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Multivariate statistical methods in bioprocess fault detection and performance forecasting

Department of Chemical and Process Engineering

J. Glassey

Department of Chemical and Process Engineering

A.C. Ward

Department of Microbiology, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK

G.A. Montague

Department of Chemical and Process Engineering

This paper demonstrates how multivariante statistical data analysis procedures used as feature extraction methods can assist in the operation of an industrial fermentation process. The quality of the production fermenter seed and the subsequent forecasting of productivity are the two examples considered, with results presented from industrial plant. The feature extraction methodologies utilised are based around principal component analysis (PCA) and the extension to batch systems through the use of multi-way PCA.

Key Words: Principal component analysis (PCA) • partial least-squares regression (PLS) • fault detection • forecasting • fermentation.

Transactions of the Institute of Measurement and Control, Vol. 19, No. 5, 271-279 (1997)
DOI: 10.1177/014233129701900507


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