A selective moving window partial least squares (SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene (PX) content. Aiming at the high frequency of model updating in previous recursive PLSmethods

a selective updating strategywas developed. Themodel adaptation is activated once the prediction error is larger than a preset threshold

or themodel is kept unchanged. As a result

the frequency of model updating is reduced greatly

while the change of prediction accuracy is minor. The performance of the proposedmodel is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary

the proposed SMW-PLS method can deal with the slow time-varying processes effectively.