Multi-Level Optimization and Prediction of Water Wall Wear Protection Model Using Ensemble Learning Method

In order to solve the problem that water-cooled walls suffer severe wear under high temperature and high pressure conditions, which leads to shortened equipment life and increased safety hazards, this paper introduces a watercooled wall wear protection model based on an integrated learning method. Firstly, this paper collects and organizes the wear data under different operating conditions and performs preprocessing. Then this paper adopts the Boosting ensemble method and uses XGBoost (eXtreme Gradient Boosting) as the base learner. The prediction errors are corrected through gradual training, and the prediction results are optimized by combining the Deep Neural Network (DNN) to improve the prediction accuracy. Next, this paper applies Bayesian optimization and grid search to tune the hyperparameters. Finally, in order to further improve the performance, this paper adopts weighted average and stacking ensemble strategies to fuse the prediction results of multiple base learners. In the experimental conclusion, the research model showed excellent accuracy and stability in predicting water wall wear. The model combining XGBoost and DNN achieved the lowest RMSE (Root Mean Squared Error) of 0.085 and MAE (Mean Absolute Error) of 0.062. In addition, the model exhibits good generalization ability at different data scales and can effectively predict the wear trend of water-cooled walls, providing reliable data support for equipment maintenance and safety assurance.