Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Analytical Chemistry
  4. Fast Track Articles
  5. Fast Track Article
image of Modeling for Copper Recovery from E-Waste by Using Machine Learning Technique: An Approach for the Circular Economy

Abstract

Background

Copper, a precious metal in e-waste, presents a substantial economic opportunity. The study estimates that ~322000 tons of copper are discarded annually worldwide as e-waste. Given the significant financial value of copper, its recovery from e-waste is beneficial and crucial. This process also plays a pivotal role in waste management and recycling hazardous waste. The potential reduction in e-waste in landfills is a direct result of this strategic approach to waste management, offering a more sustainable and optimistic outlook for the future. This research paves the way for a future where e-waste is no longer a burden on our environment.

Methodology

This study is structured around a robust two-step process. It begins with an experiment focused on copper recovery using hydrometallurgical methods. The modeling leverages the power of artificial intelligence (AI) and machine learning techniques to predict copper recovery from e-waste. This innovative approach not only promises but also has the potential to revolutionize the field of copper recovery, inspiring further innovation and progress.

Results

The model was developed using an Artificial Neural Network (ANN) and a Boosting Algorithm (BA). Based on four crucial variables (HSO, HO, Solid/Liquid ratio, and Reaction Time), this model provides a comprehensive understanding of Cu recovery. HSO is a crucial component during the leaching process; HO facilitates Cu oxidation, the Solid/Liquid ratio affects the efficiency, and Reaction Time determines the completion of the process. The ANN and BA-based models yield satisfactory results in Cu recovery, achieving over 94% yield under optimized conditions.

Conclusion

The model developed in this study can potentially revolutionize copper recovery. By automating the process, we can significantly reduce the stress of copper mining, which relieves the environment. We can also promote a circular economy, offering a promising future for sustainable copper recovery. This could be a game-changer in the field of waste management and recycling.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cac/10.2174/0115734110343714241130170537
2025-01-07
2025-05-06

