Biomass & Bioenergy最新文献

Cereal straw is a large biomass resource which may potentially be pretreated for biogas production by combined ensiling and storage. Five pilot-scale ensiling experiments were performed with straw of wheat, barley and rye, baled in midi-bales (0.9*1.2*2.0 m) with various water contents (17–68 %), ensiling additives and ensiling periods (173–230 days). The water content in the bales differed depending on previous precipitation but also the time of day for baling. Temperature in wet straw increased to 30 and 63 °C during storage for bales with and without wrapping, respectively, and mean dry weight loss was 5.1 ± 5.9 % and 10.5 ± 7.3 %, respectively.

During storage, pH dropped significantly in wrapped straw bales but not in bales without wrapping. In wrapped straw without additives, pH after ensiling decreased significantly with increasing initial water content, indicating the importance of water content above 30–40 % for ensiling of straw. Besides a more rapid pH reduction when applying acetic acid, there was no significant effect of acetic and formic acid, lactic acid bacteria and brown juice on pH after storage.

Ensiling of wet straw had a significant effect on biochemical methane potential (BMP) in some cases, with up to 32 % increase. There was limited effect of ensiling additives on BMP, whereas increasing water content in the straw significantly increased BMP. Improper wrapping of straw during storage reduced BMP with 17–30 %. Overall, proper wrapping and good ensiling of wet straw may result in up to 25 % extra methane yield whereas poor storage may result in up to 37 % loss.

Traditional Chinese medicine (TCM) plays a significant role in healthcare, yet the pharmaceutical process generates substantial amounts of organic solid waste (OSW) due to the low proportion of active species in medicinal plants. Traditional disposal methods, such as incineration or landfill, pose significant environmental challenges and fail to recycle valuable resources. Consequently, there is an urgent need for sustainable and high-value utilization strategies to address this issue. Recent research indicates that OSW of TCM is an ideal raw material for preparing biochar-based functional materials due to its high cellulose, hemicellulose, and lignin content. Notably, these biochar-based materials exhibit excellent potential in environmental remediation, bio-imaging, drug delivery, and energy storage. However, systematic reviews on the preparation, modification, and application of biochar derived from the OSWs of TCM are lacking. This review summarizes the research progress in these areas and discusses the prospects and challenges associated with biochar-based functional materials from the OSWs of TCM. It aims to provide insights into the high-value preparation and recycling of biochar-based functional materials from TCM waste.

A new process for the production of formic acid from corncob waste was constructed by using MnCeO_x/Nb₂O₅ catalyst. Among pristine hemicellulose, cellulose or lignin in corncob, hemicellulose gave the maximum formic acid yield (61.2 %). Dilute hydrochloric acid (0.37 wt%) assisted-ball milling pretreatment of pristine cellulose reduced its crystallinity and increased formic acid yield from 2.6 % to 40.2 %, while lignin was hardly converted into formic acid (<7 %). In the assessment of Mn and Ce in MnCeO_x/Nb₂O₅, a synergistic effect was observed in formic acid formation. Formic acid yield obtained over Mn₆Ce₄O_x/Nb₂O₅ was 1.5-fold higher that obtained over MnO_x/Nb₂O₅. Formic acid yield as high as 0.56 g/g could be obtained directly from pretreated corncob, demonstrating potential for converting agro-waste biomass into formic acid over Mn₆Ce₄O_x/Nb₂O₅ catalyst. Mechanistic studies revealed that arabinose, glyceric acid and glycolic acid were main intermediates in the conversion of corncob to formic acid.

A model for a heat-integrated biomass downdraft gasifier is developed and used to study the influence of changes in biomass moisture content and gasifier air flow. This one-dimensional steady-state model accounts for pyrolysis, combustion and gasification reaction kinetics as well as transport phenomena occurring within the gasifier and heat integration system. The gasifier is divided into four zones for solving the ordinary differential equations (ODEs), because each zone has different geometry for the reactor or heating system. The material and energy balance ODEs are solved as a boundary value problem (BVP), ensuring that conditions for the producer gas at the bottom of the reactor match the conditions of the countercurrent annulus gas, which is used for heating. The model also accounts for the preheating of the biomass using exhaust gas from an associated engine used to generate electricity from the producer gas. The model predicts the process gas temperature, flow rate and composition and was validated using two experimental runs with different control inputs. The model predictions show good agreement with the data. Simulations with the highest feed moisture result in lower reactor temperatures and simulations with the highest air flow result in the highest reactor temperatures.

The utilization of neglected agro-industrial waste is essential for mitigating environmental impacts. This study aims to characterize the products of using sweet potato peels, leaves and stems via pyrolysis and torrefaction techniques. Additionally, the study examined the impact of the torrefaction pre-treatment on pyrolysis products. Solid, liquid, and gaseous products were characterized. Results indicated that torrefaction pre-treatment increased biochar yield but diminished bio-oil and gas yields. Thermal treatments had minimal impact on the specific surface area of formed solids and biochars. Bio-oils showed a proclivity towards basicity with increasing temperature. Phenols predominated in the organic fraction of bio-oils, with torrefaction pre-treatment resulting in reduced alcohol and increased hydrocarbon concentration. Gas composition revealed elevated CO₂ levels during torrefaction, while torrefaction pre-treatment amplified CH₄ concentration. These findings suggest potential for extracting chemical inputs like phenolic resins and biocides from sweet potato waste bio-oils, though further treatments are required for effective solid adsorption.

A series of Mo₂N particles supported on nitrogen-doped carbon (Mo₂N@NC) catalysts for guaiacol hydrodeoxygenation were synthesized in situ through a one-step method employing dopamine as C and N resources and ammonium molybdate as Mo resource, respectively. During synthesis, molybdate ions are adsorbed on dopamine because of a complexation between them; then dopamine/molybdate ions/TMB/F127 nanoemulsions are formed. After polymerization, growth, drying, and carbonization, Mo₂N particles supported on nitrogen-doped carbon catalysts are obtained. Because of the interaction between Mo and N, Mo₂N particles are anchored onto the support, preventing the aggregation of Mo₂N during carbonization. As a result, ultrafine Mo₂N particles with a size of 1.0–1.3 nm are highly dispersed on Mo₂N@NC catalysts. The guaiacol hydrodeoxygenation for these Mo₂N@NC catalysts was performed at 280–380 °C, a H₂ flow rate of 80 mL/min and different pressures and weight hourly space velocities. Among them, Mo₂N@NC with a Mo₂N loading of 40 % presents the highest guaiacol conversion (99.9 %) and aromatic hydrocarbon selectivity (80.2 %), which is also better than Mo₂N/C with a Mo₂N loading of 40 % prepared by the wet impregnation method.

Hydrogen energy derived through biomass gasification is considered as one of the most sorted sustainable sources of renewable energy. This process enhances the H₂ production from biomass in the presence of specific catalysts. Among different kinds of models that have been employed for this process, ML models adept at approximating non-linear functions and facilitate outcome prediction without detailed mathematical descriptions. Thus, the current work focuses on understanding structural-composition-operating-target property relationships, and integrated catalyst and process modelling using ML framework for thermo-catalytic biomass gasification to H₂ production, and demonstrates outliers handling, data normalization for efficient handling of data-driven modelling with non-linear database. Linear, tree-based, kernel-based, and ANN models were developed with 589 datapoints screened from the 59 relevant papers with 24 inputs and 4 outputs (H₂, CO, CO₂, and CH₄ as vol. %). Performance of these models are evaluated through 5-fold cross-validation and test data with the help of statistical measures. ANN with Basian-regularization learning algorithm using tan-sigmoid activation function in both layers, resulted superior performance in prediction of H₂ production (RMSE = 6.85 & R² = 0.80) and other output gases with high accuracy (i.e., minimum deviation from experimental data) compared to other ML models. Further, using the best ML model, input contribution and PDP analysis were performed to interpret the significance of predominate input parameters affecting on the product composition. Feature contribution analysis reveals that temperature, S/B ratio, catalyst support type, and sulphur content in biomass are significant parameters for enhancing H2 production from catalytic-biomass gasification, and PDP analysis discloses their optimal operating region.

In this study, a continuously anaerobic two-stage pilot plant was established for bioenergy production, comprising 5 major pieces of equipment: a mixing tank, 1st anaerobic digester (AD), 2nd AD, sediment tank, aeration tank, and final sediment tank, all operating at ambient conditions. The operation of the continuously anaerobic two-stage pilot plant was automatically controlled by a programmable logic controller (PLC) using a designed control logic concept to set the hydraulic retention time (HRT) and inlet substrate concentration. The organic loading rate, pretreatment of hydrolysis pressure, and microbial community analysis were investigated for their effects on biogas production performance using different substrates: soybean residue (SR) and food waste hydrolysate (FWH), respectively. It was found that the peaks of biogas production rate on daily volumetric feeding were 1.20 m³·m⁻³·d⁻¹, and the biogas yield on VS added was 760 dm³·kg⁻¹ from food waste hydrolysate with a pretreatment hydrolysis pressure of 10 kg cm⁻², at an OLR in COD concentration of 3.56 kg m⁻³·d⁻¹, and an HRT of 11 days, respectively. The Methanobrevibacter genus was found to be abundant in the 1st AD, approximately 6.7 times more abundant than in the 2nd AD. The continuous anaerobic two-stage pilot plant was properly examined for its application in treating food waste and soybean residue with the goal of obtaining renewable bioenergy.

The quest for viable and scalable biofuel sources has been at the forefront of scientific innovation for the past three decades. Due to its rich chemical constituents, microalgal biomass has emerged as a pivotal sustainable and scalable feedstock for biorefineries. This comprehensive review critically analyzes the different types of microalgae feedstock, concurrent extraction technologies, bio-pre-treatment procedures, and the key chemical and physical parameters influencing lipid formation and algal biofuel production. We propose a novel approach of photo-initiated culturing of algal biomass using photobioreactors (PBRs) to address the limitations of concurrent space and time-related constraints. The innovative photo bio-refinery strategy presented herein aims to enhance sustainability factors while minimizing emissions, catering to the needs of futuristic non-electric vehicles. A comparative quality analysis of microalgae-derived biofuel against conventional fossil fuels and other biofuels is conducted, considering chemical, environmental, economic, and social perspectives. Furthermore, we elucidate the efficacy of bio-pre-treatment strategies such as dehydration, hydrothermal liquefaction, pyrolysis, and gasification in optimizing biofuel production. The proposed photo biorefineries exhibit the potential to yield a diverse range of value-added products, including biodiesel, biogases, bio-fertilizers, bio-pesticides, bio-alcohols, dyes, proteins, carotenoids, and drug vitals. This review provides a comprehensive framework for the development of sustainable and efficient microalgae-based biorefineries, paving the way for a greener and more economically viable future in the biofuel industry.

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.