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

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 (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NOx, unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NOx, HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multiobjective optimization problem is formulated. Nondominated 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.

[1]  Gian Bhushan,et al.  Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm , 2013 .

[2]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[3]  Md. Nurun Nabi,et al.  Biodiesel from cotton seed oil and its effect on engine performance and exhaust emissions , 2009 .

[4]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[5]  Gian Bhushan,et al.  Enhancement in Jatropha-based biodiesel yield by process optimisation using design of experiment approach , 2014 .

[6]  K. N. Sheeba,et al.  Performance, emission and combustion characteristics of biodiesel fuelled variable compression ratio engine , 2011 .

[7]  A. Louche,et al.  PV-hybrid power systems sizing incorporating battery storage: an analysis via simulation calculations , 2000 .

[8]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

[9]  S. Durairaj,et al.  Response Surface Methodology for Optimization of Biodiesel Production from High FFA Jatropha Curcas Oil , 2011 .

[10]  H.P.A. Calis,et al.  Evaluation of an artificial neural network for NOX emission prediction from a transient diesel engine as a base for NOX control , 2000 .

[11]  Chun-Pao Kuo,et al.  Effects of the injection timing on the engine performance and the exhaust emissions of a diesel engine fuelled by tyre pyrolysis oil–diesel blends , 2013 .

[12]  P. Senthil Kumar,et al.  Removal of free fatty acids in Pongamia Pinnata (Karanja) oil using divinylbenzene-styrene copolymer resins for biodiesel production , 2012 .

[13]  Metin Gumus,et al.  A comprehensive experimental investigation of combustion and heat release characteristics of a biodiesel (hazelnut kernel oil methyl ester) fueled direct injection compression ignition engine , 2010 .

[14]  Magín Lapuerta,et al.  Modeling diesel particulate emissions with neural networks , 2001 .

[15]  Gian Bhushan,et al.  Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II , 2012 .

[16]  P. Baskaralingam,et al.  Acid-catalyzed esterification of karanja (Pongamia pinnata) oil with high free fatty acids for biodiesel production , 2012 .

[17]  K. Murugesan,et al.  Optimization of performance parameters of diesel engine with Jatropha biodiesel using response surface methodology , 2011 .

[18]  Patrik Thollander,et al.  Industrial Energy Management Gap Analysis , 2015 .

[19]  Shauna L. Hallmark,et al.  Prediction of emissions from biodiesel fueled transit buses using artificial neural networks , 2011 .

[20]  Kalyanmoy Deb,et al.  On self-adaptive features in real-parameter evolutionary algorithms , 2001, IEEE Trans. Evol. Comput..

[21]  S. Jayaraj,et al.  Performance and emission evaluation of a diesel engine fueled with methyl esters of rubber seed oil , 2005 .

[22]  M. Raghuwanshi,et al.  Survey on multiobjective evolutionary and real coded genetic algorithms , 2004 .

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  B. L. Salvi,et al.  Sustainability aspects and optimization of linseed biodiesel blends for compression ignition engine , 2012 .

[25]  Zuo-hua Huang,et al.  Characteristics of the ignition and combustion of biodiesel fuel spray injected by a common-rail injection system for a direct-injection diesel engine , 2010 .