LAUSR

Enzymes as industrial biocatalysts offer numerous advantages over traditional chemical processes resulting on improvements in process economy and environmental sustainability. Because enzymes are extensively used in different industrial areas, the enzyme kinetics is an important factor for industry as it is able to estimate the extent of substrate conversion under known conditions and evaluate reactor performance. Furthermore, kinetic modelling is useful in the analysis, prediction, and optimization of an enzymatic process. Thus, kinetic modelling is a powerful tool for biochemical reaction engineering. In addition to the aforementioned, in the industrial technology, modelling together with simulation play a key role because they help to understand how a system behaves under specific conditions, and thus they allow saving on costs and lead times. Enzymatic conversion of renewable cellulosic biomass into biofuels is at the heart of advanced bioethanol production. In the production of bioethanol from cellulosic biomass, enzymatic hydrolysis of cellulose to fermentable sugars accounts for a large portion (∼30%) of the total production costs. Therefore, a thorough understanding of enzymatic hydrolysis is necessary to create a robust model which helps designing optimal conditions and economical system. Nevertheless, it is a challenging task because cellulose is a highly complex substrate and its enzymatic hydrolysis is heterogeneous in nature, and thus the whole process of cellulose conversion to glucose involves more steps than classical enzyme kinetics. This chapter describes the bases of enzyme kinetic modelling, focussing on Michaelis-Menten kinetics, and presents the models classification based on the fundamental approach and methodology used. Furthermore, the modelling of cellulose enzymatic hydrolysis is described, also reviewing some model examples developed for cellulose hydrolysis over the years. Finally, the application of enzyme kinetics modelling in food, pharmaceutical and bioethanol industry is presented.