1-1 of 1
Travis J. Kessler
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Journal Articles

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Amina SubLaban, Travis J. Kessler, Noah Van Dam, J. Hunter Mack
Journal: Journal of Energy Resources Technology
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2023, 145(10): 102302.
Paper No: JERT-22-1852
https://doi.org/10.1115/1.4062189
Published Online: April 17, 2023
View Article titled, Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design
Open the PDF for Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design in another window
Topics: Artificial neural networks, Fuels, Engines