Novel adaptive sample space expansion approach of NIR model for in-situ measurement of gasoline octane number in online gasoline blending processes

Abstract For real-time measurement of gasoline octane number in online gasoline blending processes by near infrared (NIR) spectroscopy, this paper presents an adaptive sample space expansion approach (ASSEA) to improve the real-time measurement accuracy and to shorten the time for establishing the model. To improve the adaptability of the model, a comprehensive index that combines the characteristics of the query samples and the distance between the spectra is adopted to select the guide spectra. A sample construction method is proposed to supplement the original sample set with insufficient sample size. The new spectra are constructed with the labeled spectra based on the guide spectra. Subsequently, the concentration information of the constructed spectra is obtained on the basis of multi-way partial least square method. The proposed ASSEA method is compared with six representative methods. The prediction performance of ASSEA method (RMSE is 0.2442, and R2 is 0.8767) is significantly better than other methods. And compared with the traditional PLS method, RMSE and R2 are reduced and increased by 33.4% and 45.9%, respectively. Therefore, its efficiency is validated through an industrial gasoline blending process.