Fast Nonlinear Model Predictive Control with First Principle Dynamic Models

实时优化（RTO）和模型预测控制（MPC）是化工和炼油行业过程优化操作的重要技术。NMPC和动态实时优化（D-RTO）都允许结合第一性原理过程模型，从而导致与高级任务（包括调度和计划）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，动态优化已经看到越来越多的工业应用，特别是对于固有的瞬态过程。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然需要解决，并作为时间关键的在线应用来解决。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，从而降低性能并可能破坏流程的稳定。本课题解决了这些问题，实现了基于第一性原理模型的快速在线动态优化。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两部分，后台计算执行大部分计算，在线计算快速解决扰动问题。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。这些公式将用于NMPC以及状态和参数移动视界估计（MHE）。该活动的智力价值涉及基于灵敏度的在线优化的第一性原理动力学模型的开发和分析，特别是高级步NMPC和MHE。这项工作将导致大规模化工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。该研究还扩展到多阶段动态优化，以使规划和调度决策更加紧密地集成，以及鲁棒问题公式来处理模型不匹配和不可测量的干扰。该方法将推广到移动视界估计问题。非线性模型的MHE策略与观测器和卡尔曼滤波器相比具有显著的优势，但其实现需要快速优化策略的应用。更广泛的影响更广泛的影响包括该方法在两个具有挑战性的工业应用中的应用。其中包括大规模的聚合物工艺，详细的在线反应器模型和动态多阶段操作，包括品位变化。PI还将考虑气体分离过程的在线动态优化策略。这些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优化和建模环境中。最后，强调研究生培养是一个关键组成部分。教育计划包括工业实习和开发与企业范围优化相关的课程和材料。

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.