Humic substance coagulation: Artificial neural network simulation

被引：9

作者：

Al-Abri, Mohammed ^{[1
]}

Al Anezi, Khalid ^{[2
]}

Dakheel, Akram ^{[3
]}

Hilal, Nidal ^{[4
]}

机构：

[1] Sultan Qaboos Univ, Petr & Chem Engn Dept, Al Khoud 123, Oman

[2] Coll Technol Studies PAAET, Dept Chem Engn, Shuwaikh 7605, Kuwait

[3] Al Baath Univ Hama, Dept Chem Engn, Homs, Syria

[4] Univ Nottingham, Sch Chem & Environm Engn, Ctr Clean Water Technol, Nottingham NG7 2RD, England

来源：

DESALINATION | 2010年 / 253卷 / 1-3期

关键词：

Humic acid; Polymer coagulation; ANN; Prediction; FLUX DECLINE; TRAINING ALGORITHMS; ORGANIC-MATTER; METAL-ION; FILTRATION; MODEL;

D O I：

10.1016/j.desal.2009.11.014

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

This paper investigates the use of backpropagation neural network (BPNN) to predict humic substance (HS) UV absorbance experimental results The studied experimental sets include HS and heavy metal agglomeration, HS coagulation using polyelectrolyres and HS and heavy metal coagulation using polyelectrolytes BPNN simulation showed high prediction accuracy where regression coefficient (R) was >095 for all simulations. Lower and higher than optimum training data input reduces BPNN reliability due to under training or over-fitting. The number of neurons study showed that a lower number of neurons led to under training. while a higher number of neurons resulted in the network memorizing the input dataset. (C) 2009 Elsevier B.V All rights reserved.

引用

页码：153 / 157

页数：5

共 30 条

[1] A Widrow-Hoff learning rule for a generalization of the linear auto-associator [J].