Abstract
Nowadays, the recycling of metals from electrical and electronic waste is of great relevance due to its direct and indirect impact on environmental, social, and economic fields. Therefore, this study, conducted at the laboratory level, focuses on the recovery of copper from printed circuit boards through dynamic acid leaching in an H2SO4-O2 system, with the stirring rate controlled as the main parameter. Initially, the metallic pins were characterized by SEM-EDS, revealing that they consist of 7.56 wt% of copper, the predominant element serving as the base material. A thin gold film (79 wt%) is deposited on the copper to enhance its electrical conduction properties. In the subsequent leaching step, a random sample of 10 g was taken in a 500 mL volume, with an acid concentration of 0.03 M. The system was heated to 298.15 K under an oxygen partial pressure of 101.3 kPa. The stirring rate was varied from 450 to 1000 rpm, resulting in a maximum copper concentration of 645.294 ppm in the solution. The experimental constants were calculated for low (0–60 min) and high (60–240 min) chemical attack times, yielding ranges of 0.026 to 0.923 and 0.019 to 2.577 min− 1, respectively. On the other hand, one of the main outcomes of this research lies in the implementation of an artificial neural network to intelligently model the experimental process. It exhibited a mean squared error, correlation coefficient, and determination coefficient of 0.99690. Artificial neural networks emerge as an exceptional tool in predicting hydrometallurgical processes. This innovative application not only optimizes copper recovery but also ensures a cost-effective and environmentally friendly management of electronic waste. In the same way, it is possible to generate models of problems through learning. For all the aforementioned reasons, in the present work, an artificial neural network is developed to predict the dissolution of Cu in an electronic waste leaching process, considering the stirring rate as a key factor.
Highlights
-
• Electronic waste as secondary source of precious and non-precious metals.
-
• Copper is obtained from recycled computer printed circuit boards through an environmentally friendly chemical process.
-
• Artificial neural networks enable design and fault finding in complex systems by validating, aggregating and analyzing data.
-
• Application of an artificial neural network for processing, identification and modeling of dynamic acid leaching systems.
-
• A novel combined hydrometallurgical and artificial neural network process for the recovery of copper from electronic waste.
![](http://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Figa_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig2_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig3_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig4_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig5_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig6_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig7_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig8_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig10_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig11_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig12_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig13_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig14_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12649-024-02644-8/MediaObjects/12649_2024_2644_Fig15_HTML.jpg)
References
Reuter, M.A.: Limits of design for recycling and sustainability: A review, (2011). https://springer.longhoe.net/article/10.1007/s12649-010-9061-3
Cucchiella, F., D’Adamo, I., Gastaldi, M.: Sustainable management of waste-to-energy facilities. Renew. Sustain. Energy Rev. 33, 719–728 (2014). https://doi.org/10.1016/J.RSER.2014.02.015
Wan, X., Taskinen, P., Shi, J., Klemettinen, L., Jokilaakso, A.: Reaction mechanisms of waste printed circuit board recycling in copper smelting: The impurity elements. Min. Eng. 160, 106709 (2021). https://doi.org/10.1016/J.MINENG.2020.106709
Forti, V., Baldé, C.P., Kuehr, R., Bel, G.: The global e-waste monitor 2020: quantities, flows, and the circular economy potential. (2020)
Tuncuk, A., Stazi, V., Akcil, A., Yazici, E.Y., Deveci, H.: Aqueous metal recovery techniques from e-scrap: Hydrometallurgy in recycling. Min. Eng. 25, 28–37 (2012). https://doi.org/10.1016/J.MINENG.2011.09.019
Sum, E.Y.L.: The recovery of metals from electronic scrap. JOM 1991 434. 43, 53–61 (1991). https://doi.org/10.1007/BF03220549
Attah-Kyei, D., Akdogan, G., Dorfling, C., Zietsman, J., Lindberg, D., Tesfaye, F., Reynolds, Q.: Investigation of waste PCB leach residue as a reducing agent in smelting processes. Min. Eng. 156, 106489 (2020). https://doi.org/10.1016/J.MINENG.2020.106489
Zhang, S., Forssberg, E.: Intelligent Liberation and classification of electronic scrap. Powder Technol. 105, 295–301 (1999). https://doi.org/10.1016/S0032-5910(99)00151-5
Tanskanen, P.: Management and recycling of electronic waste. Acta Mater. 61, 1001–1011 (2013). https://doi.org/10.1016/J.ACTAMAT.2012.11.005
Islam, A., Roy, S., Teo, S.H., Khandaker, S., Taufiq-Yap, Y.H., Aziz, A.A., Monir, M.U., Rashid, U., Vo, D.-V.N., Ibrahim, M.L., Znad, H., Awual, M.R.: Functional novel ligand based palladium(II) separation and recovery from e-waste using solvent-ligand approach. Colloids Surf. Physicochem Eng. Asp. 632, 127767 (2022). https://doi.org/10.1016/j.colsurfa.2021.127767
Islam, A., Swaraz, A.M., Teo, S.H., Taufiq-Yap, Y.H., Vo, D.-V.N., Ibrahim, M.L., Abdulkreem-Alsultan, G., Rashid, U., Awual, M.R.: Advances in physiochemical and biotechnological approaches for sustainable metal recovery from e-waste: A critical review. J. Clean. Prod. 323, 129015 (2021). https://doi.org/10.1016/j.jclepro.2021.129015
Nithya, R., Sivasankari, C., Thirunavukkarasu, A.: Electronic waste generation, regulation and metal recovery: A review. Environ. Chem. Lett. 19, 1347–1368 (2021)
Sengupta, D., Ilankoon, I.M.S.K., Dean Kang, K., Nan Chong, M.: Circular economy and household e-waste management in India: Integration of formal and informal sectors. Min. Eng. 184, 107661 (2022). https://doi.org/10.1016/J.MINENG.2022.107661
Hoffmann, J.E.: Recovering precious metals from electronic scrap. JOM 1992 447. 44, 43–48 (1992). https://doi.org/10.1007/BF03222275
Priya, A., Hait, S.: Feasibility of bioleaching of selected metals from Electronic Waste by Acidiphilium acidophilum. Waste Biomass Valorization 2017 95. 9, 871–877 (2017). https://doi.org/10.1007/S12649-017-9833-0
Wang, Z., Zhang, B., Guan, D.: Take responsibility for electronic-waste disposal. Nat. 2016 5367614. 536, 23–25 (2016). https://doi.org/10.1038/536023a
Yildirir, E., Onwudili, J.A., Williams, P.T.: Chemical Recycling of Printed Circuit Board Waste by depolymerization in sub- and supercritical solvents. Waste Biomass Valorization 2015 66. 6, 959–965 (2015). https://doi.org/10.1007/S12649-015-9426-8
Sodhi, M.S., Reimer, B.: Models for recycling electronics end-of-life products. OR-Spektrum 2001 231. 23, 97–115 (2001). https://doi.org/10.1007/PL00013347
Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., Böni, H.: Global perspectives on e-waste. Environ. Impact Assess. Rev. 25, 436–458 (2005). https://doi.org/10.1016/J.EIAR.2005.04.001
Cui, J., Zhang, L.: Metallurgical recovery of metals from electronic waste: A review. J. Hazard. Mater. 158, 228–256 (2008). https://doi.org/10.1016/J.JHAZMAT.2008.02.001
Lin, C., Chi, Y., **, Y.: Experimental study on Treating Waste Printed Circuit Boards by molten salt oxidation. Waste Biomass Valorization. 2017 87, 8, 2523–2533 (2017). https://doi.org/10.1007/S12649-017-9836-X
Afroz, R., Masud, M.M., Akhtar, R., Duasa, J.B.: Survey and analysis of public knowledge, awareness and willingness to pay in Kuala Lumpur, Malaysia – a case study on household WEEE management. J. Clean. Prod. 52, 185–193 (2013). https://doi.org/10.1016/J.JCLEPRO.2013.02.004
UNITAR | Instituto de las: Naciones Unidas para Formación Profesional e Investigaciones, https://2020results.unitar.org/
Nnorom, I.C., Osibanjo, O.: Overview of electronic waste (e-waste) management practices and legislations, and their poor applications in the develo** countries. Resour. Conserv. Recycl. 52, 843–858 (2008). https://doi.org/10.1016/J.RESCONREC.2008.01.004
Kim, B.S., Lee, J., Seo, S.P., Park, Y.K., Sohn, H.Y.: A process for extracting precious metals from spent printed circuit boards and automobile catalysts. JOM 2004 5612. 56, 55–58 (2004). https://doi.org/10.1007/S11837-004-0237-9
Shuey, S.A., Vildal, E.E., Taylor, P.R.: Pyrometallurgical processing of electronic waste. ME Annu. Meet 06–037 (2006)
Veasey T.J.: An overview of metals recycling by physical separation methods. Proc. Inst. Mech. Eng. Part. E J. Process. Mech. Eng. 211, 61–64 (1997). https://doi.org/10.1243/0954408971529557
Debnath, B., Chowdhury, R., Ghosh, S.K.: Sustainability of metal recovery from E-waste. Front. Environ. Sci. Eng. 2018 126. 121–12 (2018). https://doi.org/10.1007/S11783-018-1044-9
Zherlitsyn, A.A., Alexeenko, V.M., Kumpyak, E.V., Kondratiev, S.S.: Fragmentation of printed circuit boards by sub-microsecond and microsecond high-voltage pulses. Min. Eng. 176, 107340 (2022). https://doi.org/10.1016/J.MINENG.2021.107340
Habashi, F.: Principles of extractive metallurgy. Routledge (2017)
Conard, B.R.: The role of hydrometallurgy in achieving sustainable development. Hydrometallurgy. 30, 1–28 (1992)
Khaliq, A., Rhamdhani, M.A., Brooks, G., Masood, S.: Metal Extraction Processes for Electronic Waste and Existing Industrial Routes: A Review and Australian Perspective. Resour. Vol. 3, Pages 152–179. 3, 152–179 (2014). (2014). https://doi.org/10.3390/RESOURCES3010152
Williams, P.T.: Valorization of Printed Circuit Boards from Waste Electrical and Electronic Equipment by Pyrolysis. Waste Biomass Valorization 2010 11. 1, 107–120 (2010). https://doi.org/10.1007/S12649-009-9003-0
Salinas-Rodríguez, E., Hernández-Ávila, J., Reyes-Valderrama, M.I., Rodríguez-Lugo, V., Montiel-Hernández, J.F., Cerecedo-Sáenz, E.: Recovery of gold and base metals from waste printed circuits boards. Pädi Boletín Científico Ciencias Básicas E Ing. Del. ICBI. 9, 62–71 (2021). https://doi.org/10.29057/ICBI.V9IESPECIAL2.7681
Bidini, G., Fantozzi, F., Bartocci, P., D’Alessandro, B., D’Amico, M., Laranci, P., Scozza, E., Zagaroli, M.: Recovery of precious metals from scrap printed circuit boards through pyrolysis. J. Anal. Appl. Pyrol. 111, 140–147 (2015). https://doi.org/10.1016/J.JAAP.2014.11.020
Puente-Siller, D.M., Fuentes-Aceituno, J.C., Nava-Alonso, F.: An analysis of the efficiency and sustainability of the thiosulfate-copper-ammonia-monoethanolamine system for the recovery of silver as an alternative to cyanidation. Hydrometallurgy. 169, 16–25 (2017). https://doi.org/10.1016/J.HYDROMET.2016.12.003
Sharma, N., Chauhan, G., Kumar, A., Sharma, S.K.: Statistical Optimization of Heavy Metal (Cu2 + and Co2+) extraction from Printed Circuit Boards and Mobile batteries using Chelation Technology. Ind. Eng. Chem. Res. 56, 6805–6819 (2017). https://doi.org/10.1021/ACS.IECR.7B01481/SUPPL_FILE/IE7B01481_SI_001.PDF
Liu, K., Zhang, Z., Zhang, F.S.: Direct extraction of palladium and silver from waste printed circuit boards powder by supercritical fluids oxidation-extraction process. J. Hazard. Mater. 318, 216–223 (2016). https://doi.org/10.1016/J.JHAZMAT.2016.07.005
Yang, C., Li, J., Tan, Q., Liu, L., Dong, Q.: Green Process of Metal Recycling: Coprocessing Waste Printed Circuit Boards and spent tin strip** solution. ACS Sustain. Chem. Eng. 5, 3524–3534 (2017). https://doi.org/10.1021/ACSSUSCHEMENG.7B00245/SUPPL_FILE/SC7B00245_SI_001.PDF
Li, H., Eksteen, J., Oraby, E.: Hydrometallurgical recovery of metals from waste printed circuit boards (WPCBs): Current status and perspectives – A review. Resour. Conserv. Recycl. 139, 122–139 (2018). https://doi.org/10.1016/J.RESCONREC.2018.08.007
Gunarathne, V., Rajapaksha, A.U., Vithanage, M., Alessi, D.S., Selvasembian, R., Naushad, M., You, S., Oleszczuk, P., Ok, Y.S.: Hydrometallurgical processes for heavy metals recovery from industrial sludges. Crit. Rev. Environ. Sci. Technol. 52, 1022–1062 (2022). https://doi.org/10.1080/10643389.2020.1847949
Iqbal, A., Jan, M.R., Shah, J., Rashid, B.: Dispersive solid phase extraction of precious metal ions from electronic wastes using magnetic multiwalled carbon nanotubes composite. Min. Eng. 154, 106414 (2020). https://doi.org/10.1016/J.MINENG.2020.106414
Reyes-Valderrama, M.I., Salinas-Rodríguez, E., Montiel-Hernández, J.F., Rivera-Landero, I., Cerecedo-Sáenz, E., Hernández-Ávila, J., Arenas-Flores, A.: Urban Mining and Electrochemistry: Cyclic Voltammetry Study of Acidic Solutions from Electronic Wastes (Printed Circuit Boards) for Recovery of Cu, Zn, and Ni. Met. Vol. 7, Page 55. 7, 55 (2017). (2017). https://doi.org/10.3390/MET7020055
Nadirov, R., Syzdykova, L., Zhussupova, A.: Copper smelter slag treatment by ammonia solution: Leaching process optimization. J. Cent. South. Univ. 24, 2799–2804 (2017). https://doi.org/10.1007/s11771-017-3694-3
Wu, P., Zhang, L., Liu, Y., **e, X., Zhou, J., Jia, H., Wei, P.: Enhancing Cu-Zn-Cr-Ni Co-Extraction from Electroplating Sludge in Acid Leaching Process by Optimizing Fe3 + Addition and Redox Potential. https://home.liebertpub.com/ees. 36, 1244–1257 (2019). https://doi.org/10.1089/EES.2019.0127
Barragan, J.A., Castro, J.R.A., Peregrina-Lucano, A.A., Sánchez-Amaya, M., Rivero, E.P., Larios-Durán, E.R.: Leaching of metals from e-waste: From its thermodynamic analysis and design to its implementation and optimization. ACS Omega. 6, 12063–12071 (2021). https://doi.org/10.1021/ACSOMEGA.1C00724/ASSET/IMAGES/LARGE/AO1C00724_0007.JPEG.
Panda, R., Jha, M.K., Pathak, D.D., Gupta, R.: Recovery of Ag, Cu, Ni and Fe from the nitrate leach liquor of waste ICs. Min. Eng. 158, 106584 (2020). https://doi.org/10.1016/J.MINENG.2020.106584
Daware, S., Chandel, S., Rai, B.: A machine learning framework for urban mining: A case study on recovery of copper from printed circuit boards. Min. Eng. 180, 107479 (2022). https://doi.org/10.1016/J.MINENG.2022.107479
Oraby, E.A., Eksteen, J.J.: The selective leaching of copper from a gold–copper concentrate in glycine solutions. Hydrometallurgy. 150, 14–19 (2014). https://doi.org/10.1016/J.HYDROMET.2014.09.005
Tanda, B.C., Eksteen, J.J., Oraby, E.A.: An investigation into the leaching behaviour of copper oxide minerals in aqueous alkaline glycine solutions. Hydrometallurgy. 167, 153–162 (2017). https://doi.org/10.1016/J.HYDROMET.2016.11.011
Oraby, E.A., Eksteen, J.J., Tanda, B.C.: Gold and copper leaching from gold-copper ores and concentrates using a synergistic lixiviant mixture of glycine and cyanide. Hydrometallurgy. 169, 339–345 (2017). https://doi.org/10.1016/J.HYDROMET.2017.02.019
Li, H., Oraby, E., Eksteen, J.: Extraction of precious metals from waste printed circuit boards using cyanide-free alkaline glycine solution in the presence of an oxidant. Min. Eng. 181, 107501 (2022). https://doi.org/10.1016/J.MINENG.2022.107501
Oraby, E.A., Li, H., Eksteen, J.J.: An Alkaline Glycine-based Leach process of base and precious metals from Powdered Waste Printed Circuit Boards. Waste Biomass Valorization 2019 118. 11, 3897–3909 (2019). https://doi.org/10.1007/S12649-019-00780-0
Rodrigues, É.F., De Rossi, A., Rovaris, B., Valério, A., de Oliveira, D., Hotza, D.: Cleaner Pre-concentration of metals from Printed Circuit Board Waste Using Novel Dense Liquid Medium Based on Sodium Silicate. Waste Biomass Valorization 2020 127. 12, 4081–4087 (2020). https://doi.org/10.1007/S12649-020-01271-3
