Data-Driven Based Temperature Prediction of Ferroalloy Electric Furnace Smelting Process

Aiming at the features of nonlinearity, time delay, high dimension, big noise and others in the electric furnace smelting process, and the smelting process parameters some control process needs cannot be obtained by sensors, so the online parameter prediction method is of great significance for dynamic optimization control of system. In this paper, by using chaos theory and phase space reconstruction method, based on the idea of data-driven modeling, the temperature prediction model of crucible in the smelting process of ferroalloy electric furnace is established based on multivariate time series. Experiments show that the established data-driven prediction model can well predict the temperature change in the electric furnace smelting process, the root mean square errors of prediction are respectively less than 0.0715,and this has good guidance for practical production.