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A mathematical model-based optimization was used to optimize the performance of a dual-chamber microbial fuel cell (MFC) using single- and multi-objective optimization methods, revealing that substrate concentration and flow rate significantly impacted performance.

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Dual-chamber H-cell MFCRepresentative model
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Representative dual-chamber H-cell — matched on the paper’s reported configuration, not its exact geometry.

Extraction

Reported parameters

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What they did

System
MFC
Substrate
pure compound
Inoculum
mixed culture

What worked

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Abstract

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD , CD , and SRE were 2.04 W/m², 11.08 A/m², and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

Key findings

  • Maximum power density (PD) was 2.04 W/m²
  • Maximum current density (CD) was 11.08 A/m²
  • Maximum substrate removal efficiency (SRE) was 73.6%
  • Pareto-optimal solutions were generated using the weighted sum method for maximizing PD and CD, and PD and SRE simultaneously

Keywords

Microbial fuel cellMulti-objective optimizationMathematical optimizationSubstrate (aquarium)Pareto principleMATLAB

Identifiers

DOI
10.3390/pr8070839
Journal
Processes
Year
2020