Soft Sensor Model of f-CaO Content in Cement Clinker Based on Self-Attention and Time Convolutional Network

The quality of cement clinker is strongly linked to its free calcium oxide (f-CaO) content. Therefore, real-time detection of f-CaO content is crucial for reducing energy consumption and stabilizing clinker quality. This work presents a Temporal Convolutional Network (TCN) that incorporates a self-attention mechanism for handling coupled time-series data from process variables. This model utilizes TCN to capture the time series coupling relationship among multiple input variables and extract multivariable time series features that affect f-CaO content. On this basis, a self-attention mechanism is introduced to focus on nonlinear features that have a significant impact on the output variable. The self-attention mechanism enhances the model’s ability through three key aspects: dynamic feature weighting, global context awareness, and interpretable feature selection. Combined with TCN’s time feature extraction, a robust f-CaO content prediction framework is constructed. Finally, a mapping relationship between nonlinear features and output is established through a fully connected layer, enabling real-time measurement of f-CaO content. Experimental comparisons with existing deep learning-based soft sensors demonstrate the superior performance of our model.