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Title: Pattern recognition approaches to state identification in chemical plants
Keywords: Dynamic PCA, Clustering, Neural Network, Classification, Context Classification
Issue Date: 17-Feb-2005
Citation: WANG CHENG (2005-02-17). Pattern recognition approaches to state identification in chemical plants. ScholarBank@NUS Repository.
Abstract: Applying operating state-based supervisory control to chemical process becomes more and more attractive since chemical processes operate in multiple steady state operating conditions and transition between them. In this thesis, three closely related problems towards the uses of effective operation have been addressed. Offline clustering of process states in historical data can be used to compare different operating states. Different stages of a multi-step operation (such as startup of FCCU) can be assessed for similarity. Traditional clustering methods are computationally expensive and normally perform poorly on temporal signals. A two-step clustering method based on Dynamic Principal Component Analysis (DPCA) is proposed in this thesis. Dynamic PCA based similarity measures are used to compare the different modes and the different transitions and cluster them. Once offline clustering has provided the essential understanding of the process, an online classifier has to be built to monitor and identify the process state in real time. Two new neural network structures a?? One-Variable-One-Network (OVON) and One-Class-One-Network (OCON) a?? that overcome this problem are proposed in this thesis. In comparison to traditional monolithic neural networks, both the proposed architectures improve classification accuracy and minimize the training complexity. In addition, OVON is robust to sensor failures and OCON is well suited for addition of new pattern classes. Context-based pattern recognition arises when the interpretation of a pattern varies across contexts. It is shown that the identification of the state of chemical or biological processes is context-dependent. To address this problem, a neural network based architecture a?? operating state identification neural network (OSINN) a?? is proposed in this thesis. In OSINN, process measurements can be used as primary features for identifying the current process state, and the previous process state provides the context in which the primary features have to be interpreted. Three variations of the architecture, each using a different approach to identify change of context, are described.
Appears in Collections:Ph.D Theses (Open)

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