Anaerobic co-digestion of wastewater sludge and food waste: A machine learning approach to process modeling and optimization
Bibliographic record
Abstract
Due to the complex microbial interactions in anaerobic co-digestion (An-CoD), predicting process outcomes remains challenging. This study applies machine learning (ML) to model and optimize the co-digestion of wastewater sludge and food waste (FW). A generalized, transferable framework is developed that reflects real-world operational constraints by isolating the mixing ratio as the primary input variable under fixed digester conditions, an approach aligned with full-scale facility limitations. Unlike prior studies limited to data from single facilities, this model is trained on a diverse dataset compiled from multiple literature sources, enhancing its applicability across varying sludge and FW characteristics. Three ML models, random forest (RF), XGBoost, and artificial neural networks (ANN), were trained to predict methane yield and assess the influence of feedstock and operational parameters. Regularized ANN outperformed other models, reducing overfitting, and achieved an R 2 of 0.86 and NRMSE of 0.31. The trained model was then coupled with a global optimization algorithm (dual annealing) to identify mixing ratios that maximize methane yield based on feedstock properties. Optimization across seven candidate points, spanning a range of solid contents, revealed that methane yield scales with feedstock quality and process intensity. High-VS sludge and FW mixtures under mesophilic continuous operation yielded up to 510 mL/g VS, while inorganic-rich, low-VS sludge limited yields to ∼130 mL/g VS. Medium-quality mixtures reproduced experimental trends, exceeding 300 mL/g VS. Predicted yields ranged from 134 to 510 mL/g VS, with optimal mixing ratios varying from 0.99 % to 88 % VS across scenarios. • Anaerobic co-digestion process was modeled using machine learning. • Optimum mixing ratio in co-digestion varies with characteristics of the substrates. • The volatile to total solids ratio is a significant feature in anaerobic digestion modeling. • Optimization techniques showed potential in complementing experimental approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".