The invention provides a resin
tower regeneration cycle optimization method based on an AI
algorithm. The method comprises the following steps: S1, continuously monitoring the
calcium and
magnesium ion concentration of
salt water at an outlet of a resin
tower on line; s2, collecting operation parameters associated with the adsorption process of the resin
tower, wherein the operation parameters comprise the
salt water double-alkali excess alkali amount,
turbidity, pH, ORP, flow, temperature and resin tower
pressure difference; s3, constructing a resin adsorption capacity
dynamic prediction model and a resin tower outlet
calcium and
magnesium content
dynamic prediction model based on the
calcium and
magnesium ion concentration monitored in the step S1 and the operation parameters collected in the step S2; and S4, constraint conditions are set through a multi-objective optimization
algorithm, a regeneration opportunity
decision matrix is generated based on the real-time prediction data of the two
dynamic prediction models constructed in the step S3, and the constraint conditions are used for judging whether a resin tower regeneration program is triggered or not. Dynamic optimization of the regeneration period of the resin tower can be realized, the quality of
saline water is guaranteed, the regeneration frequency and
material consumption are reduced, the production cost of an enterprise is reduced, and the operation efficiency of the resin tower is improved.