Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

Sensors

Vໜtor Martínez-Martínez²  Carlos Baladrón²  Jaime Gomez-Gil²  Gonzalo Ruiz-Ruiz¹  Luis M. Navas-Gracia¹  Javier M. Aguiar² 

[1] Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:;Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:

关键词: estimation;    prediction;    Artificial Neural Networks (ANN);    tobacco drying process;    signal processing;   

DOI  :  10.3390/s121014004

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.