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Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production

Author

Listed:
  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia)

  • A. G. Olabi

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Mohammad Ali Abdelkareem

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61517, Egypt)

  • Hussein M. Maghrabie

    (Department of Mechanical Engineering, Faculty of Engineering, South Valley University, Qena 83521, Egypt)

  • Enas Taha Sayed

    (Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61517, Egypt)

Abstract

Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM.

Suggested Citation

  • Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production," Sustainability, MDPI, vol. 15(3), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1878-:d:1040391
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    References listed on IDEAS

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    Cited by:

    1. Yaping Wu & Xiaolong Wu & Yuanwu Xu & Yongjun Cheng & Xi Li, 2023. "A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    2. Hegazy Rezk & Abdul Ghani Olabi & Enas Taha Sayed & Samah Ibrahim Alshathri & Mohammad Ali Abdelkareem, 2023. "Optimized Artificial Intelligent Model to Boost the Efficiency of Saline Wastewater Treatment Based on Hunger Games Search Algorithm and ANFIS," Sustainability, MDPI, vol. 15(5), pages 1-16, March.

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