Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel-Reference-Cited by-同舟云学术

Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel

Published:2023-09 Issue: Volume:49 Page:103109
ISSN:2214-157X
Container-title:Case Studies in Thermal Engineering
language:en
Short-container-title:Case Studies in Thermal Engineering

Author:

Zhu Chao-zhe,Samuel Olusegun D.^ORCID,Elboughdiri Noureddine,Abbas Mohamed,Saleel C Ahamed,Ganesan Nataraj,Enweremadu Christopher C.,Fayaz H.

Funder

Deanship of Scientific Research, King Khalid University

Khon Kaen University

Publisher

Elsevier BV

Subject

Fluid Flow and Transfer Processes,Engineering (miscellaneous)

Reference72 articles.

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4. Effects of alternative fuel use in a vehicle with TSI (turbocharged direct-injection spark-ignition) engine technology;Özer;Int. J. Green Energy,2021

5. Comparative assessment of stoichiometric and lean combustion modes in boosted spark-ignition engine fueled with syngas;Park;Energy Convers. Manag.,2021

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