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

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

While retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression pr...

Full description

Bibliographic Details
Main Authors: Chao-zhe Zhu, Olusegun D. Samuel, Noureddine Elboughdiri, Mohamed Abbas, C Ahamed Saleel, Nataraj Ganesan, Christopher C. Enweremadu, H. Fayaz
Format: Article
Language:English
Published: Elsevier 2023-09-01
Series:Case Studies in Thermal Engineering
Subjects:
ANN
Gene expression programming
Modelling
Gasohol
Hydrogen
Spark ignition engine
Online Access:http://www.sciencedirect.com/science/article/pii/S2214157X2300415X

Internet

http://www.sciencedirect.com/science/article/pii/S2214157X2300415X

Similar Items