario, at least 39220 kWhe equivalents (cooking energy portion) of the total energy demand have to be provided by direct use of biogas. In the fourth scenario, 70% of cooking energy is met by direct use of biogas. 30 % of cooking energy is supplied by using improved biomass cook stoves utilizing cow dung, agricultural residues and wood as fuel. In this scenario, at least 27756 kWhe equivalents of energy needs have to be supplied by direct use of biogas (70% of cooking needs). Improved biomass cook stoves operate with 19%, 21% and 24% efficiency if they utilize cow dung, agricultural residues or woody biomass respectively. Table 7.8 lists the total energy demand and local energy availability from different resources in kWhe equivalents for four different scenarios considered.
Table 7.8: Energy demand and potential in kWhe equivalents in Rampura village.
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Total energy demand (kWhe) 322025 322025 58745 46958 Solar energy potential (kWhe) 20000 40000 20000 20000 Biogas energy potential (kWhe) 53800 53800 53800 52725 Biomass gasification potential (kWhe) 243000 243000 243000 204747
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The linear programming problem presented in section 7.3 is solved for each of the
7 objective functions separately for all four scenarios considered. The energy technology or energy technology combinations satisfying minimum land use, water use, GWP, total annualized cost, maximum % Re, maximum EYR and minimum ELR objectives are calculated. For all of the scenarios analyzed, energy technology combination satisfying maximum %Re, maximum EYR and minimum ELR objectives is the same combination within the same scenario, though the combination changes from scenario to scenario.
Hence, we subsume these three objectives under maximum sustainability objective and present the related results altogether.
7.4.2 Scenario 1: All Electrical Energy, Ground Mounted Solar Panels
7.4.2.1 Minimum Land Use Objective
In the first scenario, the energy combination satisfying the energy need for minimum land use objective is CL clean coal technology and multi-crystalline solar PV.
302025 kWhe is supplied from CLP and 20000 kWhe is supplied from solar PV equaling to 322025 kWhe, the total energy demand in Rampura. Land use is found to be 0.017 m2 per kWhe in this option.
In figure 7.2, land use, water use, GWP, cost, %Re, EYR, ELR and ESI values calculated for the energy combination satisfying minimum land use objective are presented. As it can be seen from figure 7.2, this energy combination has high ELR (588) and very low %Re and ESI values (0.29% and 0.0017). The cost of electricity provided is
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3.79 Rs/kWhe while the water use per kWhe is 2.2 liters. This combination has a GWP of
47 g CO2eq per kWhe utilized.
Solar PV is the least land utilizing energy technology option followed by CLP, for that reason, these technologies are favored over the other technologies for minimum land use objective.
7.4.2.2 Minimum Water Use Objective
Solar PV is also the least water intensive technology option among the five technology options analyzed. CLP is the second least water intensive technology option.
As a result, the same energy combination found in land use also satisfies the minimum water use objective. Thus, to meet 322025 kWhe equivalent of energy demand, 302025 kWhe is supplied from CLP and 20000 kWhe is supplied from solar PV. This combination utilizes 2.2 liters of water per kWhe utilized. Since this combination is the same as the combination calculated in minimum land use objective, the results presented in figure 7.2 and all conclusions inferred are also valid for minimum water use objective in scenario 1.
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Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.2: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 1.
7.4.2.3 Minimum Global Warming Potential (GWP) Objective
In calculating the energy technology combination having least GWP to meet the energy demand in Rampura, technologies having lower GWP is favored over the others.
Among the five technology options analyzed, electricity generated from biogas has a
GWP of -118 g CO2 per kWhe. Secondly, CLP has a GWP of 44.2 g CO2 per kWhe generated. To satisfy the minimum GWP objective, biogas electricity should be used in the quantity that is equal to its capacity and rest of the energy is supplied from CLP.
Consequently, to meet 322025 kWhe energy demand, 268225 kWhe is provided by CLP and 53800 kWhe is provided by electrical energy produced from biogas. The GWP of the energy combination calculated is 17.1 g CO2 eq./kWhe.
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Figure 7.3 presents the environmental impacts and emergy indicators calculated for the combination having least GWP potential. The land use and water use of this combination per kWhe utilized is 0.32 m2 and 19.7 liters. Although land use and water use of energy combination with the least GWP is higher than the least land and water utilizing energy combination, it has much higher %Re (8.8% versus 0.29%). The EYR,
ELR and ESI values of the combination are 1.18, 520 and 0.0027. In other words, the least GWP energy combination has lower ELR but higher EYR and ESI values which is a desirable situation. Additionally, electricity supplied in this scheme is slightly cheaper
(3.73 Rs/kWhe versus 3.79 Rs/kWhe). The improvement in emergy indicators is due to the utilization of electricity generated from biogas representing the most renewable energy option among five different energy technologies considered.
Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.3: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 1.
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7.4.2.4 Minimum Annual Cost Objective
When cost is the only criterion considered in supplying the energy demand in
Rampura, conventional clean coal technology is favored over other processes since it does not have a constraint in terms of capacity to meet the kind of demand in Rampura.
As a result, all of the 322025 kWhe demand is provided by conventional clean coal technology to have the cheapest electricity in Rampura. Cost of electricity provided by conventional process is 2.64 Rs/kWhe.
Figure 7.4 presents the results for cheapest electricity which can be supplied to
Rampura. These values are the same as the constants calculated for conventional clean coal technology since all the electricity is provided only by it. Despite being cheap, conventional process has the highest GWP (161 g CO2 eq. /kWhe) among all options.
Emergy indicators which are measures of process sustainability are also unfavorable. As a result, the cheapest way of providing electricity is not the most environmentally benign option to meet the energy needs in Rampura.
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Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.4: Radar diagram for energy technology combination satisfying minimum annual cost objective in scenario 1.
7.4.2.5 Maximum Sustainability Objective
The joint objective of maximum sustainability includes maximum % Re, maximum EYR and minimum ELR objectives since calculations for these objectives result in the same energy combination in all of the four scenarios considered. In the first scenario in which all energy needs are provided by electrical energy, the energy combination satisfying maximum sustainability objective is as follows: To meet the
322025 kWhe energy demand, 243000 kWh of electricity is generated from producer gas
(biomass gasification), 53800 kWh of electricity is provided by electrical energy from biogas, 20000 kWhe is provided by the solar panels and the remaining 5225 kWhe is supplied from the conventional clean coal technology.
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Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI Use(liter/kWh) 1.00E+01 1.00E+00 1.00E-01 ELR 1.00E-02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.5: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 1.
The results regarding the energy combination satisfying joint maximum sustainability objective are presented in figure 7.5. Here the % Re, EYR and ELR values of the system are 13.2%, 1.25 and 18.9, respectively. Indeed, this combination has the highest %Re and EYR and lowest ELR compared to the other energy combinations satisfying minimum land use, water use, GWP or cost objectives. However, presented
2 combination has higher land use, water use and GWP (0.51 m , 170 liters, 168 g CO2 eq. per kWhe) than the other options discussed.
A more sustainable energy system in scenario1consists of local options in greater portions, however, this energy combination provides electricity at a considerably higher price which is 9 Rs/kWhe.
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7.4.3 Scenario 2: All Electrical Energy, Rooftop Mounted Solar Panels
Scenario 2 is a modification of scenario 1. Instead of ground mounted solar panels in scenario 1, the multi-crystalline solar panels are assumed to be mounted on rooftops in scenario 2. Alleviating dedicated land area limitation of Rampura village, mounting panels on rooftops allows increasing solar electricity potential to 40000 kWhe. Other than solar energy potential, energy demand and energy potentials are the same as in scenario
1.
