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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

Alexander L. Bowler

Sarah Rodgers

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

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