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Experimental validation of single and multi-objective optimization of microbial fuel cell based on recent electric eel foraging algorithm

Author

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  • Rezk, Hegazy
  • Ghasemi, Mostafa
  • Al Saadi, Amal
  • Sayed, Enas Taha

Abstract

The performance of the microbial fuel cell (MFC) mainly depends on the operational parameters including temperature, wolf mineral solution, and pH. Therefore, the key challenge is determining the best values of temperature, wolf mineral solution, and pH corresponding to the highest performance of the MFC. The proposed methodology consists of three main phases: experimental investigation, fuzzy modelling, and parameter identification. Firstly, experimental work was conducted to evaluate the performance of the MFC by changing the input controlling parameters. Secondly, using measured data, the model of MFC is designed for simulating the MFC considering temperature, wolf mineral solution, and pH. The output performance of the MFC is measured through power density (PD) and COD removal. Thirdly, single and multiple objectives optimization using the recent electric eel foraging optimization (EEFO), the best values of temperature, wolf mineral solution, and pH are identified. During the optimization procedure, the temperature, wolf mineral solution, and pH are the decision variables, and the objective function is to simultaneously increase PD and COD removal. Regarding fuzzy model of the PD, compared to ANOVA, the RMSE was decreased from 178 using ANOVA to 10.37 using fuzzy, so it decreased by 94 %. For fuzzy model of COD removal, compared to ANOVA, the RMSE reduced from 28.49 using ANOVA to 2.9 using fuzzy. It decreased by 89.8 %. For single-objective optimization of COD removal, the COD removal increased by 6.7 % and 28.65 % respectively compared with experimental and ANOVA. For single-objective optimization of PD, PD increased by 4.9 % and 12 % respectively compared with experimental and ANOVA. For multi-objective optimization, PD increased by 1.1 % and 11.8 % respectively compared with experimental and ANOVA. At the same time, the COD removal increased by 9.16 % and 11.5 % respectively compared with experimental and ANOVA.

Suggested Citation

  • Rezk, Hegazy & Ghasemi, Mostafa & Al Saadi, Amal & Sayed, Enas Taha, 2025. "Experimental validation of single and multi-objective optimization of microbial fuel cell based on recent electric eel foraging algorithm," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225046316
    DOI: 10.1016/j.energy.2025.138989
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    References listed on IDEAS

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    1. Hamed Farahani & Mostafa Ghasemi & Mehdi Sedighi & Nitin Raut, 2024. "Employing Artificial Intelligence for Enhanced Microbial Fuel Cell Performance through Wolf Vitamin Solution Optimization," Sustainability, MDPI, vol. 16(15), pages 1-17, July.
    2. Hesham Alhumade & Iqbal Ahmed Moujdin & Saad Al-Shahrani, 2023. "Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    3. Ghasemi, Mostafa & Rezk, Hegazy, 2024. "Performance improvement of microbial fuel cell using experimental investigation and fuzzy modelling," Energy, Elsevier, vol. 286(C).
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