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Performance analysis of direct injection diesel engine fueled with diesel-tomato seed oil biodiesel blending by ANOVA and ANN CQU.pdf (8.52 MB)

Performance analysis of direct injection diesel engine fueled with diesel-tomato seed oil biodiesel blending by ANOVA and ANN

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Version 2 2023-08-14, 04:01
Version 1 2021-01-17, 10:43
journal contribution
posted on 2023-08-14, 04:01 authored by Rahim Karami, Mohammad RasulMohammad Rasul, Mohammad KhanMohammad Khan, Mohammad Anwar
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. Duncan0s 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. © 2019 MDPI AG. All rights reserved.

History

Volume

12

Issue

23

Start Page

1

End Page

18

Number of Pages

18

eISSN

1996-1073

Publisher

M D P I AG

Additional Rights

CC BY 4.0

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2019-11-19

Era Eligible

  • Yes

Journal

Energies

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