BackgroundWhile advantages of biofuel have been widely reported, studies also highlight the challenges in large scale production of biofuel. Cost of ethanol and process energy use in cellulosic ethanol plants are dependent on technologies used for conversion of feedstock. Process modeling can aid in identifying techno-economic bottlenecks in a production process. A comprehensive techno-economic analysis was performed for conversion of cellulosic feedstock to ethanol using some of the common pretreatment technologies: dilute acid, dilute alkali, hot water and steam explosion. Detailed process models incorporating feedstock handling, pretreatment, simultaneous saccharification and co-fermentation, ethanol recovery and downstream processing were developed using SuperPro Designer. Tall Fescue (Festuca arundinacea Schreb) was used as a model feedstock.ResultsProjected ethanol yields were 252.62, 255.80, 255.27 and 230.23 L/dry metric ton biomass for conversion process using dilute acid, dilute alkali, hot water and steam explosion pretreatment technologies respectively. Price of feedstock and cellulose enzymes were assumed as $50/metric ton and 0.517/kg broth (10% protein in broth, 600 FPU/g protein) respectively. Capital cost of ethanol plants processing 250,000 metric tons of feedstock/year was $1.92, $1.73, $1.72 and $1.70/L ethanol for process using dilute acid, dilute alkali, hot water and steam explosion pretreatment respectively. Ethanol production cost of $0.83, $0.88, $0.81 and $0.85/L ethanol was estimated for production process using dilute acid, dilute alkali, hot water and steam explosion pretreatment respectively. Water use in the production process using dilute acid, dilute alkali, hot water and steam explosion pretreatment was estimated 5.96, 6.07, 5.84 and 4.36 kg/L ethanol respectively.ConclusionsEthanol price and energy use were highly dependent on process conditions used in the ethanol production plant. Potential for significant ethanol cost reductions exist in increasing pentose fermentation efficiency and reducing biomass and enzyme costs. The results demonstrated the importance of addressing the tradeoffs in capital costs, pretreatment and downstream processing technologies.

Metabolic engineering to divert carbon flux from sucrose to oil in high biomass crop like sugarcane is an emerging strategy to boost lipid yields per hectare for biodiesel production. Sugarcane stems comprise more than 70% of the crops' biomass and can accumulate sucrose in excess of 20% of their extracted juice. The energy content of oils in the form of triacylglycerol (TAG) is more than twofold that of carbohydrates. Here, we report a step change in TAG accumulation in sugarcane stem tissues achieving an average of 4.3% of their dry weight (DW) in replicated greenhouse experiments by multigene engineering. The metabolic engineering included constitutive co‐expression of wrinkled1; diacylglycerol acyltransferase1‐2; cysteine‐oleosin; and ribonucleic acid interference‐suppression of sugar‐dependent1. The TAG content in leaf tissue was also elevated by more than 400‐fold compared to non‐engineered sugarcane to an average of 8.0% of the DW and the amount of total fatty acids reached about 13% of the DW. With increasing TAG accumulation an increase of 18:1 unsaturated fatty acids was observed at the expense of 16:0 and 18:0 saturated fatty acids. Total biomass accumulation, soluble lignin, Brix and juice content were significantly reduced in the TAG hyperaccumulating sugarcane lines. Overcoming this yield drag by engineering lipid accumulation into late stem development will be critical to exceed lipid yields of current oilseed crops.

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BioSTEAM, the Biorefinery Simulation and Techno-Economic Analysis Modules, is an open-source steady-state process simulator in Python that enables biorefinery design, simulation, and techno-economic analysis (TEA) under uncertainty through its fast and flexible framework. By incorporating uncertainty as a key feature, BioSTEAM aims to evaluate the landscape of design decisions and scenarios for conceptual and emerging technologies. The applicability of BioSTEAM is demonstrated here in the context of (i) the co-production of biodiesel and ethanol from lipid-cane and (ii) the production of second-generation ethanol from corn stover. Economic metrics evaluated in BioSTEAM closely match benchmark designs modeled in proprietary software (SuperPro Designer, Aspen Plus). Through the automation of unit operation sizing and characterization of utility requirements, process waste streams, and make-up water usage, BioSTEAM also generates data needed for environmental sustainability analyses (e.g., via life cycle assessment). Ultimately, BioSTEAM enables rapid and robust process design, mass and energy balances, and TEA to compare established and early-stage technologies and prioritize research, development, and deployment.

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BackgroundDuring cellulosic ethanol production, cellulose hydrolysis is achieved by synergistic action of cellulase enzyme complex consisting of multiple enzymes with different mode of actions. Enzymatic hydrolysis of cellulose is one of the bottlenecks in the commercialization of the process due to low hydrolysis rates and high cost of enzymes. A robust hydrolysis model that can predict hydrolysis profile under various scenarios can act as an important forecasting tool to improve the hydrolysis process. However, multiple factors affecting hydrolysis: cellulose structure and complex enzyme-substrate interactions during hydrolysis make it diffucult to develop mathematical kinetic models that can simulate hydrolysis in presence of multiple enzymes with high fidelity. In this study, a comprehensive hydrolysis model based on stochastic molecular modeling approch in which each hydrolysis event is translated into a discrete event is presented. The model captures the structural features of cellulose, enzyme properties (mode of actions, synergism, inhibition), and most importantly dynamic morphological changes in the substrate that directly affect the enzyme-substrate interactions during hydrolysis.ResultsCellulose was modeled as a group of microfibrils consisting of elementary fibrils bundles, where each elementary fibril was represented as a three dimensional matrix of glucose molecules. Hydrolysis of cellulose was simulated based on Monte Carlo simulation technique. Cellulose hydrolysis results predicted by model simulations agree well with the experimental data from literature. Coefficients of determination for model predictions and experimental values were in the range of 0.75 to 0.96 for Avicel hydrolysis by CBH I action. Model was able to simulate the synergistic action of multiple enzymes during hydrolysis. The model simulations captured the important experimental observations: effect of structural properties, enzyme inhibition and enzyme loadings on the hydrolysis and degree of synergism among enzymes.ConclusionsThe model was effective in capturing the dynamic behavior of cellulose hydrolysis during action of individual as well as multiple cellulases. Simulations were in qualitative and quantitative agreement with experimental data. Several experimentally observed phenomena were simulated without the need for any additional assumptions or parameter changes and confirmed the validity of using the stochastic molecular modeling approach to quantitatively and qualitatively describe the cellulose hydrolysis.

Okra (Abelmoschus esculentus) was dried to a moisture level of 0.1 g water/g dry matter using a microwave-assisted hot air dryer. Response surface methodology was used to optimize the drying conditions based on specific energy consumption and quality of dried okra. The drying experiments were performed using a central composite rotatable design for three variables: air temperature (40-70 °C), air velocity (1-2 m/s) and microwave power level (0.5-2.5 W/g). The quality of dried okra was determined in terms of color change, rehydration ratio and hardness of texture. A second-order polynomial model was well fitted to all responses and high R(2) values (>0.8) were observed in all cases. The color change of dried okra was found higher at high microwave power and air temperatures. Rehydration properties were better for okra samples dried at higher microwave power levels. Specific energy consumption decreased with increase in microwave power due to decrease in drying time. The drying conditions of 1.51 m/s air velocity, 52.09 °C air temperature and 2.41 W/g microwave power were found optimum for product quality and minimum energy consumption for microwave-convective drying of okra.