The difficulty of measuring the drying rate of biomass under hot air convection conditions due to the influence of multiple factors, such as environmental conditions and material properties; and the problems associated with the variability of desiccation curves under changing conditions makes the use of mass transfer models based on diffusion and convection generally quite inaccurate. The research proposes the use of neural networks to determine the average drying speed (g removed water in unit of biomass material (kg) in unit time (s)), highlighting its ability to handle complex and variable data, as well as its adaptability and robustness. After 62 iterations, the R of the training process reached values of 0.93. Subsequent validation provided an R of 0.88. The mean square error was less than 10 g dryed water kg biomass s. Traditional mass transfer models applied to drying processes were compared with experimental data. It has been proven that the values of the convection coefficient in mass transfer are overestimated when obtained from the Sherwood number. Values of this coefficient applied to wood are 30 times lower due to capillary phenomena and electrostatic forces between the material and the water particles.

中文翻译：

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使用神经网络确定生物质干燥速度


受环境条件、物料性质等多重因素影响，热风对流条件下生物质干燥速率测量困难；与变化条件下干燥曲线的可变性相关的问题使得基于扩散和对流的传质模型的使用通常相当不准确。该研究提出利用神经网络来确定平均干燥速度（单位时间（s）内单位生物质材料（kg）去除的水分g），强调其处理复杂和可变数据的能力，以及其适应性和适应性。鲁棒性。经过 62 次迭代后，训练过程的 R 达到 0.93。随后的验证得出 R 为 0.88。均方误差小于 10 g 干水 kg 生物质 s。将应用于干燥过程的传统传质模型与实验数据进行了比较。事实证明，从舍伍德数获得的传质对流系数值被高估了。由于材料和水颗粒之间的毛细管现象和静电力，应用于木材的该系数值低 30 倍。