MLP, ANFIS, and GRNN based real-time coagulant dosage determination and accuracy comparison using full-scale data of a water treatment plant

Real-time determination of appropriate coagulant dosage under wide fluctuation of raw water quality in a water treatment plant (WTP) is a challenging task due to nonlinearity relation between coagulant dosage and raw water characteristics. In this research, three techniques, multilayer perceptron (M...

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Bibliographic Details
Published in:Aqua (London) 2017-02, Vol.66 (1), p.49-61
Main Authors: Kim, Chan Moon, Parnichkun, Manukid
Format: Article
Language:English
Subjects:
Accuracy
Adaptive systems
Artificial neural networks
Automation
Coagulants
Data
Data collection
Dosage
Drinking
Drinking water
Engineering
Environmental engineering
Fuzzy logic
Fuzzy systems
History
Methods
Neural networks
Nonlinear systems
Nonlinearity
Plant introductions
Process controls
Rainy season
Raw water
Real time
Software
Studies
Time series
Turbidity
Water quality
Water treatment
Water treatment plants
Wet season
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Summary:Real-time determination of appropriate coagulant dosage under wide fluctuation of raw water quality in a water treatment plant (WTP) is a challenging task due to nonlinearity relation between coagulant dosage and raw water characteristics. In this research, three techniques, multilayer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and generalized regression neural network (GRNN), are applied to determine the coagulant dosage at Bansong drinking WTP. Each model is developed based on 8,760 historical data sets with hourly resolution for a whole year. Several statistical properties are determined to obtain the best-fit model from each method. The top performing models of each method are evaluated by external validation indices and absolute relative error according to nine turbidity zones. From the result, MLP and ANFIS models meet all conditions of validation indices, but GRNN cannot. The MLP shows the best result for high turbidity zones over 20 NTU as well as for overall performance. Meanwhile, ANFIS provides consistent results and better performance than MLP for low turbidity zones which have higher disorder of coagulant dosage data. The GRNN shows high accuracy for the highest turbidity zone which occurs during the rainy season. It is concluded that MLP, ANFIS, and GRNN can support operators effectively for real-time determination of coagulant dosage.
ISSN:0003-7214
1606-9935
1605-3974
DOI:10.2166/aqua.2016.022