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，Biegler 实时优化 (RTO) 和模型预测控制 (MPC) 是化工和炼油行业优化过程操作的重要技术。 NMPC 和动态实时优化 (D-RTO) 都允许合并第一原理过程模型，从而产生与更高级别任务（包括调度和规划）一致的在线优化策略。 此外，随着动态建模、仿真和优化的最新进展，动态优化的工业应用不断增加，特别是对于固有的瞬态过程。 然而，仍然需要解决反映复杂反应和分离现象以及多级动态操作的更详细的动态优化模型，并将其作为时间关键的在线应用程序来解决。 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，这可能会降低性能并可能破坏过程的稳定性。该项目解决了这些问题，并能够利用第一原理模型实现快速在线动态优化。 PI 计划开发一类基于灵敏度的算法，将动态优化分为后台计算和在线计算，前者执行大部分计算，后者快速解决扰动问题。 因此，即使对于大型、复杂的非线性模型，在线计算也减少了几个数量级并且变得非常快。 这些公式将针对 NMPC 以及状态和参数移动水平估计 (MHE) 进行开发。 智力优点 这项活动的智力优点涉及使用第一原理动态模型（特别是高级步骤 NMPC 和 MHE）开发和分析基于灵敏度的在线优化。这项工作应该能够实现大规模化学过程的非线性模型预测控制和在线动态优化，而不受计算反馈延迟的限制。该研究还涉及多阶段动态优化的扩展，以实现规划和调度决策的更紧密集成，以及稳健的问题表述，以处理模型失配和不可测量的干扰。这种方法将扩展到移动视界估计（MHE）问题。非线性模型的 MHE 策略比观测器和卡尔曼滤波器具有显着优势，但它们的实现需要应用快速优化策略。更广泛的影响更广泛的影响包括将此方法应用于两个具有挑战性的工业应用。其中包括具有详细在线反应器模型和动态多级操作（包括等级变化）的大规模聚合物工艺。 PI 还将考虑气体分离过程的在线动态优化策略。这些系统的特点是负载变化和动态具有强非线性，通过有效的 NMPC 和 MHE 策略可以大大提高其性能。这些概念也将集成到全面的优化和建模环境中。最后，强调研究生培训是一个关键组成部分。教育计划包括工业实习以及与企业范围优化相关的课程和材料的开发。