A novel soft sensor method based on slow feature regression (SFR) was proposed for industrial process with nonlinear and dynamic characteristics. First

a dynamic dataset was built by adding time-delay data and information redundancy was reduced by selecting variables according to mutual information maximization criteria. Then

kernel function was introduced into slow feature analysis(SFA)to improve capability of processing nonlinear data and the kernel slow features were used for regression. Through analysis of sample variation

kernel slow feature analysis(KSFA)could extract components with slowly varying dynamics

characterize trend of industrial process effectively

and improve precision of regression modelling. Finally

effectiveness and feasibility of the proposed method were verified by soft sensor model of constant top oil dry point and constant first line dropping point in atmospheric tower.