Diesel cetane number estimation from NIR spectra of hydrocracking total effluent

The work shown in this paper offers a fast and efficient alternative for estimating the cetane number of the diesel obtained from the distillation of the hydrocracking total effluent. In this study, the estimation of this diesel property was achieved through a partial least squares regression (PLSR) model using only the NIR spectrum of the hydrocracking total effluent. For calibrating and validating the PLS model, it was used a database containing the NIR spectra acquired on 98 total effluent samples and the cetane number measured on the 98 diesel fractions recovered from each total effluent sample distillation. The database was divided into the calibration and test data sets using the Kennard-Stone algorithm. The regression model developed exhibited good performance in estimating the studied property with errors of calibration (1.3), cross-validation (2.2), and prediction (2.0), close to the reproducibility of the reference method (+/- 3.6). The alternative method for diesel cetane number estimation discussed in this article evidences its feasibility in optimizing diesel fuel characterization by reducing the necessity of the total effluent distillation. Furthermore, the results also show the potential of the alternative proposed to be applied in predicting other properties of fuels obtained from the hydrocracking process.

Automated summary

This paper presents a fast and efficient method for estimating the cetane number of diesel fuel obtained from hydrocracking total effluent using near-infrared (NIR) spectrum. The method utilizes a partial least squares regression (PLSR) model and a calibrated database to achieve accurate estimations. The alternative method not only optimizes diesel fuel characterization but also shows potential for predicting other fuel properties.