TOWARDS ARTIFICIAL INTELLIGENCE BASED DIESEL ENGINE PERFORMANCE CONTROL UNDER VARYING OPERATING CONDITIONS USING SUPPORT VECTOR REGRESSION

Abstract

Diesel engine designers are constantly on the look-out for performance nhancement through efficient control of operating parameters. In this paper, the concept of an intelligent engine control system is proposed that seeks to ensure optimised performance under varying operating conditions. The concept is based on arriving at the optimum engine operating parameters to ensure the desired output in terms of efficiency. In addition, a Support Vector Machines based prediction model has been developed to predict the engine performance under varying operating conditions. Experiments were carried out at varying loads, compression ratios and amounts of exhaust gas recirculation using a variable ompression ratio diesel engine for data acquisition. It was observed that the SVM model was able to predict the engine performance accurately.

Keywords

Dates

  • Submission Date2012-04-13
  • Revision Date2012-08-23
  • Acceptance Date2012-10-22

DOI Reference

10.2298/TSCI120413218N

References

  1. R. Subramanian, G. Rajendiran, R. Venkatachalam, N. Nedunchezhian, K. Mayilsamy, Studies on Performance and Emission Characteristics of Multi Cylinder Diesel Engine using Hybrid Fuel Blends as Fuel, Journal of Scientific and Industrial Research, 70 (2011), 7, pp. 539-543
  2. L. Ou, C. Wang, Y. Qian, W. Huang, S. Zhu, J. Sun, Effect of Gasoline Fumigation on Diesel Engine Performance and Emissions, Applied Mechanics and Materials, 130 -134 (2012), pp. 1744-1748
  3. A. Deepak, S. Shrawan Kumar, A. Avinash Kumar, Effect of Exhaust Gas Recirculation (EGR) on Performance, Emissions, Deposits and Durability of a Constant Speed Compression Ignition Engine, Applied Energy, 88 (2011), 8, pp. 2900-2907
  4. K. Muralidharan, D. Vasudevan, Performance, Emission and Combustion Characteristics of a Variable Compression Ratio Engine using Methyl Esters of Waste Cooking Oil and Diesel Blends, Applied Energy, 88 (2011), 11, pp. 3959-3968
  5. M. I. Jahirul, R. Saidur, H. H. Masjuki, Predictability of Artificial Neural Network (ANN) in Performance Prediction of a Retrofitted CNG Engine, International Journal of Mechanical and Materials Engineering, 5 (2010), 2, pp. 268-275
  6. M. Abhisek, G. Kasthurirangan, H. Shauna, Prediction of Emissions from Biodiesel Fueled Transit Buses using Artificial Neural Networks, International Journal for Traffic and Transport Engineering, 1 (2011), 2, pp. 115 - 131
  7. T. Hari Prasad, K. Hema Chandra Reddy, M. Muralidhara Rao, Performance and Exhaust Emissions Analysis of a Diesel Engine Using Methyl Esters of Fish Oil with Artificial Neural Network Aid, International Journal of Engineering and Technology, 2 (2010), 1, pp.23-27
  8. O. Obodeh, C. I. Ajuwa, Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine NOx Emissions, European Journal of Scientific Research, 33 (2009), 4, pp. 642-653
  9. B. Adnan, T. Mustafa, S. G. Sinan, C. Ismet, Prediction of a Diesel Engine Characteristics by using Different Modelling Techniques, International Journal of the Physical Sciences, 6 (2011), 16, pp.3979-3992
  10. M. Kiani, B. Ghobadian, T. Tavakoli, A.M. Nikbakht, G. Najafi, Application of Artificial Neural Networks for the Prediction of Performance and Exhaust Emissions in SI Engine Using Ethanol- Gasoline Blends, Energy, 35 (2010), 1, pp.65-69
  11. J. T. Antonio, O. Pablo, M. Jaime, R. Carlos, A Tool for Predicting the Thermal Performance of Diesel Engine, Heat transfer Engineering, 32 (2011), 10, pp.891-904
  12. Z. Yan, C. Zhou, S. Su, Z. Liu, X. Wang, Application of Neural Network in the Study of Combustion Rate of Neural Gas/Diesel Dual Fuel Engine, Journal of Zhejiang University SCIENCE A, 4 (2003), 2, pp.170-174
  13. M. Abhisek, G. Kasthurirangan, S. Hallmark, Prediction Of Emissions From Biodiesel Fueled Transit Buses Using Artificial Neural Networks, International Journal for Traffic and Transport Engineering, 1 (2011), 2, pp.115 - 131
  14. G. J. Chen, Z. M. Liu, T. T. Liu, S. H. Su, G. J. Yuan, Y. J. Cao, Research on Emission Control of Marine Diesel Engine, Advanced Materials Research, 430 -432 (2012), pp.1198-1202
  15. X. Q. Shen, Y. X. Su, Marine Diesel Engine Speed Control System Based on Fuzzy-PID, Applied Mechanicas and Material ,152 - 154 (2012), pp.1589-1594
  16. G. J. Chen, Z. M. Liu, T. T. Liu, S. H. Su, G. J. Yuan, Y. J. Cao. Reliability Analysis on the Key Components of High-Power Low-Speed Diesel Engine, Applied Mechanics and Materials, 138 - 139 (2012), pp. 382-386
  17. Z. Ji, X. Xie, Z. Sun, P. Chen, Rail Pressure Control of Common Rail Diesel Engine Based on RBF Neural Network Adaptive PID Controller, Electronic and Mechanical Engineering and Information Technology , 3 (2011), pp.1122 - 1125
  18. F. Barghi, A.A. Safavi, An Intelligent Control Policy for Fuel Injection Control of SI Engines, Intelligent Engineering Systems, (2011), pp.115 - 119.
  19. S. Mukherjee, E. Osuna, F. Girosi, Non Linear Prediction of Chaotic Time Series using Support Vector Machines, Neural Networks for Signal Processing, (1997), pp.511-520
  20. H. Wei, P. Ping, Predicting Engine Reliability by Support Vector Machines, International Journal of Advanced Manufacturing Technology, 28 (2006), pp.154-161
  21. V. Chi, W. Pak-kin, L. Vi, Prediction of Automotive Engine Power and Torque Using Least Squares Support Vector Machines and Bayesian Inference, Engineering Applications of Artificial Intelligence,19 (2006), 3, pp. 227-297
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