Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes

Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determ...

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Detalles Bibliográficos
Autores: Escrig, Josep, Rady, A., Rangappa, S., Simeone, A., Watson, N.J., Wolley, E.
Tipo de recurso: artículo
Estado:Versión borrador
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/530725
Acceso en línea:http://hdl.handle.net/2072/530725
Access Level:acceso abierto
Palabra clave:Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
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spelling Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place ProcessesEscrig, JosepRady, A.Rangappa, S.Simeone, A.Watson, N.J.Wolley, E.Distributed Artificial IntelligenceIndustryArtificial Intelligence & Big DataCleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials.Elsevier Ltd2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/draft25 p.application/pdfhttp://hdl.handle.net/2072/530725RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésFood and Bioproducts ProcessingVolume 123;Crown Copyright © 2020 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers. All rights reserved.info:eu-repo/semantics/openAccessoai:recercat.cat:2072/5307252026-05-29T05:05:01Z
dc.title.none.fl_str_mv Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
title Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
spellingShingle Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
Escrig, Josep
Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
title_short Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
title_full Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
title_fullStr Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
title_full_unstemmed Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
title_sort Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes
dc.creator.none.fl_str_mv Escrig, Josep
Rady, A.
Rangappa, S.
Simeone, A.
Watson, N.J.
Wolley, E.
author Escrig, Josep
author_facet Escrig, Josep
Rady, A.
Rangappa, S.
Simeone, A.
Watson, N.J.
Wolley, E.
author_role author
author2 Rady, A.
Rangappa, S.
Simeone, A.
Watson, N.J.
Wolley, E.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
topic Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
description Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/draft
format article
status_str draft
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/530725
url http://hdl.handle.net/2072/530725
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Food and Bioproducts Processing
Volume 123;
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 25 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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