Abstract
In China, as an important source of renewable energy, the standard biogas production module (SBPM) plays a key role and accounts for the majority of biogas production. Given its greater environmental pollution, it is particularly important to assess its quantitative sustainability status for optimizing system design and policy-making. This paper conducted the sustainability assessments of the open-loop standard biogas production module (OL-SBPM) and closed-loop standard biogas production module (CL-SBPM). Compared with the OL-SBPM system, the CL-SBPM system integrates a power generation subsystem. To evaluate two types of systems quantitatively, energy, emergy and entropy methods have been utilized in this study. The results illustrated that only partial indicators are more sustainable in the CL-SBP system rather than all indicators by comparing with the OL-SBPM system, which is contrary to the prevailing view. Taking the information entropy as an example, the equivalent information entropy flow values of the OL-SBPM and CL-SBPM systems are 2.04 × 1025 bits and 1.47 × 1025 bits, respectively, which also demonstrate that the OL-SBPM system has more useful information resulting in higher degree of sustainability. Therefore, these two systems need to be selected according to the existing conditions, rather than the subjective ideation.
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References
Abel, T. (2013). Emergy evaluation of DNA and culture in “information cycles.” Ecological Modelling, 251, 85–98.
Agricultural statistics of China in 2017. http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp
Amaral, L. P., Martins, N., & Gouveia, J. B. (2016). A review of emergy theory, its application and latest developments. Renewable and Sustainable Energy Reviews, 54, 882–888.
Archishman, B., Richen, L., Karthik, R., Richard, O., Ao, X., & Jerry, D. M. (2019). How to optimise photosynthetic biogas upgrading: a perspective on system design and microalgae selection. Biotechnology Advances, 37, 107444–107451.
Avery, J. S. (2012). Information Theory and Evolution. World Scientific Publishing.
Aziz, N. I. H. A., & Hanafiah, M. M. (2020). Life cycle analysis of biogas production from anaerobic digestion of palm oil mill effluent. Renewable Energy, 145, 847–857.
Bejan, A., & Lorente, S. (2010). The constructal law of design and evolution in nature. Philosophical Transaction of the Royal Society B, 365, 1335–1347.
Bejan, A., & Lorente, S. (2013). Constructal law of design and evolution: Physics, biology, technology, and society. Journal of Applied Physics, 2013, 113–123.
Binyue, Z., & Bin, C. (2017). Sustainability accounting of a household biogas project based on emergy. Applied Energy, 194, 819–831.
Brown, M. T. & Bardi, E. (2001). Handbook of emergy evaluation: A compendium of data for emergy computation in a series of folios, Folio. #3. Center for Environmental Policy, University of Florida, USA.
Brown, M. T., Raugei, M., & Ulgiati, S. (2012). On boundaries and “investments” in emergy synthesis and LCA: A case study on thermal vs. photovoltaic electricity. Ecological Indicators, 15, 227–235.
Brown, M. T., & Ulgiati, S. (2004). Emergy analysis and environmental accounting. Encyclopedia Energy, 2, 329–354.
Burcu, G., Joseph, S., Paul, D., Cathal, C., & Jenny, L. (2019). Pre-treatments to enhance biogas yield and quality from anaerobic digestion of whiskey distillery and brewery wastes: A review. Renewable and Sustainable Energy Reviews, 113, 109281.
Campbell, D. E. (2016). Emergy baseline for the earth: A historical review of the science and a new calculation. Ecological Modeling, 339, 96–125.
Cao, C., & Feng, X. (2007). Distribution of emergy indices and its application. Energy and Fuels, 21, 1717–1723.
Chen, W., et al. (2016). Life cycle-based emergy analysis on China’s cement production. Journal of Cleaner Production, 131, 272–279.
Chen, W., Zhong, S., Geng, Y., Chen, Y., Cui, X., Wu, Q., Pan, H., Wu, R., Sun, L., & Tian, X. (2017). Emergy based sustainability evaluation for Yunnan Province, China. Journal of Cleaner Production, 162, 1388–1397.
