Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions-Reference-Cited by-同舟云学术

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Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

Published:2022-04-21 Issue:7 Volume:68 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE Journal

Author:

Xing Yafeng¹^ORCID,Dong Yachao¹^ORCID,Goergakis Christos²^ORCID,Zhuang Yu¹^ORCID,Zhang Lei¹^ORCID,Du Jian¹,Meng Qingwei³^ORCID

Affiliation:

1. School of Chemical Engineering Institute of Chemical Process Systems Engineering, Dalian University of Technology Dalian Liaoning China

2. Chemical and Biological Engineering and Systems Research Institute, Tufts University Medford Massachusetts USA

3. State Key Laboratory of Fine Chemicals, School of Pharmaceutical Science and Technology Department Dalian University of Technology Dalian China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17713

Reference38 articles.

1. Chemical data intelligence for sustainable chemistry

2. Changes in activation energy and kinetic mechanism during EVA crosslinking

3. Water-soluble SG1-based alkoxyamines: A breakthrough in controlled/living free-radical polymerization in aqueous dispersed media

4. Homogeneous high-temperature oxidation of methane

5. Towards the development of a chemical kinetic model for the homogeneous oxidation of mercury by chlorine species

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1. SINDy-CRN: Sparse Identification of Chemical Reaction Networks from Data;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

2. Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes;Processes;2023-07-13

3. Optimization-based simultaneous modelling of stoichiometries and kinetics in complex organic reaction system;Chemical Engineering Science;2023-07

4. Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning;Digital Discovery;2023

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