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  • Title: Prediction and optimization of CI engine performance fuelled with Calophyllum inophyllum diesel blend using response surface methodology (RSM).
    Author: Venugopal P, Kasimani R, Chinnasamy S.
    Journal: Environ Sci Pollut Res Int; 2018 Sep; 25(25):24829-24844. PubMed ID: 29931633.
    Abstract:
    The transportation demand in India is increasing tremendously, which arouses the energy consumption by 4.1 to 6.1% increases each year from 2010 to 2050. In addition, the private vehicle ownership keeps on increasing almost 10% per year during the last decade and reaches 213 million tons of oil consumption in 2016. Thus, this makes India the third largest importer of crude oil in the world. Because of this problem, there is a need of promoting the alternative fuels (biodiesel) which are from different feedstocks for the transportation. This alternative fuel has better emission characteristics compared to neat diesel, hence the biodiesel can be used as direct alternative for diesel and it can also be blended with diesel to get better performance. However, the effect of compression ratio, injection timing, injection pressure, composition-blend ratio and air-fuel ratio, and the shape of the cylinder may affect the performance and emission characteristics of the diesel engine. This article deals with the effect of compression ratio in the performance of the engine while using Honne oil diesel blend and also to find out the optimum compression ratio. So the experimentations are conducted using Honne oil diesel blend-fueled CI engine at variable load conditions and at constant speed operations. In order to find out the optimum compression ratio, experiments are carried out on a single-cylinder, four-stroke variable compression ratio diesel engine, and it is found that 18:1 compression ratio gives better performance than the lower compression ratios. Engine performance tests were carried out at different compression ratio values. Using experimental data, regression model was developed and the values were predicted using response surface methodology. Then the predicted values were validated with the experimental results and a maximum error percentage of 6.057 with an average percentage of error as 3.57 were obtained. The optimum numeric factors for different responses were also selected using RSM.
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