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第25届中国控制与决策会议

作者： Wei Ding Linbo Xie Zhihai Wu Key Laboratory of Advanced Process control for Light Industry （Ministry of Education） Jiangnan University

ISBN: (纸本)9781467355339

A pulse-width-modulation （PWM） digital controller design method in aeration control, which can reduce the energy costs of the control procedure, is proposed for wastewater treatment process based on the ASM1 model. First, an optimal linearization technique is applied to the aeration nonlinear system for finding a local optimal linear model, which has the same dynamics of the original nonlinear system at the operating point of interest. Then, a discrete-time optimal control law in pulse-amplitude-modulated （PAM） form is designed using LQR method. The PWM digital control law is then converted, using the digital redesign technique, from the corresponding PAM control law for the purpose of engineering realization. Last, simulation results demonstrate the effectiveness of the proposed method and the whole energy consumption is also computed. It has been shown that compared with the conventional multi-set point on/off control and on/off control method, the proposed method can save more energy than other approaches.

关键词： Wastewater treatment process PAM PWM aeration control

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

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Engineering 2024年第5期36卷 51-62页

作者： Hong-Cheng Wang Yu-Qi Wang Xu Wang Wan-Xin Yin Ting-Chao Yu Chen-Hao Xue Ai-Jie Wang State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China

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 ***,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 *** 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 *** 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 *** testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control *** potential application of these strategies in critical water science domains is *** 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

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