Optimization of a low-temperature combustion engine run with different compression ratios by using modified social group technique

Safieddin Ardebili, Seyed Mohamamd; Veza, Ibham; Deniz Karaoglan, Aslan; Ileri, Erol; Kiani Deh Kiani, Mostafa; Rabeti, Masoud

doi:10.22069/bere.2025.22943.1002

Document Type : Original Article

Authors

¹ Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

² Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jakarta Pusat, Indonesia

³ Balikesir University, Department of Industrial Engineering, 10145 Balikesir, Turkey

⁴ National Defense University, Army NCO Vocational HE School, Department of Automotive Sciences, 10110 Balıkesir, Turkey

⁵ Department of Mechanical Engineering, Faculty of Engineering, Sousangerd Branch, Islamic Azad University, Sousangerd, Iran

10.22069/bere.2025.22943.1002

Abstract

For the HCCI experiments, four different compression ratios were used (CR9, CR10, CR11, and CR12). The intake air temperatures varied between 313 and 373 K, while the engine speed changed from 800 to 1800 rpm. Three fuel blends were used, i.e., RON20, RON40, and RON60. The RON60 indicates 60% iso-octane and 40% n-heptane. A modified social group optimization (MSGO) algorithm was used for HCCI optimization purposes. Regression modeling was first employed to calculate the mathematical relations between the factors (compression ratio, research octane number (RON), intake air temperature, engine speed, and lambda) and the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). FThe regression models fit the given observations well with a low prediction error. The calculated R^2 obtained from this study show that the compression ratio (X_1), RON (X_2), intake air temperature (X_3), engine speed (X_4), and lambda (X_5) are sufficient to model the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). ANOVA results show p-value < α (under the 95% confidence type-I error= α=5%), indicating that the model is significant (H1 is true). Then MSGO is run via these mathematical models to determine the parameters with optimal optimization values. In the verification phase, 13 additional experimental runs that were not used in the mathematical modeling phase were used. It was found that the regression models fit the observed values well with a low PE (%). MSGO algorithm suggested the best value for studied parameters as X1=11.47, X2=60, X3=313, X4=800, and X5=1.45. The verification shows satisfying results with a high accuracy. The optimized factor levels indicates that the effective torque, IMEP, and indicated thermal efficiency were maximized while the other responses were minimized. Therefore, the findings signify the potential of the MSGO algorithm for HCCI optimization.

Graphical Abstract

Highlights

Research Highlights:

A modified social group optimization or HCCI optimization purposes was used for numerical modeling.

Improved engine performance and reduced exhaust emissions recorded.
Best engine performance condition proposed.

Keywords

References

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Biosystems Engineering and Renewable Energies

Optimization of a low-temperature combustion engine run with different compression ratios by using modified social group technique

References

References

Volume 1, Issue 1
April 2025
Pages 11-20

Optimization of a low-temperature combustion engine run with different compression ratios by using modified social group technique

References

References

Volume 1, Issue 1April 2025Pages 11-20

Volume 1, Issue 1
April 2025
Pages 11-20