Economic model predictive control of an industrial fluid catalytic cracker

Abstract

Fluid catalytic cracking (FCC) is an important refinery process by which heavy hydrocarbons are cracked to form lighter valuable products over catalyst particles. FCC plants consist of the riser (reactor), the regenerator, and the fractionator that separates the riser effluent into the useful end products. In FCC plants the product specifications and feedstocks change due to varying economic and market conditions. In addition, FCC plants operate with large throughputs and a small improvement realized by optimization and control yields significant economic return. In previous work, we developed a nonlinear dynamic model and validated it with industrial data. In this study, our focus involves the development and application of a real-time optimization framework. We propose a hierarchical structure which includes a two-layer implementation of economic model predictive control (EMPC). EMPC provides the optimal riser and the regenerator temperature reference trajectories which are determined from a dynamic optimization problem maximizing the plant profit. A regulatory model predictive controller (RMPC) manipulates the catalyst circulation rate and the air flow rate to track the reference trajectories provided by EMPC. We consider changes in product prices and the feed content, both of which necessitate online optimization. Dynamic simulations show that the proposed hierarchical control structure achieves optimal tracking of plant profit during transitions between different operating regimes thanks to the combined efforts of EMPC and RMPC.