BioEnergy Research最新文献

The extraction of Moringa oleifera oil (MOO) from its kernel under microwave irradiation and n-hexane as solvent was modeled using statistical (response surface methodology - RSM) and four machine learning (ML) approaches (gradient boosting with decision trees – GBDT, support vector machine – SVM, artificial neural network – ANN, and extreme learning machine – ELM). The D-optimal design method was used to create twenty-two experiments to assess the impact of extraction time, irradiation power, and solid/solvent ratio on the MOO yield. The characteristics of the ANN, ELM, SVM, and GBDT models were coefficient of determination, R² = 0.9842, 9340, 8478, 0.8008, and mean relative percentage deviation, MRPD = 2.1393, 5.5857, 13.4217, 14.7639%, respectively, compared to the RSM model with R² = 0.9568 and MRPD = 4.4.7758%. The results demonstrated the superiority of ANN over RSM, whereas RSM outperformed ELM, SVM, and GBDT. Optimal conditions predicted by RSM and ANN, coupled with the particle swarm optimization tool, are as follows: time = 8 min, irradiation power = 540 W, and solid/solvent ratio = 1:30 g/mL, with corresponding MOO yields of 43.60% and 39.08%. These conditions were validated in the laboratory to be 39.02%. The physicochemical properties of the MOO (acid number = 3.03 mg KOH/g, density = 874 kg/m³, and peroxide value = 8.4 meqO₂/kg oil) suggest that it could be a viable feedstock for various chemical products.

In a world where billions of people still rely on unsustainable fuels and inefficient stoves, the need for clean cooking technologies has never been more urgent. However, the complexity of varying regional dynamics hinders one-size-fits-all solutions. Biomass cookstoves have been used for many years with a number of different models, prototypes, or products which are either already available in the market or still being researched. They can be one of the answers in paving the way to clean cooking, especially in developing regions. Yet, the selection of appropriate fuels and operating conditions is a crucial aspect that is often overlooked. Effective decision-making, based on case-specific evaluations, remains essential for the adoption of suitable technologies. This review paper provides a perspective on biomass cookstove technologies, addressing design and operational characteristics, the impact of pretreatment methods, and the potential applications of by-products. On this basis, a decision-making framework is proposed to facilitate the context-specific selection of biomass cookstoves. By integrating multiple factors, and real-world examples, the proposed approach aims to guide more informed decision-making for cookstove selection and contribute to accelerating progress toward universal clean cooking access. Our aim is to provide a practical and integrative perspective rather than a comprehensive systematic review, aiming to bridge the gap between theory and application for biomass-based clean cooking.

Lignocellulose-based ethanol production remains commercially challenging due to biomass recalcitrance and high cellulase costs. This review first establishes the challenges within current techno-economic limits, then traces three analytical layers that must be solved in concert. A modelling layer couples genome-scale reconstructions with multi-omics data that builds the foundation. The engineering layer converts model-derived insights into robust genetic interventions. A decision layer applies data-efficient learning algorithms that generate sequence functions and process landscapes that guide experimentation based on information-rich variants. All layers feed an automated Design–Build–Test–Learn loop that stores genotypes, phenotypes, and processes data in a unified schema, so each iteration directly refines the next. Case studies spanning life-cycle assessment, multi-objective metabolic optimization, adaptive laboratory evolution, and AI-guided enzyme design illustrate progressive gains in titer, rate, and yield (TRY) approaching commercial thresholds. This review offers a clear route from laboratory insight to cost-competitive second-generation bioethanol.

Lignocellulosic biorefining has traditionally focused on either converting biomass into sugars for fuels or isolating solid cellulose for bioproducts. However, cost-effective strategies to maximize sugar yields while preserving crystalline cellulose remain underexplored. This study addresses that gap by optimizing cellulase enzymes to the co-production of fermentable sugars and crystalline cellulose. Laboratory-scale results also informed a techno-economic analysis (TEA) to evaluate the feasibility of industrial-scale implementation. To this end, a selective hydrolysis process was developed to target hemicelluloses and amorphous cellulose, while retaining crystalline regions, using an optimized enzyme cocktail tested on three feedstocks: unbleached hardwood pulp, wild-type poplar, and clustered regularly interspaced short palindromic repeats (CRISPR)-edited poplar. Optimization across varying pH and temperature conditions enabled effective selective hydrolysis. Low-lignin unbleached pulp and CRISPR-edited poplar, each containing less than 15% lignin on a dry weight basis, exhibited improved enzymatic accessibility and eliminated the need for pretreatment, resulting in higher sugar yields (74 g/L and 33 g/L, respectively) and more efficient downstream processing. An engineered yeast strain co-fermented C5 and C6 sugars into ethanol, leaving behind high-crystallinity cellulose. CRISPR-edited poplar outperformed wild type, with 18% more sugar and 25% more ethanol yield, while enhancing cellulose crystallinity. TEA estimated crystalline cellulose production costs at $4,438 per metric tonne from unbleached pulp and $1,474 from CRISPR-edited biomass, highlighting the economic advantage of engineered feedstocks. This work presents a novel lignocellulosic biorefining approach that, for the first time, prioritizes the co-production of fermentable sugars and crystalline cellulose from low-lignin biomass.

Sustainable biodiesel production is crucial for a net-zero scenario, but obstacles hinder its large-scale production. Effective strategies are essential to overcome these hurdles. This study aims to identify and evaluate the significant barriers that prevent the widespread production of biodiesel. Through the extensive literature review and by utilizing the knowledge of a selected team of experts, 10 barriers are identified. Total interpretive structural modelling (TISM) and matrix-based multiplication applied to a classification (MICMAC) analysis are adopted to develop a hierarchical structural model and to categorize these barriers. Using the TISM model, a 6-level hierarchy model has been developed. The hierarchy model reveals that lack of efficient technology is the most influential barrier. MICMAC analysis reveals that the barriers lack of efficient technology and high-water consumption are the bottlenecks that need to be overcome for successful large-scale biodiesel production. The structural model is used to propose commercial biodiesel roadmap and benefits.

Biochar and cover crops are often considered to improve soil properties. Yet, how low biochar application rates and multi-species cover crops affect soil properties in the long term (> 10 years) is not well known. We assessed the impact of wood biochar (2.40 Mg ha^− 1 with 701 g kg^-1 C), wood biochar (2.40 Mg ha^− 1) + 168 kg N ha^− 1, and cover crop mix on soil properties of a fertile (38 g kg^-1 organic matter concentration) silty clay loam (fine, smectitic, mesic Typic Argiudoll) in a no-till corn-soybean system in the western U.S. Corn Belt after 10 years. Biochar applied once at 2.40 Mg ha^− 1 had no effect on soil properties in the 0–15.2 cm soil depth. Cover crop mix increased soil K concentration but did not affect other soil properties. However, while differences were not statistically significant, most soil properties including wet aggregate stability, water content at -33 kPa matric potential (field capacity), microbial properties (total microbial biomass, total bacteria, actinomycetes, total fungi, arbuscular mycorrhizal, saprophytes, and protozoa), pH, and concentrations of organic matter, total C, K, and P were numerically larger under biochar than no biochar. These trends suggest that larger than 2.40 Mg biochar ha^− 1 may be needed to detect significant changes in soil properties. Results indicate fertile soils such as Mollisols may not significantly respond to low biochar rates and cover crop mixes even after 10 years, which warrant the need for research on whether these practices are more beneficial in degraded or low-fertility soils.

This research assesses the capability of cassava-derived bioenergy in Africa by employing artificial neural networks (ANN) for scenario analysis and predictive modeling. A dataset comprising 12 input variables (such as cassava yield, starch content, moisture, pH, retention time) and 3 output variables (biogas yield, ethanol yield, energy efficiency) was examined. Descriptive statistics revealed average cassava yields of 12.5 t/ha (SD = 4.2, range = 5–30 t/ha), starch percentage of 28.3% (SD = 6.5), and residual biomass of 1,250 kg/ha (range = 500–2,500 kg/ha). Environmental conditions ranged from 25 to 38 °C, with annual rainfall between 800 and 2,400 mm, and pH levels varying from 5.0 to 8.5. ANN models featuring three hidden layers (64–32–16 neurons) surpassed regression and ensemble techniques, recording MSE = 0.032, RMSE = 0.179, and R² = 0.91, in contrast to multiple linear regression (MSE = 0.084, R² = 0.72) and random forest (MSE = 0.041, R² = 0.87). Sensitivity analysis revealed that starch content (32.4% contribution), moisture (21.7%), and retention time (18.5%) are the main predictors. Scenario modeling indicated that designating 10% cassava for energy production resulted in 2,500 m³/ha of biogas with low impact on food, whereas a 50% allocation produced 12,000 m³/ha but presented significant risks to food security. Regional evaluation indicated that Central Africa has the greatest biogas potential (18,400 m³/ha at 16.8 t/ha yield), in contrast to East Africa’s semi-arid region (9,200 m³/ha at 9.5 t/ha). Life-cycle GHG assessment revealed cassava bioenergy emissions of 30–40 gCO₂-eq/MJ, which are notably less than diesel (95 gCO₂-eq/MJ), gasoline (93 gCO₂-eq/MJ), and coal-generated power (110 gCO₂-eq/MJ). These results show ANN as a revolutionary resource for enhancing cassava bioenergy, reconciling food–energy conflicts, and advancing UN SDGs 7, 12, and 13 across Africa.

