Bioresource Technology 102 (2011) 2684–2694
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Bioresource Technology
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Comparative life cycle assessment of three biohydrogen pathways ⇑ Sylvestre Njakou Djomo a,b, , Dagnija Blumberga b a University of Antwerp, Department of Biology, Research Group of Plant and Vegetation Ecology, Universiteitsplein 1, B-2610 Wilrijk, Belgium b Riga Technical University, Faculty of Power and Electrical Engineering, Institute of Energy Systems and Environment, Kronvalda Blvd. 1, LV-1010 Riga, Latvia article info abstract
Article history: A life cycle assessment was performed to quantify and compare the energetic and environmental perfor- Received 8 August 2010 mances of hydrogen from wheat straw (WS-H2), sweet sorghum stalk (SSS-H2), and steam potato peels Received in revised form 27 October 2010 (SPP-H2). Inventory data were derived from a pilot plant. Impacts were assessed using the impact Accepted 28 October 2010 2002+ method. When co-product was not considered, the greenhouse gas (GHG) emissions were Available online 4 November 2010 1 1 1 5.60 kg CO2eq kg H2 for WS-H2, 5.32 kg CO2eq kg H2 for SSS-H2, and 5.18 kg CO2eq kg H2 for SPP- H2. BioH2 pathways reduced GHG emissions by 52–56% compared to diesel and by 54–57% compared Keywords: to steam methane reforming production of H . The energy ratios (ER) were also comparable: 1.08 for Hydrogen 2 WS-H , 1.14 for SSS-H and 1.17 for SPP-H . A shift from SPP-H to WS-H would therefore not affect Energy balance 2 2 2 2 2 Wheat straw the ER and GHG emissions of these BioH2 pathways. When co-product was considered, a shift from Potato peels SPP-H2 to WS-H2 or SSS-H2 decreased the ER, while increasing the GHG emissions significantly. Co-prod- Sweet sorghum stalk uct yield should be considered when selecting BioH2 feedstocks. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction renewable energy sources; thus, they are unsustainable in the long
term. To make the future H2 economy more sustainable, renewable The transportation sector consumes about 30% of the world’s energy resources such as biomass have to be used to produce H2 energy (EIA, 2010) and contributes to about 23% of global green- (Manish and Banerjee, 2008). house gas (GHG) emissions (IEA, 2009). Petroleum fuels account Biomass today provides about 14% of the global energy needs for 40% of the energy consumed worldwide by the transportation (IEA, 1998). It is a significant contributor to the world economy sector (Tan et al., 2008). This makes transportation one of the most (Parikka, 2004; Antonopoulou et al., 2008), and it is one of the most polluting sectors, which at the same time is vulnerable to disrup- abundant renewable resources that can be used for the production tions in the oil supply. The progressive depletion of fossil fuels of biological hydrogen (BioH2). BioH2 production through consecu- and the growing concern about global warming issues have in- tive chemotrophic (i.e., dark) and phototrophic (i.e., photo) fer- creased worldwide interest in the use of alternative transportation mentation of organic substrates has received growing attention fuels that are CO2-neutral and less polluting (Rubin et al., 1992). in recent years as one of the most eco-friendly methods of produc- Alternative transportation fuels are being promoted to help reduce ing H2 because it produces clean energy while reducing waste the reliance on fossil fuels and to reduce GHG and urban air emis- (Benemann, 1996; Claassen et al., 1999; Wang and Wan, 2009; sions. Among alternative fuels, hydrogen (H2) is viewed as an Das and Veziroglu, 2008). important energy carrier of the future (Johnston et al., 2005; Carbohydrates, which include starch, sugars, celluloses, and Momirlan and Veziroglu, 2005). It has the highest energy content hemicelluloses, are the main potential fermentable substrates for 1 per unit weight of any known fuel (122 MJ kg H2), and it is the producing H2 (Nandi and Sengupta, 1998; Kim and Dale, 2004). only fuel that is not chemically bound to carbon. Current H2 pro- Wheat straw (WS), sweet sorghum stalk (SSS), and steam potato duction is based on conventional methods such as steam reforming peels (SPP) are sources of carbohydrates that have gained special of methane, coal gasification, and electrolysis of water with elec- attention as important global feedstock materials for H2 produc- tricity from fossil fuels (Das and Veziroglu, 2001). However, these tion because they are abundant and inexpensive (De Vrije et al., production methods are energy intensive, expensive, and use non- 2002; Kim and Dale, 2004; Liu et al., 2008). Their strong advanta- ges over other feedstock types are that they do not compete with the food and feed industries for biomass and land. WS, SPP, and ⇑ SSS are considered to be among the best feedstock options for Corresponding author at: University of Antwerp, Department of Biology, increasing H production through fermentative processes. The Research Group of Plant and Vegetation Ecology, Universiteitsplein 1, B-2610 2 Wilrijk, Belgium. Tel.: +32 3 2652827; fax: +32 3 2652271. physical and chemical compositions of these feedstock materials E-mail address: [email protected] (S. Njakou Djomo). vary substantially. This variation imposes different pre-treatment
