SA-MSIFF: Soft sensing the cement f-CaO content with a self-adaptive multisource information fusion framework in clinker burning process

Abstract

The accurate soft sensing of f-CaO content in cement clinker is crucial for the cement industry. However, existing methods require improvements in terms of effectiveness, practicality, and computational efficiency for industrial applications. Responding to these needs, this paper proposes a self-adaptive multisource information fusion framework (SA-MSIFF) for f-CaO content soft sensing. The SA-MSIFF utilizes a dynamic rotary kiln model for independent and real-time calcination state estimation and mechanistic feature generation, along with a dilated 3D convolution and attention-based network for direct feature extraction from flame image sequences. Subsequently, a temporal–spatial feature extraction and fusion (TSFE&F) network is introduced to utilize the multisource feature series for f-CaO content soft sensing. Offline experiments validate the SA-MSIFF’s ability to adaptively extract features from multisource information. Compared to its previous version, MSIFF, the SA-MSIFF achieves a considerable 89.65% reduction in framework training time and an 8.22% decrease in soft sensing error. The SA-MSIFF’s effectiveness is also demonstrated in its engineering applications.