A novel adaptive algorithm with near-infrared spectroscopy and its application in online gasoline blending processes

Near-infrared (NIR) spectroscopy has been widely used to estimate product qualities. Although numerous studies on NIR modeling methods have been conducted, few papers have reported the online application of an NIR spectrometer in the gasoline blending process. This study presents a novel adaptive modeling method to establish an NIR model for the gasoline blending process. This method is based on the local learning and recursive modeling framework. Based on the framework, the proposed method can adjust the model structure from two aspects: (i) in sampling intervals, the model is updated with a local learning strategy, and the weights of the training samples can be gradually adjusted; and (ii) when new reference samples become available, the new data pairs are selected and added to the training data set based on an effective evaluation mechanism. The high performance of the proposed algorithm was demonstrated through a spectroscopic data set from a real gasoline blending process. The research octane number (RON), as the most important properties of gasoline, was estimated. Several modeling methods such as recursive partial least squares (RPLS), partial least squares (PLS), and locally weighted PLS were utilized for comparison. The results show that the proposed approach produce more accurate results than the traditional RPLS and locally weighted PIS algorithms. (C) 2014 Published by Elsevier B.V.