  • From This Site

    /content/journals/cac/10.2174/0115734110343714241130170537
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Wang R. Chen S. Deng Y. Zeng X. Wang J. Liang Q. Shu J. Wang M. Chen M. Han J. Ai K. Copper recovery from waste printed circuit boards by NH3−(NH4)2S2O8–CuSO4 slurry electrolysis. J. Ind. Eng. Chem. 2024 129 511 520 10.1016/j.jiec.2023.09.010
    [Google Scholar]
  2. Esyra Hani Ku Ishak K. Ismail S. Irfan Bin Abd Razak M. Recovery of copper and valuable metals from E-waste via hydrometallurgical method. Mater. Today Proc. 2022 66 3077 3081 10.1016/j.matpr.2022.07.395
    [Google Scholar]
  3. Srivastava S.K. Ramanathan A.L. Groundwater in Vicinity of landfill: Application of Graphical and Multivariate Statistical methods for hydro-geochemical characterization of Groundwater Germany Lambert Publication 2012 1 380
    [Google Scholar]
  4. Murali A. Plummer M.J. Shine A.E. Free M.L. Sarswat P.K. Optimized bioengineered copper recovery from electronic wastes to increase recycling and reduce environmental impact. J. Hazard. Mater. Adv. 2022 5 100031 10.1016/j.hazadv.2021.100031
    [Google Scholar]
  5. Garlapati V.K. Parashar S.K. Klykov S. Vundavilli P.R. Sevda S. Srivastava S.K. Taherzadeh M.J. Invasive weed optimization coupled biomass and product dynamics of tuning soybean husk towards lipolytic enzyme. Bioresour. Technol. 2022 344 Pt B 126254 10.1016/j.biortech.2021.126254 34757227
    [Google Scholar]
  6. Yang L. Zhang T. Gao Y. Li D. Cui R. Gu C. Wang L. Sun H. Quantitative identification of the co-exposure effects of e-waste pollutants on human oxidative stress by explainable machine learning. J. Hazard. Mater. 2024 466 133560 10.1016/j.jhazmat.2024.133560 38246054
    [Google Scholar]
  7. Sun Y. Teng Y. Zhao L. Li R. Ren W. Non-negligibly negative role of e-waste-derived pyrogenic carbon in the soil washing of copper and polybrominated diphenyl ethers. J. Hazard. Mater. 2023 458 131841 10.1016/j.jhazmat.2023.131841 37331062
    [Google Scholar]
  8. Yang H. Liu J. Yang J. Leaching copper from shredded particles of waste printed circuit boards. J. Hazard. Mater. 2011 187 1-3 393 400 10.1016/j.jhazmat.2011.01.051 21300436
    [Google Scholar]
  9. Martín M.I. García-Díaz I. López F.A. Properties and perspective of using deep eutectic solvents for hydrometallurgy metal recovery. Miner. Eng. 2023 203 108306 10.1016/j.mineng.2023.108306
    [Google Scholar]
  10. Liu G. Wu Y. Tang A. Pan D. Li B. Recovery of scattered and precious metals from copper anode slime by hydrometallurgy: A review. Hydrometallurgy 2020 197 105460 10.1016/j.hydromet.2020.105460
    [Google Scholar]
  11. Muscetta M. Process intensification in metal recovery from solid waste: Challenges, opportunities and recent advances. Chem. Eng. Process. 2024 204 109937 10.1016/j.cep.2024.109937
    [Google Scholar]
  12. Han Q. Gao Y. Su T. Qin J. Wang C. Qu Z. Wang X. Hydrometallurgy recovery of copper, aluminum and silver from spent solar panels. J. Environ. Chem. Eng. 2023 11 1 109236 10.1016/j.jece.2022.109236
    [Google Scholar]
  13. Dutta D. Rautela R. Gujjala L.K.S. Kundu D. Sharma P. Tembhare M. Kumar S. A review on recovery processes of metals from E-waste: A green perspective. Sci. Total Environ. 2023 859 Pt 2 160391 10.1016/j.scitotenv.2022.160391 36423849
    [Google Scholar]
  14. Song Q. Liu Y. Zhang L. Xu Z. Selective electrochemical extraction of copper from multi-metal e-waste leaching solution and its enhanced recovery mechanism. J. Hazard. Mater. 2021 407 124799 10.1016/j.jhazmat.2020.124799 33348202
    [Google Scholar]
  15. Parashar S.K. Srivastava S.K. Dutta N.N. Garlapati V.K. Engineering aspects of immobilized lipases on esterification: A special emphasis of crowding, confinement and diffusion effects. Eng. Life Sci. 2018 18 5 308 316 10.1002/elsc.201700082 32624910
    [Google Scholar]
  16. Parashar S.K. Srivastava S.K. The mathematical modeling for optimizing triacylglycerol acylhydrolases production through artificial neural network and genetic algorithm. Int. J. Pharma Bio Sci. 2019 a 10 3 135 143 10.22376/ijpbs.2019.10.3.b135‑143
