APPLIED RESEARCH ON NEW MULTI-FUNCTION GASOLINE ADDITIVE

In order to assess the performance of a new cleansing and combustion-improving gasoline additive （MAZ）, and to explore the evaluation methods of additives, two engines with the same model number and performance indices, fueled with and without the MAZ gasoline additive respectively, are carried through 100 h strenuous tests on a bench. The results obtained in full load characteristic and load characteristics of different operational modes are compared. It indicates that the power, economy and emission of the engine fueled with the MAZ additive all have obvious improvement in comparison with the engine without adding the additive： the power increasing by 16.43%, specific fuel consumption （SFC） decreasing 5.39%, and the emission of CO, HC and NOx falling by 28.61%, 54.38% and 10.1% respectively. Wear and tear of the engine cylinder is weakened, and sediment of combustion chamber inner side is reduced. In addition, no negative effect on the catalytic conversion device is found.

Gasoline-derived methyl tert-butyl ether as a potential obesogen linked to metabolic syndrome

The power and efficiency of gasoline engines is often improved through the use of fuel with high octane ratings.The octane rating of fuel could be further increased with oxygenate additives such as alcohols and ethers,with methyl tert-butyl ether(MTBE)being one of the most common gasoline additives.

Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach

A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives,for which the knowledge of the fuels’characteristic properties forms the backbone.The aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes(YSI).In conjunction with our previously developed database for gasoline compounds,which includes the physical and chemical properties,such as octane numbers,laminar burning velocity,and heat of vaporization,for more than 600 species,the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition engines.For this purpose,a quantitative structure-property relationship(QSPR)model is developed to predict the YSI of fuel species by using artificial neural network(ANN)techniques with 21 well-selected functional group descriptors as input features.The model is trained and cross-validated with the YSI database reported by Yale University.It is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular groups.In addition,the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these properties.Finally,a selection of potential gasoline blending components is carried out exemplarily,by taking the fuels’potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI,respectively.