Layered monitoring of xylenol tail gas treatment process based on stationary subspace analysis

Abstract

To deal with the inaccurate monitoring caused by the non-stationary characteristics of the xylenol tail gas treatment process, a layered monitoring scenario utilizing stationary subspace analysis has been presented in the present study. First, principal component analysis (PCA) is applied to establish the upperlevel stationary monitoring model. Then the sliding time window is used to capture the dynamic information of the remaining non-stationary features, update the model, and establish the lower non-stationary monitoring model.Next, the Bayesian information criterion is employed to build the global monitoring model according to the detection results of the upper and lower layers. Finally, the industrial data collected from the industrial boiler is used to evaluate the effectiveness and applicability of the proposed method. It is demonstrated that layered monitoring has higher accuracy than conventional non-stationary process monitoring techniques. Moreover, it can capture the faults in the stationary features masked by the non-stationary features.