Particle swarm optimization and ordered predictors selection applied in NMR to predict crude oil properties

We used spectral information from nuclear magnetic resonance (1H NMR and 13C NMR) of nearly 150 Brazilian crude oil samples to predict some physicochemical properties. We build models to estimate API gravity (API), standardized kinematic viscosity at 50 °C (VISst), heat combustion value (HCV), total acid number (TAN), saturates (SAT), aromatics (ARO), resins (RES) and asphaltenes (ASF) content. To obtain accurate models, particle swarm optimization (PSO) and ordered predictors selection (OPS) were applied as variable selection techniques coupled to partial least squares (PLS) regression. PSO-PLS and OPS-PLS hybrid models presented higher predictive capacity than PLS regression models. We were able to find the most relevant signal areas of the NMR spectra for each property. The best results of SAT, ARO and RES content were obtained with PSO-PLS of 13C NMR dataset with root mean squared error of prediction (RMSEP) of 4.54, 2.85 and 4.08 (wt%), respectively. For API, VISst and TAN the RMSEP were equal to 0.74 (API), 0.02 and 0.16 (mg KOH·g−1), respectively, using OPS-PLS method and 1H NMR dataset. The more accurated models for ASF contents and HCV were built with PSO-PLS of 1H NMR, with RMSEP of 0.59 (wt%) and PSO-PLS of 13C NMR with RMSEP of 0.64 (MJ·kg−1), respectively. However, these two properties presented a coefficient of determination for the prediction set (R2p) lower than 0.75, which means they are not very well adjusted to the regression model. Furthermore, using 13C NMR dataset, OPS-PLS models has shown similar results than PSO-PLS models, for some properties.