化工学报 ›› 2019, Vol. 70 ›› Issue (2): 572-580.DOI: 10.11949/j.issn.0438-1157.20181340

• 过程系统工程 • 上一篇    下一篇

基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用

耿志强1,2(),景邵星1,2,白菊1,2,王仲凯1,2,朱群雄1,2,韩永明1,2()   

  1. 1. 北京化工大学信息科学与技术学院,北京 100029
    2. 智能过程系统工程教育部工程研究中心,北京 100029
  • 收稿日期:2018-11-15 修回日期:2018-11-22 出版日期:2019-02-05 发布日期:2019-02-05
  • 通讯作者: 韩永明
  • 作者简介:<named-content content-type="corresp-name">耿志强</named-content>(1973—),男,博士,教授,<email>gengzhiqiang@mail.buct.edu.cn</email>|韩永明(1987—),男,博士,副教授,<email>hanym@mail.buct.edu.cn</email>
  • 基金资助:
    国家自然科学基金项目(61673046,61374166);国家重点研发计划项目(2018YFB0803501);中央高校基本科研业务费专项资金(XK1802-4)

Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries

Zhiqiang GENG1,2(),Shaoxing JING1,2,Ju BAI1,2,Zhongkai WANG1,2,Qunxiong ZHU1,2,Yongming HAN1,2()   

  1. 1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
    2. Engineering Research Center of Intelligent PSE, Ministry of Education, Beijing 100029, China
  • Received:2018-11-15 Revised:2018-11-22 Online:2019-02-05 Published:2019-02-05
  • Contact: Yongming HAN

摘要:

石油钻井是一项高风险性、耗资巨大的系统工程。为了智能预警石油钻井过程中的异常,缩短非生产时间,降低相关风险,提出一种基于移动窗稀疏主元分析法(MWSPCA)的案例推理(CBR)异常智能预警方法(MWSPCA-CBR)。首先利用MWSPCA算法分析钻井过程中的实时数据,快速定位出异常可能发生的时间,然后使用基于案例推理方法分析异常数据,确定可能的异常类型,并为实时监控专家提供相关异常的处理方法。所提方法应用到石油钻井过程异常预警中,实验结果验证了所提方法的可行性和有效性,为钻井过程降低风险成本提供了新思路。

关键词: 主元分析, 基于案例推理, 智能预警方法, 石油钻井过程, 过程控制, 模型预测控制

Abstract:

The petroleum drilling project is a high-risk and costly system project. To effectively scan for potential problems of drilling, reduce non-productive time and lower related risks, this paper proposes an improved intelligent warning method based on moving window sparse principal component analysis (MWSPCA) integrating case-based reasoning (CBR) (MWSPCA-CBR). First, the MWSPCA is used to analyze the real-time data in the drilling process, and the time of occurrence of the anomaly is quickly located. Then the abnormal data is analyzed by using the CBR method to give possible exception types, and the associated handling methods are provided for monitoring experts. Finally, the proposed method is applied to intelligent warn abnormal problems of the petroleum drilling, the experimental results verify the feasibility and effectiveness of the proposed method and provide new ideas for reducing risks and costs during the petroleum drilling process.

Key words: principal component analysis, case-based reasoning, intelligent warning method, petroleum drilling process, process control, model-predictive control

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