Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

• VSL was designed for as specialized feature engineering approach for intelligent control of WWTPs. • Performance of Machine Learning for Multiple Classes Improved by VSL. • Machine learning models based on VSL reduce energy consumption of aeration. • An automatic Python library called 'wwtpai' packages has been made free and open sourced. Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai , which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs. The surrounding water environment represents the wastewater treatment plant, the phrase(TN, TEMP, COD, Time, DO, MLSS, Flow rate and NH 3 -N) in the bubble indicates that the commonly used indicators of the wastewater plant are used to predict the air demand of aeration at the top of the figure. Human brain represents method of artificial intelligence, multiple neurons represent 12 algorithms (GBDT,LSTM,ANN,HUBER,KNN,SVM,ROBUST,DT,LGBM,VAYER,RF and XGB). The aeration quantity can be predicted through various algorithms. Use the library of Python (wwtpai) based on variation sliding layer (VSL) encapsulation to optimize the prediction result. [Display omitted] [ABSTRACT FROM AUTHOR]