Research progress of machine learning in typical sludge treatment technologies

CAO Yihang; SONG Xin; ZHANG Chi; LUO Jingyang

doi:10.13205/j.hjgc.202607021

Volume 44 Issue 7

Jul. 2026

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2026 > 44(7): 212-221

CAO Yihang, SONG Xin, ZHANG Chi, LUO Jingyang. Research progress of machine learning in typical sludge treatment technologies[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(7): 212-221. doi: 10.13205/j.hjgc.202607021

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CAO Yihang, SONG Xin, ZHANG Chi, LUO Jingyang. Research progress of machine learning in typical sludge treatment technologies[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(7): 212-221. doi: 10.13205/j.hjgc.202607021

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Research progress of machine learning in typical sludge treatment technologies

doi: 10.13205/j.hjgc.202607021

CAO Yihang^1,2,
SONG Xin^1,2,
ZHANG Chi^1,2,
LUO Jingyang^{1,2
,
,}

1. Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of the Ministry of Education, Hohai University, Nanjing 210098, China;
2. College of Environment, Hohai University, Nanjing 210098, China

Received Date: 2026-03-11
Available Online: 2026-09-01

Abstract

Abstract

With the continuous expansion of urban sewage treatment capacity， the sludge generation continues to increase， making its efficient treatment， disposal， and resource recovery as significant research focus. Machine learning holds substantial application potential in sludge treatment prediction and optimization due to its capacity to extract non-linear features from complex operational data. Focusing on typical processes such as sludge dewatering， resource recovery （i.e.， anaerobic digestion）， and final disposal （i.e.， incineration and landfill）， the general workflow and key research advances in machine learning modeling are summarized across three dimensions： dataset preparation， algorithm selection， and model evaluation. A comparative analysis examines the applicability and limitations of support vector machines （SVM）， random forests （RF）， artificial neural networks （ANN）， and other deep learning models across diverse sludge treatment scenarios. The results show that SVMs demonstrate greater stability with small-to-medium sample sizes and high-dimensional data， while RFs typically exhibit strong generalization capabilities and provide insights into variable importance. ANNs and deep learning models show advantages in handling large-scale data and typical time-series or image tasks， though they impose higher demands on data quality. Finally， future research directions are explored through multi-source data fusion， model interpretability， and the coupling of machine learning with mechanistic models， aiming to provide guidance on the intelligent and refined management of sludge treatment.
- sludge treatment,
- machine learning,
- sludge dewatering,
- anaerobic digestion,
- terminal disposal

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References(46)

References

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