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.