UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Multivariate statistical process control of an industrial-scale fed-batch simulator

Duran-Villalobos, CA; Goldrick, S; Lennox, B; (2020) Multivariate statistical process control of an industrial-scale fed-batch simulator. Computers and Chemical Engineering , 132 , Article 106620. 10.1016/j.compchemeng.2019.106620. Green open access

[thumbnail of 1-s2.0-S0098135419304375-main.pdf]
Preview
Text
1-s2.0-S0098135419304375-main.pdf - Published Version

Download (2MB) | Preview

Abstract

This article presents an improved batch-to-batch optimisation technique that is shown to be able to bring the yield closer to its set-point from one batch to the next. In addition, an innovative Model Predictive Control technique is proposed that over multiple batches, reduces the variability in yield that occurs as a result of random variations in raw material properties and in-batch process fluctuations. The proposed controller uses validity constraints to restrict the decisional space to that described by the identification dataset that was used to develop an adaptive multi-way partial least squares model of the process. A further contribution of this article is the formulation of a bootstrap calculation to determine confidence intervals within the hard constraints imposed on model validity. The proposed control strategy was applied to a realistic industrial-scale fed-batch penicillin simulator, where its performance was demonstrated to provide improved consistency and yield when compared with nominal operation.

Type: Article
Title: Multivariate statistical process control of an industrial-scale fed-batch simulator
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.compchemeng.2019.106620
Publisher version: https://doi.org/10.1016/j.compchemeng.2019.106620
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Optimal control, Batch to batch optimisation, Model predictive control, Data-driven modelling, Missing data methods, Partial least square regression
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10086069
Downloads since deposit
141Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item