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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 13; p. 3642
• Main Authors: Simeone, Alessandro, Woolley, Elliot, Escrig, Josep, Watson, Nicholas James
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.06.2020; MDPI
• Subjects: Acoustics; artificial neural networks; Clean-in-Place; Cleaning; Clinical decision making; Data analysis; Decision making; Decomposition; Dissimilar materials; Fluids; Food; Fouling; Heat; Image processing; Machine learning; Manufacturing; Methods; Neural networks; optical sensors; regression; Sensors; Signal processing; Time series; ultrasonic sensors; United States > US
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20133642

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.