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Environmental Science and Pollution Research

, Volume 25, Issue 23, pp 22889–22902 | Cite as

Investigating the effect of MgO and CeO2 metal nanoparticle on the gasoline fuel properties: empirical modeling and process optimization by surface methodology

  • Farzin Zamankhan
  • Vahid Pirouzfar
  • Fathollah Ommi
  • Mohsen Valihesari
Research Article
  • 49 Downloads

Abstract

The main purpose of this paper is to investigate how to optimize gasoline in order to reduce the emitted pollutants caused by combustion, while the torque and power of the engine reach the maximum capabilities. To optimize gasoline formulation, an ethanol and magnesium oxide (MgO) or cerium oxide (CeO2) mixture was added to gasoline. This study explores the role of main variables such as type of metal nanoparticle additive, engine speed, and throttle on engine performance and exhaust gas emissions through the modeling and optimization methods. Experimental design conducted through the implementation of D-optimal design, taking into account the three main parameters. To review the efficiency of this novel fuel, it was tested by a four-stroke engine connected to a dynamometer and an analyzer, under different controlled environments: speeds of 1500, 2000, 2500, and 3000 rpm at both half and full throttle conditions. The analyzed data are the power and torque of the engine, the amount of emitted CO, CO2, HC, and NOx, the octane index, and the viscosity. The analyzed data were calculated and turned into models. Applying the models to data (the optimization process), close correlation between predicted and actual outcomes was found, highlighting the validity of the work. A secondary finding is that the CeO2 mixture used at higher speeds and throttles produces less emissions, while lower speeds and throttles using the MgO mixture produce less emissions.

Keywords

Gasoline CeO2 and MgO Pollutant emissions Octane index 

Nomenclature

b0, b1, bn

Regression coefficients

DOF

Degree of freedom

F-value

Ratio of variances, computed value

i and j

Subscripts (integer variables)

n

Number of factors (variables)

P-value

Statistical criterion

CV

Coefficient of variation

R

Correlation coefficient

R2

Coefficient of multiple determinations

R2adj

Adjusted statistic coefficient

X1, X2, X3

Coded variables

\( \overline{x} \)

Mean value of variables

Y

Response

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Farzin Zamankhan
    • 1
  • Vahid Pirouzfar
    • 2
  • Fathollah Ommi
    • 3
  • Mohsen Valihesari
    • 1
  1. 1.Department of Chemical Engineering, North Tehran BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Chemical EngineeringCentral Tehran Branch, Islamic Azad UniversityTehranIran
  3. 3.Department of Mechanical EngineeringTarbiat Modares UniversityTehranIran

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