Enhanced just-in-time modelling for online quality prediction in BF ironmaking

Abstract

Various data driven soft sensor models have been established for online prediction of the silicon content in blast furnace ironmaking processes. However, two main disadvantages still remain in these empirical models. First, most of traditional outlier detection methods for preprocessing the data samples assume that they (approximately) follow a Gaussian distribution and thus may be invalid for some situations. To address this problem, a support vector clustering (SVC) based efficient outlier detection method is proposed whereby the process nonlinearity and non-Gaussianity can be better handled. Second, only using a single global model is insufficient to capture all the process characteristics, especially for those complicated regions. In this paper, a reliable just-in-time modelling method is proposed. The SVC outlier detection is integrated into the just-in-time-based local modelling method to enhance the reliability of quality prediction. A healthier relevant data set is constructed to build a more reliable local prediction model. Moreover, the historical data set is updated repetitively in a reasonable way. The superiority of the proposed method is demonstrated and compared with other soft sensors in terms of online prediction of the silicon content in an industrial blast furnace in China.

Keywords:

• Industrial ironmaking process
• Reliable soft sensor
• Support vector clustering
• Outlier detection
• Just-in-time learning model
• Historical set maintenance