China statistical yearbook. (2019). http://www.stats.gov.cn/tjsj/ndsj/
Congguang, Z., & Ling, Q. (2018). Comprehensive sustainability assessment of a biogas-linked agro-ecosystem: A case study in China. Clean Technologies and Environmental Policy, 20, 1847–1860.
Daniela, T., Kay, S., Stefan, M., & Thomas, H. (2020). Governance of sustainability in the German biogas sector: Adaptive management of the renewable energy Act between agriculture and the energy sector. Energy Sustainability and Society, 10, 3.
Derovil, A. S. F., Laís, R. G. O., Maurício, C. P., Waldir, N. S., Maurício, A. M. S., & José, F. T. J. (2020). Energy sustainability of supply centers from the codigestion of organic waste. Detritus, 09, 76–82.
Dewar, R. (2003). Information theory explanation of the fluctuation theorem, maximum entropy production and self-organized criticality in non-equilibrium stationary states. J. Phys. A Math. General, 36(3), 631–640.
Dinko, D., & Ivona, H. (2020). Anaerobic digestate treatment selection model for biogas plant costs and emissions reduction. Processes, 8, 142. https://doi.org/10.3390/pr8020142
Edmund, T., John, B. K., & Wilson, B. M. (2019). Long-life performance of biogas systems for productive applications: The role of R&D and policy. Energy Reports, 5, 579–583.
Elalami, D., Carrere, H., Monlau, F., Abdelouahdi, K., Oukarroum, A., & Barakat, A. (2019). Pretreatment and co-digestion of wastewater sludge for biogas production: Recent research advances and trends. Renewable and Sustainable Energy Reviews, 114, 109287.
Erika, W., Pasi, R., Jarkko, P., & Vilja, V. (2019). Is biogas an energy or a sustainability product. Business opportunities in the Finnish biogas branch. Journal of Cleaner Production, 233, 1344–1354.
Fath, B. D., Patten, B. C., & Choi, J. S. (2001). Complementarity of ecological goal functions. Journal of Theoretical Biology, 208, 493–506.
Fatih, T., & Yavuz, D. (2020). Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technologies and Environmental Policy, 22, 713–724.
Ferreira, A. F., Toledo-Cervantes, A., de Godos, I., Gouveia, L., & Munoz, R. (2019). Life cycle assessment of pilot and real scale photosynthetic biogas upgrading units. Algal Research, 44, 101668.
Florian, K., Roh, P. L., & Bernd, M. (2020). Life cycle assessment of global warming potential, resource depletion and acidification potential of fossil, renewable and secondary feedstock for olefin production in Germany. Journal of Cleaner Production, 250, 119484.
Harte, J., & Newman, E. A. (2014). Maximum information entropy: A foundation for ecological theory. Trends in Ecology and Evolution, 29, 384–389.
Hermanowicz, S. W. (2008). Sustainability in water resources management: Changes in meaning and perception. Sustainability Science, 3, 181–188.
Huan, Z., Xiajie, Z., Lizhu, G., Kesi, L., Ding, H., Yuejuan, Y., Jiahuan, L., Shu, X., Cong, Z., Shiming, T., & Kun, W. (2019). Assessing the efficiency and sustainability of wheat production systems in different climate zones in China using emergy analysis. Journal of Cleaner Production, 235, 724–732.
Jae, M. L., & William, W. B. (2017). Building emergy analysis of Manhattan: Density parameters for high-density and high-rise developments. Ecological Modelling, 363, 157–171.
Jaynes, E. T. (1957a). Information theory and statistical mechanics. Physical Review, 106, 620–630.
Jaynes, E. T. (1957b). Information theory and statistical mechanics. II. Physical Review, 108, 171–190.
Jia, C., Congguang, Z., Jiaming, S., & Ling, Q. (2019). Sustainability accounting for the construction and operation of a plant-scale solar-biogas heating system based on emergy analysis. Energy Research, 43, 3806–3822.
