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

1160014-Biegler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重要技术。近年来，MPC已经扩展到非线性模型预测控制（NMPC），以实现高性能控制的高度非线性过程。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，特别是对于从不处于稳态的系统，例如批处理和循环过程。NMPC和动态实时优化（D-RTO）都允许将第一原理过程模型，这导致在线优化策略与更高级别的任务，包括调度和规划相一致。然而，更详细的动态优化模型，反映复杂的反应和分离现象和多阶段的动态操作仍然需要解决？并作为时间关键的在线应用程序来解决。这里，一个主要的问题是，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，这会降低性能并可能使过程不稳定。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。我们以前的工作导致了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，后台计算是执行大部分计算的地方，而在线计算是非常快速地解决扰动问题的地方。因此，在线计算减少了几个数量级，变得非常快，即使是大型，复杂的非线性模型。这些公式既适用于NMPC，也适用于状态和参数滚动时域估计（MHE）。所提出的活动的智力价值扩展了基于灵敏度的第一原理动态模型在线优化的开发和分析，特别是先进的步骤NMPC和MHE。这一转变性的工作为大规模化工过程提供了无计算反馈延迟限制的非线性模型预测控制和在线动态优化。在多个采样时间背景NLP的解决方案方面将取得进展。此外，我们建议扩展先进的步骤NMPC和MHE的混合系统，其中离散的决策（开关）是允许在任何时间点，算法遭受没有损失的计算效率。此外，我们将纳入降阶非线性动态模型，开发专门的NMPC和MHE方法为这些问题，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开发和应用这一方法来处理一些具有挑战性的发电过程。这些多级系统具有负载变化和强非线性动态特性，通过有效的NMPC和MHE策略可以大大提高系统的性能。这些概念也将被集成到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，强调研究生培训是这一建议的一个关键组成部分。教育计划包括与GE全球研究的工业互动以及与动态实时优化相关的课程和材料的开发。

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.