Return to search

Simulation of deep-bed drying of Virginia peanuts to minimize energy use

A deep-bed drying model simulating the drying of peanuts in a fixed bed is required for designing energy-efficient and automatically controlled dryers. A deep-bed drying model consists of a thin-layer drying model to calculate the moisture release from the material and a set of mass and energy balances. An experimental setup was constructed to determine drying rates of Virginia-type peanuts under 14 different drying air conditions. Selected empirical and semi-theoretical models available for modeling thin-layer drying rates were fitted to the collected data using nonlinear regression techniques. The modified Page's model and the two-term exponential model fitted the data better than other models considered. A deep-bed drying model PEATECH based on four coupled partial differential equations consisting of four variables, air temperature, peanut temperature, air humidity, and peanut moisture content was developed. Validation of the model was accomplished by using the data collected from 36 deep-bed drying experiments conducted using three laboratory dryers during 1987, 1988, and 1989. PEATECH predicted the variables within a peanut bed with an accuracy of less than ± 6%. The energy saving potential of exhaust-air recirculation was established by conducting simulated experiments using a modified version of PEATECH. / Ph. D.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/39762
Date12 October 2005
CreatorsKulasiri, G. Don
ContributorsAgricultural Engineering, Vaughan, David H., Cundiff, John S., Wilcke, William F., Wright, F. Scott, Kuppusamy, Thangavelu, Perumpral, John V.
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeDissertation, Text
Formatxiii, 207 leaves, BTD, application/pdf, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/
RelationOCLC# 23716401, LD5655.V856_1990.K753.pdf

Page generated in 0.002 seconds