Dynamic estimation of continuum and noncontinuum states for process control monitoring

用于过程控制监控的连续和非连续状态的动态估计

• 批准号: 203057-2007
• 负责人: Huang, Biao
• 金额: $ 2.93万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=364775
• 关键词: Dynamic; estimation; continuum; noncontinuum; states

The field of process control monitoring has gained significant progress over the last 20 years. Existing studies have almost exclusively been devoted to continuous-valued or continuum system. In reality, however, many systems or problems may take noncontinuum form or state, such as "stiction" vs "no stiction" in a control valve. Even for processes with continuum state, one may be interested in only the noncontinuum representation such as "good", "bad" vs "optimal" in control performance monitoring problem. There is therefore a need to consider both continuum and noncontinuum states in process control monitoring.The long-term objective of this research is therefore to develop a novel framework based on state estimation for process control monitoring, more specifically, simultaneous process fault detection and control performance monitoring problem. The shorter-term objective is to investigate and develop dynamic estimation methods for both continuum and noncontinuum states and use these methods to solve certain outstanding fault detection and control performance monitoring problems for chemical processes. The moving horizon estimation approach, in particular, will be used for dynamic estimation of continuum and noncontinuum states. Once the new framework is established and algorithms are developed, process control monitoring problems with both continuum and noncontinuum states are solved under the developed framework.Success of this work implies that many process fault detection and control performance monitoring problems can be cast as state estimation problem and be readily solved. In addition, many process control monitoring problems that appear quite disjoint can be solved in a unified way. This research outcome thus not only provides a theoretical foundation but also facilitates development of computation algorithms for industrial applications under the same computation framework for solving different monitoring problems.

在过去的20年里，过程控制监测领域取得了重大进展。现有的研究几乎都集中在连续值系统或连续统系统上。然而，在现实中，许多系统或问题可能采取非连续的形式或状态，例如控制阀中的“有粘”与“无粘”。即使对于具有连续状态的过程，在控制性能监控问题中也可能只关心“好”、“坏”和“最优”等非连续表示。因此，在过程控制监测中需要同时考虑连续和非连续状态。因此，本研究的长期目标是开发一种基于状态估计的过程控制监测的新框架，更具体地说，是过程故障检测和控制性能监测的同步问题。短期目标是研究和发展连续和非连续状态的动态估计方法，并利用这些方法解决化工过程中某些突出的故障检测和控制性能监测问题。特别是，移动视界估计方法将用于连续和非连续状态的动态估计。一旦建立了新的框架并开发了相应的算法，就可以在该框架下解决连续和非连续状态下的过程控制监测问题。这项工作的成功意味着许多过程故障检测和控制性能监控问题可以转化为状态估计问题，并且很容易解决。此外，许多看起来很不连贯的过程控制监测问题可以统一解决。这一研究成果不仅提供了理论基础，而且有利于在同一计算框架下开发工业应用的计算算法，以解决不同的监测问题。