Correlating the Process Variables and Products Involved in the Fluid Catalytic Cracking of Waste Feeds

Abstract

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 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 3 industrial catalysts. Principal component analysis (PCA) has been 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 have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

Catalytic Conversion of 1‐butene over Modified Versions of Commercial ZSM‐5 to Produce Clean Fuels and Chemicals

“…In analysing a large set of material properties that may be somehow related, one may advantageously employ a multivariate statistical tool, such as principal component analysis (PCA), since it allows to reduce a large set of variables to a smaller set of principal components that still contains most of the information of the large set, decreasing the complexity of the analyses . The PCA methodology was reported by Castaño and co‐workers for fluid catalytic cracking over faujasite Y zeolites, and by Vayenas and co‐workers for hydrotreatment of lube oil over metal oxides, allowing valuable insights into key parameters affecting the catalytic reactions.…”