Research on the prediction of water treatment plant coagulant dosage based on feed-forward artificial neutral network

2011 ◽  
Author(s):  
Wang Xiaojie ◽  
Ji Yunzhe ◽  
Li Xiaojing
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Neutral Network ◽  
Feed Forward ◽  
Artificial Neutral Network ◽  
Coagulant Dosage

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.

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 Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

2019 ◽  
Vol 9 (3) ◽  
pp. 4176-4181
Author(s):  
A. S. Kote ◽  
D. V. Wadkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Dose Model ◽  
Artificial Neural

Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.

Treatment and reuse of backwash water in Taipei water treatment plant, Taiwan

2006 ◽  
Vol 6 (6) ◽  
pp. 89-98 ◽  
Author(s):  
C.B. Yang ◽  
Y.L. Cheng ◽  
J.C. Liu ◽  
D.J. Lee
Keyword(s):  
Water Treatment ◽  
Pilot Study ◽  
Sea Water ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Coagulant Dosage ◽  
Effective Removal ◽  
Recycle Ratio ◽  
Water Recycle ◽  
The Impact

A case study on the treatment and reuse of backwash water from Chang-Hsing Water Treatment Plant (CHWTP) and Swan-Sea Water Treatment Plant (SSWTP) of Taipei Water Department was conducted. Both backwash waters showed different properties. However, the characteristics of each backwash water did not vary considerably among samples taken during different time. Results from jar tests indicated that both polyaluminium chloride (PACl) and alum could result in effective removal of turbidity. Both DOC and absorbance of UV254 decreased slightly with increasing coagulant dosage. In continuous operation of backwash water recycle in pilot study in CHWTP, it was found that treated water quality was not affected by two different modes of recycle: intermittent recycle at ratio of 1:7 (backwash water:raw water) and continuous recycle at ratio of 1:42. In the pilot study in SSWTP, no impact was found on the introduction of backwash water at recycle ratio of 4, 6 and 8%, regardless of whether the backwash water was recycled directly or went through 3 min pre-sedimentation before it is recycled. Further study on the impact of typhoon on treatment and recycle of backwash water was recommended.

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