Prediction of cadmium removal efficiency by emulsion liquid membrane using an artificial neural network
DOI:
https://doi.org/10.31699/IJCPE.2025.4.11Keywords:
emulsification; surfactant; cadmium removal; emulsion; artificial neural networkAbstract
This study presented Intelligent Network's proposed methodology for forecasting the effectiveness of cadmium removal from wastewater using emulsion liquid membrane (ELM). The research examined the removal of cadmium from an aqueous solution in the internal phase of water-in-oil emulsions. ELM was composed of kerosene as the membrane phase, Span 80 as the surfactant, di-2-ethylhexyl phosphoric acid (D2EHPA) as the extracting agent, and hydrochloric acid (HCl) as the stripping solution. Experiments were conducted to investigate the effects of five parameters: surfactant concentration, feed-phase agitation speed, internal-to-membrane phase volume ratio, emulsion-to-feed volume ratio, and stripping-phase concentration in the internal phase. More than 97.4% of cadmium can be extracted in less than 15 minutes. This study evaluates the effectiveness of various learning algorithms, including gradient descent (GD), resilient back propagation (RB), gradient descent with momentum (GDM), gradient descent with learning momentum and adaptive rate (GDX), polak-ribiére conjugate gradient (CG), and levenberg marquardt (LM), in predicting the efficiency of cadmium elimination from wastewater using liquid emulsion membrane technology. The researchers developed a neural network model with 8 input neurons, 10 hidden neurons, and 1 output neuron. This feed-forward artificial neural network (ANN) incorporated various back-propagation training algorithms to simulate the cadmium removal process using ELM. The neural network model's predictions closely aligned with results from batch experiments, as demonstrated by a correlation coefficient (R2) of 0.9723 and a Mean Squared Error (MSE) of 0.0041, calculated in MATLAB. In conclusion, the ANN models demonstrated high accuracy and reliability in predicting cadmium removal efficiency, confirming their potential as practical tools for process optimization.
Received on 21/09/2025
Received in Revised Form on 27/10/2025
Accepted on 28/10/2025
Published on 30/12/2025
References
[1] A. Benderrag, B. Haddou, Daaou, M., Benkhedja, H., Bounaceur, B., & Kameche, M. Experimental and modeling studies on Cd (II) ions extraction by emulsion liquid membrane using Triton X-100 as biodegradable surfactant. Journal of Environmental Chemical Engineering, 7(3), 103166. 2019, https://doi.org/10.1016/j.jece.2019.103166
[2] G. K. Kinuthia, V. Ngure, D. Beti, R. Lugalia, A. Wangila, and L. Kamau, “Levels of heavy metals in wastewater and soil samples from open drainage channels in Nairobi, Kenya: community health implication,” Scientific Reports, vol. 10, no. 1, Dec. 2020, https://doi.org/10.1038/s41598-020-65359-5
[3] H. K. Admawi and A. A. Mohammed, “Extraction of cadmium from aqueous solutions by emulsion liquid membrane (ELM) using three different carriers dissolved in a 70:30 ratio of sunflower oil to kerosene,” Journal of the Indian Chemical Society, vol. 100, no. 9, Sep. 2023, https://doi.org/10.1016/j.jics.2023.101081
[4] S. Lamba, Ankush, Ritambhara, Deepika, and R. Prakash, “Cadmium in Environment—An Overview,” Cadmium Toxicity in Water. Springer Water, pp. 3–20. 2024, https://doi.org/10.1007/978-3-031-54005-9_1
[5] I. Ahmad, R. U. Asad, L. Maryam, and M. Masood, “Treatment Methods for Cadmium Removal from Wastewater,” Cadmium Toxicity in Water, Springer Water; pp. 139–174, 2024, https://doi.org/10.1007/978-3-031-54005-9_7
[6] M. Yaghoobi, P. Zaheri, S. H. Mousavi, B. Arabi Ardehali, and T. Yousefi, “Evaluation of mean diameter and drop size distribution of an emulsion liquid membrane system in a horizontal mixer-settler,” Chemical Engineering Research and Design, vol. 167, pp. 231–241, Mar. 2021, https://doi.org/10.1016/j.cherd.2020.12.016
