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Abstract：
Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H2O2), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R2 values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H2O2/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent.
摘要：
合成了Jicama过氧化物酶（JP）固定化的功能化Buckypaper/聚乙烯醇（BP/PVA）膜，并将其评价为一种有前途的纳米生物复合膜，用于从水溶液中去除亚甲基蓝（MB）染料。独立过程变量的影响，包括pH值,搅拌速度,过氧化氢(H2O2)的初始浓度,系统考察了接触时间对染料去除效率的影响。响应面方法（RSM）和人工神经网络与粒子群优化（ANN-PSO）方法用于预测最佳工艺参数，以实现最大的MB染料去除效率。发现PSO嵌入式ANN架构的最佳拓扑是4-6-1。这个优化的网络为随机训练提供了更高的R2值，测试和验证数据集，分别为0.944、0.931和0.946，从而证实了ANN-PSO模型的有效性。与RSM相比，结果证实，混合ANN-PSO显示出较好的预测MB染料去除的建模能力。在pH值为5.77，179rpm时，达到了99.5％的最大MB染料去除效率，H2O2/MB染料的比率为73.2:1,在229min内。因此，这项工作表明，JP固定的BP/PVA膜是处理工业废水的一种有前途且可行的替代方法。
