Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.
Identifieur interne : 000261 ( PubMed/Corpus ); précédent : 000260; suivant : 000262Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.
Auteurs : Víctor Martínez-Martínez ; Carlos Baladr N ; Jaime Gomez-Gil ; Gonzalo Ruiz-Ruiz ; Luis M. Navas-Gracia ; Javier M. Aguiar ; Belén CarroSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Agriculture, Desiccation, Environmental Monitoring.
- methods : Agriculture, Desiccation, Environmental Monitoring.
- Environment, Controlled, Humidity, Neural Networks (Computer), Temperature, Tobacco.
Abstract
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
DOI: 10.3390/s121014004
PubMed: 23202032
Links to Exploration step
pubmed:23202032Le document en format XML
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Navas-Gracia</name></author><author><name sortKey="Aguiar, Javier M" sort="Aguiar, Javier M" uniqKey="Aguiar J" first="Javier M" last="Aguiar">Javier M. Aguiar</name></author><author><name sortKey="Carro, Belen" sort="Carro, Belen" uniqKey="Carro B" first="Belén" last="Carro">Belén Carro</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="doi">10.3390/s121014004</idno><idno type="RBID">pubmed:23202032</idno><idno type="pmid">23202032</idno><idno type="wicri:Area/PubMed/Corpus">000261</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000261</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.</title><author><name sortKey="Martinez Martinez, Victor" sort="Martinez Martinez, Victor" uniqKey="Martinez Martinez V" first="Víctor" last="Martínez-Martínez">Víctor Martínez-Martínez</name><affiliation><nlm:affiliation>Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain. vmarmar@ribera.tel.uva.es.</nlm:affiliation></affiliation></author><author><name sortKey="Baladr N, Carlos" sort="Baladr N, Carlos" uniqKey="Baladr N C" first="Carlos" last="Baladr N">Carlos Baladr N</name></author><author><name sortKey="Gomez Gil, Jaime" sort="Gomez Gil, Jaime" uniqKey="Gomez Gil J" first="Jaime" last="Gomez-Gil">Jaime Gomez-Gil</name></author><author><name sortKey="Ruiz Ruiz, Gonzalo" sort="Ruiz Ruiz, Gonzalo" uniqKey="Ruiz Ruiz G" first="Gonzalo" last="Ruiz-Ruiz">Gonzalo Ruiz-Ruiz</name></author><author><name sortKey="Navas Gracia, Luis M" sort="Navas Gracia, Luis M" uniqKey="Navas Gracia L" first="Luis M" last="Navas-Gracia">Luis M. Navas-Gracia</name></author><author><name sortKey="Aguiar, Javier M" sort="Aguiar, Javier M" uniqKey="Aguiar J" first="Javier M" last="Aguiar">Javier M. Aguiar</name></author><author><name sortKey="Carro, Belen" sort="Carro, Belen" uniqKey="Carro B" first="Belén" last="Carro">Belén Carro</name></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture (instrumentation)</term><term>Agriculture (methods)</term><term>Desiccation (instrumentation)</term><term>Desiccation (methods)</term><term>Environment, Controlled</term><term>Environmental Monitoring (instrumentation)</term><term>Environmental Monitoring (methods)</term><term>Humidity</term><term>Neural Networks (Computer)</term><term>Temperature</term><term>Tobacco</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Agriculture</term><term>Desiccation</term><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Agriculture</term><term>Desiccation</term><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Environment, Controlled</term><term>Humidity</term><term>Neural Networks (Computer)</term><term>Temperature</term><term>Tobacco</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">23202032</PMID><DateCreated><Year>2012</Year><Month>12</Month><Day>03</Day></DateCreated><DateCompleted><Year>2014</Year><Month>03</Month><Day>31</Day></DateCompleted><DateRevised><Year>2015</Year><Month>02</Month><Day>19</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>10</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.</ArticleTitle><Pagination><MedlinePgn>14004-21</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s121014004</ELocationID><Abstract><AbstractText>This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Martínez-Martínez</LastName><ForeName>Víctor</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain. vmarmar@ribera.tel.uva.es.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baladrón</LastName><ForeName>Carlos</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Gomez-Gil</LastName><ForeName>Jaime</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Ruiz-Ruiz</LastName><ForeName>Gonzalo</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Navas-Gracia</LastName><ForeName>Luis M</ForeName><Initials>LM</Initials></Author><Author ValidYN="Y"><LastName>Aguiar</LastName><ForeName>Javier M</ForeName><Initials>JM</Initials></Author><Author ValidYN="Y"><LastName>Carro</LastName><ForeName>Belén</ForeName><Initials>B</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>10</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Sensors (Basel)</MedlineTA><NlmUniqueID>101204366</NlmUniqueID><ISSNLinking>1424-8220</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>ISA Trans. 2011 Apr;50(2):177-94</RefSource><PMID Version="1">21281932</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sensors (Basel). 2010;10(10):8963-80</RefSource><PMID Version="1">22163391</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sensors (Basel). 2012;12(2):1468-81</RefSource><PMID Version="1">22438720</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sensors (Basel). 2011;11(12):11235-50</RefSource><PMID Version="1">22247663</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sensors (Basel). 2011;11(4):3640-51</RefSource><PMID Version="1">22163813</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sensors (Basel). 2010;10(12):11189-211</RefSource><PMID Version="1">22163520</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000383">Agriculture</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003890">Desiccation</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004780">Environment, Controlled</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004784">Environmental Monitoring</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006813">Humidity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D016571">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" 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