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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/2497
Title: Modelling and simulation of desalination process using artificianeural network: A review
Authors: Mahadeva R.
Manik G.
Verma O.P.
Sinha S.
Published in: Desalination and Water Treatment
Abstract: Water is the natural, yet very essential, resource for survival of humans, animals and plants. However, only 3% pure water (present in lakes, rivers, as groundwater and frozen water) is available globally and 97% being saline is not suitable for drinking and agriculture purposes. Surprisingly, only 1% of this pure water is within reach of humans for existence. Hence, it is quite imperative to improve the water quality as well as its availability. Desalination, a process for converting the saline water into fresh water, may help in achieving this objective by providing water suitable for consumption by humans and animals, for agriculture and industrial applications. In this paper, we review various desalination techniques namely: reverse osmosis, vapor compression distillation, electrodialysis, multi-stage flash, etc., and their hybrids being increasingly used for treating seawater. Modelling and simulation of such processes is vital for improving water quality and quantity as well as understanding, analysis and reporting of the physical, chemical and biological results for appropriate process measurement and control. Artificial neural network (ANN) involves representing such processes with models inspired by the architecture of a biological neural network of human brain. An exhaustive review of ANN-based models, improvised recently to more effectively simulate process behavior for optimizing operating conditions, has been presented. © 2018 Desalination Publications. All rights reserved.
Citation: Desalination and Water Treatment (2018), 122(): 351-364
URI: https://doi.org/10.5004/dwt.2018.23106
http://repository.iitr.ac.in/handle/123456789/2497
Issue Date: 2018
Publisher: Desalination Publications
Keywords: Artificial neural network
Desalination
Modelling and simulation
Optimization
ISSN: 19443994
Author Scopus IDs: 57204158068
56595314900
56594677400
7403739121
Author Affiliations: Mahadeva, R., Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Saharanpur Campus, Saharanpur, Uttar Pradesh 247001, India
Manik, G., Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Saharanpur Campus, Saharanpur, Uttar Pradesh 247001, India
Verma, O.P., Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India
Sinha, S., Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Corresponding Author: Manik, G.; Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Saharanpur Campus, India; email: manikfpt@iitr.ac.in
Appears in Collections:Journal Publications [CH]

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