Junsheng, Y., Xuemei, J., Xingzhong, Y., Xiaofeng, W., Bo, L., & Shuangshuang, L. (2017). Design of a multiplexed system for domestic wastewater of Happy Farmer’s Home (HFH) and environmental evaluation using the emergy Analysis. Journal of Cleaner Production, 156, 729–740.
Kim, I.-T. (2019). simultaneous denitrification and bio-methanol production for sustainable operation of biogas plants. Sustainability, 11, 6658. https://doi.org/10.3390/su11236658
Kleidon, A., Malhi, Y., & Cox, P. M. (2010). Maximum entropy production in environmental and ecological systems. Proceedings of the Royal Society of London Series B Biological Sciences, 365, 1297–1302.
Landauer, R. (1990). Maxwell’s Demon: Entropy, Information. Princeton University Press.
Lesne, A. (2014). Statistical entropy: At the crossroads between probability, information theory, dynamical systems and statistical physics. Mathematical Structure in Computer Science, 24, 240311.
Lucia, U. (2013). Stationary open systems: A brief review on contemporary theories on irreversibility. Physica a: Statistical Mechanics and Its Applications, 392, 1051–1062.
Luo, Z., Zhao, J., Yao, R., & Shu, Z. (2015). Emergy-based sustainability assessment of different energy options for green buildings. Energy Conversion and Management, 100, 97–102.
Martina, P., Tareq, A. H., & Sharon, J. (2020). Assessing the success and failure of biogas units in Israel: Social niches, practices, and transitions among Bedouin villages. Energy Research and Social Science, 61, 101328.
Meillaud, F., Gay, J. B., & Brown, M. T. (2005). Evaluation of a building using the emergy method. Solar Energy, 79, 204–212.
Merlin, G., & Boileau, H. (2017). Eco-efficiency and entropy generation evaluation based on emergy analysis: Application to two small biogas plants. Journal of Cleaner Production, 143, 257–268.
Merlin, G., Kohler, F., Bouvier, M., Lissolo, T., & Boileau, H. (2012). Importance of heat transfer in an anaerobic digestion plant in a continental climate context. Bio Resource Technology, 124, 59–67.
Niklas, P. E. K., Maya, H., Fawzi, H., & Marie, M. (2019). Business modelling in farm-based biogas production: Towards network-level business models and stakeholder business cases for sustainability. Sustainability Science, 14, 1071–1109.
Odum, H. T. (1996). Environmental Accounting. Emergy and Environmental Decision Making [M] (pp. 32–34). John Wiley.
Pan, H., Zhuang, M., Geng, Y., Wu, F., & Dong, H. (2019). Emergy-based ecological footprint analysis for a mega-city: The dynamic changes of Shanghai. Journal of Cleaner Production, 210, 552–562.
Ravi, S. S., William, W. B., Daniel, E. C., & Charlie, D. C. (2012). Re(De)fining net zero energy: Renewable emergy balance in environmental building design. Building and Environment, 47, 300–315.
Ricardo, L. C., Pooja, Y., Natxo, G.-L., Robert, L., Gert, N., Rocio, D.-C., Venkata, K. K. U., Christoer, B., & Dimitris, A. (2020). Environmental sustainability of bioenergy strategies in Western Kenya to address household air pollution. Energies, 13, 719. https://doi.org/10.3390/en13030719
Richard, J. C., Stephanie, L., & Jay, F. M. (2011). Emergy analysis of biogas production and electricity generation from small-scale agricultural digesters. Ecological Engineering, 37, 1681–1691.
Rizwan, R., Abdullah, Y., Yubo, W., Amtul, B. T., Sajid, R. A., Fizza, T., & Yuehong, S. (2019). Environmental impact and economic sustainability analysis of a novel anaerobic digestion waste-to-energy pilot plant in Pakistan. Environmental Science and Pollution Research, 26, 26404–26417.