7.4.3.1 Minimum Land Use and Water Use Objectives
The minimum land use and water use objectives in scenario 2 are satisfied by the same energy combination. To meet 322025 kWhe of energy demand, 28225 kWhe is provided by CLP and 40000 kWhe is provided by the solar PV in scenario 2. Being the least water and land intensive energy option, solar power is utilized in the quantity equal to its potential and rest of the energy demand is supplied from second least water and land utilizing technology option, CLP. The water use and land use values calculated for this combination are 2.21 liter and 0.016 m2 per kWhe.
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Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.6: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 2.
In figure 7.6, land use, water use, GWP, cost, %Re, EYR, ELR and ESI values calculated for this energy combination are presented. The energy combination satisfying minimum land use and water use objectives in scenario 2 has a lower ELR (552) and higher %Re and ESI values (0.42% and 0.0018) than in scenario 1. This situation is due to the increase of solar power share in the energy combination in scenario 2. The EYR value is 1, same as in scenario 1.The cost of electricity provided by this energy combination is 4.67 Rs/kWhe. Combination has a GWP of 50 g CO2eq per kWhe used.
The cost and GWP of electricity provided by the CLP- solar combination in scenario 2 for minimum land and water use objectives increase compared to scenario 1. This situation is again due to the increase of solar power share in the energy combination.
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However, energy combination in question also has low sustainability with low %Re,
EYR and high ELR values.
7.4.3.2 Minimum Global Warming Potential Objective
The same energy combination that satisfies minimum GWP objective in scenario
1 also satisfies the minimum GWP objective in scenario 2. The energy combination calculated consists of 53800 kWhe of biogas electricity and 268225 kWhe of electricity provided by CLP. Consequently, all the values calculated for this combination in scenario
1 also apply here in scenario 2. These results are presented in figure 7.7.
Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.7: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 2.
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7.4.3.4 Minimum Annual Cost Objective
As in scenario 1, 322025 kWhe of energy demand is provided by conventional clean coal technology to have the cheapest electricity in scenario 2. Cost of electricity is
2.64 Rs/kWhe. All the conclusions and values in scenario 1 are valid for the current scenario to satisfy minimum cost objective. Figure 7.8 presents the results for cheapest electricity which can be supplied to Rampura in scenario 2.
Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI 1.00E+01 Use(liter/kWh) 1.00E+00 1.00E-01 1.00E-02 ELR 1.00E-03 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.8: Radar diagram for energy technology combination satisfying minimum annual cost objective in scenario 2.
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7.4.3.5 Maximum Sustainability Objective
The energy combination satisfying maximum %Re, maximum EYR and minimum ELR objectives consists of 243000 kWhe of electrical energy generated from producer gas,
53800 kWhe of electrical energy generated from biogas and 25225 kWhe of electrical energy generated by the solar panels. The results regarding the energy combination satisfying joint maximum sustainability objective are presented in figure 7.9. Here the %
Re, EYR and ELR values of the system are 13.2%, 1.25 and 15.9, respectively. As in scenario 1, this combination has the highest %Re and EYR and lowest ELR than the other energy combinations satisfying minimum land use, water use, GWP or cost objectives in the current scenario. However, presented energy combination has higher
2 land use, water use, GWP (0.51 m , 170 liters, 167 g CO2 eq. per kWhe) than the other options discussed in scenario 2.
As in case of scenario 1, electricity supplied by this combination is more expensive, even more expensive than in scenario 1, which is 9.27 Rs/kWhe. The most important difference between scenarios 1 and 2 is that when the capacity of solar energy generation is increased to 40000 kWhe, all the energy demand in Rampura can be provided by the local resources without being dependent on external resources for electricity.
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Land Use(m2/kWh) 1.00E+03 1.00E+02 Water ESI Use(liter/kWh) 1.00E+01 1.00E+00 1.00E-01 ELR 1.00E-02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.9: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 2.
7.4.4 Scenario 3: All Biogas Energy for Cooking
Scenario 3 encompasses the case in which all the cooking energy is supplied by direct use of biogas. Excess biogas can then be utilized to generate electricity to meet other energy demands (irrigation, lighting). Biogas potential in the village is 53800 kWhe equivalents as in other scenarios. However, another constraint is applied for biogas here.
At least 39220 kWhe equivalents (cooking energy portion) of the total energy demand have to be provided by direct use of biogas.
By avoiding a second transformation from biogas to electricity, cooking energy demand now becomes providable by biogas and total energy demand reduces substantially. In this scenario, total energy demand is 58745 kWhe, solar power potential is 20000 kWhe and biomass gasification potential is 243000 kWhe as presented in table
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7.8. And, in satisfying all objectives considered, at least 39220 kWhe equivalents of biogas energy have to be utilized for cooking energy demand.
7.4.4.1 Minimum Land Use and Water Use Objectives
Similar to scenarios 1 and 2, the same energy combination satisfies the minimum land and water use objectives in scenario 3. To meet 58745 kWhe of total energy demand, 39220 kWhe equivalents of energy must be supplied by anaerobic digestion technology and 19525 kWhe of electricity is provided by solar PV. The land use for this combination is calculated as 1.20 m2/ kWhe and water use is calculated as 72.2 liter/kWhe.
Land Use(m2/kWh) 9.00E+01 ESI Water Use(liter/kWh) 4.00E+01
-1.00E+01
ELR -6.00E+01 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.10: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 3.
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Figure 7.10 presents the results calculated for the energy combination satisfying minimum land and water use objectives in scenario 3. Despite having higher water and land use than the other scenarios for the same objectives, the biogas- solar energy combination has a negative GWP (-47 g CO2 eq. /kWhe) and better emergy indicators than the CLP-solar combination in scenarios 1 and 2. The %Re, EYR and ELR values calculated for biogas-solar combination are 35%, 1.72 and 6, respectively. The system has ESI value of 0.29. The cost of biogas- solar combination provided energy is 10.9
Rs/kWhe which is 1.6-1.9 Rs/kWh more expensive than energy provided by CLP-solar combination in scenarios 1 and 2.
7.4.4.2 Minimum Global Potential Warming (GWP) Objective
Minimum GWP objective is satisfied by utilizing the whole capacity of biogas potential and supplying rest of the energy demand from CLP. Here 39220 kWhe equivalents of biogas potential is used as biogas directly for cooking. Rest of the biogas is converted into electricity (14580 kWhe) to meet other demands. The combination satisfying minimum GWP objective in scenario 3 consists of 53800 kWhe of biogas and
4945 kWhe of CLP electricity. The GWP calculated for this combination is -104 g CO2 eq./kWhe.
Figure 7.11 presents the results calculated for biogas-CLP combination satisfying the minimum GWP objective in scenario 3. 1.65 m2 of land and 98.2 liters of water are utilized per kWhe provided by this option. Cost of electricity per kWh is 7.4 Rs. % Re,
EYR and ELR values are 47.5%, 1.98 and 53.3 respectively. The ESI value of the system
251 is 0.037. The biogas-CLP combination also satisfies the minimum GWP objective in scenarios 1 and 2. However the share of biogas utilization is higher in scenario 3. As a result, this option has lower GWP, better emergy indicators than the cases in scenarios 1 and 2.
