A data-driven reaction network for the fluid catalytic cracking of waste feeds

José Ignacio Alvira, Idoia Hita, Elena Rodríguez, José M. Arandes, Pedro Castano*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Article number243
JournalProcesses
Volume6
Issue number12
DOIs
StatePublished - Dec 1 2018

Keywords

  • Fluid catalytic cracking (FCC)
  • Polyolefin pyrolysis
  • Principal component analysis (PCA)
  • Scrap tires
  • Waste valorization

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Process Chemistry and Technology

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