Developing fully mechanistic models for bioprocess is expensive and time-co
nsuming. 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 hybri
d models, combine mechanistic equations with 'black-box' techniques (reserv
ed only for the unclear parts of the system), in order to achieve an effici
ent use of the available information. The hybrid models can also be called
'grey-box' approaches. To generate the hybrid models, different level of in
formation is introduced into the 'black-box' models, allowing for an intere
sting model performance comparison in the end. Results demonstrate that the
'black-box' models present a good performance in the range of process cond
itions used to develop them. However, the inclusion of mechanistic knowledg
e into the hybrid models increase the model extrapolative capability. In th
is work, artificial neural networks (ANN) are used as the main technique fo
r both the 'black-box' models and the 'black-box' parts in the hybrid model
s. (C) 2000 Elsevier Science Ltd. All rights reserved.