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Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks

Author:
Martínez-Martínez, Víctor, Gomez-Gil, Jaime, Stombaugh, Timothy S., Montross, Michael D., Aguiar, Javier M.
Source:
Drying technology 2015 v.33 no.14 pp. 1708-1719
ISSN:
1532-2300
Subject:
Panicum virgatum, air, air temperature, correlation, drying, equations, neural networks, prediction, rain, relative humidity, water content
Abstract:
This article proposes two artificial neural network (ANN)-based models to characterize the switchgrass drying process: The first one models processes with constant air temperature and relative humidity and the second one models processes with variable air conditions and rainfall. The two ANN-based models proposed estimated the moisture content (MC) as a function of temperature, relative humidity, previous MC, time, and precipitation information. The first ANN-based model describes MC evolution data more accurately than six mathematical empirical equations typically proposed in the literature. The second ANN-based model estimated the MC with a correlation coefficient greater than 98.8%.
Agid:
4084548