Virtual Sensing f-CaO Content of Cement Clinker Based on Incremental Deep Dynamic Features Extracting and Transferring Model

Abstract

The content of free calcium oxide (f-CaO) in clinker significantly determines the quality of the final cement production. However, in practice, the value of f-CaO content in clinker is off-line sampled manually with a significant time interval and then analyzed in a laboratory with a large time delay, which could meet the needs for monitoring and control of cement quality. To tackle this problem, this article proposes a data-driven model based on deep dynamic features extracting and transferring methods to build a virtual sensor for f-CaO content prediction. First, in this model, large-scale unlabeled data collected from the process distributed control system (DCS) take a vital effect in extracting nonlinear dynamic features along with the limited labeled data samples. Then, the extracted features are transferred to a powerful regression model, the eXtreme Gradient Boosting (XGBoost), for output f-CaO prediction. Besides, an incremental model updating strategy is proposed for the augment of online data samples, which facilitates the virtual sensor to adapt the process time-variant characteristics. Finally, the proposed virtual sensor is verified by a data set acquired from a real cement production process. Comparing with traditional statistical modeling methods, the prediction accuracy of f-CaO content is significantly improved.