Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment  

作　　者：Hong-Cheng Wang Yu-Qi Wang Xu Wang Wan-Xin Yin Ting-Chao Yu Chen-Hao Xue Ai-Jie Wang 

机构地区：[1]State Key Laboratory of Urban Water Resource and Environment,School of Civil and Environmental Engineering,Harbin Institute of Technology(Shenzhen),Shenzhen 518055,China [2]Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province,College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China

出　　处：《Engineering》2024年第5期51-62,共12页工程（英文）

基　　金：the financial support by the National Natural Science Foundation of China(52230004 and 52293445);the Key Research and Development Project of Shandong Province(2020CXGC011202-005);the Shenzhen Science and Technology Program(KCXFZ20211020163404007 and KQTD20190929172630447).

摘　　要：The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising solution.Here,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML models.The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and interpretability.Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4%and a coefficient of determination of 0.948.Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control methods.The potential application of these strategies in critical water science domains is discussed.To foster accessibility and promote widespread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

关 键 词：Wastewater treatment Multimodal machine learning Deep learning Aeration control Interpretable machine learning 

分 类 号：X703[环境科学与工程—环境工程]