Can Dispersed Biomass Processing Protect the Environment and Cover the Bottom Line for Biofuel?

This paper compares environmental and profitability outcomes for a centralized biorefinery for cellulosic ethanol that does all processing versus a biorefinery linked to a decentralized array of local depots that pretreat biomass into concentrated briquettes. The analysis uses a spatial bioeconomic model that maximizes profit from crop and energy products, subject to the requirement that the biorefinery must be operated at full capacity. The model draws upon biophysical crop input-output coefficients simulated with the Environmental Policy Integrated Climate (EPIC) model as well as market input and output prices, spatial transportation costs, ethanol yields from biomass, and biorefinery capital and operational costs. The model was applied to 82 cropping systems simulated across 37 subwatersheds in a 9-county region of southern Michigan in response to ethanol prices simulated to rise from $1.78 to$3.36 per gallon. Results show that the decentralized local biomass processing depots lead to lower profitability but better environmental performance, due to more reliance on perennial grasses than the centralized biorefinery. Simulated technological improvement that reduces the processing cost and increases the ethanol yield of switchgrass by 17% could cause a shift to more processing of switchgrass, with increased profitability and environmental benefits

Supplementary Figure S2 from The effect of alkali-soluble lignin on purified core cellulases and hemicellulases activities during hydrolysis of pretreated lignocellulosic biomass

Glucan conversion optimization using five core enzymes at different enzyme mass loadings (7.5 mg/g; 15 mg/g and 30 mg/g of glucan), with βG loading at 10% supplementation

Supplementary Tables from The effect of alkali-soluble lignin on purified core cellulases and hemicellulases activities during hydrolysis of pretreated lignocellulosic biomass

Table S1. Core enzymes activities on different substrates.; Table S2. Statistical model regression coefficients for xylan conversion at three protein mass loadings for EA-CS(-) and EA-CS(+) pretreated biomass.; Table S3. The average difference of glucan conversion and their statistical significance for EA-CS(-) and EA-CS(+) under thirty-one enzyme combinations(at three different enzyme mass loadings, with 7.5, 15, and 30 mg protein/g glucan). Note: sig. here means significance. *: P<0.05; **: P<0.01; ***: P<0.001.; Table S4. The model generated optimum mixture hydrolysis predictions were verified at three total enzyme loadings for both EA-CS(-) and EA-CS(+)

Supplementary Figure S3 from The effect of alkali-soluble lignin on purified core cellulases and hemicellulases activities during hydrolysis of pretreated lignocellulosic biomass

Xylan conversion optimization using five core enzymes at different enzyme mass loadings (7.5 mg/g; 15 mg/g and 30 mg/g of glucan), with βG loading at 10% supplementation

Supplementary Figure S1 from The effect of alkali-soluble lignin on purified core cellulase and hemicellulase activities during hydrolysis of extractive ammonia-pretreated lignocellulosic biomass

SDS-PAGE of purified biomass degrading enzymes used in all of the experiments. Here, EG I (lane 1), CBH II (lane 2), CBH I (lane 3), EX (lane 4), βX (lane 5) and βG (lane 6) and marker (lane M

Supplementary Figure S2 from The effect of alkali-soluble lignin on purified core cellulase and hemicellulase activities during hydrolysis of extractive ammonia-pretreated lignocellulosic biomass

Glucan conversion optimization using five core enzymes at different enzyme mass loadings (7.5 mg/g; 15 mg/g and 30 mg/g of glucan), with βG loading at 10% supplementation

Supplementary Tables from The effect of alkali-soluble lignin on purified core cellulase and hemicellulase activities during hydrolysis of extractive ammonia-pretreated lignocellulosic biomass

Table S1. Core enzymes activities on different substrates.; Table S2. Statistical model regression coefficients for xylan conversion at three protein mass loadings for EA-CS(-) and EA-CS(+) pretreated biomass.; Table S3. The average difference of glucan conversion and their statistical significance for EA-CS(-) and EA-CS(+) under thirty-one enzyme combinations(at three different enzyme mass loadings, with 7.5, 15, and 30 mg protein/g glucan). Note: sig. here means significance. *: P<0.05; **: P<0.01; ***: P<0.001.; Table S4. The model generated optimum mixture hydrolysis predictions were verified at three total enzyme loadings for both EA-CS(-) and EA-CS(+)

Supplementary Figure S3 from The effect of alkali-soluble lignin on purified core cellulase and hemicellulase activities during hydrolysis of extractive ammonia-pretreated lignocellulosic biomass

Xylan conversion optimization using five core enzymes at different enzyme mass loadings (7.5 mg/g; 15 mg/g and 30 mg/g of glucan), with βG loading at 10% supplementation

Fermentation performance of Y128 using different WSC fractions.

<p><b>Here,</b> (A) Glucose consumption; (B) Xylose consumption; (C) Ethanol production and (D) Cell growth OD₆₀₀. ACSH: AFEX corn stover hydrolysate; SynH-W: SynH with 20 g/L water phase extract after ethyl acetate-water partitioning; SynH-P: SynH with 20 g/L ethyl acetate phase extract after ethyl acetate-water partitioning; SynH-WSC: SynH with 20 g/L WSC; SynH-Control: SynH-base media with no inhibitors added. Both phenolic compounds and nutrient components were re-dissolved in SynH-base media at 20 g/L. Fermentations were conducted in Erlenmeyer flasks (50 mL at pH 4.8, 30 °C and 150 RPM with inoculum at 0.8 OD₆₀₀.</p

Water-soluble phenolic compounds produced from extractive ammonia pretreatment exerted binary inhibitory effects on yeast fermentation using synthetic hydrolysate

<div><p>Biochemical conversion of lignocellulosic biomass to liquid fuels requires pretreatment and enzymatic hydrolysis of the biomass to produce fermentable sugars. Degradation products produced during thermochemical pretreatment, however, inhibit the microbes with regard to both ethanol yield and cell growth. In this work, we used synthetic hydrolysates (SynH) to study the inhibition of yeast fermentation by water-soluble components (WSC) isolated from lignin streams obtained after extractive ammonia pretreatment (EA). We found that SynH with 20g/L WSC mimics real hydrolysate in cell growth, sugar consumption and ethanol production. However, a long lag phase was observed in the first 48 h of fermentation of SynH, which is not observed during fermentation with the crude extraction mixture. Ethyl acetate extraction was conducted to separate phenolic compounds from other water-soluble components. These phenolic compounds play a key inhibitory role during ethanol fermentation. The most abundant compounds were identified by Liquid Chromatography followed by Mass Spectrometry (LC-MS) and Gas Chromatography followed by Mass Spectrometry (GC-MS), including coumaroyl amide, feruloyl amide and coumaroyl glycerol. Chemical genomics profiling was employed to fingerprint the gene deletion response of yeast to different groups of inhibitors in WSC and AFEX-Pretreated Corn Stover Hydrolysate (ACSH). The sensitive/resistant genes cluster patterns for different fermentation media revealed their similarities and differences with regard to degradation compounds.</p></div