    [Google Scholar]
  17. Parashar S.K. Srivastava S.K. Garlapati V.K. Dutta N.N. Production of microbial enzyme triacylglycerol Acylhydrolases by Aspergillus sydowii JPG01 by in submerged fermentation using agro residues. Asian J. Microbiol. Biotechnol. Environ. Sci. 2019 b 21 4 1076 1079
    [Google Scholar]
  18. Rao M.D. Meshram R.B. Singh K.K. Morrison C.A. Love J.B. Life cycle analysis on sequential recovery of copper and gold from waste printed circuit boards. Waste Manag. 2023 171 621 627 10.1016/j.wasman.2023.10.008 37837909
    [Google Scholar]
  19. MPCB Madhya Pradesh Pollution Control Board annual report on the assessment of electronic wastes in Mumbai-Pune area Maharashtra pollution control board, Kalpataru Point, Sion(e), Mumbai. 2007 Available from: https://mpcb.mah.nic.in
  20. Hageluken C. Improving metal returns and eco-efficiency in electronics recycling – a holistic approach for interface optimization between pre-processing and integrated metals smelting and refining. IEEE International Symposium on Electronics and the Environment 2006 218 223 10.1109/ISEE.2006.1650064
    [Google Scholar]
  21. Srivastava S.K. Ramanathan A.L. Geochemical assessment of groundwater quality in vicinity of Bhalswa landfill, Delhi, India, using graphical and multivariate statistical methods. Environ. Geol. 2008 53 7 1509 1528 10.1007/s00254‑007‑0762‑2
    [Google Scholar]
  22. CPCB Central Pollution Control Board draft guidelines for environmentally sound management of electronic waste. 2007 Available from: http://e-wasteguide.info/newsandevents/new-dr
  23. Chen W. Tang H. Yin S. Wang L. Zhang M. Copper recovery from low-grade copper sulfides using bioleaching and its community structure succession in the presence of Sargassum. J. Environ. Manage. 2024 349 119549 10.1016/j.jenvman.2023.119549 37979390
    [Google Scholar]
  24. Hao M. Tang L. Wang P. Wang H. Wang Q.C. Dai T. Chen W.Q. Mapping China's copper cycle from 1950–2015: Role of international trade and secondary resources. Resources Conservation and Recycling 2023 188 8−9 106700 10.1016/j.resconrec.2022.106700
    [Google Scholar]
  25. Elshkaki A. Graedel T.E. Ciacci L. Reck B.K. Copper demand, supply, and associated energy use to 2050. Glob. Environ. Change 2016 39 305 315 10.1016/j.gloenvcha.2016.06.006
    [Google Scholar]
  26. Srivastava S.K. Ramanathan A.L. Assessment of landfills vulnerability on the groundwater quality located near floodplain of the perennial river and simulation of contaminant transport. Model. Earth Syst. Environ. 2018 4 2 729 752 10.1007/s40808‑018‑0464‑7
    [Google Scholar]
  27. Srivastava S.K. Dhaker K.L. Data-driven approach for Cu recovery from hazardous e-waste. Process Saf. Environ. Prot. 2024 183 665 675 10.1016/j.psep.2024.01.013
    [Google Scholar]
  28. Kara I.T. Huntington V.E. Simmons N. Wagland S.T. Coulon F Extracting metal ions from basic oxygen steelmaking dust by using bio-hydrometallurgy. Heliyon 2024 10 11 e32437 10.1016/j.heliyon.2024.e32437
    [Google Scholar]
  29. Hossain M.S. Shenashen M.A. Awual M.E. Rehan A.I. Rasee A.I. Waliullah R.M. Kubra K.T. Salman M.S. Sheikh M.C. Hasan M.N. Hasan M.M. Islam A. Khaleque M.A. Marwani H.M. Alzahrani K.A. Asiri A.M. Rahman M.M. Awual M.R. Benign separation, adsorption, and recovery of rare-earth Yb(III) ions with specific ligand-based composite adsorbent. Process Saf. Environ. Prot. 2024 185 367 374 10.1016/j.psep.2024.03.026
    [Google Scholar]
  30. Awual M.E. Salman M.S. Hasan M.M. Hasan M.N. Kubra K.T. Sheikh M.C. Rasee A.I. Rehan A.I. Waliullah R.M. Hossain M.S. Marwani H.M. Asiri A.M. Rahman M.M. Islam A. Khaleque M.A. Awual M.R. Ligand imprinted composite adsorbent for effective Ni(II) ion monitoring and removal from contaminated water. J. Ind. Eng. Chem. 2024 131 585 592 10.1016/j.jiec.2023.10.062
    [Google Scholar]
  31. Rasee A.I. Awual E. Rehan A.I. Hossain M.S. Waliullah R.M. Kubra K.T. Sheikh M.C. Salman M.S. Hasan M.N. Hasan M.M. Marwani H.M. Islam A. Khaleque M.A. Awual M.R. Efficient separation, adsorption, and recovery of Samarium(III) ions using novel ligand-based composite adsorbent. Surf. Interfaces 2023 41 103276 10.1016/j.surfin.2023.103276
