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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：
2027091
负责人：
James Rawlings
金额：
$ 29.86万
依托单位：
University of California-Santa Barbara
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=2027091&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，以改善两个基本的化学制造操作：（i）化学生产调度;（ii）原材料和最终产品混合。新的理论是需要建立的性能水平，可以实现使用自动反馈和重新调度的过程测量变得可用，当大的过程干扰发生时，如设备故障和预定的任务延迟。需要计算效率高的算法来确保计算可以在真实的时间内进行;因为这些快速解决方案可能是次优的，所以必须开发一种确保最优但较慢的解决方案的性能保证的方法。最后，由于各种各样的调度问题，存在于化学加工行业，相应的范围内的优化方法必须进行调查，以实现所需的性能目标下的过程的不确定性和干扰。虽然这项研究将针对传统和新的化学生产调度和材料混合操作类的应用程序，建模，设计和解决方案的方法，在这项研究中开发的将是足够普遍的，适用于调度问题所产生的任何制造设施的生产目标和材料，工作流程和库存的限制。所提出的方法的一个重要的创新是，使自动重新安排与新的测量信息的到来时，最小的中断。这种自动使用纠正反馈的方法在当今使用的几乎所有制造调度方法中都不存在，因此这项工作将为改善许多工业部门的业务绩效提供一个变革性的机会。作为设计自动反馈控制系统的手段的基于模型的预测的在线、重复优化的直观概念现在已经在应用于化学过程工业以及许多其他工业部门（例如机器人运动控制、飞行和陆地车辆引导控制）的最先进的控制技术中占据了主导地位。所提出的研究的智力价值是在设计这样的系统和连接的闭环操作系统的性能和鲁棒性的设计参数的最先进的。该提案中的目标应用程序的特点是离散决策（调度）和非线性模型（混合）。设计的目标函数和约束条件，并证明在显着的模型不确定性下的性能，这类具有挑战性的应用程序将提高基础的基本控制理论，以及这些技术的应用，复杂的工业制造设施。在批调度中，所有事件（决策和干扰）都发生在采样时间的整数倍的假设通常是不准确的。因此，一个状态估计方法定制的批量调度，可以自动推断的过程中的状态，从可用的测量，无论何时发生的事件，将被开发。最后，在原材料和产品混合领域，我们面临着必须在真实的时间内反复求解的混合整数非线性规划（MINLP）模型的问题。要为这类具有挑战性的问题开发可靠的在线操作能力，需要更好的解决方法。努力将集中在解决方案的方法，利用已知的，几乎可行的/最佳的解决方案，因为在实时操作的上下文中，这样的解决方案通常是可用的。此外，与以前提出的解决方案的方法，该研究计划将建立在收紧和重新制定的方法，已开发的MILP调度models.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。