Understanding the low-temperature drying process of sludge with machine learning in a sewage-source heat pump drying system.

Heat pump drying technology based on sewage heat source is an eco-friendly sludge drying method. It can effectively reduce the pollution of natural water bodies by waste heat while reducing energy consumption. However, the drying characteristics of sludge in this case remain unclear. Here, we proposed and constructed a novel sewage-source heat pump sludge low-temperature drying system, and combine sludge drying theory with machine learning algorithms to model and analyze the drying process. The results revealed that the performance of the machine learning models was significantly better than that of the existing numerical simulation models. After Bayesian optimization, the XGBoost model showed superior prediction effect. In addition, the interpretable analysis of the model indicated that in the constant rate stage, the drying rate is mainly influenced by external air parameters, with temperature being the most critical factor. When temperature exceeds 50 °C, the effect of relative humidity becomes significant. During the falling rate stage, the dominant factors begin to gradually shift from external air parameters to internal characteristics within the sludge itself, with dry basis moisture content becoming the new key factor. When air velocity exceeds 1.5 m/s, the response of drying rate to air velocity is significantly influenced by changes in dry basis moisture content. The results of this study indicated that it is feasible to use machine learning models for predicting and explaining the low-temperature drying process of sludge. This provides valuable insights for the application of machine learning models in the development and management of drying strategies.