0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.10.139 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 2685 methods, which in turn could impact the energetic and environ- polymers into fermentable sugars (Mosier et al., 2005). The pre- mental performances of the fuel (i.e., H2) pathway. treated feedstocks (WS, SSS) are further broken down into fer- Most of the previous studies have focused primarily on the eco- mentable sugars using commercial enzymes such as cellulase, glu- nomic viability and biological performance (e.g., fermentability of coamylase (AMG 300), and thermamyl (Claassen and de Vrije, lignocelluloses) of BioH2 production pathways and, more recently, 2006). It should be mentioned that under the same conditions of on the suitability of different feedstock materials for H2 produc- enzyme loading, the amounts of enzyme required for SPP, SSS, tion. However, much less is known about the energetic and envi- and WS at the same feedstock flow rates differ because the feed- ronmental performance of BioH2 production systems, and it is stock materials contain different amounts of polysaccharide unclear whether a shift from one feedstock type to another im- (starch and cellulose). Similarly, the amount of chemical (NaOH) proves or worsens its environmental and energetic performances. required in the pre-treatment process is also different for SSS To fully understand the environmental implications of using and WS since their hemicelluloses and lignin levels differ. The these feedstock materials on a larger scale, a more detailed quan- THF uses thermophilic bacteria that grow at a temperature of at titative assessment is required. Here, we set out to evaluate and least 70 °C to convert the fermentable sugars into hydrogen (H2), compare the environmental and energetic performances of H2 pro- carbon dioxide (CO2), and organic acids. The PHF employs photo- duced from WS, SSS, and SPP by means of a two-stage bioprocess. heterotrophic bacteria to convert the residual organic acids to
We applied the LCA methodology to an experimental pilot plant additional H2 and CO2 using light as an extra energy source (Claas- that was built to study the production of H2 in Europe. sen et al., 2000, 2004). The mixed gas (H2,CO2, and small amounts of contaminant gases) produced in the bioreactors is then recov- ered and sent to a gas treatment unit where unwanted gases are 2. Methods removed. Well documented and efficient processes such as pres- sure swing adsorption (PSA) and amine absorption/desorption 2.1. Biomass feedstock (MEA) are used for gas upgrading (Modarresi et al., 2010; Claassen and de Vrije, 2006). The purified hydrogen is then compressed at Steam potato peels (SPP), sweet sorghum stalk (SSS), and wheat 450 bars and stored on site (i.e., at the BioH plant). Finally, the straw (WS) were considered as biomass feedstock in this study. 2 by-products (protein residues) from the pre-treatment and fer- These by-products and residues are potential feedstock materials mentation are removed and sent to the fodder industry where they for H production. The chemical compositions of these feedstock 2 are mixed with other products to produce animal fodder. materials are listed in Table 1. 2.3. Life cycle assessment (LCA) 2.2. Biohydrogen from agricultural and agrofood residues: system description The LCA methodology was used to evaluate and compare the
environmental and energetic performances of the BioH2 produc- The biological hydrogen (BioH2) production system evaluated in tion pathways outlined in Fig. 1. LCA quantifies the potential envi- this work consisted of eight main processes: collection of residues; ronmental impacts of all processes applied to the product starting pre-treatment; hydrolysis; fermentation (including thermophilic from the extraction of raw materials to the production, use, and [THF] and photoheterotrophic fermentation [PHF]); recovery of disposal of the product. LCA comprises four interrelated steps: goal hydrogen; gas treatment; compression and storage; and applica- and scope definition, inventory analysis, impact assessment, and tion of fermentation residues (Fig. 1). The most important operat- interpretation (ISO 14040, 2006). Inventory calculations for the ing conditions for the conversion of biomass to H2 in this study three BioH2 pathways were based on primary data (collected di- were similar to the earlier conditions reported by van Groenestijn rectly from a pilot plant) and secondary data (collected from the et al. (2002). The plant was assumed to operate 336 days per year. literature and databases). The characterisation of environmental Agricultural residues (e.g., WS, SSS) and by-products from agro- impacts was based on the impact assessment methodology impact food industries (e.g., SPP) are collected and transported to the 2002+ (Jolliet et al., 2003). The modelling of H2 production was BioH2 production plant. Depending on the type of feedstock, pre- facilitated by the LCA software Simapro 7.1 (Pre-consultant, 2006). treatment is used to enhance the accessibility of cellulose in the lignocellulosic feedstock to enzymes that convert carbohydrate 2.3.1. Goal definition and scope, system boundaries As stated in the introduction, the objective of this study was to quantify and compare the energy balance and the environmental Table 1 impacts of these three BioH production pathways. LCA results will Chemical characterisation and high heating values of feedstock used in the analysis 2 (Claassen et al., 2004; Duncan et al., 1991; Billa et al., 1997). The columns from left to also allow the BioH2 pathways to be compared with a fossil fuel right denote: the components of each feedstock, wheat straw, steam potato peels, and system such as gasoline and other H2 production pathways. sweet sorghum stalk. The function of the system is to supply H2 to road vehicles. The Components Wheat straw Steam potato Sweet sorghum functional unit chosen to represent the system was defined as 460 1 (% DM) (WS) peels (SPP) stalk (SSS) ton H2 year , and the amount of SPP with 56% carbohydrate-con- 1 Sugars – – 55 tent needed to deliver the functional unit was 6447 ton year . The Starch – 56.3 – amount of WS and SSS with, respectively, 68% and 77% carbohy- Cellulose 39 – 12.3 drate content necessary to produce the same quantity of H2 were Hemicellulose 29 – 10.1 5309 ton year 1 and 4689 ton year 1. Moreover, 1 MJ of fuel equiv- Lignin 15 – 4.8 Total carbohydrates* 68 56.3 77.4 alent was used as a reference energy value in order to compare the Crude protein 4.2 9.8 1.98 results with those for gasoline. Ash 6 7.6 0.3 The system boundaries (SB) encompass all the processes neces- HHV (MJ kg 1) 15.63 14.43 17 sary to deliver the system’s functional unit. The definition of the SB then guides the selection of the processes to be taken into account DM, dry matter; HHV, high heating value. * Total carbohydrates include components such as cellulose, hemicellulose, starch, (Jolliet et al., 2005). A ‘‘well-to-tank’’ scheme was used for this sugars, etc. that are essential for the production of BioH2. Minor components are not study, which means that the combustion of BioH2 in the vehicle included in this list, so the numbers do not sum to 100%. was not included in the analysis. The whole production system is 2686 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694
