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Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach

机译：混合神经网络-遗传算法在错流超滤中的建模与优化

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摘要

Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) of oily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimize operating parameters involved in the process model result in attaining desired permeate flux, is of fundamental great interest from an economical and technical point of view. Accordingly, this current research proposed a hybrid process modeling and optimization based on computational intelligence paradigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meets the challenge of specified-objective based on two steps: first the development of bio-inspired approach based on ANN, trained, validated and tested successfully with experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale in which the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and filtration time as inputs; and gave permeate flux as an output. Subsequently, the 5-dimensional input space of the ANN model portraying process input variables was optimized by applying GA, with a view to realizing maximum or minimum process output variable. The results obtained validate the estimates of the ANN-GA technique with a good accuracy. Finally, the relative importance of the controllable operation factors on flux decline is determined by applying the various correlation statistic techniques. According to the result of the sensitivity analysis based on the correlation coefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.
机译：含油废水的错流超滤（UF）中各种操作参数下的精确建模通量下降，然后采用适当的优化算法来优化过程模型中涉及的操作参数，从而获得所需的渗透通量，这是非常重要的从经济和技术角度来看。因此，当前的研究提出了一种基于计算智能范式的混合过程建模和优化，其中人工神经网络（ANN）和遗传算法（GA）的结合通过两个步骤满足指定目标的挑战：首先，生物技术的发展基于人工神经网络的方法，经过训练，验证和测试，成功地利用了聚丙烯腈（PAN）超滤过程中收集的实验数据来处理德黑兰炼油厂的含油废水，实验室规模在模型中接受了进料温度（T），进料pH ，跨膜压力（TMP），错流速度（CFV）和过滤时间作为输入;并给出渗透通量作为输出。随后，通过应用GA优化了描绘过程输入变量的ANN模型的5维输入空间，以期实现最大或最小过程输出变量。获得的结果以良好的准确性验证了ANN-GA技术的估计。最后，通过应用各种相关统计技术来确定可控操作因子对通量下降的相对重要性。根据基于相关系数的敏感性分析结果，过滤时间是最重要的，其次是T，CFV，进料pH和TMP。

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