Modeling and Kinetic Parameter Estimation of the Enzymatic Hydrolysis Process of Lignocellulosic Materials for Glucose Production

Abstract

The necessity to generate advances in the comprehension of modeling and simulation of enzymatic hydrolysis of lignocellulosic materials, based on experimentation, has attracted significant attention currently. This paper proposes a methodology for modeling and parameter estimation of the enzymatic hydrolysis process for sugarcane bagasse. The methodology is composed of three steps: experimental process, approach to the phenomenologically based semi-physical model, and parameter estimation. Experimentally, pretreated sugar cane bagasse was characterized, with 22.60, 13.39, and 23.71% for cellulose, hemicellulose, and lignin, respectively. The enzymatic hydrolysis of cellulose, cellobiose, and glucose concentrations was monitored using a commercial cellulase with an enzymatic activity of 96FPU/mL at 50 °C, pH 4.8, 130 rpm for 74 h. A model was proposed, which includes adsorption effects of the enzymatic complex, pH, temperature, transglycosylation, appearance of new cellulose, and product inhibition. Finally, a global mean percentage squared error cost function (% MSE Global ) was used, adjusting the glucose, cellobiose, and cellulose variables from the estimation of 29 kinetic parameters. It was possible to obtain a % MSE Global of 6.33%, which is lower than the proposed acceptable error of 10%. Thus, the methodology for modeling and parameter estimation achieved good results in this first approximation from experimental development and the formulation of a more complex model.

Modeling Based Identifiability and Parametric Estimation of an Enzymatic Hydrolysis Process of Amylaceous Materials

“…Therefore, J ( p ) is a measurement of the integral quadratic error of the parameter identification adjusted to the standard deviation and mean of each sample for including all information on the measured data in comparison with previous works. 7 , 9 While J ( p ) takes a lower value, p̂ will be a local minimum nearer to the real value of p .…”

“…This structure allows use of the experimental data shown in ref and obtains a point of comparison between the proposed model and the model included in the previously mentioned reference. Therefore, J ( p ) is a measurement of the integral quadratic error of the parameter identification adjusted to the standard deviation and mean of each sample for including all information on the measured data in comparison with previous works. , While J ( p ) takes a lower value, p̂ will be a local minimum nearer to the real value of p .…”

“…This method in dealing with parameter identification can be observed in ref ( 4 ), where a bioprocess identification strategy was proposed based on the generalized bioprocess model to improve the parameter adjustment associated with the reaction kinetics used in the modeling stage, or in refs ( 5 ) and ( 6 ), which use genetic algorithms to perform a global search of the parameters describing bioreactor behavior for the production of ethanol and culture of E. coli MC4110 in a semibatch reactor, respectively. Finally, in ref ( 7 ) was proposed a methodology for modeling the enzymatic hydrolysis process, considering the estimation of some parameters selected arbitrarily, using the mean square error as a cost function. However, that research does not present an analysis of identifiability, an important aspect to consider in this type of model due to its sensitivity at the moment of changing some of its parameters.…”

Mathematical Modeling for the Industrial 2-Mercaptobenzothiazole Batch Production Process

“…Among these, the most popular one is the least-squares (LS) approach, in which the objective function is generally defined as a summation of the 2-norm residuals of output variables between model prediction and measurement. 13,17,21 However, the standard LS approach assumes that different output data have same contributions to the estimation of unknown parameters. 22 Therefore, the weighted least-squares (WLS) method was developed, whereby suitable weights are assigned to the relevant residuals.…”

“…In recent decades, a great number of methods have been developed for implicit parameter estimation. Among these, the most popular one is the least-squares (LS) approach, in which the objective function is generally defined as a summation of the 2-norm residuals of output variables between model prediction and measurement. ,, However, the standard LS approach assumes that different output data have same contributions to the estimation of unknown parameters . Therefore, the weighted least-squares (WLS) method was developed, whereby suitable weights are assigned to the relevant residuals. , The relatively compact form and high computational efficiency of the WLS approach have led to its widespread application to parameter estimation problems in various chemical processes. − The WLS approach assumes that the input variables can be precisely measured and that there are no errors in the input data.…”