Evaluating kinetics of enzymatic saccharification of lignocellulose by fractal kinetic analysis

Current understanding and optimization strategies for efficient lignin-enzyme interaction: A review

From energy perspective, with abundant polysaccharides (45-85%), the renewable lignocellulosic is recognized as the 2nd generation feedstock for bioethanol and bio-based products production. Enzymatic hydrolysis is a critical pathway to yield fermentable monosaccharides from pretreated substrates of lignocellulose. Nevertheless, the lignin presence in lignocellulosic substrates leads to the low substrate enzymatic digestibility ascribed to the nonproductive adsorption. It has been reported that the water-soluble lignin (low molecular weight, sulfonated/sulfomethylated and graft polymer) enhance the rate of enzymatic digestibility, however, the catalytic mechanism of lignin-enzyme interaction remains elusive. In this review, optimization strategies for enzymatic hydrolysis based on the lignin structural modification, enzyme engineering, and different additives are critically reviewed. Lignin-enzyme interaction mechanism is also discussed (lignin and various cellulases). In addition, the mathematical models and simulation of lignin, cellulose and enzyme aims for promoting an integrated biomass-conversion process for sustainable production of value-added biofuels.

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.

Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification

The enzymatic hydrolysis of lignocellulosic biomass incorporates many physico-chemical phenomena, in a heterogeneous and complex media. In order to make the modeling task feasible, many simplifications must be assumed. Hence, different simplified models, such as Michaelis-Menten and Langmuir-based ones, have been used to describe batch processes. However, these simple models have difficulties in predicting fed-batch operations with different feeding policies. To overcome this problem and avoid an increase in the complexity of the model by incorporating other phenomenological terms, a Takagi-Sugeno Fuzzy approach has been proposed, which manages a consortium of different simple models for this process. Pretreated sugar cane bagasse was used as biomass in this case study. The fuzzy rule combines two Michaelis-Menten-based models, each responsible for describing the reaction path for a distinct range of solids concentrations in the reactor. The fuzzy model improved fitting and increased prediction in a validation data set.

In vitro and in silico characterization of metagenomic soil-derived cellulases capable of hydrolyzing oil palm empty fruit bunch

Diversification of raw material for biofuel production is of interest to both academia and industry. One attractive substrate is a renewable lignocellulosic material such as oil palm (Elaeis guineensis Jacq.) empty fruit bunch (OPEFB), which is a byproduct of the palm oil industry. This study aimed to characterize cellulases active against this substrate. Cellulases with activity against OPEFB were identified from a metagenomic library obtained from DNA extracted from a high-Andean forest ecosystem. Our findings show that the highest cellulolytic activities were obtained at pH and temperature ranges of 4-10 and 30 °C-60 °C, respectively. Due to the heterogeneous character of the system, degradation profiles were fitted to a fractal-like kinetic model, evidencing transport mass transfer limitations. The sequence analysis of the metagenomic library inserts revealed three glycosyl hydrolase families. Finally, molecular docking simulations of the cellulases were carried out corroborating possible exoglucanase and β-glucosidase activity.

Fractal-like kinetics of the solid-state anaerobic digestion

Total solid content (TS) negatively impacts the methane production efficiency (MPE) of solid-state anaerobic digestion (SS-AD), to which the classic mass action-based kinetics failed to provide a unified explanation. This study revealed that SS-AD reactions actually follow the fractal-like kinetics in light of the surface reactions in crowded SS-AD environment packed with heterogeneous media. The fractal characteristics of the SS-AD kinetics were found increasingly pronounced as TS increased. This study represents the first attempt to resolve the dilemma in SS-AD kinetics with the application of fractal theory. Employing this new concept allows explaining the reduced MPE at high TS and offers an easy assessment of the fractal characteristics of the SS-AD media.