Machine learning-based prediction of acidogenic performance in anaerobic fermentation of chemical-biological sewage sludge

ZHAO Ke; LI Tianle; LIU Changjie; PING Qian

doi:10.13205/j.hjgc.202606001

Volume 44 Issue 6

Jun. 2026

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ZHAO Ke, LI Tianle, LIU Changjie, PING Qian. Machine learning-based prediction of acidogenic performance in anaerobic fermentation of chemical-biological sewage sludge[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 1-9. doi: 10.13205/j.hjgc.202606001

Citation:

ZHAO Ke, LI Tianle, LIU Changjie, PING Qian. Machine learning-based prediction of acidogenic performance in anaerobic fermentation of chemical-biological sewage sludge[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 1-9. doi: 10.13205/j.hjgc.202606001

Citation:

ZHAO Ke, LI Tianle, LIU Changjie, PING Qian. Machine learning-based prediction of acidogenic performance in anaerobic fermentation of chemical-biological sewage sludge[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 1-9. doi: 10.13205/j.hjgc.202606001

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Machine learning-based prediction of acidogenic performance in anaerobic fermentation of chemical-biological sewage sludge

doi: 10.13205/j.hjgc.202606001

ZHAO Ke¹,
LI Tianle¹,
LIU Changjie¹,
PING Qian^{1,2
,
,}

1. State Key Laboratory of Water Pollution Control and Green Resource Recycling, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;
2. Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China

Received Date: 2025-10-23
Accepted Date: 2025-11-06
Rev Recd Date: 2025-10-30

Available Online: 2026-07-06

Abstract

Abstract

The efficiency of recovering resources from municipal sludge through anaerobic processes is generally lower in China than in developed countries. The widespread adoption of chemical phosphorus removal processes has increased the content of chemical precipitates （e.g.， iron and aluminum salts） in waste activated sludge， resulting in the formation of chemical-biological sewage sludge that reduces acidogenic efficiency during anaerobic fermentation. This study aims to identify the key factors affecting the acidogenic performance in such sludge and to develop a high-precision prediction model to facilitate its resource recovery. By integrating literature and experimental data， acidogenic performance indicators under various anaerobic fermentation conditions were obtained. The predictive capabilities of five machine learning models—Backpropagation Neural Network， Adaptive Neuro-Fuzzy Inference System， Support Vector Machine， K-Nearest Neighbors， and Random Forest —were systematically compared， and feature importance was analyzed using the optimal model. The results indicated that the Random Forest model achieved the best predictive performance， with a coefficient of determination as high as 0.9463 on the test set， significantly outperforming the other models while exhibiting minimal overfitting risk. This demonstrated its strong capability in handling the high-dimensional， nonlinear， and multi-factor coupled problems like anaerobic fermentation. Feature importance analysis based on this model revealed that pH and Volatile Suspended Solids （VSS） were the primary drivers of acidogenic efficiency， with the effect of aluminum salts being greater than that of iron salts. Consequently， for engineering applications， process optimization should follow the pathway of "adjusting pH， stabilizing organic matter content， and controlling aluminum salts". This study not only provides an intelligent predictive tool for the efficient resource recovery of chemical-biological hybrid sludge but also clarifies specific directions for process optimization， holding significant importance for promoting the precision and intelligent development of sludge treatment technology.
- municipal sludge,
- short-chain fatty acids,
- model prediction,
- anaerobic fermentation,
- resource recovery

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References(37)

References

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