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

2001 ◽  
Vol 28 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
C W Baxter ◽  
Q Zhang ◽  
S J Stanley ◽  
R Shariff ◽  
R -RT Tupas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Process Control ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process ◽  
Artificial Neural

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.Key words: artificial neural networks, water treatment process control, water treatment modelling.

scholarly journals A framework for the use of artificial neural networks for water treatment: development and application

2020 ◽  
Vol 20 (8) ◽  
pp. 3301-3317
Author(s):  
Rafael Paulino ◽  
Pierre Bérubé
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Distribution System ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Model Complex ◽  
Artificial Neural ◽  
Uv Transmittance

Abstract Artificial neural networks (ANNs) are increasingly being used in water treatment applications because of their ability to model complex systems. The present study proposed a framework to develop and validate ANNs for drinking water treatment and distribution system water quality applications. The framework was used to develop ANNs to identify the optimal ozone dose required for effective UV disinfection and to meet regulatory requirements for disinfection by-products (DBPs) in the distribution system. Treatment at a full-scale treatment plant was successfully modelled, with treated water UV transmittance as the output variable. ANNs could be used to identify operating setpoints that minimize operating costs for effective disinfection during drinking water treatment. However, because of the limited data available to train and validate the distribution system ANNs (i.e. n = 48; 15 years of quarterly measurements), these could not be used to reliably identify operating setpoints that also ensure compliance with DBP regulations.

Comparison of drinking water treatment process streams for optimal bacteriological water quality

2012 ◽  
Vol 46 (12) ◽  
pp. 3934-3942 ◽  
Author(s):  
Lionel Ho ◽  
Kalan Braun ◽  
Rolando Fabris ◽  
Daniel Hoefel ◽  
Jim Morran ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Treatment ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process

Study on the Micro-Polluted Water Treatment by GAC-UF

2010 ◽  
Vol 113-116 ◽  
pp. 2049-2052
Author(s):  
Jin Long Zuo
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Treatment ◽  
Suspended Solids ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Polluted Water ◽  
Total Removal ◽  
Water Treatment Process

Nowadays drinking water resource has been polluted, while the conventional treatment process cannot effectively remove polluted matters. In order to tackle this problem, the granular activated carbon (GAC) and ultrafiltration membrane (UF) were introduced into drinking water treatment process. The results revealed that when treat the micro-polluted water the effluent water quality of turbidity, permanganate index and color can reach 0.1NTU, 1.3mg/L-2.3mg/L and 5 degree respectively with GAC-UF process. And the total removal efficiency of turbidity, permanganate index and color can reach 98%-99%, 70%~75% and 60% respectively. The GAC can effectively remove organic matters, while the UF membrane can effectively remove suspended solids, colloids. The GAC-UF combined process can get a good water quality when treat the micro-polluted water.

Artificial neural networks for cost optimization of coagulation, sedimentation and filtration in drinking water treatment

2008 ◽  
Vol 8 (4) ◽  
pp. 383-388
Author(s):  
H.-J. Mälzer ◽  
S. Strugholtz
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Cost Optimization ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Artificial Neural ◽  
Safety And Stability

The applicability of Artificial Neural Networks (ANN) for process and costs optimization in drinking water treatment by coagulation, sedimentation and rapid filtration was investigated. The results showed that besides a considerable cost reduction, an improvement of process safety and stability can be expected. For further testing, the ANN will be installed at a water treatment plant for online coagulation control and process optimization.

Modeling the kinetics of a photochemical water treatment process by means of artificial neural networks

1999 ◽  
Vol 38 (4-6) ◽  
pp. 373-382 ◽  
Author(s):  
Sabine Göb ◽  
Esther Oliveros ◽  
Stefan H. Bossmann ◽  
André M. Braun ◽  
Roberto Guardani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Treatment Process ◽  
Water Treatment Process ◽  
Artificial Neural ◽  
Kinetics Of
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