Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy

Based on near-infrared (NIR) spectroscopy, a new quantitative calibration algorithm, called Partial Least Squares Regression Residual Extreme Learning Machine (PLSRR-ELM), was proposed for fast determination of research octane number (RON) for blended gasoline. In this algorithm, partial least square (PLS) cooperates with non-linear extreme learning machine (ELM) to separate the relationship information suitable for each other from the raw relationship information (between NIR spectrum and corresponding property) with the unknown degree of non-linearity, with aim of calibrating them respectively. Since the advantages of both PLS and ELM are fully utilized, it is expected that PLSRR-ELM can address the relationship information more effectively and leads to improved calibration performance over PLS and ELM alone. The calibration performance of PLSRR-ELM was evaluated by a set of on-line gasoline blending sample data from a refinery. As a result, it showed an enhanced prediction performance, e.g., about 13% or 11% decrease in the root mean squared error of test (RMSE-T) over PLS or ELM alone, respectively. In method comparison, the model performance of PLRR-ELM exceeds all other methods including PLS, Poly-PLS, KPLS, ELM, and ANN, demonstrating its superiority for fast prediction of gasoline RON.

Automated summary

The new quantitative calibration algorithm PLSRR-ELM utilizes the advantages of partial least squares (PLS) and non-linear extreme learning machine (ELM) to effectively handle relationship information, leading to improved calibration performance for determining the research octane number (RON) of blended gasoline.