Res-BiLSTMs model based on multi-task attention for real-time measurement of the free calcium oxide content

Abstract

Abstract The content of free calcium oxide (f-CaO) is the primary economic index to evaluate the quality of cement. A residual bidirectional long short-term memory network model (Res-BiLSTMs) based on a multi-task attention mechanism was proposed for the characteristics of cement clinker production, used for online monitoring f-CaO content. The model utilizes the Bi-LSTM as the foundational component and combines the residual network to construct the Res-BiLSTMs coding structure, which aims to summarize the multi-level characteristic information of the input sequence. Additionally, a multi-task attention mechanism is introduced, combining the attention mechanism with semi-supervision to extract control coupling and data coupling among devices and variables. The results demonstrate that the addition of the multi-task attention mechanism led to a reduction in model errors by 0.0175 and 0.022, respectively, and an improvement in the degree of fit by 14.61%. The effectiveness of the multi-task attention mechanism for quality monitoring is confirmed. Compared to traditional LSTM, this model exhibited a reduction in errors by 0.0469 and 0.019, respectively, an increase in the correlation coefficient by 45.37%, and outperformed all other models in the comparison. The model’s measurement performance under limited labeled samples is also validated.