Abstract
In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.
Highlights
The interest in the prediction of biodegradability of base oils using their chemical structure and/or chemical and physical characteristics stems from a threefold motivation
The objective of this paper is to uncover new modeling techniques that would improve base oils biodegradability through testing of a large variety of data mining techniques using the chemical and physical characteristics of 63 base oils’ data analyzed by Haus et al [1]
Several data mining techniques including continuous and binary classification techniques were applied to the prediction of base oil biodegradability using three of their most significant predictive characteristics, namely Paraffinic Carbon (PC), KV and NV
Summary
The interest in the prediction of biodegradability of base oils (e.g., motor oils and lubricants) using their chemical structure and/or chemical and physical characteristics stems from a threefold motivation. Predicting base oil biodegradability before they are produced, tested and used will make these imperatives easier to meet, and the development of environmentally friendlier oils all the more feasible. This problem has so far eluded the search for an adequately accurate solution. The objective of this paper is to uncover new modeling techniques that would improve base oils biodegradability through testing of a large variety of data mining techniques using the chemical and physical characteristics of 63 base oils’ data analyzed by Haus et al [1]. The state of the art of biodegradability modeling is first reviewed
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