System and method for predicting alkalinity of silicon-manganese alloy smelting slag

Abstract

The invention provides a silicon-manganese alloy smelting slag alkalinity prediction system and method. The system comprises a data acquisition module which is used for acquiring test data of raw material ore, proportion data of the raw material ore and ingredients and test data of silicon-manganese alloy and slag after smelting is finished; the data calculation module is used for calculating chemical components and product indexes of the ingredients; the model training module is used for training a neural network model according to the calculated numerical value to obtain a prediction model; the data preprocessing module is used for performing mapping reconstruction on the ingredient data and the product data with lagging through time cross-correlation analysis, and extracting characteristic values of the ingredient data and the product data through characteristic engineering; and the prediction module is used for inputting the characteristic value into a prediction model and predicting the alkalinity of the slag at the future moment. According to the method, the time delay of the feeding moment and the smelting ending moment is fully considered, the model trained by the processed data can correctly reflect the relationship between ingredients and products, and the calculation precision is high.

Description

technical field [0001] The invention relates to the technical field of slag component prediction of silicomanganese, in particular to a system and method for predicting the basicity of slag in smelting of silicomanganese alloys. Background technique [0002] The composition of silicomanganese slag is an important factor affecting the technical and economic indicators of smelting. It is very important to select the appropriate slag composition according to the raw materials and equipment conditions. Although the conditions for smelting silicon-manganese alloys are different, the general principle is to have suitable melting point, viscosity and electrical conductivity to ensure good technical and economic indicators. Among them, the slag basicity is the most important index in the slag composition. Appropriate basicity is one of the important conditions for improving manganese recovery rate, increasing output and reducing smelting power consumption. Slag basicity is defined ...

AI Technical Summary

Problems solved by technology

However, this patent cannot predict the change trend and range of slag alkalinity in advance before confirming that the raw material ore is put into the submerged arc furnace, and cannot realize the optimization of the operation of the submerged arc furnace and solve the feedback Untimely problems, ultimately improving the output of submerged arc furnaces and the quality of silicon-manganese alloys

Images