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Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during clean-in-place processes

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journal contribution
posted on 2020-05-11, 09:21 authored by Josep Escrig-Escrig, Alessandro Simeone, Elliot WoolleyElliot Woolley, Shreedhar Rangappa, A Rady, Nicholas Watson
Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials.

Funding

Innovate UK projects 103936 and 132205.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Food and Bioproducts Processing

Volume

123

Pages

1 - 13

Publisher

Elsevier B.V. on behalf of Institution of Chemical Engineers

Version

  • AM (Accepted Manuscript)

Rights holder

© Crown Copyright

Publisher statement

This paper was accepted for publication in the journal Food and Bioproducts Processing and the definitive published version is available at https://doi.org/10.1016/j.fbp.2020.05.003.

Acceptance date

2020-05-06

Publication date

2020-05-23

Copyright date

2020

ISSN

0960-3085

Language

  • en

Depositor

Dr Elliot Woolley. Deposit date: 7 May 2020

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