Benchmarking Machine Learning Algorithms for Microbial Electromethanogenesis: A Comprehensive Assessment with SHapley Additive exPlanation-Based Insights
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".