Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals
2015
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.
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