Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes
Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes
复制标题
DOI：
10.1016/j.compchemeng.2003.07.001
复制
发表时间：
2004-03-15
影响因子：
4.3
通讯作者：
Pike, RW
中科院分区：
文献类型：
--
作者：
Özyurt, DB;Pike, RW
在线优化通过向过程的分布式控制系统（DCS）提供设定点来提供用于将过程维持在其最佳操作条件附近的手段。为了实现优化的工厂模型匹配，过程测量是必要的。然而，需要对这些测量值进行预处理，因为它们通常包含随机误差和不太常见的粗差。在对过程进行任何评估之前，应消除这些误差，并且测量值应满足过程约束。本文论述了数据协调和粗差探测同时进行的重要性和有效性。这些程序依赖于稳健统计的结果，减少了粗差的影响。他们提供了可比的结果，如修改的迭代测量测试方法（MIMT），而不需要一个迭代过程的方法。除了推导新的鲁棒方法，新的粗差检测标准进行了描述和测试。所介绍的方法的比较结果给出了五个文献，更重要的是，两个工业案例。基于柯西分布和Hampel的降阶M估计的方法在数据协调和粗差检测方面具有良好的效果。(C)2003 Elsevier Ltd.保留所有权利。
On-line optimization provides a means for maintaining a process near its optimum operating conditions by providing set points to the process's distributed control system (DCS). To achieve a plant-model matching for optimization, process measurements are necessary. However, a preprocessing of these measurements is required since they usually contain random and-less frequently-gross errors. These errors should be eliminated and the measurements should satisfy process constraints before any evaluation on the process. In this paper, the importance and effectiveness of simultaneous procedures for data reconciliation and gross error detection is established. These procedures depending on the results from robust statistics reduce the effect of the gross errors. They provide comparable results to those from methods such as modified iterative measurement test method (MIMT) without requiring an iterative procedure. In addition to deriving new robust methods, novel gross error detection criteria are described and their performance is tested. The comparative results of the introduced methods are given for five literature and more importantly, two industrial cases. Methods based on the Cauchy distribution and Hampel's redescending M-estimator give promising results for data reconciliation and gross error detection with less computation. (C) 2003 Elsevier Ltd. All rights reserved.