Modified subspace identification for long-range prediction model for inferential control

Abstract

In a chemical plant containing a series of processing units, a model that can be used to forecast the behavior of downstream variables based on upstream measurements can be useful for feedforward and inferential control. With such, various upstream disturbances can be rejected well before they influence the downstream units. However, creating such a dynamic model can be a challenge. The conventional multivariable system identification approach, which minimizes single-step-ahead prediction error, can result in models leading to poor prediction and control in the described context. To alleviate this difficulty, a modification is proposed to the conventional subspace identification (SSID) method to emphasize the accuracy of k-step-ahead prediction, where k is a general integer. It is shown that the modified SSID method can be used in conjunction with the k-step prediction error minimization (PEM). Using illustrative examples of mixing units with a recycle loop and an industrial-relevant pulp digester, improvement from adopting the suggested modification is demonstrated.