Application of dual-rate modeling to CCR octane quality inferential control

Abstract

Octane quality control at Shell Canada's continuous catalytic reforming (CCR) units is typically done manually due to infrequent measurements of the research octane number (RON). The goal of this paper is to study automating the control loop by developing a dual-rate inferential control scheme. In particular, for a dual-rate process with fast input updating and slow output sampling, we propose a polynomial domain method to identify a fast single-rate linear model based on dual-rate input-output data; using the fast model to supply missing samples, we extend a popular model-based predictive control algorithm to the inferential control framework; the identification and control algorithms are applied to a Shell Canada's CCR reactor, and the inferential controller is implemented in real time, resulting in 40% reduction in octane quality variance-a significant improvement.