In this research, MgAl-CO32- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

中文翻译：

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人工神经网络-遗传算法混合对NLDH / PVDF超滤纳米复合膜的建模与优化。

在这项研究中，MgAl-CO32-纳米层状双氢氧化物（NLDH）通过一种简便的共沉淀方法合成，然后进行水热处理。制备的NLDH用作亲水性纳米填料，用于改善基于PVDF的超滤膜的性能。这项研究的主要目的是使用计算技术作为一种具有成本效益的方法，以获得表现出最佳性能的NLDH / PVDF纳米复合膜的优化配方。为此，开发了一个人工神经网络（ANN）模型来建模和表达纳米复合膜的性能（纯水通量，蛋白质通量和通量回收率）与包括NLDH，PVP 29000和聚合物浓度。使用开发的模型预测的轮廓图研究了上述参数的影响以及参数之间的相互作用。扫描电子显微镜（SEM），原子力显微镜（AFM）和水接触角技术被用于表征纳米复合膜并解释ANN模型的预测。将开发的ANN模型作为生物启发优化器引入遗传算法（GA），以确定导致高纯水通量，蛋白质通量和通量回收率的输入参数的最佳值。NLDH，PVP 29000和PVDF浓度的最佳值分别确定为0.54、1和18 wt％。实验研究了使用GA提出的最佳值制备的纳米复合膜的性能，结果与人工神经网络模型预测值相吻合，误差小于6％。这一良好的协议证实，可以通过ANN-GA系统成功地建模和优化纳米复合膜的性能。