Benchmarking Machine Learning Algorithms for Microbial Electromethanogenesis: A Comprehensive Assessment with SHapley Additive exPlanation-Based Insights.
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75% confidenceThis study benchmarked seven machine learning algorithms for predicting biomethane production in microbial electromethanogenesis, with the 1D-CNN model exhibiting superior performance. The predictive capabilities of these models were evaluated using experimental data from bioelectrochemical systems, considering operational parameters such as optical density and CO2 availability. The 1D-CNN model achieved an R-squared value of 0.934, outperforming traditional machine learning methods.
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Abstract
Microbial electromethanogenesis (EM) presents a promising pathway for sustainable biogas upgrading, but accurately predicting its performance is challenging due to complex, nonlinear process dynamics. Here, we systematically compared seven supervised machine learning (ML) algorithms, including one-dimensional convolutional neural network (1D-CNN), multilayer perceptron (MLP), gradient boosting regressor (GBR), adaptive boosting regressor (AdaBoost), stacking regressors, and K-nearest neighbors (kNN), for their predictive biomethane production capabilities using experimental data from EM bioelectrochemical systems (EM-BESs). The data set encompassed operational parameters such as optical density (OD 600 ), pH, electrical conductivity (EC, mS/cm), average applied current (A m -2 ), and CO 2 availability (mol). After hyperparameter optimization, the 1D-CNN model exhibited superior predictive performance ( R 2 = 0.934), significantly outperforming traditional ML methods. To move beyond prediction and uncover mechanistic insights, a feature importance analysis was conducted on the CNN model using SHapley Additive exPlanations (SHAP). The analysis revealed that average current, OD 600 , and pH were the most influential features in biomethane production, confirming that the model learned relationships grounded in fundamental bioelectrochemical principles. The SHAP analysis also identified complex, nonmonotonic effects of other variables, providing deeper process understanding. This study not only demonstrates the promising ability of ML, especially deep learning architectures, to advance EM optimization but also provides mechanistic insights into the factors governing bioelectrochemical methanogenesis. These findings are broadly applicable to analogous BESs, particularly microbial electrosynthesis (i.e., commodity chemical) and microbial electrolysis cells (i.e., biohydrogen), offering potential for enhancing system performance through data-driven operational control across sustainable biotechnology applications.