CAREER: Integrated Production Management and Process Control of Energy-Intensive Processes

职业：能源密集型工艺的集成生产管理和过程控制

• 批准号: 1454433
• 负责人: Michael Baldea
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454433&HistoricalAwards=false
• 关键词: CAREER; Integrated; Production; Management; Process

1454433 - BaldeaThe 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.

1454433 -Baldea该提案包括一个综合研究和教育计划，以解决能源密集型化学工艺系统与电网的相互作用。通过改变生产计划，可以调整化学过程的用电量，以适应其他电网用户需求的变化：在非高峰时段增加生产，在高峰时段储存和销售过剩的产品，当生产降低时。这种需求响应（DR）操作要求在短期内做出生产管理决策（例如，每小时）的时间间隔，其中过程动态和控制高度相关。受此启发，该项目的研究部分旨在提供一个新的框架，生产调度和连续DR过程的过程控制的优化集成。该方法的前提是嵌入降阶表示的闭环过程动态调度模型。该项目的教育部分将向工程专业学生介绍化学工艺系统与电网之间的关系。研究生和少数民族本科生都将参加研究活动。将开发一套新颖的实践学习活动（其中包括增材制造），并用于培养创造性思维，i）新的第一年工程课程，ii）高级过程控制课程。拟议的外展活动将使低收入家庭的中学生参与STEM学习，并支持他们成为第一代大学毕业生。所提出的集成生产调度和过程控制框架将与工业案例研究进行验证。预计它将获得从业者的认可，并扩大参与DR的工业基础（包括，例如，空气分离，水泥，氯碱，铝，占美国工业用电量的10%以上），导致电网中的净峰值功率需求的相当大的减少。几种其他化学过程（例如，聚合物、废水处理）也提出了类似的调度和控制挑战，可以部署本项目的解决方案来改善其运营。调度和控制的化学过程的集成是具有挑战性的，由于两个活动之间的时间范围的差异，并在所有相关的时间尺度上描述系统的行为所需的模型的大小。该项目探索了一个新的方向来克服这些困难：PI提出了时间尺度桥接的概念，并开发了捕获过程闭环行为的与时间相关的低阶动态模型。这些模型，然后纳入调度制定的约束条件。他还介绍了一个新的控制理论方向，预测-MPC，将这些想法扩展到广泛使用的模型预测控制（MPC）范式。这些发展预计将减少计算工作量需要考虑的动态和控制调度DR化学过程，并赋予鲁棒性的综合框架。此外，提出的故障检测技术将提供新的机制，使进程重新调度的决定。在更广泛的背景下，未来化学品供应链能源效率和经济绩效的提高需要“更智能”的制造，其基础是共享信息和同步各级运营决策，从监管和监督控制到生产调度和规划。调度和控制的集成，这是协调的制造管理和控制层的过程决策层次的关键点，得到了相对较少的关注。因此，拟议的研究解决了一个重要而开放的问题，并将开发智能制造框架中目前缺失的一个环节。