Blast furnace molten iron silicon content prediction method and device based on LSTM & DNN

Abstract

The invention discloses a blast furnace molten iron silicon content prediction method and a device based on LSTM & DNN. The method comprises the following steps: dividing for obtaining a time lag attribute, a related attribute and a redundancy attribute based on a Pearson correlation coefficient; respectively constructing an LSTM blast furnace silicon content model and a DNN blast furnace siliconcontent model by utilizing the divided attribute data; performing weighted fusion on the LSTM model and the DNN model through a BP neural network to obtain a blast furnace molten iron silicon contentprediction model; according to the method, attribute division is carried out on the basis of the Pearson correlation coefficient, redundant attributes are removed, and the correlation attributes are stripped, so that the pressure of the LSTM model can be effectively relieved, the calculation speed is increased, and the model prediction effect is improved; the long-term and short-term memory capability of the LSTM network is utilized to effectively solve the large time lag characteristic of the blast furnace data; the DNN model is used for mining high-dimensional features of related attributes,so that the LSTM & DNN-based blast furnace molten iron silicon content prediction model has memory ability and generalization ability.

Description

technical field [0001] The invention belongs to the field of predicting the silicon content of blast furnace hot metal, and in particular relates to a method and device for predicting the silicon content of blast furnace hot metal based on LSTM&DNN. Background technique [0002] In the blast furnace ironmaking process, the thermal state of the blast furnace is the decisive factor for the quality of pig iron, but it is difficult to accurately measure the internal thermal state of the complex blast furnace system with existing technologies. The thermal state of the blast furnace is linearly related to the silicon content in molten iron. Therefore, it is of great significance to grasp the change trend of the silicon content. However, limited by the harsh environment inside the blast furnace and the production process, the silicon content in molten iron cannot be measured in real time, which is not conducive to making timely adjustments to the operating parameters of the blast f...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a method and device for predicting silicon content in blast furnace molten iron based on LSTM&DNN, which mainly aims at the characteristics of non-linearity and large time lag of blast furnace data, overcomes the difficulty that a single model cannot take into account, and enables the prediction model to have a dynamic system model at the same time The memory ability and the generalization ability of the deep neural network, and ensure that the model has good prediction accuracy and processing speed

Images