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Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane.
Environmental Research ( IF 7.7 ) Pub Date : 2020-01-21 , DOI: 10.1016/j.envres.2020.109158
Lau Yien Jun 1 , Rama Rao Karri 2 , Lau Sie Yon 1 , N M Mubarak 1 , Chua Han Bing 1 , Khalid Mohammad 3 , Priyanka Jagadish 3 , E C Abdullah 4
Affiliation  

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.

中文翻译:

利用粒子群嵌入神经网络进行建模和优化,以固定在布基纸/聚乙烯醇膜上的豆薯过氧化物酶吸附亚甲基蓝。

合成了固定化的Jicama过氧化物酶(JP)的功能化Buckypaper /聚乙烯醇(BP / PVA)膜,并被评估为用于从水溶液中去除亚甲基蓝(MB)染料的有前途的纳米生物复合膜。系统地研究了独立工艺变量,包括pH,搅拌速度,过氧化氢(H2O2)的初始浓度和接触时间对染料去除效率的影响。响应面方法学(RSM)和人工神经网络结合粒子群优化(ANN-PSO)方法均用于预测最佳工艺参数,以实现最大的MB染料去除效率。发现用于PSO嵌入式ANN架构的最佳最佳拓扑是4-6-1。这个经过优化的网络为随机训练,测试和验证数据集提供了更高的R2值,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,结果证实了混合ANN-PSO显示出优异的建模能力,可预测MB染料的去除。在229分钟内,在pH-5.77、179 rpm,H2O2 / MB染料比为73.2:1的情况下,达到了99.5%的最大MB染料去除效率。因此,这项工作表明,JP固定的BP / PVA膜是用于处理工业废水的一种有前途且可行的替代方法。
更新日期:2020-01-22
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