A hybrid safety monitoring framework for industrial FCC disengager coking rate based on FPM, CFD, and ML

This work proposed a hybrid modeling framework for the safety monitoring of coking rates in FCC (Fluidized Catalytic Cracking) disengager. The framework combines CFD (Computational Fluid Dynamics), coking FPM (First Principles Model), machine learning, and industrial data to establish a comprehensive and customizable approach. The FPM of the coking rate, including the UNIFAC condensation ratio model, the coking ratio model and the capture ratio model, are established by combustion experiments, gas-solid two-phase flow simulations and DPM (Discrete Phase Model) simulations. The capture ratio model is built to calculate the capture ratio of heavy oil droplets by each high-risk region, achieved by DPM simulation. By establishing a link between the simulated position and the DCS (Distributed Control System) sensor, the simulation results and the real-time DCS data are matching. An improved LSTM (Long Short-Term Memory) network was then established to predict the real-time temperature and pressure in the high-risk coking regions of the disengager using real-time DCS and LIMS (Laboratory Information Management System) data. The LSTM network can achieve online monitoring of coking rate by coupling FPM. This hybrid coking monitoring framework has been industrially applied and validated with an accuracy of over 90% in a continuous one-month online monitoring task, which is highly interpretable and extensible, providing refining companies with a scalable and customizable approach to monitor coking problems in FCC disengager.