【作者】 谭明；

【导师】 贺高红；

【作者基本信息】 大连理工大学 ， 化学工程， 2013， 博士

【摘要】 优化分离膜制备条件可以提高膜的性能，扩大其应用范围，具有十分重要的意义。目前，膜制备条件的优化方法以正交试验和单因素实验法为主，即在多种条件下制备膜，根据膜的性能及其变化趋势优化制备条件。这种方法实验工作量大，只能得到较优的制备条件。因此，建立普适的膜制备条件与性能之间关系的数学模型，具有十分重要的意义。本研究基于Flory-Huggins理论分析得到了多种添加剂的最大加入量，制备了多种条件下的聚醚酰亚胺（PEI）超滤膜，并使用纯水通量截留率测定实验与图像分析两种方法对膜进行了表征。利用这些基础数据，建立了优化PEI超滤膜制备条件的反向传播神经网络（BPNN）和遗传算法（GA）混合模型。该模型预测了不同制备条件下PEI超滤膜和聚二甲基硅氧烷（PDMS）/陶瓷渗透汽化复合膜的性能，与实验数据吻合良好，具有较好的普适性。还预测出了高纯水通量与期望截留率的PEI超滤膜和高渗透通量与期望选择性的PDMS/陶瓷渗透汽化复合膜的制备条件。该模型减少了实验量，实现了根据需要进行膜的设计制备。利用Flory-Huggins理论研究了PEI为聚合物，N, N-二甲基乙酰胺（DMAc）为溶剂，水（H2O）为非溶剂，乙醚（DE）、聚乙二醇（PEG400）、正丁醇（BuOH）和丁内酯（GBL）为添加剂的铸膜液体系的成膜热力学过程，计算得到了H2O/DMAc/PEI、DE/DMAc/PEI、PEG400/DMAc/PEI和BuOH/DMAc/PEI体系的双节线和旋节线，分析得到了H2O、DE、PEG400和BuOH的最大加入量，并根据铸膜液的热力学性能进行了膜的制备实验。使用MATLAB软件开发了膜孔结构参数统计程序，对膜的表面扫描电镜照片进行分析，得到了膜的平均孔径、最大孔径和离散孔径等参数。提出利用离散孔径与筛分方程计算膜的截留曲线、截留分子量和截留分子尺寸比利用平均孔径准确，并通过牛血清白蛋白（BSA）和葡聚糖分子的截留实验进行了验证。在上述实验数据的基础上，分别建立了BPNN和径向基函数网络（RBFNN）模型回归相转化法五个关键超滤膜制备条件（PEI浓度、添加剂种类与浓度、停留蒸发时间、凝胶浴温度）与纯水通量、BSA截留率之间的关系。BPNN易陷于局部极值，模型多次收敛结果差别较大；而RBFNN的容错性不理想，本研究结合BPNN很强的局部收敛能力与遗传算法优异的全局寻优能力建立了BPNN-GA混合模型，并用“试差法”得到了较优的模型结构参数。尽管多种添加剂的成膜机理各异，膜性能变化趋势多样，但是BPNN-GA混合模型都能准确预测膜的纯水通量与BSA截留率，大部分测试数据的偏差都小于10%，证明该模型的预测精度良好。该模型还预测出GBL是本研究BuOH、DE、PEG400、聚乙烯吡咯烷酮（PVP）和GBL中制备PEI超滤膜最优的添加剂，预测出截留率为80-90%，纯水通量高达1.15-0.95m³m^-2 h^-1的超滤膜的制备条件，与实验结果吻合良好。为了进一步考察BPNN-GA混合模型的普适性，本研究利用文献中的实验数据预测了不同制备条件下PDMS/陶瓷渗透汽化膜的性能，并与文献中的响应曲面法进行了比较。混合模型训练数据和测试数据的最大偏差分别为11.01%和7.17%，均小于响应曲面法对应的最大偏差（17.79%和15.56%）。混合模型预测值与实验数据的一致性，说明该模型具有较好的精度。BPNN-GA混合模型预测出PDMS浓度、交联剂浓度与涂膜时间对渗透汽化复合膜性能的影响都很大，还预测出选择性为6和7，渗透通量高达10.5和9kg·m^-2·h^-1的膜的制备条件。更多还原

