Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC)

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.

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A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; Case study: Tehran-Qazvin freeway

Geocarto International ◽

10.1080/10106049.2021.1871669 ◽

2021 ◽

pp. 1-12

Author(s):

Reza Sanayei ◽

Alireza Vafaeinejad ◽

Jalal Karami ◽

Hossein Aghamohammadi Zanjirabad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Model Development ◽

Discrete Wavelet ◽

Artificial Neural ◽

Artificial Neural Network Ann

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

Water Science & Technology ◽

10.2166/wst.2000.0410 ◽

2000 ◽

Vol 42 (3-4) ◽

pp. 403-408 ◽

Cited By ~ 8

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.

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