Rodrigo, S., Murillo, V. B., José, G. P. R., Cassiano, M. P., Leila, M. L., & Antonio, C. F. (2018). Life cycle assessment of electricity from biogas: A systematic literature review. Environmental Progress and Sustainable Energy. https://doi.org/10.1002/ep.13133
Serizawa, H., Amemiya, T., & Itoh, K. (2014). Tree network formation in Poisson equation models and the implications for the maximum entropy production principle. Natural Science, 6, 514.
Shannon, E. C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.
Shaoqing, C., & Bin, C. (2012). Sustainability and future alternatives of biogas-linked agrosystem (BLAS) in China: An emergy synthesis. Renewable and Sustainable Energy Reviews, 16, 3948–3959.
Shaoqing, C., & Bin, C. (2014). Energy efficiency and sustainability of complex biogas systems: A 3-level emergetic evaluation. Applied Energy, 115, 151–163.
Simon, J. R., & Jafar, A. Z. (2020). Contribution of encouraging the future use of biomethane to resolving sustainability and energy security challenges: The case of the UK. Energy for Sustainable Development, 55, 48–55.
Sinéad, O., & Daniela, T. (2020). Energy crops in regional biogas systems: An integrative spatial LCA to assess the influence of crop mix and location on cultivation GHG emissions. Sustainability, 12, 237. https://doi.org/10.3390/su12010237
Skene, K. R. (2015). Life’s a gas: A thermodynamic theory of biological evolution. Entropy, 17, 5522–5548.
Tian, X., Geng, Y., & Ulgiati, S. (2017). An emergy and decomposition assessment of China-Japan trade: Driving forces and environmental imbalance. Journal of Cleaner Production, 141, 359–369.
Tilley, D. R., Agostinho, F., Campbell, E., Ingwersen, W., Lomas, P., Winfrey, B., Zuccaro, A., & Zhang, P., (2012). The ISAER transformity database. International Society for Advancement of Emergy Research. www.emergydatabase.org (Accessed 25 Apr 2020).
Tong, Z., John, C., & Matthew, C. (2019). Promoting agricultural biogas and biomethane production: Lessons from cross-country studies. Renewable and Sustainable Energy Reviews, 114, 109332.
Ulgiati, S., & Brown, M. T. (2002). Quantifying the environmental support for dilution and abatement of process emissions: The case of electricity production. Journal of Cleaner Production, 10, 335–348.
Vita, T., Kestutis, V., Virmantas, P., Kestutis, N., Vidmantas, Z., & Zydre, K. (2020). The effect of digestate and mineral fertilisation of cocksfoot grass on greenhouse gas emissions in a cocks foot-based biogas production system. Energy Sustainability and Society, 10, 13.
Wang, Y., Lin, C., Li, J., Duan, N., Li, X., & Fu, Y. (2013). Emergy analysis of biogas systems based on different raw materials. The Scientific World Journal, 2013, 1–9.
Wang, Y., Zhang, X., Liao, W., Jun, Wu., Yang, X., Shui, W., Deng, S., Zhang, Y., Lin, L., Xiao, Y., Xiaoyu, Yu., & Peng, H. (2018). Investigating the impact of waste reuse on the sustainability of municipal solid waste (MSW) incineration industry using emergy approach: A case study from Sichuan province, China. Waste Management, 77, 252–267.
Wu, X. F., Yang, Q., Xia, X. H., Wu, T. H., Wu, X. D., Shao, L., Hayat, T., Alsaedi, A., & Chen, G. Q. (2015). Sustainability of a typical biogas system in China: Emergy-based ecological footprint assessment. Ecological Informatics, 26, 78–84.
Xi, Y., & Qin, P. (2006). Emergy value evaluation on rice-duck organic farming mode. Chinese Journal of Applied Ecology, 17(2), 237–242.
Xiaolong, W., Yuanquan, C., Peng, S., Wangsheng, G., Feng, Q., Xia, W., & Jing, X. (2014). Efficiency and sustainability analysis of biogas and electricity production from a large-scale biogas project in China: An emergy evaluation based on LCA. Journal of Cleaner Production, 65, 234–245.
Xihui, W., Faqi, W., Jia, W., & Lu, S. (2015). Emergy-based sustainability assessment for a five-in-one integrated production system of apple, grass, pig, biogas, and rainwater on the Loess Plateau, Northwest China. Agroecology and Sustainable Food Systems, 39, 666–690.
Xihui, W., Faqi, W., Xiaogang, T., & Bi, J. (2013). Emergy-based sustainability assessment of an integrated production system of cattle, biogas, and greenhouse vegetables: Insight into the comprehensive utilization of wastes on a large-scale farm in Northwest China. Ecological Engineering, 61, 335–344.
Xihui, W., Faqi, W., Xiaogang, T., Jia, W., Lu, S., & Xiaoyu, P. (2015). Emergy and greenhouse gas assessment of a sustainable, integrated agricultural model (SIAM) for plant, animal and biogas production: Analysis of the ecological recycling of wastes. Resources, Conservation and Recycling, 96, 40–50.
Xue, S., Song, J., Wang, X., Shang, Z., Sheng, C., Li, C., Zhu, Y., & Liu, J. (2020). A systematic comparison of biogas development and related policies between China and Europe and corresponding insights. Renewable and Sustainable Energy Reviews, 117, 109474–109481.
Yang, J., & Chen, B. (2014). Emergy analysis of a biogas-linked agricultural system in rural China-A case study in Gongcheng Yao Autonomous County. Applied Energy, 118, 173–182.
Yen, J. D. L., Paganin, D. M., Thomson, J. R., & Mac Nally, R. (2014). Thermodynamic extremization principles and their relevance to ecology. Austral Ecology, 39, 619–632.
Yi, H., Srinivasan, R. S., & Braham, W. W. (2015). An integrated energy emergy approach to building form optimization: Use of EnergyPlus, emergy analysis and Taguchi regression method. Building and Environment, 84, 89–104.
Yu, X., Geng, Y., Dong, H., Fujita, T., & Liu, Z. (2016). Emergy-based sustainability assessment on natural resource utilization in 30 Chinese provinces. Journal of Cleaner Production, 133, 18–27.
Zhang, X., et al. (2017). An environmental sustainability assessment of China’s cement industry based on emergy. Ecological Indicators, 72, 452–458.
Zhang, X.-H., Zhang, R., Jun, Wu., Zhang, Y.-Z., Lin, L.-L., Deng, S.-H., Li, Li., Yang, G., Xiao-Yu, Yu., Qi, H., & Peng, H. (2016). An emergy evaluation of the sustainability of the Chinese crop production system during 2000–2010. Ecological Indicators, 60, 622–633.
Zhou, J. B., Jiang, M. M., Chen, B., & Chen, G. Q. (2009). Emergy evaluations for constructed wetland and conventional wastewater treatments. Communications in Nonlinear Science and Numerical Simulation, 14, 1781–1789.
Zhou, S. Y., Zhang, B., & Cai, Z. F. (2010). Emergy analysis of a farm biogas project in China: A biophysical perspective of agricultural ecological engineering. Communications in Nonlinear Science and Numerical Simulations, 15, 1408–1418.
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Appendices
Appendix 1
All calculated details from 1 to 17 in OL-SBPM system (Table 3).
-
1.
Sunlight energy = plant area (2 × 104m2)*Avg. annual solar radiation (5.01 × 109 J/m2)*Albedo(0.13). UEVs of sunlight = 1.00 sej/J by definition. Sunlight emergy = 1.3 × 1013sej.
-
2.
Rain chemical potential energy = plant area(2 × 104m2)*annual rainfall (0.59 m)*Evaporation rate (60.0%)*water density (1000 kg/m3)*Gibbs free energy (4.94 × 103 kJ/kg); UEV = 2.35 × 104sej/J (Jae & William, 2017). Rain chemical potential emergy = 3.5 × 1010 × 2.35 × 104 = 8.23 × 1014sej.
-
3.
