4. Conclusion
Overall, based on the current case studies, it is concluded that by
adopting the automatic model structure detection framework, it is
possible to construct a hybrid model that combines both physical
knowledge and insights from the data obtained (through machine learning
techniques). Compared to a pure kinetic or a pure data-driven model, the
hybrid model does not require deep physical knowledge of the underlying
system or a large amount of process data. Based on (computational)
experimental verification, it is found that the hybrid model shows
significant potential to be used for process optimisation and
monitoring, and its self-calibration is easy to implement in an online
operating system. Future research will be conducted to further
investigate its advantages in process scale-up and reactor design.
It is important to emphasise that although this work conducted model
structure identification and parameter estimation simultaneously, in
practice this can be executed in sequence depending on the complexity of
the kinetic part of the hybrid model. With more available physical
information, it is possible to develop more accurate kinetic models that
better quantify the process behaviour. The more accurate the kinetic
part is, the less complex the data-driven part will be. However, solving
a highly complex dynamic MINLP problem is mathematically challenging;
thus, developing more effective optimisation algorithms leaves an open
gap for the construction and industrial use of large scale hybrid
models.