Fast Nonlinear Model Predictive Control with First Principle Dynamic Models

使用第一原理动态模型的快速非线性模型预测控制

• 批准号: 0756264
• 负责人: Lorenz Biegler
• 金额: $ 29.07万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0756264&HistoricalAwards=false
• 关键词: Fast; Nonlinear; Model; Predictive; Control

CBET-0756264, BieglerReal-Time Optimization (RTO) and Model Predictive Control (MPC) are important technologies for optimal process operation in the chemical and refining industry. Both NMPC and dynamic real-time optimization (D-RTO) allow the incorporation of first principle process models, which lead to on-line optimization strategies consistent with higher-level tasks, including scheduling and planning. Moreover, with recent advances in dynamic modeling, simulation and optimization, dynamic optimization has seen increasing industrial application, particularly for inherently transient processes. However, more detailed dynamic optimization models that reflect complex reaction and separation phenomena and multi- stage dynamic operation still need to be addressed - and solved as time-critical, on-line applications. Here, a major concern is that computational times needed to solve these large-scale optimizations lead to feedback delays in implementation that can degrade performance and possibly destabilize the process.This project addresses these issues and enables the realization of fast on-line dynamic optimization with first principle models. The PI plans to develop a class of sensitivity-based algorithms that separate dynamic optimization into background calculations, where most of the computation is performed, and on-line calculations, where a perturbed problem is solved very quickly. On-line computations are thus reduced by several orders of magnitude and become very fast, even for large, complex nonlinear models. These formulations are to be developed both for NMPC as well as state and parameter moving horizon estimation (MHE).Intellectual MeritThe intellectual merit of this activity deals with the development and analysis of sensitivity-based on-line optimization with first principle dynamic models, particularly Advanced-Step NMPC and MHE. The work should lead to nonlinear model predictive control and on-line dynamic optimization for large-scale chemical processes without the limitations of computational feedback delay. The research also deals with extensions to multi-stage dynamic optimization for tighter integration of planning and scheduling decisions, and robust problem formulations to deal with model mismatch and unmeasured disturbances. This approach will be extended to moving horizon estimation (MHE) problems. MHE strategies for nonlinear models have significant advantages over observers and Kalman filters, but their realization requires application of fast optimization strategies.Broader ImpactsBroader impacts include the application of this approach on two challenging industrial applications. These include a large-scale polymer process with detailed on-line reactor models and dynamic multi-stage operation, including grade changes. The PI will also consider on-line dynamic optimization strategies for gas separation processes. Characterized by load changes and dynamics with strong nonlinearities, performance of these systems can be greatly improved through efficient NMPC and MHE strategies. These concepts will also be integrated within a comprehensive optimization and modeling environment. Finally, graduate training is emphasized as a key component. Included in the educational plan are industrial internships and the development of courses and materials related to Enterprise Wide Optimization.

CBET-0756264，Bieglereal时间优化（RTO）和模型预测控制（MPC）是化学和炼油行业最佳过程运行的重要技术。 NMPC和动态实时优化（D-RTO）都允许合并第一个主要过程模型，这会导致在线优化策略与高级任务一致，包括调度和计划。 此外，随着动态建模，模拟和优化的最新进展，动态优化的工业应用增加了，尤其是对于固有的瞬态过程。 但是，反映复杂的反应和分离现象和多阶段动态操作的更详细的动态优化模型仍需要解决 - 并将其求解为时间关键时期的在线应用。 在这里，一个主要问题是，解决这些大规模优化所需的计算时间会导致实施中的反馈延迟，从而可以降低性能并可能破坏该过程。该项目解决了这些问题，并实现了通过第一原理模型实现快速的在线动态优化。 PI计划开发一类基于灵敏度的算法，该算法将动态优化分为背景计算，其中大多数计算都已执行，并在线计算中，在线计算中，解决了扰动问题。 因此，在线计算通过几个数量级降低，甚至对于大型，复杂的非线性模型也变得非常快。 这些配方既应针对NMPC，状态和参数移动范围估计（MHE）。Intlectual值得这项活动的智力优点涉及基于敏感性的在线优化的开发和分析，该优化具有第一原理动态模型，尤其是高级步骤NMPC和MHE。这项工作应导致非线性模型预测性控制和在线化学过程的在线动态优化，而无需局限计算反馈延迟。这项研究还涉及多阶段动态优化的扩展，以严格整合计划和调度决策，以及强大的问题表述，以处理模型不匹配和未衡量的干扰。这种方法将扩展到移动视野估计（MHE）问题。非线性模型的MHE策略比观察者和卡尔曼过滤器具有显着优势，但是它们的实现需要应用快速优化策略。BroaderImpactsBroader的影响包括将这种方法应用于两个具有挑战性的工业应用程序。其中包括具有详细的在线反应堆模型和动态多阶段操作（包括等级变化）的大型聚合物过程。 PI还将考虑对气体分离过程的在线动态优化策略。具有强大非线性的负载变化和动态的特征，通过有效的NMPC和MHE策略，可以大大提高这些系统的性能。这些概念也将集成到全面的优化和建模环境中。最后，强调研究生培训是关键组成部分。教育计划中包括工业实习以及与企业广泛优化有关的课程和材料的开发。