Model of Prediction of Trihalomethanes (THMs) formation in chlorinated water in water treatment plant using Artificial Neural Network

2016 ◽  
Vol 19 (1) ◽  
pp. 79-90
Author(s):  
Khairi Ali Omar ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Artificial Neural

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

scholarly journals River Water Salinity Impact on Drinking Water Treatment Plant Performance Using Artificial neural network

2019 ◽  
Vol 25 (8) ◽  
pp. 149-159
Author(s):  
Sabreen Hayder Abbas ◽  
Basim Hussein Khudair ◽  
Mahdi Shanshal Jaafar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
River Water ◽  
Artificial Neural Network Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Water Salinity ◽  
Tigris River ◽  
Artificial Neural

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in) to predict the salinity TDSout.  Results showed that the effectiveness of the artificial neural network model to predicting the salinity is a good agreement between observed and the predicted value of the TDS, through the determination coefficient of the model is (0.998, 0.966, 0.997, 0.998, 0.996, and 0.996) for Al. Karkh, Sharq Dijla, Al.Karama, Al.Wathba, Al.Dora and Al.Wihda respectively. From this value can be shown that ANN is a successful tool for predicting the nonlinear equation of the salinity under different and complicated environmental case along the river.  

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 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.

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