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Predicting current and hydrogen productions from microbial electrolysis cells using random forest model

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  • Yoon, Jinyoung
  • Cheong, Dae-Yeol
  • Baek, Gahyun

Abstract

The current- and H2-producing performances of microbial electrolysis cells (MECs) were predicted by constructing machine learning models based on the previous 76 MEC datasets, making it the largest dataset to date. All models showed high correlation efficiency (R2 > 0.92) in predicting MEC performances. When the models were constructed separately based on the organic substrate type used in the anode of MECs, the models based solely on acetate-fed MEC data exhibited higher prediction accuracies compared to those on all kinds of substrate or complex substrate-based data. As a results of the feature importance analysis, the applied voltage and cathode surface area were identified as the two most critical factors in the acetate-fed MEC data models. Still low prediction accuracies in the models here seem to be due to several important features which could not be numerically presented and thus not be considered as input variables such as electrode material types.

Suggested Citation

  • Yoon, Jinyoung & Cheong, Dae-Yeol & Baek, Gahyun, 2024. "Predicting current and hydrogen productions from microbial electrolysis cells using random forest model," Applied Energy, Elsevier, vol. 371(C).
    • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010249
    DOI: 10.1016/j.apenergy.2024.123641
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    References listed on IDEAS

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    1. de Ramón-Fernández, A. & Salar-García, M.J. & Ruiz Fernández, D. & Greenman, J. & Ieropoulos, I.A., 2020. "Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells," Energy, Elsevier, vol. 213(C).
    2. Park, Jun-Gyu & Jun, Hang-Bae & Heo, Tae-Young, 2021. "Retraining prior state performances of anaerobic digestion improves prediction accuracy of methane yield in various machine learning models," Applied Energy, Elsevier, vol. 298(C).
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