Data-Driven Process Systems Optimization under Uncertain Environment

不确定环境下数据驱动的流程系统优化

• 批准号: RGPIN-2019-04584
• 负责人: Li, Zukui
• 金额: $ 2.4万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746877
• 关键词: Data; Driven; Process; Systems; Optimization

For today's process industries, a solid foundation in data acquisition and storage has been developed based on the established process control and information technologies. While huge volumes of data are generated through daily operations, process industries are moving towards data-driven decision making. The analysis of operations and business data is used to support process, plant, and enterprise-wide optimization initiatives. To achieve this kind of transition, it requires seamlessly merging data analytics and process control aspects with operational optimization of planning and scheduling. The target is to create added value to the industrial process, increase the agility of processes to react to changes simultaneously focusing on energy efficiency and sustainability. In the past few years, we have made achievements in process operations optimization under uncertainty using the robust optimization and stochastic programming techniques. With a large amount of operational data available, it is possible to improve the process and uncertainty model and make more practical decisions towards systems optimization. In this research program, we plan to combine the fields of process optimization and process data analytics by implementing systematic methods for data-driven optimization under uncertainty. Specifically, we will: 1) investigate the data-driven process and uncertainty modeling with the aid of data analytics; 2) develop approaches for data-driven robust and adaptive optimization of complex process systems; 3) develop software tools to enable the automation of modeling and solution of the data-driven optimization problem. The proposed research will be utilized to optimize systems of systems, from a single process unit to an entire plant site. This will provide significant theoretic and computational support that enables managers, operators, and engineers in the process industry to collaborate and work together using real-time data and analysis in an information-driven environment.

对于当今的流程行业，基于既定的流程控制和信息技术开发了数据采集和存储的坚实基础。尽管通过日常操作产生了大量数据，但过程行​​业正在朝着数据驱动的决策迈进。运营和业务数据的分析用于支持过程，工厂和企业范围的优化计划。为了实现这种过渡，它需要与计划和调度的操作优化无缝合并数据分析和过程控制方面。目标是为工业过程创造附加价值，提高过程的敏捷性，以对变化的反应同时关注能源效率和可持续性。 在过去的几年中，我们使用强大的优化和随机编程技术在不确定性下优化了过程操作的成就。有了大量的操作数据，可以改善过程和不确定性模型，并为系统优化做出更实际的决策。在该研究计划中，我们计划通过在不确定性下实施系统驱动的优化来结合过程优化和过程数据分析的领域。具体而言，我们将：1）借助数据分析研究数据驱动的过程和不确定性建模； 2）开发用于数据驱动的复杂过程系统的鲁棒和自适应优化的方法； 3）开发软件工具，以实现建模和解决数据驱动的优化问题的自动化。拟议的研究将用于优化从单个过程单元到整个工厂站点的系统系统。这将提供重要的理论和计算支持，使过程行业中的经理，运营商和工程师能够在信息驱动的环境中使用实时数据和分析一起协作和合作。