Khoshdast Hamid, Gholami Alireza, Hassanzadeh Ahmad, Niedoba Tomasz, Surowiak Agnieszka
Department of Mining Engineering, Higher Education Complex of Zarand, Zarand 7761156391, Iran.
Department of Mineral Processing, Tarbiat Modares University, Tehran 14115-111, Iran.
Materials (Basel). 2021 May 27;14(11):2880. doi: 10.3390/ma14112880.
This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60 × 5.70 × 0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process.
这项工作旨在提出一种先进的模拟方法，用于一种新型的基于鼠李糖脂的生物浮选工艺，以从废水中去除铬。为此，使用方差分析（ANOVA）方法研究并评估了关键影响操作变量的重要性，这些变量包括初始溶液pH值（2、4、6、8、10和12）、鼠李糖脂与铬的比例（RL:Cr = 0.010、0.025、0.050、0.075和0.100）、还原剂（Fe）与铬的比例（Fe:Cr为0.5、1.0、1.5、2.0、2.5、3.0）以及空气流速（50、100、150、200和250 mL/min）。鼠李糖脂作为捕收剂和起泡剂，是在特定条件下使用铜绿假单胞菌MA01的纯菌株生产的。生物浮选试验在尺寸为60×5.70×0.1 cm的鼓泡柱式浮选机中进行。基于人工神经网络（ANN）的四种优化技术，包括布谷鸟算法、遗传算法、萤火虫算法和基于生物地理学的优化算法，应用于113次实验，以确定研究因素的最佳值。方差分析结果表明，所有四个变量均通过非线性趋势影响生物浮选性能。除曝气速率外，它们的影响在统计学上具有显著性（p值<0.05），并且根据安德森 - 达林（AD）准则，所有参数均遵循正态分布。在pH值为6、鼠李糖脂与铬的比例为0.05、空气流速为150 mL/min以及Fe与Cr的比例为1.0时，实现了约98%的最大铬去除率。浮选动力学研究表明，铬生物浮选遵循一级动力学模型，速率为0.023秒。根据模型准确性的统计评估，结构为4-9-1的萤火虫算法（FFA）产生了最高的可靠性水平，测试数据的均方、均方根、百分比误差和相关系数值分别为0.0038、0.0617、3.08%和96.92%。这些值证明了结构良好的人工神经网络方法模拟该过程的一致性。
