Using computational intelligence for prediction, modeling, and optimization of chemical process behavior could save costs and time. This study’s main goal was to predict and optimize removal efficiency and permeate flux behavior of Pb²⁺ aqueous solution in a nanofiltration process through using response surface methodology (RSM) and multilayer perceptron (MLP) neural network. A regression coefficient R²=0.99 was obtained for both removal efficiency and permeate flux in the RSM model. Also, the F-value for the removal efficiency and permeate flux was 394.79 and 1888.85, respectively. Different MLP structures for predicting removal efficiency and permeate flux behavior of lead ion in aqueous solutions were investigated. The best structure was obtained for two hidden layers with nine (tansig transfer function) and three (logsig transfer function) neurons. The values of R=0.9993, R²=0.9986, MSE=0.402 and MAE=0.409 for the best structure were obtained. Finally, the the removal efficiency was optimized through RSM based on the experimental data. It was concluded that optimum mode selected for membrane composition of PSF=10.04%, NMP=88.98%, and PAN-CMC-41=0.98% (wt%) 53.17 ppm as lead ion concentration in solution and 30.31 min for filtration time achieved the maximum value of removal efficiency equal to 90.68%.

使用计算智能进行化学过程行为的预测，建模和优化可以节省成本和时间。这项研究的主要目的是通过使用响应表面方法（RSM）和多层感知器（MLP）神经网络来预测和优化纳米过滤过程中Pb ²⁺水溶液的去除效率和渗透通量行为。回归系数R ²在RSM模型中，去除效率和渗透通量均= 0.99。此外，去除效率和渗透通量的F值分别为394.79和1888.85。研究了用于预测铅离子在水溶液中的去除效率和渗透通量行为的不同MLP结构。对于具有九个（tansig传递函数）和三个（logsig传递函数）神经元的两个隐藏层，获得了最佳结构。R = 0.9993，R ^2的值＝ 0.9986，获得最佳结构的MSE ＝ 0.402和MAE ＝ 0.409。最后，根据实验数据，通过RSM对去除效率进行了优化。结论是，溶液中铅离子浓度为PSF = 10.04％，NMP = 88.98％和PAN-CMC-41 = 0.98％（wt％）的膜组成的最佳模式选择为53.17 ppm，过滤时间为30.31 min去除效率的最大值等于90.68％。