Current density performance prediction for a microbial electrolysis cell using machine learning methods

doi:10.1016/j.jwpe.2026.110840

Current density performance prediction for a microbial electrolysis cell using machine learning methods

2026-09-03

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OA

AI

A

Antonio Rivero-Cacho

M

Manuel Botejara-Antúnez

Justo García‐Sanz‐Calcedo *

DOI：10.1016/j.jwpe.2026.110840

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Abstract

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• Eight machine-learning models were evaluated to predict bioanode current-density in a MEC. • CNN-NARX achieved the highest coefficient of determination on the independent test set. • The study provides an experimental proof of concept based on 150 days of MEC operation. • SHAP identified pH, acetic acid and optical density as the most influential predictors. • Data-driven modelling showed potential for MEC monitoring in wastewater treatment applications.

Keywords:

Artificial neural networks

Microbial electrolysis cells

Process modelling

Wastewater

Energy projects

Current density performance prediction for a microbial electrolysis cell using machine learning methods

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