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

1160014-Biegler三十多年来，实时优化和模型预测控制已成为化工和炼油行业优化过程操作的基本技术。最近，为了实现对高度非线性过程的高性能控制，预测控制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，特别是对于批处理和循环过程等从不处于稳态的系统。NMPC和动态实时优化(D-RTO)都允许引入第一原理过程模型，从而产生与更高层次任务(包括调度和计划)一致的在线优化策略。然而，反映复杂反应和分离现象以及多阶段动态操作的更详细的动态优化模型仍有待解决。并作为时间关键的在线应用程序来解决。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，这可能会降低性能并可能破坏过程的稳定。该方案解决了上述问题，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，该算法将动态优化分为后台计算和在线计算，后台计算执行了大部分计算，而在线计算则非常快地解决了扰动问题。因此，在线计算减少了几个数量级，变得非常快，即使是对于大型、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滑动水平估计(MHE)。建议活动的智力优势扩展了基于灵敏度的在线优化的开发和分析，采用第一原理动态模型，特别是先进的步长NMPC和MHE。这一变革性的工作使大型化工过程的非线性模型预测控制和在线动态优化不受计算反馈延迟的限制。背景NLP的解决方案将在多个采样时间内取得进展。此外，我们还提出将改进的NMPC和MHE算法推广到混合系统中，在任意时刻允许离散决策(切换)，并且该算法在计算效率上没有损失。此外，我们还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开发这一方法并将其应用于一些具有挑战性的发电过程。这些多级系统的负载变化和动态特性具有很强的非线性，通过有效的NMPC和MHE策略可以大大提高系统的性能。这些概念还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敏感性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项提案的一个关键组成部分。教育计划包括与GE Global Research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。

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.