Data-driven modeling of refinery reactors

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A rapid development and increased popularity of data processing methods - from statistical analysis via machine leaning to computational intelligence and deep learning - have triggered an interest in utilizing new approaches to modeling processes. It is expected that applications of techniques of artificial intelligence in development of new generation of data driven models will lead to an enhanced utilization of models in design and validation of more efficient and optimal industrial control systems. Advanced data-oriented modelling methods will be able to address many issues related to systems' performance and their improvements.**For these reasons, the project focuses on development of methods and techniques for better understanding of factors influencing modeling of physical process units in refineries. It is important to utilize fast and accurate models of refineries in cases where currently used models require high performance computing, and are practically useless for quick predictability activities and processes of finding the best working configurations for given circumstances. Such knowledge is necessary to develop strategies and methodologies required by new methods of controlling and optimizing operations of refineries.**