Alvira, J.I.; Hita, I.; Rodríguez, E.; Arandes, J.M.; Castaño, P. A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds. Processes2018, 6, 243.
Alvira, J.I.; Hita, I.; Rodríguez, E.; Arandes, J.M.; Castaño, P. A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds. Processes 2018, 6, 243.
Alvira, J.I.; Hita, I.; Rodríguez, E.; Arandes, J.M.; Castaño, P. A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds. Processes2018, 6, 243.
Alvira, J.I.; Hita, I.; Rodríguez, E.; Arandes, J.M.; Castaño, P. A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds. Processes 2018, 6, 243.
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
