Artificial intelligence for monitoring and supervisory control of process systems
Introduction
Computerized control systems that monitor, control, and diagnose process variables such as pressure, flow, and temperature have been implemented for various processes. When these systems are for large-scale processes, they generate many process variable values, and operators often find it difficult to effectively monitor the process data, analyze current states, detect and diagnose process anomalies, and/or take appropriate actions to control the processes. To assist plant operators, process operational information must be analysed and presented in a manner that reflects the important underlying trends or events in the process (Rengaswamy and Venkatasubramanian, 1992). Intelligent decision support systems that incorporate a variety of AI and non-AI techniques can support this task. Our survey of some relevant literature reveals three general solution approaches for supporting the tasks of monitoring, control, and diagnosis can be identified. They include the data-driven, analytical, and knowledge based approaches (Chiang et al., 2001). Our review of the relevant literature also reveals extensive research effort has been devoted to enhancing robustness of the approaches by combining them so as to minimize their weaknesses and maximize their strengths. However, successful integration of the three approaches has not been realized. The task of integrating the solution approaches is rendered more complex due to the proliferation of software and databases, which makes it impossible to combine these approaches using the rigid structure of conventional integration methods. The objective of this paper is to explain characteristics of the three solution approaches and present efforts at integration conducted at some major research centers in both North America and Europe. The discussion also presents a summary of approaches from each of the four research groups as well as their advantages and disadvantages.
Section snippets
Solution approaches for developing intelligent support systems in process control engineering
For developing decision support systems in process control engineering, the three solution approaches of data driven, analytical, and knowledge-based have been identified; each approach will be discussed in detail as follows.
A survey of four integrated approaches for monitoring, diagnosis and control of industrial processes
Our survey on literature about development of intelligent systems for monitoring, diagnosis and control in process industries reveals that the three solution approaches described in Section 2 are often combined in system construction. Due to growing complexity of current systems, the integration of the three solution approaches into an intelligent system requires a framework which coordinates communication among the different solution modules. Tzafestas and Verbruggen (1995) stated that an
Discussion
On comparing the four integrated frameworks of IRTW, INTEMOR, OOSE, and DKIT in terms of their approaches and tasks addressed, a number of similarities and differences can be observed. The four integrated frameworks for development of intelligent systems for process systems combine the three solution approaches, but the priority they assign to the approaches are different. INTEMOR, DKIT and IRTW give the highest priority to the knowledge-based system (KBS) solution approach whereas OOSS assign
Conclusion
This paper has presented an overview of research work on development of intelligent systems for monitoring, supervisory control, and diagnosis of operations in process systems engineering. We have discussed the three solution approaches often adopted for building automated systems in process control engineering, namely the data-driven, analytical and knowledge-based approaches. Some popular algorithms and applications of each approach have also been discussed. We have also presented what we
Acknowledgement
We would like to acknowledge the generous financial support of a Strategic Grant from Natural Sciences and Engineering Research Council (NSERC) of Canada.
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Introduction (chapter 1)
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