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Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence

Author

Listed:
  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Dalia Yousri

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Abdullah G. Alharbi

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Sulaiman Alshammari

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Yahia B. Hassan

    (Electrical Engineering Department, Higher Institute of Engineering, Minia 61519, Egypt)

Abstract

In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m 3 H 2 /m 3 d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m 3 H 2 /m 3 d to 1.253 m 3 H 2 /m 3 d by around 6.7% compared to RSM.

Suggested Citation

  • Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Abdullah G. Alharbi & Sulaiman Alshammari & Yahia B. Hassan, 2023. "Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3730-:d:1072316
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    References listed on IDEAS

    as
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