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

被引：3

作者：

Sivanantham, V ^{[1
]}

Narayana, P. L. ^{[2
]}

Hyeong, Kwon Jun ^{[2
]}

Pareddy, Preetham ^{[3
]}

Sangeetha, V ^{[1
]}

Kyoung-Seok, Moon ^{[2
]}

In, Kim Hong ^{[2
]}

Sung, Hyo Kyung ^{[2
]}

Reddy, N. S. ^{[2
]}

机构：

[1] Periyar Univ, Constituent Coll Arts & Sci, Dept Comp Sci, Pappireddipatti Campus, Salem 636011, Tamil Nadu, India

[2] Gyeongsang Natl Univ, Sch Mat Sci & Engn, Engn Res Inst, Jinju, South Korea

[3] Fractal Analyt, Western Express Highway, Goregaon 400063, East Mumbai, India

来源：

CHEMOSPHERE | 2021年 / 268卷

基金：

新加坡国家研究基金会;

关键词：

Reverse osmosis; Artificial neural networks; Chlorophenol removal; Wastewater;

D O I：

10.1016/j.chemosphere.2020.129345

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：9

共 31 条

[1] Modeling of an RO water desalination unit using neural networks [J].