Fig. 1. Schematic representation of the system boundary of the production process. The boxes represent unit processes and the arrows refer to material flows. The dash- dotted box represents the displaced process. The dashed lines represent the system boundaries. represented in Fig. 1. Since protein residues are by-products that cause precise inventory data are available. Key data for this occur during the pre-treatment and fermentation processes, a conversion technology as well as for the BioH2 pathways are credit was applied for the animal feed that was not produced be- shown in Table 2. Data for this study were collected from different cause it could be replaced by the by-products of BioH2 production. sources (Table 2). The use of data from diverse sources raises the The production of enzymes was excluded because there were no issue of the consistency of the data quality. To minimise uncer- reliable data on such processes. However, the transport and use tainty about the data quality, we made sure that all primary data of enzymes in the BioH2 plant were included in the analysis. The were recent (i.e., not dating from before 2005) and represented energy costs and environmental loads of capital equipment (i.e., current technologies. European data were preferred. Mass and en- buildings, roads, and machinery) as well as the production of bac- ergy balance analyses for each unit process were conducted in or- teria were also excluded, as these impacts typically are negligible der to ensure that the data did not violate any of the basic physical when allocated over the total quantity of H2 produced over the life laws. Finally, the data from each source were checked against other cycle of the facility. Only the inputs and outputs directly associated sources to determine if they fell outside the normal range for sim- with the production and storage of BioH2 were identified and ilar products or processes. In summary, we feel that the data could quantified. The use of agricultural land was excluded from this be considered of sufficiently high quality to evaluate the BioH2 study. Indeed, no additional land is required to produce agricul- pathways and to meet the goals of the study, because most of tural residues or biomass waste from other activities (Searchinger the data sources were quite uniform and up to date. A description et al., 2008). The impact of the removal of straw and stalk was also of the assumptions considered regarding the transport activities in excluded from our analysis. In fact, straw removal has little influ- the BioH2 life cycle is presented in Table 2. ence on the environment and the carbon sequestration resulting For the purpose of comparison of BioH2 pathways with other H2 from its incorporation in the soil is only 5–10% (Gabrielle and Gag- production pathways, data from the Institute for Environment and naire, 2008). Impacts such as noise and odours were excluded in Sustainability of the European Union Commission’s Joint Research this study because there are no characterisation methods to assess Centre were also used (IES-JRC, 2008). Table 3 gives an explanation these impacts. The geographical boundary for this study is Europe: of the major characteristics of each H2 production pathway. raw materials are assumed to be produced there. The time horizon considered for the BioH2 production system is 2005 onward. 2.3.2.2. Co-product allocation. The BioH2 pathways analysed in this study produce in addition to H2, a considerable amount of protein residues which can be used as animal feed. Consequently, environ-
2.3.2. Life cycle inventory mental impacts had to be allocated between the product (i.e., H2) 2.3.2.1. Data collection. Life cycle inventory involves the collection and the co-product (i.e., protein residues). In LCAs, different alloca- and compilation of the data required to quantify all the relevant in- tion methods exist to deal with co-products such as protein resi- puts and outputs associated with the production of the functional dues. However, the ISO standard (ISO 14044, 2006) recommends unit. In this study, primary data were collected to quantify the that allocation should be avoided whenever possible by sub-divid- operational inputs and outputs associated with each BioH2 produc- ing the system or by applying system expansion. In this study, sys- tion chain, while secondary data from published literature and re- tem expansion was applied to avoid allocation, and it was assumed ports were used to characterise various background processes such that the use of protein residues as animal food would offset the as electricity production and transmission. Where no other data production of an equivalent amount of mixture of maize grain were available, the Ecoinvent database (Frischknecht et al., 2007) and soy meal in regular animal feed production. Table 4 summa- was used. rises the parameters used to calculate the amount of protein and Operational data for hydrogen production were obtained from energy that potentially can be displaced in cattle diets. Eqs. (1) the two-stage BioH2 production plant (i.e., pilot plant) in the Neth- and (2) (Lywood et al., 2009) illustrate the formulae used in this erlands. Based on a feedstock input rate of 800 kg SPP h 1, this study to calculate the co-product substitution ratio. technology produces 57 kg H h 1 (Claassen et al., 2004). It was 2 DPðsoyÞ EAðco-productÞ EAðsoyÞ DPðco-productÞ chosen because it represents a novel approach of producing hydro- a ¼ ð1Þ DPðsoyÞ EAðmaizeÞ DPðmaizeÞ EAðsoyÞ gen from biomass that is still in the stage of a pilot project, and be- S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 2687
Table 2
General inventory data for the three BioH2 pathways analysed (up) and assumptions about transportation activities linked to BioH2 production (down). The upper columns from left to right denote: H2 from steam potato peels, H2 from sweet sorghum stalk, H2 from wheat straw, while the lower columns denote: the type of materials transported, the transport mode, the truck capacity, and the average distance travelled.
