EAGER:Real-Time:Automated Control-Assisted Data-Based Model Development for Real-Time Feedback Control

美国过程工业未来的经济竞争力要求成功实施下一代控制器，以在真实的时间内优化过程经济性能，并考虑安全因素和传统上被忽视的动态因素，这些因素会显著影响控制回路性能。拟议的探索性研究旨在开发一种新的，自动化的，控制辅助的方法，用于从在线数据中构建基于物理的模型，并为实时应用制定下一代控制器。如果成功的话，拟议中的研究可能会产生影响，在广泛的化学过程industries.Techniques从数据开发模型是目前有限的状态测量信息提供给一个模型识别程序和假设代表系统动态的结构。因此，从过程状态/输入测量中获得用于控制设计的基于物理的模型的能力需要用于获得适当数据的方法，从这些数据可以推导出正确的模型结构。 拟议的研究将：a）开发控制辅助框架，用于从过程中获得在线操作数据，这有助于开发基于物理的模型; B）开发用于将数学函数结构与操作数据趋势相关联的方法; c）探索基于优化控制中的偏微分方程模型的潜在的基于类比或机器学习的计算时间减少技术; d）开发控制器更新技术，以便于控制律设计中使用的函数或参数的自动开发;（e）演示开发的方法，利用高保真过程模型的化学过程模拟，研究开发的技术促进自动化的潜力基于物理的模型开发，用于开发用于实时应用的基于模型的控制器。 建议与工业界进行互动，以验证拟议的方法，并计划通过网络广泛传播结果。除了培养研究生和本科生的研究，课程编制和向初中和高中学生的推广活动，学校学生以及公众被建议专注于吸引更广泛的受众，并鼓励代表性不足的少数民族追求工程事业。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

The future economic competitiveness of the US process industries requires the successful implementation of next-generation controllers to optimize process economic performance in real time and account for both safety considerations and traditionally-neglected dynamics that can significantly impact control loop performance. The proposed exploratory research aims to develop a novel, automated, control-assisted methodology for building physics-based models from on-line data and formulating next-generation controllers for real-time applications. If successful, the proposed research can potentially have an impact in a broad spectrum of chemical process industries.Techniques for developing models from data are currently limited by the state measurement information provided to a model identification procedure and by the structure assumed to represent the system dynamics. Therefore, the ability to derive physics-based models for control design from process state/input measurements requires methods for obtaining appropriate data from which the correct model structures can be deduced. The proposed research will : a) develop a control-assisted framework for obtaining on-line operating data from a process that is conducive to developing a physics-based model; b) develop methods for relating mathematical function structure to operating data trends; c) explore potential analogy or machine learning-based computation time reduction techniques for partial differential equation models in optimization-based control; d) develop controller update techniques which facilitate automated development of functions or parameters used in the control law design; and e) demonstrate the developed methods utilizing chemical process simulations of high fidelity process models to investigate the potential of the developed techniques to promote automated physics-based model development for use in developing model-based controllers for real-time applications. Interactions with industry are proposed to validate the proposed methodologies and broad dissemination of results via the web is planned. In addition to training graduate and undergraduate students in research, curriculum development and outreach activities to middle- and high-school students as well as the general public are proposed focused on engaging a broader audience and encouraging underrepresented minorities to pursue careers in engineering.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.