Machine learning-aided prediction of COD removal in the electrocoagulation process using a super learner model
<p dir="ltr">A new predictive machine learning stacking model was developed to examine chemical oxygen demand (COD) removal efficiency in electrocoagulation. The model used a comprehensive dataset consisting of 379 points containing no missing data collected from different studies in...
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2025
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| Summary: | <p dir="ltr">A new predictive machine learning stacking model was developed to examine chemical oxygen demand (COD) removal efficiency in electrocoagulation. The model used a comprehensive dataset consisting of 379 points containing no missing data collected from different studies investigating COD removal efficiency using electrocoagulation, encompassing different wastewater types. The newly developed model included 10 input parameters, namely initial COD concentration, pH, conductivity, anode material, cathode material, inter-electrode distance, number of electrodes, current density, the ratio between the effective electrode area and the reactor volume, and electrolysis time. The stacking model uses three ensemble models, specifically, gradient boosting regression (GBR), eXtreme Gradient Boosting (XGB), and random forest regression (RFR), as the base learners, while the meta learner is a linear regression model. The developed model has a prediction accuracy of 95.3 % for the R<sup>2</sup> value in the test dataset. Additionally, the study used sensitivity analysis and Partial Dependence Plots (PDPs) to determine the impact of each input parameter on COD removal efficiency. The results show that the three most influential parameters are electrolysis time, inter-electrode distance, and current density.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Environmental Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jece.2025.117469" target="_blank">https://dx.doi.org/10.1016/j.jece.2025.117469</a></p> |
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