Deepak R. Keshwani

Sodium hydroxide pretreatment and enzymatic hydrolysis of coastal Bermuda grass

Coastal Bermuda grass was pretreated with NaOH at concentrations from 0.5% to 3% (w/v) for a residence time from 15 to 90min at 121 degrees C. The pretreatments were evaluated based on total lignin removal and production of total reducing sugars, glucose and xylose from enzymatic hydrolysis of the pretreated biomass. Up to 86% lignin removal was observed. The optimal NaOH pretreatment conditions at 121 degrees C for total reducing sugars production as well as glucose and xylose yields are 15min and 0.75% NaOH. Under these optimal pretreatment conditions, total reducing sugars yield was about 71% of the theoretical maximum, and the overall conversion efficiencies for glucan and xylan were 90.43% and 65.11%, respectively.

High temperature dilute acid pretreatment of coastal Bermuda grass for enzymatic hydrolysis.

Dilute sulfuric acid was used to pretreat coastal Bermuda grass at high temperature prior to enzymatic hydrolysis. After both pretreatment and enzymatic hydrolysis processes, the highest yield of total sugars (combined xylose and glucose) was 97% of the theoretical value. The prehydrolyzate liquor was analyzed for inhibitory compounds (furfural, hydroxymethylfurfural (HMF)) in order to assess potential risk for inhibition during the following fermentation. Accounting for the formation of the inhibitory compounds, a pretreatment with 1.2% acid at 140 °C for 30 min with a total sugar yield of 94% of the theoretical value may be more favorable for fermentation. From this study, it can be concluded that dilute sulfuric acid pretreatment can be successfully applied to coastal Bermuda grass to achieve high yields of monomeric glucose and xylose with acceptable levels of inhibitory compound formation.

Modeling changes in biomass composition during microwave-based alkali pretreatment of switchgrass.

This study used two different approaches to model changes in biomass composition during microwave‐based pretreatment of switchgrass: kinetic modeling using a time‐dependent rate coefficient, and a Mamdani‐type fuzzy inference system. In both modeling approaches, the dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors for changes in amounts of lignin, cellulose, and xylan during the pretreatment. Training and testing data sets for development and validation of the models were obtained from pretreatment experiments conducted using 1–3% w/v NaOH (sodium hydroxide) and pretreatment times ranging from 5 to 20 min. The kinetic modeling approach for lignin and xylan gave comparable results for training and testing data sets, and the differences between the predictions and experimental values were within 2%. The kinetic modeling approach for cellulose was not as effective, and the differences were within 5–7%. The time‐dependent rate coefficients of the kinetic models estimated from experimental data were consistent with the heterogeneity of individual biomass components. The Mamdani‐type fuzzy inference was shown to be an effective approach to model the pretreatment process and yielded predictions with less than 2% deviation from the experimental values for lignin and with less than 3% deviation from the experimental values for cellulose and xylan. The entropies of the fuzzy outputs from the Mamdani‐type fuzzy inference system were calculated to quantify the uncertainty associated with the predictions. Results indicate that there is no significant difference between the entropies associated with the predictions for lignin, cellulose, and xylan. It is anticipated that these models could be used in process simulations of bioethanol production from lignocellulosic materials. Biotechnol. Bioeng. 2010;105: 88–97.

Optimal control strategy for fed-batch enzymatic hydrolysis of lignocellulosic biomass based on epidemic modeling

A mathematical optimal control strategy for feeding operation was developed for fed‐batch enzymatic hydrolysis of dilute acid pretreated lignocellulosic biomass based on a modified epidemic model. Cellulose conversion was maximized and glucose concentration achieved highest possible value over a fixed hydrolysis time. Boundaries of feeding rate and lignin content were set for feasible controls. Using the optimal control feeding strategy, glucose concentration and accumulated cellulose conversion reached up to 77.31 g/L and 72.08% in 100 h, which are 108.76% and 37.50% higher than in batch hydrolysis with same amount of enzyme consumption. Solids content in feeding source has a significant interference on system mass transfer. Optimal control is a useful tool for guiding operations in fed‐batch and continuous processes as it enables process optimization through clear objective functions and feasible controls. Biotechnol. Bioeng. 2015;112: 1376–1382.

Agricultural Industry Advanced Vehicle Technology: Benchmark Study for Reduction in Petroleum Use

agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness, of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.

