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

A Novel Kinetic Modeling of Enzymatic Hydrolysis of Sugarcane Bagasse Pretreated by Hydrothermal and Organosolv Processes

Lignocellulosic biomasses have a complex and compact structure, requiring physical and/or chemical pretreatments to produce glucose before hydrolysis. Mathematical modeling of enzymatic hydrolysis highlights the interactions between cellulases and cellulose, evaluating the factors contributing to reactor scale-up and conversion rates. Furthermore, this study evaluated the influence of two pretreatments (hydrothermal and organosolv) on the kinetics of enzymatic hydrolysis of sugarcane bagasse. The kinetic parameters of the model were estimated using the Pikaia genetic algorithm with data from the experimental profiles of cellulose, cellobiose, glucose, and xylose. The model considered the phenomenon of non-productive adsorption of cellulase on lignin and inhibition of cellulase by xylose. Moreover, it included the behavior of cellulase adsorption on the substrate throughout hydrolysis and kinetic equations for obtaining xylose from xylanase-catalyzed hydrolysis of xylan. The model for both pretreatments was experimentally validated with bagasse concentration at 10% w/v. The Plackett-Burman design identified 17 kinetic parameters as significant in the behavior of process variables. In this way, the modeling and parameter estimation methodology obtained a good fit from the experimental data and a more comprehensive model.

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

This work presents the modeling of an enzymatic hydrolysis process of amylaceous materials considering the parameter identification problem as a basis for the construction of the model. For this, a modeling methodology is modified in order to apply the identifiability property and improve the proposed model structure. A brief theoretical explanation of the identifiability is described. This concept is based on the observability property of a nonlinear dynamic system. The used methodology is based on the phenomenological based semiphysical model (PBSM). This methodology visualizes that the structure of a dynamic model can only improve with new mass or energy balances suggested by model suppositions. Additionally, a computer algorithm is included in the methodology to validate if the model is structurally locally identifiable or know if the parameters are unidentifiable. Also, an optimization algorithm is used to obtain the numeric values of the identifiable parameters and, hence, guarantee the validity of the result. The methodology focuses on the liquefaction and saccharification stages of an enzymatic hydrolysis process. The results of the model are compared with experimental data. The comparison shows low errors of 7.96% for liquefaction and 7.35% for saccharification. These errors show a significant improvement in comparison with previous models and validate the proposed modeling methodology.

Mathematical Modeling for the Industrial 2-Mercaptobenzothiazole Batch Production Process

As an important chemical intermediate, 2-mercaptobenzothiazole (MBT) is widely used in various processes, especially in the rubber industry. However, there is no first-principles model that describes the synthetic process of MBT. This paper focuses on the formulation of a reliable mathematical model represented by a series of differential and algebraic equations for the industrial batch MBT production process. It is difficult to estimate all of the unknown parameters in the model because of the lack of sufficient industrial/experimental data. Thus, a reduced estimable parameter set is derived by performing estimability analysis on the original estimation problem. A multiple-starting-point strategy is then applied to numerically solve the non-convex parameter estimation problem with the weighted least-squares approach. Subsequently, a cross-validation technique is employed to evaluate the generalizability of the proposed model. Finally, it is confirmed that the proposed model produces encouraging prediction performance with regard to independent test data.

The enzyme interactome concept in filamentous fungi linked to biomass valorization

Biomass represents an abundant and inexpensive source of sugars and aromatic compounds that can be used as raw materials for conversion into value-added bioproducts. Filamentous fungi are sources of plant cell wall degrading enzymes in nature. Understanding the interactions between enzymes is crucial for optimizing biomass degradation processes. Herein, the concept of the interactome is presented as a holistic approach that depicts the interactions among enzymes, substrates, metabolites, and inhibitors. The interactome encompasses several stages of biomass degradation, starting with the sensing of the substrate and the subsequent synthesis of hydrolytic and oxidative enzymes (fungus-substrate interaction). Enzyme-enzyme interactions are exemplified in the complex processes of lignocellulosic biomass degradation. The enzyme-substrate-metabolite-inhibitor interaction also provides a better understanding of biomass conversion, allowing bioproduct production from recalcitrant agro-industrial residues, thus bringing greater value to residual biomass. Finally, technological applications are presented for optimizing the interactome at various levels.