An End-to-End Multisource Information Fusion Framework for f-CaO Content Soft Sensing in Cement Clinker Burning Process
Yuchen Zhao, Chunjie Yang, Hang Xiao, Yaoyao Bao, Siwei Lou, Jiayun Mao, Huanyu Liao
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.