Application of artificial neural networks (ANNs) in drying technology: a comprehensive review

Aghbashlo, Mortaza, Hosseinpour, Soleiman and Mujumdar, Arun S. (2015) Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying Technology, 33 12: 1397-1462. doi:10.1080/07373937.2015.1036288


Author Aghbashlo, Mortaza
Hosseinpour, Soleiman
Mujumdar, Arun S.
Title Application of artificial neural networks (ANNs) in drying technology: a comprehensive review
Journal name Drying Technology   Check publisher's open access policy
ISSN 1532-2300
0737-3937
Publication date 2015-09-10
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1080/07373937.2015.1036288
Open Access Status Not yet assessed
Volume 33
Issue 12
Start page 1397
End page 1462
Total pages 66
Place of publication New York, NY 10017 United States
Publisher Taylor and Francis
Collection year 2016
Language eng
Abstract Inspired by the functional behavior of the biological nervous system of the human brain, the artificial neural network (ANN) has found many applications as a superior tool to model complex, dynamic, highly nonlinear, and ill-defined scientific and engineering problems. For this reason, ANNs are employed extensively in drying applications because of their favorable characteristics, such as efficiency, generalization, and simplicity. This article presents a comprehensive review of numerous significant applications of the ANN technique to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in drying technology. We summarize the use of the ANN approach in modeling various dehydration methods; e.g., batch convective thin-layer drying, fluidized bed drying, osmotic dehydration, osmotic-convective drying, infrared, microwave, infrared- and microwave-assisted drying processes, spray drying, freeze drying, rotary drying, renewable drying, deep bed drying, spout bed drying, industrial drying, and several miscellaneous applications. Generally, ANNs have been used in drying technology for modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products. Moreover, a limited number of researchers have focused on control of drying systems to achieve desired product quality by online manipulating of the drying conditions using previously trained ANNs. Opportunities and limitations of the ANN technique for drying process simulation, optimization, and control are outlined to guide future R&D in this area.
Keyword Artificial neural network (ANN)
Controlling
Drying processes
Modeling
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: School of Agriculture and Food Sciences
Official 2016 Collection
 
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