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Analysis of the pre-injection system of a marine diesel engine through multiple-criteria decision-making and artificial neural networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The present work proposes several pre-injection patterns to reduce nitrogen oxides in the Wartsila 6L 46 marine engine. A numerical model was carried out to characterise the emissions and consumption of the engine. Several pre-injection quantities, durations, and starting instants were analysed. It was found that oxides of nitrogen can be noticeably reduced but at the expense of increasing consumption as well as other emissions such as carbon monoxide and hydrocarbons. According to this, a multiple-criteria decision-making (MCDM) model was established to select the most appropriate parameters. Besides, an artificial neural network (ANN) was developed to complement the results and analyse a huge quantity of alternatives. This hybrid MCDM-ANN methodology proposed in the present work constitutes a useful tool to design new marine engines.
Rocznik
Tom
Strony
88--96
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
  • University of Coruńa, Paseo Ronda, 15011 Coruńa, Spain
autor
  • University of Coruna, Mendizabal, 15403 Ferrol, Spain
  • University of Coruńa, 19 de Febreiro, 15405 Ferrol, Spain
autor
  • Missisipi State University, Rood Hoad, 39762 Missisipi, USA
Bibliografia
  • 1. J. Kowalski and W. Tarelko, “NOx emission from a twostroke ship engine. Part 1: Modeling aspect,” Appl. Therm. Eng., vol. 29, no. 11–12, pp. 2153–2159, Aug. 2009, doi: 10.1016/j.applthermaleng.2008.06.032.
  • 2. J. Kowalski and W. Tarelko, “NOx emission from a twostroke ship engine: Part 2 – Laboratory test,” Appl. Therm. Eng., vol. 29, no. 11–12, pp. 2160–2165, Aug. 2009, doi: 10.1016/j.applthermaleng.2008.06.031.
  • 3. J. Girtler, “A method for evaluating the performance of a marine piston internal combustion engine used as the main engine on a ship during its voyage in different sailing conditions,” Polish Marit. Res., vol. 17, no. 4, Jan. 2010, doi: 10.2478/v10012-010-0033-0.
  • 4. R. Zhao et al., “A numerical and experimental study of marine hydrogen–natural gas–diesel tri-fuel engines,” Polish Marit. Res., vol. 27, no. 4, pp. 80–90, Dec. 2020, doi: 10.2478/pomr-2020-0068.
  • 5. X. Lu, P. Geng, and Y. Chen, “NOx emission reduction technology for marine engine based on Tier-III: A review,” J. Therm. Sci., vol. 29, no. 5, pp. 1242–1268, Oct. 2020, doi: 10.1007/s11630-020-1342-y.
  • 6. S. Lion, I. Vlaskos, and R. Taccani, “A review of emissions reduction technologies for low and medium speed marine Diesel engines and their potential for waste heat recovery,” Energy Convers. Manag., vol. 207, p. 112553, Mar. 2020, doi: 10.1016/j.enconman.2020.112553.
  • 7. J. Deng, X. Wang, Z. Wei, L. Wang, C. Wang, and Z. Chen, “A review of NOx and SOx emission reduction technologies for marine diesel engines and the potential evaluation of liquefied natural gas fuelled vessels,” Sci. Total Environ., vol. 766, p. 144319, Apr. 2021, doi: 10.1016/j.scitotenv.2020.144319.
  • 8. A. N. Bhatt and N. Shrivastava, “Application of artificial neural network for internal combustion engines: A state of the art review,” Arch. Comput. Methods Eng., May 2021, doi: 10.1007/s11831-021-09596-5.
  • 9. J. Kowalski, “ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine,” Polish Marit. Res., vol. 16, no. 2, Jan. 2009, doi: 10.2478/v10012-008-0023-7.
  • 10. V. Çelik and E. Arcaklioğlu, “Performance maps of a diesel engine,” Appl. Energy, vol. 81, no. 3, pp. 247–259, Jul. 2005, doi: 10.1016/j.apenergy.2004.08.003.
  • 11. E. Siami-Irdemoosa and S. R. Dindarloo, “Prediction of fuel consumption of mining dump trucks: A neural networks approach,” Appl. Energy, vol. 151, pp. 77–84, Aug. 2015, doi: 10.1016/j.apenergy.2015.04.064.
