Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber
Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber
复制标题
风干片材 (ADS) 橡胶的经验模型和人工神经网络模型方法
DOI：
10.4028/www.scientific.net/amr.622-623.69
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Y. Tirawanichakul
中科院分区：
文献类型：
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作者：
T. Ninchuewong;S. Tirawanichakul;Y. Tirawanichakul
本研究的目的是使用经验模型（EM）和人工神经网络模型（ANN）预测热风干燥的干燥行为。初始含水量范围为23-40%干基的橡胶片通过40-70°C的温度范围和0.7m/s的空气流速进行干燥。所需的最终水分含量设定为0.15%干基。结果表明，热风对流干燥橡胶板的干燥速率比传统的自然通风干燥快。用EM和ANN对产品的干燥行为进行了模拟。此外，EM和人工神经网络之间的预测结果与实验数据进行了比较。在本研究中，我们发现人工神经网络可以有效地描述干燥行为。此外，还发现，多层前馈Levenberg-Maqurdt的反向传播神经网络的预测结果与实验结果相比，EM的结果是很好的协议。该模型是预测热风干燥过程中水分迁移过程的最佳模型。
The objective of this research was to predict drying behavior of hot air drying using an empirical model (EM) and an artificial neural network model (ANN). Rubber sheet with initial moisture content ranging of 23-40% dry-basis was dried by temperature ranging of 40-70°C and air flow rate of 0.7 m/s. The desired final moisture content was set at 0.15% dry-basis. The results showed that drying rate of rubber sheet dried with hot air convection was faster than conventional natural aeration. The EM and ANN were simulated to describe the drying behavior of products. Furthermore, prediction results between EM and ANN were compared with the experimental data. In this research, it was obviously found that ANN can describe the drying behavior effectively. Additionally, it was also found that predicted results of Multilayer feed forward Levenberg-Maqurdt’s Back-propagation ANN were good agreement with the experimental results compared to those results of EM. It is the optimum architecture for prediction the evolution of moisture transfer for hot air drying.