Epidemic based modeling of enzymatic hydrolysis of lignocellulosic biomass

An epidemic based model was developed to describe the enzymatic hydrolysis of a lignocellulosic biomass, dilute sulfuric acid pretreated corn stover. The process of substrate getting adsorbed and digested by enzyme was simulated as susceptibles getting infected by viruses and becoming removed and recovered. This model simplified the dynamic enzyme “infection” process and the catalysis of cellulose into a two‐parameter controlled, enzyme behavior guided mechanism. Furthermore, the model incorporates the adsorption block by lignin and inhibition effects on cellulose catalysis. The model satisfactorily predicted the enzyme adsorption and hydrolysis, negative role of lignin, and inhibition effects over hydrolysis for a broad range of substrate and enzyme loadings. Sensitivity analysis was performed to evaluate the incorporation of lignin and other inhibition effects. Our model will be a useful tool for evaluating the effects of parameters during hydrolysis and guide a design strategy for continuous hydrolysis and the associated process control.

Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass

A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.

Alkaline Pretreatment of Coastal Bermudagrass for Bioethanol Production

Lignocellulosic materials are regarded as an alternative energy source for bioethanol production to reduce our reliance on fossil fuels. Pretreatment is important for improving the enzymatic digestibility of lignocelluloses to increase the yield of fermentable sugars. Alkaline (sodium hydroxide and lime (calcium hydroxide)) pretreatment of coastal bermudagrass for enhanced reducing sugars recovery was investigated in this study. The effect of NaOH pretreatment at 121°C using 1%, 2% and 3% (w/v) NaOH for 15, 30, 60 and 90 minutes was evaluated first. Lower NaOH concentrations (0.5% and 0.75%) and lower temperatures (50, 80 and 100°C) were then examined. Lime (0.1 g Ca(OH)2/g raw biomass) pretreatment of the biomass was conducted at room temperature, 50°C, 80°C, and 121°C. Total reducing sugars, glucose and xylose were analyzed. The optimal NaOH pretreatment conditions at 121°C for glucose and xylose production are 15 minutes and 0.75% NaOH. However, to maximize total reducing sugars production, pretreatment at 121°C for 30 minutes using 1% NaOH is needed. The highest reducing sugars yield reached up to approximate 86% of theoretical maximum for NaOH pretreatment. Sodium hydroxide is more efficient than lime at 121°C for improved reducing sugars yield. Increasing temperature reduced the optimal pretreatment time at the same lime loading. The reducing sugars production under optimal pretreatment times was enhanced by 8% of theoretical maximum from room temperature to 80°C.

An integrated modeling framework for crop and biofuel systems using the DSSAT and GREET models

Abstract As global demand for food, energy, and water resources continues to increase, decision-makers in these sectors must find sustainable ways to produce and provide for the growing population. While many models have been created to aid in decision-making in these systems, there is a lack of robust integrated models that enable an understanding of the interconnections of these systems. This study develops a modeling framework that explores the connections of the corn and ethanol systems, two major food and energy resources. A crop modeling tool (DSSAT) and a biofuel life cycle assessment tool (GREET) are connected using a service-oriented architecture programming approach. A Python program is developed to connect these two models and run scenario analyses to assess environmental impacts of the integrated system. This paper explores the impact of decisions such as fertilizer use and plant population on environmental effects of greenhouse gases, energy use, and water in the integrated system.

Process Simulation of Dilute Acid Pretreatment of Coastal Bermudagrass for Bioethanol Production

Coastal bermudagrass is a promising lignocellulosic feedstock for bioethanol production. It is well suited for the Southeastern United States where it is currently grown for hay production and nutrient management in animal farming operations. Prior experiments have generated sugar and sugar degradation data from the dilute acid pretreatment and enzymatic hydrolysis of bermudagrass over a range of pretreatment conditions. Experimentally, the yield of total glucose and xylose was maximized at 93 % of the theoretical value for the pretreatment conditions 140 oC and 1.2 % sulfuric acid (w/w) for a residence time of 30 minutes. To explore further potential optimum pretreatment conditions, an artifical neural network (ANN) was created to model both the pretreatment and enzymatic hydrolysis steps using the prior experimental data to train it. The ANN took the only the three pretreatment conditions as inputs and output glucose from the enzymatic hydrolysis step, with an R2 of 0.97, xylose from the pre-hydrolyzate, with an R2 of 0.95, total glucose and xylose from both steps, with an R2 of 0.97, and furfural from the pre-hydrolyzate, with an R2 of 0.93. From the ANN, several optimal sets of pretreatment conditions were found with total glucose and xylose levels greater than 93% of the theoretical yield with the maximum being just under 100% for the conditions 150 oC and 0.9 % sulfuric acid (w/w) for a residence time of 30 minutes. A simple fermentation simulation reinforced the need for co-fermenting xylose and glucose.