An End-to-End Multisource Information Fusion Framework for f-CaO Content Soft Sensing in Cement Clinker Burning Process

Abstract

In the cement clinker burning process, the soft sensing of free calcium oxide (f-CaO) content has been a challenging task due to the dynamic time delay between f-CaO content and process variables, the different time scale between process variables and f-CaO content and the strong nonlinearity of the process data. With the development of data-driven modeling techniques, numerous soft sensing methods for f-CaO content based on process data have emerged. However, under this circumstance, the monotony of soft sensor input may become a bottleneck that limits further improvement of the f-CaO prediction performance. To address this issue, this paper proposes a novel end-to-end multi-source information fusion framework (MSIFF) for soft sensing the f-CaO content within the cement clinker. The MSIFF takes process data and flame images as inputs, and utilizes mechanistic knowledge by generating mechanistic features from process data using a first-principle rotary kiln model. The explainable dynamic features are extracted from the matched process data and flame image sequences with a multi-source dynamic feature extraction network (MSDFE), which further participates in the end-to-end modeling of f-CaO content together with the mechanistic features. The proposed MSIFF method is validated on a real cement production line. While providing valuable operating information for the cement clinker burning process, the MSIFF exhibits an improved f-CaO soft sensing performance compared to existing f-CaO estimation methods.