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Keywords: quantitative structure-property relationship
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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
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
... (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors...
View Article titled, Artificial Neural Network Models for Octane Number and Octane Sensitivity: A <span class="search-highlight">Quantitative</span> <span class="search-highlight">Structure</span> <span class="search-highlight">Property</span> <span class="search-highlight">Relationship</span> Approach to Fuel Design
Open the PDF for Artificial Neural Network Models for Octane Number and Octane Sensitivity: A <span class="search-highlight">Quantitative</span> <span class="search-highlight">Structure</span> <span class="search-highlight">Property</span> <span class="search-highlight">Relationship</span> Approach to Fuel Design in another window
Topics: Artificial neural networks, Fuels, Engines