Parameters SPP-H2 SSS-H2 WS-H2 References General system parameters Geographic setting Europe Europe Europe Assumed Size of thermo bioreactor (m3) 959595Claassen et al. (2004) Size of photo bioreactor (m3) 300 300 300 Claassen et al. (2004) Plant capacity factor 0.92 0.92 0.92 Assumed Plant life time (years) 20 20 20 Assumed Input 1 Feedstock (tonfw year ) 36,894 5490 6071 Claassen et al. (2004) Chemical (NaOH) (ton year 1) 129 188 318 Claassen et al. (2004) Enzyme (ton year 1) 253 278 312 Claassen et al. (2004) Water for reaction (m3 year 1) 395 103 396 103b 400 103b Claassen et al. (2004) Water as solvent (m3 year 1) 2369 103 2377 103 2402 103 Claassen et al. (2004) 1 Electricity (MWhe year ) 4291 4320 4332 Claassen et al. (2004) Steam (MWh year 1) 3546 3612a 3618a Markowski et al., 2010 Output Hydrogen (ton year 1) 460 460 460 Claassen et al. (2004) Protein residues (ton year 1) 4030 1728c 815c Claassen et al. (2004) Carbon dioxide (ton year 1) 5053 5053 5053 Claassen et al. (2004) Waste water (m3 year 1) 2369 103 2377 103 2402 103 Estimated
Total hydrogen produced during life time (ton) 9200 9200 9200 Calculated Type of materials Transport mode Capacity (ton) Average distance (km)
SPP, SSS, and WS from collection to H2 plant Diesel truck 16 10
NaOH from wholesalers to H2 plant Diesel truck 28 50
Enzyme from wholesalers to H2 plant Diesel truck 28 80 SPP, steam potato peels; SSS, sweet sorghum stalk, WS, wheat straw, fw, fresh weight. a The values were obtained by assuming a 5% and 10% increase in heat demand of SSS and WS during pre-treatment processes. b The values were obtained by assuming a 5% and 10% increase in water demand for SSS and WS feedstock during pre-treatment processes. c The values were obtained by assuming a 7% increase of protein in residue yield after fermentation and considering the crude protein content of the feedstock.
DPðco-productÞ aDPðmaizeÞ its production. Eq. (3) (Sheehan et al., 1998) illustrates the formu- b ¼ ð2Þ DPðsoyÞ lae used to calculate the energy ratio (ER).
In these equations, DP is the digestible protein, EA is the avail- EH2 ER ¼ ð3Þ able energy, a and b are the substitution ratios for maize grain and kEinput soy meal, respectively. Regarding the background system, the allo- E is the energy density (based on low heating value) of H , E cation rules as applied in the ecoinvent database were adopted H2 2 in- is the total energy input, k is the fraction of the total energy in- (Frischknecht et al., 2007). put put associated with the BioH2. When the co-product is not considered k = 1. However, when the co-product is considered 2.3.3. Life cycle impact assessment k <1. Life cycle impact assessment was conducted using the impact 2002+ method (Jolliet et al., 2003). This method was chosen be- 3. Results and discussion cause it provides a way to present the most important results of the study to the general public in an understandable format. Im- 3.1. Environmental impacts of the investigated BioH2 pathways pact 2002+ groups the impacts of 14 midpoint categories into four damage-oriented impacts. The four final damage categories are: A comparison of the environmental impacts of the H2 produced human health, ecosystem quality, climate change, and resources, from the fermentation of SSP, SSS, and WS is shown in Fig. 2. When 2 and they are expressed, respectively, in DALY, PDF m yr, kg CO2eq, the co-product was not considered, the total environmental impact 1 1 and MJ. The scores in the damage categories are further normalised was 1.44 mPt kg H2 for SPP; 1.48 mPt kg H2 for SSS; and 1 based on the average impact on one person in one year in Europe. 1.58 mPt kg H2 for WS. Although SPP had a slightly better total The resulting unit is a ‘‘point’’ (Pt), where one point represents the environmental impact compared to both SSS and WS, the differ- average damage caused to one person during one year in Europe. In ences in impacts between these feedstock types were within a very this way, the four damage categories can be compared to each close range. However, these three feedstocks showed different other. environmental patterns when the co-product was considered in the analysis. Indeed, the valorisation of the co-product led to a sig-
nificant reduction of the impacts of the three studied BioH2 pro- 2.4. Energy balance duction pathways. Depending on the type of feedstock, this decrease ranged from 66% to 178% and the resulting net environ- 1 1 An important factor used to determine the energetic and envi- mental impact was 1.12 mPt kg H2 for SPP; 0.50 mPt kg H2 1 ronmental usefulness of a fuel is the energy balance (Scholz for SSS; and 0.03 mPt kg H2 for WS (Fig. 2). The ranking of these et al., 1998). The energy balance for a BioH2 pathway is defined feedstocks was also disturbed. Steam potato peels remained the in this study as the ratio of the energy contained in one kilogram more environmentally friendly feedstock, while SSS become the of H2 divided by the energy (or fraction of energy) required for least environmentally friendly one. The reverse ranking observed 2688 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694
Table 3
Energy ratios and greenhouse gas performances of H2 production from other feedstocks (IES-JRC, 2008). The columns from left to right denote: the code, the pathways description, the energy ratio, and the GHG emissions.
