In the development of soft sensors for an industrial process, the colinearity of the predictor variables and the time-varying nature of the process need to be addressed. In many industrial applications, the partial least-squares (PLS) has been proven to capture the linear relationship between input and output variables for a local operating region; therefore, the PLS model needs to be adapted to accommodate the time-varying nature of the process. In this paper, a fast moving window algorithm is derived to update the PLS model. The proposed approach adapted the parameters of the inferential model with the dissimilarities between the new and oldest data and incorporated them into the kernel algorithm for the PLS. The computational loading of the model adaptation was therefore independent of the window size. In addition, the prediction performance of the model is only dependent on the retained latent variables (LVs) and the window size that can be predetermined from the historical data. Since a moving window approach is sensitive to outliers, the confidence intervals for the primary variables were created based on the prediction uncertainty. The inferential model would not be misled by the outliers from the online analyzers, whereas the model could be updated during the transition stage. The prediction performance of a soft sensor is not only dependent on the capability of the inferential model, but also relies on the data quality of the input measurements. In this paper, the input sensors were validated before performing a prediction. The deterioration of the prediction performance due to the failed sensors was removed by the reconstruction approach. A simulated example of a continuous stirred tank reactor (CSTR) with feedback control systems illustrated that the process characteristics captured by the PLS could be adapted to accommodate a nonlinear process. An industrial example, predicting oxygen concentrations in the air separation process, demonstrated the effectiveness of the proposed approach for the process industry.

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