Process control applications of subspace and regression-based identification and monitoring methods

Abstract

This tutorial paper summarizes the application of a variety of identification techniques to simulations of two realistic chemical processes, a continuous stirred-tank reactor (CSTR) and the Tennessee Eastman challenge process. Both subspace identification methods (N4SID and CVA) and regression techniques (PLS and CCR) are considered. Emphasis is placed on the relative performance of the various identification methods, and their strengths and weaknesses. Also, the use of these identification methods in monitoring and fault detection is discussed. in the CSTR case study. Dynamic ARX and FlR models are identified using two regression techniques, PLS and CCR. and the predictive error method, are compared with state-space models identified using two subspace algorithms. CVA and N4SID. The objective functions for PLS and CCR are shown to be related. A comprehensive simulation study of the CSTR with different characteristics and noise properties is used to compare the identification methods. The results indicate that, if the time delay structure is known or estimated accurately, the identified subspace models tend to be more accurate than the models identified using regression. The state-space models identified using the CVA algorithm are especially accurate. The Tennessee Eastman challenge process is a realistic simulation of a chemical process that has been widely used in process control studies. In this case study, several identification methods are examined and used to develop MIMO models that contain seven inputs and ten outputs. ARX and finite impulse response models are identified using reduced-rank regression techniques (PLS and CCR) and state-space models identified with prediction error methods and subspace algorithms. For a variety of reasons, the only successful models are the state-space models produced by two popular subspace algorithms, N4SID and canonical variate analysis (CVA). The CVA model is the most accurate. Important issues for identifying the Tennessee Eastman challenge process and comparisons between the subspace algorithms are also discussed.