【Abstract】 Optimization of membrane preparation condition which can enhance membraneperformances and expand application range of membranes is of great significance. Nowadays,it mainly relies on single factor experiment and orthogonal test. That is, membranes undervarious preparation conditions are fabricated and characterized. According to the membraneperformances, preparation conditions are optimized. Obviously, this method has disadvantagesof large experimental data scattering, and the optimal preparation condition is not guaranteed.Therefore, there is an urgent need to develop a universal mathematical model to capture therelationship between preparation conditions and membrane performances. In this study,Flory-Huggins theory was employed to analyse the maximal addition amount of additive, andseveral membranes were fabricated, then image analysis and determination experiment of purewater flux and rejection ratio were used to characterize the membranes. Based on theexperimental data, hybrid models based on backpropagation neural network (BPNN) andgenetic algorithm (GA) were established to optimize the preparation conditions ofpolyetherimide (PEI) ultrafiltration membrane via dry/wet phase inversion. The hybrid modelspredict performances of PEI ultrafiltration membranes and polydimethylsiloxane(PDMS)/ceramic pervaporation composite membranes under various preparation conditions.The hybrid models can contribute to designing the preparation conditions to obtain desiredmembrane performances and avoiding large experimental data scattering in the fabrication ofmembranes.Flory-Huggins theory was employed to investigate the thermodynamic mechanism of thecasting solutions whose polymer is PEI, solvent is N, N-dimethylacetamide (DMAc),nonsolvent is water (H2O), and additives are diethyl ether (DE), polyethylene glycol(PEG400),1-Butanol (BuOH) and1,4-butyrolactone (GBL). The binodal curves and thespinodal lines for H2O/DMAc/PEI, DE/DMAc/PEI, PEG400/DMAc/PEI andBuOH/DMAc/PEI systems were calculated, which reveal the maximal addition amount ofadditive H2O, DE, PEG400and BuOH. According to the thermodynamic property of thecasting solution, preparation conditions of membranes were primarily explored. A statisticalprocedure was developed to measure microstructure parameters of membranes. It disposed thescanning electronic microscope (SEM) images, including gray translation, binarization andnoise reduction of the images, and then maximal pore size, discrete pore size and average poresize were obtained. It was proposed that using discrete pore size and sieving equation tocalculate rejection curve, molecular weight cutoff and molecular size cutoff of the membrane was more accurate that using average pore size, which was proved by the rejection experimentsof bovine serum albumin (BSA) and dextran.Based on the experimental data, BPNN and radial basis function neural network (RBFNN)models were constructed to capture the relationship of five key prepatation conditions (PEIconcentration, additive type and concentration, evaporation time in air and temperature ofcoagulation bath) to the performance of membranes, i.e., pure water flux and BSA rejectionratio. BPNN is easy to be convergent at suboptimal solutions, and there are great deviationsbetween several convergences. RBFNN has dissatisfactory fault tolerance. Therefore, hybridmodels which united perfect local convergent ability of BPNN and ideal global searchcapability of GA were proposed, whose model arctectures were optimized by trial-and-errormethod. Membrane formation mechanisms of various additives are numerous, and theperformance trends were different, but the predictions of the hybrid models were accurate, withmost of deviation in testing data less than10%. The hybrid models can predict membraneperformances under different preparation conditions and hereby indicate H2O/DMAc/PEI/GBLis the best of six casting systems in the study. In addition, the hybrid models can contribute todesigning preparation conditions to obtain higher performances of ultrafiltration membranes(BSA rejection is80-90%and pure water flux is up to1.15-0.95m3m-2h-1) and avoiding largeexperimental data scattering in the fabrication of phase inversion membranes.The hybrid models were used to optimize the preparation conditions of PDMS/ceramiccomposite membrane with the experimental data from the literature to discuss the modeluniversality. The maximal deviations of the training and testing data between the experimentsand the hybrid model predictions were11.01%and7.17%, smaller than those between theexperiments and response surface methodology (RSM) model predictions in the literature. Theaccordances between the the experiments and the hybrid model predictions show that thehybrid models have sufficient accuracy. Connection weight analyses show that PDMSconcentration, crosslinking agent concentration and dip-coating time have great influences onthe performances of pervaporation membranes. In addition, the models predict the preparationconditions to fabricate pervaporation membranes whose permeation fluxes reached10.5and9kg m-2h-1and selectivities were6and7.更多还原