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.

废水处理厂（WWTP）的智能控制具有显着减少能源消耗和温室气体排放的潜力。机器学习 (ML) 提供了一种很有前途的解决方案来处理不断增加的生成数据的数量和复杂性。然而，废水数据集特征之间的关系通常不明显，这阻碍了人工智能（AI）在污水处理厂智能控制中的应用。在本研究中，我们开发了一种基于变化滑动层（VSL）的特征工程自动框架来精确控制空气需求。结果表明，在经典机器学习、深度学习和集成学习中使用VSL可以显着提高污水处理厂曝气智能控制的效率。贝叶斯回归和集成学习实现了空气需求预测的最高准确度。采用 VSL-ML 模型开发的模型也在全规模污水处理厂中成功实施，与预设溶解氧 (DO) 和反馈鼓风机的传统曝气控制相比，需求减少了 16.12%。VSL-ML模型在精确空气需求预测和控制方面显示出巨大的应用潜力。该包作为Python三方库，名为wwtpai，可在GitHub和CSDN上免费获取，以消除AI技术在污水处理厂应用的技术障碍。