MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR

Raihan Tayeb; Yuwen Zhang

doi:10.1115/IMECE2022-94564

MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR

Raihan Tayeb
, Yuwen Zhang

University of Missouri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A feed forward machine-learning (ML) model is applied to study bubble induced turbulence and bubble mass transfer in a bubble column reactor. Using direct numerical simulation data for forced turbulence, bubble deformations and flow velocities are predicted. To predict mass transfer, ML sub-grid scale (SGS) modeling technique is introduced for the concentration of reactants and products undergoing parallel competitive reactions in the oxidation of toluene. The ML model replaces the iterative approach associated with the use of analytical profiles for previous SGS models for correcting concentration profiles in boundary layers. The present model, thus, offers a significant performance bonus as well as the flexibility to extend to more complex scenarios due to its data-driven nature.

Original language	English
Title of host publication	Fluids Engineering; Heat Transfer and Thermal Engineering
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791886700
DOIs	https://doi.org/10.1115/IMECE2022-94564
State	Published - 2022
Externally published	Yes
Event	ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 - Columbus, United States Duration: 30 Oct 2022 → 3 Nov 2022

Publication series

Name	ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume	8

Conference

Conference	ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022
Country/Territory	United States
City	Columbus
Period	30/10/22 → 3/11/22

Keywords

Data-driven approach
Machine learning
Reactive-diffusive-convective system
Subgrid-scale modeling
Toluene oxidation

Access to Document

10.1115/IMECE2022-94564

Cite this

Tayeb, R., & Zhang, Y. (2022). MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR. In Fluids Engineering; Heat Transfer and Thermal Engineering Article V008T11A006 (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 8). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2022-94564

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title = "MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR",

abstract = "A feed forward machine-learning (ML) model is applied to study bubble induced turbulence and bubble mass transfer in a bubble column reactor. Using direct numerical simulation data for forced turbulence, bubble deformations and flow velocities are predicted. To predict mass transfer, ML sub-grid scale (SGS) modeling technique is introduced for the concentration of reactants and products undergoing parallel competitive reactions in the oxidation of toluene. The ML model replaces the iterative approach associated with the use of analytical profiles for previous SGS models for correcting concentration profiles in boundary layers. The present model, thus, offers a significant performance bonus as well as the flexibility to extend to more complex scenarios due to its data-driven nature.",

keywords = "Data-driven approach, Machine learning, Reactive-diffusive-convective system, Subgrid-scale modeling, Toluene oxidation",

author = "Raihan Tayeb and Yuwen Zhang",

note = "Publisher Copyright: Copyright {\textcopyright} 2022 by ASME.; ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 ; Conference date: 30-10-2022 Through 03-11-2022",

year = "2022",

doi = "10.1115/IMECE2022-94564",

language = "英语",

series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",

publisher = "American Society of Mechanical Engineers (ASME)",

booktitle = "Fluids Engineering; Heat Transfer and Thermal Engineering",

}

Tayeb, R & Zhang, Y 2022, MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR. in Fluids Engineering; Heat Transfer and Thermal Engineering., V008T11A006, ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 8, American Society of Mechanical Engineers (ASME), ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022, Columbus, United States, 30/10/22. https://doi.org/10.1115/IMECE2022-94564

MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR. / Tayeb, Raihan; Zhang, Yuwen.
Fluids Engineering; Heat Transfer and Thermal Engineering. American Society of Mechanical Engineers (ASME), 2022. V008T11A006 (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 8).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tayeb, Raihan

AU - Zhang, Yuwen

PY - 2022

Y1 - 2022

N2 - A feed forward machine-learning (ML) model is applied to study bubble induced turbulence and bubble mass transfer in a bubble column reactor. Using direct numerical simulation data for forced turbulence, bubble deformations and flow velocities are predicted. To predict mass transfer, ML sub-grid scale (SGS) modeling technique is introduced for the concentration of reactants and products undergoing parallel competitive reactions in the oxidation of toluene. The ML model replaces the iterative approach associated with the use of analytical profiles for previous SGS models for correcting concentration profiles in boundary layers. The present model, thus, offers a significant performance bonus as well as the flexibility to extend to more complex scenarios due to its data-driven nature.

AB - A feed forward machine-learning (ML) model is applied to study bubble induced turbulence and bubble mass transfer in a bubble column reactor. Using direct numerical simulation data for forced turbulence, bubble deformations and flow velocities are predicted. To predict mass transfer, ML sub-grid scale (SGS) modeling technique is introduced for the concentration of reactants and products undergoing parallel competitive reactions in the oxidation of toluene. The ML model replaces the iterative approach associated with the use of analytical profiles for previous SGS models for correcting concentration profiles in boundary layers. The present model, thus, offers a significant performance bonus as well as the flexibility to extend to more complex scenarios due to its data-driven nature.

KW - Data-driven approach

KW - Machine learning

KW - Reactive-diffusive-convective system

KW - Subgrid-scale modeling

KW - Toluene oxidation

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DO - 10.1115/IMECE2022-94564

M3 - 会议稿件

AN - SCOPUS:85148487502

T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)

BT - Fluids Engineering; Heat Transfer and Thermal Engineering

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022

Y2 - 30 October 2022 through 3 November 2022

ER -

MACHINE-LEARNING APPROACH TO MODELING OXIDATION OF TOLUENE IN A BUBBLE COLUMN REACTOR

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