An integrated approach to discovery and optimal operation in process and reaction engineering

过程和反应工程中发现和优化操作的综合方法

• 批准号: RGPIN-2019-04600
• 负责人: Prasad, Vinay
• 金额: $ 3.35万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715060
• 关键词: integrated; approach; discovery; optimal; operation

The focus of this proposal is on an integrated approach to discovery and optimal operation in process and reaction engineering. It addresses the various issues presented by complexity in chemical processes: a large number of species/reactions, spatial heterogeneity, and tightly coupled and networked interacting subsystems. The first cause arises from the use of complex feeds, the second from spatial heterogeneity of the processes, and the third because of tightly coupled heat and mass transfer operations that are a natural consequence of process intensification and attempts to improve the efficiency of operation. Complexity presents many challenges to optimal dynamic operation. In systems like biomass conversion and bitumen upgrading, the inability to even identify all species presents a significant challenge to determining optimal operation and understanding the underlying chemistry. We propose a method to hypothesize and discover the dominant chemistry in such systems based on statistical, machine learning and graph-theoretic methods. A method is developed to incorporate information from multiple spectroscopic sources to improve identification of the chemistry. The identification of chemistry and reaction mechanism enable the development of accurate process models and advanced process control. The issues presented by spatial heterogeneity of petroleum reservoirs are addressed by transforming the process models to network or graph-based models. Concepts of reachability of nodes in graphs are invoked to identify the sensor and actuator locations that will result in optimal operation and control. In the case of tightly coupled networks of chemical processes and their units, a thermodynamics-based framework for the process models and controllers is combined with the network models to develop a framework for optimally reconfiguration of the plantwide control system. The integrated network or graph-based approach described in the proposal has the potential to address two major issues: smart and adaptive optimal operation of process systems under uncertainty, and sustainable and optimal process operation. The application to energy-related processes also ensures significant impact when the results of the research are implemented.

该提案的重点是在过程和反应工程中发现和优化操作的综合方法。它解决了化学过程中的复杂性所带来的各种问题：大量的物种/反应，空间异质性，以及紧密耦合和网络化的相互作用的子系统。第一个原因来自复杂进料的使用，第二个原因来自工艺的空间异质性，第三个原因是紧密耦合的传热和传质操作，这是工艺强化的自然结果，并试图提高操作效率。复杂性对最佳动态操作提出了许多挑战。 在生物质转化和沥青升级等系统中，甚至无法识别所有物种对确定最佳操作和了解潜在化学性质提出了重大挑战。我们提出了一种基于统计、机器学习和图论方法来假设和发现此类系统中的主导化学的方法。开发了一种方法，将来自多个光谱来源的信息结合起来，以提高化学成分的鉴定。化学和反应机理的识别使得能够开发精确的过程模型和先进的过程控制。 通过将过程模型转化为网络或图形模型，解决了油藏空间非均质性所带来的问题。图中的节点的可达性的概念被调用，以确定传感器和执行器的位置，将导致最佳的操作和控制。 在化学过程及其单元的紧密耦合网络的情况下，用于过程模型和控制器的基于图解法的框架与网络模型相结合，以开发用于全工厂控制系统的最佳重构的框架。 该提案中描述的集成网络或基于图的方法有可能解决两个主要问题：不确定性下过程系统的智能和自适应优化操作，以及可持续和优化的过程操作。应用到能源相关的过程中，也确保了重大影响时，研究结果的实施。