Enzymatic hydrolysis of several pretreated lignocellulosic biomasses: Fractal kinetic modelling

Impact of straw returning on soil ecology and crop yield: A review

Several studies have demonstrated the effect of straw return on enhancing soil ecology, promoting sustainable agricultural practices, and cumulative effects on plant yield. Recent studies have focused on straw return methods and their impact on soil nutrient cycling and the overall physicochemical composition of the soil. Despite the substantial progress and successes, several research gaps in these studies require further investigations to harness the full potential of straw return. This review provides a thorough examination of straw diversity and decomposition mechanisms, the effects of straw on soil microorganisms, the interactions between cellulolytic nitrogen-fixing microbes and lignocellulose biomass, as well as nutrient mineralization, organic matter content, and their influence on plant growth and yield. This review also examined the effects of straw return on plant pathogens and its allelopathic impact on plant growth, highlighting research gaps to encourage further studies that could fully realize the potential benefits of straw return in agricultural fields for optimal plant growth.

Analysis of the conversion of cellulose present in lignocellulosic biomass for biofuel production

Among the steps for the conversion of biomass into bioenergy, there is enzymatic hydrolysis. However, factors such as composition, formation of inhibitors, inhibition and enzymatic deactivation can affect the yield and productivity of this process. Lignocellulosic biomass is composed of cellulose, hemicellulose and lignin. However, lignin is organized in a complex and non-uniform way, promotes biomass recalcitrance, which repress the enzymatic attack on cellulose to be converted into glucose, and, consequently, the production of biofuel. Thus, a challenge in enzymatic hydrolysis is to model the reaction behavior. In this context, this study aims to evaluate the performance in enzymatic hydrolysis for the conversion of cellulose present in sugarcane bagasse into glucose. Therefore, modeling and optimization will be proposed to produce high glucose concentration rates. Therefore, a previously developed study will be used, in which the authors proposed a kinetic model for the hydrolysis step. However, as a differential to what has been proposed, the calculation will be carried out evaluating the evaporation, in order to maximize the response to the glucose concentration. Thus, considering evaporation and optimized kinetic parameters, it was possible to obtain high rates of glucose concentration at 204.23 $g.L^{-1.

Different pre-treatments and kinetic models for bioethanol production from lignocellulosic biomass: A review

Lignocellulosic biomass is the generally explored substrate to produce bioethanol for environmental sustainability due to its availability in abundance. However, the complex network of cellulose-hemicellulose-lignin present in it makes its hydrolysis as a challenging task. To boost the effectiveness of conversion, biomass is pre-treated before enzymatic hydrolysis to alter or destroy its original composition. Enzymes like Cellulases are widely used for breaking down cellulose into fermentable sugars. Enzymatic hydrolysis is a complex process involving many influencing factors such as pH, temperature, substrate concentration. This review presents major four pre-treatment methods used for hydrolysing different substrates under varied reaction conditions along with their mechanism and limitations. A relative comparison of data analysis for most widely studied 10 kinetic models is briefly explained in terms of substrates used to get the brief insight about hydrolysis rates. The summary of pre-treatment methods and hydrolysis rates including cellulase enzyme kinetics will be the value addition for upcoming researchers for optimising the hydrolysis process.

Effect of corn straw or corncobs in total mixed ration during peri-puberty on testis development in Hu lambs

