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Tables Showing Gibbs Free Energy As a Function of Temperature of Formation Reactions

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Tables Showing Gibbs Free Energy As a Function of Temperature of Formation Reactions Appendix A: Tables Showing Gibbs Free Energy as a Function of Temperature of Formation Reactions Table A.1 Gibbs free energy of methane formation at differ- ent temperatures (Adapted from David 2012) C+2H2 ! CH4 T (K) ΔG (kJ/mol) 298.15 À50.53 300 À50.381 400 À41.827 500 À32.525 600 À22.69 700 À12.476 800 À1.993 900 8.677 1000 19,475 1100 30.358 1200 41.294 1300 52.258 1400 63.231 1500 74.2 Table A.2 Gibbs free energy of ethane formation at different temperatures (Adapted from David 2012) 2C + 3H2 ! C2H6 T (K) ΔG (kJ/mol) 298.15 À32.015 300 À31.692 (continued) © Springer International Publishing Switzerland 2017 177 J.L. Silveira (ed.), Sustainable Hydrogen Production Processes, Green Energy and Technology, DOI 10.1007/978-3-319-41616-8 178 Appendix A: Tables Showing Gibbs Free Energy... Table A.2 (continued) 2C + 3H2 ! C2H6 T (K) ΔG (kJ/mol) 400 À13.473 500 5.912 600 26.086 700 46.8 800 67.887 900 89.231 1000 110.75 1100 132.385 1200 154.096 1300 175.85 1400 197.625 1500 219.404 Table A.3 Gibbs free energy of ethanol formation at different temperatures (Adapted from David 2012) 2C + 3H2 + 1/2O2 ! C2H5OH T (K) ΔG (kJ/mol) 298.15 À167.874 300 À167.458 400 À144.216 500 À119.82 600 À94.672 700 À69.023 800 À43.038 900 À16.825 1000 9.539 1100 36 1200 62.52 1300 89.07 1400 115.63 1500 142.185 Table A.4 Gibbs free energy of carbon dioxide formation at different temperatures (Adapted from David 2012) C+O2 ! CO2 T (K) ΔG (kJ/mol) 298.15 À394.373 300 À394.379 400 À394.656 500 À394.914 600 À395.152 (continued) Appendix A: Tables Showing Gibbs Free Energy... 179 Table A.4 (continued) C+O2 ! CO2 T (K) ΔG (kJ/mol) 700 À395.367 800 À395.558 900 À395.724 1000 À395.865 1100 À395.984 1200 À396.081 1300 À396.159 1400 À396.219 1500 À396.264 Table A.5 Gibbs free energy of carbon monoxide formation at different temperatures (Adapted from David 2012) C + 1/2O2 ! CO T (K) ΔG (kJ/mol) 298.15 À137.168 300 À137.333 400 À146.341 500 À155.412 600 À164.48 700 À173.513 800 À182.494 900 À191.417 1000 À200.281 1100 À209.084 1200 À217.829 1300 À226.1518 1400 À235.155 1500 À243.7424 Table A.6 Gibbs free energy of water formation from carbon at different temperatures (Adapted from David 2012) H2 + 1/2O2 ! H2O T (K) ΔG (kJ/mol) 298.15 À228.582 300 À228.5 400 À223.9 500 À219.05 600 À214.008 700 À208.814 800 À203.501 (continued) 180 Appendix A: Tables Showing Gibbs Free Energy... Table A.6 (continued) H2 + 1/2O2 ! H2O T (K) ΔG (kJ/mol) 900 À198.091 1000 À192.603 1100 À187.052 1200 À181.45 1300 À175.807 Appendix B: Adaptation of Cardu and Baica’s Methodology As it is proposed by Cardu and Baica (1999), it is assumed that the ecological efficiency has the following form: n ε ¼ ½c Á φηðÞÁ, indicador ψðÞindicador Constant “c” and the exponent “n” are going to be calculated by the boundary conditions. The Pollutant Indicator is used to quantify the environmental impact of a technology. Its unit is kgCO2(eq)/kgH2. By following the concepts of the authors, it is proposed that φ(η, indicator)isas follows: η φ ¼ ÀÁsystem η þ system