(617d) A Dynamic Model for Cellulosic Biomass Hydrolysis: Validation of Hydrolysis and Product Inhibition Mechanism | AIChE

(617d) A Dynamic Model for Cellulosic Biomass Hydrolysis: Validation of Hydrolysis and Product Inhibition Mechanism


Tsai, C. - Presenter, Technical University of Denmark
Morales-Rodriguez, R. - Presenter, Technical University of Denmark
Gernaey, K. V. - Presenter, Technical University of Denmark
Meyer, A. S. - Presenter, Technical University of Denmark
Sin, G. - Presenter, Technical University of Denmark

Production of bioethanol from agricultural waste such as lignocellulosic biomass forms a candidate alternative renewable energy resource. The process of converting biomass to ethanol is complex with many unit operations including the physical pre-treatment and hydrolysis of lignocellulosic material to free simple sugars. One of the challenges in the process is the underlying enzymatic hydrolysis mechanism. The kinetics of multiple enzymes on insoluble substrate is a challenging phenomenon to describe. Although a number of hydrolysis models has been proposed so far in the literature (e.g. Kadam et al., 2004), few or none of the models were independently subject to experimental validation. This sets the aim of this contribution, which is to perform a validation of a dynamic cellulosic biomass model with particular focus on the validation of the hydrolysis and product inhibition mechanisms.

In order to carry out this analysis a number of dedicated experiments were performed. Avicel® and cellobiose were used as substrates. The end product glucose and/or intermediate cellobiose was added to the initial samples in different concentrations, to better identify their inhibition effect. The enzymes used in the experiments were Novozyme 188 and Celluclast 1.5L. Three sets of experiments were performed focusing exclusively on three different conversion reactions:

(i) conversion of cellulose to cellobiose (set 1),

(ii) conversion of cellulose to glucose (set 2) and

(iii) conversion of cellobiose to glucose (set 3).

A total of nine parameters (k1r, k2r, k3r, K1IG2, K2IG2, K3M, K1IG, K2IG and K3IG) involved in the conversion reaction of cellulose to glucose were estimated from the data set 2. The cellobiose to glucose conversion contains three parameters (k3r, K3M and K3IG) which were estimated from the dedicated experimental data (set 3). These three parameters were then used (assumed fixed) when estimating the parameters involved in the reaction for conversion of cellulose to glucose.

Experimental results show that the reaction from cellobiose to glucose goes much faster than initially assumed by the Kadam model (k3r; 3106.7 versus 285.5 and K3M; 2.377 versus 24.3; both result in a reaction that is ±20 times faster, depending on glucose and cellobiose concentrations). This means that inhibition of cellobiose is less critical than originally described by Kadam et al. (2004). Some calculated parameters (k1r, k2r, K1IG and K2IG2) are similar to the values proposed by Kadam et al. (2004) for cellulose to glucose reaction. However due to the fixed parameters of the cellobiose to glucose reaction (which were found faster), some of the model parameters change as well (e.g. faster cellobiose conversion to glucose results in more inhibition for the cellulose to cellobiose conversion). In general, simulation results (using the estimated parameters) fit well for all the experiments.

Overall the model of Kadam et al. (2004) was found to remain largely valid when describing experimental data collected under different initial concentrations of product inhibition (glucose and cellobiose respectively) as inhibitors. One caveat has to be mentioned, that is that some of the model parameters had to be fine-tuned to get a better fit to the experimental data. Nonetheless the results add credit to the reliability of the model and thereby provide confidence in utilising the model for process development and optimisation studies.


[1]. Sin, G., Meyer, A. S., Gernaey, K. V., 2010. Comput. Chem. Eng. doi:10.1016/j.compchemeng.2010.02.012.

[2]. Kadam, K.L. Rydholm, E.C. McMillan, J.D., 2004. Biotechnol. Prog. 20, 698-705.