A data-driven approach to generate pseudo-reaction sequences for the thermal conversion of Athabasca bitumen-Reference-Cited by-同舟云学术

A data-driven approach to generate pseudo-reaction sequences for the thermal conversion of Athabasca bitumen

Published:2021 Issue:3 Volume:6 Page:505-537
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Sivaramakrishnan Kaushik¹²³^ORCID,Puliyanda Anjana¹²³^ORCID,de Klerk Arno¹²³^ORCID,Prasad Vinay¹²³^ORCID

Affiliation:

1. Department of Chemical and Materials Engineering

2. Edmonton

3. Canada

Abstract

We use self-modeling multivariate curve resolution to identify pseudo-components and chemical transformations in thermal conversion of Athabasca bitumen.

Funder

Natural Sciences and Engineering Research Council of Canada

Alberta Innovates

University of Alberta

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2021/RE/D0RE00321B

Reference104 articles.

1. M. R. Gray , Upgrading oilsands bitumen and heavy oil , The University of Alberta Press , Edmonton, Canada , 2015

2. A Comprehensive Approach to In-Situ Combustion Modeling

3. Kintetics Of Thermal Visbreaking Of A Cold Lake Bitumen

4. Advances in Asphaltene Petroleomics. Part 1: Asphaltenes Are Composed of Abundant Island and Archipelago Structural Motifs

5. L. Fortuna , S.Graziani , A.Rizzo and M. G.Xibilia , Soft Sensors for Monitoring and Control of Industrial Processes , Springer Science & Business Media , 2007

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Simultaneous spectrophotometric determination of Co (II) and Co (III) in acidic medium with partial least squares regression and artificial neural networks;Heliyon;2024-02

2. Partial hydrogenation of 1,3-butadiene over nickel with alumina and niobium supported catalysts;Arabian Journal of Chemistry;2024-01

3. Semi-supervised machine learning approach for reaction stoichiometry and kinetic model identification using spectral data from flow reactors;Reaction Chemistry & Engineering;2024

4. Prediction of Thermogravimetric Data for Asphaltenes Extracted from Deasphalted Oil Using Machine Learning Techniques;Industrial & Engineering Chemistry Research;2023-10-19

5. Benchmarking chemical neural ordinary differential equations to obtain reaction network-constrained kinetic models from spectroscopic data;Engineering Applications of Artificial Intelligence;2023-10