Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network

The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.

Fine Characterization and Analysis of Drying Strain of the ELM Board via DIC Technology

In this paper,the occurrence and development mechanism of strain on the cross-section during the wood drying is explored.Therefore,strain regularity on the cross-section of 50 mm thickness elm(Ulmus rubra)board at the temperature of 40℃and 80℃is detected via digital image correlation technology.Hence,the difference between tangential and radial strain at surface and core layers was denoted.The results showed that strain distribution in the width direction of the board is uneven.Moreover,a large drying shrinkage strain occurs at the near-core layer,while the maximum strain difference reaches 4.08%.Hence,the surface of the board is cracked along the thickness direction.The radial strain of the board is higher than the tangential strain in the early stage of drying,while these strains are reversed in the later stage of drying.The temperature is related to the difference between the tangential and radial strains of the elm board.These differences at the core layer are larger than those of the surface layer.The conducted research results provide a theoretical basis for process optimization.

Experimental study and modeling of particle drying in a continuously-operated horizontal fluidized bed

Multistage fluidized beds are frequently used for product drying in industry. One advantage of these fluidized beds is that they can achieve a high throughput, when operated continuously. In this study, γ- A1203 particles were dried in a pilot-scale horizontal fluidized bed, without considering any comminution effects. For each experiment, the particle moisture content distribution and residence time distribution were determined. To take into account particle back mixing in our experiments, a one-dimensional pop- ulation balance model that considers particle residence time was introduced into a fluidized bed-drying model. Experimental particle residence time distributions were reproduced using a tank-in-series model. Subsequently, the moisture content distribution was implemented, as a second dimension to the popu- lation balance in this model. These two-dimensional simulations were able to describe the experimental data, especially the spread in the residual particle moisture distribution, much more accurately than one-dimensional simulations. Using this novel two-dimensional model, the effects of different operating parameters （process gas temperature, solid feed rate, superficial air velocity） on the particle moisture content distribution were systematically studied.