Development of Regression Models by Closed–Loop Identification of Distillation Column - A Case Study

Abstract

Objectives: Closed-loop identification is reported to provide better results for identification of systems for control applications. This study conducted closed-loop identification on an Aspen Plus® dynamic simulation based on a pilot-plant distillation column to develop discrete-time linear time-invariant models. Methods/Statistical Analysis: Identification data was generated using set-point perturbations in control variables under proportional-integral control. Identified models were compared with a model identified using open-loop data using 20-step ahead predictions. Findings: Results indicate that closed-loop identification provides more precise prediction models than open-loop identification in this case study. 20-step predictions for closed loop models exceeded 90% fit, whereas the open loop model predictions provided a 70% fit and missed the steady-state values. Application/Improvements: Thus closed-loop identification is more appropriate for applications in model-based controllers. Keywords: Aspen Plus, Closed-Loop Identification, Distillation Column, Identification for Control, System Identification