To deal with the nonlinear characteristics of batch processes

a final product quality control strategy based on kernel partial least squares(KPLS) was proposed.KPLS model of batch process was calibrated with initial conditions

batch-wise unfolding of process data and final product quality.An estimate method based on principal component analysis(PCA) mapping was used to supplement the unknown process data

and online prediction of product quality was achieved.To cope with the final product quality control

T2 statistic was used to determine the field that KPLS model was applicable

and the model was introduced into product quality control problem as constraint to enhance the feasibility of the control strategy.Particle swarm optimization(PSO) algorithm was used to solve the optimization problem efficiently.Simulation results showed that comparing with partial least squares(PLS) model based control strategy

higher prediction accuracy was obtained with the proposed method

and various problems in product quality control could be effectively solved.