  • 12. M. Bietresato, A. Calcante, and F. Mazzetto, “A neural network approach for indirectly estimating farm tractors engine performances,” Fuel, vol. 143, pp. 144–154, Mar. 2015, doi: 10.1016/j.fuel.2014.11.019.
  • 13. K. Goudarzi, A. Moosaei, and M. Gharaati, “Applying artificial neural networks (ANN) to the estimation of thermal contact conductance in the exhaust valve of internal combustion engine,” Appl. Therm. Eng., vol. 87, pp. 688–697, Aug. 2015, doi: 10.1016/j.applthermaleng.2015.05.060.
  • 14. E. Arcaklioğlu and İ. Çelıkten, “A diesel engine’s performance and exhaust emissions,” Appl. Energy, vol. 80, no. 1, pp. 11–22, Jan. 2005, doi: 10.1016/j.apenergy.2004.03.004.
  • 15. K. Nikzadfar and A. H. Shamekhi, “Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network,” Fuel, vol. 125, pp. 116–128, Jun. 2014, doi: 10.1016/j.fuel.2014.02.021.
  • 16. K. Muralidharan and D. Vasudevan, “Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel,” J. Brazilian Soc. Mech. Sci. Eng., vol. 37, no. 3, pp. 915–928, May 2015, doi: 10.1007/s40430-014-0213-4.
  • 17. S. Arumugam, G. Sriram, and P. R. S. Subramanian, “Application of artificial intelligence to predict the performance and exhaust emissions of diesel engine using rapeseed oil methyl ester,” Procedia Eng., vol. 38, pp. 853– 860, 2012, doi: 10.1016/j.proeng.2012.06.107.
  • 18. A. Duran, M. Lapuerta, and J. Rodriguez-Fernandez, “Neural networks estimation of diesel particulate matter composition from transesterified waste oils blends,” Fuel, vol. 84, no. 16, pp. 2080–2085, Nov. 2005, doi: 10.1016/j.fuel.2005.04.029.
  • 19. S. Gürgen, B. Ünver, and İ. Altın, “Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network,” Renew. Energy, vol. 117, pp. 538–544, Mar. 2018, doi: 10.1016/j.renene.2017.10.101.
  • 20. H. Oğuz, I. Sarıtas, and H. E. Baydan, “Prediction of diesel engine performance using biofuels with artificial neural network,” Expert Syst. Appl., vol. 37, no. 9, pp. 6579–6586, Sep. 2010, doi: 10.1016/j.eswa.2010.02.128.
  • 21. P. Shanmugam, V. Sivakumar, A. Murugesan, and M. Ilangkumaran, “Performance and exhaust emissions of a diesel engine using hybrid fuel with an artificial neural network,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 33, no. 15, pp. 1440–1450, May 2011, doi: 10.1080/15567036.2010.539085.
  • 22. K. Çelebi, E. Uludamar, E. Tosun, Ş. Yıldızhan, K. Aydın, and M. Özcanlı, “Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas addition,” Fuel, vol. 197, pp. 159–173, Jun. 2017, doi: 10.1016/j.fuel.2017.01.113.
  • 23. N. Akkouche, K. Loubar, F. Nepveu, M. E. A. Kadi, and M. Tazerout, “Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion,” Energy Convers. Manag., vol. 205, p. 112407, Feb. 2020, doi: 10.1016/j.enconman.2019.112407.
  • 24. S. Javed, R. U. Baig, and Y. V. V. S. Murthy, “Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model,” Energy, vol. 160, pp. 774–782, Oct. 2018, doi: 10.1016/j.energy.2018.07.041.
  • 25. S. Javed, Y. V. V. Satyanarayana Murthy, R. U. Baig, and D. Prasada Rao, “Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends,” J. Nat. Gas Sci. Eng., vol. 26, pp. 549–557, Sep. 2015, doi: 10.1016/j.jngse.2015.06.041.
  • 26. T. F. Yusaf, D. R. Buttsworth, K. H. Saleh, and B. F. Yousif, “CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network,” Appl. Energy, vol. 87, no. 5, pp. 1661–1669, May 2010, doi: 10.1016/j. apenergy.2009.10.009.