Rain geopotential energy = plant area (2 × 104m2)*annual rainfall (0.59 m)*Runoff rate (40%)*water density (1000 kg/m3)*average elevation (4 m)*gravity (9.8 m/s2); UEV = 2.79 × 104sej/J (Jae & William, 2017). Rain geopotential emergy = 1.85 × 108 × 2.79 × 104sej = 5.16 × 1012sej.
-
4.
Wind kinetic energy = plant area (2 × 104m2)*air density (1.29 kg/m3) *drag coefficient (0.001) * velocity of geostrophic wind3(27)* (3.15 × 107 s/year). UEV = 1.9 × 103sej/J (Jae & William, 2017). Wind kinetic emergy = 2.2 × 1010 × 1.9 × 103sej = 4.18 × 1013sej.
-
5.
Geothermal energy = plant area (2 × 104m2)*heat flow (1.45 × 106 J/(M2*year). UEV = 3.44 × 104sej/J (Brown & Bardi, 2001). Geothermal emergy = 2.9 × 1010 × 3.44 × 104sej = 9.98 × 1014sej.
-
6.
Dairy wastewater emergy
As the major effluent, COD of dairy wastewater needs to be calculated in the open loop SBPM process. Therein, 75% of water is responsible for washing project and 25% of others for chemical reaction of dairy wastewater. The average input flow is about 25 m3/d and COD amount load is 15.8 kg/m3.
$${\text{The amount of COD}} = {\text{25 m}}^{{3}} /{\text{d}} \times {15}.{\text{8 kg}}/{\text{m}}^{{3}} \times {\text{365d}} = {1}.{44} \times {1}0^{{5}} {\text{kg}}/{\text{y}}.$$Lactic acid molecular mass = 90 g.mol−1. The free enthalpy of lactic acid:
$$\Delta H_{f}^{0} ({\text{C}}_{3} {\text{H}}_{6} {\text{O}}_{3} ) = - 672\;{\text{KJ}} \cdot {\text{mol}}^{ - 1} = 7.47 \times 10^{6} {\text{J/kg}}$$The energy of lactic acid in the dairy wastewater = 1.44 × 105 kg/y × 7.47 × 106 J/kg = 1.08 × 1012 J/y.
Unit emergy value of lactic acid is 4.83 × 105seJ/J (Zhou et al., 2009).
Emergy of lactic acid in the dairy wastewater = 1.08 × 1012 J/y × 4.83 × 105seJ/J = 5.22 × 1017sej/y.
-
7.
Concrete amount calculation
See Table
8.
-
8.
Reinforced fibers amount calculation
See Table
-
9.
Steel amount calculation
See Table
10.
-
10.
PVC amount calculation
PVC pipe is a vital part in open-loop SBPM system and the collected data are about 110 m lengths (60 mm diameter pipe and 100 diameter pipe), with 25 MWh/m3 and the lifespan is designed to be 15 years.
$${\text{PVC}}_{{{\text{amount}}}} = {{(\pi } \mathord{\left/ {\vphantom {{(\pi } {4) \times (D_{{{\text{outside}}}}^{2} - D_{{{\text{inside}}}}^{2} ) \times L \times \rho (1.4 \times 10^{3} {\text{kg/m}}^{3} }}} \right. \kern-\nulldelimiterspace} {4) \times (D_{{{\text{outside}}}}^{2} - D_{{{\text{inside}}}}^{2} ) \times L \times \rho (1.4 \times 10^{3} {\text{kg/m}}^{3} }}) = 1.1\;{\text{t}}$$Unit emergy values of PVC = 7.46 × 1015 sej/t (Tilley et al., 2020)
$${\text{PVC emergy}} = {1}.{1} \times {7}.{46} \times {1}0^{{{15}}} {\text{sej}} = {8}.{21} \times {1}0^{{{15}}} {\text{sej}}$$ -
11.
Transportation amount calculation (material and device)
In Table
11, the material and devices transport data have been listed in open-loop SBPM system. Meanwhile, an assumption is made: 12L fuel per 50km for the transport (35MJ/L).