indicator where ηsystem is the efficiency of the hydrogen production system. In order to reduce the range of values and approximate the efficiencyÀ curves of various technologies, the authors propose using function ψ indicator )as ψ ¼ lnðÞK À indicator . As the logarithm transforms a high number into a lower number, the distance between extreme points is attenuated. For example, ln 10 ¼ 2.3 and ln 100 ¼ 4.6. Therefore, an interval of [10–100] became [2.3–4.6]. In order to validate the equation, three boundary conditions are adopted, which are different from those defined by Cardu and Baica and with the indicator in kgCO2(eq)/kgH2: Condition 1. If the indicator ¼ 0, ε ¼ 1 for any η. ¼ = ε ¼ η Condition 2. If the indicator 50 kgCO2eqðÞkgH2, 0 for any . This indi- cator corresponds to the impact of Lignite (coal with high carbon content). © Springer International Publishing Switzerland 2017 181 J.L. Silveira (ed.), Sustainable Hydrogen Production Processes, Green Energy and Technology, DOI 10.1007/978-3-319-41616-8 182 Appendix B: Adaptation of Cardu and Baica’s Methodology Condition 3. In this case, the hydroelectric power plant was adopted because the ecological efficiency of this hydrogen production process has already been previously calculated by Braga (2010), Siqueira and Silveira (2011), which corresponds to 0.99. It is obtained the indicator ¼ 0.34 kgCO2(eq)/kgH2 and η ¼ 0:78 (value calculated for the hydrogen production process with electricity from a hydroelectric power plant). If: ψ ¼ lnðÞK À indicator Thus, through condition 2 and formulation 1, it is obtained: ψ ¼ lnðÞ 51 À indicator consequently: "# η n ε ¼ c Á ÀÁsystem Á lnðÞ 51 À indicator η þ system indicator From condition 1, it is found that 1 ¼ ½c Á lnðÞ 51 n Then c ¼ 0.25 From condition 3, it is found that n ¼ 0.023 for ε ¼ 0:99n and the ecological efficiency equation for hydrogen production process is written as: "#: η 0 023 ε ¼ 0:25 Á ÀÁsystem Á lnðÞ 51 À indicator η þ system indicator where: Indicator: [kgCO2(eq)/kgH2] References Braga LB (2010) Ana´lise econoˆmica do uso de ce´lula a combustı´vel para acionamento de oˆnibus urbano. Dissertation—Curso de Engenharia Mec^anica, Departamento de Energı´a, UNESP, Guaratingueta´ Cardu M, Baica M (1999) Regarding a global methodology to estimate the energy-ecologic efficiency of thermopower plants. Energy Convers Manage 40:71–87 David R (2012) CRC handbook of chemistry and physics, 87th edn. Internet version 2007 Siqueira RBP, Silveira JL (2011) Eficieˆncia ecolo´gica aplicada a uma PCH em func¸~ao da operac¸~ao de um reservato´rio hipote´tico. PCH Notı´cias e SHP News 50:24–28 Index A E Algae growth, 104, 105 Ecological efficiency Annuity factor, 109–111 electrolysis, 134–135 hydrogen production from microalgae, 135–136 B steam reforming process, 129–134 Belo Monte hydroelectric power plant Economic analysis, 111–124 project, 140 Economic indicators, 162–163 Biocrude, 154 Economical comparison, 123–124 Biogas, 77, 79, 82, 84–86, 88, 90, 92, 94, 95, Electrolysis, 2, 128, 135, 136, 153 99–100, 106, 107, 111, 115, 124, 175, 176 Electrolytic process, 116–120 ecological efficiency, 175 electrolysis using hydroelectric power, 103 steam reforming process, 133–134, 136 electrolysis using solar energy, 103 Biological hydrogen production, 155 