摘要
在钢铁生产过程中,针对不同的产品原料,找出连铸环节的最佳温度曲线对生产出质量优良的产品有着重要的意义。钢铁加工厂引入MES系统后,MES的实时数据库记录了生产过程的数据,对历史数据进行分析可以挖掘出有效、可靠的温度曲线信息。数据挖掘技术中的关联规则算法Apriori针对的是离散型、线性的事务型数据,本文针对Apriori算法的缺陷,对其进行了改进,提出可以处理连续、非线性的生产数据的算法—基于粗糙集的关联规则挖掘算法Apriori_MES。并将其应用在钢铁厂MES的实时数据库中,挖掘出不同原料在连铸环节中的最佳温度控制曲线,为生产过程提供了辅助。
In the steel production process, for different raw materials, to identify the optimum temperature curve of the casting for producing good quality products has an important significance. After the steel processing plant introduced MES system, real-time database of MES recorded data of the production process, and analyze the historical data that can be tapped out an effective, reliable temperature curve information. The data mining technique of association rules algorithm Apriori is designed for the discrete, linear, transactional data, for the defects of the Apriori algorithm, we improved it and suggested that a new algorithm which can deal with continuous, non-linear production data-based on rough set of association rule mining algorithm Apriori_MES. And with its application in real-time database of the Steel Plant＇ MES, find out the temperature control curves for the different raw materials in the casting session, and provides an auxiliary to the production process.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2012年第10期1185-1188,共4页
Computers and Applied Chemistry
基金
国家自然科学基金资助项目(61074087)
上海市教育委员会科研创新项目(12ZZ144)
关键词
MES
粗糙集
关联规则
连铸温度控制
MES, rough set, association rules, casting temperature control
