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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737717
• 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.

对于当今的过程工业，基于已建立的过程控制和信息技术，已经在数据采集和存储方面建立了坚实的基础。虽然日常运营产生了大量数据，但流程工业正在向数据驱动的决策方向发展。对运营和业务数据的分析用于支持流程、工厂和企业范围的优化计划。为了实现这种过渡，需要将数据分析和过程控制方面与规划和调度的运营优化无缝融合。其目标是为工业流程创造附加值，提高流程的灵活性，以应对变化，同时关注能源效率和可持续性。 在过去的几年中，我们已经取得了成果，在不确定性下的过程操作优化使用鲁棒优化和随机规划技术。有了大量可用的操作数据，就有可能改进过程和不确定性模型，并为系统优化做出更实际的决策。在这项研究计划中，我们计划通过实施不确定性下数据驱动优化的系统方法，将过程优化和过程数据分析领域联合收割机结合起来。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）开发复杂过程系统的数据驱动的鲁棒和自适应优化方法; 3）开发软件工具，以实现数据驱动优化问题的建模和解决方案的自动化。拟议的研究将被用来优化系统的系统，从一个单一的过程单元到整个工厂的网站。这将提供重要的理论和计算支持，使过程工业中的管理人员，操作员和工程师能够在信息驱动的环境中使用实时数据和分析进行协作和共同工作。