Bowler, A.L. orcid.org/0000-0003-3209-2774, Rodgers, S., Cook, D.J. et al. (1 more author) (2023) Bayesian and ultrasonic sensor aided multi-objective optimisation for sustainable clean-in-place processes. Food and Bioproducts Processing, 141. pp. 23-35. ISSN 0960-3085
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Crown Copyright. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Clean in place; Energy use; Bayesian optimisation; Multi-objective optimisation; Ultrasonic sensors; Neural networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jul 2024 16:20 |
Last Modified: | 10 Jul 2024 16:20 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.fbp.2023.06.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214604 |