MESSAI.iO

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This study benchmarked seven machine learning algorithms for predicting biomethane production in microbial electromethanogenesis, with the 1D-CNN model showing superior performance. The predictive capabilities were evaluated using experimental data from bioelectrochemical systems, considering operational parameters such as optical density and electrical conductivity. 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 (OD600), pH, electrical conductivity (EC, mS/cm), average applied current (A m⁻²), and CO2 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, OD600, 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.

Key findings

  • The 1D-CNN model exhibited superior predictive performance with an R-squared value of 0.934
  • Average current, OD600, and pH were identified as key factors influencing biomethane production through SHAP analysis
  • Hyperparameter optimization significantly improved the predictive capabilities of the machine learning models

Keywords

Machine learningArtificial intelligenceHyperparameterBoosting (machine learning)Multilayer perceptronGradient boosting

Identifiers

DOI
10.1021/acssuschemeng.5c09770
Journal
ACS Sustainable Chemistry and Engineering
Year
2025