For the missing information problem caused by faulted temperature sensor in temperature detecting system on blast furnace (BF) wall

a soft-sensing method based on dynamic neural network is proposed. Firstly

the temperature sensor position model and regional temperature measurement model are built based on the structure of BF. Then

according to heat transfer mechanism

the correlation between temperature sensors in regional temperature measurement model is quantitatively calculated by using maximal information coefficient (MIC) method. Finally

the soft-sensing model for missing temperature information is proposed by using Elman neural network to identify the structure of the model. The effectiveness and feasibility of the proposed method is proved by the simulation results of the real-time producing data of blast furnace which satisfies the field detection accuracy requirement.