Hybrid modelling and optimization of process systems

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Prediction of a process system behaviour (pharmaceutical, chemical, refining or similar plants) via simulation has enabled their optimal design and operation.  These systems are modeled by networks where nodes (equipment) transform energy or material, while arcs are material, energy, or information flows.  Current abilities to model process systems are limited by the accuracy of equipment models, as well as the algorithms for solving the entire network.  First principles equipment models often require a very large effort to achieve desired accuracy.  Models combining the first principles and empirical models (so called hybrid models) have been explored as an alternative.   At present there are no well defined methods that determine what types of equations to include in each part of a hybrid model.   Currently, process networks are simulated or optimized by algorithms that are inherently limited by being single computational processes on single CPUs and do not exploit multi-processor computing capabilities.Goal of the research is to enable an order of magnitude improvement in simulation speed for large process systems and a significant reduction of the effort required to build accurate plant equipment models by:I. Development of typical hybrid model structures for specific types of equipments - thereby providing means for rapid equipment modeling.II. A new class of computational architecture for a process network - simulation via node models that behave autonomously and can run simultaneously (agent-based network computations),  andIII. Algorithms enabling optimization of discrete & continuous variables in such agent-based systems.  The research will provide a basis for a new generation of applications in process design and optimal operation of processes in refining, polymers, pharmaceuticals, or chemicals for bio applications.