Application and Research Progress of Machine Learning in Typical Sludge Treatment Technologies
The continuous expansion of urban sewage treatment capacity has led to a sustained increase in sludge generation, making efficient treatment, disposal, and resource recovery critical in environmental engineering. Machine learning (ML) offers substantial potential for prediction and optimization in sludge treatment by extracting non-linear features from complex operational data. This review systematically examines the application of ML across typical sludge treatment processes, including dewatering, resource recovery (anaerobic digestion), and terminal disposal (incineration and landfill). The general modeling workflow is summarized across three dimensions: dataset preparation, algorithm selection, and model evaluation. A comparative analysis evaluates the applicability and limitations of support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and other deep learning models. SVMs demonstrate greater stability with small-to-medium sample sizes and high-dimensional data, while RFs exhibit strong generalization and provide variable importance insights. ANNs and deep learning models excel in large-scale data and time-series or image tasks but require high data quality. Key findings from the literature include ANN achieving R²=0.99 and RMSE=0.02 in dewatering prediction, and R²=0.86 with NRMSE=0.31 in anaerobic digestion, while gradient boosting reached R²=0.90 and RMSE=0.33. Future directions emphasize multi-source data fusion, model interpretability (e.g., SHAP), and coupling ML with mechanistic models to enhance predictive accuracy and generalization, supporting intelligent and refined sludge treatment management.