Fluidized Catalytic Cracking (FCC) and Chemical Looping Combustion (CLC): Kinetics , First Principle Models (FPM), Data Driven Models (DDM) and Artificial Intelligence (AI)

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This research will be developed in partnership with Imperial Oil, Canada with the aim of improving FCC (Fluidized Catalytic Cracking) and CLC (Chemical Lopping Combustion) processes. The proposed approach involves: a) The establishment of catalytic cracking kinetics using catalysts and VGO (vacuum gas oil) provided by Imperial Oil, in the CREC Riser Simulator-Mark II, b) The determination of FPMs (First Principle Models) for FCC large scale units using Barracuda CPFDs (Computational Particle Fluid Dynamics), c) The establishment of DDMs (Data Driven Models) involving adjustable parameters and model trainers, using the FMP project data library and Imperial Oil operational data, d) The development of a new CREC-Hybrid AI (Artificial Intelligence) algorithm using the Matlab/Python software. This project addresses challenges faced nowadays by Imperial Oil refineries as well as other refineries in North America. These issues are the result of rapid changes in the cost of VGO feedstocks, gasoline value and increased CO2 and other greenhouse gas emissions. Thus, the CREC Riser Simulator together with AI training models for FCC and CLC, will provide a unique platform to determine the best approaches to enhance the performance of FCC processes and VGO refinery operations worldwide.