Grey-Fuzzy Taguchi approach for multi-objective optimization of performance and emission parameters of a single cylinder crdi engine coupled with EGR

  • S. Roy
  • A. K. Das
  • R. Banerjee


The present study attempts to address the challenges of the multiobjective optimization problem of the BSFC-NOx-PM trade-off paradox of an existing diesel engine by harnessing the synergetic benefit of PM and BSFC reduction through CRDI operation and simultaneous NOx reduction by EGR application. Load, FIP and EGR were chosen as the input parameters while NOx, PM and BSFC were the response variables. In order to reduce the experimental effort, the Taguchi L16 orthogonal array technique was employed to obtain the corresponding values of the response variables. The grey relational analysis coupled with fuzzy logic has been employed as the optimization routine. The optimal combination of the input parameters corresponding to the calibrated values of the response variables were obtained by employing the Grey-Fuzzy Grade and S-N ratio strategy as a performance index. The computed optimal combination so obtained were further validated through actual experimentation. EGR was found to be the most influencing factor in the present optimization endeavour. The study also established that the Grey-Fuzzy-Taguchi method was not only comparable but superior to the Grey-Taguchi method usually employed for such optimization studies.

Key Words

CRDI EGR Grey relational analysis Fuzzy decision making logic Taguchi method 



baseline diesel operation


brake power


brake specific fuel consumption


before top dead centre


brake thermal efficiency




compression ignition


common rail diesel injection


exhaust gas recirculation


fuel injection pressure


grey fuzzy grade


grey relational grade

IC Engine

internal combustion engine


oxides of nitrogen


particulate matter


parts per million


mass flow rate of air with EGR


mass flow rate of air without EGR


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  1. Badami, M., Nuccio, P. and Trucco, G. (1999). Influence of Injection Pressure on the Performance of a DI Diesel Engine with a Common Rail Fuel Injection System. SAE Paper No. 1999-01-0193.Google Scholar
  2. Balusamy, T. and Marappan, R. (2010). Effect of injection time and injection pressure on CI engine fuelled with methyl ester of thevetia peruviana seed oil. Int. J. Green Energy 7, 4, 397–409.CrossRefGoogle Scholar
  3. Bose, P. K., Banerjee, R. and Roy, S. (2013). An Experimental Investigation on the Efficacy of EGR on the Soot-NOx-BSFC Trade-off Characteristics of a CRDI Assisted 4-stroke Single Cylinder Diesel Engine under Varying Injection Durations.Google Scholar
  4. Broge, J. L. (2009). Optimizing Diesel Engine Operating Conditions. http://articlessaeorg/6279Google Scholar
  5. Chiang, K.-T. and Chang, F.-P. (2006). Application of greyfuzzy logic on the optimal process design of an injection-molded part with a thin shell feature. Int. Communications in Heat and Mass Transfer 33, 1, 94–101.CrossRefGoogle Scholar
  6. Chiang, K.-T., Liu, N.-M. and Chou, C.-C. (2008). Machining parameters optimization on the die casting process of magnesium alloy using the grey-based fuzzy algorithm. Int. J. Adv. Manuf. Technol. 38, 3-4, 229–237.Google Scholar
  7. Cooper, B., Penny, I., Beasley, M., Greaney, A. and Crump, J. (2006). Advanced Diesel Technology to Achieve Tier 2 Bin 5 Emissions Compliance in US Light-duty Diesel Applications. SAE.CrossRefGoogle Scholar
  8. Datta, S., Bandyopadhyay, A. and Pal, P. (2008). Greybased taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding. Int. J. Adv. Manuf. Technol. 39, 11-12, 1136–1143.Google Scholar
  9. Deng, J. (1989). Introduction to grey system. J. Grey System 1, 1, 1–24.MathSciNetzbMATHGoogle Scholar
  10. Desantes, J. M., Benajes, J., Molina, S. and González, C. A. (2004). The modification of the fuel injection rate in heavy-duty diesel engines: Part 2: Effects on combustion. Applied Thermal Engineering 24, 17-18, 2715–2726.CrossRefGoogle Scholar
  11. Devan, P. K. and Mahalakshmi, N. V. (2009). Performance, emission and combustion characteristics of poon oil and its diesel blends in a DI diesel engine. Fuel 88, 5, 861–867.CrossRefGoogle Scholar
  12. Ganapathy, T., Murugesan, K. and Gakkhar, R. P. (2009). Performance optimization of Jatropha biodiesel engine model using Taguchi approach. Applied Energy 86, 11, 2476–2486.CrossRefGoogle Scholar
