期刊
DRYING TECHNOLOGY
卷 33, 期 14, 页码 1708-1719出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/07373937.2015.1005228
关键词
Artificial neural networks; Drying; Modeling; Prediction; Switchgrass
资金
- Formacion de Personal Investigador program
- Universidad de Valladolid (Spain)
- Banco de Santander
- University of Kentucky
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%.
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