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首页> 外文期刊>Journal of Applied Polymer Science >Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks
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Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

机译：响应表面法和人工神经网络对电纺PAN纳米纤维直径的建模和优化

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摘要

Response surface methodology (RSM) based on a three-level, three-variable Box-Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input-output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle-collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High-regression coefficient between the variables and the response (R ~2 = 0.998) indicates excellent evaluation of experimental data by second-order polynomial regression model. The R ~2 value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle-collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed.
机译：比较了基于三级，三变量Box-Benkhen设计（BBD）和人工神经网络（ANN）技术的响应面方法（RSM），以模拟电纺聚丙烯腈（PAN）纳米纤维的平均直径。使用缩放的共轭梯度反向传播算法，通过输入-输出模式集训练多层感知器（MLP）神经网络。研究了三个重要的静电纺丝因素，包括聚合物浓度（w / v％），施加电压（kV）和喷嘴-收集器距离（cm）。预测的纤维直径与ANN和RSM技术的实验结果一致。变量与响应之间的高回归系数（R〜2 = 0.998）表明通过二阶多项式回归模型可以很好地评估实验数据。 R〜2值为0.990，这表明ANN模型与实验数据非常吻合。而且，RSM模型显示的绝对百分比误差比ANN模型低得多。因此，所得结果表明RSM的性能优于ANN。 RSM模型预测了在10 w / v％的聚合物浓度，12 cm的喷嘴-收集器距离和12 kV的施加电压的条件下，最细纳米纤维直径的118 nm值。预测值（118 nm）仅显示2.5％，与在相同设置下观察到121 nm的实验结果有所不同。

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