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An Integrated Application of Control Performance Assessment and Root Cause Analysis in Refinery Control Loops

机译：炼油费控制回路控制性能评估和根本原因分析的综合应用

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Assessing the performance of control loops is an important component of Control Performance Monitoring (CPM) systems. Most of the industrial chemical processes have a large number of control loops interacting with each other in a complex way due to material and energy integration in the plant. A problem occurring in a certain control loop can easily upset the performance of the other control loops. Therefore, identification of the ”bad” control loops causing a plant-wide disturbances is a crucial task. In this work, an integrated approach covering performance assessment and interaction analysis is proposed to detect the ”bad” loops based on their performances. First, Minimum Variance Control (MVC) benchmark is used to screen-out the poor performing loops. Then, the spectral envelope method utilizing frequency analysis is used to identify the common oscillation periods among the loops under study. Finally, Granger causality is used to plot the interaction map between the loops. even though these methods are well developed and used for several purposes separately, we present an integrated approach which focuses and analyzes the ”bad loops”. The developed approach has been tested in a refinery plant having 18 control loops. The results show that the proposed method is clearly able to identify and isolate the root-cause control loops. The validation of results and further improvements in the control loops under study have been given.
机译：评估控制循环的性能是控制性能监测（CPM）系统的重要组成部分。由于植物中的材料和能量集成，大多数工业化学工艺具有大量的控制回路以复杂的方式互相交互。在某个控制循环中发生的问题可以很容易地扰乱其他控制环的性能。因此，识别造成植物范围扰动的“糟糕”控制环是一个重要的任务。在这项工作中，提出了一种覆盖性能评估和交互分析的综合方法，以检测基于其性能的“坏”循环。首先，最小方差控制（MVC）基准用于筛选可差的循环。然后，利用频率分析的光谱包络方法用于识别研究下的循环中的常见振荡周期。最后，Granger因果关系用于绘制循环之间的交互映射。尽管这些方法很好地发达并分别用于多种目的，但我们介绍了一种集成方法，侧重于并分析“坏环”。开发的方法已在具有18个控制环的炼油厂进行测试。结果表明，该方法显然能够识别和隔离根本原因控制环。已经给出了研究结果的验证和进一步改善研究。

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