With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs.

Biomass fractions within municipal solid waste present significant fire hazards and environmental pollution risks, amplified by their distinct physical architectures. Discarded cotton wadding and poplar fluff, characterized by porous, fluffy morphologies and high specific surface areas, readily form combustible air-premixed systems during storage and transport, posing risks of uncontrolled fires and associated pollutant release. Understanding the combustion kinetics of such waste streams is critical not only for fire safety but also for assessing their potential for efficient energy conversion and minimizing incomplete combustion emissions. This study focused on a representative elongated fibrous biomass: waste cotton floc. By integrating microscopic structural characterization with theoretical combustion modeling, we systematically uncovered the unique deflagration behavior and latent hazards associated with this class of materials, linking them to potential environmental impacts. A custom setup with high-speed imaging quantified flame spread (1.5 m/s in confined conditions vs. 0.8 m/s in open conditions) and reaction times. Confined burning, which mimics common waste accumulation scenarios, such as containers or piles, displayed 85% faster propagation but lower combustion efficiency (stabilizing at ~20% with higher fuel loads) and ultra-short combustion durations (0.2 s at peak loading); these conditions favor incomplete combustion and elevated pollutant generation. The proposed structural fuel theory identified porosity as the key control parameter, linking fiber network topology to combustion dynamics and pollutant formation potential. These insights are vital for advancing strategies to mitigate combustion-related pollution events, optimize waste biomass energy recovery efficiency, and enhance fire safety protocols within the waste management sector to protect environmental quality.

The valorization of forestry waste into densified biofuels is critical for sustainable energy development. This study investigates the optimization of the densification process for pine sawdust by examining the effects of key parameters on the final product quality, specifically focusing on the uniformity of the internal temperature field. A four-factor, mixed-level orthogonal experiment was designed, investigating forming pressure, moisture content, binder addition ratio, and heating temperature. The temperature mean square deviation (MSD) was utilized as the primary response variable to quantify thermal distribution uniformity. Analysis of variance (ANOVA) was performed to determine the statistical significance of each factor, and a multivariate regression model was established. Results from ANOVA indicated that the descending order of significance for factors impacting temperature MSD was: moisture content > forming pressure > heating temperature > binder addition ratio. A statistically significant interaction effect was identified between forming pressure and heating temperature. Response surface methodology was employed to optimize these two significant factors. The optimal conditions for minimizing temperature MSD, while maintaining constant moisture content and binder ratio, were determined to be a forming pressure of 10 MPa and a heating temperature of 190°C. By optimizing process parameters to achieve a more uniform temperature field, the quality and stability of the resulting pine sawdust densified fuel were significantly improved. This work provides a quantitative theoretical basis and key technical parameters for the scale-up and industrial application of biomass fuels in boilers and residential heating systems, thereby promoting the development of a low-carbon circular economy.