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Optimal catalyst texture in macromolecule conversion : A computational and experimental study. / Semeykina, V. S.; Malkovich, E. G.; Bazaikin, Ya V. и др.

в: Chemical Engineering Science, Том 188, 12.10.2018, стр. 1-10.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

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Vancouver

Semeykina VS, Malkovich EG, Bazaikin YV, Lysikov AI, Parkhomchuk EV. Optimal catalyst texture in macromolecule conversion: A computational and experimental study. Chemical Engineering Science. 2018 окт. 12;188:1-10. doi: 10.1016/j.ces.2018.05.005

Author

Semeykina, V. S. ; Malkovich, E. G. ; Bazaikin, Ya V. и др. / Optimal catalyst texture in macromolecule conversion : A computational and experimental study. в: Chemical Engineering Science. 2018 ; Том 188. стр. 1-10.

BibTeX

@article{66066c0bd3bb4b4897edeead5dd4c1c0,
title = "Optimal catalyst texture in macromolecule conversion: A computational and experimental study",
abstract = "Evolution of alumina catalyst texture during macromolecule conversion with an emphasis on heavy oil hydroprocessing was theoretically estimated using geometrical characteristics of the porous media that were in turn calculated via Monte-Carlo methods and methods of the graph theory. Two types of alumina texture have been modeled: unimodal mesoporous structure of conventional catalyst and bimodal meso-macroporous structure of the catalyst, which can be prepared by hard-templating method. To estimate the decreasing of the effectiveness coefficient for these two types of catalysts, a solution for the diffusion equation on the cylinder pellet was found. Deactivation was modeled by the most simple way of monotonic increase of alumina grain radius, which represented deposition of coke and metal species onto the surface of grains. The comparison of theoretical predictions with experimental results on heavy oil conversion under conditions close to industrial ones showed the correlation between the experiment and the model – hierarchical texture prolonged the catalyst lifetime in both cases. Nevertheless, to obtain accurate predictions of the necessary properties of the catalyst texture, the deactivation model should be complicated.",
keywords = "Deactivation, Diffusion modeling, Hierarchical catalyst, Macromolecule, Macropores, Percolation theory, HYDROTREATING REACTIONS, HYDRODEMETALATION, DIFFUSION, DEACTIVATION",
author = "Semeykina, {V. S.} and Malkovich, {E. G.} and Bazaikin, {Ya V.} and Lysikov, {A. I.} and Parkhomchuk, {E. V.}",
note = "Publisher Copyright: {\textcopyright} 2018 Elsevier Ltd",
year = "2018",
month = oct,
day = "12",
doi = "10.1016/j.ces.2018.05.005",
language = "English",
volume = "188",
pages = "1--10",
journal = "Chemical Engineering Science",
issn = "0009-2509",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Optimal catalyst texture in macromolecule conversion

T2 - A computational and experimental study

AU - Semeykina, V. S.

AU - Malkovich, E. G.

AU - Bazaikin, Ya V.

AU - Lysikov, A. I.

AU - Parkhomchuk, E. V.

N1 - Publisher Copyright: © 2018 Elsevier Ltd

PY - 2018/10/12

Y1 - 2018/10/12

N2 - Evolution of alumina catalyst texture during macromolecule conversion with an emphasis on heavy oil hydroprocessing was theoretically estimated using geometrical characteristics of the porous media that were in turn calculated via Monte-Carlo methods and methods of the graph theory. Two types of alumina texture have been modeled: unimodal mesoporous structure of conventional catalyst and bimodal meso-macroporous structure of the catalyst, which can be prepared by hard-templating method. To estimate the decreasing of the effectiveness coefficient for these two types of catalysts, a solution for the diffusion equation on the cylinder pellet was found. Deactivation was modeled by the most simple way of monotonic increase of alumina grain radius, which represented deposition of coke and metal species onto the surface of grains. The comparison of theoretical predictions with experimental results on heavy oil conversion under conditions close to industrial ones showed the correlation between the experiment and the model – hierarchical texture prolonged the catalyst lifetime in both cases. Nevertheless, to obtain accurate predictions of the necessary properties of the catalyst texture, the deactivation model should be complicated.

AB - Evolution of alumina catalyst texture during macromolecule conversion with an emphasis on heavy oil hydroprocessing was theoretically estimated using geometrical characteristics of the porous media that were in turn calculated via Monte-Carlo methods and methods of the graph theory. Two types of alumina texture have been modeled: unimodal mesoporous structure of conventional catalyst and bimodal meso-macroporous structure of the catalyst, which can be prepared by hard-templating method. To estimate the decreasing of the effectiveness coefficient for these two types of catalysts, a solution for the diffusion equation on the cylinder pellet was found. Deactivation was modeled by the most simple way of monotonic increase of alumina grain radius, which represented deposition of coke and metal species onto the surface of grains. The comparison of theoretical predictions with experimental results on heavy oil conversion under conditions close to industrial ones showed the correlation between the experiment and the model – hierarchical texture prolonged the catalyst lifetime in both cases. Nevertheless, to obtain accurate predictions of the necessary properties of the catalyst texture, the deactivation model should be complicated.

KW - Deactivation

KW - Diffusion modeling

KW - Hierarchical catalyst

KW - Macromolecule

KW - Macropores

KW - Percolation theory

KW - HYDROTREATING REACTIONS

KW - HYDRODEMETALATION

KW - DIFFUSION

KW - DEACTIVATION

UR - http://www.scopus.com/inward/record.url?scp=85047184084&partnerID=8YFLogxK

U2 - 10.1016/j.ces.2018.05.005

DO - 10.1016/j.ces.2018.05.005

M3 - Article

AN - SCOPUS:85047184084

VL - 188

SP - 1

EP - 10

JO - Chemical Engineering Science

JF - Chemical Engineering Science

SN - 0009-2509

ER -

ID: 13488006