Microbial communities for valorizing biomass using the carboxylate platform to produce volatile fatty acids: A review.

The carboxylate platform employs a diverse microbial consortium of anaerobes in which the methanogens are inhibited. Nearly all biomass components are digested to a mixture of C1-C8 monocarboxylic acids and their corresponding salts. The methane-arrested anaerobic digestion proceeds readily without needing to sterilize biomass or equipment. It accepts a wide range of feedstocks (e.g., agricultural residues, municipal solid waste, sewage sludge, animal manure, food waste, algae, and energy crops), and produces high product yields. This review highlights several important aspects of the platform, including its thermodynamic underpinnings, influences of inoculum source and operating conditions on product formation, and downstream chemical processes that convert the carboxylates to hydrocarbon fuels and oxygenated chemicals. This review further establishes the carboxylate platform as a viable and economical route to industrial biomass utilization.

Multiple linear regression model for predicting biomass digestibility from structural features.

A total of 147 model lignocellulose samples with a broad spectrum of structural features (lignin contents, acetyl contents, and crystallinity indices) were hydrolyzed with a wide range of cellulase loadings during 1-, 6-, and 72-h hydrolysis periods. Carbohydrate conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the simplified HCH-1 model is valid for predicting lignocellulose digestibility. The HCH-1 model is a modified Michaelis-Menton model that accounts for the fraction of insoluble substrate available to bind with enzyme. The slopes and intercepts of a simplified HCH-1 model were correlated with structural features using multiple linear regression (MLR) models. The agreement between the measured and predicted 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses indicate that lignin content, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility. The 1-, 6-, and 72-h glucan, xylan, and total sugar conversions predicted from structural features using MLR models and the simplified HCH-1 model fit satisfactorily with the measured data (R(2) approximately 1.0). The parameter selection suggests that lignin content and cellulose crystallinity more strongly affect on digestibility than acetyl content. Cellulose crystallinity has greater influence during short hydrolysis periods whereas lignin content has more influence during longer hydrolysis periods. Cellulose crystallinity shows more influence on glucan hydrolysis whereas lignin content affects xylan hydrolysis to a greater extent.

Carboxylate platform: the MixAlco process part 2: process economics.

The MixAlco process employs a mixed culture of acid-forming microorganisms to convert biomass to carboxylate salts, which are concentrated via vapor-compression evaporation and subsequently chemically converted to other chemical and fuel products. To make alcohols, hydrogen is required, which can be supplied from a number of processes, including gasifying biomass, separation from fermentor gases, methane reforming, or electrolysis. Using zeolite catalysts, the alcohols can be oligomerized into hydrocarbons, such as gasoline. A 40-tonne/h plant processing municipal solid waste ($45/tonne tipping fee) and using hydrogen from a pipeline or refinery ($2.00/kg H(2)) can sell alcohols for $1.13/gal or gasoline for $1.75/gal with a 15% return on investment ($0.61/gal of alcohol or $0.99/gal of gasoline for cash costs only). The capital cost is $1.95/annual gallon of mixed alcohols. An 800-tonne/h plant processing high-yield biomass ($60/tonne) and gasifying fermentation residues and waste biomass to hydrogen ($1.42/kg H(2)) can sell alcohols for $1.33/gal or gasoline for $2.04/gal with a 15% return on investment ($1.08/gal of alcohol or $1.68/gal of gasoline for cash costs only). The capital cost for the alcohol and gasification plants at 800 tonne/h is $1.45/annual gallon of mixed alcohols.

Carboxylate platform: the MixAlco process part 1: comparison of three biomass conversion platforms.

To convert biomass to liquid fuels, three platforms are compared: thermochemical, sugar, and carboxylate. To create a common basis, each platform is fed "ideal biomass," which contains polysaccharides (68.3%) and lignin (31.7%). This ratio is typical of hardwood biomass and was selected so that when gasified and converted to hydrogen, the lignin has sufficient energy to produce ethanol from the carboxylic acids produced by the carboxylate platform. Using balanced chemical reactions, the theoretical yield and energy efficiency were determined for each platform. For all platforms, the ethanol yield can be increased by 71% to 107% by supplying external hydrogen produced from other sources (e.g., solar, wind, nuclear, fossil fuels). The alcohols can be converted to alkanes with a modest loss of energy efficiency (3 to 5 percentage points). Of the three platforms considered, the carboxylate platform has demonstrated the highest product yields.

Neural network prediction of biomass digestibility based on structural features.

Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.

Structural features affecting biomass enzymatic digestibility.

The rate and extent of enzymatic hydrolysis of lignocellulosic biomass highly depend on enzyme loadings, hydrolysis periods, and structural features resulting from pretreatments. Furthermore, the influence of one structural feature on biomass digestibility varies with the changes in enzyme loading, hydrolysis period and other structural features as well. In this paper, the effects of lignin content, acetyl content, and biomass crystallinity on the 1-, 6-, and 72-h digestibilities with various enzyme loadings were investigated. To eliminate the cross effects among structural features, selective pretreatment techniques were employed to vary one particular structural feature during a pretreatment, while the other two structural features remained unchanged. The digestibility results showed that lignin content and biomass crystallinity dominated digestibility whereas acetyl content had a lesser effect. Lignin removal greatly enhanced the ultimate hydrolysis extent. Crystallinity reduction, however, tremendously increased the initial hydrolysis rate and reduced the hydrolysis time or the amount of enzyme required to attain high digestibility. To some extent, the effects of structural features on digestibility were interrelated. At short hydrolysis periods, lignin content was not important to digestibility when crystallinity was low. Similarly, at long hydrolysis periods, crystallinity was not important to digestibility when lignin content was low.

Enzymatic hydrolysis of lime-pretreated corn stover and investigation of the HCH-1 Model: inhibition pattern, degree of inhibition, validity of simplified HCH-1 Model.

The inhibition pattern was identified for a reaction system composed of Trichoderma reesei cellulase enzyme complex and lime-pretreated corn stover. Also, the glucose inhibition effect was quantified for the aforementioned reaction system over a range of enzyme loadings and substrate concentrations. Lastly, the range of substrate concentrations and enzyme loadings were identified in which the linear form of the simplified HCH-1 Model is valid. The HCH-1 Model is a modified Michaelis-Menton Model with non-competitive inhibition and the fraction of insoluble substrate available to bind with enzyme. With a high enzyme loading, the HCH-1 Model can be integrated and simplified in such a way that sugar conversion is linearly proportional to the logarithm of enzyme loading. A wide range of enzyme loadings (0.25-50 FPU/g dry biomass) and substrate concentrations (10-100g/L) were investigated. All experiments were conducted with an excess cellobiase loading to ensure the experimental results were not influenced by cellobiose inhibition. A non-competitive inhibition pattern was identified for the corn stover-cellulase reaction system, thereby validating the assumptions of the HCH-1 Model. At a substrate concentration of 10 g/L, glucose inhibition parameters of 0.986 and 0.979 were measured for enzyme loadings of 2 FPU/g dry biomass and 50 FPU/g dry biomass, respectively. At 5 FPU/g dry biomass, glucose inhibition parameters of 0.985 and 0.853 were measured for substrate concentrations of 10 and 100g/L, respectively. The linear form of the HCH-1 Model predicted biomass digestibility for lime-pretreated corn stover over an enzyme loading range of 0.25-50 FPU/g dry biomass and substrate concentration range of 10-100g/L.