Ultrasonic and Fungal Pretreatment of Switchgrass for Biofuel and Bioproduct Applications
Bibliographic record
Abstract
Cellulosic biomass, including agricultural residues and energy grasses, shows great potential as feedstock for bioethanol and fuel pellet production. Despite clear benefits in terms of greenhouse gas mitigation and bioproduct potential, the commercial production of cellulosic biofuels remains economically challenging in the current state of technology. Biomass pretreatment is a major economic bottleneck that affects the feasibility of a cellulosic biorefinery. Additionally, most of the conventional biomass pretreatment methods result in environmental pollution. The current study addressed this challenge by employing an energy-saving and environmentally benign pretreatment strategy, which features ultrasonic treatment combined with delignification using fungi that have evolved to metabolize the most recalcitrant plant polymers. Ultrasonic pretreatment and solid-state fermentation of switchgrass using Phanerochaete chrysosporium (PC), Trametes versicolor 52J (TV52J), and an engineered mutant strain of T. versicolor (m4D) were employed for improving the enzymatic digestibility and pellet quality of switchgrass. The pretreatment process conditions were optimized using response surface methodology (RSM) featuring a fourfactor, three-level Box-Behnken experimental design. The factors considered in the ultrasonic pretreatment were acoustic power (120, 180, and 240 W), solid–solvent ratio (1/25, 1/20, and 1/15 g/mL), hammer mill screen size (1.6, 3.2, and 6.4 mm), and sonication time (10, 30, and 50 min), while fermentation time (21, 28, and 35 d), fermentation temperature (22, 28, and 34°C), inoculum volume (5, 10, and 15 mL), and hammer mill screen size (1.6, 3.2, and 6.4 mm) were the independent variables for the fungal pretreatment. Information was obtained from microscopic and spectrometric studies, namely epifluorescence imaging, scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR) and Xray diffractometry (XRD), to gain insights into changes in morphology and chemical composition resulting from the ultrasonic and fungal pretreatment. The process model of a fungal pretreatment-based cellulosic ethanol plant with a processing capacity of 2000 tons of switchgrass per day was designed and simulated using SuperPro Designer. The results of ultrasonic delignification of switchgrass showed that the percent delignification ranged between 1.86% and 20.11%. The multivariate quadratic regression model developed for ultrasonic delignification was statistically significant at p < 0.05. SEM and TEM micrographs of the ultrasonic-treated switchgrass revealed that ultrasonic pretreatment resulted in cell wall disruption at the micro- and nano-scales. Among the fungal strains, PC had the shortest optimum fermentation time (21 d) and had the most positive impact on pellet tensile strength (3.1-fold increase) and hydrophobicity. Furthermore, the highest delignification (23.6%) using fungi was observed in the PC-treated switchgrass sample, while the Tv m4D-treated sample gave the highest available carbohydrate of 73.4%. The result of enzymatic hydrolysis with fungus-treated switchgrass at optimum pretreatment conditions showed that pretreatment with the white-rot fungi improved fermentable sugar yield upon enzymatic saccharification with Tv 52J-treated switchgrass, yielding approximately 64.9% and 74% more total reducing sugar before and after pelletization, respectively, than the untreated switchgrass sample. In addition, ultrasonic-assisted enzymatic saccharification of fungus-treated switchgrass led to an approximately 3-fold increase in cellulose digestion in comparison to the untreated switchgrass. The technoeconomic analysis of the modeled fungal pretreatment-based cellulosic ethanol plant showed that the plant’s ethanol yield, capital investment per unit capacity, and unit ethanol production cost were estimated to be 211.9 L/ ton of switchgrass, $ 3.6/L, and $ 1.44/L of ethanol, respectively. A positive net present value (NPV) was generated for the baseline model at an ethanol selling price of $ 1.5/L, which increased by 5-fold for 80% glucose yield.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".