Modeling and simulation techniques have for some time been an important feature of biocatalysis research, often applied as a complement to experimental studies. In this short review, we report on the state-of-the-art process and kinetic modeling for biocatalysis with the aim of identifying future research needs. We have particularly focused on four aspects of modeling: (i) the model purpose, (ii) the process model boundary, (iii) the model structure, and (iv) the model identification procedure. First, one finds that most of the existing models describe biocatalyst behavior in terms of enzyme selectivity, mechanism, and reaction kinetics. More recently, work has focused on extending these models to obtain process flowsheet descriptions. Second, biocatalysis models remain at a relatively low level of complexity compared with the trends observed in other engineering disciplines. Hence, there is certainly room for additional development, i.e., detailed mixing and hydrodynamics, more process units (e.g., biorefinery). Third, biocatalysis models have been only partially subjected to formal statistical analysis. In particular, uncertainty analysis is needed to ascertain reliability of the predictions of the process model, which is necessary to make sound engineering decisions (e.g., the optimal process flowsheet, control strategy, etc). In summary, for modeling studies to be more mature and successful, one needs to introduce Good Modeling Practice and that asks for (i) a standardized and systematic guideline for model development, (ii) formal identifiability analysis, and (iii) uncertainty analysis. This will advance the utility of models in biocatalysis for more rigorous application within process design, optimization, and control strategy evaluation.
(c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.