آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 634 |
| تعداد مقالات | 6,615 |
| تعداد مشاهده مقاله | 8,948,597 |
| تعداد دریافت فایل اصل مقاله | 8,477,038 |
Optimization of a low-temperature combustion engine run with different compression ratios by using modified social group technique | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 1، تیر 2025، صفحه 11-20 اصل مقاله (1.5 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2025.22943.1002 | ||
| نویسندگان | ||
| Seyed Mohamamd Safieddin Ardebili* 1؛ Ibham Veza2؛ Aslan Deniz Karaoglan3؛ Erol Ileri4؛ Mostafa Kiani Deh Kiani1؛ Masoud Rabeti5 | ||
| 1Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jakarta Pusat, Indonesia | ||
| 3Balikesir University, Department of Industrial Engineering, 10145 Balikesir, Turkey | ||
| 4National Defense University, Army NCO Vocational HE School, Department of Automotive Sciences, 10110 Balıkesir, Turkey | ||
| 5Department of Mechanical Engineering, Faculty of Engineering, Sousangerd Branch, Islamic Azad University, Sousangerd, Iran | ||
| چکیده | ||
| 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. | ||
تازه های تحقیق | ||
Research Highlights: A modified social group optimization or HCCI optimization purposes was used for numerical modeling.
| ||
| کلیدواژهها | ||
| Modified social group (MSGO) optimization؛ Regression modelling optimization؛ Low-temperature HCCI optimization؛ Low-temperature HCCI performance؛ Low-temperature HCCI emission؛ Low-temperature HCCI combustion | ||
| مراجع | ||
|
Abdelmalek, Z., Alamian, R., Safdari Shadloo, M., Maleki, A., & Karimipour, A. (2021). Numerical study on the performance of a homogeneous charge compression ignition engine fueled with different blends of biodiesel. Journal of Thermal Analysis and Calorimetry, 143(3), 2695-2705. https://doi.org/10.1007/s10973-020-09513-1
AlShabi, M., Ghenai, C., Bettayeb, M., Ahmad, F. F., & El Haj Assad, M. (2021). Multi-group grey wolf optimizer (MG-GWO) for estimating photovoltaic solar cell model. Journal of Thermal Analysis and Calorimetry, 144(5), 1655-1670. https://doi.org/10.1007/s10973-020-09895-2
Atmanlı, A., Yüksel, B., Ileri, E., & Karaoglan, A. D. (2015). Response surface methodology based optimization of diesel–n-butanol–cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics. Energy Conversion and Management, 90, 383-394. https://doi.org/10.1016/j.enconman.2014.11.029
Bhadoria, A., & Marwaha, S. (2020). Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system. Journal of Computational Design and Engineering, 7(5), 668-683. https://doi.org/10.1093/jcde/qwaa050
Calam, A., Solmaz, H., Yılmaz, E., & İçingür, Y. (2019). Investigation of effect of compression ratio on combustion and exhaust emissions in A HCCI engine. Energy, 168, 1208-1216. https://doi.org/10.1016/j.energy.2018.12.023
Deh Kiani, M. K., Ghobadian, B., Tavakoli, T., Nikbakht, A. M., & Najafi, G. (2010). Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy, 35(1), 65-69. https://doi.org/10.1016/j.energy.2009.08.034
Eiben, A. E., & Schippers, C. A. (1998). On evolutionary exploration and exploitation. Fundamenta Informaticae, 35(1-4), 35-50. https://doi.org/10.3233/FI-1998-35123403
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.https://doi.org/10.1016/j.future.2019.02.028
Ileri, E., Karaoglan, A. D., & Akpinar, S. (2020). Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm. Fuel, 273, 117784. https://doi.org/10.1016/j.fuel.2020.117784
Ileri, E., Karaoglan, A. D., & Atmanli, A. (2013). Response surface methodology based prediction of engine performance and exhaust emissions of a diesel engine fuelled with canola oil methyl ester. Journal of Renewable and Sustainable Energy, 5(3), 033132. https://doi.org/10.1063/1.4811801
Karaoglan, A. D. (2021). Optimizing Plastic Extrusion Process via Grey Wolf Optimizer Algorithm and Regression Analysis. Journal of Scientific and Industrial Research, 80(01), 34-41.
Karaoglan, A. D., & Baydeniz, B. (2021). Optimizing Plastic Injection Process Using Whale Optimization Algorithm in Automotive Lighting Parts Manufacturing. Journal of Scientific and Industrial Research , 80(04), 360-368.
