太阳能空气加热器能量预测与火用分析的前馈反向传播神经模型的建立

Harish Kumar Ghritlahre
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引用次数: 18

摘要

本文建立了人工神经网络(ANN)模型来预测粗化太阳能空气加热器(SAH)的能量和火用效率。通过对吸收板上具有三种不同规格的线肋粗糙度的SAHs进行实验获得的总共50个数据集样本已用于本工作。这些实验数据和热效率和火用效率的计算值被用来建立一个人工神经网络模型。采用Levenberg-Marquardt (LM)和缩放共轭梯度(SCG)学习算法对所提出的人工神经网络模型进行训练。在统计误差分析的基础上,用LM学习算法在隐层找到6个数的神经元作为最优值。输入层以实验时间、质量流量、环境温度、空气平均温度、吸收板温度和太阳辐射强度为输入参数;在输出层中以能效和火用效率作为输出参数。得到了6-6-2神经网络模型作为预测的最优模型。使用人工神经网络的性能预测与实验数据进行了比较,结果非常吻合。采用统计误差分析对结果进行评价。引文:Ghritlahre, H. K.(2018)。建立前馈反向传播神经网络模型,对太阳能空气加热器进行能量预测和火用分析。可再生能源发展趋势,4,213-235。DOI: 10.17737 / tre.2018.4.2.0078
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Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater
In the present work, Artificial Neural Network (ANN) model has been developed to predict the energy and exergy efficiency of a roughened solar air heater (SAH).  Total fifty data sets of samples, obtained by conducting experiments on SAHs with three different specification of wire-rib roughness on the absorber plates, have been used in this work. These experimental data and calculated values of thermal efficiency and exergy efficiency have been used to develop an ANN model. Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) learning algorithm were used to train the proposed ANN model. Six numbers of neurons were found with LM learning algorithm in the hidden layer as the optimal value on the basis of statistical error analysis. In the input layer, the time of experiments, mass flow rate, ambient temperature, mean temperature of air, absorber plate temperature and solar radiation intensity have been taken as input parameters; and energy efficiency and exergy efficiency have been taken as output parameters in the output layer. The 6-6-2 neural model has been obtained as the optimal model for prediction. Performance predictions using ANN were compared with the experimental data and a close agreement was observed. Statistical error analysis was used to evaluate the results. Citation:  Ghritlahre, H. K. (2018). Development of feed-forward back-propagation neural model to predict the energy and exergy analysis of solar air heater. Trends in Renewable Energy, 4, 213-235. DOI: 10.17737/tre.2018.4.2.0078
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