Code Pathway description Energy GHG emissions 1 ratio (kg CO2eq kg H2)
A1 H2 produced via on-site reforming of EU-mix natural gas; on-site compression 1.19 12.81
A2 Piped (7000 km) NG to on-site production of H2 and compression 0.90 15.01
A3 Piped (4000 km) NG to on-site production of H2 and compression 1.05 13.66
A4 Piped (7000 km) NG to central production of H2; distribution: pipeline; compression: on-site 1.16 13.30
A5 Piped (4000 km) NG to central production of H2; distribution: pipeline; compression: on-site 1.39 12.08
A6 Piped (4000 km) NG to central production of H2; distribution: pipeline; compression: on-site; (+CCS option) 1.30 4.64
A7 Piped (4000 km) NG to central production of H2; distribution: road; compression: on-site 1.39 12.20
A8 Piped (4000 km) NG to central production of liquid H2; distribution: road; vaporisation/compression: on-site 0.78 16.35
A9 Remote LNG to on-site production H2; vaporisation/compression: on-site 0.89 14.64
A10 Remote LNG to central production of H2; vaporisation/compression: on-site 1.15 12.93
A11 Remote NG to methanol to on-site production of H2; compression: on-site 0.89 14.52
A12 Centralised H2 production via gasification of EU mix coal; distribution: pipeline 0.71 28.55
A13 Centralised H2 production via gasification of EU mix coal; distribution: pipeline; (+CCS option) 0.56 6.47
A14 H2 produced via on-site gasification of waste wood 0.82 1.34
A15 Centralised H2 production via gasification of waste wood; distribution: pipeline 1.03 1.46
A16 H2 produced via gasification of wood waste black liquor; 1.96 1.22
A17 H2 produced via on-site gasification of farmed wood waste; 0.81 1.83
A18 H2 produced via centralised gasification of farmed wood waste; distribution: pipeline 1.03 1.71
A19 Piped (7000 km) NG to compressed H2 via on-site electrolysis 0.37 27.69
A20 Piped (7000 km) NG to compressed H2 via on-site electrolysis 0.42 24.77
A21 Piped (7000 km) NG to compressed H2 via centralised electrolysis, distribution: pipeline 0.41 24.89
A22 LNG to compressed H2 via on-site electrolysis 0.36 26.96
A23 Farmed wood to compressed H2 via on-site electrolysis (using combined cycle gas turbine) 0.38 1.46
A24 Farmed wood to compressed H2 via on-site electrolysis (using conventional steam turbine) 0.23 3.66
A25 H2 produced via on-site electrolysis using EU-mix electricity; compression: on-site 0.28 25.50
A26 H2 produced via on-site electrolysis using electricity from coal EU-mix; compression: on-site 0.32 51.97
A27 H2 produced via centralised electrolysis using electricity from coal EU-mix; distribution: pipeline; compression: on-site 0.31 50.87
A28 H2 produced via on-site electrolysis using electricity from nuclear power; compression: on-site 0.20 0.84
A29 H2 produced via centralised electrolysis using electricity from wind turbine; distribution: pipeline; compression: on-site 1.27 1.10
A30 Piped (7000 km) NG to centralised production of liquid H2; distribution: road 0.75 17.32
A31 Piped (4000 km) NG to centralised production of liquid H2; distribution: road 0.88 15.49
A32 Remote NG to methanol to liquid H2 production, transport; sea; distribution: road 0.70 16.96
A33 LNG to centralised production of liquid H2; distribution: road 0.75 16.71
A34 Liquid H2 produced via centralised gasification of farmed wood; distribution: road 0.67 0.85
A35 Piped (4000 km) NG to liquid H2 produced via centralised electrolysis (using combined cycle gas turbine); distribution: road 0.38 28.30
A36 Liquid H2 produced via centralised electrolysis using EU-mix electricity; distribution: road 0.24 28.91
A37 Liquid H2 produced via centralised electrolysis using electricity from coal EU-mix; distribution: road 0.27 58.19
NG, natural gas, H2, hydrogen, EU-mix, European Union natural gas, or coal, or electricity mix. LNG, liquefied natural gas, CCS, carbon capture and storage. between SSS and WS was due to the low protein content of SSS ative (i.e., contribution to environmental benefit) in most cases. compared to WS. This latest result suggests that in addition to The net impacts in ecosystem quality were 15.50 2 1 2 1 the carbohydrate content, the protein content of the feedstock PDF m yr kg H2 for SPP, 6.49 PDF m yr kg H2 for SSS, and 2 1 should also be considered when selecting feedstock materials for 10.80 PDF m yr kg H2 for WS (Fig. 3c) while the net impacts 6 1 BioH2 production. in human health were 2.94 10 DALY kg H2 for SPP, 8 1 7 1 Resource depletion and climate change were the largest con- 2.54 10 DALY kg H2 for SSS, and 8.3 10 DALY kg tributors to the environmental impacts of the investigated BioH2 H2 for WS (Fig. 3d). As before, SPP had a better environmental per- pathways. When the co-product was not considered, the total re- formance followed by WS. Sweet sorghum stalks ranked last. These 1 1 sources consumed were 105 MJ kg H2 for SPP, 108 MJ kg H2 results suggest not only that resource depletion and climate 1 for SSS, and 113 MJ kg H2 for WS, while the GHG emissions were change were the key damages in the life cycle of H2 from these 1 1 5.18 kg CO2eq kg H2 for SPP, 5.32 CO2eq kg H2 for SSS, and 5.60 feedstocks, but also that using SPP, SSS, or WS to make H2 benefits 1 CO2eq kg H2 for WS. However, when the co-product was consid- the environment because the accompanying co-product can be ered, the resources consumed and greenhouse gases emitted de- used to feed animals and offsets the need for feed production creased significantly but the net impacts remained positive (i.e., elsewhere. contribution to damage) in these two damage categories. The net 1 resources consumed were 45.70 MJ kg H2 for SPP, 3.2. Sources of impacts 1 1 87.30 MJ kg H2 for SSS, and 79.09 MJ kg H2 for WS (Fig. 3a). 1 The net GHG emissions were 1.28 kg CO2eq kg H2 for SPP, Electricity consumption was the main source of impacts in 1 1 4.05 kg CO2eq kg H2 for SSS, and 3.48 kg CO2eq kg H2 for WS three of the four damage categories. It accounted for over 64% of (Fig. 3b). Steam potato peels retained it leading position. However, impacts in the damage category resources and over 63% of impacts in both the resources and climate change categories, a reversed in climate change (Fig. 4a). More in detail, impacts in the resources ranking order was observed between SSS and WS, with SSS losing category were due in general to the consumption of non-renew- its second position in favour to WS. able energy and in particular natural gas, which contributed about The damage categories ecosystem quality and human health 51–59% to this category depending on the feedstock. In the climate showed different patterns compared to the resources and climate change category, impacts were caused by emissions of CO2 (about change categories. Indeed, when the co-product was considered, 57–66%) arising from burning natural gas used for the production the net impacts in ecosystem quality and human health were neg- of electricity. Human health impacts originated from emissions S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 2689
Table 4 Substitution ratios of the co-products with reference animal feed. The columns from left to right denote: the crop, feed ingredients, the fermentation co-products, and appropriate references.
