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

Autor:	Michael D. Montross, Javier M. Aguiar, Jaime Gomez-Gil, Víctor Martínez-Martínez, Timothy S. Stombaugh
Rok vydání:	2015
Předmět:	Engineering Correlation coefficient Meteorology biology Artificial neural network business.industry General Chemical Engineering Computer Science::Neural and Evolutionary Computation Function (mathematics) biology.organism_classification Panicum virgatum Relative humidity Precipitation Physical and Theoretical Chemistry business Biological system Constant (mathematics) Water content Physics::Atmospheric and Oceanic Physics
Zdroj:	Drying Technology. 33:1708-1719
ISSN:	1532-2300 0737-3937
Popis:	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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::74f6371322b8cf30e514c60fc9821694 https://doi.org/10.1080/07373937.2015.1005228 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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