Experimental study on the effects of ethanol blends on the combustion process, power performance and emission reduction of a motorcycle spark-ignition engine

Lee, Chang Sik; Huu, Tuyen Pham; Le, Anh Tuan; Minh, Tuan Pham; The, Luong Nguyen; Duy, Tien Nguyen; Duc, Khanh Nguyen

doi:10.1080/01430750.2022.2063383

Cited by 3 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The engine's efficiency, the amount of fuel it uses, and the pollution levels may all be predicted by these algorithms via the analysis of sensor data and historical performance reports [225]- [227]. The literature reveals that engine researchers mainly focused on engine performance metrics like brake thermal efficiency (BTE), brake-specific fuel/energy consumption (BSFC/BSEC), peak cylinder pressure (PCP), and heat release rate (HRR) [228]- [232]. Also, on the emission side oxides of nitrogen (NOx), carbon mono oxide (CO), unburnt hydrocarbons (UHC), and carbon dioxide (CO2), particulate matter (PM) [233]- [236] were reported.…”

Section: ) Biofuel-based Energy Forecastingmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

show abstract