Identification of the Tennessee Eastman Challenge Process with Subspace Methods

Abstract The Tennessee Eastman Challenge Process is a realistic simulation of a real chemical process that has been widely used in process control studies. In this case study, several identification methods are examined and used to develop models that contain seven inputs and ten outputs. ARX and FIR models are identified using reduced-rank regression techniques (PLS and CCR) and state-space models identified with predictive 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.