Corn straw and corncobs contain large amounts of crude fibers and are widely used in mutton sheep husbandry in northwest China. The aim of this study was to determine whether feeding with corn straw or corncobs affects lamb testis development. A total of 50 healthy Hu lamb at two-month-old (average body weight of 22.3 ± 0.1 kg) were randomly and equally divided into two groups, and the lambs were equally allocated to five pens in each group. The corn straw group (CS) received a diet containing 20% corn straw, whereas the corncobs group (CC) received a diet containing 20% corncobs. After a 77-day feeding trial, the lambs, except the heaviest and lightest in each pen, were humanely slaughtered and investigated. Results revealed no differences in body weight (40.38 ± 0.45 kg vs. 39.08 ± 0.52 kg) between the CS and CC groups. Feeding diet containing corn straw significantly (P < 0.05) increased testis weight (243.24 ± 18.78 g vs. 167.00 ± 15.20 g), testis index (0.60 ± 0.05 vs. 0.43 ± 0.04), testis volume (247.08 ± 19.99 mL vs. 162.31 ± 14.15 mL), diameter of seminiferous tubule (213.90 ± 4.91 μm vs. 173.11 ± 5.93 μm), and the number of sperm in the epididymis (49.91 ± 13.53 × 10⁸/g vs. 19.34 ± 6.79 × 10⁸/g) compared with those in the CC group. The RNA sequencing results showed 286 differentially expressed genes, and 116 upregulated and 170 downregulated genes were found in the CS group compared with the CC group. The genes affecting immune functions and fertility were screened out. Corn straw decreased the mtDNA relative copy number in the testis (P < 0.05). These results suggest that compared with corncobs, feeding corn straw in the early reproductive development stage of lambs increased the testis weight, diameter of seminiferous tubule and the number of cauda sperm.

Multivariate regression and artificial neural network modelling of sugar yields from acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass

Reducing sugar generation from lignocellulosic biomass (LCB) is closely linked with biomass characteristics, pretreatment and enzymatic hydrolysis conditions. In this study curated experimental data from literature was used to develop multivariate regression and artificial neural network (ANN) model considering nine predictors (i.e., cellulose, hemicellulose, lignin content, cellulose-lignin ratio, acid concentration, temperature, time, pretreatment severity, and enzyme concentration). Selected reduced polynomial model (R²: 0.891, Adj. R²: 0.849) suggests positive influence of acid and enzyme, while negative influence of treatment severity, temperature and time on reducing sugar generation. Genetic algorithm-optimized ANN model offered excellent fitness for LCB hydrolysis on training (R²: 0.997), validation (R²: 0.984), and test sets (R²: 0.967). Sensitivity analysis of the ANN predictors suggests lignin and to some extent hemicellulose contents can be inhibitory. Though polynomial models can have simple interpretation, use of optimized ANN offers better predictability in dataset with diverse biomass compositions.

Increased mixing intensity is not necessary for more efficient cellulose hydrolysis at high solid loading

To enhance the cellulose hydrolysis at high solid loadings, an increased mixing intensity is generally required for the high solid loading hydrolysis, while it leads to higher energy consumption. In this study, the impact of mixing intensity on cellulose conversion during hydrolysis at different solid loadings were systematically studied. It was found that the increased mixing intensity is not necessary for more efficient cellulose hydrolysis. For cellulose hydrolysis at higher solid loadings, a lower mixing intensity is needed for a higher cellulose conversion. Although the increased mixing intensity promoted enzyme adsorption, it strengthened product inhibition and caused severer enzyme deactivation. Besides, mixing at the initial stage of cellulose hydrolysis was more crucial, while continuous mixing throughout the hydrolysis was not required for more efficient cellulose hydrolysis. Based on the mechanism study, a combined mixing strategy was developed to achieve efficient cellulose hydrolysis with about two-third reduction in energy consumption.

Potential of time-stepping stochastic models as tools for guiding the design and operation of processes for the enzymatic hydrolysis of polysaccharides - A review

Processes for the enzymatic hydrolysis of polysaccharides in biorefineries are becoming increasingly important. The complex network of reactions involved in polysaccharide hydrolysis can be described by stochastic models that advance in steps of time. Such models have the potential to be important tools for guiding process design and operation, and several have been developed over the last two decades. We evaluate these models. Many of the current stochastic models for the hydrolysis of colloidal polysaccharides use empirical parameters that have no recognized biological meaning. Only one model uses classical parameters of enzyme kinetics, namely specificity constants and saturation constants. Recent stochastic models for the hydrolysis of insoluble cellulose give valuable insights into the molecular-level phenomenon that limit hydrolysis rates. We conclude that, if stochastic models of enzymatic polysaccharide hydrolysis are to become widely used tools for guiding process development, then further improvements are required.