Poplar fiber mass is a non-uniform medium that is composed of discrete microelements making it an imperative raw material in the production of ultra-thin high-density wood fiberboards. Preheating, therefore, becomes a crucial process in producing ultra-thin boards from poplar fiber masses. This study aims to investigate the thermal conductivity properties of wood fiber pellets with the objective of guiding the process parameters in the preheating section.Basic size and composition of poplar fiber masses were observed using an optical microscope. Measured parameters such as bark content and stacking density were combined with observations to establish the heat transfer unit of poplar fiber masses which were then used to develop a one-dimensional equivalent heat transfer model.The steady-state images of the surface layer of poplar fiber masses were captured under different parameters using infrared thermography. The results indicated that the relationships between thickness, density, and moisture content were negatively correlated with surface layer temperature, while the relationships between bottom heating temperature and surface layer temperature were positively correlated. From these findings, the surface layer temperature of poplar fiber mass was derived, and equivalent thermal conductivity as well as convective heat transfer coefficients were solved.Simulation results showed that the average error of the equivalent heat transfer model of poplar fiber mass was 1.584 indicating that the model is usable. This study contributes to efficient simulation of steady-state heat transfer in wood fiber masses, and could be useful in guiding decision-making processes in the preheating section of ultra-thin high-density fiberboard production.

Circumferential milling is used in wood processing, yet it generates vast quantities of dust and chips in a single pass, highlighting the need to predict chip dispersion and prevent associated hazards. This article presents findings from a theoretical and experimental analysis of chip size and kinematics of pine wood during cutting. A chip diffusion boundary surface model was established and its key parameters were determined through CCD testing. Results reveal that chip diffusion can be divided into three distinct areas based on motion state: main diffusion, random diffusion, and vortex. Notably, spindle speed and feed rate are most influential on the orthogonal diffusion angle of the main diffusion zone, whereas cutting depth most heavily impacts the top view diffusion angle. Chip scattering on the table showed an exponential increase in average chip size with sampling distance, whereas the boundary surface model accurately characterizes chip motion and demonstrates a reasonable degree of reliability, offering potential in predicting chip morphology and diffusion state. This model has important implications for wood milling practices, particularly in controlling chip dispersion.

The surface roughness of wood has a great influence on its performance and is a very important indicator in processing and manufacturing. In this paper, we use the central composite design experiment (CCD experiment) and artificial neural network (ANN) model to study the changing pattern of surface roughness during the high-speed milling process of pine wood. In the CCD experiments, the spindle speed, feed speed, and depth of cut are used as the influencing factors, and the surface roughness is used as the index to analyze the variation law and fit the surface roughness parameter equation. By measuring the chip size in each group in the CCD experiment, the ANN model is used to predict the surface roughness under this machining parameter by measuring the chip size in each test group. The experimental results showed that the mean error of the surface roughness prediction values in the CCD experiment (12.2%) was larger than that of the ANN model (7.8%), and the mean squared error (MSE) of the ANN model was 0.025, the mean absolute percentage error(MAPE) was 0.01, and the coefficient of determination R2 was 0.95. Compared with the CCD experiment, the ANN model had a higher prediction accuracy. The results of this paper can provide some guidance for the prediction of surface roughness during wood processing.

Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire. Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification. Due to the isolation of ground, temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious, making it difficult to identify by detection equipment. CO gas is an important detection index for indoor smoldering fire detection, and an important identification feature of hidden smoldering ground fires. However, there is no research on locating smoldering fires through CO detection. We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors. According to the motion decomposition search algorithm, the detection device realizes the function of automatically searching for smoldering charcoal. Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height. The size of the search step is related to the reliability of the search algorithm. The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure. The introduction of step and rotation factors in search algorithm improves the search efficiency. This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height. Based on this law, a CO gas sensor detection device for hidden smoldering fires has been designed, which enriches the technique of residual fire detection.