  13. Gopalsamy, B., Mondal, B. and Ghosh, S. (2009). Optimisation of machining parameters for hard machining: Grey relational theory approach and ANOVA. Int. J. Adv. Manuf. Technol. 45, 11-12, 1068–1086.Google Scholar
  14. Ho, C. Y. and Lin, Z. C. (2003). Analysis and application of grey relation and ANOVA in chemical–mechanical polishing process parameters. Int. J. Adv. Manuf. Technol. 21, 1, 10–14.Google Scholar
  15. Hountalas, D. T., Mavropoulos, G. C. and Binder, K. B. (2008). Effect of exhaust gas recirculation (EGR) temperature for various EGR rates on heavy duty DI diesel engine performance and emissions. Energy 33, 2, 272–283.CrossRefGoogle Scholar
  16. Johnson, T. V. (2006). Diesel Emission Control in Review. SAE.CrossRefGoogle Scholar
  17. Johnson, T. V. (2008). Diesel emission control in review. SAE Int. J. Fuels Lubr. 1, 1, 68–81.CrossRefGoogle Scholar
  18. Johnson, T. V. (2010). Review of diesel emissions and control. SAE Int. J. Fuels Lubr. 3, 1, 16–29.CrossRefGoogle Scholar
  19. Johnson, T. V. (2011). Diesel Emissions in review. SAE Int. J. Engines 4, 1, 143–157.Google Scholar
  20. Kannan, G. R. and Anand, R. (2011). Experimental investigation on diesel engine with diestrol–water micro emulsions. Energy 36, 3, 1680–1687.CrossRefGoogle Scholar
  21. Karnwal, A., Hasan, M. M., Kumar, N., Siddiquee, A. N. and Khan, Z. A. (2011). Multi-response optimization of diesel engine performance parameters using thumba biodiesel-diesel blends by applying the Taguchi method and grey relational analysis. Int. J. Automotive Technology 12, 4, 599–610.CrossRefGoogle Scholar
  22. Krishnamoorthy, A., Rajendra Boopathy, S., Palanikumar, K. and Paulo Davim, J. (2012). Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement 45, 5, 1286–1296.CrossRefGoogle Scholar
  23. Kuo, C.-F., Su, T.-L. and Tsai, C.-P. (2007). Optimization of the needle punching process for the nonwoven fabrics with multiple quality characteristics by grey-based taguchi method. Fibers Polym 8, 6, 654–664.CrossRefGoogle Scholar
  24. Ladommatos, N., Abdelhalim, S. and Zhao, H. (1998). Control of oxides of nitrogen from diesel engines using diluents while minimising the impact on particulate pollutants. Applied Thermal Engineering 18, 11, 963–980.CrossRefGoogle Scholar
  25. Lee, D. H., Park, J. S., Ryu, M. R. and Park, J. H. (2013). Development of a highly efficient low-emission diesel engine-powered co-generation system and its optimization using Taguchi method. Applied Thermal Engineering 50, 1, 491–495.CrossRefGoogle Scholar
  26. Lin, C. L. (2004). Use of the taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Materials and Manufacturing Processes 19, 2, 209–220.CrossRefGoogle Scholar
  27. Lin, C. L., Lin, J. L. and Ko, T. C. (2002). Optimisation of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. Int. J. Adv. Manuf. Technol 19, 4, 271–277.Google Scholar
  28. Liu, N.-M., Horng, J.-T. and Chiang, K.-T. (2009). The method of grey-fuzzy logic for optimizing multiresponse problems during the manufacturing process: A case study of the light guide plate printing process. Int. J. Adv. Manuf. Technol. 41, 1-2, 200–210.Google Scholar
  29. Maiboom, A., Tauzia, X. and Hétet, J.-F. (2008). Experimental study of various effects of exhaust gas recirculation (EGR) on combustion and emissions of an automotive direct injection diesel engine. Energy 33, 1, 22–34.CrossRefGoogle Scholar
  30. Mani, M. and Nagarajan, G. (2009). Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on waste plastic oil. Energy 34, 10, 1617–1623.CrossRefGoogle Scholar
  31. Mcgeehan, J. A., Yeh, S., Couch, M., Hinz, A., Otterholm, B., Walker, A. and Blakeman, P. (2005). On the Road to 2010 Emissions: Field Test Results and Analysis with DPF-SCR System and Ultra Low Sulfur Diesel Fuel. SAE.Google Scholar
  32. Minato, A., Tanaka, T. and Nishimura, T. (2005). Investigation of Premixed Lean Diesel Combustion with Ultra High Pressure Injection. SAE.CrossRefGoogle Scholar
  33. Nagata, K., Tanaka, Y. and Yano, K. (2004). Technologies of DENSO Common Rail for Diesel Engine and Consumer Values. Convergence Transportation Electronics Association.Google Scholar
  34. Pandey, R. K. and Panda, S. S. (2014). Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement, 47, 386–392.CrossRefGoogle Scholar
  35. Payri, F., Benajes, J., Arrègle, J. and Riesco, J. M. (2006). Combustion and exhaust emissions in a heavy-duty diesel engine with increased premixed combustion phase by means of injection retarding. Oil & Gas Science and Technology - Rev. IFP 61, 2, 247–258.Google Scholar
