In order to reduce the model updating frequency of recursive partial least squares (RPLS) modeling methods

a RPLS model based on the model performance assessment (MPA-RPLS) is developed. Firstly

a confidence limit of the model is generated automatically based on the initial behavior of a process. And a root mean squared error of prediction (RMSEP) is used as a performance index to evaluate the model. Base on the results of the model performance assessment

the model updating is selectively activated

in the meanwhile

the confidence limit is also updated. Subsequently

a moving average filter is integrated into the model to eliminate the noise embbeded in variables

and the effect of the noise on the model performance is then investigated. At last

the developed model is applied to a chemical reaction process

hydro-isomerization process of C8-aromatics. Simulation is run based on a large number of industrial data. The simulation results show that the computational efficiency is improved greatly (model updating frequency is reduced greatly) by the developed model

while a minor loss of the prediction accuracy is found. The noise embedded in variables could be dealt with effectively by the moving average filter

hence the prediction accuracy is improved.