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Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN

Author

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  • Rahim Karami

    (School of Engineering and Technology, Central Queensland University, Rockhampton, Queensland 4702, Australia
    School of Bio System Engineering, Shiraz University, Shiraz 71454, Iran)

  • Mohammad G. Rasul

    (School of Engineering and Technology, Central Queensland University, Rockhampton, Queensland 4702, Australia)

  • Mohammad M. K. Khan

    (School of Engineering and Technology, Central Queensland University, Rockhampton, Queensland 4702, Australia)

  • Mohammad Anwar

    (School of Engineering and Technology, Central Queensland University, Rockhampton, Queensland 4702, Australia)

Abstract

Biodiesel is an alternative fuel for diesel engine. Considering the differences between diesel and biodiesel fuels, the engine condition should be modified based on the fuel or fuel blends to achieve optimum performance. This study presented a performance analysis of a direct-injected (DI) diesel engine with a dynamometer fueled with diesel-tomato seed biodiesel (TSOB) blends employing ANOVA and universal nonlinear model based on ANN. The experiments were carried out under conditions of some independent variables including different engine loads (0, 50, 100%) and speed (1800, 2150, and 2500 rpm) for four diesel-biodiesel combinations (B0, B5, B10, and B20). In this research, the effect of these factors on dependent variables including power, torque, SFC, FC, and Exhaust Gas Temperature (EGT) are investigated. Duncan′s multi-domain test at a significance level of R < 0.01 shows that the highest and lowest of the torque and power are produced from B5 and B20, respectively. These results show that the lowest EGT of 613 K is related to B20 and the highest EGT is related to B5 and B10. The regression models showed that the torque decreases with increasing the engine speed and biodiesel percentage. These results also show that the highest and the lowest SFC is related to B0 and B20, respectively. The ANN model shows high capability of predicting the engine performance parameters and emissions, without running costly and time-consuming experiments with the histogram error of 0.004 and R = 0.96. It also proved that ANN is a non-linear model of choice to deal with these data, instead of multivariate linear regression employed for preliminary analysis.

Suggested Citation

  • Rahim Karami & Mohammad G. Rasul & Mohammad M. K. Khan & Mohammad Anwar, 2019. "Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN," Energies, MDPI, vol. 12(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4421-:d:289358
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    References listed on IDEAS

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    1. Xue, Jinlin & Grift, Tony E. & Hansen, Alan C., 2011. "Effect of biodiesel on engine performances and emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1098-1116, February.
    2. Shaafi, T. & Velraj, R., 2015. "Influence of alumina nanoparticles, ethanol and isopropanol blend as additive with diesel–soybean biodiesel blend fuel: Combustion, engine performance and emissions," Renewable Energy, Elsevier, vol. 80(C), pages 655-663.
    3. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2017. "Effect of added alumina as nano-catalyst to diesel-biodiesel blends on performance and emission characteristics of CI engine," Energy, Elsevier, vol. 124(C), pages 543-552.
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    1. Rahim Karami & Mohammad G. Rasul & Mohammad M. K. Khan, 2020. "CFD Simulation and a Pragmatic Analysis of Performance and Emissions of Tomato Seed Biodiesel Blends in a 4-Cylinder Diesel Engine," Energies, MDPI, vol. 13(14), pages 1-21, July.

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