BioEnergy Research最新文献

Haematococcus pluvialis microalgae have emerged as a prevalent source of antioxidants in cosmetics and nutritional products. Additionally, numerous researchers have posited the potential of this microalgae to produce fatty acid methyl esters (FAME). Nevertheless, the optimization of the production of FAME from H. pluvialis oil has not been investigated. In this study, the transesterification reaction of H. pluvialis bio-oil was optimized using the response surface methodology, resulting in optimal experimental conditions for an oil to methanol ratio of 1:4.17, at a temperature of 80 °C, with a reaction time of 47 min. The resulting FAME was found to not comply with the biodiesel standard in terms of the content of polyunsaturated fatty acids (6.02%), as well as kinematic viscosity (7.02 mm²/s). Further study is required to reduce these parameters in order to ensure biodiesel quality and compliance with the standard. Nevertheless, its high flash point value of 150 °C and its high thermal stability within the temperature range of 211–290 °C suggest the potential for utilization as a biolubricant.

Electrocatalytic upgradation of biomass for chemicals and energy production is an emerging approach to address the environmental issues related to chemicals and energy production. If coupled with renewable energy, this approach will further enhance the sustainability goals for the future energy and chemical sector. This work critically reviews the progress on oxidative and reductive electrocatalytic upgrading of biomass-derived chemicals such as glycerol, sorbitol, levulinic acid, 5-hydroxymethylfurfural, furfural, and bio-oil to value-added products, including 2.5-dimethyl tetrahydrofuran, 2.5-dihydroxy methyl tetrahydro furan, 2-hydroxymethyl-5-(methyl amino methyl) furan, and 2,5-furan dicarboxylic acid with simulations production of hydrogen (H₂) energy. The role of the mediator in electrocatalytic upgradation serves as a high-efficiency catalytic platform for oxidation and reduction reactions. Pd and Ru exhibit promising attributes such as durability and superior electrocatalytic hydrogenation performance. Additionally, this review discusses various methods for enhancing biofuel through a multitude of approaches, such as hydrocracking, hydrotreatment, supercritical fluid processing, steam reforming, catalytic cracking, esterification, emulsification, hydrodeoxygenation, and electrocatalytic hydrogenation. Techno-economic assessment of electrocatalytic conversion of biomass to chemicals and energy are explored to identify the key contributing factors toward the economic viability of electrocatalytic upgradation of biomass for chemical and energy. Finally, research gaps are identified for further work along with economic assessment of electrocatalytic upgradation of biomass technology with and without integration of renewable energy.

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Effective pretreatment is essential for successfully utilizing renewable resources such as lignocellulosic biomass in the production of bioethanol. In this study, ternary deep eutectic solvents (DESs), namely choline chloride/lactic acid/glycerol (ChCl/LA/Gly), choline chloride/oxalic acid/glycerol (ChCl/OA/Gly), choline chloride/lactic acid/ethylene glycol (ChCl/LA/EG), and choline chloride/oxalic acid/ethylene glycol (ChCl/OA/EG) were prepared and employed for the pretreatment of cellulose-rich Napier grass (NG). Post treatment, the NG hydrolysate was subjected to enzymatic saccharification followed by ethanol fermentation. The results showed effective delignification of NG after treatment with the prepared ternary DESs, with ChCl/LA/EG removing a maximum of 92.89% lignin. The efficiency of the prepared DESs is attributed to their low densities, pH, and viscosity. Enzymatic saccharification of ChCl/LA/EG-treated NG resulted in a 1.68 fold increase in reducing sugar yield compared to that of untreated NG. All pretreated NG produced more bioethanol via a separate hydrolysis and fermentation (SHF) process than untreated NG after Saccharomyces cerevisiae fermentation. A maximum of 0.37 g bioethanol/g of biomass was obtained from the one-pot process using ChCl/LA/Gly pretreatment. FTIR and XRD analyses of untreated and pretreated NG corroborated the efficacy of the ternary DESs on cellulose recovery and delignification. Also, enzymatic and microbial inhibition studies on the prepared DESs show their potential to be employed in a one-pot process for biorefinery. The results of the present investigation show the potential of utilizing eco-friendly DESs and renewable resources for the production of bioethanol, a viable option to fossil fuels.