If we compare the combinations within scenario 3, combination satisfying minimum GWP objective has higher share biogas energy utilization so that higher land use and water use, but lower GWP. Additionally, minimum GWP satisfying combination is cheaper since solar energy is replaced by biogas and CLP. As a result, biogas-CLP combination is 3.5 Rs cheaper than biogas-solar combination satisfying land and water use objectives in scenario 3.
Land Use(m2/kWh) 2.00E+02 1.50E+02 Water ESI 1.00E+02 Use(liter/kWh) 5.00E+01 0.00E+00 -5.00E+01 -1.00E+02 ELR -1.50E+02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.11: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 3.
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7.4.4.3 Minimum Cost Objective
To meet the minimum cost objective in this scenario, again at least 39220 kWhe equivalents of biogas energy is used for cooking. Rest of the energy is provided by cheapest energy option, conventional clean coal technology. As a result, 39220 kWhe equivalents of biogas energy and 19525 kWh of electrical energy from conventional process meet the energy demand in this option. Cost of electricity is calculated as 6.1
Rs/kWhe.
Land Use(m2/kWh) 8.00E+01 6.00E+01 Water ESI 4.00E+01 Use(liter/kWh) 2.00E+01 0.00E+00 -2.00E+01 ELR -4.00E+01 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.12: Radar diagram for energy technology combination satisfying minimum cost objective in scenario 3.
Figure 7.12 presents the results regarding cheapest energy in scenario 3. Land use
2 and water use values are 1.25 m and 72.4 liters per kWhe. GWP is -25.3 g CO2
253 eq./kWhe. The % Re, EYR and ELR values are 34.7 %, 1.72 and 77.6, respectively.
And, the system ESI is 0.022.
It can be said that as the portion of biogas energy increases, system emergy indicators and GWP improve. And, water and land use values increase. As the portion of non- renewable centralized energy in the combination increases, emergy indicators become worse.
7.4.4.4 Maximum Sustainability Objective
The energy combination satisfying maximum %Re, EYR and minimum ELR objectives in scenario 3 is 53800 kWhe biogas and 4945 kWh of electricity generated from producer gas. The %Re, EYR and ELR values are calculated to be 48.0 %, 1.99 and
2.23, respectively. ESI of the system is 0.89. The water and land use values are 115 liter and 1.67 m2 per kWhe equivalents energy utilized. The system GWP is -88.1 g
CO2/kWhe and the energy cost is 7.9 Rs/kWhe. All these results are presented in figure
7.13.
Utilization of biogas directly for cooking makes it possible to meet energy demand in
Rampura with local resources. Importing energy from centralized energy options is not needed in this scenario. This energy combination has higher %Re, EYR ESI and lower
ELR values than the combinations in scenarios 1 and 2.
Another point is to meet the energy demand completely from electricity in scenarios 1 and 2, all the biomass gasification potential in Rampura has to be utilized.
However, agricultural residues in Rampura are utilized as fodder. If all agricultural
254 residues are used in gasification, then villagers have to buy all the fodder for their animals from the market. Additionally, leftover agricultural residues cover the surface of the fields diminishing soil erosion. If agricultural residues are utilized completely, all these services are lost. Harvesting woody biomass for gasification creates pressure on the environment. Consequently, meeting energy demand with biogas reduces the demand for wood and eases the pressure on environment. Furthermore, CH4 emissions from cow dung are also avoided while sanitation improves in the area.
Land Use(m2/kWh) 1.50E+02
ESI 1.00E+02 Water Use(liter/kWh) 5.00E+01 0.00E+00 -5.00E+01 ELR -1.00E+02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.13: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 3.
7.4.5 Scenario 4: 70% Biogas and 30% Traditional Biomass Energy for Cooking,
Scenario 4 is a modification of scenario 3. 70% of the cooking energy demand is provided by direct use of biogas. 30 % of cooking is performed by improved biomass cook stoves. When a new technology is introduced in a rural setting, its adoption is not 255 immediate and 100%. People will want to cook some traditional items such as their bread or certain dishes in the traditional way. They will look for a certain taste possible only with a certain way of cooking (Ruiz-Markado et al, 2011). For that reason, we assumed a 70% adaptation of new biogas cook stoves. And, 30 % of cooking can be performed in the traditional manner but utilizing improved biomass cook stoves. Improved biomass cook stoves using cow dung, agricultural residues and wood operate with higher efficiency alleviating the inefficient resource use handicap of traditional cook stoves and still enable traditional way of cooking for people.
As stated earlier improved biomass cook stoves have an efficiency of 19 %, 21% and 24 % if they are operated with cow dung, agricultural residues or wood, respectively
(Battacharya and Salam, 2002). As a result, less biomass is needed to supply the same amount of useful energy for cooking. Energy potential from different sources for scenario
4 is presented in table 7.8 in the beginning of section 7.4. In this scenario, at least 27756 kWhe equivalents of biogas energy (70% of cooking energy demand) have to be used in satisfying each objective.
7.4.5.1 Minimum Land Use and Water Use Objectives
As in other scenarios, the same energy combination satisfies minimum land use and water use objectives in scenario 4. This combination consists of 27756 kWhe equivalents of direct biogas energy use for cooking and 19202 kWhe of solar electricity to meet 46958 kWhe equivalents of energy demand. The land use and water use values calculated are 1.07 m2 and 64.1 liter per kWhe energy utilized.
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Figure 7.14 presents the results calculated for this combination. GWP is found to be -31 g CO2 eq./kWhe and cost of energy is 11.6 Rs/kWhe. The %Re, EYR and ELR values are 31.6 %, 1.64 and 7.23, respectively. The ESI value is 0.23.
Land Use(m2/kWh) 9.00E+01 ESI Water Use(liter/kWh) 4.00E+01
-1.00E+01
ELR -6.00E+01 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.14: Radar diagram for energy technology combination satisfying minimum land and water use objectives in scenario 4.
Compared to energy combination satisfying minimum land and water use objectives in scenario 3, the %Re and EYR of the system reduces while ELR increases.
This is due to the decreased share of biogas and increased share of the solar PV in the combination. Based on the same reason, the GWP is higher than it is in scenario 3. Since share of solar electricity increased, the cost of energy provided is higher. With decreased share of biogas energy use, land use and water use values per kWhe in scenario 4 are lower than scenario 3.
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7.4.5.2 Minimum Global Warming Potential Objective
When 30% of cooking energy is supplied by the traditional methods, the biogas potential in Rampura is enough to meet the remaining 70% of the cooking energy directly and the other energy needs (irrigation, lighting) after excess biogas is converted into electricity. Here, 27756 kWhe equivalents of biogas is utilized directly and 19202 kWhe is utilized after biogas is converted into electricity to meet the total 46958 kWhe equivalents of energy demand. Being the technology with lowest GWP, anaerobic digestion is favored over other energy option in satisfying minimum GWP objective in scenario 4.
Land Use(m2/kWh) 2.00E+02 1.50E+02 Water ESI 1.00E+02 Use(liter/kWh) 5.00E+01 0.00E+00 -5.00E+01 -1.00E+02 ELR -1.50E+02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.15: Radar diagram for energy technology combination satisfying minimum GWP objective in scenario 4.
The values calculated for this option are the same as electricity from biogas since all the energy is provided by anaerobic digestion or electricity generated from biogas. 258
The GWP is -118 g CO2 eq./kWhe. This system has the best emergy indicators, meaning highest %Re, EYR, ESI values and lowest ELR value. Water and land use values are comparatively high. Cost of energy per kWhe is 7.8 Rs/kWhe which is more expensive than the energy combinations dominated by centralized energy options.