Graphical Abstract

This study investigated fungal isolates enriched under strictly anaerobic conditions from buffalo (Bubalus bubalis) rumen fluid for their potential in lignocellulosic biomass conversion. Two isolates, B031 and E033, were obtained using serial dilution in Hungate tubes with cellobiose as a carbon source. Their characterization included morphological examination, ITS-5.8 S-LSU sequencing, substrate utilization profiling, and lignocellulosic degradation assays. Phylogenetic analysis indicated that B031 is related to uncultured Basidiomycota-affiliated fungi, while E033 is closely affiliated with the yeast-like fungus Trichosporon asahii. Although these taxa are not obligate anaerobic fungi, both isolates exhibited stable growth and sustained lignocellulosic degradation under oxygen-deprived conditions across successive anaerobic subcultures. Isolate B031 demonstrated greater versatility, achieving 60% microcrystalline cellulose (MCC), 66% lignin, and 58% rice straw degradation within 7 days, whereas E033 reached 55% MCC, 58% lignin, and 33% rice straw degradation. Given that rice straw is a major lignocellulosic residue in Thailand, these fungi have significant potential to enhance biogas conversion. Based on annual production of 23 million tons of rice straw, B031 could yield 3.93 billion Nm³ CH₄ (39.13 TWh primary energy; 13.70 TWh electricity), while E033 yielded 2.24 billion Nm³ CH₄ (22.26 TWh primary energy; 7.79 TWh electricity). These results highlight the importance of strain selection based on functional anaerobic degradation capacity rather than taxonomic affiliation alone and support the potential application of these isolates as bioaugmentation agents in anaerobic digestion systems. Further genomic and functional studies are recommended to refine their taxonomic classification and assess their practical application in sustainable biogas production.

This study investigates optimization of net energy recovery from biodegradable solid waste (BSW) using a two-stage anaerobic digestion (TAD) system. BSW was pretreated by hydrolysis at 37 °C (pH 4.5–6.5), with hydraulic retention time (HRT) of 5 days and total solid (TS) concentration of 120 g/L. The liquid hydrolysate was collected, diluted, and fed continuously to a methane reactor (MR) operated at organic loading rate (OLR) of 1.9–11.4 g-TS/L/d (corresponding HRT: 15.9–3.6 d). Maximum biogas yield and methane content (327 mL/g-TS and 71.6% CH₄) were observed at the lowest OLR (1.9 g-TS/L/d) and pH 6.5; the minimum yield (193 mL/g-TS, 54.1% CH₄) occurred at pH 4.5 and the highest OLR. The relationship between operating conditions and net energy recovery was modeled using a quadratic multiple regression analysis, yielding a high correlation coefficient (R² = 0.8823). The model predicted the optimal energy recovery ∆E_optimal was 6703 J/g-TS (1862 Wh/kg-TS) at a pH of 6.5, OLR of 5.6 g-TS/L/d, and HRT of 5.4 d. The optimized TAD configuration outperformed typical single-stage systems in net energy recovery and provides practical operating guidelines for waste-to-energy applications. 

Graphical Abstract

Physicochemical and operational parameters affect microbial communities, impacting anaerobic digestion (AD) systems. Microbial dynamics in response to these parameters were temporally investigated by DNA and cDNA of the 16 S rrna gene in a pilot-scale hybrid anaerobic reactor (HAnR) fed with sugarcane vinasse. Key microbes for biogas production, including Bacteroidetes-vadinHA17, Paludibacteraceae-H1, Prolixibacteraceae, Desulfovibrio, Syntrophobacter and Methanospirillum, were favored by the maintenance of volatile fatty acids (VFA) < 3700 mg.L^− 1, pH ≥ 5.6, temperature ≥ 29.8 °C, chemical oxygen demand (COD) < 31.4 g.L^− 1 and organic loading rate (OLR) < 9.7 kg.COD.m^− 3.d^− 1. Temperature and pH also favored the redundancy of fermentation and H₂-mediated methanogenesis and the evenness and diversity indexes, all related to biogas quality. Concentrations up to 2,050 mg.SO₄²⁻.L^− 1, 3,270 mg.K.L^− 1, 367 mg.Mg.L^− 1, 0.4 mg.Cu.L^− 1 and 1.1 mg.Zn.L^− 1 led to adverse effects over microbial abundance, functional redundancy and ecology, but tolerant microorganisms, like Paludibacteraceae, Anaerolineaceae and Methanosaeta, kept fermentation and methanogenesis ongoing, although less efficiently. Microbial interactions were also affected by evaluated parameters beyond metabolic dependence. This study highlights the importance of parameter monitoring in scaled-up AD reactors to promote the adequate establishment of microbial communities and therefore contribute to system stability.