Artificial intelligence for monitoring and supervisory control of process systems |
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| Affiliation: | 1. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;2. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;3. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;1. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei 106, Taiwan;2. Faculty of Project Management, The University of Danang – University of Science and Technology, 54 Nguyen Luong Bang, Danang, Vietnam;3. Del E. Webb School of Construction, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA;1. Department of Geology, Faculty of Science, University of Tabriz, Tabriz, East Azarbaijan, Iran;2. Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA;3. Department of Environmental Science, Policy and Geography, University of South Florida, Saint Petersburg, Davis 209, 140 Seventh Ave. South, Saint Petersburg, FL 33701, USA;1. MRI Unit, Radiology Department, Health Time, Jaén, Spain;2. 3D Printing Unit, Engineering Department, Health Time, Jaén, Spain;3. SINAI Research Group, Computer Science Department, Advanced Studies Center in ICT (CEATIC), Universidad de Jaén, Jaén, Spain;4. Department of Radiology, Mayo Clinic, Scottsdale, Arizona |
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| Abstract: | Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49–54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses. |
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