Using of Support Vector Machine (SVM), Gradient Boosting (GB) and Artificial Neural Network (ANN) Techniques in Internal Combustion Engine Tests: A Review

Abstract

As a result of the literature review, it is seen that researchers tend to use alternative machine learning methods in order to determine the complex relationship between engine performance data, diesel-biodiesel fuel mixture ratios and exhaust emissions. As a result of the researches, it was observed that gradient boosting algorithm, support vector machine and artificial neural network machine learning methods are frequently used methods. Among these three methods, it was concluded that the method that has been the subject of the studies and stated to improve the results at an optimum level is the artificial neural network. In this study, the gradient boosting algorithm, support vector machine and artificial neural network methods are discussed and the reasons for using the artificial neural network method more than the other methods are investigated.

Keywords

• artificial neural network
• gradient boosting
• machine learning
• support vector machine
• internal combustion engines

References

1. M. S. Grabosk and R. L. McCormick, “Combustion Of Fat And Vegetable Oil Derived Fuels in Diesel Engines,” Science (80-. )., vol. 24, no. 97, pp. 125–164, 1998.
2. A. S. Ramadhas, S. Jayaraj, and C. Muraleedharan, “Use of vegetable oils as I.C. engine fuels - A review,” Renew. Energy, vol. 29, no. 5, pp. 727–742, 2004.
3. K. I. Wong, P. K. Wong, C. S. Cheung, and C. M. Vong, “Modeling and optimization of biodiesel engine performance using advanced machine learning methods,” Energy, vol. 55, no. x, pp. 519–528, 2013.
4. J. Grahovac, A. Jokić, J. Dodić, D. Vučurović, and S. Dodić, “Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks,” Renew. Energy, vol. 85, pp. 953–958, 2016.
5. V. Cocco Mariani, S. Hennings Och, L. dos Santos Coelho, and E. Domingues, “Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models,” Appl. Energy, vol. 249, no. February, pp. 204–221, 2019.
6. A. Domínguez-Sáez, G. A. Rattá, and C. C. Barrios, “Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression,” Energy, vol. 149, pp. 675–683, 2018.
7. B. Liu, J. Hu, F. Yan, R. F. Turkson, and F. Lin, “A novel optimal support vector machine ensemble model for NO X emissions prediction of a diesel engine,” Meas. J. Int. Meas. Confed., vol. 92, no. X, pp. 183–192, 2016.
8. S. Roy, R. Banerjee, A. K. Das, and P. K. Bose, “Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine,” J. Nat. Gas Sci. Eng., vol. 21, no. x, pp. 147–158, 2014.

