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

针对安全关键的化学工艺和工厂的过程诊断和事件驱动控制

• 批准号: 2133810
• 负责人: Costas Kravaris
• 金额: $ 40.39万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133810&HistoricalAwards=false
• 关键词: Process; diagnostics; event; driven; control

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.

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，它们必须科普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。异常状况是否是由于传感器测量误差、致动器故障（例如，卡滞的控制阀）或泄漏、结垢或其它设备故障时，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是至关重要的。当人类（过程操作员和工程师）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，清楚地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。这项研究旨在满足这些需求，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中异常情况的根本原因，并制定自动控制算法，应用必要的纠正措施以防止潜在的灾难。该研究小组还将测试他们关于过程稳定性的理论，以及他们在实验室规模的反应堆系统上开发的控制系统软件，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实现，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，安全关键制造过程的控制需要自动检测异常事件已经发生，明确识别哪个传感器，执行器，或过程设备部件发生故障，并重新配置控制系统以响应故障，以保持过程的稳定性和安全性。为满足这些要求，该项目的具体研究目标是：（a）基于第一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算法;（B）在与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，在该反应器中发生高度放热和潜在爆炸性反应（但以安全的方式）;以及（c）根据警报数据对异常事件的诊断，以及对其对强制执行安全约束的能力的影响的动态分析，开发事件驱动的控制算法。为了满足目标（a）和（c），将采用系统理论工具，推进非线性函数观测器，统计推断和约束非线性控制的方法和结果。会议目标（B）将包括化学反应器的建模、模拟和诊断算法开发，以及实验实施和测试。该项目的创新特点包括：（i）基于第一原理模型或统计模型的过程和工厂诊断理论和算法的进展，沿着实验研究，该研究将作为真实的化学过程中未来故障诊断应用的范例;以及（ii）在面对可能构成安全威胁的异常事件时的动态分析和基于模型的容错控制方面的进展。拟议工作的结果将通过在国内和国际会议上的介绍、学术期刊出版物和项目专用网站向学术界和工业界的研究人员广泛传播。PI还将免费提供所有案例研究和建议工作的实施，并将创建一个网络工具，以方便访问。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Design of linear unknown input observers for sensor fault estimation in nonlinear systems

• DOI: 10.1016/j.automatica.2023.111152
• 发表时间: 2023-07
• 期刊: Autom.
• 作者: Sunjeev Venkateswaran;C. Kravaris

Model-Based Fault Diagnosis and Fault Tolerant Control for Safety-Critical Chemical Reactors: A Case Study of an Exothermic Continuous Stirred-Tank Reactor

安全关键化学反应器基于模型的故障诊断和容错控制：以放热连续搅拌釜反应器为例

• DOI: 10.1021/acs.iecr.3c01205
• 发表时间: 2023
• 期刊: Industrial & Engineering Chemistry Research
• 影响因子: 4.2
• 作者: Du, Pu;Venkidasalapathy, Joshiba Ariamuthu;Venkateswaran, Sunjeev;Wilhite, Benjamin;Kravaris, Costas
• 通讯作者: Kravaris, Costas