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

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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.