To provide a new water quality controlling method having excellent learning ability and self-performance diagnostic ability against the characteristics of water to be treated.
After calculating the deviation between a target water quality and a treated water quality predicted by neural network from the water quality of the water to be treated and the injection ratio of a flocculating agent, review of the neural network is executed so that the deviation may be fixed within a prescribed range. The flocculating agent injection ratio is obtained by adding the flocculating agent supplementary injection ratio obtained by fuzzy inference from the membership function between a chlorine agent injection ratio and an alkali agent injection ratio to a basic design injection ratio, or the like, as a parameter. The membership function is adjusted for improving the review of the neural network.
SHIMAZAKI HIROSHI
IKEDA KAZUHARU
HATANO KAORU
