Detection of dairy fouling: Combining ultrasonic measurements and classification methods. Issue 3 (27th April 2013)

• Record Type: Journal Article
• Authors: Wallhäußer, E.; Hussein, W.B.; Hussein, M.A.; Hinrichs, J.; Becker, T.
• Is Part Of: Engineering in life sciences. Volume 13:Issue 3(2013:May)
• Page Start: 292
• Page End: 301
• Publication Date: 2013-04-27
• Subjects: Bioengineering -- Periodicals; 660.605
• DOI: 10.1002/elsc.201200081
• Languages: English
• ISSNs: 1618-0240

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.</p> </abstract>