Details

Autor(en) / Beteiligte

• Kubic, William L
• Jenkins, Rhodri W
• Moore, Cameron M
• Semelsberger, Troy A
• Sutton, Andrew D

Titel

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

Ist Teil von

• Industrial & engineering chemistry research, 2017-10, Vol.56 (42), p.12236-12245

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2017

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.