RT info:eu-repo/semantics/article
T1 Temperature and relative humidity estimation and prediction in the tobacco drying process using artificial neural networks
A1 Martínez Martínez, Víctor
A1 Baladrón García, Carlos
A1 Gómez Gil, Jaime
A1 Ruiz Ruiz, Gonzalo
A1 Navas Gracia, Luis Manuel
A1 Aguiar Pérez, Javier Manuel
A1 Carro Martínez, Belén
K1 Estimation
K1 Prediction
K1 Artificial Neural Networks (ANN)
K1 Tobacco drying process
K1 Signal processing
K1 33 Ciencias Tecnológicas
K1 31 Ciencias Agrarias
AB 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.
PB MDPI
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/57270
UL https://uvadoc.uva.es/handle/10324/57270
LA eng
NO Sensors, 2012, vol. 12, n. 10, p. 14004-14021
NO Producción Científica
DS UVaDOC
RD 23-nov-2024