Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending

Abstract Gasoline is one of the major products of oil and petrochemical industry. Blending is the final step and key to improve the efficiency of gasoline production. As an important property that can reflect the quality of gasoline products, the research octane number (RON) of final products has been widely used to evaluate the running status of gasoline blending. However, the real-time and direct acquisition of RON from this process is difficult because it have to be determined by running the fuel in a test engine with a variable compression ratio under controlled conditions. This work proposes a data-driven soft sensor based on near-infrared (NIR) spectroscopy for online RON estimation. A modified semisupervised Gaussian mixture algorithm is adopted to automatically discover meaningful modeling samples and initialize the quality prediction model. Besides, a monitoring model is integrated into the quality prediction sensor to monitor the running status and the accuracy of the NIR-based quality prediction sensor. Datasets from a numerical experiment and industrial gasoline blending are provided to reveal the effectiveness and superiority of the proposed method.