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

这项建议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联网的相互作用的子系统。第一个原因是使用复杂的进料，第二个原因是过程的空间异质性，第三个原因是紧密耦合的传热和传质操作，这是过程强化和试图提高操作效率的自然结果。复杂性对优化动态操作提出了许多挑战。 在生物质转化和沥青升级这样的系统中，甚至无法识别所有物种，这对确定最佳操作和了解潜在化学物质构成了重大挑战。我们提出了一种基于统计、机器学习和图论的方法来假设和发现这类系统中的主导化学成分。开发了一种方法，以结合来自多个光谱源的信息，以提高对化学的识别。化学和反应机理的识别使开发精确的过程模型和先进的过程控制成为可能。 通过将过程模型转换为基于网络或图形的模型，解决了油藏空间非均质性所带来的问题。图中节点的可达性概念被用来识别将导致最佳操作和控制的传感器和执行器位置。 在化工过程及其单元的紧耦合网络的情况下，将基于热力学的过程模型和控制器框架与网络模型相结合，开发了植物控制系统的优化重构框架。 建议中描述的综合网络或基于图的方法有可能解决两个主要问题：不确定情况下过程系统的智能和自适应优化操作，以及可持续和最优过程操作。应用于与能源有关的进程也确保了研究结果在实施时产生重大影响。

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.