Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran 158754413, Iran.
Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran 158754413, Iran.
J Environ Manage. 2017 Dec 15;204(Pt 1):311-317. doi: 10.1016/j.jenvman.2017.09.011. Epub 2017 Sep 9.
Prediction of Ni(II) removal during ion flotation is necessary for increasing the process efficiency by suitable modeling and simulation. In this regard, a new predictive model based on the hybrid neural genetic algorithm (GANN) was developed to predict the Ni(II) ion removal and water removal during the process from aqueous solutions using ion flotation. A multi-layer GANN model was trained to develop a predictive model based on the important effective variables on the Ni(II) ion flotation. The input variables of the model were pH, collector concentration, frother concentration, impeller speed and flotation time, while the removal percentage of Ni(II) ions and water during ion flotation were the outputs. The most effective input variables on Ni(II) removal and water removal were evaluated using the sensitivity analysis. The sensitivity analysis of the model shows that all input variables have a significant impact on the outputs. The results show that the proposed GANN models can be used to predict the Ni(II) removal and water removal during ion flotation.
为了通过适当的建模和模拟提高工艺效率，有必要对离子浮选过程中的 Ni(II)去除进行预测。为此，开发了一种新的基于混合神经网络遗传算法（GANN）的预测模型，用于预测从水溶液中使用离子浮选去除 Ni(II)离子和水的过程。训练多层 GANN 模型以基于对 Ni(II)离子浮选的重要有效变量来开发预测模型。该模型的输入变量为 pH 值、捕收剂浓度、起泡剂浓度、叶轮转速和浮选时间，而离子浮选过程中 Ni(II)离子和水的去除率则为输出。使用敏感性分析评估对 Ni(II)去除和水去除影响最大的输入变量。模型的敏感性分析表明，所有输入变量对输出都有显著影响。结果表明，所提出的 GANN 模型可用于预测离子浮选过程中 Ni(II)的去除和水的去除。
