Effect of Atmospheric Residue Incorporation in the Fluidized Catalytic Cracking (FCC) Feed on Product Stream Yields and Composition

Arandes, José M.; Torre, Iker; Azkoiti, Miren J.; Ereña, Javier; Bilbao, Javier

doi:10.1021/ef800031x

Cited by 32 publications

(33 citation statements)

References 33 publications

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“…It is worth mentioning that the variables describing process conditions and catalyst properties have very low impact, expressed as low loadings along PC1. The critical role of feed composition on the overall performance of the FCC process has been recurrently indicated before [12,24,28,55]. Based on their loadings (highest and lowest along PC1), the distribution of boiling point fractions of the feed (Hf, Gf or Lf) has a stronger impact on process performance than its chemical composition (Pf, Of, Af or Nf).…”

Section: Multivariate Analysismentioning

confidence: 99%

“…However, taking the remarkable versatility of FCC, other non-conventional feeds have also been studied [10]: (i) secondary refinery streams; (ii) streams derived from the consumer society; or (iii) oxygenated bio-derived feedstock. As secondary refinery streams (i), Arandes et al [11,12] studied the effect of co-feeding atmospheric distillation residue (20 wt %) with VGO in a riser simulator, observing a crucial role of the reactant accessibility for the Processes 2018, 6, 243 2 of 15 efficient conversion of bulky aromatic molecules, and also the great influence of the temperature and catalyst/oil (C/O) ratio on gasoline composition. Garcia et al [13] observed an improvement of conversion of the blend of atmospheric distillation residue (10 wt %) and VGO, compared with the VGO alone.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Multivariate Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Multivariate Analysismentioning

confidence: 99%

“…However, taking the remarkable versatility of FCC, other non-conventional feeds have been studied [10]: (i) secondary refinery streams, (ii) streams derived from the consumer society, or (iii) oxygenated bio-derived feedstock. As secondary refinery streams (i), Arandes et al [11,12] studied the effect of co-feeding atmospheric distillation residue (20 wt%) with VGO in a riser simulator, observing a crucial role of the reactant accessibility for the efficient conversion of bulky aromatic molecules, and also the great influence of the temperature and catalyst/oil (C/O) ratio on gasoline composition. Garcia et al [13] observed an improvement of conversion of the blend of atmospheric distillation residue (10 wt%) and VGO, compared with the VGO alone.…”

Section: Introductionmentioning

confidence: 99%

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

Ji¹,

Hita²,

Rodríguez³

et al. 2018

Preprint

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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.

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“…A real‐time prediction model for product properties and output variables of the FCC and RFCC processes is important in order to propose process changes and to deal with a large amount of process data. Mathematical models of the product properties of FCC and RFCC processes have been proposed . However, such approaches have limitations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive prediction model for fluidized catalytic cracking processes based on the PLS method

Kim

Lee

2018

Asia-Pacific J Chem Eng

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Based on the partial least squares (PLS) method, an adaptive predictive PLS (AP-PLS) method was developed for sensitive adaptation to process changes using large-scale data (Big Data) from chemical processes. Utilizing data sets of fluidized catalytic cracking (FCC) and residue FCC (RFCC) processes as the basis, the AP-PLS method was developed and its prediction ability was compared with the simple PLS method. The required parameters for the prediction model of the FCC and RFCC processes are readily available from reported process data using time-varying model updating by the AP-PLS

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Effect of Atmospheric Residue Incorporation in the Fluidized Catalytic Cracking (FCC) Feed on Product Stream Yields and Composition

Cited by 32 publications

References 33 publications

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

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

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

Adaptive prediction model for fluidized catalytic cracking processes based on the PLS method

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