基于粗糙集的转炉炼钢知识发现模型

针对转炉炼钢知识发现的特点，采用粗糙集理论进行分析，应用数据清洗、标准化及离散等方式对转炉炼钢生产数据进行预处理，以炼钢生产的主要影响因素作为知识发现的条件属性，以转炉冶炼终点控制目标作为知识发现的决策属性，建立了基于粗糙集方法的转炉炼钢知识发现模型，实现转炉炼钢生产知识的自动发现、获取和规则提取。以转炉冶炼终点钢水温度的变化规律做为知识发现的决策属性，采用210 t转炉炼钢实际生产数据进行模型的应用测试，结果表明提取出的铁水硅含量、铁矿石质量、氧气消耗量等影响因素对转炉冶炼钢水终点温度存在重要影响，且模型提取出的转炉炼钢终点钢水温度知识规则与现行转炉炼钢现场的变化规律一致，证明基于粗糙集方法的转炉炼钢知识发现模型的有效性。

Knowledge discovery model of basic oxygen furance steelmaking based on Rough Set Theory

The characteristics of the knowledge discovery for basic oxygen furnace(BOF) steelmaking are analyzed by using the Rough Set Theory. The production data of BOF steelmaking are preprocessed by using the methods of data withdrawal, standardization, discretization and so on. The main influencing factors of steelmaking production are set as the knowledge discovery property. The endpoint control objectives of BOF steelmaking are used as the decision attribute of knowledge discovery. Then the knowledge discovery model of BOF steelmaking based on rough set theory is established, which makes the automation of the production knowledge discovery, access and rule extraction come true. The model is tested by using the production data of 210 t BOF, and takes the temperature variation of smelting endpoint as the decision attribute. The results show that the influencing factors, such as silicon content, iron ore weight, oxygen consumption and so on, are of very importance to the endpoint temperature of molten steel. Besides, the rules of molten steel temperature extracted by the model vary with current converter steelmaking process, which proves the validity of the model.