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.