Data driven hybrid model identification for control and optimisation of petrochemical and refining plants

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Excellence in operation of process plants is attained by determining the best operating conditions (optimisation**of operation) and controlling their real-time operation to maintain these best conditions. Such control and**optimisation require accurate process models.**Since 1970s there have been two separate streams of model development: (i) rigorous models derived from**the first principles, and (ii) empirical models identified from plant data via methods developed in the**automatic control community. The former usually require a large effort in deriving the first principles**equations and construction of efficient algorithms for solving them. The latter have proceeded along the path**of identifying empirical models from the plant operating data. Even though significant advances have been**made, and there are many instances of very successful use of both types of models for optimisation and control,**respectively, there are still many opportunities for improvement. For instance, identification of models for**processes with large time delays**During the last decade there have been significant advances in artificial intelligence methods for speech**recognition, image recognition and classification, handwriting recognition etc. Foundation for these advances**are deep neural networks comprised on many layers. It has been found that specific neural network structures**are best at creating models for specific types of applications. This research proposes to identify very accurate**models from operating data by developing specific model structures for specific types of processes. It will**combine some first principles equations (e.g. mass and energy balances) with deep neural networks or with**models developed via identification methods from the automatic control field. Models predicting both**steady-state and dynamic behaviour will be developed, with particular attention devoted to models with large**time delays (e.g. ethane/ethylene splitter). Having developed a "standard form" of the model for specific**equipment will enable such models to be readily adjusted to represent specific equipment and be re-used.