Crop Feed ingredients Fermentation co-products References Names Maize Soy meal WS protein residues SPP protein residues SSS protein residues Digestible protein content Cattle-crude protein (% as fed) 8.40 48.01 11.20 16.80 8.98 Premier et al. (2008) DP Energy availability Cattle-MER (MJ kg 1) 12.10 12.30 12.40 12.04 12.04 Premier et al. (2008) EA Substitution ratios Maize/co-product (t t 1) 0.96 0.77 0.98 a Soy meal/co-product (t t 1) 0.04 0.23 0.02 b
DP = digestible protein, EA = available energy, a and b are substitution ratio, SPP = steam potato peels, SSS = sweet sorghum stalk, WS = wheat straw.
Fig. 2. Comparative analysis of total and net impact scores of H2 from SSP, SSS, and WS. The dark grey bars represent the impact of H2 production (from feedstock collection to compression and storage), the grey bars represent the benefit due to valorisation of co-product, and the shaded bars represent the net impacts. Impacts are expressed in points (Pt), where a point is the unit of environmental penalties. Based on Impact 2002+, 1 point represents 0.0071 DALY for human health, 13,700 PDF m2 yr for ecosystem quality, 9950 kg CO2eq for climate change, and 152,000 MJ for resources.
of nitrogen oxides (about 24–26%) and aromatic hydrocarbons 3.3. Contribution of stages to the environmental impacts of BioH2 (about 15–19%) arising from electricity generation. It can also be production pathways noticed that all three BioH2 pathways investigated in this study have similar electricity consumption (Fig. 4a). This suggests that In order to obtain a better understanding of the results ob- one might be able to improve the environmental impacts by seek- tained, we further broke down the contributions to total environ- ing a different electricity source or by reducing the amount of elec- mental impacts. Fig. 4b shows the contribution of each stage to tricity consumed in the BioH2 production process. the production chain of H2 for each feedstock type. Fig. 4b makes Steam/heat consumption was the second largest source of envi- clear that fermentation is a crucial stage in the life cycle of H2 from ronmental impact. Its shares were about one third of those of elec- these three feedstock types, since it accounted for between 41% tricity in the damage categories resources and climate change. In and 43% of the positive impacts (i.e., damages) in the resources cat- the human health damage, it was half of the share of electricity egory and for between 42% and 44% of the positive impacts in the in each Bio2 pathway. As in the case of electricity, the main non- climate change category; the reason was that the bioreactors con- renewable energy source contributing to resource depletion was sumed a large amount of electricity and steam. The compression natural gas ( 19%) while the main pollutant contributing to cli- and storage stage was the second largest contributor to environ- mate change was CO2 emissions ( 23%). Chemical use (NaOH) mental impacts and its share was between 25% and 26% in climate was the third most contributing source to environmental impact change, and between 26% and 28% in resources. The recovery stage and accounted for between 5% and 12% in the main damage cate- was the third largest contributor to impacts and it share was only gories of concern (i.e., resources and climate change). Water use half of that of the compression and storage stage. Depending on the was also noteworthy as its shares amounted to about 3% of envi- type of feedstock, the pre-treatment stage contributed between ronmental impacts. Transport related activities represented a neg- 11% and 17% and between 12% and 18% of impacts in climate ligible contribution for each pathway (less than 1% of the total change and resources categories, respectively. The hydrolysis environmental). The valorisation of protein residues reduced sig- stages had a very low contribution ( 3%) in the damage categories nificantly the impacts in all four damage categories as shown in of concern (i.e., resources and climate change), while the impacts Fig. 4a. of the purification stage were negligible (less than 0.5%) in all cat- 2690 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694
Fig. 3. Total environmental damages of the BioH2 pathways within the four damage categories: resources (a), climate change (b), ecosystem quality (c), and human health (d). 2 The bars represent the three BioH2 pathways. The impacts are expressed in MJ, kg CO2, PDF m yr, and DALY per kg H2 produced.
egories (Fig. 4b). The waste application portion of each bar is neg- when the co-product is not considered, BioH2 pathways produced 8– ative because the protein residues generated by each feedstock 17% more energy than they consumed in the production phase. Con- type offset emissions from the production of the replaced maize sidering that (1) the ER of BioH2 pathways was still larger than unity grain and soy meal. even when the co-product was excluded, and (2) the ER can be ex-
pected to improve further as the technology matures, BioH2 may of- fer an opportunity in the future to reduce reliance on fossil fuels if 3.4. Energetic performance one assumes that it can be made viable on commercial scale.
