UNS: Real-Time Economic Model Predictive Control of Nonlinear Processes

1506141-克里斯托弗发展下一代先进制造(例如，智能制造、市场驱动的制造和实时能源管理)对于维持美国化学工业的未来竞争力至关重要，化学工业是美国经济的重要组成部分。下一代制造目标的核心涉及紧密集成制造过程的组件，以提供更高的安全性、盈利能力、效率、可变性、产能和可持续性。在化工过程操作的背景下，过程控制系统在控制行动的计算中应考虑诸如可变需求、不断变化的能源价格、可变的原料和产品过渡等经济过程考虑因素，并应能够以动态的方式运行过程以考虑到波动的市场条件。大多数现有的控制基础设施都被设计为在稳态(时不变)操作方面实现可能的最佳性能。从稳态操作到动态或时变操作的转变代表着化工过程操作和化工过程控制的重大范式转变。传统上，化学过程的经济优化是通过两层体系结构来解决的。在上层，利用稳态过程模型计算最优过程操作设定值，完成经济过程优化。下层的反馈控制系统使用这些最优设定值来迫使过程在这些稳态下运行。在下层，模型预测控制(MPC)因其能够对具有输入和状态约束的多变量系统进行最优控制而被广泛应用于化工过程工业中。传统的预测控制公式使用沿有限预测范围的二次型性能指标来引导系统到达最优(经济)稳态。虽然这一策略(稳态优化和操作)传统上用于化工流程工业，但稳态操作不一定是经济上最好的操作策略。经济预测控制(EMPC)是一种使用直接考虑过程经济性的成本函数的预测控制方案，它被引入作为两层经济过程优化和控制的另一种方法。EMPC以可能随时间变化的方式运行系统，以优化工艺经济性。然而，实时EMPC系统的严格设计解决了关键的实际考虑因素(计算控制动作所需的时间、保证稳态操作的性能改进、时变的成本函数以及监控和安全)，这带来了重大的基本和实施挑战。出于这些考虑，本研究计划的主要目的是发展设计和实施由非线性动态模型描述的化工过程的实时经济模型预测控制系统所需的理论和方法，并在具有工业重要性的化工过程的背景下证明所提出的方法的有效性。具体地说，这项研究将解决：a)能够处理实时实现问题的实时EMPC系统的开发和实际考虑，包括实时计算时间和考虑可变能源价格和需求的显式依赖于时间的成本函数；b)开发用于评估考虑时变操作的EMPC系统的性能的监控方案；以及EMPC系统的设计，其明确地考虑过程安全约束并处理由于控制系统部件可能的故障而引起的操作限制，C)将实时EMPC方案应用于大型化工厂模拟器，使用高保真过程模型和最先进的实验超滤/储备渗透水淡化系统，证明EMPC可以显著降低能耗。实时EMPC系统方法的发展有望显著改善非线性过程的运行和性能，从而增强美国经济的竞争力。将研究成果整合到研究生和高级本科课程中，并撰写一本关于《经济模型预测控制》的新书，将使该领域的学生和研究人员受益。

1506141 - ChristofidesThe development of next-generation advanced manufacturing (e.g., Smart Manufacturing, market-driven manufacturing, and real-time energy management) is of paramount importance to sustaining the future competitiveness of the U.S. chemical industry, a vital sector of the U.S. economy. The core of next-generation manufacturing objectives involves tightly integrating the components of the manufacturing processes to deliver increased safety, profitability, efficiency, variability, capacity and sustainability. Within the context of chemical process operations, process control systems should account for economic process considerations such as variable demand, changing energy prices, variable feedstock, and product transitions in the computation of the control actions and should be able to operate a process in a dynamic fashion to account for the volatile market conditions. Most of the existing control infrastructure has been designed to achieve the best possible performance with respect to steady-state (time-invariant) operation. Transitioning from steady-state operation to dynamic or time-varying operation represents a significant paradigm shift in chemical process operations and chemical process control. Economic optimization of chemical processes has traditionally been addressed through a two- layer architecture. In the upper layer, economic process optimization is completed by computing optimal process operation set-points using steady-state process models. These optimal set-points are used by the feedback control systems in the lower layer to force the process to operate on these steady-states. In the lower layer, model predictive control (MPC) has been widely adopted in the chemical process industry because of its ability to optimally control multivariable systems subject to input and state constraints. The conventional formulations of MPC use a quadratic performance index along a finite prediction horizon to steer the system to the optimal (economically) steady- state. While this strategy (steady-state optimization and operation) has been traditionally used in chemical process industries, steady-state operation may not necessarily be the economically best operation strategy. Recently, economic MPC (EMPC), an MPC scheme that uses a cost function that directly accounts for the process economics, has been introduced as an alternative approach to the two-layer economic process optimization and control. EMPC operates systems in a possibly time-varying fashion to optimize the process economics. However, the rigorous design of real-time EMPC systems, which address key practical considerations (time needed to compute control actions, guaranteed performance improvement over steady-state operation, time-varying cost functions, and monitoring and safety) poses significant fundamental and implementation challenges. Motivated by these considerations, the main objective of this research program is to develop the theory and methodology needed for the design and implementation of real-time economic model predictive control systems for chemical processes described by nonlinear dynamic models and to demonstrate the effectiveness of the proposed methods in the context of chemical processes of industrial importance. Specifically, this research will address: a) the development of real-time EMPC systems capable of handling real-time im- plementation issues and practical considerations including real-time calculation time and explicitly time-dependent cost functions accounting for variable energy price and demand, b) the development of monitoring schemes for evaluating the performance of EMPC systems accounting for time-varying operation and the design of EMPC systems that explicitly account for process safety constraints and deal with operational limitations due to possible malfunction of control system components, and c) applications of the real-time EMPC schemes to large-scale chemical plant simulators us- ing high-fidelity process models and a state-of-the-art experimental ultra-filtration/reserve osmosis water desalination system to demonstrate that EMPC can significantly reduce energy consumption. The development of real-time EMPC system methods is expected to signifi- cantly improve the operation and performance of nonlinear processes, thereby enhancing the com- petitiveness of the US economy. The integration of the research results into graduate and senior undergraduate courses and the writing of a new book on "Economic Model Predictive Control" will benefit students and researchers in the field.