Multi-objective steady-state optimization of two-chamber microbial fuel cells

doi:10.1016/j.cjche.2017.03.019

›› 2017, Vol. 25 ›› Issue (8): 1000-1012.DOI: 10.1016/j.cjche.2017.03.019

Multi-objective steady-state optimization of two-chamber microbial fuel cells

Ke Yang, Yijun He, Zifeng Ma

Shanghai Electrochemical Energy Devices Research Center, Department of Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

收稿日期:2016-09-18 修回日期:2016-11-28 出版日期:2017-08-28 发布日期:2017-09-11
通讯作者: Yijun He
基金资助:
Supported by the National Natural Science Foundation of China (21576163), the Major State Basic Research Development Program of China (2014CB239703), the Science and Technology Commission of Shanghai Municipality (14DZ2250800), and the Project-sponsored by SRF for ROCS, SEM.

Multi-objective steady-state optimization of two-chamber microbial fuel cells

Ke Yang, Yijun He, Zifeng Ma

Shanghai Electrochemical Energy Devices Research Center, Department of Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2016-09-18 Revised:2016-11-28 Online:2017-08-28 Published:2017-09-11
Supported by:
Supported by the National Natural Science Foundation of China (21576163), the Major State Basic Research Development Program of China (2014CB239703), the Science and Technology Commission of Shanghai Municipality (14DZ2250800), and the Project-sponsored by SRF for ROCS, SEM.

摘要/Abstract

摘要： A microbial fuel cell (MFC) is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment. Three non-comparable objectives, i.e. power density, attainable current density and waste removal ratio, are often conflicting. A thorough understanding of the relationship among these three conflicting objectives can be greatly helpful to assist in optimal operation of MFC system. In this study, a multiobjective genetic algorithm is used to simultaneously maximizing power density, attainable current density and waste removal ratio based on a mathematical model for an acetate two-chamber MFC. Moreover, the level diagrams method is utilized to aid in graphical visualization of Pareto front and decision making. Three biobjective optimization problems and one three-objective optimization problem are thoroughly investigated. The obtained Pareto fronts illustrate the complex relationships among these three objectives, which is helpful for final decision support. Therefore, the integrated methodology of a multi-objective genetic algorithm and a graphical visualization technique provides a promising tool for the optimal operation of MFCs by simultaneously considering multiple conflicting objectives.

关键词: Microbial fuel cell, Multi-objective optimization, Genetic algorithm, Level diagrams, Pareto front

Abstract: A microbial fuel cell (MFC) is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment. Three non-comparable objectives, i.e. power density, attainable current density and waste removal ratio, are often conflicting. A thorough understanding of the relationship among these three conflicting objectives can be greatly helpful to assist in optimal operation of MFC system. In this study, a multiobjective genetic algorithm is used to simultaneously maximizing power density, attainable current density and waste removal ratio based on a mathematical model for an acetate two-chamber MFC. Moreover, the level diagrams method is utilized to aid in graphical visualization of Pareto front and decision making. Three biobjective optimization problems and one three-objective optimization problem are thoroughly investigated. The obtained Pareto fronts illustrate the complex relationships among these three objectives, which is helpful for final decision support. Therefore, the integrated methodology of a multi-objective genetic algorithm and a graphical visualization technique provides a promising tool for the optimal operation of MFCs by simultaneously considering multiple conflicting objectives.

Key words: Microbial fuel cell, Multi-objective optimization, Genetic algorithm, Level diagrams, Pareto front

Ke Yang, Yijun He, Zifeng Ma. Multi-objective steady-state optimization of two-chamber microbial fuel cells[J]. , 2017, 25(8): 1000-1012.

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Multi-objective steady-state optimization of two-chamber microbial fuel cells

Multi-objective steady-state optimization of two-chamber microbial fuel cells

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