The energy balance (energy output/energy input ratio) for H2 from SPP, SSS, and WS with and without co-product credit is given 3.5. Comparison with conventional diesel (CD) and steam reforming of in Table 5. When the co-product (i.e., protein residues) was not methane (SRM-H2) considered it appeared that the energy ratio was 1.08 for WS-H2; The greenhouse gas emissions of the investigated systems were 1.14 for SSS-H2; and 1.17 for SPP-H2. Thus, for every unit of energy compared to those of conventional diesel (CD) and those from con- used to produce H2 via fermentation of agrofood waste or agricul- tural residues, there was an energy gain of between 8% and 17%. ventional steam reforming of methane (SRM-H2). Data on GHG emissions for CD were derived from Stichnothe and Azapagic This modest net gain means that these BioH2 pathways have only a modest effect on greenhouse gas emissions since a higher energy (2009). It was also assumed that diesel and H2 engines have similar ratio implies a lower net carbon dioxide emission. When compar- efficiencies. The results show that the use of BioH2 as an alternative to diesel could offer significant reductions in GHG emissions. The ing the ERs of the three BioH2 pathways under analysis, the BioH2 potential savings were 52% for WS, 54% for SSS, and 56% for SPP. pathway that performed best was the SPP-H2. However, the differ- ence was insignificant with respect to ERs and virtually without These potential savings achieved by BioH2 increase slightly when importance as can be seen from result in Table 5. Therefore a shift compared to steam-reformed H2 of methane (SRM-H2). Indeed, on a well-to-tank assessment such as the one performed in this from SPP based H2 to either SSS or WS based H2 would neither sub- stantially improve nor worsen the energy balance. study, SMR-H2 leads to an additional 4% increase in GHG emissions The energy gain was increased by 23–128% when the co-prod- as compared to CD (Table 6). This makes BioH2 even more attrac- uct (i.e., protein residues) was taken into account (Table 5). SPP- tive. However, this latest result must be interpreted with great care, since on a well-to-wheel assessment SMR-H could offer sig- H2 retained its leading position whereas a reverse ranking was ob- 2 nificant savings compared to conventional diesel owing to the high served between SSS-H2 and WS-H2. This was not surprising given that SSS has the lowest protein content compared to WS and efficiency of fuel cells compared to internal combustion engines. SPP. Under this condition (i.e., when co-product is considered) a shift from SPP-H2 to WS-H2 would reduce the ER by 42%, and a shift 3.6. Comparison with other H2 production pathways to SSS-H2 would reduce the ER by 48%. One of the main drivers for the transition to an H2 economy is the The BioH2 pathways investigated in this study were also com- security of energy supply. The results of this study demonstrate that pared to other H2 production pathways. For the purpose of this S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 2691
Fig. 4. Contributions of processes (a) and different life cycle stages (b) to the environmental impacts for the three BioH2 pathways analysed in this study. The columns from left to right compare the environmental performances of steam potato peels (SPP), sweet sorghum stalk (SSS), and wheat straw (WS) in each damage category. The fill patterns represent the contribution of processes (a) or the different stages (b) of H2 production. Impacts are expressed in percentage (a) and in points (b) where a point is a 2 unit of environmental penalties. Based on Impact 2002+, 1 point represents 0.0071 DALY for human health, 13,700 PDF m yr for ecosystem quality, 9950 kg CO2eq for climate change, and 152,000 MJ for resources.
comparison, these BioH2 pathways were assigned the codes A38 for they emit more than two times more GHGs than the BioH2 path- SPP-H2,A39 for SSS-H2, and A40 for WS-H2. Fig. 5 shows the well-to- ways. SMR-H2 accounts for about 48% of worldwide production tank (WTT) most efficient and less polluting H2 pathways ranked of H2 today (Dunn, 2002). It is a commercial technology not likely according to the ER. The pathways A5 and A7 (i.e., centralised to benefit significantly from further technical improvements in the SMR-H2), which are the most efficient and least polluting pathways efficiency, and so the ER will remain fairly stable. In contrast, BioH2 for H2 production from fossil fuels without carbon sequestration, is an emerging technology where process optimisation will have ER values (i.e., 1.39) slightly higher than those of the path- undoubtedly lead to further energy cost reductions and increases ways A38 (ER = 1.17), A39 (ER = 1.14), and A40 (ER = 1.08). However, in the ER. The GHG emission profiles of pathways with carbon 2692 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694
Table 5 ering only the energy balance and GHG emissions of the systems, Energy balance of the biohydrogen production chains. The columns from left to right they reported an energy ratio of 3.1 and GHG emission intensities denote: the processes, and the different feedstocks. 1 of 3.4 kg CO2 kg H2 for the two-stage bioprocess fed with sugar- Processes Feedstock cane. Our results compared well with those reported by Manish WS SPP SSS and Banerjee (2008). Indeed, when the contributions of major sys-
1 tem differences (i.e., the gas treatment and compression and stor- Process stage (MJ kg H2) Pre-treatment 19.91 11.67 14.56 age stage), were excluded in our study, the resulting energy ratio 1 Hydrolysis 4.13 4.13 4.13 and GHG emission intensities were 2.03 and 3.04 kg CO2 kg H2, Fermentation 45.14 45.14 45.14 respectively. H2 recovery 15.35 15.35 15.35
