This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Assessing the Combined Outcome of Rice Husk Nano Additive and Water Injection Method on the Performance, Emission and Combustion Characters of the Low Viscous Pine Oil in a Diesel Engine
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on October 22, 2019 by SAE International in United States
The research work intends to assess the need and improvement of using a low viscous bio oil, RH (Rice Husk) Nano Particles and water injection method in enhancing the performance, emission and combustion characters of a diesel engine. One of the major setbacks for using biodiesel was its higher viscosity. Hence, a low viscous oil (Pine oil) which doesn’t need transesterification process was used as a biofuel in this study. To further improve its characteristics a non-metallic Nano additive produced from rice husk was added at 3 proportions (50, 100, 200 ppm) and the optimal quantity was found as 100ppm based on the BTE (brake thermal efficiency) value of 30.2% at peak load condition. This efficiency value was accompanied by a considerable decrease in pollutants like HC (Hydrocarbon), Smoke, CO (Carbon monoxide). On the contrary NOx (Oxides of Nitrogen) emission was found to be increased for all load values. At peak load, when compared with diesel, pine oil with RH has 19.2% increased NOx emission. To reduce this increased NOx emission, water was injected along with the incoming fresh air at 1%, 2% and 3% by volume. Pine oil provided better Peak pressure and heat release rate values which was well aided with the RH additive. The RH additive helped in providing better performance and emissions results also. The negative effect being the increased amount of NOx formation, which was reduced considerably (-12.15%) by using water injection process. 2% of water injection was found to be a well-balanced quantity, that does not greatly affect the performance and other emissions. Additionally, ANN (Artificial Neural Network) was used as a theoretical analysis. The network was trained and validated using the known experimental values to help in predicting unknown values during necessity.