scholarly journals Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

2017 ◽  
Vol 7 (7) ◽  
pp. 3885-3902 ◽  
Author(s):  
Chan Moon Kim ◽  
Manukid Parnichkun
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Fuzzy Inference ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Water Turbidity ◽  
Inference System ◽  
Coagulant Dosage ◽  
Neuro Fuzzy

Control of primary disinfection in a drinking water treatment plant based on a fuzzy inference system

2021 ◽  
Vol 145 ◽  
pp. 63-70
Author(s):  
Lluís Godo-Pla ◽  
Jose Javier Rodríguez ◽  
Jordi Suquet ◽  
Pere Emiliano ◽  
Fernando Valero ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Inference System ◽  
Drinking Water Treatment Plant

scholarly journals Intelligent system for improving dosage control

2017 ◽  
Vol 39 (1) ◽  
pp. 33 ◽  
Author(s):  
Fabio Cosme Rodrigues dos Santos ◽  
André Felipe Henriques Librantz ◽  
Cleber Gustavo Dias ◽  
Sheila Gozzo Rodrigues
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Intelligent System ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Test Method ◽  
Raw Water ◽  
Water Turbidity ◽  
Coagulant Dosage

Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant. 

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