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Open AccessArticle

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

1
Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou 515063, China
2
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK
3
i2CAT Foundation, Calle Gran Capita, 2 -4 Edifici Nexus (Campus Nord Upc), 08034 Barcelona, Spain
4
Food, Water, Waste, Research Group, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3642; https://doi.org/10.3390/s20133642
Received: 14 May 2020 / Revised: 8 June 2020 / Accepted: 16 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Sensors for Manufacturing Process Monitoring)
Effectively cleaning 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 optimize 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. View Full-Text
Keywords: ultrasonic sensors; optical sensors; machine learning; regression; artificial neural networks; Clean-in-Place; digital manufacturing; industry 4.0; process optimisation ultrasonic sensors; optical sensors; machine learning; regression; artificial neural networks; Clean-in-Place; digital manufacturing; industry 4.0; process optimisation
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Simeone, A.; Woolley, E.; Escrig, J.; Watson, N.J. Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression. Sensors 2020, 20, 3642.

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