Collaborative Proposal: Feedback Control Theory, Computation, and Design for Scheduling and Blending

协作提案：用于调度和混合的反馈控制理论、计算和设计

基本信息

批准号：
2026980
负责人：
Christos Maravelias
金额：
$ 27.65万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026980&HistoricalAwards=false
关键词：
Collaborative Proposal Feedback Control Theory

项目摘要

The objectives of this project are to develop new theory, design methods, and computational algorithms to improve two essential chemical manufacturing operations: (i) chemical production scheduling; and (ii) raw material and final product blending. New theory is needed to establish the level of performance that can be achieved using automatic feedback and rescheduling as process measurements become available and when large process disturbances occur, such as equipment breakdowns and scheduled task delays. Computationally efficient algorithms are required to ensure the calculations can be carried out in real time; because these fast solutions may be suboptimal, a means of assuring the performance guarantees of the optimal, but slower solution, must be developed. Finally, because of the wide variety of scheduling problems that exist in the chemical processing industries, a corresponding range of optimization methods must be investigated to achieve required performance goals under process uncertainties and disturbances. While this research will target applications in both traditional and new classes of chemical production scheduling and material blending operations, the modeling, design, and solution methods developed in this research will be sufficiently general to be applied to scheduling problems arising in any manufacturing facility having production targets and constraints on materials, workflows, and inventories. A significant innovation of the proposed approach is to enable automatic rescheduling with minimal disruption on the arrival of new measurement information. This automated use of corrective feedback is absent in almost all manufacturing scheduling approaches in use today, and so this work will provide a transformative opportunity for improved business performance across many industrial sectors. The intuitive notion of online, repeated optimization of a model-based forecast as a means of designing an automatic feedback control system has now taken hold in most advanced control technologies applied in the chemical process industries as well as many other industrial sectors such as robotic motion control, flight and land vehicle guidance control, etc. The intellectual merit of the proposed research is to advance the state of the art in designing such systems and linking the design parameters to the performance and robustness properties of the closed-loop operating systems. The target applications in this proposal are characterized by discrete decisions (scheduling) and nonlinear models (blending). Designing the objective function and constraints, and demonstrating the performance under significant model uncertainty for this challenging class of applications will enhance both the underlying fundamental control theory as well as the application of these technologies to complex industrial manufacturing facilities. In batch scheduling, the assumption that all events (both decisions and disturbances) take place at an integer multiple of the sample time is often inaccurate. Therefore, a state estimation method tailored to batch scheduling that can automatically infer the state of the process from the available measurements, regardless of when an event occurs, will be developed. Finally, in the area of raw material and product blending, we face the problem of mixed-integer nonlinear programming (MINLP) models that must be solved repeatedly in real time. To develop reliable online operational capabilities for this challenging class of problems, better solution methods are required. Efforts will focus on solution methods that exploit a known, nearly feasible/optimal solution because, in the context of real-time operations, such a solution is typically available. Moreover, unlike previously proposed solution approaches, this research program will build upon tightening and reformulation methods that have been developed for MILP scheduling models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是开发新的理论，设计方法和计算算法来改善两个基本的化学制造运营：（i）化学生产计划；（ii）原材料和最终产品混合。需要新的理论来确定可以使用自动反馈和重新安排在过程测量中以及发生较大的过程干扰（例如设备故障和预定的任务延迟）时可以实现的绩效水平。需要计算有效的算法以确保可以实时执行计算；由于这些快速解决方案可能是次优的，因此必须开发确保最佳但较慢的解决方案的性能保证的手段。最后，由于化学加工行业中存在的各种调度问题，必须研究相应的优化方法，以实现在处理不确定性和干扰下所需的绩效目标。尽管这项研究将针对传统和新的化学生产计划和材料融合操作的新类别的应用，但在本研究中开发的建模，设计和解决方案方法将足够笼统地应用于在任何制造商中产生的计划问题，该设施对材料，工作流和库存的生产目标和约束。提出的方法的一个重大创新是在新的测量信息到达时，可以自动重新安排自动重新安排。在当今使用的几乎所有制造计划方法中都没有这种自动使用纠正反馈的使用，因此这项工作将为改善许多工业领域的业务绩效提供变革的机会。 The intuitive notion of online, repeated optimization of a model-based forecast as a means of designing an automatic feedback control system has now taken hold in most advanced control technologies applied in the chemical process industries as well as many other industrial sectors such as robotic motion control, flight and land vehicle guidance control, etc. The intellectual merit of the proposed research is to advance the state of the art in designing such systems and linking the design parameters to the performance and robustness properties闭环操作系统。该提案中的目标应用的特征是离散决策（调度）和非线性模型（融合）。设计目标函数和约束，并在这种挑战性的应用类别中证明在重要模型不确定性下的性能将增强基本基本控制理论以及这些技术在复杂的工业制造设施中的应用。在批处理计划中，假设所有事件（决策和干扰）都发生在样本时间的整数倍数上通常是不准确的。因此，将根据可用的测量结果自动推断该过程的状态估计方法，无论发生什么何时发生事件。最后，在原材料和产品融合的领域，我们面临着必须实时反复解决的混合企业非线性编程（MINLP）模型的问题。为了为这个具有挑战性的问题开发可靠的在线运营能力，需要更好的解决方案方法。努力将集中在利用已知的，几乎可行/最佳解决方案的解决方案方法上，因为在实时操作的背景下，这种解决方案通常可用。此外，与以前提出的解决方案方法不同，该研究计划将基于为MILP调度模型开发的收紧和重新制定方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来通过评估来支持的。