GOALI: Fast Nonlinear Model Predictive Control for Dynamic Real-time Optimization

GOALI：用于动态实时优化的快速非线性模型预测控制

• 批准号: 1160014
• 负责人: Lorenz Biegler
• 金额: $ 33.19万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1160014&HistoricalAwards=false
• 关键词: GOALI; Fast; Nonlinear; Model; Predictive

1160014-BieglerFor over three decades, Real-Time Optimization (RTO) and Model Predictive Control (MPC) have emergedas essential technologies for optimal process operation in the chemical and refining industry. More recently,MPC has been extended to Nonlinear Model Predictive Control (NMPC) in order to realize high-performancecontrol of highly nonlinear processes. Moreover, for many applications there is a need for RTO to evolvefrom steady-state optimization models to dynamic models, especially for systems, such as batch and cyclicprocesses, that are never in steady state. Both NMPC and dynamic real-time optimization (D-RTO) allowthe incorporation of first principle process models, which lead to on-line optimization strategies consistentwith higher-level tasks, including scheduling and planning. 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 proposal addresses these issues and furthers the realization of fast on-line dynamic optimization with first principle models. Our previous work led to a class of sensitivity-based algorithms that separate dynamic optimization into background calculations, where most of the computation is performed, and online 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 were developed both for NMPC as well as state and parameter moving horizon estimation (MHE).The intellectual merit of the proposed activity extends the development and analysis of sensitivity-basedon-line optimization with first principle dynamic models, particularly advanced-step NMPC and MHE. Thistransformative proposed work leads to nonlinear model predictive control and on-line dynamic optimizationfor large-scale chemical processes without the limitations of computational feedback delay. Advances will bedeveloped in the solution of background NLPs over multiple sampling times. In addition, we propose toextend advanced-step NMPC and MHE to hybrid systems, where discrete decisions (switches) are allowedat any point in time, and the algorithm suffers no loss in computational efficiency. Moreover, we willincorporate reduced order nonlinear dynamic models, develop specialized NMPC and MHE approaches forthese problems and extend them to dynamic real-time optimization. Broader Impacts:Broader impacts resulting from the proposed activity include the development and application of this approach to a number of challenging power generation processes. Characterized by load changes and dynamics with strong nonlinearities, performance of these multi-stage systems can be greatly improved through efficient NMPC and MHE strategies. These concepts will also be integrated within a comprehensive real-time optimization framework that combines open source optimization and sensitivity codes with a state of the art modeling environment. Finally, graduate training is emphasized as a key component of this proposal. Included in the educational plan are industrial interactions with GE Global Research and the development of courses and materials related to Dynamic Real-Time Optimization.

三十多年来，实时优化（RTO）和模型预测控制（MPC）已经成为化工和炼油行业优化过程操作的基本技术。近年来，为了实现对高度非线性过程的高性能控制，MPC已扩展到非线性模型预测控制（NMPC）。此外，对于许多应用来说，RTO需要从稳态优化模型发展到动态模型，特别是对于从未处于稳态的系统，例如批处理和循环过程。NMPC和动态实时优化（D-RTO）都允许结合第一性原理过程模型，从而导致与更高级别任务一致的在线优化策略，包括调度和计划。然而，反映复杂反应分离现象和多阶段动态操作的更细致的动态优化模型仍有待研究。并解决了时间紧迫的在线应用程序。这里的一个主要问题是，解决这些大规模优化所需的计算时间会导致实现中的反馈延迟，从而降低性能并可能破坏流程的稳定。该方案解决了这些问题，进一步实现了基于第一性原理模型的快速在线动态优化。我们之前的工作导致了一类基于灵敏度的算法，这些算法将动态优化分为后台计算和在线计算，其中大部分计算是执行的，其中扰动问题可以很快得到解决。在线计算因此减少了几个数量级，并且变得非常快，甚至对于大型，复杂的非线性模型也是如此。这些公式既适用于NMPC，也适用于状态和参数移动视界估计（MHE）。所提出的活动的智力优点扩展了基于灵敏度的在线优化的发展和分析，特别是先进的步NMPC和MHE。这种变革性的工作导致了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。在多个采样时间的背景nlp解决方案方面将取得进展。此外，我们建议将进阶NMPC和MHE扩展到混合系统，在混合系统中，允许在任何时间点进行离散决策（开关），并且算法不会损失计算效率。此外，我们将结合降阶非线性动态模型，针对这些问题开发专门的NMPC和MHE方法，并将其扩展到动态实时优化。更广泛的影响：拟议活动产生的更广泛的影响包括对一些具有挑战性的发电过程开发和应用这种方法。这些多级系统具有负载变化和强非线性的动态特性，通过有效的NMPC和MHE策略可以大大提高系统的性能。这些概念也将集成在一个全面的实时优化框架中，该框架将开源优化和灵敏度代码与最先进的建模环境相结合。最后，研究生培训被强调为本建议的关键组成部分。教育计划包括与GE全球研究中心的行业互动以及与动态实时优化相关的课程和材料的开发。