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Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression

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journal contribution
posted on 19.06.2020, 10:47 by Alessandro Simeone, Elliot WoolleyElliot Woolley, Josep Escrig, Nicholas James Watson
Effective cleaning of equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy and chemicals. To optimise the cleaning of food production equipment there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97% respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

Funding

Innovate UK projects 103936 and 132205

Research Startup Fund Subsidized Project of Shantou University, China, (No. NFT17004)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Sensors

Volume

20

Issue

13

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

16/06/2020

Publication date

2020-06-29

Copyright date

2020

ISSN

1424-8220

eISSN

1424-8220

Language

en

Depositor

Dr Elliot Woolley Deposit date: 18 June 2020

Article number

3642