The proposal comprises an integrated research and education plan to address the interaction of energy-intensive chemical process systems with the power grid. The electricity use of chemical processes can be modulated to accommodate the variation of the demand of other grid users by changing production schedules: production is increased during off-peak hours and products generated in excess are stored and sold at peak times, when production is lowered. This demand response (DR) operation calls for making production management decisions over short (e.g., hourly) time intervals, where process dynamics and control are highly relevant. Motivated by this, the research component of the project aims to provide a new framework for the optimal integration of production scheduling and process control of continuous DR processes. The approach is predicated on embedding reduced-order representations of the closed-loop process dynamics in the scheduling model. The educational component of the project will introduce engineering students to the nexus between chemical process systems and the electric grid. Both graduate and minority undergraduate students will be engaged in the research activities. A suite of novel hands-on learning activities will be developed (relying, amongst others, on additive manufacturing), and used to foster creative thinking in, i) a new first-year engineering course and, ii) in the senior process control class. The proposed outreach activities will engage middle school students from low-income families in STEM learning and support their efforts to become first-generation college graduates. The proposed integrated production scheduling and process control framework will be validated with industrial case studies. It is expected to gain practitioner acceptance and expand the industrial base participating in DR (including, e.g., air separation, cement, chlor-alkali, aluminum, which account for over 10% of industrial electricity use in the U.S.), leading to a sizable reduction in net peak power demand in the grid. Several other chemical processes (e.g., polymers, wastewater treatment) pose similar scheduling and control challenges, and solutions from this project can be deployed to improve their operations.

The integration of scheduling and control for chemical processes is challenging due to the discrepancy in time horizons between the two activities and to the size of the models required to describe system behavior over all relevant time scales. The project explores a new direction to overcome these difficulties: the PI proposes the concept of time scale-bridging, and the development of scheduling-relevant low-order dynamic models that capture the closed-loop behavior of a process. These models are then incorporated as constraints in the scheduling formulation. He also introduces a new control-theoretical direction, scheduling-MPC, to extend these ideas to the widely used model predictive control (MPC) paradigm. These developments are expected to reduce the computational effort required to account for dynamics and control in scheduling DR chemical processes, and to impart robustness to the integrated framework. Additionally, the proposed fault detection techniques will provide novel mechanisms for making process rescheduling decisions. In a broader context, future improvements in the energy efficiency and economic performance of the chemical supply chain call for "smarter" manufacturing, based on sharing information and synchronizing all levels of operational decisions, from regulatory and supervisory control, to production scheduling and planning. The integration of scheduling and control, which is the pivotal point for coordinating the manufacturing management and control layers of the process decision-making hierarchy, has received relatively little attention to date. The proposed research thus addresses an important and open problem, and will develop a currently missing link in the smart manufacturing framework.

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University of Texas Austin
United States
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