Data-Driven Process Systems Optimization under Uncertain Environment

对于今天的过程工业来说，在建立过程控制和信息技术的基础上，已经建立了数据采集和存储的坚实基础。虽然日常操作产生了大量数据，但流程工业正在朝着数据驱动决策的方向发展。操作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。为了实现这种转变，它需要无缝地将数据分析和过程控制方面与计划和调度的操作优化相结合。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。

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.