Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

Abstract

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO_x, unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO_x, HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO_x, HC, smoke, a multi-objective optimization problem is formulated. Non-dominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

Keywords

jatropha biodiesel / fuel properties / response surface methodology / multi-objective optimization / non-dominated sorting genetic algorithm-II

References

[1] ThiruvengadaraviK V, NandagopalJ, BaskaralingamP, BalaV S S, SivanesanS. Acid-catalyzed esterification of karanja (Pongamia Pinnata) oil with high free fatty acid for biodiesel production. Fuel, 2012, 98: 1-4

[2] BalaV S S, ThiruvengadaraviK V, Sentil KumarP S, PremkumarM P, KumarV V, SankarS S, HariM. Removal of free fatty acid in Pongamia Pinnata (Karanja) oil using divinylbenzene-styrene copolymer resins for biodiesel production. Biomass and Bioenergy, 2012, 37: 335-341

[3] DhingraS, DubeyK K, BhushanG.Enhancement in Jatropha based biodiesel yield by process optimization using design of experiment approach. International Journal of Susainable Energy, 2013, (Published online)

[4] DhingraS, BhushanG, DubeyK K. Development of combined approach for improvement and optimization of Karanja (Pongamia pinnata) biodiesel using response surface methodology and genetic algorithm. Frontiers in Energy, 2013,

[5] BojanS G, ChelladuraiS, DurairajS K. Chelledurai, Durairaj S K. Response surface methodology for optimization of biodiesel production from high FFA Jatropha curcas oil. International Journal of Green Energy, 2011, 8(6): 607-617

[6] NabiN, RehmanM, AkhterS. Biodiesel from cotton seed oil and its effect on engine performance and exhaust emissions.Applied Thermal Engineering, 2008, 29(11-12): 2265-2270

[7] GumusM A. Comprehensive experimental investigation of combustion and heat release characteristics of a biodiesel (hazelnut kernel oil methyl ester) fuelled direct injection compression ignition engine. Fuel, 2010, 89(10): 2802-2814

[8] BalajiganeshN, Chandra Mohan ReddyB.Optimization of C.I Engine parameters using Artificial Neural. International Journal of Mechanical and Industrial Engineering, 2011, 1(2), ISSN No. 2231-6477

[9] Kiani Deh KianiM, GhobadianB, TavakoliT, NikbakhtA M, NajafiG. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy, 2009, 4: 1-5

[10] KaruppasamyK, Syed Abu ThaheerA, Ahmed BashaC, MannivannanA. The effect of biodiesel blends on single cylinder DI diesel engine and optimization using response surface methodology. European Journal of Scientific Research, 2012, 84(3): 365-376

[11] De LucasA, DuranA, CarmonaM, LapuertaM. Modelling diesel particulate emissions with neural networks. Fuel, 2001, 80(4): 539-548

[12] KrijnsenH, Van KootenW, CalisH, VerbeekR, Venden BleekC. Evaluation of an artificial neural network for NO prediction from a transient diesel engine as a base for ANN would be well suited to inventory prediction from transient diesel engine as base for NOx control. Canadian Journal of Chemical Engineering, 2000, 78(2): 408-417

[13] MudgalA, GopalkrishnanK, HallmarkS. Prediction of emissions from biodiesel fuelled transit buses using artificial neural networks. International Journal of Traffic and Transport Engineering, 2011, 1(2): 115-131

[14] MuralidharanK, VasudevanD, SheebaK N. Performance emission and combustion characteristics of biodiesel fuelled variable compression ratio engine. Energy, 2011, 36(8): 5385-5393

[15] GanapathyT, GakharR P, MurugesanK. Optimization of performance parameters of diesel engine with jatropha biodiesel using response surface methodology. International Journal of Sustainable Energy, 2011, 30(sup1 S1): S76-S90

[16] KutiO A, XiangangW G, NishidaK, HuangZ H. Characteristics of ignition and combustion of biodiesel fuel spray injected by a common-rail injection system for a direct-injection diesel engine. Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering, 2010, 224(12): 1581-1596

[17] RamdhasA S, MuraleedharanC, JayarajS. Performance and emission evaluation of a diesel engine fuelled with methyl esters of rubber seed oil. Renewable Energy, 2005, 20: 1-12

[18] LinG H, KuoC P. Effects of the injection timing on the engine performance and the exhaust emissions of a diesel engine fuelled by tyre-pyrolysis oil-diesel blends. Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering, 2013, 227(8): 1153-1161

[19] JindalS, SalviB L. Sustainability aspects and optimization of linseed biodiesel blends for compression gnition engine. Journal of Renewable and Sustainable Energy, 2012, 4(4): 043111

[20] MuralidharanK, VasudevanD, SheebaK N. Performance emission and combustion characteristics of biodiesel fuelled variable compression ratio engine. Energy, 2011, 36(8): 5385-5393

[21] DebK, AgarwalR B. Simulated Binary Crossover for Continuous Search Space. Complex Systems, 1995, 9: 115-148

[22] RaghuwanshiM M, KakdeO G. Survey on multi-objective evolutionary and real coded genetic algorithms. Proceedings of the 8th Asia Pacīc Symposium on Intelligent and Evolutionary Systems, 2004, 150-161

[23] BeyerH G, DebK. On Self-Adaptive Features in Real-Parameter Evolutionary Algorithm. IEEE Transactions on Evolutionary Computation, 2001, 5(3): 250-270

[24] SrinivasN, DebK. Multi-objective Optimization Using Non-dominated Sorting in Genetic Algorithms. Evolutionary Computation, 1994, 2(3): 221-248

[25] DebK, PratapA, AgarwalS, MeyarivanT. A Fast Elitist Multi- objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197

[26] GargM P, JainA, BhushanG. Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture

Acknowledgements

The authors gratefully acknowledge Mr. Mohinder Pal Garg, Assistant professor, Mechanical Engineering Department, M. M. University, Ambala, Haryana and Mr. Deepak Chhabra, Assistant professor, Mechanical Engineering Department, UIET, M.D.U, Rohtak, Haryana for helping them in carrying out their research.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg