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

这项研究将与加拿大帝国石油公司合作开发，旨在改进FCC（流化催化裂化）和CLC（化学燃烧）工艺。拟议的办法包括：a）使用催化剂和VGO建立催化裂化动力学（真空瓦斯油），在CREC立管模拟器-Mark II中，B）FPM的测定（第一原理模型）用于使用Barracuda CPFD的FCC大型装置（计算粒子流体动力学），c）DDM的建立（数据驱动模型）涉及可调参数和模型训练器，使用FMP项目数据库和帝国石油运营数据，d）使用Matlab/Python软件开发新的CREC混合AI（人工智能）算法。 该项目解决了帝国石油炼油厂以及北美其他炼油厂目前面临的挑战。这些问题是VGO原料成本、汽油价值以及CO2和其他温室气体排放增加的快速变化的结果。因此，CREC提升管模拟器与FCC和CLC的人工智能培训模型将提供一个独特的平台，以确定提高全球FCC工艺和VGO炼油厂运营性能的最佳方法。

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.