Development of Process Inverse Neural Network Model to Determine the Required Alum Dosage at Segama Water Treatment Plant Sabah, Malaysia

2009 ◽  
pp. 525-530 ◽  
Author(s):  
A. Robenson ◽  
S.R. Abd. Shukor ◽  
N. Aziz
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Treatment Plant ◽  
Water Treatment Plant

Estimation of Turbidity in Water Treatment Plant using Hammerstein-Wiener and Neural Network Technique

2017 ◽  
Vol 5 (3) ◽  
pp. 666 ◽  
Author(s):  
M. S Gaya ◽  
L. A. Yusuf ◽  
Mamunu Mustapha ◽  
Bashir Muhammad ◽  
Ashiru Sani ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Wiener Model ◽  
The Neural Network ◽  
Effective Removal ◽  
Reliable Model

Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.

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.

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