The study aimed to assess the efficiency of higher xylanase and lower cellulase-producing bacteria as a whole-cell biocatalyst for surface modification of banana pseudostem fibers in an eco-friendly and cost-effective manner. The ability of bacterial biocatalysts to alter fibers’ surface during fiber-biocatalyst interaction in liquid media was determined by analyzing fibers' chemical composition (cellulose, hemicellulose, and lignin), surface color, thickness, surface morphology, and spectral attributes. Results indicated that the production of xylanase by Bacillus licheniformis (1.23 IU/mg of protein) and Bacillus pumilus (1.29 IU/mg of protein) was almost 15 times more than cellulase produced by them. The content of alpha-cellulose (46.7%), hemicelluloses (21.6%), and lignin (11.7%) was slightly decreased in B. licheniformis-treated BPFs. The surface color (whiteness index) was positively improved, indicating color changes (ΔE) of 6.37 and 8.28 for B. pumilus and B. licheniformis-treated fibers, respectively. The thickness of fibers pretreated with B. lichenifiormis (160.75 ± 22.43 mm) and B. pumilus (202.655 ± 24.83) was reduced by 31.90 and 14.14%, respectively. Scanning electron micrograph studies revealed the increased roughness and grooves on the biocatalysts-treated fiber surface. Spectral analysis confirmed the stretching and deformation of inter and intra-molecular bonds of components of banana fibers. Briefly, the study highlights the effectiveness of whole-cell bacterial biocatalysts as a greener and cheaper tool for the surface modification of banana pseudostem fibers.

Biochar production from biomass sources is a highly complex, multistep process that depends on several factors, including feedstock composition (e.g., type of biomass, particle size) and operating conditions (e.g., reaction temperature, pressure, residence time). However, the optimal set of variables for producing the maximum amount of biochar with the required characteristics can be determined by using machine learning (ML). In light of this, the purpose of this paper is to examine ML applications in biochar processes for the production of sustainable fuels. First, recent developments in the field are summarized, and then, a detailed review of ML applications in biochar production is presented. Following that, a bibliometric analysis is done to illustrate the major trends and construct a comprehensive perspective for future studies. It is found that biochar yield is the most common target variable for ML applications in biochar production. It is then concluded that ML can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties of biochar which can be later used for decision-making, resource allocation, and fuel production.

Production of fermentable sugars from renewable lignocellulosic biomass could provide a sustainable substrate for biofuel production by microbial fermentation. However, the inherent structure of lignocellulose largely hinders enzymatic saccharification efficiency to obtain sugars, thus the development of efficient pretreatment methods to reduce biomass recalcitrance is an important issue. In this study, the effects of two typical processes including hydrothermal (HTP) and citric acid pretreatments (CAP) on the enzymatic hydrolysis yield of reed biomass were investigated. The results indicate that, when performing HTP at 180 °C for 120 min to deconstruct reed, the pretreated biomass led to a higher glucose yield of 87.1% after 72 h enzymatic hydrolysis with cellulase (10 FPU/g substrate). Furthermore, under the pretreatment conditions with 6% (w/v) citric acid, 150 °C, and 60 min, the glucose yield of pretreated reed was 63.4% after hydrolysis with cellulase (10 FPU/g substrate). In this case, adding Tween 80 (100 mg/g substrate) significantly facilitated the enzymatic saccharification activity resulting in the glucose yield of CA-pretreated reed to 84.2%. Compared with the untreated reed, the glucose yield of pretreated residues after HTP and CAP was largely improved by 4.97–5.18 folds which was mainly due to the enhanced crystallinity (50.0–53.2% vs. 45.9%). The total fermentable sugars of 34.7–34.8 g can be produced from 100 g of raw reed biomass based on the proposed HTP and CAP processes. The mechanism of enhanced enzymatic hydrolysis after pretreatments was elucidated through physicochemical characterization techniques. In summary, the proposed pretreatments show high potential application for biorefinery from renewable, abundant reed biomass.

Heterogeneous acid catalysis provides a sustainable and cost-effective replacement to homogeneous catalysts, as it is easily accessible and reusable. In this research, biodiesel was synthesized from palm fatty acid distillate (PFAD) by way of esterification utilizing a heterogeneous acid catalyst composed of CuO/WPLAHC-S, which was derived from waste palm leaves-based hydrochar. The catalyst was characterized using TGA, XRD, FESEM, EDX, BET, FT-IR, Raman, and acid density. The synthesized catalyst was found to have remarkable stability during the thermal test. The BET surface area, pore volume, and pore width of the synthesized catalyst were measured at 118.47 m²/g, 0.05 cm³/g, and 8.15 nm, respectively. The significant acidity density (18.57 mmol/g) facilitated the simultaneous esterification process. The investigation found that the copper-doped activated hydrochar, treated with sulfuric acid, exhibited excellent catalytic activity. A high yield of 98.34% fatty acid methyl ester (FAME) was achieved under mild conditions with the use of a catalyst concentration of 2 wt.%, a methanol-to-PFAD molar ratio of 15:1, and a reaction temperature of 80 °C for a duration of 3 h. For statistical analysis, one-way ANOVA was applied to verify the experimental data set for PFAD esterification optimization parameters by predetermining a 95% confidence interval. All of the esterification parameters met the expectation of a p-value less than 0.05 indicating its significant impact on FAME production. The catalyst exhibited excellent performance by maintaining a FAME yield of over 90% after four reaction cycles without requiring reactivation. The study demonstrates the efficacy of CuO/WPLAHC-S as a promising acid catalyst derived from waste palm leaves-based hydrochar for biodiesel production from PFAD.