Soto, C.E.B., Hernández, J.F.M., Muñoz, E.J., Renteria, M.Á.F., Flores, D.A.A.: Efecto De La concentración De H3O + en recuperación de Los metales au, Cu, Ni Y Zn contenidos en la chatarra electrónica. Ingenio Y Concienc. Boletín Científico La Esc. Super Ciudad Sahagún. 8, 8–11 (2021). https://doi.org/10.29057/ESCS.V8I15.6497
Eksteen, J.J., Oraby, E.A., Tanda, B.C.: A conceptual process for copper extraction from chalcopyrite in alkaline glycinate solutions. Min. Eng. 108, 53–66 (2017). https://doi.org/10.1016/J.MINENG.2017.02.001
Deng, Z., Oraby, E.A., Eksteen, J.J.: The sulfide precipitation behaviour of cu and au from their aqueous alkaline glycinate and cyanide complexes. Sep. Purif. Technol. 218, 181–190 (2019). https://doi.org/10.1016/J.SEPPUR.2019.02.056
Nithya, R., Sivasankari, C., Thirunavukkarasu, A., Selvasembian, R.: Novel adsorbent prepared from bio-hydrometallurgical leachate from waste printed circuit board used for the removal of methylene blue from aqueous solution. Microchem J. 142, 321–328 (2018)
Nithya, R., Thirunavukkarasu, A., Sivasankari, C.: Comparative profile of green and chemically synthesized nanomaterials from bio-hydrometallurgical leachate of e-waste on crystal violet adsorption kinetics, thermodynamics, and mass transfer and statistical models. Biomass Convers. Biorefinery. 13, 17197–17221 (2023)
Ortega, R., Lor, A., Nicklasson, J.: Passivity-based Control of Euler-Lagrange Systems: Mechanical, Electrical and Electromechanical Applications
Chemical Reaction Engineering - Octave Levenspiel: Chemical reaction engineering. John wiley %26 sons.&f = false (1998). https://books.google.com.mx/books?hl=es&lr=&id=vw48EAAAQBAJ&oi=fnd&pg=PP1&dq=55.%09Levenspiel,+O.+(1998).+Chemical+reaction+engineering.+John+wiley+%26+sons.&ots=5x03pv6VaA&sig=79b4avkaVbD6Wc6xVHHiazqJykE&redir_esc=y#v=onepage&q=55.%09Levenspiel%2 CO
Hernandez, E., Arkun, Y.: Neural network modeling and an extended DMC algorithm to control nonlinear systems. Proc. Am. Control Conf. 2454–2459 (1990). https://doi.org/10.23919/ACC.1990.4791169
Nahas, E.P., Henson, M.A., Seborg, D.E.: Nonlinear internal model control strategy for neural network models. Comput. Chem. Eng. 16, 1039–1057 (1992). https://doi.org/10.1016/0098-1354(92)80022-2
Psichogios, D.C., Ungar, L.H.: Direct and Indirect Model Based Control using Artificial neural networks. Ind. Eng. Chem. Res. 30, 2564–2573 (1991). https://doi.org/10.1021/IE00060A009/ASSET/IE00060A009.FP.PNG_V03
Cubillos, F.A., Lima, E.L.: Adaptive hybrid neural models for process control. Comput. Chem. Eng. 22, S989–S992 (1998). https://doi.org/10.1016/S0098-1354(98)00197-5
Honório, K.M., De Lima, E.F., Quiles, M.G., Romero, R.A.F., Molfetta, F.A., Da Silva, A.B.F.: Artificial neural networks and the study of the psychoactivity of cannabinoid compounds. Chem. Biol. Drug Des. 75, 632–640 (2010). https://doi.org/10.1111/J.1747-0285.2010.00966.X
Fu, Y., Aldrich, C.: Flotation froth image recognition with convolutional neural networks. Min. Eng. 132, 183–190 (2019). https://doi.org/10.1016/J.MINENG.2018.12.011
Koh, E.J.Y., Amini, E., McLachlan, G.J., Beaton, N.: Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Min. Eng. 170, 107026 (2021). https://doi.org/10.1016/J.MINENG.2021.107026
Ruhatiya, C., Shaosen, S., Wang, C.-T., Jishnu, A.K., Bhalerao, Y.: Optimization of process conditions for maximum metal recovery from spent zinc-manganese batteries: Illustration of statistical based automated neural network approach. Energy Storage. 2, e111 (2020). https://doi.org/10.1002/EST2.111
Sadrzadeh, M., Mohammadi, T., Ivakpour, J., Kasiri, N.: Separation of lead ions from wastewater using electrodialysis: Comparing mathematical and neural network modeling. Chem. Eng. J. 144, 431–441 (2008). https://doi.org/10.1016/J.CEJ.2008.02.023
Sobouti, A., Hoseinian, F.S., Rezai, B., Jalili, S.: The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization. (2019) 22, 319–327.https://doi.org/10.1080/12269328.2019.1644205.
