Author(s)

Yang Xu

Affiliation(s)

Gongshu District Water and Air Pollution Control Office, Hangzhou 310015, Zhejiang, China.

Corresponding Author

Yang Xu

ABSTRACT

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.

KEYWORDS

Industrial wastewater treatment efficiency; XGBoost-based intelligent analysis; Feature engineering; Principal component analysis (PCA); Time-series sliding window

CITE THIS PAPER

Yang Xu. An intelligent analysis model of factors influencing industrial wastewater treatment efficiency by incorporating XGBoost. World Journal of Agriculture and Forestry Sciences. 2024, 2(3): 46-53. DOI: https://doi.org/10.61784/wjafs3019.

REFERENCES

[1] Lv J, Du L, Lin H, et al. Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning. Bioresource Technology, 2024, 393: 130008.

[2] Li J, Du Z, Liu J, et al. Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations. Science of The Total Environment, 2024, 920: 171033.

[3] Aparna K G, Swarnalatha R. Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies. Process Safety and Environmental Protection, 2024, 188: 995-1008.

[4] Al-Jamimi H A, BinMakhashen G M, Saleh T A. From data to clean water: XGBoost and Bayesian optimization for advanced wastewater treatment with ultrafiltration. Neural Computing and Applications, 2024, 36(30): 18863-18877.

[5] Shao S, Fu D, Yang T, et al. Analysis of machine learning models for wastewater treatment plant sludge output prediction. Sustainability, 2023, 15(18): 13380.

[6] Nasir F B, Li J. Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables. Advances in Environmental and Engineering Research, 2024, 5(4): 1-23.

[7] Nguyen D V, Park J, Lee H, et al. Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development. Environmental Pollution, 2024, 341: 123017.

[8] Shu Y, Kong F, Lin X, et al. Leveraging ionic information for machine learning-enhanced source identification in integrated wastewater treatment plant. Journal of Water Process Engineering, 2025, 74: 107784.

[9] Saddiqi H A, Javed Z, Ali Q M, et al. Optimization and predictive modeling of membrane based produced water treatment using machine learning models. Chemical Engineering Research and Design, 2024, 207: 65-76.

[10] Tenneti S, Divya P D, Tejaswini E S S, et al. Interpretability and performance assessment of advanced machine learning models for α-factor prediction in wastewater treatment plants. Journal of Water Process Engineering, 2025, 72: 107637.