Development of adaptive modeling techniques to describe the temperature-dependent kinetics of biotechnological processes

Bioprocesses are quite difficult and expensive to model, since their operation involves microbial growth under constantly changing conditions, with impact on process kinetics and performance. Hence, there is a need and incentive for the improvement of methods for rapid development of simple, though realistic, mathematical models. In this work the modeling of biotechnological processes is studied with focus on developing methodologies that can be used whenever a re-estimation of parameters is necessary. The ethanol fermentation process is used as a case study. The performance of a hybrid neural model and a balance based model, both considering the effect of temperature on the kinetics, are evaluated not only by their accuracy in describing experimental data, but also by the difficulties involved in the adaptation of their parameters. Experiments are performed to develop the two models and further experiments (using sugar cane molasses from a different harvest and a different production medium) validate the methodologies for re-estimation of kinetic parameters.