Investigating the engine vibration in MF285 tractor effected by different blends of biodiesel fuel using statistical methods and ANFIS

Document Type : Research Article

Authors

• A. Safrangian ¹
• L. Naderloo ¹
• H. Javadikia ¹
• M. Mostafaei ¹
• S. S. Mohtasebi ²

¹ Department of Mechanical Engineering of Biosystems, Razi University, Kermanshah, Iran

² Department of Mechanical Engineering of Biosystems, University of Tehran, Karaj, Iran

https://doi.org/10.22067/jam.v7i1.52397

Abstract

Introduction
Vibrations include a wide range of engineering sciences and discuss from different aspects. One of the aspects is related to various types of engines vibrations, which are often used as power sources in agriculture. The created vibrations can cause lack of comfort and reduce effective work and have bad influence on the health and safety. One of the important parameters of the diesel engine that has the ability to create vibration and knocking is the type of fuel. In this study, the effects of different blends of biodiesel, bioethanol and diesel on the engine vibration were investigated. As a result, a blend of fuels such as synthetic fuel that creates less vibration engine can be identified and introduced.
Materials and Methods
In this study, canola oil and methanol alcohol with purity of 99.99% and the molar ratio of 6:1 and sodium hydroxide catalyst with 1% by weight of oil were used for biodiesel production. Reactor configurations include: maintaining the temperature at 50 ° C, the reaction time of 5 minutes and the intensity of mixing (8000 rpm), and pump flow, 0.83 liters per minute. A Massey Ferguson (MF) 285 tractor with single differential (2WD), built in 2012 at Tractor factory of Iran was used for the experiment. To measure the engine vibration signals, an oscillator with model of VM120 British MONITRAN was used. Vibration signals were measured at three levels of engine speed (2000, 1600, 1000 rpm) in three directions (X, Y, Z). The analysis performed by two methods in this study: statistical data analysis and data analysis using Adaptive neuro-fuzzy inference system (ANFIS).
Statistical analysis of data: a factorial experiment of 10×3 based on completely randomized design with three replications was used in each direction of X, Y and Z that conducted separately. Data were compiled and analyzed by SPSS 19 software. Ten levels of fuel were including of biodiesel (5, 15 and 25%) and bioethanol (2, 4 and 6%), and diesel fuel.
Data analysis by ANFIS: ANFIS is the combination of fuzzy systems and artificial neural network so that it has both benefits. This system is useful to solve the complex non-linear problems in agricultural engineering applications such as systems involved in the soil, plant and air. ANFIS by linguistic concepts can establish and inference non-linear relationship between inputs and outputs. In this research, modeling was generally performed by Toolbox of ANFIS and coding in MATLAB too. Five important and effective factors in modeling were optimized until the best ANFIS model is obtained. The five factors were: type of input fuzzy sets, the number of input fuzzy sets, fuzzy set of output, methods of optimization and the number of epochs.
Results and Discussion
Based on the total vibration acceleration values for different fuels in different rpm, pure diesel (B5E4D91) had the highest vibration and the lowest vibration was seen in the mixed fuel of B25E4D71. Based on the results, two combined fuel of (B25E2D73, B25E4D71) have the lowest vibration and highest amount of biodiesel fuel (25%). After them, three combined fuels of (B5E2D83, B5E4D81, and B5E6D79) have created more vibration and the lowest amount of biodiesel fuel in this study (5%) has created the greatest amount of vibration. With increasing engine speed, the number of combustion courses and piston shock per unit of time increases. As a result, the engine body vibration increases. The results are consistent with results from other researchers.

Conclusion
In this study, motor vibration of MF285 tractors, by replacing a portion of diesel fuel with biodiesel produced from canola oil and bioethanol, was investigated. In the beginning, necessary biodiesel fuel was produced by research reactor in biodiesel workshop, and then different percentages of diesel and bio-ethanol were mixed to biodiesel and ten combined fuels were created. Finally the effect of different fuel combinations and different engine rotational speeds on the tractor engine vibrations was studied based on a factorial randomized complete block design and then analyzed and modeled by ANFIS. The results showed that the vibration of pure diesel fuel had the highest vibration. Also, with increasing biodiesel fuel blends, the amount of vibration reduced significantly. Increase in engine speed had direct effect on increasing the amount of vibration. Also by increasing the percent of bioethanol from 0 to 4%, the amount of vibration was reduced then vibration value increased by raising the percent of bioethanol. After modeling and analyzing, our results showed that the best fuel in terms of having the lowest vibration motor was B25E4D71.

Keywords

• Bio-ethanol
• Bio-fuel
• Diesel
• Oscillato
• Vibration

20.1001.1.22286829.1396.7.1.14.9

References

1. Ahmadian, H. 2011. Measurement, analysis and evaluation of vibration of the two-wheel tractor in transportation, Faculty of Agriculture, University of Tehran, Karaj. (In Farsi).