9. H. Almér, “Machine learning and statistical analysis in fuel consumption prediction for heavy vehicles,” Master Sci. Thesis, p. 75, 2015.
10. Y. Pan, S. Chen, F. Qiao, S. V. Ukkusuri, and K. Tang, “Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees,” Sci. Total Environ., vol. 660, pp. 741–750, 2019.
11. H. Taghavifar, S. Khalilarya, and S. Jafarmadar, “Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm,” Energy, vol. 71, pp. 656–664, 2014.
12. J. Rezaei, M. Shahbakhti, B. Bahri, and A. A. Aziz, “Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks,” Appl. Energy, vol. 138, pp. 460–473, 2015.
13. S. O. Giwa, S. O. Adekomaya, K. O. Adama, and M. O. Mukaila, “Prediction of selected biodiesel fuel properties using artificial neural network,” Front. Energy, vol. 9, no. 4, pp. 433–445, 2015.
14. A. Banerjee, D. Varshney, S. Kumar, P. Chaudhary, and V. K. Gupta, “Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation,” Int. J. Ind. Chem., vol. 8, no. 3, pp. 253–262, 2017.
15. J. Tao, C. Qin, W. Li, and C. Liu, “Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time–frequency information of vibration signals,” Sensors (Switzerland), vol. 19, no. 15, 2019.
16. X. Niu, H. Wang, S. Hu, C. Yang, and Y. Wang, “Multi-objective online optimization of a marine diesel engine using NSGA-II coupled with enhancing trained support vector machine,” Appl. Therm. Eng., vol. 137, no. April 2017, pp. 218–227, 2018.
17. M. Ghanbari, G. Najafi, B. Ghobadian, R. Mamat, M. M. Noor, and A. Moosavian, “Support Vector Machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel,” IOP Conf. Ser. Mater. Sci. Eng., vol. 100, no. 1, 2015.
18. S. Uslu, “Optimization of diesel engine operating parameters fueled with palm oil-diesel blend: Comparative evaluation between response surface methodology (RSM) and artificial neural network (ANN),” Fuel, vol. 276, no. January, p. 117990, 2020.
19. S. V. Khandal, S. B. Gadwal, V. A. Raikar, T. M. Yunus Khan, and I. A. Badruddin, “An experimental-based artificial neural network performance study of common rail direct injection engine run on plastic pyrolysis oil,” Int. J. Sustain. Eng., vol. 00, no. 00, pp. 1–10, 2020.
20. M. Canakci, A. Erdil, and E. Arcaklioǧlu, “Performance and exhaust emissions of a biodiesel engine,” Appl. Energy, vol. 83, no. 6, pp. 594–605, 2006.
21. T. F. Yusaf, B. F. Yousif, and M. M. Elawad, “Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches,” Energy, vol. 36, no. 8, pp. 4871–4878, 2011.
22. T. Ozgur, G. Tuccar, M. Ozcanli, and K. Aydin, “Prediction of emissions of a diesel engine fueled with soybean biodiesel using artificial neural networks,” Energy Educ. Sci. Technol. Part A Energy Sci. Res., vol. 27, no. 2, pp. 301–312, 2011.
23. Shivakumar, P. Srinivasa Pai, and B. R. Shrinivasa Rao, “Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings,” Appl. Energy, vol. 88, no. 7, pp. 2344–2354, 2011.
24. H. Mohamed Ismail, H. K. Ng, C. W. Queck, and S. Gan, “Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends,” Appl. Energy, vol. 92, pp. 769–777, 2012.
25. A. U. Osarogiagbon, F. Khan, R. Venkatesan, and P. Gillard, “Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations,” Process Saf. Environ. Prot., 2020.
26. R. James, G., Witten, D., Hastie, T., Tibshirani, An Introduction to Statistical Learning -with Applications in R. 2013.
27. C. Gambella, B. Ghaddar, and J. Naoum-Sawaya, “Optimization Models for Machine Learning: A Survey,” no. xxxx, 2019.
28. J. Wei et al., “Machine learning in materials science,” InfoMat, vol. 1, no. 3, pp. 338–358, 2019.
29. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, pp. 281–305, 2012.
30. T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” J. Mach. Learn. Res., vol. 20, pp. 1–21, 2019.
31. M. Wistuba, A. Rawat, and T. Pedapati, “A Survey on Neural Architecture Search,” 2019.
32. T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning,” Encycl. Syst. Biol., pp. 358–360, 2008.
33. G. P. Gupta and M. Kulariya, “A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark,” Procedia Comput. Sci., vol. 93, no. September, pp. 824–831, 2016.
34. J. Elith, J. R. Leathwick, and T. Hastie, “A working guide to boosted regression trees,” J. Anim. Ecol., vol. 77, no. 4, pp. 802–813, 2008.
35. R. M. Yang et al., “Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem,” Ecol. Indic., vol. 60, pp. 870–878, 2016.
36. C. Persson, P. Bacher, T. Shiga, and H. Madsen, “Multi-site solar power forecasting using gradient boosted regression trees,” Sol. Energy, vol. 150, pp. 423–436, 2017.
37. V. Vapnik, S. E. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” Adv. Neural Inf. Process. Syst., pp. 281–287, 1997.
38. P. Golbayani, I. Florescu, and R. Chatterjee, “A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees,” North Am. J. Econ. Financ., vol. 54, no. August 2019, p. 101251, 2020.
39. W. Li and Z. Liu, “A method of SVM with normalization in intrusion detection,” Procedia Environ. Sci., vol. 11, no. PART A, pp. 256–262, 2011.
40. N. K. Natt, H. Kaur, and G. P. S. Raghava, “Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods,” Proteins Struct. Funct. Genet., vol. 56, no. 1, pp. 11–18, 2004.
41. J. Zareei and A. Rohani, “Optimization and study of performance parameters in an engine fueled with hydrogen,” Int. J. Hydrogen Energy, vol. 45, no. 1, pp. 322–336, 2020.
42. S. Osowski, L. T. Hoai, and T. Markiewicz, “Support Vector Machine-Based Expert System for Reliable Heartbeat Recognition,” IEEE Trans. Biomed. Eng., vol. 51, no. 4, pp. 582–589, 2004.
43. S. Raghuvaran, B. Ashok, B. Veluchamy, and N. Ganesh, “Evaluation of performance and exhaust emission of C.I diesel engine fuel with palm oil biodiesel using an artificial neural network,” Mater. Today Proc., no. xxxx, 2020.
44. R. Kenanoğlu, M. K. Baltacıoğlu, M. H. Demir, and M. Erkınay Özdemir, “Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches,” Int. J. Hydrogen Energy, vol. 45, no. 49, pp. 26357–26369, 2020.
45. A. Di Mauro, H. Chen, and V. Sick, “Neural network prediction of cycle-to-cycle power variability in a spark-ignited internal combustion engine,” Proc. Combust. Inst., vol. 37, no. 4, pp. 4937–4944, 2019.
46. S. G. Herawan, K. Talib, and A. Putra, “Prediction of heat energy from the naturally aspirated internal combustion engine exhaust gas using artificial neural network,” Procedia Comput. Sci., vol. 135, pp. 267–274, 2018.
47. J. M. Luján, H. Climent, L. M. García-Cuevas, and A. Moratal, “Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks,” Appl. Therm. Eng., vol. 123, pp. 625–634, 2017.
48. D. Hao, R. K. Mehra, S. Luo, Z. Nie, X. Ren, and M. Fanhua, “Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition,” Int. J. Hydrogen Energy, vol. 45, no. 8, pp. 5309–5325, 2020.
49. H. Duan et al., “Experimental study of premixed hydrogen enriched natural gas under an alternating-current (AC) electric field and application of support vector machine (SVM) on electric field assisted combustion,” Fuel, vol. 258, no. June, p. 115934, 2019.
50. H. Duan, Y. Huang, R. K. Mehra, P. Song, and F. Ma, “Study on influencing factors of prediction accuracy of support vector machine (SVM) model for NOx emission of a hydrogen enriched compressed natural gas engine,” Fuel, vol. 234, no. June, pp. 954–964, 2018.
51. Y. Pan, F. Qiao, K. Tang, S. Chen, and S. V. Ukkusuri, “Understanding and estimating the carbon dioxide emissions for urban buses at different road locations: A comparison between new-energy buses and conventional diesel buses,” Sci. Total Environ., vol. 703, p. 135533, 2020.
52. Y. Pan, S. Chen, F. Qiao, S. V. Ukkusuri, and K. Tang, “Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees,” Sci. Total Environ., vol. 660, pp. 741–750, 2019.
53. X. Niu, C. Yang, H. Wang, and Y. Wang, “Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine,” Appl. Therm. Eng., vol. 111, pp. 1353–1364, 2017.