基于向量自回归算法的锌熔池铁渣含量预测研究

    Forecasting of Iron Slag Content in Zinc Melting Pool Based on Vector Autoregressive Algorithm

    • 摘要: 薄带钢在热镀锌工艺中不可避免会产生锌渣。锌渣中铁渣的含量占比大约为5%。锌渣中铁含量的增加,更容易产生底渣,底渣沉积在锌锅内不易清除,还会给锌渣中锌资源回收利用带来很大的困难。采用向量自回归算法,选取了锌液温度、带钢速度、总铝含量这3个工艺变量,来建立铁渣含量的预测模型。结果表明:在实际生产中,应用该模型对未来15 min内铁渣含量进行预测是可行的。通过对这3个工艺参数的调整,可有效减少铁渣的生成。

       

      Abstract: Zinc slag is inevitable in the production of hot-dip galvanizing process for thin strip steel. The percentage of iron in zinc dross is about 5%. The increase of iron content in zinc slag makes it easier to produce bottom slag, which is deposited in the zinc pot and not easy to remove, and it will also bring great difficulties to the recycling of zinc resources in zinc slag. A vector auto-regressive algorithm was used to select three process variables, the temperature of zinc liquid, strip speed, and total aluminum content, to build a prediction model for iron slag content. The results show that it is feasible to apply the model to predict the iron slag content in the next 15 min. By adjusting the three process parameters, the generation of iron slag can be effectively reduced.

       

    /

    返回文章
    返回