Digital twins-based process monitoring for wastewater treatment processes

Liu W.; Mou J.; Xiong W.He S.Xue T.Chen H.

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Digital twins-based process monitoring for wastewater treatment processes

机译：Digital twins-based process monitoring for wastewater treatment processes

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? 2023 Elsevier LtdDigital twins are a significant way to achieve fault detection of various smart manufacturing, which provide a new paradigm for complex industrial process monitoring. Wastewater treatment processes play a crucial role in water recycling, its failures may cause risks of adverse environmental impacts. This paper studies the digital twins fault detection framework based on the convolutional autoencoder for wastewater treatment processes monitoring. The designed digital twins fault detection framework can simulate the sludge bulking failure and the toxic impact failure conditions in the virtual space to construct the simulation data with continuous updating through wastewater data. The simulation data is divided into rate of change information sub-block, original sub-block, and cumulative information sub-block using the multi-block modeling strategy to fully explore the hidden information. Further, the sliding window method is utilized to resample the reconstructed sub-blocks to enhance the effects of the detection performance. Bayesian fusion is adopted, and the final decision is made based on the fused statistical value and the control limit. The comparison experiments tested on the digital twins fault detection framework demonstrate the superiority and feasibility of detection performance.

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来源
《Reliability engineering & system safety》 |2023年第10期|1.1-1.9|共9页
作者
Liu W.; Mou J.; Xiong W.He S.Xue T.Chen H.;
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作者单位

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University;

Department of Civil and Environmental Engineering the Hong Kong Polytechnic University;

College of Electrical Engineering and Automation Shandong University of Science and TechnologyDepartment of Automation Shanghai Jiao Tong University;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Convolutional autoencoder; Digital twins; Fault detection; Wastewater treatment processes;

Digital twins-based process monitoring for wastewater treatment processes

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