Modeling the Hydrocracking Process with Deep Neural Networks-Reference-Cited by-同舟云学术

Modeling the Hydrocracking Process with Deep Neural Networks

Published:2020-01-16 Issue:7 Volume:59 Page:3077-3090
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Song Wenjiang¹^ORCID,Mahalec Vladimir²^ORCID,Long Jian¹^ORCID,Yang Minglei¹,Qian Feng¹^ORCID

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

2. Department of Chemical Engineering, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4L8, Canada

Funder

State Administration of Foreign Experts Affairs

Ministry of Education of the People's Republic of China

National Natural Science Foundation of China

International Cooperation and Exchange Project

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.9b06295

Reference34 articles.

1. Development of Vacuum Residue Hydrodesulphurization–Hydrocracking Models and Their Integration with Refinery Hydrogen Networks

2. Kinetic modeling of hydrocracking of heavy oil fractions: A review

3. Reaction pathways and product yields in mild thermal cracking of vacuum residues: A multi-lump kinetic model

4. Kinetic model for hydrocracking of heavy oil in a CSTR involving short term catalyst deactivation

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