electrolyzer, 102 Biophotolysis, 155 using wind power, 102 Brazil, 104, 111 Electrolyzer, 101–103, 109, 110, 116, 124 Brazil’s energy policy, 145, 147, 148, 171 Endothermic process, 87 Brazilian conditions, 145, 149, 152, 157, Environmental analysis, 127, 128 162, 168, 171 Environmental indicators, 161–162 Eolic energy, 135 Equilibrium constant (K), 2, 77, 78, 87, C 89, 91, 92 Carbon cycle, 130, 131, 136 Ethanol fuel car, 149 Carbon dioxide (CO2), 127, 128, 130–133, Ethanol industry, 146 135, 136, 159 Carbon monoxide, 151, 152, 154 Chemoheterotrophic species, 154 F Closed reactors, 105 Fossil fuels, 142, 147, 153, 154, 157, 159, Coal gasification, 159, 165, 166, 168 160, 162, 165, 166, 168, 169 Fuel cell program, 150 D Degree of advancement (α), 2, 78, 87–94 G Direct ethanol fuel cells (DEFC), 150 Gas emissions, 127 Dry reforming reactions, 99 Gasification, 154, 159 © Springer International Publishing Switzerland 2017 183 J.L. Silveira (ed.), Sustainable Hydrogen Production Processes, Green Energy and Technology, DOI 10.1007/978-3-319-41616-8 184 Index GHG emissions. See Green house gas (GHG) I emissions Indicators Gibbs free energy economic, 162–163 reforming process and shift reaction, environmental, 161–162 82–86 social, 163 change as function of temperature, 79–94 weighting of criteria/indicators, 164 degree of advancement, 87–94 Interest rate, 111–123 equilibrium Constant, 87 Internal combustion engines (ICE), 151, 168 Gibbs free energy change (ΔGo), 77 Investment costs, 124 Global sustainable development, 141 Global warming potential (GWP), 128 Green house gas (GHG) emissions, 143, L 165, 167 Laboratory for optimization of energy Group of optimization of energy systems systems (LOSE), 79, 111 (GOSE ), 170 Life cycle assessment (LCA), 128, 145 Lower heating value (LHV), 96–100 H M H2 production process, 100 Hydraulic power, 165 Maintenance cost, 109, 110, 124 Hydroelectric power, 103, 128 Methane, 159 Hydroelectric power plants, 135, 136 Microalgae, 135–136 Hydrogen, 1–3 Microwave plasma reforming, 101 production from algae, 104–106, 176 Microwave plasma sources (MPSs), 101 production from microalgae, 104 Multi-criteria analysis (MCA), 139, 143–145, production process from algae, 156–158, 160, 161, 166, 168, 170, 171 121–124 storage and distribution, 155–156 Hydrogen cost, 109–124, 175 N Hydrogen from biological processes Natural gas (NG), 77, 79, 82, 83, 85, 86, 88, 90, (biophotolysis) (HBP), 159 91, 93–95, 97–99, 101, 106, 107, 111, Hydrogen from coal gasification with 113, 114, 124, 160, 165, 175 carbon capture (HCGCC), 159 steam reforming process, 131–132, 136 Hydrogen from electrolysis powered by National Renewable Energy Laboratory renewable sources (HEPRS), 159 (NREL), 116 Hydrogen from the steam reforming of ethanol (HSRE), 160 Hydrogen from the steam reforming of O natural gas (HSRNG), 160 Organisation for Economic Co-operation and Hydrogen production, 128, 134, 136 Development (OECD), 148 bacteria/algae, 165 Operation cost, 109, 110, 116, 124 biological processes, 154–155 Operation time, 111–113, 115–120, 122, 123 in Brazil, 146–156 electrolysis, 153 fuel cells, 150, 152, 165 P pipelines, 155 Payback period, 112, 114, 115, 117, 119, process, 166 120, 122 pyrolysis/gasification, 154 Performance
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