Ghasemi, Mostafa, Chalak Qazani, Mohammad Reza, Lennon, Christopher W., and Sedighi, Mehdi (2023) Analysis and prediction of microbial fuel cell behaviour using MLP and SVR. Journal of the Taiwan Institute of Chemical Engineers, 151. 105101.

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Abstract

Background: Microbial fuel cell (MFC) is a device for simultaneous wastewater treatment and clean energy production. In this study, different amounts of yeast extract (1, 2, 3, 4, or 5 g/L) were varied and the MFC performance was measured in terms of power generation, COD removal and coulombic efficiency.

Methods: The first part of this study examined a dual chamber MFC with varying volumes of yeast extract for power output, COD removal, and coulombic efficiency. Soft computing approaches were utilized in the second part of the study to estimate MFC performance, and multilayer perceptron (MLP) with varied numbers of hidden layers was used to improve model accuracy. The method was next implemented in MATLAB software using 70% and 30% of the dataset for training and testing purposes of the system, which was validated with the traditional support vector regression (SVR) estimation method.

Findings: The experimental results have shown that the MFC4 (4 g/L yeast extract) had the highest catalytic activity and produced the maximum power (308 mW/m2). Comparing the experimental results and soft computing model proved that MFC behavior can be predicted 5.1819 times more accurately using MLP compared with traditional SVR.

Item ID:	86725
Item Type:	Article (Research - C1)
ISSN:	1876-1089
Copyright Information:	© 2023 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Date Deposited:	10 Sep 2025 01:02
FoR Codes:	40 ENGINEERING > 4004 Chemical engineering > 400410 Wastewater treatment processes @ 40% 40 ENGINEERING > 4008 Electrical engineering > 400804 Electrical energy storage @ 20% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 40% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 60%
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