Industrial process plants are often characterized with problems of variable drifts

nonlinearity and time-variant. The time difference (TD) model was proposed by researchers to handle the drifting problems. However

the global model used under TD model cannot describe the data characteristic like the time-variant and high nonlinearity well. Moreover

the prediction accuracy will greatly decrease when change of process state occurs. In this paper

the time difference model and locally weighted partial least squares (LWPLS) are synthesized to enhance the adaptability of soft sensor models. In the TD-LWPLS based soft sensor framework

TD is used to reduce the effect of process drifts. Moreover

as a just-in-time (JITL) method

LWPLS is utilized to tackle nonlinearity and change of process state. A numerical example and an industrial application example have been carried out to test the effectiveness and feasibility of the proposed method. The results demonstrate that the TD technique with the LWPLS model can achieve the best prediction accuracy in both cases compared to two other methods.