Applying a neural network-based approach for estimating instantaneous emissions of the heavy-duty buses: A case study

Le, Anh‐Tuan; Nguyen, Yen-Lien T.; Duc, Khanh Nguyen; Nghiem, Trung-Dung; Huu, Tuyen Pham; Bui, Ngoc Dung

doi:10.1080/15567036.2022.2118903

Cited by 2 publications

(1 citation statement)

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“…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

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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.

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