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Cited 3 time in webofscience Cited 6 time in scopus
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Modeling the relationship between forward osmosis process parameters and permeate flux

Authors
Reddy, B. S.Maurya, A. K.Narayana, P. L.Kori, S. A.Sung, HyokyungReddy, M. R.Cho, Kwon-KooSharada, Y. S.Reddy, N. S.
Issue Date
1-Nov-2022
Publisher
ELSEVIER
Keywords
Artificial neural networks; Permeate flux; Membrane; Forward osmosis; Quantitative; Qualitative
Citation
SEPARATION AND PURIFICATION TECHNOLOGY, v.300
Indexed
SCIE
SCOPUS
Journal Title
SEPARATION AND PURIFICATION TECHNOLOGY
Volume
300
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/782
DOI
10.1016/j.seppur.2022.121830
ISSN
1383-5866
Abstract
Artificial neural networks (ANN) models are becoming more popular than mathematical and transport-based models due to their high performance and accuracy. Previous literature shows a lack of application of power-ful ANN techniques for predicting forward osmosis (FO) performance. In this study, we developed a feedforward network to predict and analyze the permeate flux in the FO process. The ANN model was developed based on a lab-scale experimental database from various published articles. The permeate flux was modeled as a function of membrane-type, membrane orientation, feed and draw solution molarity, feed and draw velocity, molecular weight, feed solution temperature, and draw solution temperature. The influence of foulants on permeate flux has not been considered for developing the ANN model to avoid over-complication of the present work. The adj. R-squared values for train, unseen test and total datasets are 0.99, 0.92, and 0.95, respectively. These values are higher than those found in previously published literature (0.97, 0.85, and 0.82). Moreover, this is the first time that the effect of individual variables on permeate flux has been estimated quantitatively.
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공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles

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Reddy, N. Subba
공과대학 (나노신소재공학부금속재료공학전공)
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