Unit emergy values of transportation = 8.4 × 104 sej/j (Tilley et al., 2020)
$${\text{Transportation emergy}} = {2}.{16} \times {1}0^{{9}} \times {8}.{4} \times {1}0^{{4}} {\text{sej}} = {1}.{81} \times {1}0^{{{14}}} {\text{sej}}$$ -
12.
Power consumption amount calculation (electricity)
See Table
12.
-
13.
Service technician emergy
In China, the unit emergy value of service is 7.96×1009sej/$. The average cost of the machine service is 5×1005$/y and the working time is about 40 h.
$${\text{Emergy}}_{t} = 7.96 \times 10^{9} \times 5 \times 10^{5} = 3.98 \times 10^{15} {\text{Sej/year}}$$ -
14.
Service engineer emergy
In China, the unit emergy value of service is 7.96×1009sej/$. The average cost of the engineering service is 6.3×1005$/y and the working time is about 40 h.
$$E_{e} = 7.96 \times 10^{9} \times 6.3 \times 10^{5} = 5.01 \times 10^{15} {\text{sej/year}}$$ -
15.
Biogas emergy
The biogas amount can be collected by the flow meter and was 75 m3/J. According to the standard biogas energy (2.1×107 J/m3) and transformity (1.68×106 sej/J) (Merlin et al., 2012), the biogas emergy can be obtained by using the following equation.
$$E_{{{\text{biogas}}}} = 75 \times 365 \times 2.1 \times 10^{7} \times 1.68 \times 10^{6} {\text{sej/year}} = 9.66 \times 10^{17} {\text{sej/year}}$$ -
16.
Effluent and sludge emergy
There are two types of residues in the outflows, including effluent and sludge, respectively. The inflow loss rate of COD is 95% and the transformity is 4.83×105sej/J (Zhou et al., 2009). Effluent and sludge emergy can be got by as the following calculation.
Appendix 2
All calculated details from 1 to 28 in CL-SBPM system (Table 4).
-
1.
Sunlight emergy has been shown in “Appendix 1”.
-
2.
Rain chemical potential emergy has been shown in “Appendix 1”.
-
3.
Rain geopotential emergy has been shown in “Appendix 1”.
-
4.
Wind kinetic emergy has been shown in “Appendix 1”.
-
5.
Geothermal emergy has been shown in “Appendix 1”.
-
6.
Dairy wastewater emergy has been shown in “Appendix 1”.
-
7.
Crops residues emergy (Tilley et al., 2020):
$$E_{{{\text{crop}}}} = 5.73 \times 10^{11} \times 1.9 \times 10^{5} = 1.09 \times 10^{17} {\text{sej/year}}$$ -
8.
Concrete emergy has been shown in “Appendix 1”.
-
9.
Reinforced fibers emergy has been shown in “Appendix 1”.
-
10.
Steel emergy has been shown in “Appendix 1”.
-
11.
PVC emergy has been shown in “Appendix 1”.
-
12.
Asphalt emergy (Tilley et al., 2020):
$$E_{{{\text{asphalt}}}} = 200 \times 6.02 \times 10^{14} = 1.2 \times 10^{17} {\text{sej/year}}$$ -
13.
Polyane emergy (Tilley et al., 2020):
$$E_{{{\text{polyane}}}} = 0.67 \times 3.44 \times 10^{15} = 2.3 \times 10^{15} {\text{sej/year}}$$ -
14.
Plastic emergy (Tilley et al., 2020):
$$E_{{{\text{plastic}}}} = 2.41 \times 1.25 \times 10^{15} = 3.01 \times 10^{16} {\text{sej/year}}$$ -
15.
Waterproofing emergy (Tilley et al., 2020):
$$E_{{{\text{crop}}}} = 3.59 \times 1.25 \times 10^{15} = 4.49 \times 10^{16} {\text{sej/year}}$$ -
16.
