Alexander L. Bowler
Bayesian and ultrasonic sensor aided multi-objective optimisation for sustainable clean-in-place processes
Bowler, Alexander L.; Rodgers, Sarah; Cook, David J.; Watson, Nicholas J.
Authors
Sarah Rodgers
Professor DAVID COOK david.cook@nottingham.ac.uk
SABMILLER CHAIR BREWING SCIENCE
Nicholas J. Watson
Abstract
In food and drink manufacturing, clean-in-place procedures are essential for hygienic and efficient operations but often over-clean process equipment leading to unnecessary use of energy, water, and chemicals. Previous attempts in the literature to optimise clean-in-place processes have focused on trialling cleaning over a range of parameter (e.g. temperature and chemical concentration) combinations or modelling the process using equations. However, these methods do not aim to minimise the number of experimental trials that a manufacturer must conduct and only determine the optimal cleaning parameters for the average fouling condition. In this work, Bayesian optimisation is used to minimise the number of cleaning parameter combinations that require trialling thereby reducing the disruption to a manufacturing process during the optimisation procedure. Secondly, ultrasonic sensors are used to monitor the cleaning process and enable real-time optimisation of the parameters to adapt to variations in the fouling condition. Multi-objective optimisation was used in both tasks to simultaneously minimise the economic cost, carbon footprint, and water usage of a clean-in-place process. Bayesian optimisation was able to optimise the process after trialling only nine cleaning parameter combinations (achieving between 98.7% and 100% optimisation of the objective function compared with the global optimum). Bayesian optimisation displayed a small advantage (0.0–4.7% decrease in the objective function) compared with methods used in previous literature. Real-time optimisation of the cleaning parameters using ultrasonic sensor data improved the optimisation objective function by 0.0 – 4.8% for all fouling instances tested when utilising results from ten trials conducted during the Bayesian optimisation procedure along with five additional cleaning processes under normal operation.
Citation
Bowler, A. L., Rodgers, S., Cook, D. J., & Watson, N. J. (2023). Bayesian and ultrasonic sensor aided multi-objective optimisation for sustainable clean-in-place processes. Food and Bioproducts Processing, 141, 23-35. https://doi.org/10.1016/j.fbp.2023.06.010
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 30, 2023 |
Online Publication Date | Jul 1, 2023 |
Publication Date | 2023-09 |
Deposit Date | Sep 29, 2023 |
Publicly Available Date | Oct 19, 2023 |
Journal | Food and Bioproducts Processing |
Print ISSN | 0960-3085 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 141 |
Pages | 23-35 |
DOI | https://doi.org/10.1016/j.fbp.2023.06.010 |
Public URL | https://nottingham-repository.worktribe.com/output/25393236 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S0960308523000755?via%3Dihub |
Files
1-s2.0-S0960308523000755-main
(3 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Untargeted metabolomic profiling of 100% malt beers versus those containing barley adjunct
(2024)
Journal Article
Formation of staling aldehydes in different grain bed layers in an industrial scale maltings
(2023)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search