Recent trends in the modeling of cellulose hydrolysis

Abstract

This work reviews recent trends in the modeling of cellulose hydrolysis, within the perspective of application of kinetic models in a bioreactor engineering framework, including scale-up, design and process optimization. From this point of view, despite the phenomenological insight that mechanistic models can provide, the expectation that more detailed approaches could be a basis for extrapolations to different substrates and/or enzymatic pools is still not fulfilled. The complexity of the lignocellulosic matrix, the different mechanisms of catalytic action, the role of mass transfer limitations and the deviations from ideal mixing are important difficulties for the modeler, which will continue to impose more conservative approaches for scale-up. Nevertheless, the search for more robust models is a very important task, provided that the engineer is aware of their limitations. Data-driven, non-mechanistic models such as artificial neural networks, perhaps in combination with other approaches in the so-called hybrid models, is also a promising alternative

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

“…However, taking into account all these factors at once is a complex task, especially for heterogeneous substrates like apple pomace, a waste product from the apple juice industry. These factors make the modelling of enzyme kinetics in these complex substrates difficult and poorly understood (Al-Zuhair 2008;Andric et al 2010b;Kadam et al 2004;Sousa et al 2011). Most models are able to explain hydrolysis during the initial stages of hydrolysis, but not at the later stages, where the rate of reaction slows down due to end-product inhibition, mass transfer limitations, inactivation of the enzymes and unproductive adsorption of enzymes on the substrate (Bommarius et al 2008;Sarkar and Etters 2004;Zhang et al 1999).…”

“…Most models are able to explain hydrolysis during the initial stages of hydrolysis, but not at the later stages, where the rate of reaction slows down due to end-product inhibition, mass transfer limitations, inactivation of the enzymes and unproductive adsorption of enzymes on the substrate (Bommarius et al 2008;Sarkar and Etters 2004;Zhang et al 1999). A number of kinetic models to predict enzymatic hydrolysis of biomass have been developed and reviewed in the literature which include MichaelisMenten models; empirical models; mechanistic models; models accounting for adsorption; models for soluble substrates; functionally based and structurally based models (Bansal et al 2009;Sousa et al 2011;Wang et al 2011).…”

“…O'Dwyer et al (2008) used ANN to successfully predict the effect of acetyl and lignin contents and crystallinity indices on the digestibility of poplar wood. ANN was also successfully used to model the effect of pretreatment conditions (temperature, time, solid content and sulphuric acid concentration) on glucose production from biomass (Sousa et al 2011). Glucose yield from sugarcane bagasse using different loadings of cellulase and b-glucosidase was predicted by Rivera et al (2010).…”

“…Complex interactions of variables can be evaluated at the same time as more data can be analysed at a time by ANNs (Bhotmange and Shastri 2011;Sousa et al 2011). ANNs offer enormous potential in complex systems such as the bioconversion of lignocellulose (Nodeh 2012;O'Dwyer et al 2008;Sasikumar and Viruthagiri 2010).…”

Continuous and Semicontinuous Reaction Systems for High-Solids Enzymatic Hydrolysis of Lignocellulosics

“…The kinetic model of Zheng et al (2009) is a semi-mechanistic three-reaction kinetic model intended for optimization, economical evaluation and process design. There are more kinetic models particularly useful either for developing and testing understanding at the level of substrate features and multiple enzyme activities (Zhang and Lynd, 2004) or for scale-up, design and process optimization (Sousa et al, 2011;Carvalho et al, 2013). On the other hand, micromixing refers to the contact among fluid elements at the microscopic or molecular scales and is characterized by the dynamic environment renovation around each molecule.…”