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Reactor design and optimization for converting crude (and refinery wastes) to chemicals in one step through steam-fluidized catalytic cracking

Problem statement

The direct catalytic cracking from crude oil to chemicals could dominate the petrochemical industry shortly, with less fuel consumption and increasing production of light olefins and aromatics. We aim to simplify the refinery into a unique one-step conversion scheme, targeting the production of the most demanded petrochemicals.

Using a bottom-up holistic approach, we design a catalytic crude-to-chemicals process toward this goal using a bottom-up holistic approach. We investigate advanced reactors with intrinsic kinetic data and controlled hydrodynamics to improve the process. We study the non-linear multiscale phenomena by coupling the hydrodynamics, heat transfer, and reaction kinetics.

We use particle image/tracking velocimetry experiments, kinetic modeling, computational particle fluid dynamic modeling, and optimization approaches to improve operating scenarios and develop innovative reactor prototypes.

We focus on the catalyst, reactor, and process levels for system enhancement and intensification. We are optimizing several state-of-the-art laboratory and pilot-scale units, including a circulating Berty, downer, and multifunctional fluidized bed reactors.

Goals

  • Develop and scale up advanced reactors for converting crude oil to chemicals through fluid catalytic cracking approaching intrinsic kinetics
  • Model process dynamics using reactive particle fluid dynamics coupled with experimental validations
  • Establish a design workflow for short-contact time reactors based on modeling, prototyping, and testing
  • Analyze the novel process developments in fluid catalytic cracking: novel feedstock, process modifications…
C2C-FCC2023

Related People

Related Publications

A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds

by Alvira, Hita, Rodriguez, Arandes, Castaño
Processes Year: 2018

Extra Information

Open Access.

Abstract

Establishing a reaction network is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). This step is the seed for a faithful reactor modeling and the subsequent catalyst re-design, process optimization or prediction. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, was obtained in an FCC simulator using six types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and three industrial catalysts. Principal component analysis (PCA) was applied over the dataset, showing that the main components are associated with feed composition (27.41% variance), operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component were correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others. Then, a data-driven reaction network was proposed for the cracking of waste feeds based on the previously obtained correlations.

Keywords

FCC W2C MKM