Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

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.

A Novel Computational Platform for Steady-State and Dynamic Simulation of Dual-Chambered Microbial Fuel Cell

“…15,16 In order to pave the path of applications in industries, there are two approaches of MFC optimization: (i) experimental optimization and (ii) mathematical modeling. 17 A substantial amount of experimental optimization studies has been done by researchers in previous studies. Behera and Ghangrekar used full factorial design of experiment techniques to optimize dual-chamber MFC, by considering hydraulic retention time (HRT), influent pH and influent COD as independent variables.…”

Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method

“…One of the most widely used MOO algorithms for the optimization of engineering problems is the non-dominated sorting hybrid algorithm [24]. In the last five years, there has been a significant usage of MOO in chemical engineering [25][26][27][28][29][30][31][32][33]. The advantage of MOO over SOO is that it allows for a multi-criteria analysis for choosing the best solution [26].…”

“…In the last five years, there has been a significant usage of MOO in chemical engineering [25][26][27][28][29][30][31][32][33]. The advantage of MOO over SOO is that it allows for a multi-criteria analysis for choosing the best solution [26]. Additionally, MOO does not depend on user-supplied initialization procedures.…”

Engineered nanomaterials in microbial fuel cells – Recent developments, sustainability aspects, and future outlook

“…1. A single-chamber microbial fuel cell (SCMFC) in which activated carbon (AC) derived from ground nutshell utilized as a metal-free oxygen reduction catalyst for the aerated cathode (up), and a dual-chamber MFC with a PEM (down), the organic compounds are degraded by the microorganisms in the anode to produce electrons, which are transferred to the cathode and participate in oxygen reduction reactions, adopted from Karthick et al, [33], Abu-Reesh, [34]. Kamali et al Fuel xxx (xxxx) 122347…”