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 (pvalue<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 A2 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-1. 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.

中文翻译：

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应用混合遗传和人工蜂群算法来模拟使用鼠李糖脂生物表面活性剂对合成染料污染废水的生物处理。

目前的工作旨在优化和高级模拟使用本地生成的鼠李糖脂 (RL) 生物表面活性剂从合成废水中去除染料材料的效率。为此，在 RL 存在下，通过离子浮选工艺对染料污染的合成废水的生物处理进行了实验、动力学和统计研究。作为染料材料的亚甲蓝 (MB) 的去除率通过紫外 (UV)-可见光吸收测量来评估。操作变量的影响包括 RL 浓度（作为染料收集剂，5-50 ppm）、甲基异丁基甲醇 (MIBC) 剂量（作为起泡剂，10-70 ppm）、溶液 pH (2-12) 和曝气率 (1 -5 l/min) 通过单向方差分析 (ANOVA) 和 Anderson-Darling 作为正态分析策略进行评估。该过程使用两种人工神经网络 (ANN) 优化算法进行模拟，即遗传算法 (GA) 和人工蜂群 (ABC) 作为一种新方法。统计结果表明，染料去除过程受所有操作变量（pvalue<0.05）的显着影响，而它们的相对强度遵循曝气速率>溶液pH>RL浓度>MIBC剂量的顺序。Anderson-Darling 方法表明所有因素都完全符合正常趋势，A2 小于 1，p 值大于 0.05，置信度为 95%。主效应图显示，除 MIBC 用量呈非线性趋势外，其余因素对去除效率的影响呈上升趋势。通过解释各种变量之间的相互作用来优化该过程，以达到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 以及浮选动力学速率为 0.015 sec-1 时，实现了 97 ± 0.13% 的最大去除率。最后，智能仿真结果表明，该过程可以使用 4-7-1 结构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度分析方面优于遗传算法。分别具有 0.015 sec-1 的浮选动力学速率。最后，智能仿真结果表明，该过程可以使用 4-7-1 结构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度分析方面优于遗传算法。分别具有 0.015 sec-1 的浮选动力学速率。最后，智能仿真结果表明，该过程可以使用 4-7-1 结构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度分析方面优于遗传算法。