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. 2021 Apr:268:129345.
doi: 10.1016/j.chemosphere.2020.129345. Epub 2020 Dec 16.

Modeling and optimization of chlorophenol rejection for spiral wound reverse osmosis membrane modules

V Sivanantham  1 , P L Narayana  2 , Kwon Jun Hyeong  2 , Preetham Pareddy  3 , V Sangeetha  4 , Moon Kyoung-Seok  2 , Kim Hong In  2 , Hyo Kyung Sung  2 , N S Reddy  5
Affiliations

Modeling and optimization of chlorophenol rejection for spiral wound reverse osmosis membrane modules

V Sivanantham et al. Chemosphere. 2021 Apr.

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.

Keywords: Artificial neural networks; Chlorophenol removal; Reverse osmosis; Wastewater.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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