Moving Horizon Estimation and Nonlinear, Large-Scale Model Predictive Control of Chemical Processes

化学过程的移动水平估计和非线性、大规模模型预测控制

• 批准号: 0105360
• 负责人: James Rawlings
• 金额: $ 21.06万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-15 至 2004-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0105360&HistoricalAwards=false
• 关键词: Moving; Horizon; Estimation; Nonlinear; Large

ABSTRACTPI: James B. Rawlings Institution: University of WisconsinProposal Number: 0105360To maintain economic competitiveness, maximize product quality and operate more environmentally benign processes, the chemical process industries need to invest in process monitoring and control technology. Industrial process operations are becoming increasingly integrated and complex, operation against constraints is common and the dynamic process behavior requires more sophisticated online analysis and decision making in order to optimize the process performance. The goal of this research is to address three outstanding challenges in the further development of advanced chemical control methodology:1. State estimation based on dynamic models and online measurements is used to further improve process operations. The moving horizon estimation (MHE) approach addresses this need as it allows both nonlinear models and estimator constraints as part of the problem formulation. The PI plans to study a series of issues and application problems in order to understand and expand the current applicability of MHE theory.2. Use model predictive control (MPC) for online optimization of an integrated, large-scale plant. The PI plans to exploit the inherent communication capability provided by the forecast horizon in the MPC controllers.3. Develop a suite of nonlinear MPC test problems for closed-loop control. These problems should provide a convenient standard for the research community to test new nonlinear MPC theories, approaches and algorithms.Expected impacts for this research include: improved understanding of the fundamental issues surrounding the online use of models for monitoring, inference and decision making; direct impact on industrial plant operations and control; and rapid and wide dissemination of new research results to educators by the development and release of high-level source code.

摘要PI：James B. Rawlings机构：威斯康星大学港口编号：0105360维持经济竞争力，最大化产品质量并更加环保过程，化学过程行业需要投资于过程监测和控制技术。 工业过程操作变得越来越集成和复杂，针对约束的操作很普遍，动态过程行为需要更复杂的在线分析和决策，以优化过程性能。 这项研究的目的是应对进一步发展高级化学控制方法的三个重大挑战：1。基于动态模型和在线测量的状态估计用于进一步改善过程操作。 移动范围估计（MHE）方法解决了这一需求，因为它允许非线性模型和估计器约束作为问题制定的一部分。 PI计划研究一系列问题和应用问题，以了解和扩大MHE理论的当前适用性2。使用模型预测控制（MPC）在线优化集成的大型工厂。 PI计划利用MPC控制器中预测范围提供的固有通信能力3。为闭环控制开发一套非线性MPC测试问题。 这些问题应该为研究社区提供方便的标准，以测试新的非线性MPC理论，方法和算法。预期的对本研究的影响包括：对围绕在线使用模型进行监测，推理和决策的在线使用的基本问题的了解得到了改进；直接影响工业工厂的运营和控制；通过开发和发布高级源代码，将新的研究结果快速传播给教育工作者。