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

该项目的目标是发展新的理论、设计方法和计算算法，以改进两个基本的化学制造操作：(i)化学生产调度；（ii）原料和最终产品的混合。需要新的理论来建立性能水平，可以使用自动反馈和重新调度，当过程测量变得可用时，当大的过程干扰发生时，如设备故障和计划任务延迟。需要计算效率高的算法来确保计算可以实时进行；由于这些快速解决方案可能不是最优的，因此必须开发一种方法来确保最优但较慢的解决方案的性能保证。最后，由于化学加工工业中存在各种各样的调度问题，必须研究相应的优化方法，以在过程不确定性和干扰下实现所需的性能目标。虽然本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普遍性，可以应用于任何具有生产目标和材料、工作流程和库存限制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在新测量信息到达时，以最小的干扰实现自动重新调度。这种对纠正反馈的自动化使用在目前使用的几乎所有制造调度方法中都是缺失的，因此这项工作将为许多工业部门提供改进业务绩效的变革性机会。在线的直观概念，基于模型的预测重复优化作为设计自动反馈控制系统的一种手段，现在已经在化学过程工业中应用的最先进的控制技术以及许多其他工业部门，如机器人运动控制，飞行和陆地车辆制导控制等。所提出的研究的智力价值在于推动了设计此类系统的最新技术，并将设计参数与闭环操作系统的性能和鲁棒性联系起来。该方案的目标应用具有离散决策（调度）和非线性模型（混合）的特点。为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，将增强潜在的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。在批调度中，假设所有事件（包括决策和干扰）发生在样本时间的整数倍上通常是不准确的。因此，将开发一种适合批调度的状态估计方法，该方法可以从可用的度量中自动推断流程的状态，而不管事件何时发生。最后，在原料和产品混合领域，我们面临着必须实时反复求解的混合整数非线性规划（MINLP）模型问题。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。努力将集中在利用已知的、几乎可行的/最优解决方案的解决方法上，因为在实时操作的背景下，这样的解决方案通常是可用的。此外，与先前提出的解决方法不同，本研究计划将建立在已经为MILP调度模型开发的紧缩和重新制定方法的基础上。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.