    [Google Scholar]
  32. Sheikh M.C. Hasan M.M. Hasan M.N. Salman M.S. Kubra K.T. Awual M.E. Waliullah R.M. Rasee A.I. Rehan A.I. Hossain M.S. Marwani H.M. Islam A. Khaleque M.A. Awual M.R. Toxic cadmium(II) monitoring and removal from aqueous solution using ligand-based facial composite adsorbent. J. Mol. Liq. 2023 389 122854 10.1016/j.molliq.2023.122854
    [Google Scholar]
  33. Awual M.R. Hasan M.M. Khaleque M.A. Sheikh M.C. Treatment of copper(II) containing wastewater by a newly developed ligand based facial conjugate materials. Chem. Eng. J. 2016 288 368 376 10.1016/j.cej.2015.11.108
    [Google Scholar]
  34. Waliullah R.M. Rehan A.I. Awual M.E. Rasee A.I. Sheikh M.C. Salman M.S. Hossain M.S. Hasan M.M. Kubra K.T. Hasan M.N. Marwani H.M. Islam A. Rahman M.M. Khaleque M.A. Awual M.R. Optimization of toxic dye removal from contaminated water using chitosan-grafted novel nanocomposite adsorbent. J. Mol. Liq. 2023 388 122763 10.1016/j.molliq.2023.122763
    [Google Scholar]
  35. Salman M.S. Hasan M.N. Kubra K.T. Hasan M.M. Optical detection and recovery of Yb(III) from waste sample using novel sensor ensemble nanomaterials. Microchem. J. 2021 162 105868 10.1016/j.microc.2020.105868
    [Google Scholar]
  36. Shahat M.R. Large-pore diameter nano-adsorbent and its application for rapid lead (II) detection and removal from aqueous media. Chem. Eng. J. 2015 265 210 218
    [Google Scholar]
  37. Awual M.R. Novel nanocomposite materials for efficient and selective mercury ions capturing from wastewater. Chem. Eng. J. 2017 307 456 465 10.1016/j.cej.2016.08.108
    [Google Scholar]
  38. Tang W. Li Y. Yu Y. Wang Z. Xu T. Chen J. Lin J. Li X. Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms. Chemosphere 2020 253 126666 10.1016/j.chemosphere.2020.126666 32289603
    [Google Scholar]
  39. Tušek A.J.U.R.I.N.J.A.K. Jurina T. Benković M. Valinger D. Belščak-Cvitanović A. Application of multivariate regression and artificial neural network modelling for prediction of physical and chemical properties of medicinal plants aqueous extracts. J. Appl. Res. Med. Aromat. Plants 2020 16 100229 10.1016/j.jarmap.2019.100229
    [Google Scholar]
  40. Jalal M. Jalal H. Grasley Z. Bullard J.W. Jalal H. RETRACTED: Behavior assessment, regression analysis and support vector machine (SVM) modeling of waste tire rubberized concrete. J. Clean. Prod. 2020 273 122960 10.1016/j.jclepro.2020.122960
    [Google Scholar]
  41. Abrougui K. Gabsi K. Mercatoris B. Khemis C. Amami R. Chehaibi S. Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil Tillage Res. 2019 190 202 208 10.1016/j.still.2019.01.011
    [Google Scholar]
  42. Akbari E. Moradi R. Afroozeh A. Alizadeh A. Nilasha M.A. A new approach for graphene-based prediction isfet using regression tree and neural network. Superlattices Microstruct. 2019 130 241 248 10.1016/j.spmi.2019.04.011
    [Google Scholar]
  43. Zegler C.H. Renz M.J. Brink G.E. Ruark M.D. Using regression tree analysis to assess the importance of plant, soil, and management factors affecting potential milk production on organic pastures. Agric. Syst. 2020 180 102776 10.1016/j.agsy.2019.102776
    [Google Scholar]
  44. Shahani N.M. Kamran M. Zheng X. Liu C. Guo X. Application of gradient boosting machine learning algorithms to predict the uniaxial compressive strength of soft sedimentary rocks at Thar coalfield. Adv. Civ. Eng. 2021 2021 1 2565488 10.1155/2021/2565488
    [Google Scholar]
  45. Pethkar A.V. Paknikar K.M. Recovery of gold from solutions using Cladosporium cladosporioides biomass beads. J. Biotechnol. 1998 63 2 121 136 10.1016/S0168‑1656(98)00078‑9
    [Google Scholar]
  46. Luda M.P. Balabanovich A.I. Camino G. Thermal decomposition of fire retardant brominated epoxy resins. J. Anal. Appl. Pyrolysis 2002 65 1 25 40 10.1016/S0165‑2370(01)00175‑9
    [Google Scholar]
  47. Yamane L.H. Moraes V.T. Espinosa D.C. Tenorio J.A. Recycling of WEEE, Characterization of spent PCBs from mobile phones and computers. J. Waste Manag. 2011 12 2553 2558 10.1016/j.wasman.2011.07.006
