Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals

Abstract This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using 1 H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6 °C was obtained in comparison with 15.6 °C for PLS, 15.1 °C for ePLS and 28.4 °C for SVR. The RMSEPs for T50% were 24.2 °C, 23.4 °C, 22.8 °C and 14.4 °C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0 °C, 39.9 °C and 39.9 °C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS.