Process modelling development through artificial neural networks and hybrid models
Developing fully mechanistic models for bioprocess is expensive and time-consuming. On the other hand, using pure 'black-box' approaches can lead to a misuse of available information, because there are aspects of the process that can be accurately described by simple equations as, for example, mass balances. This work analyses the use of different types of 'black-box' and hybrid models to outline the dynamics of a batch beer production. The hybrid models, combine mechanistic equations with 'black-box' techniques (reserved only for the unclear parts of the system), in order to achieve an efficient use of the available information. The hybrid models can also be called 'grey-box' approaches. To generate the hybrid models, different level of information is introduced into the 'black-box' models, allowing for an interesting model performance comparison in the end. Results demonstrate that the 'black-box' models present a good performance in the range of process conditions used to develop them. However, the inclusion of mechanistic knowledge into the hybrid models increase the model extrapolative capability. In this work, artificial neural networks (ANN) are used as the main technique for both the 'black-box' models and the 'black-box' parts in the hybrid models. (C) 2000 Elsevier Science Ltd. All rights reserved.
process modelling development artificial neural networks hybrid models