Abstract
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.
Highlights
Water is one of the most important elements for sustaining life
Coagulant dosage is set by an automatic control by Supervisory Control and Data Acquisition (SCADA) systems and Programmable Logic Controller (PLC) devices
A range of parameters has been used for determining the best water process control conditions according to the plant’s historical data
Summary
Water is one of the most important elements for sustaining life. Undesired substances and microorganisms should be removed when it is used for human consumption. Since decisions on the coagulation process are often carried out based on the experience of the human operator, several studies have been conducted on resources of computational intelligence at this stage of water treatment process, Acta Scientiarum. Artificial intelligence techniques and mathematical models have demonstrated their efficiency and optimal results, when applied in a multivariable and nonlinear system, such as water treatment plants (Böling, Seborg, & Hespanha, 2007). The use of mathematical modeling in water treatment process foregrounds the study of variations in costs and inputs at various stages, including clotting, according to operating conditions (Malzer & Strugholtz, 2008; Mostafa, Bahareh, Elahe, & Pegah, 2013; Ogwueleka & Ogwueleka, 2009)
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