Process diagnostics and event-driven control for safety-critical chemical processes and plants

当自动控制系统用于保护安全关键的制造过程和化工厂时，它们必须应对异常情况下的重大挑战，以避免产品和设备损坏，避免经济损失，最重要的是防止危及生命的情况。无论异常情况是由于传感器测量错误、执行器故障（例如控制阀卡死）、泄漏、结垢或其他设备故障，检测、诊断异常事件并在情况升级为安全威胁之前采取适当的控制措施都是至关重要的。当人类（流程操作员和工程师）被要求采取行动时，情况变得更加具有挑战性，在这种情况下，明确指示发生了什么是至关重要的，以便可以进行适当的操作员干预。这项研究旨在解决这些需求，并将推进和改变化学和其他制造过程安全的工业实践。该项目的研究团队将开发诊断算法，可以快速检测和识别化学过程或整个工厂异常情况的根本原因，并将制定自动控制算法，应用必要的纠正措施，以防止潜在的灾难。研究小组还将在实验室规模的反应堆系统上测试他们的过程稳定性理论和控制系统软件，该系统可以（安全地）模拟工业反应堆。这种诊断和控制算法的发展将通过系统科学领域的基本进步来完成，特别是在数据驱动的建模和反馈控制方法方面。该项目将支持研究生和本科生教育，包括来自代表性不足群体的学生，并将为化学过程设计和过程控制课程提供重要的安全相关内容。面对潜在的异常事件，对安全关键制造过程的控制需要自动检测异常事件的发生，明确识别传感器、执行器或过程设备组件发生故障，并重新配置控制系统以应对故障，以保持过程的稳定性和安全性。为了满足这些要求，该项目的具体研究目标是：(a)基于第一性原理模型或数据驱动的统计模型，开发化学过程和工厂诊断的算法；(b)在工业相关但实验室规模的化学反应器中对诊断算法进行实验测试，其中发生高度放热和潜在爆炸性反应（但以安全的方式）；(c)开发基于从报警数据中诊断异常事件的事件驱动控制算法，并动态分析其对执行安全约束能力的影响。为了实现目标(a)和(c)，将采用系统理论工具，推进非线性函数观察者，统计推断和约束非线性控制的方法和结果。实现目标(b)将涉及化学反应器的建模、仿真和诊断算法开发，以及实验实施和测试。该项目的创新特点包括：(i)基于第一性原理模型或统计模型的过程和设备诊断理论和算法的进步，以及将作为未来实际化学过程故障诊断应用范例的实验研究；面对可能构成安全威胁的异常事件，在动态分析和基于模型的容错控制方面取得了进展。拟议工作的结果将通过在国内和国际会议上的报告、在学术评审的期刊出版物上以及通过该项目的专门网站广泛传播给学术界和工业界的研究人员。PI还将免费提供拟议工作的所有案例研究和实施，并将创建一个易于访问的网络工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

When automatic control systems are used to protect safety-critical manufacturing processes and chemical plants, they must cope with major challenges under abnormal situations to avoid product and equipment damage, avert financial loss, and most importantly, prevent life-threatening situations. Whether the abnormal condition is due to sensor measurement errors, actuator failure (e.g., a stuck control valve), or leakage, fouling, or other equipment malfunction, it is critical that the abnormal event be detected, diagnosed, and that proper control action be taken before the situation escalates into a safety threat. The circumstances become even more challenging when humans (process operators and engineers) are called to take action, situations in which a clear indication of what happened is vital so that appropriate operator intervention can be made. This research effort intends to address these needs and will advance and transform industrial practice for chemical and other manufacturing process safety. The research team of this project will develop diagnostic algorithms that can quickly detect and identify the root cause of an abnormal situation in a chemical process or entire plant and will formulate automatic control algorithms that apply the necessary corrective action to prevent a potential disaster. The research team also will test their theories on process stability and the control system software they develop on a laboratory scale reactor system that (safely) can emulate an industrial reactor. The development of such diagnostic and control algorithms will be accomplished through fundamental advancements in the field of systems science, particularly in data-driven modeling and feedback control methods. This project will support graduate and undergraduate education, including students from underrepresented groups, and will generate important safety related content for chemical process design and process control courses.Control of safety critical manufacturing processes in the face of potential abnormal events requires the automated detection that an unusual event has taken place, unambiguous identification of which sensor, actuator, or process equipment component has failed, and reconfiguration of the control system to respond to the failure to maintain process stability and safety. To satisfy these requirements, the specific research objectives of the project are to: (a) develop algorithms for chemical process and plant diagnostics, based on either a first-principles model or data-driven statistical model; (b) perform experimental testing of the diagnostic algorithms in an industrially relevant but lab-scale chemical reactor, where a highly exothermic and potentially explosive reaction takes place (but in a safe manner); and (c) develop event-driven control algorithms based on the diagnosis of the abnormal event from alarm data, as well as dynamic analysis of its effect on the ability to enforce safety constraints. To meet objectives (a) and (c), system-theoretical tools will be employed, advancing methods and results in nonlinear functional observers, statistical inference, and constrained nonlinear control. Meeting objective (b) will involve modeling, simulation, and diagnostic algorithm development for chemical reactors, as well as experimental implementation and testing. The innovative features of the project consist of: (i) advances in the theory and algorithms for process and plant diagnostics, based on either first-principles models or statistical models, along with an experimental study that will serve as a paradigm for future fault diagnosis applications in real chemical processes; and (ii) advances in dynamic analysis and model-based fault-tolerant control in the face of possible abnormal events that can pose safety threats. The results of the proposed work will be broadly disseminated to researchers in academia and industry by presentations at domestic and international meetings, in scholarly refereed journal publications, and through a dedicated web site for the project. The PI also will make freely available all case studies and implementations of the proposed work, and will create a web tool for easy access.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.