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

doi:10.1007/s11465-014-0287-9

Front. Mech. Eng.

2014, Vol. 9

Issue (1) : 81-94 https://doi.org/10.1007/s11465-014-0287-9

RESEARCH ARTICLE

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

Sunil Dhingra^1,^*(

),Gian Bhushan²,Kashyap Kumar Dubey³

1. Department of Mechanical Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra
2. Department of Mechanical Engineering, National Institute of Technolo- gy, Kurukshetra
3. Department of Biotechnology, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak

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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

Corresponding Author(s): Sunil Dhingra

Issue Date: 16 May 2014

Cite this article:

Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey. Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II[J]. Front. Mech. Eng., 2014, 9(1): 81-94.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-014-0287-9
https://academic.hep.com.cn/fme/EN/Y2014/V9/I1/81

Fig.1 Schematic diagram of the engine setup

Tab.1 Fuel properties of Jatropha oil, its biodiesel blends, diesel fuel and standard values of biodiesel

Tab.2 Engine Specification

Tab.3 Variable constraints in response surface methodology

Tab.4 Experimental design for combination of input variables in the engine and their responses

Tab.5 Precision index values of different response models

Tab.6 Probability values (p-values) of different performance and emission parameter models by Analysis of variance (ANOVA)

Fig.2 Surface variation of BSFC response model across blending ratio, load torque and compression ratio

Fig.3 Surface variation of BTE response model across blending ratio, load torque and compression ratio

Fig.4 Surface variation of P_max response model across blending ratio, load torque and compression ratio

Fig.5 Surface variation of Sqrt(CO) response model across blending ratio, load torque and compression ratio

Fig.6 Surface variation of Sqrt(NO_x) response model across blending ratio, load torque and compression ratio

Fig.7 Surface variation of Log₁₀(HC) response model across blending ratio, load torque and compression ratio

Fig.8 Surface variation of Sqrt(Smoke) response model across blending ratio, load torque and compression ratio

Fig.9 NSGA-II flowchart.

Fig.10 (a) Variation of brake thermal efficiency against the blending ratio (b) Variation of brake thermal efficiency against the compression ratio. (c) Variation of brake thermal efficiency against load torque

Tab.7 NSGA-II operators used in MATLAB code

Tab.8 Pareto optimum solution sets predicted by NSGA-II package

Tab.9 Confirmatory experiments by randomly selecting the five solution sets.

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