Experimental and ANN modeling investigations of energy traits for rough rice drying

Mohsen Beigi, Mehdi Torki-Harchegani and Mojtaba Tohidi

Energy, 2017, vol. 141, issue C, 2196-2205

Abstract: In the present work, the kinetics of convective deep-bed drying of rough rice was investigated at different conditions. The energy performance of the process was investigated in terms of specific energy consumption as well as energy, drying and thermal efficiencies. Different well-known artificial neural networks (ANNs) were used to predict the traits and the best topologies, transfer functions and training algorithms were determined. Increasing drying air temperature and velocity decreased drying duration while higher relative humidity increased the process time. The results showed that applying higher temperatures together with lower levels of flow rate and relative humidity of drying air improved the energy indices. Artificial neural network modeling technique can be used, as a powerful tool, to predict and determine the energy efficient drying conditions.

Keywords: Deep-ed drying; Drying time; Specific energy consumption; Thermal efficiency; Artificial neural network (ANN) (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217320236
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:141:y:2017:i:c:p:2196-2205

DOI: 10.1016/j.energy.2017.12.004

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).