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

Ke Yang; Yijun He; Zifeng Ma

doi:10.1016/j.cjche.2017.03.019

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

Article | Updated：2026-01-06

- Multi-objective steady-state optimization of two-chamber microbial fuel cells
- Chinese Journal of Chemical Engineering Vol. 25, Issue 8, Pages: 1000-1012(2017)
- Affiliations：
  
  Shanghai Electrochemical Energy Devices Research Center, Department of Chemical Engineering, Shanghai Jiao Tong University,Shanghai,China,200240
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.03.019
  CLC：
- Published：2017
- Accepted：
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Ke Yang, Yijun He, Zifeng Ma. Multi-objective steady-state optimization of two-chamber microbial fuel cells[J]. , 2017, 25(8): 1000-1012.
DOI：

Ke Yang, Yijun He, Zifeng Ma. Multi-objective steady-state optimization of two-chamber microbial fuel cells[J]. , 2017, 25(8): 1000-1012. DOI： 10.1016/j.cjche.2017.03.019.

摘要

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.

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