Opportunity to Improve Diesel-Fuel Cetane-Number Prediction from Easily Available Physical Properties and Application of the Least-Squares Method and Artificial Neural Networks-Reference-Cited by-同舟云学术

Opportunity to Improve Diesel-Fuel Cetane-Number Prediction from Easily Available Physical Properties and Application of the Least-Squares Method and Artificial Neural Networks

Published:2015-02-19 Issue:3 Volume:29 Page:1520-1533
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Stratiev Dicho¹,Marinov Ivaylo¹,Dinkov Rosen¹,Shishkova Ivelina¹,Velkov Ilian¹,Sharafutdinov Ilshat¹,Nenov Svetoslav²,Tsvetkov Tsvetelin²,Sotirov Sotir³,Mitkova Magdalena³,Rudnev Nikolay⁴

Affiliation:

1. LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria

2. Department of Mathematics, University of Chemical Technology and Metallurgy, 8, Kliment Ohridsky Boulevard, 1756 Sofia, Bulgaria

3. Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”, 1 “Prof. Yakimov” Boulevard, 8010 Burgas, Bulgaria

4. Ufa State Petroleum Technological University, 1 Kosmonavtov Street, 450062 Ufa, Russia

Publisher

American Chemical Society (ACS)

Subject

Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Link

https://pubs.acs.org/doi/pdf/10.1021/ef502638c

Reference36 articles.

1. Detailed Composition-Based Model for Predicting the Cetane Number of Diesel Fuels

2. Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS

3. Measurements and Modeling Study on a High-Aromatic Diesel Fuel

4. Determination of diesel cetane number by consensus modeling based on uninformative variable elimination

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