Application and Research Progress of Machine Learning in Typical Sludge Treatment Technologies

• • ANN achieved the highest prediction accuracy in sludge dewatering with R²=0.99 and RMSE=0.02, demonstrating superior capability for precise moisture content estimation, which is critical for optimizing dewatering operations and reducing disposal costs. • • In anaerobic digestion, ANN outperformed other models with R²=0.86 and NRMSE=0.31, enabling reliable prediction of biogas yield and process stability, which is essential for efficient energy recovery and digester control. • • Gradient boosting delivered R²=0.90 and RMSE=0.33 in sludge treatment predictions, indicating its robustness for capturing complex non-linear relationships, making it suitable for process optimization in variable feed conditions. • • SVM showed stable performance in small-to-medium sample sizes and high-dimensional data, providing a reliable alternative when data availability is limited, which is common in pilot-scale studies.