    [Google Scholar]
  48. Ranjan R. Srivastava S.K. Ramanathan A.L. An assessment of the hydrogeochemistry of two wetlands located in Bihar State in the subtropical climatic zone of India. Environ. Earth Sci. 2017 76 1 16 10.1007/s12665‑016‑6330‑x
    [Google Scholar]
  49. Prasad M.B.K. Ramanathan A.L. Shrivastav S.K. Anshumali R. Saxena R. Metal fractionation studies in surfacial and core sediments in the Achankovil River Basin in India. Environ. Monit. Assess. 2006 121 1-3 77 102 10.1007/s10661‑005‑9108‑2 16758286
    [Google Scholar]
  50. Castro L.A. Martins A.H. Recovery of tin and Cu by recycling PCBs from obsolete computers. Braz. J. Chem. Eng. 2009 26 04 649 657
    [Google Scholar]
  51. Anderson J.A. An Introduction to Neural Networks. Cambridge, MA MIT Press 1995 10.7551/mitpress/3905.001.0001
    [Google Scholar]
  52. Hastie T. Tibshirani R. Friedman J. The Elements of Statistical Learning Springer New York 2001
    [Google Scholar]
  53. Samarasinghe S. Neural Networks for Applied Science and Engineering. New York, U.S.A. Auerbach Publications 2007
    [Google Scholar]
  54. Freund Y. Schapire R.E. Decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 1997 55 1 119 139 10.1006/jcss.1997.1504
    [Google Scholar]
  55. Thapliyal D. Shrivastava R. Verros G.D. Verma S. Arya R.K. Sen P. Prajapati S.C. Chahat A. Gupta. Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-. Xylene Coatings Using Artificial Neural Network. 2024 12 2 260 10.3390/pr12020260
    [Google Scholar]
  56. Awual M.R. A facile composite material for enhanced cadmium(II) ion capturing from wastewater. J. Environ. Chem. Eng. 2019 7 5 103378 10.1016/j.jece.2019.103378
    [Google Scholar]
  57. Awual M.R. Hasan M.M. Fine-tuning mesoporous adsorbent for simultaneous ultra-trace palladium(II) detection, separation and recovery. J. Ind. Eng. Chem. 2015 21 507 515 10.1016/j.jiec.2014.03.013
    [Google Scholar]
  58. Awual M.R. Hasan M.M. Islam A. Rahman M.M. Asiri A.M. Khaleque M.A. Sheikh M.C. Offering an innovative composited material for effective lead(II) monitoring and removal from polluted water. J. Clean. Prod. 2019 231 214 223 10.1016/j.jclepro.2019.05.125
    [Google Scholar]
  59. Hasan M.N. Shenashen M.A. Hasan M.M. Znad H. Awual M.R. Assessing of cesium removal from wastewater using functionalized wood cellulosic adsorbent. Chemosphere 2021 270 128668 10.1016/j.chemosphere.2020.128668 33268087
    [Google Scholar]
  60. Shahat A. Kubra K.T. El-marghany A. Equilibrium, thermodynamic and kinetic modeling of triclosan adsorption on mesoporous carbon nanosphere: Optimization using Box-Behnken design. J. Mol. Liq. 2023 383 122166 10.1016/j.molliq.2023.122166
    [Google Scholar]
  61. Srivastava S.K. New Challenges on Natural Resources and their Impact on Climate Change in the Indian Context. India: Climate Change Impacts, Mitigation, and Adaptation in Developing Countries. Islam M.N. van Amstel A. Cham Springer 2021 1 15 10.1007/978‑3‑030‑67865‑4_1
    [Google Scholar]
  62. Srivastava S.K. Soil and Environmental Variables Influencing Greenhouse Gas Cycling in an Agroecosystem. Greenhouse Gas Regulating Microorganisms in Soil Ecosystems Mohanty S.R Kollah B. Cham Springer 2024
    [Google Scholar]
  63. Srivastava S.K. Delineation of groundwater potential zone through geospatial technique, multi-criteria decision analysis, and analytical hierarchy process. Res. Sq. 2021 2021 10.21203/rs.3.rs‑419796/v1
    [Google Scholar]
/content/journals/cac/10.2174/0115734110343714241130170537
Loading
Modeling for Copper Recovery from E-Waste by Using Machine Learning Technique: An Approach for the Circular Economy
Bentham Science Publishers ; https://doi.org/10.2174/0115734110343714241130170537
/content/journals/cac/10.2174/0115734110343714241130170537
/content/journals/cac/10.2174/0115734110343714241130170537
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: hazardous waste ; artificial neural network (ANN) ; machine learning technique ; boosting algorithm (BA) ; E-waste

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test