Fast Nonlinear Model Predictive Control with First Principle Dynamic Models

CBET-0756264、Biegler实时优化（RTO）和模型预测控制（MPC）是化工和炼油工业中优化过程操作的重要技术。 NMPC和动态实时优化（D-RTO）都允许纳入第一原理过程模型，这导致与更高级别的任务（包括调度和规划）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，更详细的动态优化模型，反映复杂的反应和分离现象和多级动态操作仍然需要解决-和解决的时间紧迫，在线应用。 在这里，一个主要的问题是，需要解决这些大规模的优化计算时间导致反馈延迟的实施，可以降低性能，并可能不稳定的process.This项目解决这些问题，并实现快速在线动态优化与第一原理模型。 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，后台计算执行大部分计算，在线计算可以非常快速地解决扰动问题。 因此，在线计算减少了几个数量级，变得非常快，即使是大型，复杂的非线性模型。 这些公式将被开发用于NMPC以及状态和参数移动时域估计（MHE）。智力价值这项活动的智力价值涉及开发和分析基于灵敏度的在线优化与第一原理动态模型，特别是先进的步骤NMPC和MHE。该工作将为大规模化工过程的非线性模型预测控制和在线动态优化提供理论基础，而不受计算反馈延迟的限制。该研究还涉及扩展到多阶段的动态优化规划和调度决策的更紧密的集成，和强大的问题配方，以处理模型失配和不可测的干扰。这种方法将扩展到滚动时域估计（MHE）问题。MHE战略的非线性模型有显着的优势，观察员和卡尔曼滤波器，但他们的实现需要应用快速优化strategies.Broader ImpactsBroader影响包括应用这种方法在两个具有挑战性的工业应用。其中包括具有详细的在线反应器模型和动态多阶段操作（包括等级变化）的大规模聚合物工艺。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优化和建模环境中。最后，强调研究生培训是一个关键组成部分。教育计划包括工业实习以及与企业范围优化相关的课程和材料的开发。

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.