Model-free framework for dynamic characterization and optimization of plant-wide operation

用于全厂运行动态表征和优化的无模型框架

• 批准号: 341217-2007
• 负责人: Lee, JongMin
• 金额: $ 1.57万
• 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: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362643
• 关键词: Model; free; framework; dynamic; characterization

In today's dynamic market and complex manufacturing environment for chemical process industries, operating conditions of all units of a plant must be carefully coordinated in order to remain near an economic optimum. Real time optimization (RTO) solves the problem of coordinating various plant units and has attracted the attention of process industry. The conventional RTO system is based on a steady-state flow-sheet model of a plant, and its formulation can be extremely limited for most integrated plants with their units interacting across different time scales. Other approaches utilizing dynamical behaviour of an entire plant are unlikely to scale to real plant-wide problems in any foreseeable future because of exorbitant computational requirements. Contrary to the paucity of scalable plant-wide dynamics and dynamic optimization scheme for practical systems, the rapid growth of distributed control systems and data acquisition tools has resulted in the availability of massive and high dimensional data. Such data capture the history of various operation modes and events. The objective of my research is to develop fundamental theories and computational frameworks for deriving an improved plant-wide operation strategy by extracting valuable information from historical plant operation data without constructing a plant-wide dynamic model. It would be expected that applications of the suggested framework to the decision hierarchy in process industry will result in significantly improved process and economic performance.

在当今充满活力的市场和复杂的化学过程工业制造环境中，必须仔细协调工厂所有单元的操作条件，以保持接近经济最佳。实时优化（RTO）解决了工厂各单元之间的协调问题，已引起过程工业的重视。传统的RTO系统基于工厂的稳态流程图模型，其公式对于大多数集成工厂来说是非常有限的，因为它们的单元在不同的时间尺度上相互作用。其他利用整个工厂动态行为的方法在任何可预见的未来都不太可能扩展到真正的工厂范围的问题，因为过高的计算要求。与实际系统缺乏可扩展的全厂动态和动态优化方案相反，分布式控制系统和数据采集工具的快速增长导致了大量高维数据的可用性。这些数据捕获各种操作模式和事件的历史。我的研究目标是发展基础理论和计算框架，通过从历史工厂操作数据中提取有价值的信息，而无需构建工厂范围的动态模型，从而获得改进的工厂范围的操作策略。可以预期，将建议的框架应用于流程工业的决策层次将显著改善流程和经济绩效。