Kocakulak, T., Babagiray, M., Nacak, Ç., Safieddin Ardebili, S. M., Calam, A., & Solmaz, H. (2022). Multi objective ptimization of HCCI combustion fuelled with fusel oil and n-heptane blends. Renewable Energy, 182, 827-841. https://doi.org/https://doi.org/10.1016/j.renene.2021.10.041
Kocakulak, T., Halis, S., Ardebili, S. M. S., Babagiray, M., Haşimoğlu, C., Rabeti, M., & Calam, A. (2023). Predictive modelling and optimization of performance and emissions of an auto-ignited heavy naphtha/n-heptane fueled HCCI engine using RSM. Fuel, 333, 126519.https://doi.org/https://doi.org/10.1016/j.fuel.2022.126519
Leo, G. M. L., Sekar, S., & Arivazhagan, S. (2020). Experimental investigation and ANN modelling of the effects of diesel/gasoline premixing in a waste cooking oil-fuelled HCCI-DI engine. Journal of Thermal Analysis and Calorimetry, 141(6), 2311-2324. https://doi.org/10.1007/s10973-020-09418-z
Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133. https://doi.org/10.1016/j.knosys.2015.12.022
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161-185. https://doi.org/10.1016/j.asoc.2017.11.043
Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
Naik, A. (2021). Modified Social Group Optimization Algorithm. MATLAB Central File Exchange. Retrieved 21 September from https://www.mathworks.com/matlabcentral/fileexchange/78272-modified-social-group-optimization-algorithm
Naik, A., Satapathy, S. C., & Abraham, A. (2020). Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling. Applied Soft Computing, 95, 106524. https://doi.org/10.1016/j.asoc.2020.106524
Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2017). A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Applied Soft Computing, 59, 596-621. https://doi.org/10.1016/j.asoc.2017.06.033
Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2020). A novel meta-heuristic optimization method based on golden ratio in nature. Soft Computing, 24(2), 1117-1151. https://doi.org/10.1007/s00500-019-03949-w
Parsa, S., & Neshat, E. (2022). Thermodynamic and statistical analysis on the effect of exhaust gas recirculation on waste heat recovery from homogeneous charge compression ignition engines. Journal of Thermal Analysis and Calorimetry, 147(11), 6349-6361. https://doi.org/10.1007/s10973-021-10923-y
Rao, R. V., & Keesari, H. S. (2020). A self-adaptive population Rao algorithm for optimization of selected bio-energy systems. Journal of Computational Design and Engineering, 8(1), 69-96. https://doi.org/10.1093/jcde/qwaa063
Roushangar, K., & Shahnazi, S. (2019). Bed load prediction in gravel-bed rivers using wavelet kernel extreme learning machine and meta-heuristic methods. International Journal of Environmental Science and Technology, 16(12), 8197-8208. https://doi.org/10.1007/s13762-019-02287-6
Satapathy, S., & Naik, A. (2016). Social group optimization (SGO): a new population evolutionary optimization technique. Complex & Intelligent Systems, 2(3), 173-203. https://doi.org/10.1007/s40747-016-0022-8
Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36, 315-333. https://doi.org/10.1016/j.asoc.2015.07.028
Tejani, G. G., Savsani, V. J., Patel, V. K., & Mirjalili, S. (2018). An improved heat transfer search algorithm for unconstrained optimization problems. Journal of Computational Design and Engineering, 6(1), 13-32. https://doi.org/10.1016/j.jcde.2018.04.003
Yazdani, M., & Jolai, F. (2015). Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24-36. https://doi.org/10.1016/j.jcde.2015.06.003
Yilmaz, N., Ileri, E., Atmanlı, A., Deniz Karaoglan, A., Okkan, U., & Sureyya Kocak, M. (2016). Predicting the engine performance and exhaust emissions of a diesel engine fueled with hazelnut oil methyl ester: the performance comparison of response surface methodology and LSSVM. Journal of Energy Resources Technology, 138(5), 052206. https://doi.org/10.1115/1.4032941
Zhao, W., Wang, L., & Zhang, Z. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems, 91, 601-610. https://doi.org/10.1016/j.future.2018.05.037 | ||
|
آمار تعداد مشاهده مقاله: 27 تعداد دریافت فایل اصل مقاله: 21 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب