BioEnergy Research最新文献

During anaerobic digestion, the addition of biochar is expected to enhance biogas production by adsorbing inhibitory substances, increasing the surface area available for microbial growth and the density of surface functional groups. Moreover, it promotes direct interspecies electron transfer (DIET) attributed to its high electrical conductivity. With repeated use, biochar develops a microbial biofilm on its surface, which reduces the effects of the surface area and functional groups while maintaining high electrical conductivity. Therefore, increasing the number of repeated uses allows for evaluating the effects of electrical conductivity separately from the other two factors. In this study, the pressed juice from water hyacinth was utilized as wastewater, and anaerobic digestion was performed through repeated-batch operations with the addition of biochar. In the first cycle, biochar addition improved the cumulative methane yield, with a maximum increase of 21.5%. However, regardless of biochar addition, an increase in the number of repeated cycles resulted in stabilization of the methane yield at approximately 170-mL/g-VS. This observation clearly demonstrated the minimal effect of electrical conductivity on biogas production. The subsequent evaluation of microbial community structure exhibited high similarity in microbial composition, except for Anaerolineae bacteria, irrespective of biochar addition. Although two distinct Anaerolineae groups emerged depending on the presence or absence of biochar, both possessed similar gene clusters related to acetate supply for acetoclastic methanogens. These findings are in line with the observation that biochar addition did not selectively enrich specific microorganisms involved in DIET, and its effect on electrical conductivity was negligible.

The increasing demand for sustainable energy solutions has intensified interest in lignocellulosic biomass as a renewable alternative to fossil fuels. This study assesses the bioenergy potential from maintenance activities on a university campus in southern Brazil, typically underutilized resource for energy recovery. Residues, though poorly managed, present a viable pathway for waste valorization and renewable energy generation. A comprehensive physicochemical characterization was conducted through proximate and ultimate analyses, along with moisture content and higher heating value (HHV) determination. Biomass characterization recorded fixed carbon (20.14–22.45%), volatile matter (76.02–77.95%), ash content (1.56–2.39%), and calorific values (18.63–19.59 MJ/kg). The ultimate analysis recorded carbon (46.10–48.79%), hydrogen (5.96–6.35%), oxygen (44.30–47.14%), and nitrogen (0.32–0.79%). To evaluate its energy recovery potential, a steady-state thermodynamic model simulated the combustion of 50 kg/h lignocellulosic biomass in an incineration system coupled with a Rankine cycle power plant, achieving a peak output of 63.32 kW. The novelty lies in modeling the energy recovery potential of a highly heterogeneous biomass stream, derived from non-industrial forest maintenance, an area scarcely explored in the literature. This research provides valuable data and modeling insights to guide energy systems design using low-grade, heterogeneous biomass fuels. Additionally, the study demonstrates how targeted heat exchanger optimization in the Rankine cycle can improve overall efficiency. The findings advance waste-to-energy strategies and open opportunities for future research in scaling up this approach to regions and types of biomass waste.

Graphical Abstract

Anaerobic digestion is a highly preferred technology for energy production and waste disposal because of its adaptability, sustainability, and environmental protection. Kinetic analysis is crucial in anaerobic digestion to represent biomethane production performance. However, the connection between the kinetic models and process parameters is not universal. This study investigates the performance of three kinetic models: first order, logistic, and Gompertz on the simulation of biomethane yield from oxidative pretreated and combined oxidative and Fe₃O₄ nanoparticle additive pretreated substrate. Xyris capensis was pretreated before anaerobic digestion. The cumulative biomethane released was used to simulate the digestion process using selected kinetic models. The results indicated that pretreatment conditions influence the performance of the models, and the cumulative biomethane yield of the single pretreated Xyris capensis fitted more accurately with the Gompertz model. In contrast, the total biomethane released from the combined pretreated feedstock best fits the logistic model. All the model’s performance metrics of lag phase (λ), correlation coefficient (R²) of 0.8269–0.9978, Root Mean Square Error (RMSE) of 3.0193–156.3094, AIC of 65.6708–175.1098, and %diff of 0.0329–4.7751, show acceptable values. Comparison along different pretreatment conditions using the performance metrics shows that the Gompertz model produced superior accuracy. This study has established the performance of kinetic models in simulating the biomethane release from varying pretreatment conditions and provides a scientific conceptualization for process optimization. This finding can be helpful in enhancing energy recovery that will support a decarbonization approach and can be investigated on a commercial scale.

The third-generation bioenergy crops as microalgae have a wide range of advantages, including high growth rate, high oil content, and low land occupation. In particular, their placement on abandoned croplands will not compete for croplands with food production. However, the bioenergy potential of microalgae on abandoned croplands is still unclear. In this paper, we identified abandoned cropland in China from 2000 to 2020. On this basis, we estimated the bioenergy potential of microalgae on abandoned cropland by conducting historical and scenario experiments. The results showed total abandoned croplands reached about 29.75 million ha in China from 2000 to 2020, and the abandonment rate was 11.07%. In addition, the projection indicates that the potential production of microalgal bioenergy on such abandoned croplands is to increase in different SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios in future decades. It can offset 8.26 × 10⁹ GJ year⁻¹, about 22.83% of carbon emission from fossil energy consumption in China in 2020. The average annual production of bioenergy from microalgae in the historical period accounts for 4.76% of the global oil consumption in 2020. The bioenergy potential of microalgae is about 8.8 and 1.2 times higher than that of the first- and second-generation bioenergy, respectively. Spatially, microalgal bioenergy production is significantly higher in the south-western and south-central China than in the other regions. The phenomenon can be attributed to the differences in elevation, NDVI (normalized difference vegetation index), and sunshine duration. Furthermore, their interactions among these factors were significantly stronger than the effects of any single one. The findings provide a guidance to the effective utilization of abandoned cropland and bioenergy deployment at large scales.

The increasing accumulation of sugarcane agricultural waste poses a significant environmental challenge, highlighting the urgent need for innovative valorization strategies such as converting sugarcane leaves into bioethanol through efficient pretreatment technologies. Therefore, this study investigates the effect of pretreatment using a combination of ultrasonic and deep eutectic solvents (DESs), ethylene glycol: citric acid (EG/CA) and choline chloride: citric acid (ChCl/CA), on sugarcane leaves’ compositions and properties. DES-assisted pretreatment was conducted using a solid-to-liquid ratio of 1:5 (w/w) at 90 °C and 100 rpm stirring speed for 3.15 h. Ultrasound-assisted pretreatment was optimized for amplitude and duration. The reducing sugar concentration obtained after pretreatment increased by 2.89 times for EG/CA and 3.41 times for ChCl/CA compared to the untreated sample (3.75 g/L). However, combining EG/CA pretreatment with ultrasound at 40% amplitude for 30 min enhanced reducing sugar concentration to 8.49 g/L with the highest crystallinity index (CrI) of 50.09%. Applying ultrasonic pretreatment before DES effectively improved the sugar release, resulting in an ethanol yield of 17.14%. Additionally, Fourier-transform infrared spectroscopy (FT-IR) was employed to examine chemical structural modifications of the biomass, verifying the effectiveness of ultrasound-assisted DES pretreatment. These findings highlight the potential of ultrasound-assisted DES pretreatment as an effective alternative for lignocellulosic biomass processing.

Graphical Abstract

In recent years, the use of nanobubbles (NBs) in anaerobic digestion (AD) has received significant research attention, with numerous studies reporting notable improvements in AD efficiency when nanobubble water (NBW) is introduced in digesters. The improvements are often attributed to more efficient substrate degradation, higher activity of key enzymes, and the more complex and diverse microbial communities associated with NBW-integrated AD. Despite these promising results, detailed explanations of the mechanisms and modes of action behind NBW, particularly its impact on each step of the AD process—hydrolysis, acidogenesis, acetogenesis, methanogenesis—are still limited. This paper aims to provide a comprehensive review and critical assessment of NBW’s impacts on AD, with a particular focus on how operational conditions can influence NBW-related effects on AD performance. Finally, energy efficiencies, future opportunities and challenges of expanding NB technology to boost AD efficiency are discussed.

Graphical Abstract

Biochar is a highly efficient and clean fuel. In recent years, significant progress has been made in machine learning technology to predict the higher heating value (HHV) of biochar. This study innovatively proposes a method to enhanced data for the HHV of biochar. The dataset was divided into three groups according to the characteristics of biomass, and the prediction model of HHV of biochar was established on the basis of three machine learning algorithms: LightGBM, CatBoost, and DNN. The effect of “enhanced data” on the prediction accuracy of the model is evaluated. Experiment results reveal that inclusion of “enhanced data” improves the model-fitting performance of the model, and the model of LightGBM is more suitable for biochar HHV prediction. The introduction of enhanced data improves the prediction accuracy of the model, with R² increasing by 0.068, MAE decreased by 0.421, and RMSE decreased by 0.180. The SHAP analysis demonstrated that inclusion of “enhanced data” changed the ranking of feature importance in that ash content of pyrolysis and temperature of pyrolysis stayed at the forefront of importance features. PDP and ICE analysis demonstrated that inclusion of “enhanced data” significantly changed the contribution of some of the features to HHV of biochar. This study provides significant reference and guidance for predicting other characteristics of biomass pyrolysis products.

The treatment of kitchen waste and agricultural solid waste are two major challenges in urban and agricultural environmental protection. The conversion of organic solid waste into bio-oil through mild hydrothermal methods can not only alleviate the environmental problems caused by traditional Lignocellulosic biomass processing but also provide technical support for the sustainable production of fuel oil. Traditional research on hydrothermal conversion of organic solid waste mostly focuses on areas such as catalysts and hydrothermal parameters, while studies on environmental impact assessments are relatively rare. In this study, a process for the co-hydrothermal conversion of kitchen waste and agricultural solid waste biomass was firstly established, with bio-oil as the target product. The analysis results show that the higher the yield and calorific value of bio-oil, the greater its negative impact on the environment, which accounts for 41.41 to 55.24% of the total impact. Groups with more significant Maillard reactions had higher bio-oil yields but also had higher net CO2 emissions, with the highest reaching 25.05 kg CO₂ kg⁻¹. For the consumption of mineral, fossil, and renewable resources, dichloromethane was the largest contributor. Overall, the hydrothermal Liquefaction stage had the greatest environmental impact among all stages, accounting for 42.26 to 56.55% of the total impact. Changes in fertilizer application rates had a certain effect on impact categories other than mineral, fossil, and renewable resource consumption. Reducing the energy consumption of hydrothermal liquefaction can significantly reduce its consumption of fossil fuels and other energy sources, while changes in grinding and filtering energy consumption had little impact on the environment. The research content of this paper will provide some references for the resourceful conversion of kitchen waste and agricultural solid waste from an industrial perspective.

Industrial CO(_2) emissions, particularly from flue gases, are a major barrier to climate change mitigation. Carbon capture, particularly with bioenergy (BECCS), offers a pathway to negative emissions. This study models an MEA-based carbon capture system using operational data from a stand-alone pulp mill, focusing on its energy impact and optimization strategies. The analysis distinguishes between high-quality steam, critical for electricity generation, and waste heat, which can be valorized for carbon capture. The thermal energy demand for capture was found to be 3.6 MJ/kg CO(_2), leading to a reduction in energy efficiency. Integration scenarios such as flue gas valorization, combustion air preheating, and a steam generation heat pump (SGHP) were evaluated to address this. The electrical energy penalty (EEP) was introduced as a metric to quantify power losses due to steam extraction. The results show that while flue gas valorization and air preheating offer limited benefits, the steam generation heat pump effectively reduces both steam dependency and electrical energy losses, enabling high CO(_2) capture efficiency with minimal impact on mill operations, with the SGHP achieving the lowest EEP of 0.50 MJ/kg CO(_2) and enabling a 90% CO(_2) capture rate.

Anaerobic digestion-derived volatile fatty acids (VFAs) have emerged as key intermediates in sustainable biofuels and biochemicals production, offering a circular approach to organic waste valorization. This review explores recent advancements in enhancing VFA production through tailored pre-treatment strategies, optimized feedstock selection, and controlled process conditions. Furthermore, VFA extraction methodologies are discussed, with a focus on energy-efficient separation techniques. The integration of VFAs as feedstock for microbial lipid production is analyzed, highlighting substrate preferences, metabolic insights, and growth conditions for oleaginous yeast, which can thereby be cultivated in non-sterile conditions because of using VFAs as feedstock. Additionally, innovative lipid extraction technologies, including pulsed electric fields, hydrothermal liquefaction, and ionic liquids, are reviewed for their potential to enhance lipid recovery. By consolidating advancements across these domains, this review provides a comprehensive understanding of the technological landscape, identifying key challenges and future research directions to optimize VFA-driven biofuel production.