首页> 外文期刊>Journal of The Institution of Engineers (India), Series E. Chemical Engineering and Textile Engineering >Optimality in Microwave-Assisted Drying of Aloe Vera (Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling
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Optimality in Microwave-Assisted Drying of Aloe Vera (Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling

机译:使用响应面方法和人工神经网络建模的微波辅助干燥芦荟(芦荟Barbadensis Miller)凝胶的最优性

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The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. 'Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.
机译:目前的作品说明了芦荟凝胶的微波辅助干燥(MWAD)特征与过程优化和人工神经网络建模相结合。研究了微波功率(160-480W),凝胶量(4-8g)和干燥时间(1-9分钟)对水分比的影响。芦荟凝胶的干燥表现出典型的扩散控制特性,其具有输入功率和干燥时间之间的主要相互作用。对于整个芦荟凝胶的整个MWAD,观察到下降率。面向中心的中央复合设计(FCCD)开发了一种回归模型,以评估它们对水分比的影响。最佳MWAD条件是建立为227.9W的微波功率,样品量为4.47g和5.78分钟的干燥时间,对应于0.15的水分比。生成计算机刺激的人工神经网络(ANN)模型用于在处理变量和所需响应之间进行映射。 'Levenberg-Marquardt Back传播'算法具有3-5-1架构师的算法,给出了最优质的预测,并在FCCD上表现出明显的优势。

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