Prediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data

Abstract

A model for the prediction of the derived cetane number (DCN) and carbon/hydrogen ratio (C/H) of hydrocarbon mixtures, diesel fuels, and diesel−gasoline blends has been developed on the basis of infrared (IR) spectroscopy data of pure components. IR spectra of 65 neat hydrocarbon species were used to generate spectra of 127 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 44 real fuels were calculated using n-paraffin, isoparaffin, olefin, naphthene, aromatic, and oxygenate (PIONA-O) class averages of pure components. It is shown that this strategy retains knowledge of C/H, an important indicator of the chemical structure. Three methods were compared to assess the prediction of DCN and C/H ratio from the assembled IR spectra, i.e., partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). It was found that ANNs gave the best performance with DCN prediction errors of ±1.1 on average and C/H prediction errors of ∼0.8%. Lasso-regularized linear models were also used to find simple combinations of wavenumbers that yield acceptable estimations.

Formulation of 7-Component Surrogate Mixtures for Military Jet Fuel and Testing in Diesel Engine

“…), − 1 H NMR to get carbon types, − 13 C NMR, and physical properties . ASTM methods (ASTM D976 or D4737) estimate CN using density and distillation curve temperature information and call the estimate a cetane index. , Machine learning and neural networks have also been used to predict the CN of diesel and gasoline from IR spectroscopy data, , the CN of hydrocarbons with and without oxygenates using molecular descriptors (e.g., polar surface area, average bond enthalpy, etc. ), , major chemical categories, functional group classification within molecules (e.g., aromatic bond, double bond, quaternary carbon, etc.…”

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

“…303 Fuel blend components can be represented as paraffinic, iso-paraffinic, olefinic, naphthenic, aromatic, and oxygenate (PIONA-O) class averaged approximations. 304,305 Chemometric models may be used to capture nonlinear functions as is the case in the prediction of the derived cetane and octane numbers. 304−310 While it is practical to deal with fuels in the liquid phase, increased intermolecular forces can cause deviations from the Beer−Lambert law.…”

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

“…It can record spectra in real time to capture process properties for analyzing the composition of substances in chemical mixtures. Moreover, it has shown great potential in the prediction of qualitative and quantitative properties in a wide range , (e.g., agricultural products, plants, biomedicals, and pharmaceutical samples). Numerous methods, including principal component regression, multivariate linear regression, partial least squares (PLS), neural network, nonlinear PLS, and locally weighted regression, have been proposed to determine the presence of a linear or nonlinear relationship with NIR spectral data. − …”