Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment

2010 ◽  
Vol 22 (3) ◽  
pp. 256-270 ◽  
Author(s):  
Magdalena Bieroza ◽  
Andy Baker ◽  
John Bridgeman
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Organic Matter ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Drinking Water Treatment ◽  
Analysis Methods ◽  
Artificial Neural ◽  
Operational Tool

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.

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.

Artificial neural networks for cost optimization of coagulation, sedimentation and filtration in drinking water treatment

2008 ◽  
Vol 8 (4) ◽  
pp. 383-388
Author(s):  
H.-J. Mälzer ◽  
S. Strugholtz
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Cost Optimization ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Artificial Neural ◽  
Safety And Stability

The applicability of Artificial Neural Networks (ANN) for process and costs optimization in drinking water treatment by coagulation, sedimentation and rapid filtration was investigated. The results showed that besides a considerable cost reduction, an improvement of process safety and stability can be expected. For further testing, the ANN will be installed at a water treatment plant for online coagulation control and process optimization.

Current knowledge in the field of algal organic matter adsorption onto activated carbon in drinking water treatment

2021 ◽  
pp. 149455
Author(s):  
Martin Pivokonsky ◽  
Ivana Kopecka ◽  
Lenka Cermakova ◽  
Katerina Fialova ◽  
Katerina Novotna ◽  
...  
Keyword(s):  
Drinking Water ◽  
Organic Matter ◽  
Activated Carbon ◽  
Water Treatment ◽  
Current Knowledge ◽  
Drinking Water Treatment ◽  
Algal Organic Matter

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 Natural organic matter removal by ion exchange at different positions in the drinking water treatment lane

2013 ◽  
Vol 6 (1) ◽  
pp. 1-10 ◽  
Author(s):  
A. Grefte ◽  
M. Dignum ◽  
E. R. Cornelissen ◽  
L. C. Rietveld
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Organic Matter ◽  
Water Treatment ◽  
Organic Carbon ◽  
Natural Organic Matter ◽  
Drinking Water Treatment ◽  
Biological Stability ◽  
Sand Filtration ◽  
Slow Sand Filtration

Abstract. To guarantee a good water quality at the customers tap, natural organic matter (NOM) should be (partly) removed during drinking water treatment. The objective of this research was to improve the biological stability of the produced water by incorporating anion exchange (IEX) for NOM removal. Different placement positions of IEX in the treatment lane (IEX positioned before coagulation, before ozonation or after slow sand filtration) and two IEX configurations (MIEX® and fluidized IEX (FIX)) were compared on water quality as well as costs. For this purpose the pre-treatment plant at Loenderveen and production plant Weesperkarspel of Waternet were used as a case study. Both, MIEX® and FIX were able to remove NOM (mainly the HS fraction) to a high extent. NOM removal can be done efficiently before ozonation and after slow sand filtration. The biological stability, in terms of assimilable organic carbon, biofilm formation rate and dissolved organic carbon, was improved by incorporating IEX for NOM removal. The operational costs were assumed to be directly dependent of the NOM removal rate and determined the difference between the IEX positions. The total costs for IEX for the three positions were approximately equal (0.0631 € m−3), however the savings on following treatment processes caused a cost reduction for the IEX positions before coagulation and before ozonation compared to IEX positioned after slow sand filtration. IEX positioned before ozonation was most cost effective and improved the biological stability of the treated water.

Characterization of organic matter and disinfection by-products in membrane backwash water from drinking water treatment

2009 ◽  
Vol 168 (2-3) ◽  
pp. 753-759 ◽  
Author(s):  
Lingling Zhang ◽  
Ping Gu ◽  
Zijie Zhong ◽  
Dong Yang ◽  
Wenjie He ◽  
...  
Keyword(s):  
Drinking Water ◽  
Organic Matter ◽  
Water Treatment ◽  
Drinking Water Treatment ◽  
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