Benchmarking Machine Learning Algorithms for Microbial Electromethanogenesis: A Comprehensive Assessment with SHapley Additive exPlanation-Based Insights.

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

SUBMITTER: Gadkari S 

PROVIDER: S-EPMC12801386 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature