Search Results (223)

This study experimentally investigated the movement, combustion, and potassium (K) and chlorine (Cl) migration behaviors of three biomass types: densified wood pellets (heavy), corn straw (lightweight), and wheat straw (lightweight, friable). The experiments were conducted under conditions representative of industrial coal-fired circulating fluidized bed (CFB) boilers, with a temperature range of 850–950 °C and a fluidization velocity of 6–8 m/s. Results show that densified wood pellets sink into the dense-phase zone and release volatiles slowly, in about 50 s. As the volatiles are nearly fully released, the pellets fracture multiple times along their length, eventually forming nearly spherical particles. Their movement and combustion processes closely resemble those of coal, making them suitable for direct co-firing in coal-fired CFB boilers. Conversely, corn straw and wheat straw exhibit low density, high volatile release rates (2 and 10 times that of wood pellets, respectively), rapid char fragmentation and abrasion, and high inherent K and Cl content (with >50% of K and >90% of Cl released). These properties lead to particle segregation, shortened gas-phase combustion time, an upward shift in heat release distribution, and potential risks such as high-temperature KCl corrosion, HCl dew point corrosion, ash slagging, and bed agglomeration. Therefore, untreated corn straw and wheat straw are unsuitable for co-firing in conventional coal-fired CFB boilers. This study provides essential data and engineering guidance: strict quality control is necessary for wood pellets to prevent Cl contamination, while pretreatment is mandatory for straw fuels. These findings offer practical insights for implementing diverse biomass co-firing strategies in coal-fired CFB boilers. Full article

This study evaluated the effects of straw particle size (short or long) and corn physical form (ground or whole) in diets on growth performance, rumen fermentation and fecal score in calves. Sixty female newborn calves were randomly assigned to one of the four treatments: 90% pelleted starter and 10% short straw (PSS); 70% pelleted starter, 20% whole corn and 10% short straw (PWCSS); 90% pelleted starter and 10% long straw (PLS); 70% pelleted starter, 20% whole corn and 10% long straw (PWCLS). In PSS and PLS treatments, all of the corn was within the pelleted starter. Calves were weaned at 68 days of age. Body weight (BW), wither height and heart girth were measured at 3 and 68 days of age. Feed intakes and fecal scores were measured daily. Rumen fluid and blood samples were collected for rumen pH, rumen volatile fatty acid (VFA) and blood β-hydroxy butyrate (BHB) measurements at 68 days of age. Weaning BW, average daily weight gain (ADG) and weaning wither height were significantly lower in PLS compared to other treatments. Weaning heart girth was significantly lower in PSS and PLS than PWCSS and PWCLS. Feed intake was significantly higher for PWCSS than PWCLS. PWCLS had a significantly lower feed efficiency (starter feed intake/ADG) than PLS. No significant differences were observed for ruminal pH, ruminal acetate and blood BHB among the treatments. In the diets including short straw, ruminal propionate, butyrate and total VFA concentrations were significantly higher for PWCSS than PSS. In the diets including long straw, the ruminal propionate concentration was significantly greater for PLS than PWCLS, and ruminal butyrate and total VFA concentrations were not different for PLS and PWCLS. This study indicated that the effect of corn physical form (ground or whole) on ruminal propionate, butyrate and total VFA concentrations could vary depending on straw particle size. Fecal score was significantly lower in PSS compared to other treatments. In conclusion, long straw combined with pelleted concentrate reduced growth performance in pre-weaning calves. Whole corn inclusion in the diets with long straw increased ADG and weaning BW and improved feed efficiency. Full article

Rotary tillage blades are critical soil-engaging components in conservation tillage systems but are prone to adhesion of soil particles under cohesive soil conditions, which increases tillage resistance, degrades tillage quality, and lowers operational efficiency. To address these issues, this study proposed a collaborative strategy that combines parameter optimization of rotary tillage blades with a bionic microtexture design to reduce adhesion and resistance and improve operation performance. A coupled soil–wheat straw–rotary tillage blade model based on the Discrete Element Method (DEM) and Multibody Dynamics (MBD) was established in loessial soil environment. The structure and working parameters of the rotary tillage blade were optimized using a Box–Behnken experimental design. On this basis, a bionic microtexture design was introduced on regions prone to adhesion of the rotary tillage blade, inspired by the non-smooth convex hull microstructure on the head surface of the dung beetle. The results indicated that the optimal parameter combination (rotational speed 244 r·min⁻¹, tillage depth 110 mm, and bending angle 122°) reduced soil adhesion mass and tillage resistance by 74.47% and 23.44%, respectively. After applying the bionic microtexture, the corresponding reductions further increased to 82.93% and 28.35%. Moreover, the bionic-optimized rotary tillage blade outperformed the original design in disturbance depth and range and exhibited improved energy consumption performance. Overall, the results demonstrated that coupling parameter optimization with bionic microtexture design substantially enhanced adhesion and resistance reduction and improved soil-disturbance performance, thereby providing theoretical support for the development of high-performance rotary tillage blades. Full article

This study investigates a sustainable and efficient approach to valorising barley straw by producing nanocellulose via an integrated subcritical alkaline hydrolysis and HDES-assisted processing pathway. Subcritical alkaline pretreatment under best processing conditions (150 bar, 200 °C, 125 min) enabled effective biomass fractionation, achieving average hemicellulose and lignin solubilisation of 57.72% and 82.69%, respectively. Subsequent purification of the pretreated solid fraction yielded cellulose fibres with an average cellulose yield of 41.97% and a purity of 87.87%. Nanocellulose was then obtained using a sequential HDES treatment followed by high-intensity ultrasound (HIUS), producing a sample (NC-BTW-3) in which 66% of particles exhibited diameters below 100 nm, 15.2% were between 100 and 200 nm, and 19% were within the 200–1000 nm range. The resulting nanocellulose demonstrated good colloidal stability, with an average zeta potential of −33.0 mV. Overall, the work highlights a green and effective processing strategy for the valorisation of agricultural residues into high-value nanocellulose suitable for bio-based material applications. Full article

This study produces high-purity nano-silica from corn straw ash (biomass power plants) using an alkaline fusion-derived sodium silicate solution. CO₂ replaces traditional acids in the carbonation reaction, enabling high extraction yield (93.11%). The process addresses the gap in directly utilizing combustion ash for such high-purity silica. Key optimal conditions identified were 5 M aq. HCl concentration, NaOH fusion reagent, 1:1.2 mixing ratio, 3 M aq. NaOH solvent, and 12 h ripening. The resulting nano-silica achieved 92.73% purity, 10–50 nm particle size, 270 × 10⁻⁵ m³/kg dibutyl phthalate (DBP) absorption, 55.9916 m²/g specific surface area, 6.38% loss on drying (LOD), and 6.69% loss on ignition (LOI). These properties meet national standards for premium, loosely structured nano-silica. This method provides an economical and effective silicon source, reducing costs and offering economic-environmental benefits. Full article

Anaerobic digestion of straw is a crucial method for agricultural waste valorization, yet its efficiency is limited by complex factors. This study employed machine learning to predict methane yield and optimize process parameters in biochar-amended straw digestion. A comprehensive dataset integrating experimental and literature data (100 samples, 15 input variables) was constructed, incorporating operational conditions, straw characteristics, and biochar properties (e.g., dosage, particle size, specific surface area, and elemental composition). Prediction models were developed using Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Results indicated that the RF model achieved the best predictive accuracy, with an R² of 0.81 and RMSE of 36.9, significantly surpassing other models. Feature importance analysis identified feeding load, biochar dosage, and biochar carbon content (C%) as the key governing factors, collectively accounting for 65.7% of the total contribution. The model-predicted optimal ranges for practical operation were 15–30 g for feeding load and 5–20 g/L for biochar dosage. This study provides data-driven validation of biochar’s enhancement mechanisms and demonstrates the utility of RF in predicting and optimizing anaerobic digestion performance, offering critical support for sustainable agricultural waste recycling and clean energy generation. Full article

In fluidized bed conversion processes such as pyrolysis and combustion, defluidization mainly arises from particle agglomeration, which originates from the surface coating of primary bed materials (e.g., sand) by partially liquefied feedstock components, e.g., plastics or biomass. For reliable operation, the probability of occurrence of defluidization must be quantifiable. However, existing models are either computationally expensive or difficult to transfer across feedstocks with different rheological behaviors. Furthermore, such transferability challenges are particularly pronounced in technically relevant systems involving liquefied components, such as molten polymers and ash-derived silicate melts. In this study, we propose two new coating approaches: (i) a simplified full coating model, where a fraction of bed particles is directly assumed to be fully covered upon feed introduction, and (ii) a partial coating model, where only local surface regions of particles are coated. The proposed models are implemented within a Monte Carlo framework and validated against experimental data reported in the literature for polyethylene and polypropylene pyrolysis as well as for wheat straw combustion. Across all cases, the model predictions capture the experimentally observed defluidization behavior reported in reference studies (e.g., with coefficients of determination of $R^2=0.912$ for the polymer series and $R^2=0.917$ for the wheat straw series). Beyond model validation, several model-based analyses and discussions are further conducted based on the characteristics of the proposed framework. Overall, the developed methodology provides a generalized basis for analyzing coating-driven defluidization across polymers and biomass, with potential extensions to co-pyrolysis, co-gasification, and other thermochemical conversion processes. Full article

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

The objective of this study was to establish the kinetic of gasification reactions involved in chemical looping gasification (CLG) using pelletized biomass as solid fuel. However, significant limitations have been found in obtaining such kinetics using a traditional methodology from a large number of tests in a thermogravimetric analyzer (TGA) for pelleted biomass. A novel methodology is presented in this article, namely: (i) the determination of the intrinsic gasification rate for several biomasses; (ii) the determination of the gasification rate of pelletized biomass under selected operating conditions; (iii) the development and validation of a reaction model for pelletized biomass considering the determined intrinsic kinetics and gas diffusion in the biomass particles; and (iv) obtaining an apparent kinetics from data calculated with the developed model, which will be easy to implement in the modeling of gasifiers. To evaluate the applicability of this methodology, it was demonstrated with three different types of biomasses: pine forest residue (PFR), industrial wood pellets (IWP), and wheat straw pellets (WSP). The intrinsic kinetics was derived from tests with powdered char under several operating conditions: reacting temperature (1073–1223 K), concentration of gasifying agent (10–40 vol.% H₂O or CO₂), and concentration of gasification product (0–40 vol.% H₂ or CO). The evolution of the char conversion with the reacting time was predicted using a model involving three different regimes: (I) deactivation at the beginning; (II) uniform progress in the main middle part following a n-order model; and (III) catalytic activation as complete conversion is approached. The second regime was included for all biomasses, being 1, 0.4, and zero-order for WSP, IWP, and PFR, respectively. However, the third regime was observed for PFR and IWP, and the first regime only for IWP. The intrinsic kinetics was successfully used in a theoretical model to properly predict the gasification rate of pelletized biomass, and, eventually, to determine an apparent gasification kinetics as simple as possible in order to be easily implemented in future gasifier modeling works. Full article

In the Republic of Moldova, orchard biomass represents an important resource for the production of densified solid biofuels, with peach having the highest sustainable energy potential (33.5 ± 6.54 GJ·ha⁻¹). However, the quality of solid biofuels derived from orchard biomass is often constrained by heterogeneity in moisture content, uneven particle size distribution, and inadequate drying or blending practices along the supply chain. Optimizing the solid biofuel supply chain is therefore essential to minimize feedstock variability, ensure consistent densification quality, and reduce production costs. The aim of this study was to improve the process of producing densified solid biofuels from orchard biomass. Specifically, the study investigated how raw material moisture and particle size influence briquette density and durability, and how ternary mixtures of peach biomass, wheat straw, and sunflower residues can be optimized for enhanced energy performance. All experimental determinations were performed using validated methods and calibrated equipment. The results showed that optimal performance is achieved by shredding the biomass with 4–8 mm sieves and maintaining the moisture content between 6 and 14%, resulting in briquettes with the density of 1.00–1.05 g·cm⁻³, ash content below 3–5%, and an energy yield of 18.4–19.2 MJ·kg⁻¹. Ternary diagrams confirmed the decisive role of peach lignocellulosic residues in achieving high density, low ash content, and increased energy yield, while wheat straw and sunflower residues can be used in controlled proportions to diversify resources and reduce costs. These findings provide quantitative insights into how mixture formulation and process parameters influence the briquette quality, contributing to the optimization of solid biofuel supply chains for orchard and agricultural residues. Overall, this study demonstrates that competitive solid biofuels can be produced through careful balancing of mixture composition and optimization of technological parameters, offering practical guidelines for sustainable bioenergy development in regions with abundant orchard residues. Full article

Petroleum hydrocarbons are pervasive soil pollutants that detrimentally affect the soil structure, nutrients, and microbial ecosystems. However, the effect of biochar particle size on the remediation effectiveness remains a critical, unresolved parameter. Here, a soil remediation experiment was conducted to evaluate the synergy between biochars of different particle sizes and nutrient addition. Total petroleum hydrocarbons (TPHs) were quantified gravimetrically, and specific hydrocarbon fractions were analysed via gas chromatography mass spectroscopy (GC-MS) while the microbial community composition was analysed via high-throughput sequencing. The results revealed that granular bulrush straw biochar (0.85 mm) with nutrients achieved the greatest TPH degradation (73.35%), significantly outperforming both powder biochar and soybean straw biochar. This enhanced remediation was associated with a significant shift in the microbial community (p < 0.05), characterized by substantial increases in hydrocarbon-degrading bacteria, particularly Actinobacteria and the genus Mycobacterium. This study revealed that the synergistic application of granular biochar and nutrients is a highly effective, nature-based strategy for petroleum-contaminated soil, which functions by resolving a critical biochar parameter to enhance key microbial degraders. Full article

Accurate load prediction is essential for optimizing the performance and design of agricultural machinery. However, obtaining field-based load data is challenging due to the limited harvesting period of crops. To address this, the Discrete Element Method (DEM) has been widely applied to simulate crop–machine interactions under controlled virtual conditions. Previous DEM studies on rice straw often assumed uniform mechanical properties throughout the stem, neglecting sectional heterogeneity and limiting the accuracy of tensile and shear response prediction. This study developed an optimized DEM-based modeling approach by dividing rice straw into four sections—Top, Mid, Node, and Bottom—and experimentally determining their mechanical properties for model calibration. The Mid section exhibited the highest average tensile strength (178.71 N), while the Node showed the greatest shear resistance (114.08 N). One-way ANOVA confirmed significant sectional differences in both tensile (F = 18.12, p < 0.001) and shear (F = 23.61, p < 0.001) strengths. Two DEM models were validated: a multi-particle (Model A) and a simplified single-particle (Model B) configuration. Both achieved over 95% prediction accuracy, with Model B reducing computation time by 77.5% (80→18 min). Although the modeling was based on fully dried straw, future studies should incorporate moisture-dependent properties to enhance predictive fidelity. The proposed approach improves both accuracy and efficiency, providing a foundation for raking and baling load simulations. Full article

For sustainable soil management, the link between carbon amendment structure and soil health is paramount, yet how the particle size of carbon governs hydrolase activity through kinetic and thermodynamic mechanisms remains poorly understood. A three-year field experiment with four treatments, including Control, Straw, Biochar, and Nanocarbon, was conducted in black soil. After harvest, the activities of invertase (INV), urease (URE), and acid phosphatase (ACP) were assayed from 15 to 55 °C. Kinetic parameters—including half-saturation constant (K_m), maximal reaction rate (V_max) and catalytic efficiency (K_a)—and thermodynamic parameters—including Gibbs free energy (ΔG), enthalpy (ΔH) and entropy (ΔS)—were determined. INV and ACP activities increased with temperature, peaking at 55 °C, whereas URE peaked at 45 °C. The V_max, K_a, and ΔG of the enzymes also increased with temperature. With straw, INV activity remained stable, whereas INV-K_a, INV-ΔH, and INV-ΔS increased with decreased INV-K_m. URE activity declined with thermodynamic elevation. For ACP, ACP-K_m and ACP-V_max increased, whereas ACP-K_a and ACP-ΔG decreased. With biochar or nanocarbon, the enzyme activities, V_max, and K_a decreased, whereas ∆G increased, with stronger inhibition by nanocarbon. Correlation analysis revealed ∆G as the dominant factor for activity after carbon addition, while redundancy analysis identified organic carbon (OC) and total phosphorus (TP) as the key regulators. Overall, straw, biochar, and nanocarbon had different sustainable values on hydrolase systems, with thermodynamic parameters, especially ∆G, better reflecting system shifts than kinetic traits. Full article

The transition toward sustainable panel technologies is driving intensive research on binderless boards and self-bonded lignocellulosic composites. Particleboard, an engineered wood composite made by hot pressing wood particles with synthetic adhesives, is among the most widely produced wood-based panels due to cost-effectiveness and versatility. However, pressure on forest-derived raw materials and concern over formaldehyde emissions are accelerating the search for renewable resources and greener routes. Residues and underutilized materials from agro-industrial, food, and forestry sectors (such as cereal straws, sugarcane bagasse, brewer’s spent grain, and fruit-processing by-products) offer a sustainable alternative, enabling waste valorization, lowering environmental burdens, and supporting circular bioeconomy models. Binderless boards, produced without adhesives, exploit natural bonding among lignocellulosic components, including lignin softening, thermoplasticization, and covalent crosslinking during hot pressing. This review adopts a complex network approach to systematically map and analyze the scientific landscape of binderless board production. Using citation-based networks from curated seed papers and their first- and second-degree neighbors, we identify thematic clusters, with cluster “A” as the research core. The examination of this cluster, complemented by word-cloud analysis of titles and abstracts, highlights prevalent raw materials and key research lines, like raw-material sources and lignocellulosic composition, processing parameters, and pretreatment strategies. Based on these findings, brewer’s spent grain is selected as a representative case study for cost analysis. This approach synthesizes the state of the art and reveals emerging directions, research gaps, and influential works, providing a data-driven foundation for advancing self-bonded lignocellulosic composites. Full article

The heterogeneous Fenton-like catalysts TiO₂/Fe₃O₄ were fabricated using maize straw as template (MST-TiO₂/Fe₃O₄) by calcination followed by the hydrothermal method. The characterization showed that higher Fe₃O₄ particle dispersion, closer interaction between TiO₂ and Fe₃O₄, stronger electron transfer ability, and lower leaching of Fe ions of MST-TiO₂/Fe₃O₄ catalyst resulted in higher catalytic activity towards the degradation of tetracycline (TC) compared to pure Fe₃O₄. The best conditions for TC degradation were initial pH = 6.74, 11.52 mmol/L of H₂O₂, 0.38 g/L of MST-TiO₂/Fe₃O₄, and a reaction time of 56.63 min according to the response surface methodology (RSM) result based on the Box–Behnken design (BBD). The quadratic model was well-fitted to the experimental data with R2 (0.9843) and adj-R2 (0.9660) by the analysis of variance (ANOVA). Under the optimum reaction conditions, a maximum removal rate of 98.67% was achieved. The findings of the present study revealed that heterogeneous UV–Fenton process catalyzed by MST-TiO₂/Fe₃O₄ was a suitable way for the degradation of TC from aqueous environment. Full article