Using ultrasonic measurements and machine learning for the cleaning of food fouling in pipes

Lack of knowledge of when to clean pipelines can result in significant economic and environmental costs. Ultrasonic measurements and machine learning can be used to detect fouling in food pipes. A study published in Food Control described 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 data 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 technique performed marginally better in the plastic pipe. All machine learning methods studied had a good performance. The study showed the potential of a low-cost ultrasonic sensor to monitor and therefore optimize cleaning processes within pipes.  @ https://www.sciencedirect.com/science/article/abs/pii/S0956713520302255?dgcid=rss_sd_all