Gholami Alireza, Khoshdast Hamid, Hassanzadeh Ahmad
Department of Mineral Processing, Faculty of Engineering, Tarbiat Modares University, 14115111, Tehran, Iran.
Department of Mining Engineering, Higher Education Complex of Zarand, 7761156391, Zarand, Iran.
J Environ Manage. 2021 Dec 1;299:113666. doi: 10.1016/j.jenvman.2021.113666. Epub 2021 Sep 13.
The present work aims at optimization and advanced simulation of removal efficiency of dye material from a synthetic wastewater using a locally generated rhamnolipid (RL) biosurfactant. For this purpose, bio-treatment of dye polluted synthetic wastewater was experimentally, kinetically, and statistically investigated by the ion flotation process in the presence of the RL. The removal rate of methylene blue (MB) as the dye material was assessed by the ultraviolet (UV)-visible absorbance measurements. The impact of operating variables including RL concentration (as a dye collector, 5-50 ppm), methyl isobutyl carbinol (MIBC) dosage (as a frother, 10-70 ppm), solution pH (2-12) and aeration rate (1-5 l/min) were assessed through one-way analysis of variance (ANOVA) and Anderson-Darling as the normality analysis strategy. The process was simulated using two artificial neural network (ANN) optimization algorithms, i.e., genetic algorithm (GA) and artificial bee colony (ABC) as a novel approach. The statistical results indicated that the dye removal process was significantly influenced by all operating variables (p<0.05) while their relative intensity followed the order of aeration rate > solution pH > RL concentration > MIBC dosage. Anderson-Darling approach disclosed that the all factors were perfectly followed the normal trend with A less than unity and p-value of greater than 0.05 at 95% confidence level. Main effect plots revealed that except MIBC dosage with nonlinear trend, the rest of factors had an ascending influence on the removal efficiency. The process was optimized by interpreting the interaction effect among various variables to reach the maximum dye bioflotation. The maximum removal of 97 ± 0.13% was achieved at pH 12, airflow rate of 5 l/min, MIBC and rhamnolipid concentrations of 30 and 40 ppm, respectively with a flotation kinetic rate of 0.015 sec. Finally, the intelligent simulation results showed that the process could be modelled using an artificial bee colony algorithm of 4-7-1 structure with 99% and 98.8% accuracies in the training and testing steps, respectively. Further, we found that the artificial bee colony algorithm was superior to the genetic algorithm in terms of complexity analysis.
本研究旨在利用本地生产的鼠李糖脂（RL）生物表面活性剂对合成废水中染料物质的去除效率进行优化和高级模拟。为此，通过离子浮选法，在RL存在的情况下，对染料污染的合成废水进行了生物处理，并进行了实验、动力学和统计学研究。通过紫外（UV）-可见吸光度测量评估了作为染料物质的亚甲基蓝（MB）的去除率。通过单因素方差分析（ANOVA）和作为正态性分析策略的安德森-达林检验，评估了包括RL浓度（作为染料捕集剂，5-50 ppm）、甲基异丁基甲醇（MIBC）用量（作为起泡剂，10-70 ppm）、溶液pH值（2-12）和曝气速率（1-5 l/min）等操作变量的影响。使用两种人工神经网络（ANN）优化算法，即遗传算法（GA）和人工蜂群算法（ABC）作为一种新方法对该过程进行了模拟。统计结果表明，所有操作变量对染料去除过程均有显著影响（p<0.05），其相对影响强度顺序为曝气速率>溶液pH值>RL浓度>MIBC用量。安德森-达林检验方法表明，所有因素在95%置信水平下均完全符合正态趋势，A值小于1且p值大于0.05。主效应图显示，除MIBC用量呈非线性趋势外，其他因素对去除效率均有上升影响。通过解释各种变量之间的相互作用效应来优化该过程，以实现最大的染料生物浮选。在pH值为12、气流速率为5 l/min、MIBC和鼠李糖脂浓度分别为30和40 ppm时，浮选动力学速率为0.015秒，实现了97±0.13%的最大去除率。最后，智能模拟结果表明，该过程可以使用结构为4-7-1的人工蜂群算法进行建模，在训练和测试步骤中的准确率分别为99%和98.8%。此外，我们发现人工蜂群算法在复杂性分析方面优于遗传算法。
