Evaluation of the Performance of the Riser in the Fluid Catalytic Cracking Process

Alvarez-Castro, H. C.; Matos, E.M.; Mori, Milton; Martignoni, W.P.; Ocone, Raffaella

doi:10.1080/10916466.2014.968285

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…[ 1–3 ] Furthermore, with the improvement in computational performance since the 2000s, there has been an increase in the reports of complex analyses, such as a large number of lumps, parameters related to catalyst deactivation, and risers' and cyclones' performance with computational fluid dynamics (CFD). For example, Bollas et al studied five Lump models and catalyst deactivation; [ 4 ] Yakubu et al studied six‐Lump models and reaction analysis in risers; [ 5 ] Alvarez‐Castro et al studied twelve Lump models and riser analysis using numerical software; [ 6 ] Olafadehan et al reported a riser study using the numerical computation software MATLAB; and Yang et at. reported one using EQUATRAN‐G.…”

Section: Introductionmentioning

confidence: 99%

Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model

Kawai¹,

Sato²,

Furuichi³

et al. 2022

J Adv Manuf & Process

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Because of the recent declining demand for gasoline, the key to making refineries competitive is to maximize the yields of propylene and aromatics by converting heavier feedstock into basic petrochemicals through the residue fluid catalytic cracking (RFCC) process. This study presents an artificial intelligence (AI) hybrid reaction model to optimize the catalyst make‐up rate and maximize the product yield in a real‐time operation by (1) developing a catalyst activity evaluation method, (2) integrating the catalyst to oil (Cat/Oil) ratio to evaluate the reaction performance, and (3) incorporating the yield prediction model into the latest digital technologies. To this end, the catalyst deactivation function, which uses a deep neural network of the basic machine learning method, was added to the past RFCC reaction model. Under actual operational conditions, this study shows that the AI hybrid reaction model using the catalyst deactivation function can minimize catalyst loss and produce an accurate yield prediction as a production planning support tool.

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Section: Introductionmentioning

confidence: 99%

Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model

Kawai¹,

Sato²,

Furuichi³

et al. 2022

J Adv Manuf & Process

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show abstract

“…Essas reações são indesejáveis, pois reduzem a eficiência do processo por aumentar o rendimento do coque, além de gerar produtos de qualidade inferior aos produtos gerados pelo craqueamento catalítico. Portanto, é de grande importância investigar parâmetros do processo que podem ser modificados para amenizar esses fenômenos indesejados (ALVAREZ-CASTRO et al, 2012;BARBOSA, 2012).…”

Section: Leito Fluidizado Gás-sólidounclassified

Análise numérica da influência de defletores sobre o escoamento gás-sólido em risers de FCC via fluidodinâmica computacional

Brito¹

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Many industrial processes with fluids flow include multiphase phenomena, such as fluidized catalytic cracking (FCC), which is present in oil refineries. This process consists of cracking the heavy oil fractions and converting them into high added-value products. Despite its economic viability, this process operates under extreme conditions of temperature and pressure and is characterized by the presence of multiphase reactive flow and a series of complex phenomena, which makes the process difficult to be evaluated experimentally. In this context, Computational Fluid Dynamics (CFD) has proved to be a powerful tool for the study of many practical engineering problems in which a flow pattern is observed because, besides the reduced costs, it is possible to evaluate equipment even under extreme operating conditions. The fluid dynamics inside the riser is heterogeneous and characterized by accumulation of catalyst particles onto the wall. This flow pattern known as core-annulus causes a decrease in process yields. In this sense, the use of baffles in FCC risers has attracted the interest of researchers with the purpose of minimizing the accumulation of solid particles onto the wall and ensuring better distribution and uniformity between the phases within FCC risers. Therefore, the main objective of this work was to evaluate the influence of baffles in FCC riser on the gas-solid flow using computational fluid dynamics. Numerical simulations were performed using a three-level factorial design by varying the geometric parameters: quantity (Z), length (H) and throttling (E) of baffles in the FCC riser. The results of the numerical simulations showed that baffles in FCC riser can cause changes to the flow, providing greater homogenization in the system. The statistical analysis of the results pointed out the parameters that most influenced the yields of gasoline, coke, light fractions and heavy fractions, as well as the increase or decrease of pressure.

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Computational fluid dynamic modelling of FCC riser: A review

Shah

Utikar

Pareek

et al. 2016

Chemical Engineering Research and Design

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Evaluation of the Performance of the Riser in the Fluid Catalytic Cracking Process

Cited by 4 publications

References 7 publications

Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model

Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model

Análise numérica da influência de defletores sobre o escoamento gás-sólido em risers de FCC via fluidodinâmica computacional

Computational fluid dynamic modelling of FCC riser: A review

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