Abstract

In this study, artificial neural network (ANN) and response surface methodology (RSM) were applied to analyze the fixed bed adsorption of FD&C red 40 dye (Food, drug and cosmetic dye red 40) on polyurethane/chitosan foam (PU/CS foam). The adsorbent was prepared and characterized. The effects of the process variables, including flow rate and bed height, were investigated through two different levels using RSM. Breakthrough curves were used as training data set for the ANN. The ANN was customized with 10 neurons in the hidden layer using the hyperbolic tangent sigmoid transfer function as activation function and the linear transfer function in the output layer. The optimal range of bed operation was 5–6 cm for bed height and 15–17.05 mL min⁻¹ for flow rate. The values of experimental adsorption capacity of the column ranged from 44.3 to 108.1 mg g⁻¹, and were compared with the resulting values of the ANN and RSM models. The ANN can predict the experimental data with more accuracy than the RSM. The values found for the coefficient of determination ​​were 0.9911 for the ANN and 0.8853 for the RSM. The various error functions tested between predicted and experimental values ​​of the ANN and RSM models demonstrated a better applicability and efficiency of the ANN model. Finally, PU/CS foam proved to be a promising, low-cost adsorbent with excellent potential for removing FD&C red 40 dye.

摘要

本研究应用人工神经网络 (ANN) 和响应面法 (RSM) 分析 FD&C 红 40 染料（食品、药物和化妆品染料红 40）在聚氨酯/壳聚糖泡沫（PU/CS 泡沫）上的固定床吸附）。制备并表征了吸附剂。过程变量的影响，包括流速和床层高度，通过两个不同的水平使用 RSM 进行研究。突破曲线被用作人工神经网络的训练数据集。人工神经网络在隐藏层中定制了 10 个神经元，使用双曲正切 sigmoid 传递函数作为激活函数，输出层中使用线性传递函数。床层操作的最佳范围是床高 5-6 cm 和 15-17.05 mL min ^-1为流量。柱的实验吸附容量值范围为 44.3 至 108.1 mg g ^-1，并与 ANN 和 RSM 模型的结果值进行比较。ANN 可以比 RSM 更准确地预测实验数据。ANN 的决定系数为 0.9911，RSM 为 0.8853。在ANN和RSM模型的预测值和实验值之间测试的各种误差函数证明了ANN模型更好的适用性和效率。最后，PU/CS 泡沫被证明是一种很有前途的低成本吸附剂，具有去除 FD&C 红 40 染料的出色潜力。