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Modeling and optimization of chlorophenol rejection for spiral wound reverse osmosis membrane modules
- Authors
- Sivanantham, V; Narayana, P. L.; Kwon, Jun Hyeong; Pareddy, Preetham; Sangeetha, V; Moon, Kyoung Seok; Kim, Hong In; Sung, Hyo Kyung; Reddy, N. S.
- Issue Date
- Apr-2021
- Publisher
- Pergamon Press Ltd.
- Keywords
- Reverse osmosis; Artificial neural networks; Chlorophenol removal; Wastewater
- Citation
- Chemosphere, v.268
- Indexed
- SCIE
SCOPUS
- Journal Title
- Chemosphere
- Volume
- 268
- ISSN
- 0045-6535
1879-1298
- Abstract
- This study shows an artificial neural network (ANN) model of chlorophenol rejection from aqueous solutions and predicting the performance of spiral wound reverse osmosis (SWRO) modules. This type of rejection shows complex non-linear dependencies on feed pressure, feed temperature, concentration, and feed flow rate. It provides a demanding test of the application of ANN model analysis to SWRO modules. The predictions are compared with experimental data obtained with SWRO modules. The overall agreement between the experimental and ANN model predicted was almost 99.9% accuracy for the chlorophenol rejection. The ANN model approach has the advantage of understanding the complex chlorophenol rejection phenomena as a function of SWRO process parameters. (C) 2020 Elsevier Ltd. All rights reserved.
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