Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures-Reference-Cited by-同舟云学术

Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

Published:2020-11 Issue: Volume:280 Page:118589
ISSN:0016-2361
Container-title:Fuel
language:en
Short-container-title:Fuel

Author:

Li Runzhao^ORCID,Herreros Jose Martin,Tsolakis Athanasios^ORCID,Yang Wenzhao

Funder

Innovate UK

Technology Strategy Board

EPSRC

University of Birmingham

Publisher

Elsevier BV

Subject

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

Reference73 articles.

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2. Standard test method for determination of ignition delay and derived cetane number (DCN) of diesel fuel oils by combustion in a constant volume chamber;ASTM D6890-18;American Society for Testing and Materials (ASTM) international,2018

3. Standard test method for determination of derived cetane number (DCN) of diesel fuel oils—ignition delay and combustion delay using a constant volume combustion chamber method;ASTM D7668-17;American Society for Testing and Materials (ASTM) international,2017

4. Standard test method for determination of indicated cetane number (ICN) of diesel fuel oils using a constant volume combustion chamber—reference fuels calibration method;ASTM D8183-18;American Society for Testing and Materials (ASTM) international,2018

5. Standard test method for research octane number of spark-ignition engine fuel;ASTM D2699-19;American Society for Testing and Materials (ASTM) international,2019

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