Machine learning prediction of hydrocarbon mixture lower flammability limits using quantitative structure‐property relationship models-Reference-Cited by-同舟云学术

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Machine learning prediction of hydrocarbon mixture lower flammability limits using quantitative structure‐property relationship models

Published:2019-11-04 Issue:2 Volume:39 Page:
ISSN:1066-8527
Container-title:Process Safety Progress
language:en
Short-container-title:Proc Safety Prog

Author:

Jiao Zeren¹^ORCID,Yuan Shuai¹^ORCID,Zhang Zhuoran¹^ORCID,Wang Qingsheng¹^ORCID

Affiliation:

1. Mary Kay O'Connor Process Safety Center, Artie McFerrin Department of Chemical EngineeringTexas A&M University College Station Texas

Publisher

Wiley

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/prs.12103

Reference31 articles.

1. From Ignition to Suppression, a Thermal View of Flammability Limits

2. Experimental measurement and numerical analysis of binary hydrocarbon mixture flammability limits

3. Prediction of upper flammability limits for fuel mixtures using quantitative structure–property relationship models

4. Prediction of lower flammability limits of blended gases based on quantitative structure–property relationship

5. A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine

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1. Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model;Chemical Engineering Science;2024-09

2. QSPR models to predict the physical hazards of mixtures: a state of art;SAR and QSAR in Environmental Research;2023-09-02

3. Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding;AIChE Journal;2023-07-11

4. Machine learning based quantitative consequence prediction models for toxic dispersion casualty;Journal of Loss Prevention in the Process Industries;2023-02

5. Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction;ACS Applied Engineering Materials;2022-11-07

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