PCA based data reconciliation in soft sensor development - Application for melt flow index estimation

Barbara Farsang, Imre Balogh, Sándor Németh, Zoltán Székvölgyi, János Abonyi

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

Melt flow index (MFI) is a very important property of thermoplastic polymers. Laboratory measurements follow standard methods (ASTM D1238 or ISO 1133) to determine MFI and give accurate values, but these measurements are available only in 2-4 hour sampling intervals. Using soft sensors real time estimation of MFI is available for process control and monitoring. When detailed knowledge about the process is not available, data-driven soft sensors can be applied. In this case historical process data are used to build statistical models to determine the relationship between inputs and outputs. Since these methods are based on measurements, the performance of soft sensor depends on the quality of data. Measurements are always affected by errors so pre-processing of data should be necessary. The measurement noise and process variable can be correlated with each other so one opportunity to improve measurement accuracy is using multivariate statistical methods (PCA, PLS). Statistical methods can be improved when phenomenological knowledge is taken into account (e.g. balance equations). The aim of the presented research is to propose a methodology to support the data-driven development of process monitoring systems. We developed a method which improves the effectiveness of data based MFI soft sensor. This method includes advantages of a priori knowledge based models and data-driven multivariate statistical process monitoring tools. As a case study we developed a soft-sensor to estimate MFI of the products of industrial polypropylene reactor at TVK Plc. The proposed method is able to detect undesirable operation states and it can be used for fault detection.

Original languageEnglish
Title of host publicationChemical Engineering Transactions
EditorsSauro Pierucci, Jiri J. Klemes
PublisherItalian Association of Chemical Engineering - AIDIC
Pages1555-1560
Number of pages6
Volume43
ISBN (Electronic)9788895608341
DOIs
Publication statusPublished - Jan 1 2015

ASJC Scopus subject areas

  • Chemical Engineering(all)

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    Farsang, B., Balogh, I., Németh, S., Székvölgyi, Z., & Abonyi, J. (2015). PCA based data reconciliation in soft sensor development - Application for melt flow index estimation. In S. Pierucci, & J. J. Klemes (Eds.), Chemical Engineering Transactions (Vol. 43, pp. 1555-1560). Italian Association of Chemical Engineering - AIDIC. https://doi.org/10.3303/CET1543260