Abstract:  Accurate prediction of ash transportation output in the power ash transportation system of thermal power plants is of great significance for improving the overall efficiency of power generation. At present， the pneumatic ash transportation systems in thermal power plants mainly rely on manual experience for operation. Based on this， an intelligent ash transportation prediction model based on the XGBoost （eXtreme Gradient Boosting） and SHAP （Shapley Additive Explanation） framework is proposed. Firstly， data such as air pressure and equipment temperature are acquired from the DCS （Distributed Control System） of the power plant’s ash transportation system. Secondly， to enhance the accuracy of the model predictions and prevent overfitting， RFECV （Recursive Feature Elimination with Cross-Validation） is used for feature selection. The selected feature set is then imported into the XGBoost-based intelligent ash transportation prediction model. Concurrently， the SHAP model is utilized for visual causal analysis， thereby discovering useful information from the power ash transportation data to form a knowledge base for the power plant’s ash transportation system， aiming to achieve more intelligent and efficient operation. The research results can provide data support for early warning technology for ash transportation in thermal power plants and the intelligent upgrade of ash transportation systems， which helps to energy saving and consumption reduction in power plant ash transportation systems.

Key words:  , thermal power plant, ash conveying prediction, XGBoost model, SHAP