H2 Purification 0.18 0.18 0.18 Compression and storage 28.12 28.12 28.12 3.8. Sensitivity analyses on key parameters and assumptions Protein residues 33.74 58.88 20.22 Total energy input without 112.83 104.59 107.48 Sensitivity analyses were conducted to assess the effects of the 1 co-product (MJ kg H2) variation of key input parameters and assumptions on the results Total energy input with 79.09 45.71 87.26 1 (i.e., ER and GHG emissions) of the study. The elasticity (i.e., the ra- co-product (MJ kg H2) 1 a a a tio of the change in the results to the change in data) method was Hydrogen output (MJ kg H2) 122 122 122 Energy ratio (without co-product) 1.08 1.17 1.14 used to perform the sensitivity analysis. For simplicity and because Energy ratio (with co-product) 1.54 2.67 1.40 no change in the ranking of BioH2 pathways was observed, only the a The energy output is based on low heating value of hydrogen (i.e., results of the H2 production from SPP are presented. Fig. 6a and b 1 122 MJ kg H2); SPP, steam potato peels; SSS, sweet sorghum stalk; WS, wheat shows that the ER and GHG emissions were most sensitive to straw. changes in H2 yield, protein residues yield, and electricity con- sumption. When the H2 yield, protein residues yield and electricity consumption were varied by 10%, the ER and GHG emissions chan- capture sequestration (A6 and A13) were comparable to those of ged by more than 11% in most cases. The results of the elasticity BioH2 pathways. However, their ERs might in most cases be lower were between 1 to 1.4 for ER, and between 1 and 3 for GHG emis- because of the additional energy required for CO capture. 2 sions. This means that overall, the ER and GHG emissions of BioH2 Renewable H2 production pathways such as hydrogen from pathways significantly change since elasticity greater than or equal wind (pathway A29) and the pathway based on gasification of to one is acceptable. The effect of varying the transport distance on farmed wood waste (pathway A16) outperformed the BioH2 path- ER (Fig. 6a) and GHG emissions (Fig. 6b) was less significant. ways both in terms of ERs and GHG emissions. However, the bio- A sensitivity analysis was also carried out for electricity gener- mass gasification pathways (A A ,A ,A , and A ), and the 14, 15 17 18 34 ation source. It was assumed in this study that all three BioH2 path- pathways based on electrolysis with electricity from biopower ways were using electricity generated from a natural gas plant. plant (A23 and A24) had lower ER values compared to BioH2 path- However, it is possible that electricity could come from other ways, although their GHG emissions performances were better. sources. Sensitivity analyses where these three BioH2 pathways The pathway using nuclear energy (A28) had the lowest GHG emis- were assumed to operate either on the same European electricity sion intensities but it consumed a significant amount of non- mix or on electricity from a renewable source such as hydropower renewable energy as indicated by its low ER value (0.20). Thus, were carried out in order to assess the effect of changing the elec- about five times more energy is needed to produce 1 kg H2 via tricity generation source on ERs and GHG emissions. It appeared the nuclear pathway than is needed with BioH2 pathways. that the ERs and GHG emissions were less sensitive to a change Although it is difficult at this stage of development to pick up a of electricity source from natural gas to European grid mix. How- winner, Fig. 5 shows that BioH2 pathways should be accounted ever, a change in electricity source from natural gas to hydropower among preferred H production pathways owing to their high 2 substantially affects the ERs and GHG emissions of these BioH2 ERs and low GHG emissions. production pathways. Again, no change in ranking of the three
BioH2 pathways was observed. 3.7. Comparison with other studies 3.9. Limitations of the study Manish and Banerjee (2008) performed a net energy analysis on different biological H2 production systems. The boundaries of the There are a number of limitations to this LCA study. This study BioH2 pathways investigated in their study did not include the assesses the life cycle of a technology that currently does not exist gas treatment and the compression and storage processes. Consid- on a commercial scale. Thus, there exists the possibility that the
Table 6
Greenhouse gas emission and saving from BioH2 compared to conventional diesel (CD) and conventional reforming of methane (SRM). The columns from left to right denote: the
CO2 emissions from fuel production, the CO2 emissions from fuel use, the equivalency factor, and total CO2 emissions.
1 GHG Fuel production Fuel use Equivalency factor Total (kg CO2eq kg diesel) GHG reduction (%) 1 1 (kg CO2eq kg )[a] (kg CO2eq kg )[b] (LHVeq)[c] S =[(a + b) c] SCD SP 100 SCD
Fuel types CD (LHV = 43.2 MJ kg 1) 0.57 3.50a 1 4.07 – 1 SRM-H2 (LHV = 122 MJ kg ) 12.08 0 0.35 4.23 3.93 1 SPP-H2 (LHV = 122 MJ kg ) 5.18 0 0.35 1.81 55.53 1 SSS-H2 (LHV = 122 MJ kg ) 5.32 0 0.35 1.86 54.30 1 WS-H2 (LHV = 122 MJ kg ) 5.60 0 0.35 1.96 51.84
CD, conventional diesel; SRM-H2, hydrogen from steam methane reforming, SPP-H2, hydrogen from steam potato peels; SSS-H2, hydrogen from sweet sorghum stalk; WS-H2, hydrogen from wheat straw; CO2, carbon dioxide; GHG, greenhouse gas; LHV, low heating value; SCD, GHG emissions from diesel; SP, GHG emissions from H2 production pathways (i.e., SMR-H2, SPP-H2, SSS-H2, WS-H2). a Based on current efficiency of internal combustion engine. S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 2693
Fig. 5. Well-to-tank comparisons of energy ratios (ER) and greenhouse gas (GHG) emissions of various hydrogen (H2) production pathways. The bars represent the GHG emissions, and the solid line the ER. The codes A1 to A40 next to the horizontal axis represent the H2 production pathways analysed in this study.
Fig. 6. Sensitivity analyses of key parameters and assumptions related to energy ratio (a) and greenhouse gas emissions (b). The solid lines represent the variation of energy ratios or greenhouse gas emissions due to changes in key parameters and assumptions. 2694 S. Njakou Djomo, D. Blumberga / Bioresource Technology 102 (2011) 2684–2694 inclusion of new data might change the outcome, even though we report No. 3, V2.0, Swiss Centre for Life Cycle Inventory. Dübendorf, believe that our estimates were reasonable in those cases where Switzerland. Gabrielle, B., Gagnaire, N., 2008. Life cycle assessment of straw use in bio-ethanol complete data were unavailable. Despites these limitations, we be- production: a case study based on biophysical modelling. Biomass and lieve that the LCA results presented in this study are useful in so far Bioenergy 32, 431–441. IEA, 1998. 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