Attempts to improve quality control in complex bioproduction processes
require the efficient use of as much knowledge about the process as i
s available. Knowledge of the process conditions, and the relationship
between process variables and product quality may be expressed by a p
rocess model. This article reviews methods that aim to make better use
of empirical data, or of process knowledge derived from such data, in
order to develop and improve the models. Application of these methods
leads to hybrid process models, which combine mathematical models wit
h artificial neural networks and fuzzy expert systems.