Key Takeaways & Executive Findings
- •• • Random Forest model achieved a test set R² of 0.9463, outperforming BP neural network, ANFIS, SVM, and KNN, with minimal overfitting risk, enabling reliable prediction of acidogenic performance in chemical-biological sludge fermentation. • • pH and VSS were identified as the most critical variables driving acidogenic efficiency, with feature importance scores of 1.17 and 0.894, respectively, indicating that process control should prioritize pH adjustment and organic matter stabilization. • • Aluminum salts (importance 0.657) exhibited a stronger inhibitory effect on acidogenesis than iron salts (importance 0.527), guiding the need for targeted control of aluminum-based coagulants in sludge management. • • The proposed optimization pathway 'adjust pH, stabilize organic matter, control aluminum salts' provides a data-driven strategy to enhance short-chain fatty acid production, potentially improving resource recovery efficiency in municipal sludge treatment.
Abstract
Municipal sludge anaerobic resource recovery efficiency in China lags behind developed countries. Widespread chemical phosphorus removal increases iron and aluminum salt precipitates in waste activated sludge, forming chemical-biological sludge that reduces acidogenic efficiency. This study identified key factors and developed a high-precision prediction model. Integrating literature and experimental data, acidogenic performance indicators under various conditions were compiled. Five machine learning models—Backpropagation Neural Network, Adaptive Neuro-Fuzzy Inference System, Support Vector Machine, K-Nearest Neighbors, and Random Forest—were systematically compared. Random Forest achieved the best predictive performance with a test set coefficient of determination (R²) of 0.9463, significantly outperforming others with minimal overfitting risk, demonstrating strong capability for high-dimensional, nonlinear, multi-factor coupled problems. Feature importance analysis revealed pH and Volatile Suspended Solids (VSS) as primary drivers, with aluminum salts exerting greater influence than iron salts. Engineering optimization should follow the pathway: 'adjust pH, stabilize organic matter, control aluminum salts'. This study provides an intelligent predictive tool and clarifies optimization directions, advancing precision and intelligent sludge treatment.
1. Introduction
Municipal sludge anaerobic digestion in China suffers from lower resource recovery efficiency compared to developed nations, primarily due to the widespread adoption of chemical phosphorus removal processes. These processes introduce iron and aluminum salts into waste activated sludge, forming chemical-biological hybrid sludge that inhibits acidogenic fermentation—the critical first step for short-chain fatty acid production. Traditional empirical optimization struggles with the high-dimensional, nonlinear interactions among pH, solids content, metal ions, and operational parameters, leaving a bottleneck in process design and control.
This study addresses that bottleneck by applying machine learning to model acidogenic performance. By systematically comparing five algorithms, the authors identified Random Forest as the most accurate and robust predictor, achieving an R² of 0.9463 on test data. Feature importance analysis quantified the dominant roles of pH and VSS, and revealed that aluminum salts are more inhibitory than iron salts. These findings translate into a concrete engineering pathway—'adjust pH, stabilize organic matter, control aluminum salts'—offering a data-driven framework to optimize fermentation conditions and enhance resource recovery from chemical-biological sludge.
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ZHAO Ke, LI Tianle, LIU Changjie, PING Qian (2026). Machine Learning-Based Prediction of Acidogenic Performance in Anaerobic Fermentation of Chemical-Biological Sewage Sludge. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202606001
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Frequently Asked Questions
What is the comparative predictive performance of Random Forest against other models in terms of R² and overfitting risk?
Random Forest achieved the highest test set R² of 0.9463, significantly outperforming BP neural network, ANFIS, SVM, and KNN. It also exhibited the lowest overfitting risk, as evidenced by the minimal gap between training and test performance, making it the most reliable model for this high-dimensional, nonlinear problem.
Which operational parameters are most critical for optimizing acidogenic performance, and how should they be controlled?
Feature importance analysis identified pH (importance 1.17) and VSS (importance 0.894) as the primary drivers. Aluminum salts (importance 0.657) had a stronger inhibitory effect than iron salts (importance 0.527). Therefore, process optimization should focus on adjusting pH to optimal ranges, stabilizing VSS levels, and minimizing aluminum salt input to enhance acid production.
How does the presence of aluminum and iron salts affect acidogenic efficiency, and why is aluminum more inhibitory?
Both aluminum and iron salts were identified as inhibitory factors, but aluminum exhibited a higher feature importance (0.657 vs. 0.527), indicating a stronger negative impact on acidogenic performance. This may be due to aluminum's higher affinity for organic matter and phosphate, potentially forming complexes that reduce substrate bioavailability and microbial activity.
What is the practical significance of the proposed 'adjust pH, stabilize organic matter, control aluminum salts' pathway for engineering applications?
This pathway provides a clear, data-driven strategy for operators: first, adjust pH to optimal conditions (typically near neutral for acidogens); second, maintain stable VSS concentrations to ensure consistent organic loading; third, control aluminum salt inputs, possibly by optimizing chemical phosphorus removal dosages or using alternative coagulants. Implementing these steps can improve acidogenic efficiency and overall resource recovery from chemical-biological sludge.
What are the limitations of the machine learning approach, and how can the model be generalized to other sludge types?
The model's accuracy depends on the quality and diversity of training data. While it performed well on the compiled dataset, generalization to other sludge types may require retraining with site-specific data. Future work should incorporate additional variables such as sludge age, temperature dynamics, and microbial community data to enhance model robustness and applicability.
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