7.4.5.3 Minimum Cost Objective
To meet the minimum cost objective in this scenario, at least 27756 kWhe equivalents of biogas energy has to be used for cooking. Rest of the energy is provided by cheapest energy option, conventional clean coal technology. As a result, 27756 kWhe equivalents of biogas energy and 19202 kWh of electrical energy from conventional process meet the energy demand in this combination. And, the cost of electricity is calculated as 5.7 Rs/kWhe.
Figure 7.16: Radar diagram for energy technology combination satisfying minimum cost objective in scenario 4.
259
Figure 7.16 presents the results calculated for the combination in question. Land and water use values are 1.12 m2 and 64.4 liters per kWhe equivalent energy utilized.
GWP is -39.1 g CO2 per kWhe. The %Re, EYR and ELR values are 30.8 %, 1.63 and
95.2. The calculated ESI value for the system is 0.017.
Since the share of conventional process electricity increases here compared to scenario 3, the emergy indicators are worse, and GWP is higher. However, the land and water use values per kWhe are lower than the combination satisfying minimum cost objective in scenario 3. Since the share of conventional process increases, the cost of energy is 0.4 Rs cheaper than in scenario 3 per kWhe equivalents energy utilized.
7.4.5.4 Maximum Sustainability Objective
Again, when 30% of cooking energy is supplied by the traditional methods, the biogas potential in Rampura is enough to meet the remaining 70% of the cooking energy directly and the other energy needs (irrigation, lighting) after excess biogas is converted into electricity. Anaerobic digestion provides the energy with highest %Re, EYR and
ELR among all the energy options analyzed. As a result, it is favored in satisfying maximum sustainability objective. Once again, 27756 kWhe equivalents of biogas is utilized directly, 19202 kWhe is utilized after biogas is converted into electricity to meet
46958 kWhe equivalents of energy demand. The % Re, EYR and ELR of the system are
51.8%, 2.07 and 0.94, respectively. All the inferences made about this technology in
260 minimum GWP objective section 7.4.5.2 are also valid here. Figure 7.17 presents these results graphically.
Land Use(m2/kWh) 1.50E+02 1.00E+02 Water ESI Use(liter/kWh) 5.00E+01 0.00E+00 -5.00E+01 ELR -1.00E+02 GWP(CO2eq/kWh)
EYR Cost(Rs/kWh)
%Re
Figure 7.17: Radar diagram for energy technology combination satisfying maximum sustainability objective in scenario 4.
7.4.6 Greenhouse Gas (GHG) Mitigation
Table 7.9 summarizes the current energy use and GHG emissions for cooking activity in Rampura. The incomplete combustion in traditional stoves causes high GHG emissions combined with high resource use (Panwar, 2009 and Bhattacharya and Salam,
2002). Utilization of efficient cook stoves such as biogas cook stoves and improved biomass cook stoves can help mitigating GHG emissions caused by cooking activities.
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Table 7.9: Cooking energy use and related GHG emission currently in Rampura.
Amount(g) Energy(MJ) gCO2 eq./MJ Fuel Cow Dung 1.90E+08 2.67E+06 2.02E+07 Agricultural Residues 1.00E+08 1.45E+06 1.09E+07 Wood 3.56E+08 4.84E+06 5.84E+07
Bhattacharya and Salam studied the GHG emissions caused by cooking activities using different cook stoves. Table 7.10 summarizes their results and emissions per MJ of fuel utilized in cooking. Since biomass cook stoves utilize bio-fuels, only CH4 and N2O emissions have a net GWP. CO2 emitted is sequestered during photosynthesis while the plants grow.
Table 7.10: Cooking related GHG emissions using different cook stoves and fuels. (Adapted from Bhattacharya and Salam, 2002).
Traditional Stove CH4(kg/TJ fuel) N2O(kg/TJ fuel) gCO2 eq./MJ Fuel Wood 519.6 3.74 12.07 Residue 300 4.00 7.54 Dung 300 4.00 7.54 Improved Stoves Biogas 57.8 5.20 2.83 Wood 408 4.83 10.07 Residue 131.8 4.00 4.01 Dung 300 4.00 7.54
In scenario 3, 60% efficient biogas cook stoves are utilized to meet cooking energy demand 100%. As a result, 3.59E+06 g CO2 equivalents of GHG are emitted. By utilization of biogas for cooking, 8.59E+07 g CO2 equivalents of GHG emission can be mitigated annually. 262
Table 7.11: GHG emissions related to biogas use for cooking in scenario 3.
Energy (MJ) gCO2 eq. Emissions Biogas 1.27E+06 3.59E+06
In scenario 4, 70% of cooking energy is provided by direct biogas energy use and
30 % is met by biomass use in improved biomass cook stoves. Table 7.12 summarizes the cooking energy use and related emission in scenario 4. Here, 1.10E+07 g CO2 equivalents of GHG are emitted. By utilization of biogas and improved biomass cook stoves for cooking, 7.85E+07 g CO2 equivalents of GHG emission can be mitigated annually in scenario 4.
Table 7:12: GHG emissions related to biogas and improved biomass cook stove use for cooking in scenario 4
Energy (MJ) gCO2 eq. Emissions Biogas 8.89E+05 2.51E+06 Cow Dung 2.11E+05 1.59E+06 Agricultural Residues 2.06E+05 8.28E+05 Wood 6.05E+05 6.09E+06
The mitigation by utilization of electric cook stoves depends on the source of electricity and how it is generated. However, our result regarding biogas or biogas- biomass cook stove use reveals that utilization of these efficient cooking schemes has a substantial potential to mitigate cooking related GHG emissions in Rampura
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Chapter 8: Conclusions and Future Work
8.1 Conclusions
In this study, we perform emergy analysis (EA) and life cycle assessment (LCA) of two centralized clean coal and three localized energy technology options. Joint use of
LCA and emergy analysis enables us to capture and evaluate downstream (LCA) and upstream (EA) effects of these processes. Following the analysis of different energy technologies, we analyze the current status of sustainability in Rampura village and its interacting subsystems utilizing EA. Here, we determine the energy and material exchanges and how these exchanges affect the subsystems and the overall village system.
Going one step further, we determine the energy needs in Rampura and try to answer the question, “How can the energy demand in Rampura be met in the most sustainable manner possible?”
Analysis results of centralized clean coal technologies reveal that these systems have low % Re, high ELR and low ESI and EYR values. Based on these results, it is concluded that clean coal technologies are unsustainable energy systems dependent on purchased external inputs almost 100%. However, implementation of CO2 capture to our
IGCC systems reduce energy generation related environmental impacts significantly. For instance our process- LCA scale GWP is 44.2 and 161 g CO2 eq./ kWhe for CLP and conventional process , respectively. Corresponding numbers are 884 and 800 g CO2 eq./ kWhe for pulverized coal combustion (PCC) plants and 725 g CO2 eq./ kWhe for
264 integrated gasification combined cycle (IGCC) plants as reported by Hurst and Corti
(Corti and Lombardi, 2004 and Hurst et al,2012). Additionally, energy efficiency of
IGCC systems is higher than PCC plants further reducing the environmental impacts and resource use per kWhe power generation (Penth and Henkel, 2009). As discussed in chapters 1 and 4, coal is a major energy source and is used and will be used in greater portions in developing countries in the future. Increased efficiency and significantly lower environmental impacts of clean coal technologies can lead to more environmentally benign utilization of coal as an energy source. Despite its sustainability drawbacks, the centralized clean coal technologies supply electricity at a lower price than the localized energy options investigated.