[7] A. Kusumastuti et al., “Emulsion Liquid Membrane Modelling for Cadmium Removal using Taylor-Couette Column,” Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, vol. 112, no. 1, pp. 155–175, Dec. 2023, https://doi.org/10.37934/arfmts.112.1.155175
[8] A. Benderrag, B. Haddou, M. Daaou, H. Benkhedja, B. Bounaceur, and M. Kameche, “Experimental and modeling studies on Cd (II) ions extraction by emulsion liquid membrane using Triton X-100 as biodegradable surfactant,” Journal of Environmental Chemical Engineering, vol. 7, no. 3, Jun. 2019, https://doi.org/10.1016/j.jece.2019.103166
[9] H. M. Ghargan, O. Al-Fatlawi, and Y. Bashir, “Reservoir permeability prediction based artificial intelligence techniques,” Iraqi Journal of Chemical and Petroleum Engineering, vol. 25, no. 4, pp. 49–60, Dec. 2024, https://doi.org/10.31699/IJCPE.2024.4.5
[10] L. Sima, J. Bucukovski, E. Carlson, and N. L. Yien, “Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks,” Neural and Evolutionary Computing, Sep. 2023, https://doi.org/10.48550/arXiv.2309.04426
[11] S. Schmidgall et al., “Brain-inspired learning in artificial neural networks: a review,” Neural and Evolutionary Computing, May 2023, https://doi.org/10.48550/arXiv.2305.11252
[12] M. Li, “Academic Journal of Science and Technology Comprehensive Review of Backpropagation Neural Networks”, Academic Journal of Science and Technology, 2024, https://doi.org/10.54097/51y16r47
[13] R. Jang, “Learning representations by forward-propagating errors,” Neural and Evolutionary Computing:2308.09728, Aug. 2023, https://doi.org/10.48550/arXiv.2308.09728
[14] S. A. A. Akkar and S. A. M. Mohammed, “Design of intelligent network to predicate phenol removal from waste water by emulsion liquid membrane,” in Materials Science Forum, vol. 1021, pp. 115–128, 2021, https://doi.org/10.4028/www.scientific.net/MSF.1021.115
[15] X. Deng, T. Sun, D. Li, and X. Lu, “Exploring the Inefficiency of Heavy Ball as Momentum Parameter Approaches 1,” Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence 2024, https://doi.org/10.24963/ijcai.2024/431
[16] J. Jepkoech, D. M. Mugo, B. K. Kenduiywo, and E. C. Too, “The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks.” International Journal of Advanced Computer Science and Applications, Vol. 12, No. 8, 2021, https://doi.org/10.14569/IJACSA.2021.0120885
[17] A. Silas, I. C. Peace, A. O. Uzoma, and S. Abasiama Ita, “Effect of Learning Rate on Artificial Neural Network in Machine Learning,” International Journal of Engineering Research & Technology, 2015.
[18] C. Sekhar and P. S. Meghana, “A Study on Backpropagation in Artificial Neural Networks,” Asia-Pacific Journal of Neural Networks and Its Applications, vol. 4, no. 1, pp. 21–28, Aug. 2020, https://doi.org/10.21742/AJNNIA.2020.4.1.03
[19] M. Li, “Comprehensive Review of Backpropagation Neural Networks”, Academic Journal of Science and Technology, 2024, https://doi.org/10.54097/51y16r47
[20] A. Orooji, M. Shanbehzadeh, E. Mirbagheri, and H. Kazemi-Arpanahi, “Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19,” BMC Infectious Diseases, vol. 22, no. 1, p. 923, Dec. 2022, https://doi.org/10.1186/s12879-022-07921-2
[21] D. Wang, Q. Wang, X. Zhang, T. Liu, and H. Zhang, “Conversion of Waste Oil from Oil Refinery into Emulsion Liquid Membrane for Removal of Phenol: Stability Evaluation, Modeling and Optimization,” Membranes (Basel), vol. 12, no. 12, p. 1202, Nov. 2022, https://doi.org/10.3390/membranes12121202
[22] G. Sznejer and A. Marmur, “Cadmium removal from aqueous solutions by an emulsion liquid membrane The effect of resistance to mass transfer at the outer oil-water interface,” Colloids and Surfaces A: Physicochemical and Engineering Aspects, 1999, https://doi.org/10.1016/S0927-7757(98)00505-6
[23] A. T. Abd Khalil, M. M. H. Shah Buddin, N. F. Mokhtar, and S. W. Puasa, “Performance evaluation of emulsion liquid membrane for simultaneous copper and cadmium removal: Dispersion tool comparison,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Dec. 2020, https://doi.org/10.1088/1755-1315/616/1/012077
