Dynamic estimation of continuum and noncontinuum states for process control monitoring

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“静摩擦”与“无静摩擦”。即使对于连续状态的过程，在控制性能监控问题中，人们也可能只对“好”、“坏”和“最优”等非连续表示感兴趣。因此，有必要同时考虑连续和非连续状态的过程控制monitoring.The长期的目标，因此，本研究是开发一种新的框架，基于状态估计的过程控制监控，更具体地说，同时过程故障检测和控制性能监测问题。短期目标是研究和开发连续和非连续状态的动态估计方法，并使用这些方法来解决化学过程中某些突出的故障检测和控制性能监测问题。滚动时域估计方法，特别是，将用于连续和非连续状态的动态估计。在此框架下，对连续和非连续状态的过程控制监控问题进行了求解，表明许多过程故障检测和控制性能监控问题都可以转化为状态估计问题，并易于求解。此外，许多过程控制监控问题，似乎相当不相交，可以解决一个统一的方式。因此，该研究成果不仅提供了一个理论基础，但也有利于在同一计算框架下解决不同的监测问题的工业应用程序的计算算法的发展。

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.