The localized energy options analyzed include a multi-crystalline solar PV, a floating drum biogas digester and a downdraft biomass gasifier. The solar PV has the lowest water and land use values among all the options analyzed, however, electricity generated by the solar PV has the highest price with a high GWP. Contrary to general opinion, solar electricity is highly non-renewable. Although solar energy is100% renewable, materials utilized in the production of solar panels are non-renewable.
Furthermore, solar panels emit poisonous heavy metals or radioactive metals during their life cycle. Despite being a better option than the clean coal technologies in terms of life cycle impacts, the solar PV is not also an environmentally sustainable system with its low
%Re, EYR, ESI and high ELR values.
The analysis of biomass gasifier and biogas digester is performed for different operation schemes. These schemes are current, ideal and full capacity cases for biogas
265 production and current, ideal dual fuel mode and ideal single fuel modes for biomass gasification. As a result of our analysis, we determine that the current operation schemes for the biomass gasifier and the biogas digester are not optimum or feasible. The best sustainability results are obtained for full capacity operation in anaerobic digestion and for single fuel operation scheme in biomass gasification. As these systems are operated closer to optimum, %Re, EYR, ESI values increase whereas ELR and EIR values decrease pointing to increased sustainability for the systems. In case of biogas digester, life cycle GWP and resource uses per kWh electricity generated also decline from current case to full capacity operation scheme. In biomass gasification, significant difference occurs between dual fuel mode and single fuel operation schemes. While GWP in ideal dual fuel mode is 238 g CO2 eq/kWhe, it is -39 g CO2 eq./kWhe in single fuel mode. This huge difference is due to utilization of diesel together with producer gas in dual fuel mode. Water and land use values are higher for single fuel mode since more ipomea is needed to compensate diesel to generate the same amount of power. For both of the processes, cost of electricity reduces 2-3 times if they are operated properly.
Consequently, we recommend biogas digester plant to be operated in full capacity scheme and there is enough cow dung feedstock in Rampura to operate the biogas digester in this scheme. And, we recommend the biomass gasifier to be operated in single fuel mode. However, there is not enough ipomea or agricultural residues in Rampura to operate the biomass gasifier in single fuel mode. The optimum operation scheme in that case is ideal dual fuel mode operation for the biomass gasifier. In solving linear programming problem for the energy needs of Rampura, full capacity operation scheme
266 for the biogas digester and ideal case dual fuel mode operation scheme for the biomass gasifier are considered.
Emergy analysis of Rampura village and its subsystems reveal that the village and its subsystems are not self-sufficient and are highly dependent on non-renewable external material and energy inputs. This dependency means that sustainability is not achieved both at the village and at the subsystems level. In husbandry sector, the largest emergy input is fodder for feeding animals. Fodder is mainly wheat straw mixed with other agricultural residues. In emergy analysis of agricultural sector, renewability of agricultural residues is found to be around 20%. If renewability of fodder is considered as 20%, renewability of animal power and husbandry products becomes 25%. 75 % of emergy inputs to the sector come from purchased external and non-renewable inputs.
Even when fodder is considered as 20% renewable, husbandry sector has high ELR
(3.06) and low EYR (1.33) and ESI (0.43) values. If fodder were considered 100% purchased, these indicators would be worse. In agricultural sector, human labor and animal draft are the largest emergy inputs; renewability of animal draft input depends on fodder renewability. As a result, agricultural products and residues have a renewability of
26% when fodder is accounted as 20% renewable. Agricultural system has better emergy indicators (higher %Re, EYR, and ESI, lower ELR) than the husbandry sector due to higher share of renewable inputs and being a more efficient system. However, agricultural system in Rampura is also a not self-sufficient system which is dependent on non- renewable external inputs such as fertilizer, pesticide and fossil fuels.
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As discussed in chapter 6, agricultural sector provides food and energy resources
(agricultural residues) to the domestic sector and husbandry sector provides cow dung and milk. Renewability of fodder indirectly affects the renewability of products of the domestic sector. When fodder is considered as 20% Re, the renewability of human labor in Rampura becomes 30%. Domestic sector has more favorable emergy indicators than the agricultural and husbandry sectors, however, this subsystem is also not self- maintaining or sustainable.
These three subsystems constitute the overall village system. When fodder is considered 20% Re, village system has a renewability of 19.5%. EYR, ELR and ESI values are 1.6, 4.2 and 0.38, respectively. These values indicate that overall Rampura village is not a self sufficient, sustainable system. To improve the sustainability in
Rampura, dependency on purchased inputs fodder, fertilizer and diesel and non- renewable cooking fuel wood should be reduced.
As stated earlier in section 7.3, a linear programming problem is set up to determine the energy mix which meets the energy demand in Rampura in the most sustainable manner possible. First scenario considered is the case where all energy needs are met by electricity and solar panels are ground mounted (Electricity-GM). In the second scenario, solar panels are rooftop mounted (Electricity-RM). In the third scenario, cooking energy is met by direct use of biogas 100% (Biogas cooking). And finally, in the fourth scenario, 70% biogas and 30% improved biomass cook stoves are utilized for cooking (70%Biogas cooking). Table 8.1 presents the linear programming results for energy combinations to meet the energy demand in Rampura.
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Table 8.1: Linear programming results for the energy combinations to meet the energy demand in Rampura under different scenarios considered.
Land and Water Use Objectives Electricity-GM Electricity-RM Biogas Cooking 70% Biogas Cooking Land Use(m2/kWhe) 1.70E-02 1.64E-02 1.20E+00 1.07E+00 Water Use(liter/kWhe) 2.21E+00 2.21E+00 7.22E+01 6.41E+01 GWP(CO2eq/kWhe) 4.73E+01 5.05E+01 -4.73E+01 -3.10E+01 Cost(Rs/kWhe) 3.79E+00 4.66E+00 1.09E+01 1.16E+01 %Re 0.29% 0.42% 35.34% 31.55% EYR 1.00E+00 1.00E+00 1.72E+00 1.64E+00 ELR 5.88E+02 5.52E+02 6.05E+00 7.23E+00 ESI 1.71E-03 1.82E-03 2.85E-01 2.27E-01 GWP Objective Electricity-GM Electricity-RM Biogas Cooking 70% Biogas Cooking Land Use(m2/kWhe) 3.15E-01 3.15E-01 1.65E+00 1.80E+00 Water Use(liter/kWhe) 1.97E+01 1.97E+01 9.82E+01 1.07E+02 GWP(CO2eq/kWhe) 1.71E+01 1.71E+01 -1.04E+02 -1.18E+02 Cost(Rs/kWhe) 3.73E+00 3.73E+00 7.39E+00 7.80E+00 %Re 8.79% 8.79% 47.48% 51.82% EYR 1.18E+00 1.18E+00 1.98E+00 2.07E+00 ELR 5.20E+02 5.20E+02 5.33E+01 9.35E-01 ESI 2.27E-03 2.27E-03 3.72E-02 2.21E+00 Cost Objective Electricity-GM Electricity-RM Biogas Cooking 70% Biogas Cooking Land Use(m2/kWhe) 1.35E-01 1.35E-01 1.25E+00 1.12E+00 Water Use(liter/kWhe) 2.84E+00 2.84E+00 7.24E+01 6.44E+01 GWP(CO2eq/kWhe) 1.61E+02 1.61E+02 -2.53E+01 -3.91E+00 Cost(Rs/kWhe) 2.64E+00 2.64E+00 6.08E+00 5.69E+00 %Re 0.43% 0.43% 34.74% 30.81% EYR 1.00E+00 1.00E+00 1.72E+00 1.63E+00 ELR 2.32E+02 2.32E+02 7.76E+01 9.52E+01 ESI 4.34E-03 4.34E-03 2.21E-02 1.72E-02 Maximum Sustainability Objective Electricity-GM Electricity-RM Biogas Cooking 70% Biogas Cooking Land Use(m2/kWhe) 5.08E-01 5.06E-01 1.67E+00 1.80E+00 Water Use(liter/kWhe) 1.70E+02 1.70E+02 1.15E+02 1.07E+02 GWP(CO2eq/kWhe) 1.68E+02 1.67E+02 -8.81E+01 -1.18E+02 Cost(Rs/kWhe) 9.04E+00 9.27E+00 7.88E+00 7.80E+00 %Re 13.17% 13.20% 47.95% 51.82% EYR 1.25E+00 1.25E+00 1.99E+00 2.07E+00 ELR 1.89E+01 1.59E+01 2.23E+00 9.35E-01 ESI 6.61E-02 7.87E-02 8.93E-01 2.21E+00 269
Figure 8 1: Linear programming results for energy combinations satisfying different objectives under different scenarios considered.
