Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression

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 fou...

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Detalhes bibliográficos
Autores: Escrig, Josep, Simeone, Alessandro, Watson, Nicholas, Wooley, E.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos: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:2072/531581
Acesso em linha:http://hdl.handle.net/2072/531581
Access Level:acceso abierto
Palavra-chave:Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
621.3
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oai_identifier_str oai:recercat.cat:2072/531581
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spelling Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regressionEscrig, JosepSimeone, AlessandroWatson, NicholasWooley, E.Distributed Artificial IntelligenceIndustryArtificial Intelligence & Big Data621.3Effectively 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.MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion20 p.application/pdfhttp://hdl.handle.net/2072/531581RECERCAT (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)InglésSensorsSensors 2020;20(13), 3642L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2072/5315812026-05-29T05:05:01Z
dc.title.none.fl_str_mv Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
title Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
spellingShingle Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
Escrig, Josep
Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
621.3
title_short Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
title_full Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
title_fullStr Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
title_full_unstemmed Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
title_sort Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression
dc.creator.none.fl_str_mv Escrig, Josep
Simeone, Alessandro
Watson, Nicholas
Wooley, E.
author Escrig, Josep
author_facet Escrig, Josep
Simeone, Alessandro
Watson, Nicholas
Wooley, E.
author_role author
author2 Simeone, Alessandro
Watson, Nicholas
Wooley, E.
author2_role author
author
author
dc.subject.none.fl_str_mv Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
621.3
topic Distributed Artificial Intelligence
Industry
Artificial Intelligence & Big Data
621.3
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/531581
url http://hdl.handle.net/2072/531581
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors
Sensors 2020;20(13), 3642
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20 p.
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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 15,198674

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