Multi-objective optimization of methane production system from biomass through anaerobic digestion

doi:10.1016/j.cjche.2018.01.001

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (10): 2084-2092.DOI: 10.1016/j.cjche.2018.01.001

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

Multi-objective optimization of methane production system from biomass through anaerobic digestion

Weijun Li^1,2, Jakob Kjøbsted Huusom³, Zhimao Zhou¹, Yi Nie¹, Yajing Xu¹, Xiangping Zhang^1,2

1 Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
2 Sino-Danish Center for Education and Research, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Department of Chemical & Biochemical Engineering, Technical University of Denmark, DK 2800 Kgs. Lyngby, Denmark

Received:2017-10-15 Revised:2017-12-22 Online:2018-11-14 Published:2018-10-28
Contact: Xiangping Zhang,E-mail address:xpzhang@ipe.ac.cn
Supported by:
Supported by the National Natural Science Fund for Distinguished Young Scholars (21425625), the National Basic Research Program of China (2013CB733506, 2015CB251403), the National Natural Science Foundation of China (U1610222), and the Beijing Hundreds of Leading Talents Training Project of Science and Technology (Z171100001117154).

Multi-objective optimization of methane production system from biomass through anaerobic digestion

Weijun Li^1,2, Jakob Kjøbsted Huusom³, Zhimao Zhou¹, Yi Nie¹, Yajing Xu¹, Xiangping Zhang^1,2

1 Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
2 Sino-Danish Center for Education and Research, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Department of Chemical & Biochemical Engineering, Technical University of Denmark, DK 2800 Kgs. Lyngby, Denmark

通讯作者: Xiangping Zhang,E-mail address:xpzhang@ipe.ac.cn
基金资助:
Supported by the National Natural Science Fund for Distinguished Young Scholars (21425625), the National Basic Research Program of China (2013CB733506, 2015CB251403), the National Natural Science Foundation of China (U1610222), and the Beijing Hundreds of Leading Talents Training Project of Science and Technology (Z171100001117154).

Abstract

Abstract: This work addressed the multi-objective optimization of a biogas production system considering both environmental and economic criteria. A mixed integer non-linear programming (MINLP) model was established and solved with non-dominated sorting genetic algorithm Ⅱ, from which the Pareto fronts, the optimal technology combinations and operation conditions were obtained and analyzed. It's found that the system is feasible in both environmental and economic considerations after optimization. The most expensive processing section is decarbonization; the most expensive equipment is anaerobic digester; the most power-consuming processing section is digestion, followed by decarbonization and waste management. The positive green degree value on the process is attributed to processing section of digestion and waste management. 3:1 chicken feces and corn straw, solar energy, pressure swing adsorption and 3:1 chicken feces and rice straw, solar energy, pressure swing adsorption are turned out to be two robust technology combinations under different prices of methane and electricity by sensitivity analysis. The optimization results provide support for optimal design and operation of biogas production system considering environmental and economic objectives.

Key words: Biogas production system, MINLP, Multi-objective optimization, Non-dominated sorting genetic algorithm II, Green degree value

摘要： This work addressed the multi-objective optimization of a biogas production system considering both environmental and economic criteria. A mixed integer non-linear programming (MINLP) model was established and solved with non-dominated sorting genetic algorithm Ⅱ, from which the Pareto fronts, the optimal technology combinations and operation conditions were obtained and analyzed. It's found that the system is feasible in both environmental and economic considerations after optimization. The most expensive processing section is decarbonization; the most expensive equipment is anaerobic digester; the most power-consuming processing section is digestion, followed by decarbonization and waste management. The positive green degree value on the process is attributed to processing section of digestion and waste management. 3:1 chicken feces and corn straw, solar energy, pressure swing adsorption and 3:1 chicken feces and rice straw, solar energy, pressure swing adsorption are turned out to be two robust technology combinations under different prices of methane and electricity by sensitivity analysis. The optimization results provide support for optimal design and operation of biogas production system considering environmental and economic objectives.