As can be seen from these graphs, scenario biogas cooking and 70% biogas cooking performs better than electricity options in all of the objectives considered.
Electricity-GM scenario has higher ELR than electricity-RM scenario in satisfying land and water use objectives. In all the other objectives considered, electricity-RM and electricity-GM scenarios overlap and do not have a significant difference in terms of performance.
Based on these results, best option to meet the energy demand in Rampura would be to meet all the cooking energy with direct use of biogas. Considering that adoption of
270 a new technology by the villagers will not be 100%, 70% biogas and 30% improved biomass cook stoves can be implemented in Rampura both satisfying energy demand in an environmentally benign manner and satisfying the cultural needs of Rampura people.
Hence, we favor scenario 4 over the other scenarios analyzed.
Out of the four energy combinations satisfying different objectives of scenario 4
(70% biogas), we choose the option that satisfies the energy demand solely by using biogas directly for cooking and converting excess biogas into electricity to meet lighting and irrigation energy needs. When 30% of cooking is performed by utilizing improved biomass cook stoves in the traditional way, the biogas potential becomes enough to meet all the remaining energy demand (70% of cooking, lighting and irrigation) in Rampura.
Utilization of biogas directly for cooking makes it possible to meet all the energy demand in Rampura with local resources. Importing energy from centralized energy options is not needed, hence energy security and reliability are ensured. Utilizing biogas reduces the amount of agricultural residues used for cooking. By this way, more agricultural residues can be utilized as fodder. Additionally, leftover agricultural residues cover the surface of the fields diminishing soil erosion. Furthermore, excessive consumption of woody biomass for cooking creates pressure on the environment. Consequently, utilization of biogas reduces the demand for wood and eases the pressure on environment. CH4 emissions from cow dung are avoided via production of biogas while the sanitation improves in the area. The GHG emissions related to cooking with inefficient cook stoves are also significantly mitigated through the use of biogas and improved biomass cook stoves.
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Our results about the state of sustainability in Rampura are compatible with the results found for Chinese villages (Yang et al, 2009). Even at village level, sustainability is not achieved in these developing countries. However, if the renewable local energy resources are utilized in rural areas of these developing countries to meet the whole energy demand or to complement the government grid, the dependence on external resources for energy can be reduced also increasing the self-sufficiency and sustainability of the region considered.
Energy demand in developing countries is subject to increase with increasing prosperity and consumerism. This increasing energy demand will necessitate the utilization of centralized energy options even in rural areas of developing countries in the near future. Utilizing centralized clean coal technologies to meet this demand can ease energy related environmental problems especially global warming significantly.
Following the path developed countries followed will not lead to sustainability for these developing countries as can be inferred from the current status in the world today. Adopting conscious and renewable energy oriented consumption patterns and avoiding consumption beyond the carrying capacity of these regions can contribute to achieve global sustainability and ease the environmental burdens and problems in developing countries.
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8.2 Future Work
Our future research focuses on developing a general framework to provide a robust basis for design of sustainable energy systems. This framework is aimed to be applicable at different scales and in rural as well as in urban settings. Although we analyzed clean coal technologies as centralized; solar, biomass and biogas as localized options in this study, the aimed framework is to be extended to include other technologies such as wind, geothermal, natural gas etc. Additionally, we analyzed only one type of solar PVs, biogas digesters and biomass gasifiers. However, there are multiple types of these plants. Hence, mathematical models included in the framework should be extended for different types of plants of the same energy technology option. Currently, we consider life cycle GWP, land and water use impacts. Other life cycle impacts and ecosystems services critical for a technology or a region should also be captured and be evaluated in future analyses and designs.
In this study, we mainly focused on electricity generation. Our research can be extended in a way to include alternative technologies which can replace transportation fuels. Bioethanol, biobutanol, biodiesel production, their environmental impacts and the sustainability of production pathways are crucial aspects today. Constructing a framework for determining the optimum energy mix to meet transportation fuel demand is another direction for future work.
Future research to improve the analysis techniques utilized is needed in terms of accuracy and reproducibility. LCA is a well standardized but data intensive methodology.
Creating life cycle inventory for numerous processes and products requires time and
273 intensive human effort. Moreover, since these data are obtained from multiple sources by multiple people, uncertainties occur in life cycle inventory data reducing the data quality
(Guinee et al, 2011). For that reason, uncertainty analysis should be part of LCA in future studies Industry- researcher communications should be increased to obtain better quality data. New and more integrated life cycle inventory databases should be created with open access to researchers and practitioners.
While LCA can evaluate multiple downstream environmental impacts due to resource use and emissions, it cannot account for the environmental work invested into natural resources for their formation or evaluate and quantify inputs having no economic value such as human labor. New research efforts are emerging to improve LCA in these directions. To evaluate human labor in LCA studies, new frameworks are developed
(Rugani et al, 2012). Further, new techniques based on LCA concept are developed to increase ability of LCA to evaluate sustainability. Life cycle sustainability assessment
(LCSA) is an attempt with this motivation (Ginee, 2011).
Emergy analysis is a nature oriented or donor-side thermodynamic analysis technique. Emergy analysis can evaluate natural capital, sources not having monetary or market value. However, emergy analysis does not have a standardized analysis framework creating problems of reproducibility. To improve emergy analysis in terms of accuracy and reproducibility of results, there are attempts to integrate emergy analysis within LCA framework and harmonize their calculation techniques (Marvuglia et al,
2013 and Rugani and Benetto, 2012). In our opinion, standardization of emergy analysis
274 through a robust framework construction will be the trend for methodological research related to emergy in the future.
Neither LCA nor emergy analysis evaluates carrying capacity of a region despite evaluating environmental impacts or natural resources. Developing analysis techniques which can account for the carrying capacity of a region and integrating them with LCA and emergy are important steps to be taken in the future.
275
Appendix A: Supporting Information for Clean Coal Technologies
276
The supporting information regarding clean coal technologies compared is organized as follows:
1. The assumptions
2. Raw data utilized and calculation details
A.1 Assumptions
A.1.1 Conventional Process:
1. The life time of the plant is 20 years (NETL, 2008).
2. Coal mine is 150 miles = 240 km away from the plant (NETL, 2008).
3. Coal transportation is performed via rail transportation (NETL, 2008).
4. Solid disposal is done to the mining site and transported via railway (NETL,
2008).
