BioEnergy Research最新文献

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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This article presents an energy-efficient biochar kiln that produces biochar from agricultural crop residues. The kiln is designed to be easy to operate and has minimal requirements for special operations. It works by heating biomass in a combustion chamber using recirculated pyrogas. The study optimized the process parameters and economics of producing biochar from wheat straw using Response Surface Methodology (RSM) based on Central Composite Design (CCD). The sustainable pyrolyzer had a thermal efficiency of 43.69%, with steady-state operation at 517.68 ± 98.18 °C. It produced an average of 54.57 ± 1.86 kg of biochar per batch, using 36.96 ± 3.66 kg of subabul (Leucaena leucocephala) as fuel and 179.61 ± 2.87 kg of wheat straw as feedstock. Subabul is chosen as a fuel due to its rapid growth, high calorific value, low moisture content, efficient combustion, and minimal smoke emission. The thermogravimetric index (TGI) and calorific value of WSB were 7.14 and 25.91 ± 0.74 MJ/kg, respectively. The benefit–cost ratio and payback period were 2.27 ± 0.16 and 4.92 ± 0.44 months, respectively.

The co-pyrolysis behavior of pine sawdust (PS) biochar doped with copper slag (CS) and the adsorption performance of the prepared CS catalyzed composite adsorbent is studied. Thermogravimetric, SEM, and BET are used to analyze the co-pyrolysis characteristics and the adsorption performance of PS and its mixed samples with different ratios of PS and CS. The co-pyrolysis of CS and PS effectively improves the pyrolysis characteristics. Compared with PS pyrolysis alone, with little change in activation energy, the reaction order changed from 1.5 to 0.5. When the mixing ratio PS:CS = 3:1, the pyrolysis release characteristics were the highest; the most favorable for the pyrolysis reaction. The H₃PO₄, KOH, and ZnCl₂ used to modify PS biochar improved its adsorption capacity. The results show that the modified PS biochar has a larger specific surface area and provides more adsorption sites, effectively improving the adsorption effect. The adsorption capacity of the PS biochar is inversely proportional to the concentration of the Cr(VI) solution. Under the conditions of modified PS at 1:1 based on H₃PO₄, KOH, and ZnCl₂, the concentration of the Cr(VI) solution is 20 mg, and the adsorption effect is best in an acidic environment with pH = 1. The high value-added utilization of metallurgical solid waste and agricultural waste was realized, namely the governance concept of “treating danger with waste.”

Straw management is extremely challenging, and irrational treatment can cause environmental pollution and affect the development of a circular economy in agriculture. The high-value utilization of agricultural straw as an important pathway to promote circular economy and achieve carbon neutrality goals has attracted much attention. Herein, the environmental and economic performance of the main straw-based fuel (straw-to-biochar, ethanol, and biogas) and material (straw-to-board and paper) utilization scenarios in China were compared from a life cycle perspective. This study is based on data from the eFootprint platform and the China Life Cycle Database. The results show that straw production for fuel has a better environmental performance than the production of materials option for water use, acidification potential, eutrophication potential, and respiratory inorganics. Utilizing straw to produce fuels or materials can reduce greenhouse gas emissions by 155–1296 kg CO₂ eq. and decrease primary energy consumption by 8949–31270 MJ. Straw-to-board has the worst energy conservation and emission reduction performance but has the best economic performance with an LCC value of − 46.38 USD. The scenario with the shortest payback time is straw-to-ethanol, which take only 2 years. Sensitivity analysis reveals that the product has the most significant impact on the environmental and economic performance. The study analyzed the environmental impacts and economic benefits of these straw utilization pathways to provide solutions for sustainable straw management.