A Data Mining Approach To Support The Development Of New Fuels And Technology
Free (open access)
G. S. Terra, C. L. Curotto & N. F. F. Ebecken
In the present work, data mining techniques are used to model the non trivial relationships between properties that characterize the fuels, engine technologies and car emissions. Using models to predict car emissions from fuel properties and technologies engines can improve their development process. To support models of relational data, using the Object Linking and Embedding Database for Data Mining technology, a Simple Naive Bayes Incremental classifier was implemented in Microsoft® SQL Server™, supporting numeric input attributes, multiple prediction attributes and incremental update of data. Computational experiments using real word data sets were made to evaluate the results obtained by this classifier. 1 Introduction The automobile industries are stimulated to develop an efficient car, with new technologies, that improves the autonomy and reduce the pollutant emission. The downstream oil company also contributes when develops new fuels that provides a positive interaction with those new car technologies. The fuel characteristics are modified when additives are incorporated in their formulation or changes are made in the proportion of their basic composition. During the fuel development phase different combinations are tested in cars where theirs emissions are measured in controlled tests. The results were stored in a database and a classification model was built to improve this research area.