  • 27. E. Uludamar et al., “Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel–biodiesel blends,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23352–23360, Sep. 2017, doi: 10.1016/j.ijhydene.2017.01.192.
  • 28. J. Syed, R. U. Baig, S. Algarni, Y. V. V. S. Murthy, M. Masood, and M. Inamurrahman, “Artificial neural network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach,” Int. J. Hydrogen Energy, vol. 42, no. 21, pp. 14750–14774, May 2017, doi: 10.1016/j.ijhydene.2017.04.096.
  • 29. H. Taghavifar, H. Taghavifar, A. Mardani, A. Mohebbi, S. Khalilarya, and S. Jafarmadar, “On the modeling of convective heat transfer coefficient of hydrogen fueled diesel engine as affected by combustion parameters using a coupled numerical-artificial neural network approach, ”Int. J. Hydrogen Energy, vol. 40, no. 12, pp. 4370–4381, Apr. 2015, doi: 10.1016/j.ijhydene.2015.01.140.
  • 30. S. Tasdemir, I. Saritas, M. Ciniviz, and N. Allahverdi, “Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine,” Expert Syst. Appl., May 2011, doi: 10.1016/j.eswa.2011.04.198.
  • 31. J. Martínez-Morales, H. Quej-Cosgaya, J. Lagunas-Jiménez, E. Palacios-Hernández, and J. Morales-Saldańa, “Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data,” Sci. China Technol. Sci., vol. 62, no. 6, pp. 1055–1064, Jun. 2019, doi: 10.1007/s11431-017-9235-y.
  • 32. M. M. Etghani, M. H. Shojaeefard, A. Khalkhali, and M. Akbari, “A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel,” Appl. Therm. Eng., vol. 59, no. 1–2, pp. 309–315, Sep. 2013, doi: 10.1016/j.applthermaleng.2013.05.041.
  • 33. M. Deb, P. Majumder, A. Majumder, S. Roy, and R. Banerjee, “Application of artificial intelligence (AI) in characterization of the performance–emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimization,” Int. J. Hydrogen Energy, vol. 41, no. 32, pp. 14330–14350, Aug. 2016, doi: 10.1016/j.ijhydene.2016.07.016.
  • 34. J. K. Dukowicz, “A particle-fluid numerical model for liquid sprays,” J. Comput. Phys., vol. 35, no. 2, pp. 229–253, Apr. 1980, doi: 10.1016/0021-9991(80)90087-X.
  • 35. L. M. Ricart, J. Xin, G. R. Bower, and R. D. Reitz, “In-cylinder measurement and modeling of liquid fuel spray penetration in a heavy-duty diesel engine,” May 1997, doi: 10.4271/971591.
  • 36. Y. Ra and R. D. Reitz, “A reduced chemical kinetic model for IC engine combustion simulations with primary reference fuels,” Combust. Flame, vol. 155, no. 4, pp. 713–738, Dec. 2008, doi: 10.1016/j.combustflame.2008.05.002.
  • 37. H. Yang, S. R. Krishnan, K. K. Srinivasan, K. C. Midkiff, “Modeling of NOx emissions using a superextended Zeldovich mechanism,” ASME 2003 Internal Combustion Engine and Rail Transportation Divisions Fall Technical Conference, 2003, doi: 10.1115/ICEF2003-0713.
  • 38. J. A. Miller and P. Glarborg, “Modeling the formation of N2O and NO2 in the thermal DeNOx process,” Springer Ser. Chem. Phys., vol. 61, pp. 318–333, 1996.
  • 39. J. Sietsma and R. J. F. Dow, “Creating artificial neural networks that generalize,” Neural Networks, vol. 4, no. 1, pp. 67–79, Jan. 1991, doi: 10.1016/0893-6080(91)90033-2.
  • 40. D. Golmohammadi, “Neural network application for fuzzy multi-criteria decision making problems,” Int. J. Prod. Econ., vol. 131, no. 2, pp. 490–504, Jun. 2011, doi: 10.1016/j.ijpe.2011.01.015.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-71591bbd-6341-4374-9d9b-acb0d842c5f2
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