[24] H. K. Admawi and A. A. Mohammed, “Simultaneous extraction of cadmium, nickel, and cobalt ions from acidic aqueous solutions by emulsion liquid membrane using sunflower-kerosene mixture as a diluent; breakage and mass transfer studies,” Results in Surfaces and Interfaces, vol. 15, May 2024, https://doi.org/10.1016/j.rsurfi.2024.100216
[25] M. Zamouche et al., “Optimization and Prediction of Stability of Emulsified Liquid Membrane (ELM): Artificial Neural Network,” Processes, vol. 11, no. 2, Feb. 2023, https://doi.org/10.3390/pr11020364
[26] S. Laki, A. Arabi Shamsabadi, S. S. Madaeni, and M. Niroomanesh, “Separation of manganese from aqueous solution using an emulsion liquid membrane,” RSC Advances, vol. 5, no. 102, pp. 84195–84206, 2015, https://doi.org/10.1039/c5ra08547k
[27] P. C. Bolne, S. A. Ghodke, and B. A. Bhanvase, “Intensified Hydrodynamic Cavitation-Based Process for the Production of Liquid Emulsion Membrane (LEM) for the Extraction of Chromium(VI) Ions,” International Journal of Environmental Research, vol. 15, no. 2, pp. 313–320, Apr. 2021, https://doi.org/10.1007/s41742-021-00322-4
[28] A. Mohammed and R. Al-Khateeb, “Application of Emulsion Liquid Membrane Using Green Surfactant for Removing Phenol from Aqueous Solution: Extraction, Stability and Breakage Studies,” Journal of Ecological Engineering, vol. 23, no. 1, pp. 305–314, Jan. 2022, https://doi.org/10.12911/22998993/143970
[29] N. Q. Jaber, Ahmed. A. Mohammed, and Q. Nasir, “Emulsion Liquid Membrane for Pesticides Removal from Aqueous Solution: Emulsion Stability, Extraction Efficiency and Mass Transfer Studies,” Iraqi Journal of Chemical and Petroleum Engineering, vol. 24, no. 2, pp. 1–10, Jun. 2023, https://doi.org/10.31699/IJCPE.2023.2.1
[30] P. Ghorbanpour and M. Jahanshahi, “Removal of zinc by emulsion liquid membrane using lecithin as biosurfactant,” Journal of Dispersion Science and Technology, vol. 43, no. 14, pp. 2218–2226, 2022, https://doi.org/10.1080/01932691.2021.1929287
[31] A. L. Ahmad, M. M. H. Shah Buddin, B. S. Ooi, and A. Kusumastuti, “Utilization of environmentally benign emulsion liquid membrane (ELM) for cadmium extraction from aqueous solution,” Journal of Water Process Engineering, vol. 15, pp. 26–30, 2017, https://doi.org/10.1016/j.jwpe.2016.05.010
[32] A. Najwa Saber Majeed Manal Adnan Mohammed, “Phenol Removal from Aqueous Solution Using Emulsion Liquid Membrane Process: Batch Experimental Studies,” Association of Arab Universities Journal of Engineering Sciences, 2017, https://jaaru.org/index.php/auisseng/article/view/59
[33] S. Sujatha, N. Rajamohan, Y. Vasseghian, and M. Rajasimman, “Conversion of waste cooking oil into value-added emulsion liquid membrane for enhanced extraction of lead: Performance evaluation and optimization,” Chemosphere, vol. 284, Dec. 2021, https://doi.org/10.1016/j.chemosphere.2021.131385
[34] A. A. Mohammed, H. M. Selman, and G. Abukhanafer, “Liquid surfactant membrane for lead separation from aqueous solution: Studies on emulsion stability and extraction efficiency,” Journal of Environmental Chemical Engineering, vol. 6, no. 6, pp. 6923–6930, Dec. 2018, https://doi.org/10.1016/j.jece.2018.10.021
[35] I. N. S. Kahar, A. S. Ali, N. Othman, N. F. M. Noah, and S. S. Suliman, “Copper Extraction Using LIX 84 as a Mobile Carrier in the Emulsion Liquid Membrane Process,” Journal of Applied Membrane Science & Technology, vol. 27, no. 3, pp. 69–80, Nov. 2023, https://doi.org/10.11113/amst.v27n3.275
[36] M. Esfandyari, M. Khodadadi, R. N. Ghadirli, and D. Jafari, “Prediction and optimization of heavy metal ions removal efficiency from the active sludge using intelligent systems,” Desalination Water Treat, vol. 252, pp. 167–176, Mar. 2022, https://doi.org/10.5004/dwt.2022.28254
[37] J. Kabuba and A. V. Maliehe, “Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines,” Water Science and Technology, vol. 84, no. 12, pp. 3489–3507, Dec. 2021, https://doi.org/10.2166/wst.2021.494
Downloads
Published
Issue
Section
License
Copyright (c) 2025 The Author(s). Published by College of Engineering, University of Baghdad.
This work is licensed under a Creative Commons Attribution 4.0 International License.
