Roughing filtration as an effective pre-treatment system for high turbidity water

2011 ◽  
Vol 64 (7) ◽  
pp. 1419-1427 ◽  
Author(s):  
Zahiruddin Khan ◽  
Rahimuddin Farooqi
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Dose Requirement ◽  
Water Turbidity ◽  
Effluent Quality ◽  
Coagulant Dosage ◽  
High Turbidity ◽  
Pre Treatment ◽  
Effective Water

Effective water treatment is the prime goal of every water treatment facility. Chakwal Water Treatment Plant in Pakistan has been treating high-turbidity surface water through crude coagulation, sedimentation and slow sand filtration since the early 1980s. The process has always been tedious in terms of high coagulant dosage, large volumes of sludge and short filter runs especially after wet spells. A laboratory-scale study was conducted to see if roughing filtration, as the pre-treatment process, would help in reducing coagulant dose and sludge volume and improving effluent quality. Results indicated that up-flow rouging filtration with media grades decreasing in the direction of flow could reduce wet weather raw water turbidity (by more than 90%) and coagulant dose. Overall, the plant could save over US $54,000 annually in terms of coagulant cost only. Longer filter runs, improved product water quality leading to lower chlorine dose requirement, would be additional benefits.

scholarly journals Intelligent system for improving dosage control

2017 ◽  
Vol 39 (1) ◽  
pp. 33 ◽  
Author(s):  
Fabio Cosme Rodrigues dos Santos ◽  
André Felipe Henriques Librantz ◽  
Cleber Gustavo Dias ◽  
Sheila Gozzo Rodrigues
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Intelligent System ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Test Method ◽  
Raw Water ◽  
Water Turbidity ◽  
Coagulant Dosage

Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. 

Cost-effective water treatment of polluted surface water by using direct filtration and granular activated carbon filtration

2002 ◽  
Vol 2 (1) ◽  
pp. 233-240 ◽  
Author(s):  
J. Cromphout ◽  
W. Rougge
Keyword(s):  
Activated Carbon ◽  
Water Treatment ◽  
Treatment Process ◽  
Treatment Plant ◽  
Granular Activated Carbon ◽  
Cost Effective ◽  
Water Treatment Plant ◽  
Direct Filtration ◽  
Carbon Filtration ◽  
Effective Water

In Harelbeke a Water Treatment Plant with a capacity of 15,000 m3/day, using Schelde river water has been in operation since April 1995. The treatment process comprises nitrification, dephosphatation by direct filtration, storage into a reservoir, direct filtration, granular activated carbon filtration and disinfection. The design of the three-layer direct filters was based on pilot experiments. The performance of the plant during the five years of operation is discussed. It was found that the removal of atrazin by activated carbon depends on the water temperature.

Rehabilitation of water treatment plants with operational constrains: a study on turbidity removal

2013 ◽  
Vol 3 (4) ◽  
pp. 549-556 ◽  
Author(s):  
Kaveh Sookhak Lari ◽  
Morteza Kargar
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Term Effect ◽  
High Rate ◽  
Water Turbidity ◽  
Turbidity Removal ◽  
Water Treatment Plants ◽  
Long Term Effect

High-rate lamella settlers in clarifiers and triple media filters have been implemented in Isfahan water treatment plant (known as ‘Baba-Sheikh-Ali’) in Iran to upgrade existing clarification/filtration processes during the recent years. The applied technologies are mainly used to reduce finished water turbidity as the primary regional criterion on water quality. However, application of both technologies faced some operational limitations since they began to work. These problems are due to the existing layout of the process units and available materials. The current study focuses on performance of restricted application of the two technologies with respect to turbidity removal. Online measured turbidity data from a two-year field observation (since March 2010) are used. In particular, results show a more promising and long-term effect on turbidity removal due to tripling filter media rather than application of the lamella settlers in clarifiers. The reasons for these observations are discussed.

Application of artificial neural network to control the coagulant dosing in water treatment plant

2000 ◽  
Vol 42 (3-4) ◽  
pp. 403-408 ◽  
Author(s):  
R.-F. Yu ◽  
S.-F. Kang ◽  
S.-L. Liaw ◽  
M.-c. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Ann Model ◽  
Coagulant Dosage ◽  
Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

scholarly journals IL CONTRIBUTO DEI SERVIZI IDRICI INTEGRATI AL FABBISOGNO IRRIGUO DEI COMPRENSORI AGRICOLI: IL CASO DI MILANO

2017 ◽  
Author(s):  
Maurizio Brown
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Forage Production ◽  
Sewerage System ◽  
Xix Century ◽  
The City ◽  
Effective Water ◽  
Drainage Waters ◽  
Significant Model

During its history Milan city has been able to treasure its wastewater collected by Vettabbia irrigation ditch. Since the XII century, also thanks to Cistercensi (monks from Chiaravalle Abbey) great endeavour, the use of effusing the filthy waters from Vettabbia over the rotten lawns of a great and huge agricultural district, situated in the South the City, was going to become the most spread method used to regain the nutrients contained into the wastewaters and, at the same time, a great procedure to optimize the fresh forage production. During the second half of XIX century this technique was refined and made more efficient in order to allow the sustainable and ecofriendly disposal of the drainage waters raised from the new sewerage system of the City. Since 2004 Milan adopted an effective water treatment plant which permits the reuse of the purified waters for what concerns agriculture, becoming, in this field, the most significant model in Europe.

scholarly journals Using sulfuric acid (H2SO4) for pH adjustment in water treatment

2021 ◽  
Vol 11 (3) ◽  
pp. 19-27
Author(s):  
Ali Salim Abd Al-Hussein
Keyword(s):  
Sulfuric Acid ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Strong Acid ◽  
Raw Water ◽  
Water Turbidity ◽  
Ph Adjustment ◽  
Oil Company ◽  
Clarified Water

The aim of this paper is to explain the advantages of using sulfuric acid in Qarmat Ali water treatment plant belong to Basrah Oil Company, which produces water for injection into the Rumaila reservoirs. Sulfuric acid is a strong acid providing rapid and effective pH reduction. Maintaining the coagulation pH within the optimum value (6.4) by inject specific value of sulfuric acid to RAW water enhances the clarification performances by reducing the clarified water turbidity to minimum value (5.1). It was preferable for  operating at a pH below the saturation pH to prevent the precipitation of minerals such as calcium carbonate which are contributing to blocking the surface filters installed downstream (auto back wash filters) and The clarifiers that cause increased the feed from 500 MBD  to 1000 MBD. With a fast and rapid dissociation in Water, Sulfuric acid is an effective and practical way to lower the pH on Qarmat Ali plant which producing in excess of 1,000MBD of export water.

Prediction of Clarified Water Turbidity of Moyog Water Treatment Plant Using Artificial Neural Network

2007 ◽  
Vol 7 (15) ◽  
pp. 2006-2010 ◽  
Author(s):  
Duduku Krishnaiah ◽  
Siva Kumar Kumaresan . ◽  
Matthew Isidore . ◽  
Rosalam Sarbatly .
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Water Turbidity ◽  
Artificial Neural ◽  
Clarified Water
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