  36. Pickett, L. M. and Siebers, D. L. (2004). Non-Sooting, Low Flame Temperature Mixing-Controlled DI Diesel Combustion. SAE.CrossRefGoogle Scholar
  37. Pierpont, D. A. and Reitz, R. D. (1995). Effects of Injection Pressure and Nozzle Geometry on D.I. Diesel Emissions and Performance. SAE.CrossRefGoogle Scholar
  38. Pohit, G. and Misra, D. (2013). Optimization of performance and emission characteristics of diesel engine with biodiesel using grey-taguchi method. J. Engineering 2013, 8.CrossRefGoogle Scholar
  39. Pradeep, V. and Sharma, R. P. (2007). Use of HOT EGR for NOx control in a compression ignition engine fuelled with bio-diesel from Jatropha oil. Renewable Energy 32, 7, 1136–1154.CrossRefGoogle Scholar
  40. Rajmohan, T., Palanikumar, K. and Prakash, S. (2013). Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites. Composites Part B: Engineering, 50, 297–308.CrossRefGoogle Scholar
  41. Rakopoulos, C. D., Dimaratos, A. M., Giakoumis, E. G. and Rakopoulos, D. C. (2010). Investigating the emissions during acceleration of a turbocharged diesel engine operating with bio-diesel or n-butanol diesel fuel blends. Energy 35, 12, 5173–5184.CrossRefGoogle Scholar
  42. Reitz, R. D. (1998). Controlling D.I. Diesel engine emissions using multiple injections and EGR. Combustion Science and Technology 138, 1-6, 257–278.CrossRefGoogle Scholar
  43. Ross, P. J. (1988). Taguchi Tachniques for Quality Engineering. McGraw-Hill. New York.Google Scholar
  44. Roy, S., Banerjee, R. and Bose, P. K. (2014a). Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network. Applied Energy, 119, 330–340.CrossRefGoogle Scholar
  45. Roy, S., Banerjee, R., Das, A. K. and Bose, P. K. (2014b). Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine. J. Natural Gas Science and Engineering, 21, 147–158.CrossRefGoogle Scholar
  46. Roy, S., Das, A. K., Banerjee, R. and Bose, P. K. (2014c). A TMI based CNG dual-fuel approach to address the soot–NOx–BSFC trade-off characteristics of a CRDI assisted diesel engine–An EPA perspective. J. Natural Gas Science and Engineering, 20, 221–240.CrossRefGoogle Scholar
  47. Saravanan, S., Nagarajan, G. and Sampath, S. (2010). Multi response optimization of NOx emission of a stationary diesel engine. Fuel 89, 11, 3235–3240.CrossRefGoogle Scholar
  48. Saravanan, S., Nagarajan, G. and Sampath, S. (2013). Combined effect of injection timing, EGR and injection pressure in NOx control of a stationary diesel engine fuelled with crude rice bran oil methyl ester. Fuel, 104, 409–416.CrossRefGoogle Scholar
  49. Shimazaki, N., Tsurushima, T. and Nishimura, T. (2003). Dual Mode Combustion Concept with Premixed Diesel Combustion by Direct Injection Near Top Dead Center. SAE.CrossRefGoogle Scholar
  50. Suh, H. K. (2011). Investigations of multiple injection strategies for the improvement of combustion and exhaust emissions characteristics in a low compression ratio (CR) engine. Applied Energy 88, 12, 5013–5019.CrossRefGoogle Scholar
  51. Tarng, Y. S. and Yang, W. H. (1998). Application of the Taguchi method to the optimization of the submerged ARC welding process. Materials and Manufacturing Processes 13, 3, 455–467.CrossRefGoogle Scholar
  52. Tarng, Y. S., Yang, W. H. and Juang, S. C. (2000). The use of fuzzy logic in the taguchi method for the optimisation of the submerged arc welding process. Int. J. Adv. Manuf. Technol. 16, 9, 688–694.Google Scholar
  53. Wu, H.-W. and Wu, Z.-Y. (2013). Using Taguchi method on combustion performance of a diesel engine with diesel/biodiesel blend and port-inducting H2. Applied Energy, 104, 362–370.CrossRefGoogle Scholar
  54. Yang, Y.-S. and Huang, W. (2012). A grey-fuzzy Taguchi approach for optimizing multi-objective properties of zirconium-containing diamond-like carbon coatings. Expert Systems with Applications 39, 1, 743–750.CrossRefGoogle Scholar
  55. Yeh, J.-H. and Tsai, T.-N. (2014). Optimizing the fine-pitch copper wire bonding process with multiple quality characteristics using a grey-fuzzy Taguchi method. Microelectronics Reliability 54, 1, 287–296.CrossRefGoogle Scholar
  56. Zadeh, L. A. (1965). Fuzzy sets. Information and Control 8, 3, 338–353.CrossRefMathSciNetzbMATHGoogle Scholar
  57. Zhao, H. (2010). Advanced Direct Injection Combustion Engine Technologies and Development, 2: Diesel Engines. Woodhead Publishing Limited. Cambridge, UK.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringAgartalaIndia

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