Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models-Reference-Cited by-同舟云学术

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

Published:2021-05-19 Issue:11 Volume:35 Page:9332-9340
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Correa Gonzalez Sandra¹,Kroyan Yuri¹,Sarjovaara Teemu²,Kiiski Ulla²,Karvo Anna²,Toldy Arpad I.¹^ORCID,Larmi Martti¹,Santasalo-Aarnio Annukka¹^ORCID

Affiliation:

1. Research Group of Energy Conversion, Department of Mechanical Engineering, School of Engineering, Aalto University, PO Box 14400, FI-00076 Aalto, Finland

2. Technology Centre, Neste Corporation, POB 310, 06101 Porvoo, Finland

Funder

Neste Corporation

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/acs.energyfuels.1c00749

Reference50 articles.

1. Data & Statistics; International Energy Agency. https://www.iea.org/data-and-statistics (accessed 2020-03-05).

2. Challenges for Increased Efficiency through Gasoline Engine Downsizing

3. The Effect of Compression Ratio, Fuel Octane Rating, and Ethanol Content on Spark-Ignition Engine Efficiency

4. Test Method for Research Octane Number of Spark-Ignition Engine Fuel

5. Calculating Gasoline Blend Octane Ratings

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