A robust strategy for real-time process monitoring

Abstract

An operator support system (OSS) is proposed to reliably retain salient information in a high dimensional and correlated database, to uncover linear and nonlinear correlations among variables, to reconstruct failed/unavailable sensors, and to assess process-operating performance in the presence of noise and outliers. The proposed strategy carries out the task in three steps. In the first step, a robust tandem filter is used to suppress noise and reject any outlying observations. Next, an orthogonal nonlinear principal component analysis network is utilized to optimally retain a parsimonious representation of the system. In the final step, the process status is checked against the normal operating region defined by kernel density estimation, and failed/unavailable sensors are reconstructed via constrained optimization and the trained network. The strategy is demonstrated in real-time using a pilot-scale distillation column.