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首页> 外文期刊>International Journal of Heat and Mass Transfer >Experimental investigation of horizontal tube immersed in gas-solid fluidized bed of large particles using artificial neural network
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Experimental investigation of horizontal tube immersed in gas-solid fluidized bed of large particles using artificial neural network

机译：利用人工神经网络将水平管浸入大颗粒气固流化床中的实验研究

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摘要

The average heat transfer coefficient is determined between the fluidizing bed and horizontal tube surface immersed in the bed of large particles. The mustard (d_p= 1.8 mm), raagi (d_p =1.4 mm) and bajara (d_p = 2.0 mm) were used as particles in the bed. The effect of fluidizing gas velocity on the heat transfer coefficient in the immersed horizontal tube is discussed. The results obtained by experiment are compared with correlations and artificial neural network modeling. The parameters particle size, temperature difference between bed and immersed surface were used in the neural network modeling along with fluidizing velocity. The feed-forward network with back propagation structure implemented using Levenberg-Marquardt's learning rule in the neural network approach. The network's performance tested with regression analysis. The predictions of the artificial neural network were found to be in good agreement with the experiment's values, as well as the results achieved by the developed correlations.
机译：在流化床和浸入大颗粒床的水平管表面之间确定平均传热系数。芥末（d_p = 1.8毫米），raagi（d_p = 1.4毫米）和bajara（d_p = 2.0毫米）被用作床上的颗粒。讨论了流化气体速度对水平管内传热系数的影响。将实验获得的结果与相关性和人工神经网络建模进行比较。在神经网络建模中使用参数粒度，床和浸入表面之间的温差以及流化速度。在神经网络方法中，使用Levenberg-Marquardt的学习规则实现具有后向传播结构的前馈网络。网络的性能通过回归分析进行了测试。发现人工神经网络的预测与实验值以及通过开发的相关性获得的结果非常吻合。

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