Drinking water quality and treatment: The use of artificial neural networks

被引:37
作者
Baxter, CW [1 ]
Zhang, Q
Stanley, SJ
Shariff, R
Tupas, RRT
Stark, HL
机构
[1] Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB T6G 2M8, Canada
[2] EPCOR Water Serv Inc, Edmonton, AB T5J 3B1, Canada
关键词
artificial neural networks; water treatment process control; water treatment modelling;
D O I
10.1139/l00-053
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modelling as well as drinking water treatment process modelling and control through the presentation of several case studies at two large-scale water treatment plants in Edmonton, Alberta.
引用
收藏
页码:26 / 35
页数:10
相关论文
共 18 条
[1]   Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation [J].
Baxter, CW ;
Stanley, SJ ;
Zhang, Q .
JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 1999, 48 (04) :129-136
[2]   Experience in industrial plant model development using large-scale artificial neural networks [J].
Boger, Z .
INFORMATION SCIENCES, 1997, 101 (3-4) :203-216
[3]   DYNAMIC MODELING OF THE ACTIVATED-SLUDGE PROCESS - IMPROVING PREDICTION USING NEURAL NETWORKS [J].
COTE, M ;
GRANDJEAN, BPA ;
LESSARD, P ;
THIBAULT, J .
WATER RESEARCH, 1995, 29 (04) :995-1004
[4]  
Daniel T. M., 1991, P INT HYDR WAT RES S, P797
[5]   PREDICTING SALINITY IN THE CHESAPEAKE BAY USING BACKPROPAGATION [J].
DESILETS, L ;
GOLDEN, B ;
WANG, QW ;
KUMAR, R .
COMPUTERS & OPERATIONS RESEARCH, 1992, 19 (3-4) :277-285
[6]   NEURAL NETWORKS IN CIVIL ENGINEERING .1. PRINCIPLES AND UNDERSTANDING [J].
FLOOD, I ;
KARTAM, N .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 1994, 8 (02) :131-148
[7]   An evaluation of some factors affecting the accuracy of classification by an artificial neural network [J].
Foody, GM ;
Arora, MK .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1997, 18 (04) :799-810
[8]  
GARRETT JH, 1992, EXPERT SYSTEMS CIVIL, P259
[9]  
Harvey S, 1998, APPITA J, V51, P20
[10]  
Hasham FA, 1998, TRANSPORTATION, LAND USE, AND AIR QUALITY, CONFERENCE PROCEEDINGS, P246