Clean-in-Place is an autonomous technique used to clean the internal surfaces of processing equipment in the food and drink sector. However, these systems clean for a longer time than required with negative economic and environmental impacts. In this work, an ultrasonic sensor system was developed to monitor the cleaning of different food fouling materials at laboratory scale. The fouling removal of three different food materials was also studied at different cleaning fluid temperatures. The three food materials had different cleaning mechanisms, which could be monitored successfully with the ultrasonic system. Tomato paste and gravy appeared to be cleaned by mechanical forces whereas malt extract dissolved into the cleaning water. The results yielded from the cleaning of the malt was found to be repeatable whereas the tomato and gravy were more variable between repeat experiments. It was found that changes in recorded ultrasonic signals were mainly affected by the area of fouling that covered the transducer's active element.
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.
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