Publication: Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework
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Issued Date
2024-12-01
Resource Type
eISSN
25901230
Scopus ID
2-s2.0-85206192644
Journal Title
Results in Engineering
Volume
24
Rights Holder(s)
SCOPUS
Bibliographic Citation
Results in Engineering Vol.24 (2024)
Suggested Citation
Klinkaew N., Wiangkham A., Ariyarit A., Aengchuan P., Pumpuang A., Sripratum S., Maneedaeng A., Srisertpol J., Sukjit E. Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework. Results in Engineering Vol.24 (2024). doi:10.1016/j.rineng.2024.103072 Retrieved from: https://hdl.handle.net/20.500.14740/20489
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Abstract
This study investigates the effects of blending bio-hydrogenated diesel and biodiesel on engine performance and emissions across various engine speeds (2000–3000 rpm) and loads (25–90 %), addressing the growing demand for sustainable transportation fuels. Using a single-cylinder diesel engine from POLAWAT ENGINE Company Limited, we evaluated different bio-hydrogenated diesel and biodiesel blends, optimizing their composition through the Reference Vector Guided Evolutionary Algorithm with a surrogate objective function via Generalized Regression Neural Networks. Results indicate that engine performance is closely linked to combustion temperature, which significantly affects fuel consumption and thermal efficiency. Bio-hydrogenated diesel demonstrated superior performance compared to conventional diesel, with lower fuel consumption and higher thermal efficiency, attributed to its higher cetane index (78 vs. 56) and heating value (47.02 MJ/kg vs. 43.48 MJ/kg). However, increasing biodiesel content in blends tended to increase fuel consumption and decrease efficiency due to biodiesel's lower heating value (39.62 MJ/kg) and higher viscosity (5.16 cSt vs. 2.58 cSt for bio-hydrogenated diesel). Emission analysis revealed that bio-hydrogenated diesel generally produced lower hydrocarbon, carbon monoxide, and smoke emissions than conventional diesel, though nitrogen oxide emissions were slightly higher. Biodiesel blends often showed increased emissions, particularly at higher blend ratios, due to poor fuel atomization. The Generalized Regression Neural Networks model demonstrated high accuracy in predicting engine performance and emissions, with coefficient of determination (R2) values exceeding 0.98 and mean absolute percentage errors below 5 %. Optimization results indicated that bio-hydrogenated diesel ratios centered around 90 % provided the best balance of performance and emissions. This research bridges critical gaps in combined bio-hydrogenated diesel and biodiesel usage and artificial intelligence-driven biofuel optimization, providing valuable insights for practical implementation in Thailand's agricultural sector.