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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.

这个项目的目标是开发新的理论、设计方法和计算算法来改进两个基本的化学制造操作：(I)化学生产调度；(Ii)原料和最终产品混合。需要新的理论来确定当过程测量变得可用时以及当发生大的过程扰动时，例如设备故障和预定任务延迟时，使用自动反馈和重新调度可以达到的性能水平。需要计算效率高的算法来确保计算可以实时进行；因为这些快速解可能是次优的，所以必须开发一种方法来确保最优解的性能保证，但速度较慢。最后，由于化工加工行业中存在着各种各样的调度问题，因此必须研究相应的优化方法，以在过程不确定性和扰动下实现所需的性能目标。虽然这项研究将针对传统和新型化学生产调度和材料混合操作的应用，但本研究中开发的建模、设计和解决方法将具有足够的通用性，适用于任何具有生产目标和材料、工作流程和库存约束的制造设施中出现的调度问题。提出的方法的一个重要创新是能够在新的测量信息到达时以最小的中断实现自动重新调度。这种纠正性反馈的自动使用在目前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一种手段，在线重复优化基于模型的预测的直观概念现已在应用于化工过程工业以及许多其他工业部门的最先进控制技术中站稳脚跟，如机器人运动控制、飞行和陆地车辆制导控制等。拟议研究的智力价值是促进此类系统设计的最新水平，并将设计参数与闭环系统的性能和鲁棒性特性联系起来。该方案中的目标应用具有离散决策(调度)和非线性模型(混合)的特点。对于这类具有挑战性的应用，设计目标函数和约束，并展示在显著模型不确定性下的性能，将增强基本控制理论以及这些技术在复杂工业制造设施中的应用。在批处理调度中，假设所有事件(决策和干扰)都发生在采样时间的整数倍，这往往是不准确的。因此，将开发一种为批量调度量身定做的状态估计方法，该方法可以根据可用的测量结果自动推断过程的状态，无论事件何时发生。最后，在原料和产品配料方面，我们面临着混合整数非线性规划(MINLP)模型必须实时重复求解的问题。要为这类具有挑战性的问题开发可靠的在线操作能力，就需要更好的解决方法。努力将重点放在利用已知的、几乎可行的/最佳解决方案的解决方法上，因为在实时操作的背景下，这种解决方案通常是可用的。此外，与之前提出的解决方案方法不同，该研究计划将建立在为MILP调度模型开发的收紧和重新制定方法的基础上。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。