Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM

Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM

The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to model the performance and the emissions of a single cylinder diesel engine fueled by diesel and propylene glycol additive. The evaluations are...

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Bibliographic Details
Main Authors: Haleh Karimmaslak, Bahman Najafi, Shahab S. Band, Sina Ardabili, Farid Haghighat-Shoar, Amir Mosavi
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
propylene glycol
biodiesel
machine learning
artificial intelligence
sustainable fuel
Online Access:http://dx.doi.org/10.1080/19942060.2021.1880970

Internet

http://dx.doi.org/10.1080/19942060.2021.1880970

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