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

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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.