融合大数据技术和工艺经验的高炉参数优化

 为了更精准地评判高炉运行情况，量化高炉指导方针。根据高炉生产过程的特点，应用大数据技术对高炉生产参数与铁水产量和高炉能耗等指标进行数据驱动分析，提出了一种优化高炉生产参数的新方法。首先，对某钢铁厂高炉各工序历史数据进行了采集、清洗、过滤和整合，建立了高炉数据仓库。然后，将多种聚类算法相结合，完成了对高炉炉况变化的详细划分。运用工艺经验与递归特征消除算法相结合，全面筛选得到能够反映炉况波动的强相关变量。应用统计学方法分析得出Class_a炉况对应的核心参数的最优范围，这对指导现场生产、保持高炉长期稳定顺行具有重要意义。

Optimization of blast furnace parameters based on #br# big data technology and process experience

In order to evaluate the operation of blast furnace more accurately, the guidelines of blast furnace are quantified. According to the characteristics of blast furnace production process, the big data technology to the data driven analysis of blast furnace production parameters was applied, hot metal production and blast furnace energy consumption, and proposes a new method to optimize blast furnace production parameters. Firstly, the data collection, cleaning, filtering and integration of the historical data of blast furnace in a steel plant are carried out, and the data warehouse of the blast furnace is established. Then, a variety of clustering algorithms are combined to complete the detailed division of the blast furnace condition changes. The combination of process experience and recursive feature elimination algorithm is used to comprehensively select strong correlation variables that can reflect fluctuations in furnace conditions. The statistical method is applied to analyze the optimal range of core parameters corresponding to class a furnace conditions, which is of great significance for guiding on site production and maintaining the long term stable and smooth operation of the blast furnace.