Modeling of an RO water desalination unit using neural networks

被引：105

作者：

Abbas, A ^{[1
]}

Al-Bastaki, N ^{[1
]}

机构：

[1] Univ Bahrain, Coll Engn, Dept Chem Engn, Isa Town, Bahrain

来源：

CHEMICAL ENGINEERING JOURNAL | 2005年 / 114卷 / 1-3期

关键词：

reverse osmosis; water desalination; neural networks; process modeling;

D O I：

10.1016/j.cej.2005.07.016

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg-Marquardt (LM) optimization technique was employed for training the NN. The network learned the input-output mappings with accuracy for interpolation cases, but not for extrapolation. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：139 / 143

页数：5

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