Multivariate monitoring of batch process startup

Robin C. Wurl, Susan L. Albin

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

A batch process is finite in duration and can be separated into two stages: startup and production. We develop a methodology to monitor a batch process during the startup stage to reduce the length of the startup stage. We focus on processes that are characterized by multiple process parameters and product characteristics. Because of the complex interdependencies characterizing the process parameters and product characteristics, it is more effective to evaluate them simultaneously. To address the multivariate nature of the process we use a multivariate statistical model: PLS (Projection to Latent Structures). PLS has been applied to several applications in statistical process monitoring. We present a new application of PLS to the startup stage of a batch process. Iterative adjustments made during startup in search of an acceptable production zone consume considerable amounts of material, labor and equipment time. We develop a monitoring procedure to reduce the time as well as the number of iterations and adjustments needed for startup. A PLS model is constructed, using baseline data, to characterize the relationship among process parameters during good production. The startup stage is monitored using the PLS characterization to determine if the process is consistent with good production. We illustrate the improved startup operations with an example from a batch process in filament extrusion, the application that motivates this work.

Original languageEnglish (US)
Pages (from-to)269-278
Number of pages10
JournalQuality and Reliability Engineering International
Volume17
Issue number4
DOIs
StatePublished - Jul 2001

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Keywords

  • Batch process startup
  • Multivariate statistical process control
  • Projection to latent structures (PLS)
  • Statistical process monitoring

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