关键词: 软测量, 模型, 优化, 数据驱动融合, 近红外

Abstract: Training sample selection is a key step to establish soft sensor models. According to traditional selection methods, the information of dependent variables cannot be used well. In addition, it is difficult to evaluate the impact of training samples on soft sensor models. To handle these issues, in the present paper, a new training sample selection strategy based on intelligent optimization algorithms was proposed. The objective function of our proposed method was combined with a loss function and a compression-ratio operator of training samples which can tune the direction of searching. The advantage is that it can make full use of dependent variables and the effectiveness of training samples in a certain soft sensor model. As a result, it can optimize the structure of selected training samples. The performance of our proposed methods was demonstrated by its practical applications on research octane number(RON)-near infrared(NIR) spectrum data set, which were selected from gasoline blending process. Besides, a diesel NIR spectrum benchmark data set were also provided. Based on these data sets, we analysis and discuss the impact of training samples on soft sensor model, some useful results were gained. Compared with traditional partial least squares method(PLS), locally weighted PLS, and several other modeling strategies, the proposed method was found to achieve good accuracy and robustness, it is very suitable for industrial application.

Key words: soft sensor, model, optimization, data driven fusion, near-infrared spectroscopy