Robust Regulatory and Economic Predictive Control of Chemical Process Systems

化学过程系统的稳健监管和经济预测控制

• 批准号: RGPIN-2017-05627
• 负责人: Swartz, Christopher
• 金额: $ 2.04万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682753
• 关键词: Robust; Regulatory; Economic; Predictive; Control

Model predictive control (MPC) rapidly became the advanced control method of choice in the chemical process industry after its introduction about three decades ago, and has since been widely adopted across a wide range of industrial sectors. The use of an economic performance objective has given rise to so-called economic MPC (EMPC) algorithms that may be applied either as a single-level control algorithm, or in a cascade manner with EMPC providing set-points to an inner regulatory MPC system. MPC relies on a dynamic model for prediction of future process behavior in order to calculate the control input. No model is perfect, and depending on the level of uncertainty, poor control performance can result if the uncertainty is not accounted for in the control calculation, particularly with regard to process constraint violations. This has led to the development of several robust MPC strategies over the past several years. However, these methods for the most part do not rigorously capture the closed-loop evolution of the process under the assumed uncertainty.*******The primary objective of the proposed research program is to develop robust regulatory and economic MPC strategies based on uncertainty propagation through use of rigorous closed-loop prediction under the assumed uncertainty. The research will utilize a recent formulation for the closed-loop response of a plant under constrained MPC developed within our group in the context of dynamic real-time optimization (DRTO). By more accurately representing the uncertainty propagation, this approach would potentially provide superior performance over current robust MPC methods. The research program will involve the investigation of a number of areas consistent with the overall program objective. This will include performance evaluation over selected alternative robust MPC paradigms, assessment of the computational effort required and methodologies for reducing the computational load, its use in single-layer and cascade EMPC structures, performance analysis under various uncertainty types and characterizations, application to nonlinear MPC, and stability analysis. The efficacy of developed algorithms will be evaluated through application to several case studies, ranging from relatively simple processes to isolate specific features of the algorithm, to more complex, industrially-oriented case studies. Due to the ubiquitous application of MPC technology and its role in the economics of process operation, the potential impact of the proposed research is significant.*******The research involves elements of control, optimization, numerical computation, dynamic modeling, and simulation. HQP trained during the course of the research will consequently be exposed to a range of process systems technologies that will prepare them to make high-level technical contributions to the sectors in which they will be employed.***

模型预测控制(MPC)自三十年前被引入以来，迅速成为化工过程工业中的先进控制方法，并被广泛应用于各个工业部门。经济绩效目标的使用已经引起了所谓的经济MPC(EMPC)算法，其可以作为单级控制算法或者以级联方式应用，其中EMPC向内部监管MPC系统提供设置点。MPC依靠一个动态模型来预测未来的过程行为，以计算控制输入。没有一个模型是完美的，根据不确定性的程度，如果在控制计算中不考虑不确定性，特别是在违反工艺约束的情况下，可能会导致较差的控制性能。这导致在过去几年中制定了几项强有力的货币政策控制战略。然而，这些方法在很大程度上没有严格地捕捉到假设不确定性下过程的闭环演化。*所提出的研究计划的主要目标是通过使用假设的不确定性下的严格的闭环预测来开发基于不确定性传播的稳健的监管和经济MPC策略。这项研究将利用我们小组在动态实时优化(DRTO)背景下开发的受约束MPC下的对象闭环系统响应的最新公式。通过更准确地表示不确定性传播，该方法潜在地提供了优于当前稳健预测方法的性能。研究计划将涉及与总体计划目标一致的一些领域的调查。这将包括对选定的稳健预测控制范例的性能评估、对所需计算工作量和减少计算负荷的方法的评估、其在单层和级联预测预测控制结构中的使用、在各种不确定性类型和表征下的性能分析、在非线性预测预测控制中的应用以及稳定性分析。开发的算法的有效性将通过应用于几个案例研究来评估，从相对简单的过程来分离算法的特定特征，到更复杂的、面向工业的案例研究。由于MPC技术的广泛应用及其在过程操作经济学中的作用，拟议中的研究的潜在影响是显著的。*研究涉及控制、优化、数值计算、动态建模和仿真。因此，在研究过程中培训的HQP将接触到一系列工艺系统技术，使他们做好准备，为他们将受雇的部门做出高水平的技术贡献。*