Ultrasonic and Fungal Pretreatment of Switchgrass for Biofuel and Bioproduct Applications
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».