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Microalgae show great potential as a biodiesel feedstock, primarily attributed to their rapid growth rates and higher lipid content. Biomass pretreatment is a critical step in biodiesel production, as it is essential for providing unsaturated and saturated fatty acids to produce fatty acid methyl ester (FAME). The present study investigates the effect of two-stage cultivation of nutrient starvation and ultrasonic-assisted solvent extraction technique from Scenedesmus dimorphus on lipid content and productivity. Preliminary studies facilitated the identification of an appropriate range to which the variables must be optimized. Scenedesmus dimorphus was obtained from the National Repository for Microalgae and Cyanobacteria [NRMC-F]. The medium optimization resulted in a higher lipid content (38.45%) in the M4N medium under starved conditions. The generated model (R² = 0.998) forecasted the lipid content of 0.317 g/L after 87.19 min of sonication under the sonication power (65.77 W) with a molar ratio [chloroform:methanol] (1.3:1), which was experimentally validated. Under optimized conditions, the efficiency of extraction has been improved from 64.6 to 72.5% with a maximum biodiesel yield of 25.4 wt.%. In summary, this research successfully identified the optimal growth medium and pretreatment conditions, ultimately maximizing the production of biodiesel.

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This study aims to optimize bio-oil production through microwave pyrolysis of Triplochiton scleroxylon sawdust (Ayous). After a physicochemical characterization of the sawdust, response surface methodology via centered composite design was used to investigate the influence of pyrolysis factors on bio-oil yield and determine the optimal pyrolysis conditions. The studied pyrolysis factors were microwave power (W), irradiation time (min), and biochar (%) as wave absorber. Finally, the bio-oil produced under optimal conditions was characterized by GC–MS. It emerges from this study that Ayous biomass has physicochemical properties that can be valorized for bio-oil production, with a high volatile matter content (63.2 ± 2%) and low ash content (2.8 ± 0.3%). The optimization study of bio-oil yield shows that all factors have significant effects with a statistical significance level of 5% (p < 0.05) on the measured parameters. The optimal bio-oil yield of 44.82% is obtained at optimal conditions: microwave power of 576 W, irradiation time of 28 min, and a biochar (wave absorber) input of 3.18%. The bio-oil produced under optimal conditions has a pH of 4.6 ± 0.7 and a water content of 25 ± 1.2%. Compound identification of this bio-oil by GC–MS identified families of compounds including alkanes (13.90%), esters (5.88%), alcohols (1.10%), and high molecular weight phenolic compounds (58%). The produced bio-oil can be used as biofuel or in industrial applications. Nevertheless, further processing steps are needed to lower the water content and acidity of the oil.

Whey, a cheese production byproduct, can be anaerobically digested to reduce pollution and generate energy. Yet, stability is challenging due to organic content sensitivity and influent fluctuations. The present work attempts to implement the mechanistic model and machine learning (ML) models (support vector regression (SVR) and artificial neural networks (ANNs)) together to predict the concentration dataset of substrate 1 (S₁) (i.e., carbohydrates and proteins), substrate 2 (S₂) (i.e., glucids and amino acids), VFA, and methane (CH₄) as a function of input independent variables, namely time and organic loading rate (OLR). The R² values for S₁, S₂, VFA, and CH₄ obtained through the mechanistic model remained as 0.953, 0.918, 0.84, and 0.976, respectively; for ANN models, 0.982, 0.928, 0.958, and 0.99; and for SVR models, 0.984, 0.939, 0.938, and 0.999, respectively. ML models have been discovered to be among the most precise and versatile compared to the mechanistic model. Moreover, other performance metrics, such as RMSE (0.022–2.177), MRE (0.007–0.100), and AARE (0.008–0.104) for ANN and RMSE (0.083–1.961), MRE (0.021–0.091), and AARE (0.037–0.089) for SVR, are obtained, indicating good prediction performances for both ML models. SVR and ANN models excel, aligning concentration curves to the optimum line when input parameters are adjusted, unlike the subpar traditional-based mechanistic model. Therefore, ML methods offer a tool to predict anaerobic digestion more effectively, enhancing design and operations.

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