Desirability-based optimization of dual-fuel diesel engine using acetylene as an alternative fuel

Nguyen, Van Giao; Dager, Brijesh; Chhillar, Ajay; Sharma, Prabhakar; Osman, Sameh M.; Nguyen Le, Duc Trong; Kowalski, Jerzy; Truong, Thanh Hai; Yadav, Prem Shanker; Cao, Dao Nam; Tran, Viet Dung

doi:10.1016/j.csite.2024.104488

Case Studies in Thermal Engineering

2024

DOI: 10.1016/j.csite.2024.104488

|View full text |Cite

Desirability-based optimization of dual-fuel diesel engine using acetylene as an alternative fuel

Van Giao Nguyen,

Brijesh Dager,

Ajay Chhillar

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A coupling study of scavenging effect on pollutant formation and unclean combustion of a linear vibration engine

Chunzhi,

Yuan,

et al. 2025

Case Studies in Thermal Engineering

View full text Add to dashboard Cite

A coupling study of scavenging effect on pollutant formation and unclean combustion of a linear vibration engine

Chunzhi,

Yuan,

et al. 2025

Case Studies in Thermal Engineering

View full text Add to dashboard Cite

Utilization of sterculia foetida oil as a sustainable feedstock for biodiesel production: optimization, performance, and emission analysis

Devarajan,

2024

Results in Engineering

View full text Add to dashboard Cite

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Desirability-based optimization of dual-fuel diesel engine using acetylene as an alternative fuel

Cited by 3 publications

References 91 publications

A coupling study of scavenging effect on pollutant formation and unclean combustion of a linear vibration engine

A coupling study of scavenging effect on pollutant formation and unclean combustion of a linear vibration engine

Utilization of sterculia foetida oil as a sustainable feedstock for biodiesel production: optimization, performance, and emission analysis

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Contact Info

Product

Resources

About