公开(公告)号：US20060074599A1

公开(公告)日：2006-04-06

申请号：US11212434

申请日：2005-08-26

申请人： Kenneth Emigholz ,  Richard McLain ,  Robert Wang ,  Stephen Woo

发明人： Kenneth Emigholz ,  Richard McLain ,  Robert Wang ,  Stephen Woo

IPC分类号： G06F11/30

CPC分类号： C10G11/187 ,  G05B23/0254 ,  G05B2219/31477 ,  G05B2219/31478

摘要： The present invention is a method for detecting an abnormal event for process units of an ethylene processing system. The method compares the operation of the process units to a model developed by principal 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： 本发明是用于检测乙烯处理系统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。

公开(公告)号：US20060058898A1

公开(公告)日：2006-03-16

申请号：US11212189

申请日：2005-08-26

申请人： Kenneth Emigholz ,  Robert Wang ,  Stephen Woo ,  Richard McLain ,  Sourabh Dash ,  Thomas Kendi

发明人： Kenneth Emigholz ,  Robert Wang ,  Stephen Woo ,  Richard McLain ,  Sourabh Dash ,  Thomas Kendi

IPC分类号： G05B13/02

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。