5. One third of coal price is spent for transportation (EIA, 2010).
6. Plant construction cost is 327.107 M$ (NETL, 2008).
7. Equipment production cost is 711.539 M$ (NETL, 2008).
8. For energy quality accounting, we assume that 1 joule of electrical energy is equal
to 2 joules of hydrogen energy (50 % conversion efficiency) (Cleaveland, 1992,
Thomas, 2009 and EIA, 2008).
9. Price for CO2 is 37 $/tonne for year 2008 (Analysis of the European CO2 Market,
2012 and X-Rates, 2011).
277
A.1.2 Calcium Looping Process (CLP):
1. The life time of the plant is 20 years (Ramkumar, 2010).
2. Coal mine is 150 miles = 240 km away from the plant (Ramkumar, 2010).
3. Coal transportation is performed via rail transportation (Ramkumar, 2010).
4. Solid disposal and solid purge is done to the mining site and transported via
railway (Ramkumar, 2010).
5. One third of coal price is spent for transportation (EIA, 2010).
6. Limestone at mine price is 8.3 $/tones (IMI, 2012
7. Limestone transportation via railway is 11.7 $/tonne (IMI, 2012).
8. Plant construction cost is 485 M$ (Ramkumar, 2010).
9. Equipment production cost is 1142.25 M$(Ramkumar, 2010).
10. For energy quality accounting, we assume that 1 joule of electrical energy is equal
to 2 joules of hydrogen energy (50 % conversion efficiency) (Cleveland, 1992,
Thomas, 2009 and EIA, 2008).
11. Price for CO2 is 37 $/tonne for year 2008 (Analysis of the European CO2 Market,
2012 and X-Rates, 2011).
A.2 Raw Data and Calculations:
A.2.1 Raw Data
The raw data regarding conventional process is obtained from NTEL case study
for hydrogen production plants implemented with CO2 capture (NTEL, 2008). The
278
raw data of CLP is obtained from an ASPEN simulation performed by Swetha
Ramkumar and presented in her Ph.D thesis (Ramkumar, 2010).
Table A.1: Conventional process raw data: Inputs, outputs and unit prices (NREL, 2008).
Amount Unit Price Inputs (Tonnes/day) ($/unit) Coal 5426 45.7 Air 0 Water 21787 0.1 Shift Catalyst 0.0018 1086800 Selexol Solution 0.043 29524 Activated C 0.047 2288 Water Treatment Chemicals 6.18 374 Outputs H2 560 2110 Electricity 30.3 1800 CO2 to sequestration 11253.45 37 Solid Disposal 617 16 CO2 from stack 1293.7 SO2 0.120547945 NOx 0.4 Particulates 0.4 Hg 0.00019
279
Table A.2: CLP raw data: Inputs, outputs and unit prices (Ramkumar, 2010).
Amount Unit Inputs (Tonnes/day) Price($/unit) Coal 7990 45.7 Air 59788 0 Water 17374 0.12 Limestone(Solid Make- up) 1699 20 Activated C 0.047 2288 Water Treatment Chemicals 6.18 374
Outputs H2 Produced 495 2110 Electricity(Mwe/day) 266 1800 CO2 to sequestration 18826 37 Spent Sorbent( Solid Purge) 1569 8 CaS 275.5 CaCO3 1196.4 CaCl2 24.5 Ca(OH)2 72.6 Particulates 148 Solid Waste Disposal 617 16
A.2.2 Calculation Details for Each Analysis Scale
A.2.2.1. Equipment Scale Data and Calculations
Equipment scale data is also obtained from the sources that raw data are provided.
If the water or energy use per year calculated and divided by the total energy generated per kWhe, the equipment scale water and energy use per kWh can be calculated. As an example, CLP water use per kWh generated is calculated below. (Energy content of H2 is
280 taken as 141.9 MJ/kg) (Engineering Toolbox , 2012). Data for equipment scale emission is given in raw data section A.2.1.
Water use per year =17374*365 = 6341510 tonnes/ year
Energy Generated = [Hydrogen energy/2 + Electricity Generated]/ (3.6*10^6) in(kWh)
= 5.89*10^9 kWh/year
Water Use (liter/kWh) = 6341510000/ (5.89*10^9) = 1.08 liter/kWhe
Table A.3: Breakdown of equipment scale water make-up in CLP (Ramkumar, 2010).
Amount (tonne/day) Slag Handling 734 Hydration Steam 3939 Cooling tower 12701 Total 17374
Table A.4: Breakdown of equipment scale water make-up in conventional process (NETL, 2008).
Amount (tonnes/day) Slag handling 734.4 Condenser Makeup 4176 Shift Steam 4176 Cooling Tower 12700.8 Total 21787.2
281
Table A.5: Breakdown of equipment scale energy use and electricity production in CLP (Ramkumar, 2010).
CLP Energy Balance MWe/day Electricity Produced 514 Consumptions Coal handling, milling, slurry pumps 3.19 Slag Handling 1.12 ASU Air and O2 compressors 109 CO2 Compressor 28.3 Feed water pumps 2.27 Hydration water pump 0.152 Circulating Water Pumps 2.31 Steam turbine aux. 0.1 Transformer losses 0.33 Balance of Plant 1.5 Total 248 Net electricity Produced 266
282
Table A.6: Breakdown of equipment scale energy use and electricity generation in conventional process (NETL, 2008).
Conventional Energy Balance MWe/day Electricity Produced 172.5 Consumptions Coal handling, milling, slurry pumps 3.19 Slag Handling 1.12 ASU Air and O2 compressors 77.98 CO2 Compressor 27.3 Feed water pumps 2.27 Condensate Pumps 0.19 Quench Water Pump 2.38 Shift Pumps 0.34 Circulating Water Pumps 2.31 Cooling tower fans 1.51 Scrubber pumps 0.03 Acid gas removal 19.26 Steam turbine aux. 0.1 Claus Process 2.4 Transformer losses 0.33 Balance of Plant 1.5 TOTAL 142.21 Net Elect. Produced 30.29
283
A.2.2.2 Process LCA Scale Data and Calculations
In this scale, equipment scale data is combined with process LCA scale data regarding resource extraction, transportation and waste disposal. Process LCA scale data for these life cycle steps are obtained from NREL life cycle assessment database (NREL,
2012). NREL database includes data regarding fossil fuel use for each life cycle steps and related emissions. Multipliers for energy use and GHG emissions have been calculated per unit based on NREL data. Multiplication of multipliers with the total amount of resource gives the total energy use or emissions for that life cycle steps. Addition of all life cycle steps results in total life cycle emissions or energy use which in turn, can be utilized in per kWh environmental impact or EROI calculations in process LCA scale if they are calculated in annual amounts.
284
Table A.7:GWP of conventional process at process LCA scale
Conventional Steps included Amount Distance Emissions INPUTS (tonnes/day) Travelled(km) Multipliers (MT CO2eq) Coal Coal Mining 5426 0 83.79 454.64 Coal Transportation 5426 240 1.89E-02 24.61 OUTPUTS H2 560 0 0.00 CO2 to sequestration 11253.5 0 0.00 Sulfur 135.6 0 0.00 Gasification CO2 from stack 1293.69 0 1293.69 SO2 0.120547945 0 NOx 0.4 0 124.00 Particulates 0.405479452 0 0.00 Hg 0.000191781 0 0.00 Solid Disposal 617 240 1.89E-02 2.80 Total 1900
285
Table A.8: GWP of CLP at process LCA scale (Ramkumar, 2010 and NREL, 2012).
