Optimization of coagulant dosing process in water purification system

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focuse on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A fuzzy model for normal condition and a neural network model for abnormal condition are developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived from the fuzzy model or the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, in order to consider the variation of algae which is nor able to be measured by sensor, the variation of pH is regarded as a new input variable. The ability of the proposed control scheme validated through the field test is proved to be of considerable practical value.