AN INTELLIGENT ANALYSIS MODEL OF FACTORS INFLUENCING INDUSTRIAL WASTEWATER TREATMENT EFFICIENCY BY INCORPORATING XGBOOST

In order to improve the accuracy and system understanding of industrial wastewater treatment efficiency prediction, an intelligent analysis model integrating XGBoost is constructed with a typical A2/O process wastewater treatment system as an example. 14 high-frequency operational variables are collected and processed in the system, and a multi-dimensional input system containing ratio features and time-difference features is designed. Combining the PCA dimensionality reduction and the temporal sliding window mechanism, the model effectively compresses the redundant information and enhances the expression ability of the dynamic features. The model stability and generalization ability are improved by the joint tuning strategy of grid search and Bayesian optimization. Comparison of the SVR, RF and MLP models shows that XGBoost is better in terms of prediction accuracy, robustness and feature interpretation, and SHAP analysis further clarifies the dominant roles of COD, NH??-N and other variables in the performance of the system, which verifies the potential and scalability of the constructed model in complex industrial scenarios. The constructed model is validated for its practical potential and extension value in complex industrial scenarios.