Compactor emergy:
The power of compactor is 250kw/h and the operating time is 130 h. UEVs of compactor are 1.43×105 sej/J (Tilley et al., 2020).
$$E_{{{\text{compactor}}}} = 250 \times 130 \times 3.6 \times 10^{6} {\text{J}} \times 1.43 \times 10^{5} {\text{sej/J}} = 1.67 \times 10^{16} {\text{sej/year}}$$ -
17.
Mobile crane emergy:
The power of mobile crane is 200kw/h and the operating time is 24 h. UEVs of mobile crane are 1.43×105 sej/J (Tilley et al., 2020).
$$E_{{\text{mobile - crane}}} = 200 \times 24 \times 3.6 \times 10^{6} J \times 1.43 \times 10^{5} {\text{sej/J}} = 2.47 \times 10^{15} {\text{sej/year}}$$ -
18.
Perforator emergy:
The power of perforator is 2.5 kw/h and the operating time is 100 h. UEVs of perforator are 2.54×105 sej/J (Derovil et al., 2020).
$$E_{{{\text{perforator}}}} = 2.5 \times 100 \times 3.6 \times 10^{6} {\text{J}} \times 2.54 \times 10^{5} {\text{sej/J}} = 2.29 \times 10^{14} {\text{sej/year}}$$ -
19.
Vibrator emergy:
The power of vibrator is 1.1 kw/h and the operating time is 120 h. UEVs of vibrator are 2.54×105 sej/J (Derovil et al., 2020).
$$E_{{{\text{vibrator}}}} = 1.1 \times 120 \times 3.6 \times 10^{6} {\text{J}} \times 2.54 \times 10^{5} {\text{sej/J}} = 1.21 \times 10^{14} {\text{sej/year}}$$ -
20.
Air compressor emergy:
The power of air compressor is 3.5 kw/h and the operating time is 110 h. UEVs of air compressor are 2.54×105 sej/J (Derovil et al., 2020).
$$E_{{\text{air - compressor}}} = 3.5 \times 110 \times 3.6 \times 10^{6} {\text{J}} \times 2.54 \times 10^{5} {\text{sej/J}} = 3.52 \times 10^{14} {\text{sej/year}}$$ -
21.
Transportation emergy has been shown in “Appendix 1”.
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22.
Power consumption has been shown in “Appendix 1”.
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23.
Biogas generator emergy:
The Biogas generator amount is 3.5×104 $. UEVs are 2.45×1012 sej/$ (Tilley et al., 2020).
$$E_{{\text{Biogas - generator}}} = 3.5 \times 10^{4} \$ \times 2.45 \times 10^{12} {\text{sej/}}\$ = 8.58 \times 10^{16} {\text{sej}}$$ -
24.
Oil emergy:
The oil amount is 4.5×1010 J. UEVs are 8.39×104 sej/J (Tilley et al., 2020).
$$E_{{{\text{oil}}}} = 4.5 \times 10^{10} {\text{J}} \times 8.39 \times 10^{4} {\text{sej/J}} = 3.78 \times 10^{15} {\text{sej}}$$ -
25.
Maintenance emergy:
The maintenance cost is 5.18×105 $. UEVs are 3.38×1012 sej/$ (Tilley et al., 2020).
$$E_{{{\text{Maintenance}}}} = 5.18 \times 10^{4} \$ \times 3.38 \times 10^{12} {\text{sej}}/\$ = 1.75 \times 10^{17} {\text{sej}}$$ -
26.
Biogas emergy has been shown in “Appendix 1”.
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27.
Effluent and sludge emergy has been shown in “Appendix 1”.
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28.
Power generation emergy:
The power of generation is 2.8 kw/h and the operating time is 10×106 hours. UEVs of power generation are 1.59×105 sej/J (Xi and Qin, 2006).
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Zhang, H., Asutosh, A.T. & Zhang, J. A quantitative sustainable comparative study of two biogas systems based on energy, emergy and entropy methods in China. Environ Dev Sustain 24, 13583–13609 (2022). https://doi.org/10.1007/s10668-021-02002-x
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DOI: https://doi.org/10.1007/s10668-021-02002-x