关键词: Biogas production system, MINLP, Multi-objective optimization, Non-dominated sorting genetic algorithm II, Green degree value

Weijun Li, Jakob Kjøbsted Huusom, Zhimao Zhou, Yi Nie, Yajing Xu, Xiangping Zhang. Multi-objective optimization of methane production system from biomass through anaerobic digestion[J]. Chin.J.Chem.Eng., 2018, 26(10): 2084-2092.

References

[1] A.M. Khan, N. Fatima, Biodiesel synthesis via metal oxides and metal chlorides catalysis from marine alga Melanothamnus afaqhusainii, Chin. J. Chem. Eng. 24(3) (2016) 388-393.

[2] F.R. Abdeen, M. Mel, M.S. Jami, S.I. Ihsan, A.F. Ismail, A review of chemical absorption of carbon dioxide for biogas upgrading, Chin. J. Chem. Eng. 24(6) (2016) 693-702.

[3] F. Sgroi, A.M. Di-Trapani, M. Fodera, R. Testa, S. Tudisca, Economic performance of biogas plants using giant reed silage biomass feedstock, Ecol. Eng. 81(2015) 481-487.

[4] C.A.G. And, A.E. Rodrigues, Layered vacuum pressure-swing adsorption for biogas upgrading, Ind. Eng. Chem. Res. 46(23) (2007) 7844-7848.

[5] H. Yu, Q. Wang, K.E. Ileleji, C. Yu, Z. Luo, K. Cen, J. Gore, Design and analysis of geographic distribution of biomass power plant and satellite storages in China. Part 1:Straight-line delivery, Biomass Bioenergy 46(2012) 773-784.

[6] H. Yu, Q. Wang, K.E. Ileleji, C. Yu, Z. Luo, K. Cen, J. Gore, Design and analysis of geographic distribution of biomass power plant and satellite storages in China. Part 2:Road delivery, Biomass Bioenergy 46(2012) 785-792.

[7] J. Singh, B.S. Panesar, S.K. Sharma, A mathematical model for transporting the biomass to biomass based power plant, Biomass Bioenergy 34(4) (2010) 483-488.

[8] J. Ariunbaatar, A. Panico, G. Esposito, F. Pirozzi, P.N.L. Lens, Pretreatment methods to enhance anaerobic digestion of organic solid waste, Appl. Energy 123(2014) 143-156.

[9] C. Mendes, K. Esquerre, L.M. Queiroz, Application of anaerobic digestion model No. 1 for simulating anaerobic mesophilic sludge digestion, Waste Manag. 35(2015) 89-95.

[10] B.Wu,X.Zhang, Y.Xu, D.Bao,S.Zhang,Assessment oftheenergyconsumption of the biogas upgrading process with pressure swing adsorption using novel adsorbents, J. Clean. Prod. 101(2015) 251-261.

[11] T. Rehl, J. Mueller, Life cycle assessment of biogas digestate processing technologies, Resour. Conserv. Recycl. 56(1) (2011) 92-104.

[12] B. Wu, X. Zhang, D. Bao, Y. Xu, S. Zhang, L. Deng, Biomethane production system:Energetic analysis of various scenarios, Bioresour. Technol. 206(2016) 155-163.

[13] B. Wu, X. Zhang, D. Shang, D. Bao, S. Zhang, T. Zheng, Energetic-environmentaleconomic assessment ofthe biogassystem with threeutilization pathways:Combined heat and power, biomethane and fuel cell, Bioresour. Technol. 214(2016) 722-728.

[14] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ, IEEE Trans. Evol. Comput. 6(2) (2002) 182-197.

[15] V.S.P. Bitra, A.R. Womac, C. Igathinathane, P.I. Miu, Y.T. Yang, D.R. Smith, N. Chevanan, S. Sokhansanj, Direct measures of mechanical energy for knife mill size reduction of switchgrass, wheat straw, and corn stover, Bioresour. Technol. 100(24) (2009) 6578-6585.