CLP Steps included Amount Distance GWP Inputs (tonnes/Day) Travelled(km) Multipliers (MTCO2) Coal Coal Mining 7990 0 83.79 669.5 Coal Transportation 7990 240 1.89E-02 36.2 0 Limestone Limestone mining 1699 Limestone Transportation 1699 240 1.89E-02 7.7
Outputs H2 Produced 495 0 0.0 CO2 to sequestration 18826 0 0.0 Spent Sorbent (Solid Purge) Solid Purge 1569 240 1.89E-02 7.1 Particulates 148 0 0.0 Solid Disposal Solid Disposal 617 240 1.89E-02 2.8 723.3
286
Table A.9: Energy use of conventional process at the process LCA scale (NREL, 2012 and Ramkumar, 2010).
Steps Conventional included Amount Distance Energy Use INPUTS (tonnes/day) Travelled(km) Multipliers (MJ/day) Coal Coal Mining 5426 0 0.493 2675018 Coal Transportation 5426 240 0.247 321653 OUTPUTS H2 560 0 CO2 to sequestration 11253.5 0 Sulfur 135.6 0
CO2 from stack 1293.69 0 SO2 0.120547945 0 NOx 0.4 0 Particulates 0.405479452 0 Hg 0.000191781 0 Solid Disposal 617 240 0.247 36575 Total 3030000
287
Table A.10: Energy use of CLP at process LCA scale (NREL, 2012 and Ramkumar,
2010). Steps CLP included Amount Distance Energy Use Inputs (tonnes/Day) Travelled(km) Multipliers (MJ/day) Coal Coal Mining 7990 0 0.493 3940147 Coal Transportation 7990 240 0.247 473647 Limestone Limestone mining 1699 0.023 39000 Limestone Transportation 1699 240 0.247 100716
Outputs H2 Produced 495 0 CO2 to sequestration 18826 0 Spent Sorbent (Solid Purge) Solid Purge 1568.9 240 0.247 93004 Particulates 148 0 Solid Disposal Solid Disposal 559.62 240 0.247 33173 Total 4670000
A.2.2.3 Economy Scale Data and Calculations
In economy scale equipment scale data is combined with economy scale data. The
monetary values regarding each life cycle step, input or output are fed as an input to Eco-
LCA software (OSU, 2013). In tables A.11 and A.12, these data are presented. By
entering monetary vales and selecting the related environmental impact or energy use,
economy scale impacts or energy use is calculated.
288
Table A.11: Economy scale data for CLP: Life cycle steps, related sectors and monetary data (Ramkumar, 2010 and OSU, 2013).
Life Cycle Monetary Inputs Step Sector Value(M$/year) Equipment 33329A Other industrial machinery production manufacturing 57.11 Power plant 230102 Non-residential manufacturing construction structures 24.25 Coal Coal mining 212100 Coal mining 88.96 Coal transportation 482000 Rail transportation 44.48 Limestone 212390 Other non-metallic Limestone mining mineral mining 5.15 Limestone transportation 482000 Rail transportation 7.26 Water treatment Chemicals 325188 All other basic inorganic chemicals production chemical manufacturing 0.84 Chemicals 325188 All other basic inorganic Active Carbon production chemical manufacturing 0.04
Outputs Waste water 221300 Water, sewage and other Water disposal systems 0.76 Solid waste 562000 Waste management and Solid Disposal disposal remediation services 3.60 Rail Solid Purge Transportation 482000 Rail transportation 4.58
289
Table A 12: Economy scale data for conventional process: Life cycle steps, related sectors and monetary data (NETL, 2008 and OSU, 2013).
Life Cycle Monetary Inputs Step Sector Value(M$/year) Equipment 33329A Other industrial machinery production manufacturing 35.58 Power plant 230102 Non-residential manufacturing construction structures 16.36 Coal Coal mining 212100 Coal mining 60.4 Coal transportation 482000 Rail transportation 30.2 Gasification - Water treatment Chemicals 325188 All other basic inorganic chemicals production chemical manufacturing 0.84 Chemicals 325188 All other basic inorganic Active Carbon production chemical manufacturing 0.04 Chemicals 325188 All other basic inorganic Shift Catalyst Production chemical manufacturing 0.72 Selexol Chemicals 325188 All other basic inorganic Solutions Production chemical manufacturing 0.47 Outputs Waste water 221300 Water, sewage and other Water disposal systems 0.95 Solid waste 562000 Waste management and Solid Disposal disposal remediation services 3.60
290
Appendix B: Rampura Village
291
In this section, we present raw data obtained from Development alternatives and our surveys in Rampura village. In table B.1, agricultural crop yields per year, crop consumption in the village as food item and the amount sold in the market is given in percentages.
Table B.1: Agricultural crop yields per year in Rampura (Development Alternatives, 2011).
Village Crops Area(acre) Yield(kg/acre) Total(kg) Utilization (%) Sold Amount (%) Wheat 72.5 1900 137750 25 75 Gram 32.0 400 12800 20 80 Mustard 53.7 300 16110 0 100 Maize 38.5 550 21175 5 95 Urad Dal 35.3 400 14120 12.5 87.5 Soya 39.2 900 35280 12.5 87.5 Groundnut 31.2 400 12480 5 95 Vegetables 11.0 7500 82500 30 70
Table B.2: Agricultural residue amounts per year in Rampura (Development Alternatives, 2011).
Agricultural Residue Area Residues Residues (kg/acre) (acre) (kg/year) Wheat 1800 72.5 130500 Gram 200 32.0 6400 Mustard 650 53.7 34905 Maize 885 38.5 34092 Urad Dal 520 35.3 18356 Soya 1080 39.2 42336 Groundnut 920 31.2 28704
292
In table B.2, agricultural residue obtained from each crop and the total agricultural
residues amount in a year is given. Table B.3 includes the information regarding fodder
consumption and manure production by the animals in a day. Lastly, table B.4 presents
ground water use and diesel use data for irrigation in a year time period.
Table B.3: Fodder consumption and manure production per day by different types of animals (Development Alternatives, 2011).
Animal Total Fodder/Animal Manure/Animal Total Fodder Total Manure Type Number (kg/day) (kg/day) (kg) (kg) Buffalo 117 16.5 23.5 1930.5 2749.5 Cow 55 10 14.5 550 797.5 Goat 155 0 1.8 0 279 Bullock 45 15 23.5 675 1057.5 Calf 36 3.0 5.0 108 180
Table B.4: Year around irrigation data in Rampura: Ground water and diesel consumption (Development Alternatives, 2011).
# of Irrigation/ Irrigation Area Cultivated Total Water Diesel Irrigation Acre Hours/Acre (acre) Pump Hours (liter) (liter) Wheat 5.0 4.5 72.5 1631 30993750 2039 Gram 1.0 4.5 32.0 144 2736000 180 Mustard 1.0 4.5 53.7 242 4591350 302 Maize 1.0 4.5 38.5 173 3291750 217 Urad Dal 1.0 4.5 35.3 159 3018150 199 Soya 2.0 4.5 39.8 358 6805800 448 Ground nut 2.0 4.5 31.2 281 5335200 351 Vegetable 10.0 4.5 11.0 495 9405000 619 Total 3483 66177000 4354
293
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