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Evaluation of Anti-Knock Quality of Dicyclopentadiene-Gasoline Blends
Technical Paper
2017-01-0804
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Increasing the anti-knock quality of gasoline fuels can enable higher efficiency in spark ignition engines. In this study, the blending anti-knock quality of dicyclopentadiene (DCPD), a by-product of ethylene production from naphtha cracking, with various gasoline fuels is explored. The blends were tested in an ignition quality tester (IQT) and a modified cooperative fuel research (CFR) engine operating under homogenous charge compression ignition (HCCI) and knock limited spark advance (KLSA) conditions. Due to current fuel regulations, ethanol is widely used as a gasoline blending component in many markets. In addition, ethanol is widely used as a fuel and literature verifying its performance. Moreover, because ethanol exhibits synergistic effects, the test results of DCPD-gasoline blends were compared to those of ethanol-gasoline blends. The experiments conducted in this work enabled the screening of DCPD auto-ignition characteristics across a range of combustion modes. The synergistic blending nature of DCPD was apparent and appeared to be greater than that of ethanol. The data presented suggests that DCPD has the potential to be a high octane blending component in gasoline; one which can substitute alkylates, isomerates, reformates, and oxygenates.
Authors
- Mohannad Al-Khodaier - King Abdullah University of Science and Technology
- Vijai Shankar Bhavani Shankar - King Abdullah University of Science and Technology
- Muhammad Waqas - King Abdullah University of Science and Technology
- Nimal Naser - King Abdullah University of Science and Technology
- Mani Sarathy - King Abdullah University of Science and Technology
- Bengt Johansson - King Abdullah University of Science and Technology
Citation
Al-Khodaier, M., Bhavani Shankar, V., Waqas, M., Naser, N. et al., "Evaluation of Anti-Knock Quality of Dicyclopentadiene-Gasoline Blends," SAE Technical Paper 2017-01-0804, 2017, https://doi.org/10.4271/2017-01-0804.Data Sets - Support Documents
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