Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 2: Fluid Catalytic Cracking (FCC) Process

This work presents the methodology to develop, validate, and apply a predictive model for an integrated fluid catalytic cracking (FCC) process. We demonstrate the methodology using data from a commercial FCC plant in the Asia Pacific with a feed capacity of 800 000 tons per year. Our model accounts for the complex cracking kinetics in the riser–regenerator with a 21-lump kinetic model. We implement the methodology with Microsoft Excel spreadsheets and a commercial software tool, Aspen HYSYS/Petroleum Refining from Aspen Technology, Inc. The methodology is equally applicable to other commercial software tools. This model gives accurate predictions of key product yields and properties given feed qualities and operating conditions. In addition, this work presents the first lumped FCC kinetic model integrated with a gas plant model in the literature. We validate this work using 6 months of plant data. We also perform several case studies to show how refiners may apply this work to improve the gasoline yield and increase unit throughput. A key application of the integrated FCC model is to generate DELTA–BASE vectors for linear programming (LP)-based refinery planning to help refiners choose an optimum slate of crude feeds. DELTA–BASE vectors quantify changes in FCC product yields and properties as functions of changes in feed and operating conditions. Traditionally, refiners generated DELTA–BASE vectors using a combination of historical data and correlations. Our integrated model can eliminate guesswork by providing more robust predictions of product yields and qualities. This work differentiates itself from previous work in this area through the following contributions: (1) detailed models of the entire FCC plant, including the overhead gas compressor, main fractionator, primary and sponge oil absorber, primary stripper, and debutanizer columns, (2) process to infer molecular composition required for the kinetic model using routinely collected bulk properties of feedstock, (3) predictions of key liquid product properties not published alongside previous related work (density, ASTM D86 distillation curve, and flash point), (4) case studies showing industrially useful applications of the model, and (5) application of the model with an existing LP-based planning tool.