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.

1160014-Bieglerfor三十年来，实时优化（RTO）和模型预测控制（MPC）具有Emergedas Essential Technologies，用于化学和炼油行业的最佳过程运行。最近，MPC已扩展到非线性模型预测控制（NMPC），以实现高度非线性过程的高表象对照。此外，对于许多应用，RTO需要从稳态优化模型发展到动态模型，尤其是对于从未处于稳定状态的系统，例如批处理和环形程序等系统。 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一致，包括调度和计划，包括调度和计划。但是，反映复杂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吗？并将其作为关键时期的在线应用程序解决。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，从而可以降低性能并可能破坏该过程的稳定。该建议解决了这些问题，并通过第一个原理模型进一步实现了快速的在线动态优化。我们以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背景计算，其中大多数计算都被执行，并在线计算，在线计算，在该计算中，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。这些配方既是针对NMPC，状态和参数移动范围估计（MHE）开发的。拟议活动的智力优点扩展了基于敏感性的基于灵敏度的基于灵敏度的开发和分析，尤其是第一个原理动态模型，尤其是先进的NMPC和MHE。此转换提出的工作导致非线性模型预测控制和在线动态优化，从而在没有计算反馈延迟的情况下进行了大规模化学过程。在多个采样时间的背景NLP的溶液中，进步将在底部。此外，我们建议将高级步骤NMPC和MHE扩展到混合系统，在此，在任何时间点都允许离散决策（开关），并且该算法在计算效率中没有损失。此外，我们将裁定降低订单非线性动态模型，开发专业的NMPC和MHE方法提出问题，并将其扩展到动态实时优化。更广泛的影响：拟议活动造成的更广泛的影响包括将这种方法开发和应用到许多具有挑战性的发电过程中。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，可以极大地改善这些多阶段系统的性能。这些概念还将集成到一个综合的实时优化框架中，该框架将开源优化和灵敏度代码与最先进的建模环境结合在一起。最后，强调研究生培训是该提案的关键组成部分。教育计划中包括与GE全球研究的工业互动以及与动态实时优化有关的课程和材料的开发。