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

作　　者：Ke Yang Yijun He Zifeng Ma 

机构地区：[1]Shanghai Electrochemical Energy Devices Research Center, Department of Chemical Engineering, Shanghai Jiao Tong University

出　　处：《Chinese Journal of Chemical Engineering》2017年第8期1000-1012,共13页中国化学工程学报（英文版）

基　　金：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);the Project-sponsored by SRF for ROCS,SEM

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.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 con- flicting objectives can be greatly helpful to assist in optimal operation of MFC system. In this study, a multi- objective 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 bi- objective 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 

分 类 号：TM911.45[电气工程—电力电子与电力传动]