Sujatha, S., Rajamohan, N., Anbazhagan, S., Vanithasri, M., Rajasimman, M.: Extraction of nickel using a green emulsion liquid membrane – Process intensification, parameter optimization and artificial neural network modeling. Chem. Eng. Process. - Process Intensif. 165, 108444 (2021). https://doi.org/10.1016/J.CEP.2021.108444
Pazhoohan, J., Beiki, H., Esfandyari, M.: Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank. Int. J. Min. Metall. Mater. 2019 265. 26, 538–546 (2019). https://doi.org/10.1007/S12613-019-1762-4
Ebrahimzade, H., Khayati, G.R., Schaffie, M.: Leaching kinetics of valuable metals from waste Li-ion batteries using neural network approach. J. Mater. Cycles Waste Manag. 20, 2117–2129 (2018). 204 https://doi.org/10.1007/S10163-018-0766-X
Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ (1992)
Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Fuzzy Rule-Based System. Introd. to Fuzzy Log. using MATLAB. 113–149 (2007). https://doi.org/10.1007/978-3-540-35781-0_6
Montiel-Hernández, J.F.: Lixiviación dinámica ácida De Desechos electrónicos en El Sistema H2SO4-O2. Obtención de oro metálico y recuperación electrolítica de Cu. Ni y Zn (2015)
Montiel, J.F., Reyes, M.I., Rivera, I., Patiño, F., Hernández, J.: Caracterización De circuitos impresos vía SEM-EDS Y Su lixiviación en El Sistema O2-H2SO4. Bol. Soc. Quim. Mex. 6, 21–23 (2012)
Kalman, B.L., Kwasny, S.C.: Why Tanh: Choosing a Sigmoidal Function. Proc. Int. Jt. Conf. Neural Networks. 4, 578–581 (1992). https://doi.org/10.1109/IJCNN.1992.227257
Yu, H., Wilamowski, B.M.: Levenberg—Marquardt Training. In: Wilamowski, B. M., & Irwin, J.D. (ed.) The Industrial Electronics Handbook. pp. 12–1 to 12–15. CRC Press, Boca Raton (2011)
Almalki, M.M., Alaidarous, E.S., Maturi, D., Raja, M.A.Z., Shoaib, M.: A Levenberg–Marquardt Backpropagation Neural Network for The Numerical Treatment of Squeezing Flow with Heat Transfer Model. IEEE Access. (2020). https://doi.org/10.1109/ACCESS.2020.3044973
Sapna, S., Tamilarasi, A., Kumar, M.P.: Backpropagation learning algorithm based on Levenberg Marquardt Algorithm. Comp. Sci. Inf. Technol. (CS IT) 2, 393–398. (2012)
Azadeh, A., Sheikhalishahi, M., Tabesh, M., Negahban, A.: The effects of pre-processing methods on forecasting improvement of Artificial neural networks. Aust J. Basic. Appl. Sci. 5, 570–580 (2011)
Ogata, K.: Modern control engineering fifth edition. (2010)
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing Interests
The authors did not receive support from any organization for the submitted work.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ordaz-Oliver, M., Jiménez-Muñoz, E., Gutiérrez-Moreno, E. et al. Application of Artificial Neural Networks for Recovery of Cu from Electronic Waste by Dynamic Acid Leaching: A Sustainable Approach. Waste Biomass Valor (2024). https://doi.org/10.1007/s12649-024-02644-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12649-024-02644-8