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Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning

journal contribution
posted on 29.04.2020 by Josep Escrig, Elliot Woolley, Alessandro Simeone, Nicholas Watson
Food and drink production equipment is routinely cleaned to ensure it remains hygienic and operating under optimal conditions. A limitation of existing cleaning systems is that they do not know when the fouling material has been removed so nearly always over-clean, incurring significant economic and environmental costs. This work has studied the use of ultrasonic measurements and a range of different machine learning classification methods to monitor the fouling removal of food materials in plastic and metal cylindrical pipes. The experimental results showed that the developed techniques could predict the presence of fouling with prediction confidence as high as 100% for both plastic and metal pipes. The sensor technique performed marginally better in the plastic pipe and similar performance was found for the all of the machine learning methods studied. This work has demonstrated the potential of low-cost ultrasonic sensors to monitor and therefore optimise cleaning processes within pipes. It is discussed how new data set labelling strategies will be required for the techniques to be used effectively within production environments.

Funding

Innovate UK projects 103936 and 132205

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Food Control

Volume

116

Issue

October 2020

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier Ltd

Publisher statement

This paper was accepted for publication in the journal Food Control and the definitive published version is available at https://doi.org/10.1016/j.foodcont.2020.107309.

Acceptance date

21/04/2020

Publication date

2020-04-25

Copyright date

2020

ISSN

0956-7135

Language

en

Depositor

Dr Elliot Woolley. Deposit date: 28 April 2020

Article number

107309

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