Abstract
This paper presents the results of simulation study conducted on a water-cooled, single-cylinder, 4-stroke spark ignition engine. The engine was simulated using both original fuel produced in Iraq and a modified formula made by the authors. The results show great improvement in some of the fuel properties like calorific value, sulfur content, total water content, MON and RON and gum content. On the engine side, the engine power, torque, combustion efficiency, sulfur dioxide levels were greatly improved, while the heat loss, bsfc and NOx emissions increased. Further, Artificial Neural Networks (ANN) was used to predict the engine performance and emission characteristics of the engine. Separate models were developed for performance parameters as well as emission characteristics. ANN results showed that there is a good correlation between the ANN predicted values and the experimental values for various engine performance parameters and exhaust emission characteristics and the relative mean error values were within 5 %, which is acceptable.
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Abbreviations
- Symbol:
-
Description
- ANN:
-
Artificial Neural Networks
- BP:
-
Back propagation
- BFGS:
-
Broyden–Fletcher–Goldfarb–Shanno algorithm
- BSFC:
-
Brake-specific fuel consumption
- HSRN:
-
Heavy straight run naphtha
- IC:
-
Internal combustion
- LPG:
-
Liquefied petroleum gas
- LSRN:
-
Light straight run naphtha
- MON:
-
Motor octane number
- MRE:
-
Mean relative error
- MLP:
-
Multi-layer perception
- MTBE:
-
Methyl tertiary butyl ether
- NO x :
-
Nitrogen oxides
- RAFR:
-
Relative air–fuel ratio
- RMSE:
-
Root mean square error
- RON:
-
Research octane number
- SFC:
-
Specific fuel consumption
- SI:
-
Spark ignition
- SOS:
-
Sum of square errors
- WOT:
-
Wide open throttle
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Sheet, E.A.E., Yamin, J. Improvement of an SI Engine Performance Using Modified Al-Doura Pool Gasoline Formulae: Simulation Study. Arab J Sci Eng 38, 2855–2864 (2013). https://doi.org/10.1007/s13369-012-0474-0
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DOI: https://doi.org/10.1007/s13369-012-0474-0