Determination of diesel cetane number by consensus modeling based on uninformative variable elimination

Abstract

Consensus modeling based on improved Boosting algorithm (Boosting-PLS, BPLS) combined with wavelength (variable) selection by MC-UVE (Monte Carlo-Uninformative Variable Elimination) method is applied to determination of cetane number (CN) of diesel. MC-UVE is firstly used to select characteristic variables from Near-infrared (NIR) spectra of diesel based on principles of MC simulation and UVE, and then the selected variables instead of the full spectra are used for BPLS modeling to predict results. From predicted results, the proposed MC-UVE-BPLS algorithm improves the performance of conventional linear PLS modeling in terms of accuracy and robustness, so it is more efficient and parsimonious with few numbers of useful variables when applied to the relationship between CN and diesel NIR spectra. Simultaneously, the prediction results of MC-UVE-BPLS compared with those of MC-UVE-PLS, BPLS and CPLS (Consensus modeling based on Bagging) show that MC-UVE-BPLS is superior to other models, and also verifies the efficiency of MC-UVE and improved BPLS. So the proposed MC-UVE-BPLS method provides a new approach for determination of diesel CN by NIR spectra.