[16] S. Mani, L.G. Tabil, S. Sokhansanj, Specific energy requirement for compacting corn stover, Bioresour. Technol. 97(12) (2006) 1420-1426.

[17] P. Haro, P. Ollero, A.L. Villanueva-Perales, A. Gomez-Barea, Thermochemical biorefinery based on dimethyl ether as intermediate:Technoeconomic assessment, Appl. Energy 102(2013) 950-961.

[18] R. Braun, A. Wellinger, In Potential of Co-Digestion, IEA Bioenergy, Task, 37, 2003.

[19] O. Guo, Y. Li, J. Bai, G. Yang, Z. Zhou, G. Ren, Y. Feng, Effect of temperature on gasification characteristics of mixture of chicken feces and crop residue, J. Northwest A F Univ. (Nat. Sci. Ed.) 37(6) (2009) 137-144.

[20] L.T. Biegler, I.E. Grossmann, A.W. Westerberg, Systematic Methods for Chemical Process Design, 1997.

[21] H.P. Loh, J. Lyons, C.W.W. Iii, Process equipment cost estimation, Final Report, Office of Scientific & Technical Information Technical Reports, 2002.

[22] X.D. Pu, L.W. Deng, Y. Yong, S. Li, Z.Y. Wang, Economic benefit analysis on large and middle-scale biogas plants with different heating methods, Trans. Chin. Soc. Agric. Eng. 26(7) (2010) 281-284.

[23] B.H. Gebreslassie, M. Slivinsky, B. Wang, F. You, Life cycle optimization for sustainable design and operationsofhydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking, Comput. Chem. Eng. 50(2013) 71-91.

[24] J. Krischan, A. Makaruk, M. Harasek, Design and scale-up of an oxidative scrubbing process for the selective removal of hydrogen sulfide from biogas, J. Hazard. Mater. 215(2012) 49-56.

[25] E. Ryckebosch, M. Drouillon, H. Veruaeren, Techniques for transformation of biogas to biomethane, Bioresour. Technol. 35(5) (2011) 1633-1645.

[26] Y. Xu, Y. Huang, B. Wu, X. Zhang, S. Zhang, Biogas upgrading technologies:Energetic analysis and environmental impact assessment, Chin. J. Chem. Eng. 23(1) (2015) 247-254.

[27] X. Liu, Y. Huang, Y. Zhao, R. Gani, X. Zhang, S. Zhang, Ionic liquid design and process simulation for decarbonization of shale gas, Ind. Eng. Chem. Res. 55(20) (2016) 5931-5944.

[28] C.A. Grande, A.E. Rodrigues, Biogas to fuel by vacuum pressure swing adsorption-I. Behavior of equilibrium and kinetic-based adsorbents, Ind. Eng. Chem. Res. 46(13) (2007) 4595-4605.

[29] H. Ruan, Z. Yang, J. Lin, J. Shen, J. Ji, C. Gao, B. Van der Bruggen, Biogas slurry concentration hybrid membrane process:Pilot-testing and RO membrane cleaning, Desalination 368(2015) 171-180.

[30] H. Yu, Q. Wang, K.E. Ileleji, C. Yu, Z. Luo, K. Cen, J. Gore, Design and analysis of geographic distribution of biomass power plant and satellite storages in China. Part 2:Road delivery, Biomass Bioenergy 46(2012) 785-792.

[31] A. Hussain, M.-B. Hagg, A feasibility study of CO₂ capture from flue gas by a facilitated transport membrane, J. Membr. Sci. 359(1) (2010) 140-148.

[32] D. De Clercq, Z. Wen, F. Fei, Economic performance evaluation of bio-waste treatment technology at the facility level, Resour. Conserv. Recycl. 116(2017) 178-184.

[33] X. Zhang, C. Li, C. Fu, S.Zhang, Environmental impact assessment of chemical process using the green degree method, Ind. Eng. Chem. Res. 47(4) (2008) 1085-1094.

Multi-objective optimization of methane production system from biomass through anaerobic digestion

Multi-objective optimization of methane production system from biomass through anaerobic digestion

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