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Exploring the Preparation and Usage of Low Viscous Pine Oil and Rice Husk Nano Additives in a CRDI Engine under the Effect of Water Emulsion
Technical Paper
2020-01-2128
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In the search for an alternate energy source, biodiesel, a denser and viscous fuel that needs transesterification process have been widely used in diesel engines. However, in this study, a low viscous bio oil utilisation was analysed along with a non-metallic Nano additive (rice husk). Pine oil is obtained from steam distillation of the pine tree parts like cones and spines. Rice husk (RH) Nano additive is ground using planetary ball milling to reduce the size to 250nm. The test is conducted in a common rail direct injection diesel engine for different load values (0, 25, 50, 75 and 100%). Considering the brake thermal efficiency (BTE), 100mg/l and 5% were selected as the best ratios after testing 4 proportions (10, 30, 50, 100mg/l) of RH and 3 concentrations (1, 3, 5 and 8% by vol.) of water addition. The pine oil in this optimum ratio produced 30.12% BTE in contrast to the 30.67% of diesel fuel. This also accompanied with the very slight increase (1.27%) of fuel consumption and reduction of HC (26.32%), CO (31.14%) and smoke (22.4%) compared with diesel fuel usage. Oxides of nitrogen emission also reduced by 14.69% than diesel fuel with the inclusion of 3% by vol. of water in the form of emulsion. In the emulsification process, Span-80 surfactant was used in this study. This study shows that with the usage of a low viscous oil, the transesterification process is eliminated and the Nano additive helps in the improvement of the combustion process. Emulsion helps in the reduction of NOx emission. Hence, this blend can be effectively used to reduce the dependency on the fossil fuel as well as to reduce the harmful emissions.
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Panithasan, M., "Exploring the Preparation and Usage of Low Viscous Pine Oil and Rice Husk Nano Additives in a CRDI Engine under the Effect of Water Emulsion," SAE Technical Paper 2020-01-2128, 2020, https://doi.org/10.4271/2020-01-2128.Data Sets - Support Documents
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