Prediction of oxygen consumption in steelmaking based on LAOA-TSVR

Abstract

To solve the issue of oxygen consumption forecasting, the researchers suggested a twin support vector machine for regression (LAOA-TSVR) prediction model based on an improved arithmetic optimization algorithm. The model has beneficial generalization, high prediction accuracy, and the ability to jump out of the local optimum and other characteristics. The group used the method of mechanism analysis to determine the main influencing factors of oxygen consumption. To confirm the model’s prediction effect, it is compared to the Back Propagation, Radial Basis Function, and Twin Support Vector Regression prediction models. The LAOA-TSVR oxygen consumption forecasting prediction model was then tested on actual steel mill production. The test phase consisted of 200 production cycles, and the results revealed that the LAOA-TSVR model had an 85,1 % hit rate for oxygen consumption within 5 m3/t. The model can suit the actual needs of predicting oxygen consumption in steel.

Keywords

• steelmaking oxygen consumption forecasting hit rate industrial test
• LAOA-TSVR