Part 3: Artificial neural networks in drinking water and wastewater process control / Partie 3 : les réseaux neuronaux artificiels dans le contrôle des processus d’eau potable et d’eaux usées

10.1139/part3 ◽  
2004 ◽  
Vol 3 (S1) ◽  
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Process Control ◽  
Eau Potable ◽  
Water And Wastewater ◽  
Artificial Neural

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.

Predicting the concentration of sulfate (SO42-) in drinking water using artificial neural networks: a case study: Médéa-Algeria

2021 ◽  
Vol 217 ◽  
pp. 181-194
Author(s):  
Hichem Tahraoui ◽  
Abd-Elmouneïm Belhadj ◽  
Adhya-eddine Hamitouche ◽  
Mounir Bouhedda ◽  
Abdeltif Amrane
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Artificial Neural

Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

2018 ◽  
Vol 41 (2) ◽  
pp. 118-127 ◽  
Author(s):  
Erdal Karadurmuş ◽  
Nur Taşkın ◽  
Eda Göz ◽  
Mehmet Yüceer
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chamaka Karunanayake ◽  
Miyuru B. Gunathilake ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Resources ◽  
Sri Lanka ◽  
Water Availability ◽  
Training Algorithm ◽  
Climate Scenarios ◽  
Artificial Neural

Prediction of water resources for future years takes much attention from the water resources planners and relevant authorities. However, traditional computational models like hydrologic models need many data about the catchment itself. Sometimes these important data on catchments are not available due to many reasons. Therefore, artificial neural networks (ANNs) are useful soft computing tools in predicting real-world scenarios, such as forecasting future water availability from a catchment, in the absence of intensive data, which are required for modeling practices in the context of hydrology. These ANNs are capable of building relationships to nonlinear real-world problems using available data and then to use that built relationship to forecast future needs. Even though Sri Lanka has an extensive usage of water resources for many activities, including drinking water supply, irrigation, hydropower development, navigation, and many other recreational purposes, forecasting studies for water resources are not being carried out. Therefore, there is a significant gap in forecasting water availability and water needs in the context of Sri Lanka. Thus, this paper presents an artificial neural network model to forecast the inflows of one of the most important reservoirs in northern Sri Lanka using the upstream catchment’s rainfall. Future rainfall data are extracted using regional climate models for the years 2021–2050 and the inflows of the reservoir are forecasted using the validated neural network model. Several training algorithms including Levenberg–Marquardt (LM), BFGS quasi-Newton (BFG), scaled conjugate gradient (SCG) have been used to find the best fitting training algorithm to the prediction process of the inflows against the measured inflows. Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. In addition, the forecasted results using the projected climate scenarios clearly showcase the benefit of using the forecasting model in solving future water resource management to avoid or to minimize future water scarcity. Therefore, the validated model can effectively be used for proper planning of the proposed drinking water supply scheme to the nearby urban city, Jaffna in northern Sri Lanka.

scholarly journals A framework for the use of artificial neural networks for water treatment: development and application

2020 ◽  
Vol 20 (8) ◽  
pp. 3301-3317
Author(s):  
Rafael Paulino ◽  
Pierre Bérubé
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Distribution System ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Model Complex ◽  
Artificial Neural ◽  
Uv Transmittance

Abstract Artificial neural networks (ANNs) are increasingly being used in water treatment applications because of their ability to model complex systems. The present study proposed a framework to develop and validate ANNs for drinking water treatment and distribution system water quality applications. The framework was used to develop ANNs to identify the optimal ozone dose required for effective UV disinfection and to meet regulatory requirements for disinfection by-products (DBPs) in the distribution system. Treatment at a full-scale treatment plant was successfully modelled, with treated water UV transmittance as the output variable. ANNs could be used to identify operating setpoints that minimize operating costs for effective disinfection during drinking water treatment. However, because of the limited data available to train and validate the distribution system ANNs (i.e. n = 48; 15 years of quarterly measurements), these could not be used to reliably identify operating setpoints that also ensure compliance with DBP regulations.

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