摘要
转炉冶炼过程包含着复杂的多相、高温的物理化学反应,建立可靠的转炉终点预测模型对有效减少钢水成分波动、提高钢铁品质有重要的意义。以某钢厂200 t转炉实际生产数据为依据,采用粒子群优化算法选取支持向量机模型最优惩罚参数C和核参数g的方法建立预测模型,对转炉终点碳质量分数和温度进行预测。将数据处理后得到425组数据,数据划分为训练集数据和测试集数据,并对其进行归一化预处理,其中,随机选取50组为测试集数据。结果表明,转炉终点预测模型的终点钢水碳含量(误差±0.015%)的命中率为84%,终点温度(误差±15℃)的命中率为80%。与BP神经网络模型和RBF模型相比,基于粒子群算法优化的支持向量机模型具有精度高、泛化能力强的特点。
The converter smelting process contains complex multi-phase and high-temperature physical and chemical reactions,and it is of great significance to establish a reliable converter endpoint prediction model to effectively reduce the fluctuation of molten steel composition and improve the quality of steel.Based on the actual production data of a 200 t converter in a steel mill,the particle swarm optimization algorithm is used to select the optimal penalty parameter C and kernel parameter g of the support vector machine model to establish a prediction model,and the carbon mass fraction and temperature at the end point of the converter are predicted.After data processing 425 sets of data were obtained and divided into training set data and test set data,and normalized them,of which 50 groups were randomly selected as test set data.The results show that the accuracy of carbon mass fraction(error±0.015%)and temperature(error±15℃)is 81.8%and 80%respectively.Compared with BP neural network model and RBF model,support vector machine model optimized by particle swarm optimization has higher accuracy and better generalization ability.
作者
刘增山
冯亮花
康小兵
Liu Zengshan;Feng Lianghua;Kang Xiaobing(School of Materials and Metallurgy,University of Science and Technology"Liaoning,Anshan 114051,China;Hebei Yanshan Iron and Steel Group Co.,Ltd.,Qian'an 064400,China)
出处
《特殊钢》
2024年第3期27-32,共6页
Special Steel
基金
国家自然科学基金资助项目(52074151)
辽宁省科学技术厅资助项目(2022 JH2/101300079)。
