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Multi-variable optimization of diesel engine fuelled with biodiesel using grey-Taguchi method

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Abstract

The current work is aimed at the correlated multiple criteria optimization of various parameters of a turbocharged, direct injection, compression ignition engine alternatively fuelled with neat biodiesel and its 20 % blend with commercial diesel (B20) using grey–Taguchi method (GTM). The GTM converts multiple objectives of a problem into a single-objective function by an optimization technique. The process environment consisting of three input variables such as type of fuel, engine speed, and load was considered in this study. An orthogonal array was used for the design of experiments on the basis of L9 (33). The optimal parameters were determined by the grey relational grade based on GTM. The consequent optimal combination of input parameters was used to maximize the output parameters including engine torque, brake power, heat release, and injection pressure with the possible diminution of brake specific fuel consumption of the engine. During the study, it was found that B20 as a fuel, 1,800 rpm as a speed, and 100 % as a load offer an optimal parametric combination at which the desired output results are achieved. Moreover, analysis of variance approach based on statistical software of Minitab 16 was used to investigate the comparative impacts of input variables on the output responses. It was known that load is the predominant factor with an influence of 92.42 % on the output parameters. Finally, a confirmatory test was performed to validate the results using artificial neural network in MATLAB.

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Abbreviations

CI:

Compression ignition

DI:

Direct injection

H.R:

Heat release

IP:

Injection pressure

BSFC:

Brake specific fuel consumption

DOE:

Design of experiments

OA:

Orthogonal array

GRA:

Grey relational analysis

GRG:

Grey relational grade

OGRG:

Overall grey relational grade

GRC:

Grey relational coefficient

QLF:

Quality loss function

ANOVA:

Analysis of variance

DOF:

Degree of freedom

ANN:

Artificial neural network

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Acknowledgments

Authors are indebted to Dr. Ge and Dr. Tan for their guidelines and encouraging attitude, and the lab staff for their help in the conduct of experiments. The experiments were performed in the Laboratory of Auto Performance and Emission Test. School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan

    Mustabshirha Gul, Asad Naeem Shah, Younis Jamal & Imran Masood

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  1. Mustabshirha Gul
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  2. Asad Naeem Shah
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Correspondence to Mustabshirha Gul.

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Technical Editor: Luis Fernando Figueira da Silva.

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Gul, M., Shah, A.N., Jamal, Y. et al. Multi-variable optimization of diesel engine fuelled with biodiesel using grey-Taguchi method. J Braz. Soc. Mech. Sci. Eng. 38, 621–632 (2016). https://doi.org/10.1007/s40430-015-0312-x

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