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

## TL;DR

This paper compares machine learning models to predict and understand microbial electromethanogenesis, finding that a deep learning model outperforms others and reveals key factors influencing biomethane production.

## Contribution

The study introduces a novel application of SHAP-based analysis with deep learning to uncover mechanistic insights in microbial electromethanogenesis.

## Key findings

- The 1D-CNN model achieved an R² of 0.934, outperforming traditional ML methods in predicting biomethane production.
- SHAP analysis identified average current, OD600, and pH as the most influential features in the process.
- The study revealed nonmonotonic effects of variables, offering deeper understanding of bioelectrochemical dynamics.

## 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.