Enhancing waste cooking oil biodiesel yield and characteristics through machine learning, response surface methodology, and genetic algorithms for optimal utilization in CI engines-Reference-Cited by-同舟云学术

Enhancing waste cooking oil biodiesel yield and characteristics through machine learning, response surface methodology, and genetic algorithms for optimal utilization in CI engines

Published:2023-09-03 Issue: Volume: Page:1-21
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Ahmad Aqueel¹,Yadav Ashok Kumar²,Singh Achhaibar¹

Affiliation:

1. Mechanical Engineering Department, Netaji Subhas University of Technology, New Delhi, India

2. Department of Mechanical Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, India

Funder

funding

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2023.2253870

Reference43 articles.

1. Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil

2. Multi-objective exergetic and technical optimization of a piezoelectric ultrasonic reactor applied to synthesize biodiesel from waste cooking oil (WCO) using soft computing techniques

3. Neat diesel beats waste-oriented biodiesel from the exergoeconomic and exergoenvironmental point of views

4. Optimization of cavitation-assisted biodiesel production and fuel properties from Neochloris oleoabundans microalgae oil using genetic algorithm and response surface methodology

5. Process optimization of spirulina microalgae biodiesel synthesis using RSM coupled GA technique: a performance study of a biogas-powered dual-fuel engine

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