ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study |
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Authors: | Salim Heddam Abdelmalek Bermad Noureddine Dechemi |
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Institution: | 1.Faculty of Science Agronomy Department,University 20 Ao?t 1955,Skikda,Algeria;2.Laboratory Construction and Environment,Polytechnical National School,Alger,Algeria |
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Abstract: | Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water
quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing
rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such,
coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests
are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses
to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive
Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou,
Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet
absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy
systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID,
and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient
values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB
outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem. |
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