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.

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.

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

Removal of pesticides during the drinking water treatment process at Florence water supply, Italy

1999 ◽  
Vol 48 (5) ◽  
pp. 177-185 ◽  
Author(s):  
O. Griffini ◽  
M. L. Bao ◽  
D. Burrini ◽  
D. Santianni ◽  
C. Barbieri ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Water Supply ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process

scholarly journals OPTIMIZATION OF THE DRINKING WATER TREATMENT PROCESS OF A SUGAR PLANT STATION IN COTE DIVOIRE

2021 ◽  
Vol 9 (01) ◽  
pp. 512-524
Author(s):  
Konan Lopez Kouame ◽  
◽  
Nogbou Emmanuel Assidjo ◽  
Andre Kone Ariban ◽  
◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Treatment Process ◽  
Polynomial Regression ◽  
Drinking Water Treatment ◽  
Optimal Dose ◽  
Raw Water ◽  
Water Treatment Process ◽  
Jar Test ◽  
Coagulation And Flocculation

This article presents an optimization of the drinking water treatment process at the SUCRIVOIRE treatment station. The objective is to optimize the coagulation and flocculation process (fundamental process of the treatment of said plant)by determining the optimal dosages of the products injected and then proposes a program for calculating the optimal dose of coagulant in order to automatically determine the optimal dose of the latter according to the raw water quality. This contribution has the advantage of saving the user from any calculations the latter simply enters the characteristics of the raw effluent using the physical interface of the program in order to obtain the optimum corresponding coagulant concentration. For the determination of the optimal coagulant doses, we performed Jar-Test flocculation tests in the laboratory over a period of three months. The results made it possible to set up a polynomial regression model of the optimal dose of alumina sulfate as a function of the raw water parameters. A program for calculating the optimal dose of coagulant was carried out on Visual Basic. The optimal doses of coagulant obtained vary from 25, 35, 40 and 45 mg/l depending on the characteristics of the raw effluent. The model obtained is: . Finally, verification tests were carried out using this model on the process. The results obtained meet the WHO drinkability standards for all parameters for a settling time of two hours.

Sign in / Sign up

Export Citation Format

Share Document