Online monitoring and diagnosis of batch processes: empirical model-based framework and a case study

Online monitoring and diagnosis of batch processes: empirical model-based framework and a case study

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Article ID: iaor2007110
Country: United Kingdom
Volume: 44
Issue: 12
Start Page Number: 2361
End Page Number: 2378
Publication Date: Jan 2006
Journal: International Journal of Production Research
Authors: , ,
Keywords: statistics: multivariate
Abstract:

An empirical model-based framework for monitoring and diagnosing batch processes is proposed. With the input of past successful and unsuccessful batches, the off-line portion of the framework constructs empirical models. Using online process data of a new batch, the online portion of the framework makes monitoring and diagnostic decisions in a real-time basis. The proposed framework consists of three phases: monitoring, diagnostic screening, and diagnosis. For monitoring and diagnosis purposes, the multiway principal-component analysis (MPCA) model and discriminant model are adopted as reference models. As an intermediate step, the diagnostic screening phase narrows down the possible cause candidates of the fault in question. By analysing the MPCA monitoring model, the diagnostic screening phase constructs a variable influence model to screen out unlikely cause candidates. The performance of the proposed framework is tested using a real dataset from a PVC batch process. It has been shown that the proposed framework produces reliable diagnosis results. Moreover, the inclusion of the diagnostic screening phase as a pre-diagnostic step has improved the diagnosis performance of the proposed framework, especially in the early time intervals.

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