粒子群优化支持向量机的BOF用氧量预报研究

Study on Blowing Oxygen Volume Prediction of BOF Steelmaking Based on PSO-SVM

针对BOF用氧量预报模型精度不高的问题,提出利用粒子群优化支持向量机的用氧量预报建模方法,以此来优化用氧量,进而提高钢液的质量。结果表明,与RBF、BP及SVM相比,粒子群优化支持向量机的BOF吹氧量预报模型精度高、推广能力强。

Aiming at the low accuracy of prediction models for BOF blowing oxygen volume, the modeling method of blowing oxygen volume prediction using particle swarm optimization support vector machine was proposed. The results show that, comparing to other algorithms, such as RBF, BP and SVM, the prediction model of BOF blowing oxygen volume using particle swarm optimization support vector machine has a higher modeling accuracy and better generalization ability.