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Title: Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

Abstract

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1407892
Report Number(s):
LA-UR-17-24313
Journal ID: ISSN 0888-5885; TRN: US1703060
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Industrial and Engineering Chemistry Research
Additional Journal Information:
Journal Volume: 56; Journal Issue: 42; Journal ID: ISSN 0888-5885
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Energy Sciences; Organic Chemistry

Citation Formats

Kubic, William Louis, Jenkins, Rhodri W., Moore, Cameron M., Semelsberger, Troy Allen, and Sutton, Andrew. Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds. United States: N. p., 2017. Web. doi:10.1021/acs.iecr.7b02753.
Kubic, William Louis, Jenkins, Rhodri W., Moore, Cameron M., Semelsberger, Troy Allen, & Sutton, Andrew. Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds. United States. doi:10.1021/acs.iecr.7b02753.
Kubic, William Louis, Jenkins, Rhodri W., Moore, Cameron M., Semelsberger, Troy Allen, and Sutton, Andrew. Thu . "Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds". United States. doi:10.1021/acs.iecr.7b02753. https://www.osti.gov/servlets/purl/1407892.
@article{osti_1407892,
title = {Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds},
author = {Kubic, William Louis and Jenkins, Rhodri W. and Moore, Cameron M. and Semelsberger, Troy Allen and Sutton, Andrew},
abstractNote = {Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.},
doi = {10.1021/acs.iecr.7b02753},
journal = {Industrial and Engineering Chemistry Research},
number = 42,
volume = 56,
place = {United States},
year = {Thu Sep 28 00:00:00 EDT 2017},
month = {Thu Sep 28 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 1 work
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