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Quality Prediction for a Fed-Batch Fermentation Process Using Multi-Block PLS

Lookup NU author(s): Jeong Hong, Dr Jie ZhangORCiD


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Quality prediction is usually required for product quality monitoring and setting up control strategy can reduce operating cost and improve production efficiency. Partial least square (PLS) regression is a popular statistical method for predictive modelling. The amount of data measured and stored in a typical industrial process is dramatically increased due to the fast development of computer and measuring system. It is hard to analyse all measured data using one matrix for its complexity. Multi-Block PLS model allows the data to be separated into sub-blocks and the sub-blocks can be analysed independently. Data from the fed-batch fermentation process is used to build models. Data is divided by different modes and different phases and model parameters are used to select variables that can be used as good predictors. The new set of data after variable selections is used to build a new model again. In most cases, new models show improved prediction performances compared with results from the conventional method.

Publication metadata

Author(s): Hong JJ, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: EKC 2009 Proceedings of EU-Korea Conference on Science and Technology

Year of Conference: 2010

Pages: 155-162

Publisher: Springer

Library holdings: Search Newcastle University Library for this item

Series Title: Springer Proceedings in Physics

ISBN: 9783642136238