English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The stability of blast furnace temperature is an important condition to ensure the efficient production of hot metal. Accurate prediction of silicon content in hot metal is of great significance to the control of blast furnace temperature in iron and steel plants. At present, the accuracy of most silicon prediction models can only be guaranteed when the furnace condition is stable. However, due to many factors affecting the silicon content in hot metal of blast furnace, and there are large noises, large delays and large fluctuations in the data, the previous prediction results are of limited guiding significance to the actual production. In this paper, combined with the actual situation, the convolution neural network is used to extract the furnace condition characteristics, and then combined with the attention mechanism and the IndRNN model to get the prediction results, so that the prediction can better adapt to the fluctuating data set. The experimental results show that the prediction error of this model is lower than that of other models, which provides a new solution for the research of silicon content in hot metal of blast furnace.

Silicon content prediction of hot metal in blast furnace based on attention mechanism and CNN-IndRNN model