2. Andrew, L. G., C. H. Dianne, and J. S. Brian. 2007. The effect of piston friction on the torsional natural frequency of a reciprocating engine. Mechanical Systems and Signal Processing 21 (7): 2833-2837.

3. Arkhipov, M., E. Krueger, and D. Kurtener. 2008. Evaluation of ecological conditions using bioindicators: application of fuzzy modeling. Paper presented at Lecture Notes in Computer Science.

4. Buragohain, M., and C. Mahanta. 2008. A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing 8: 609-625.

5. Cheng, C. B., C. J. Cheng, and E. S. Lee. 2002. Neuro-fuzzy and genetic algorithm in multiple response optimization. Computers and Mathematics with Applications 44: 1503-1514.

6. Ferella., F., G. Mazziotti Di Celso, I. De Michelis, V. Stanisci, and F. Veglio. 2010. Optimization of the trasesterification reaction in biodiesel production. Fule 89 (1): 36-42.

7. Gruber, G., and H. Zipper Man. 1994. Relation between WBV and morbidity patterns among motor coach operations. DHEW / MIOSH publication no 75-104 washington DC:US.Govermment printing office.

8. Hasan-Beigi, S. R., E. Johar, B. Ghobadian, M. Abonajmi, and V. Astan. 2012. Measurement and analysis of vibration signals 6-cylinder diesel engine with diesel and bio fuel mixtures in the time domain. In: Proceedings of 7th International Congress on Mechanization and Machinery in Agriculture. (In Farsi).

9. Hashemifard-Dehkordi, S. H., M. Almasi, A. M. Borghaee, and B. Beheshti. 2014. The impact of bioethanol and diesel fuel blends on diesel engine vibration. Journal of Agricultural Machinery 4 (2): 243-236. (In Farsi).

10. Heidari, B., S. R. Hasan-Beigi, and B. Ghobadian. 2012. Checking tiller engine vibration in the time domain using a mixture of diesel fuel and bio-diesel In: Proceedings of 7th International Congress on Mechanization and Machinery in Agriculture. (In Farsi).

11. Keskin, A. 2010. The in flounce of ethanol-gasoline blends on spark ignition engine vibration characteristics and noise emissions. Energy sources 32: 1851-1860.

12. Metin, E. H., and H. Murat. 2008. Comparative analysis of an evaporativecondenser using artificial neural network and adaptive neuro-fuzzyinference system. International Journal of Refrigeration 31: 1426-1436.

13. Milby, T. H., and R. C. Spear. 1994. Relation between EBV and morbidtily patterns among heavy equioment operator. DHEVV / NIOSH publication NO: 77-167 washington DC:US. Govermment printing office.

14. Min-Soo, P., F. Takabumi, K. Tae-gu, and M. Setsuo. 2013. Health Risk Evaluation of Whole-body Vibration by ISO 2631-5 and ISO 2631-1 for operators of agricultural tractors and recreational vehicles. Industrial Health 51: 364-370.

15. Naderloo, L., R. Alimardani, M. Omid, F. Sarmadian, P. Javadikia, M. Y. Torabi, and F. Alimardani. 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45: 1406-1413.

16. Rao, S. S. 1995. Mechanical vibration. Addison-Wesley, UK

17. Salokhe, V. M., B. Majumder and M. S. Islam. 1995. Vibration characteristics of power tiller. Journal of Terramechanics 32: 181-196.

18. Saadatifard, A., M. Mosleh, S. Setayeshi, and M. Kamari. 2012. Sensitivity of accuracy of artificial neural networks to the quantity of incoming data and compare it with the predictions of ANFIS in making artificial petrophysical graphs. Exploration and Production 96: 83-90. (In Farsi).

19. Sam, B., and K. Kathirvel. 2006. vibration charac terisztics of walking and riding type power tillers. Bosystems Engineering 95 (4): 517-528.

20. Selim, M. Y. E. 2001. pressure-time charactristics in dirsel engine fueled whit natural gas. Renewable entergy 22 (4): 473-489.

21. Serge, G. 2001. Designing fuzzy inference systems from data:Interpretability oriented review. IEEE Transaction on Fuzzy Systems 9: 426-442.

22. Shabani, Z., S. Rafiee, B. Ghobadian, and H. Ahmadi. 2012. Optimize engine performance Tractors with a mixture of biofuels, diesel, and ethanol by response surface methodology. Journal of Engine Research 99. (In Farsi).

23. Taghizadewqh-Alisaraei, A., B. Ghobadian, T. Tavakoli–hashjin, and S. S. mohtasebie. 2012. Vibration analysis of a diesel engine using biodiesel and pet diesel fuel blends. Fuel 102: 414-422.

24. Tewari, V. K., K. N. Dewangan, and S. Karmakar. 2004. operators fatigue in field operation of hand tractors. Biosystems Engineering 89 (1): 1-11.