Development of an optimum control software package for coagulant dosing process in water purification system

In the water purification plant raw water is promptly purified by injection of chemicals. The amount of chemicals is directly related to water qualities such as turbidity, temperature, pH and alkalinity, However, the process of chemical reaction to the turbidity is not yet to be clarified. Since the process of coagulant dosage has no feedback signal, the amount of chemicals can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by jar-test data which were performed by skilled operators. In this paper, an optimum control S/W package was developed using jar-test results in order to predict the optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). Considering the relations to the reaction of coagulation and flocculation; six independent variables (turbidity, temperature, pH, alkalinity of raw water, PAC feed rate, turbidity in flocculation) are used in developing a neural network model for coagulant dosing process in water purification system. This model is utilized to predict an optimum coagulant dosage which enables one to minimize turbidity of water in flocculator. The efficacy of the proposed control scheme was examined by the field test.