Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
Identifieur interne : 000857 ( Ncbi/Curation ); précédent : 000856; suivant : 000858Temperature 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.
Descripteurs français
- KwdFr :
- MESH :
- instrumentation : Agriculture, Dessiccation, Environnement contrôlé, Humidité, Réseaux neuronaux (ordinateur), Surveillance de l'environnement, Tabac, Température.
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.
Url:
DOI: 10.3390/s121014004
PubMed: 23202032
PubMed Central: 3545603
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PMC:3545603Le document en format XML
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<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>
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<term>Agriculture (instrumentation)</term>
<term>Dessiccation ()</term>
<term>Dessiccation (instrumentation)</term>
<term>Environnement contrôlé</term>
<term>Humidité</term>
<term>Réseaux neuronaux (ordinateur)</term>
<term>Surveillance de l'environnement ()</term>
<term>Surveillance de l'environnement (instrumentation)</term>
<term>Tabac</term>
<term>Température</term>
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<term>Desiccation</term>
<term>Environmental Monitoring</term>
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<term>Desiccation</term>
<term>Environmental Monitoring</term>
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<keywords scheme="MESH" xml:lang="en"><term>Environment, Controlled</term>
<term>Humidity</term>
<term>Neural Networks (Computer)</term>
<term>Temperature</term>
<term>Tobacco</term>
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<front><div type="abstract" xml:lang="en"><p>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.</p>
</div>
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