Artificial neural network based modeling of liquid membranes for separation of dysprosium |
| |
| Affiliation: | 1. School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Environmental Science and Engineering, Nanjing Tech University, Nanjing 211816, China;1. Department of Physics, Erciyes University, 38039, Kayseri, Turkey;2. Ministry of Higher Education and Scientific Research, Allraqia University, 10011, Baghdad, Iraq;3. Science and Technology Application and Research Center, Nevşehir Hacı Bektaş Veli University, 50300, Nevşehir, Turkey;1. Key Laboratory for Ecological Metallurgy of Multimetallic Ores (Ministry of Education), Northeastern University, Shenyang 110819, China;2. School of Metallurgy, Northeastern University, Shenyang 110819, China;1. State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China;2. GRIMAT Engineering Institute Co., Ltd., Beijing 101402, China;3. State Key Laboratory of Non-ferrous Metals and Processes, GRINM Group Co., Ltd., Beijing 100088, China;4. GRIKIN Advanced Material Co. Ltd., Beijing 100220, China;5. GRIREM Advanced Materials Co. Ltd., Beijing 100088, China;1. Key Laboratory of Advanced Structural-Functional Integration Materials & Green Manufacturing Technology, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Nuclear Fuel Cycle Research Theme, Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW, 2232, Australia |
| |
| Abstract: | In recent years, the liquid membrane process has been widely investigated to remove rare earth metals. However, transport modeling of this process requires the accurate values of several parameters, which are difficult to measure. Thus, the accurate simulation of this process is a challenging task. In this study, the artificial neural network (ANN) based approach is used to model the liquid membrane process for removing dysprosium. Experimental results from a previous study were used to train the ANN. Initially, the number of neurons in the hidden layer was optimized. The minimum mean squared error between experimental results and model predictions is found with ten neurons. Model predictions were successfully validated with experimental results with correlation factor (R) of 0.9987, which confirms the authenticity of the trained network. Trained ANN was then used to study the effects of different operating parameters on transport rate. The higher volume ratio of membrane solution to feed solution (3–4) with 50–60 min of operation, higher feed pH (5), HCl concentration in stripping solution of 2 mol/L, and moderate concentration of carrier species (0.5 mol/L) with 0.5 × 10−4 mol/L dysprosium initial concentration are found to be optimum values of operating conditions for maximizing the transport rate. |
| |
| Keywords: | Rare earth metal separation Artificial neural network Dysprosium separation Liquid membranes |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
