Artificial neural networks: applications in the drinking water sector

2018 ◽  
Vol 18 (6) ◽  
pp. 1869-1887 ◽  
Author(s):  
G. O'Reilly ◽  
C. C. Bezuidenhout ◽  
J. J. Bezuidenhout
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Membrane Filtration ◽  
Distribution Systems ◽  
Water Quality Management ◽  
Future Research ◽  
Water Sector ◽  
Artificial Neural

Abstract Artificial neural networks (ANNs) could be used in effective drinking water quality management. This review provides an overview about the history of ANNs and their applications and shortcomings in the drinking water sector. From the papers reviewed, it was found that ANNs might be useful modelling tools due to their successful application in areas such as pipes/infrastructure, membrane filtration, coagulation dosage, disinfection residuals, water quality, etc. The most popular ANNs applied were feed-forward networks, especially Multi-layer Perceptrons (MLPs). It was also noted that over the past decade (2006–2016), ANNs have been increasingly applied in the drinking water sector. This, however, is not the case for South Africa where the application of ANNs in distribution systems is little to non-existent. Future research should be directed towards the application of ANNs in South African distribution systems and to develop these models into decision-making tools that water purification facilities could implement.

Drinking water quality and treatment: the use of artificial neural networks

2001 ◽  
Vol 28 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
C W Baxter ◽  
Q Zhang ◽  
S J Stanley ◽  
R Shariff ◽  
R -RT Tupas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Process Control ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process ◽  
Artificial Neural

To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modelling as well as drinking water treatment process modelling and control through the presentation of several case studies at two large-scale water treatment plants in Edmonton, Alberta.Key words: artificial neural networks, water treatment process control, water treatment modelling.

Drinking water quality and treatment: the use of artificial neural networks

2001 ◽  
Vol 28 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
C.W. Baxter ◽  
Q. Zhang ◽  
S.J. Stanley ◽  
R. Shariff ◽  
R.-R.T. Tupas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Drinking Water Quality ◽  
Artificial Neural

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Draw on artificial neural networks to assess and predict water quality

2020 ◽  
Vol 612 ◽  
pp. 012028
Author(s):  
A Fernandes ◽  
H Chaves ◽  
R Lima ◽  
J Neves ◽  
H Vicente
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Artificial Neural
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