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.
Funder
National Natural Science Foundation of China
China Scholarship Council
Cited by
1 articles.
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