Prediction of research octane number loss and sulfur content in gasoline refining using machine learning-Reference-Cited by-同舟云学术

Prediction of research octane number loss and sulfur content in gasoline refining using machine learning

Published:2022-12 Issue: Volume:261 Page:124823
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Zhang Fengyu,Su Xinchao,Tan Aoli,Yao Jingjing^ORCID,Li Haipu

Funder

Science and Technology Program of Hunan Province

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference34 articles.

1. Octane number prediction for gasoline blends;Pasadakis;Fuel Process Technol,2006

2. Characterization, determination and elimination technologies for sulfur from petroleum: toward cleaner fuel and a safe environment;Saleh;Trend. Environ. Anal. Chem.,2020

3. An overview of new approaches to deep desulfurization for ultra-clean gasoline, diesel fuel and jet fuel;Song;Catal Today,2003

4. Engine parameters assessment for alcohols fuels application in compression ignition engines;Shamun,2020

5. Ethanol in dual-fuel and blend fueling modes for advanced combustion in compression ignition engines;Ianniello,2021

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