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Fault Detection & SPC of Batch Process using Multi-way Regression Method  

Woo, Kyoung Sup (School of Chemical & Biological Engineering Seoul National University)
Lee, Chang Jun (School of Chemical & Biological Engineering Seoul National University)
Han, Kyoung Hoon (School of Chemical & Biological Engineering Seoul National University)
Ko, Jae Wook (Department of Chemical Engineering Kwangwoon University)
Yoon, En Sup (School of Chemical & Biological Engineering Seoul National University)
Publication Information
Korean Chemical Engineering Research / v.45, no.1, 2007 , pp. 32-38 More about this Journal
Abstract
A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.
Keywords
Batch Process; Fault Detection; Statistical Process Control; Multi-way Unfolding; Support Vector Regression; Partial Least Squares;
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