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11 条记录 (耗时 0.032 秒)

    Application of abnormal event detection (AED) technology to polymers
    1.
    发明授权
    Application of abnormal event detection (AED) technology to polymers 有权
    异常事件检测（AED）技术应用于聚合物

    公开(公告)号：US07761172B2

    公开(公告)日：2010-07-20

    申请号：US11725227

    申请日：2007-03-16

    申请人： Anh T. Nguyen ,  Kenneth F. Emigholz ,  Stephen S. Woo ,  Stephen D. Vercher ,  Steven M. Orsak ,  Perry Alagappan

    发明人： Anh T. Nguyen ,  Kenneth F. Emigholz ,  Stephen S. Woo ,  Stephen D. Vercher ,  Steven M. Orsak ,  Perry Alagappan

    IPC分类号： G06F19/00 ,  G05B11/00

    CPC分类号： G05B23/0254 ,  G05B17/02

    摘要： The present invention is a method and system for detecting an abnormal event for process units of a Polymers Unit. The method compares the operation of the process units to statistical, engineering or heuristic models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on correlation analysis between variables or simple engineering calculations. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

    摘要翻译： 本发明是用于检测聚合物单元的处理单元的异常事件的方法和系统。 该方法将过程单元的操作与统计，工程或启发式模型进行比较。 统计模型通过对这些单位的正常运行的主要组成部分进行分析。 此外，工程模型基于变量之间的相关分析或简单的工程计算。 如果处理单元的操作与正常模型结果之间的差异指示异常状况，则确定和校正异常状况的原因。

    Application of abnormal event detection technology to delayed coking unit
    2.
    发明授权
    Application of abnormal event detection technology to delayed coking unit 有权
    异常事件检测技术应用于延迟焦化装置

    公开(公告)号：US07720641B2

    公开(公告)日：2010-05-18

    申请号：US11786950

    申请日：2007-04-13

    申请人： Perry Alagappan ,  Kenneth F. Emigholz ,  Kevin R. Worden ,  Stephen S. Woo ,  Anh T. Nguyen

    发明人： Perry Alagappan ,  Kenneth F. Emigholz ,  Kevin R. Worden ,  Stephen S. Woo ,  Anh T. Nguyen

    IPC分类号： G06F11/30

    CPC分类号： C10B55/00 ,  G05B23/0254 ,  G05B23/0289

    摘要： The present invention is a method for detecting an abnormal event for process units of a Delayed Coking Unit. The method compares the operation of the process units to statistical and engineering models. The statistical models are developed by principal components analysis of the normal operation for these units. The engineering models are based statistical and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

    摘要翻译： 本发明是一种用于检测延迟焦化单元的处理单元的异常事件的方法。 该方法将过程单元的操作与统计和工程模型进行比较。 统计模型是通过对这些单位的正常运行的主要组成部分进行分析而开发的。 工程模型是基于变量之间的统计和相关分析。 如果处理单元的操作与正常模型结果之间的差异指示异常状况，则确定和校正异常状况的原因。

    Application of abnormal event detection technology to hydrocracking units
    3.
    发明授权
    Application of abnormal event detection technology to hydrocracking units 有权
    异常事件检测技术应用于加氢裂化装置

    公开(公告)号：US08005645B2

    公开(公告)日：2011-08-23

    申请号：US11710654

    申请日：2007-02-23

    申请人： Kenneth F. Emigholz ,  Thomas A. Kendi ,  Stephen S. Woo

    发明人： Kenneth F. Emigholz ,  Thomas A. Kendi ,  Stephen S. Woo

    IPC分类号： G06F15/00

    CPC分类号： G05B23/021 ,  C10G47/36 ,  G05B23/0254 ,  G06K9/6247

    摘要： The present invention is a method for detecting an abnormal event for process units of a hydrocracking unit. The method compares the operation of the process units to a model developed by principle components analysis of normal operation for these units. If the difference between the operation of a process unit and the normal operation indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

    摘要翻译： 本发明是用于检测加氢裂化装置的处理单元的异常事件的方法。 该方法将过程单元的操作与通过这些单元的正常操作的主要组件分析开发的模型进行比较。 如果处理单元的操作与正常操作之间的差异指示异常状况，则确定和校正异常状况的原因。

    Application of abnormal event detection technology to fluidized catalytic cracking unit
    4.
    发明授权
    Application of abnormal event detection technology to fluidized catalytic cracking unit 有权
    异常事件检测技术应用于流化催化裂化装置

    公开(公告)号：US07567887B2

    公开(公告)日：2009-07-28

    申请号：US11212188

    申请日：2005-08-26

    申请人： Kenneth F. Emigholz ,  Sourabh K. Dash ,  Stephen S. Woo

    发明人： Kenneth F. Emigholz ,  Sourabh K. Dash ,  Stephen S. Woo

    IPC分类号： G06F19/00 ,  G06F17/40

    CPC分类号： C10G11/18

    摘要： The present invention is a method for detecting an abnormal event for process units of a Fluidized Catalytic Cracking Unit. The method compares the operation of the process units to a statistical and engineering models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on partial least squares analysis and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

    摘要翻译： 本发明是用于检测流化催化裂化装置的处理单元的异常事件的方法。 该方法将过程单元的操作与统计和工程模型进行比较。 统计模型通过对这些单位的正常运行的主要组成部分进行分析。 此外，工程模型基于变量之间的偏最小二乘法分析和相关分析。 如果处理单元的操作与正常模型结果之间的差异指示异常状况，则确定和校正异常状况的原因。

    System and method for abnormal event detection in the operation of continuous industrial processes
    5.
    发明授权
    System and method for abnormal event detection in the operation of continuous industrial processes 有权
    在连续工业过程运行中异常事件检测的系统和方法

    公开(公告)号：US07349746B2

    公开(公告)日：2008-03-25

    申请号：US11212189

    申请日：2005-08-26

    申请人： Kenneth F. Emigholz ,  Robert K. Wang ,  Stephen S. Woo ,  Richard B. McLain ,  Sourabh K. Dash ,  Thomas A. Kendi

    发明人： Kenneth F. Emigholz ,  Robert K. Wang ,  Stephen S. Woo ,  Richard B. McLain ,  Sourabh K. Dash ,  Thomas A. Kendi

    IPC分类号： G05B11/01 ,  G01R27/28

    CPC分类号： C10G11/187 ,  G05B23/0208 ,  G05B23/0243 ,  G06K9/6247

    摘要： Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally.These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.

    摘要翻译： 由炼油厂和化工厂部署的现代数字过程控制系统收集了数千个过程和设备测量。 这些数据的几年历史在数据库中用于分析和报告。 这些数据库可以用于在正常操作期间发生的数据模式，以及用于确定进程何时异常运行的模式。 这些正常的操作模式由一组模型表示。 这些模型包括简单的工程方程，其表示在正常操作中应该是真实的已知关系，并且基于主成分分析的变化的多变量统计模型。 可以通过将每分钟收集的数据与正常操作模型的预测进行比较来检测设备和过程问题。 模糊Petri网解释过程操作数据中预期模式与实际数据模式之间的偏差，以确定流程操作的正常性。 然后，这用于帮助操作员本地化并诊断问题的根本原因。