Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition Temperature
Author:
Affiliation:
1. Chemical Engineering Department, Kuwait University, P.O.Box 5969, Safat 13060, Kuwait
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/ie0300373
Reference16 articles.
1. Molecular representation of complex hydrocarbon feedstocks through efficient characterization and stochastic algorithms
2. Quantitative structure?property relationships for auto-ignition temperatures of organic compounds
3. Structural Group Contribution Method for Predicting the Octane Number of Pure Hydrocarbon Liquids
4. Additivity Rules for the Estimation of Molecular Properties. Thermodynamic Properties
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