Optimizing acid leaching of copper from the wastewater treatment sludge of a printed circuit board industry using factorial experimental design

  • Usarat ThawornchaisitEmail author
  • Kamalasiri Juthaisong
  • Kasama Parsongjeen
  • Phonsiri Phoengchan


Heavy metal containing sludge from printed circuit board production (PCB) is not only classified as hazardous waste, but also as a potentially valuable raw material for copper recovery. Since sludge leaching is a significant first step in copper extraction yield via hydrometallurgy, we assessed the optimum copper acid-leaching conditions using a Design of Experiment (DOE) approach (33 full factorial design) and the multiple response method called the desirability (D) function. The effects of acid concentration, the ratio of acid volume to sludge quantity (“L/S ratio”), and leaching time on copper-leaching efficiency were evaluated. The model equation derived using Minitab 17.0 predicts that copper-leaching efficiency can reach 97.0% at optimal values of 0.84 M sulfuric acid, L/S ratio of 100:1, and a leaching time of 80 min. Experiments verified a 96.8% (\(\sigma = 0.4\% )\) copper-leaching efficiency. The small discrepancy between predicted and experimental data showed that the DOE approach was a suitable tool for predicting leaching efficiency from PCB sludge by sulfuric acid.


Acid leaching Optimization Copper extraction Wastewater sludge Printed circuit board 



This research was financially supported by the Department of Chemistry, Faculty of Science, KMITL under a special project supporting budget. English checking and editing was provided by John Morris, KMITL Research and Innovation Services (KRIS).


  1. 1.
    Chen S-Y, Huang Q-Y (2014) Heavy metals recovery from printed circuit board industry wastewater sludge by thermophilic bioleaching process. J Chem Technol Biotechnol 89:158–164. CrossRefGoogle Scholar
  2. 2.
    Workshop Materials on WEEE Management in Taiwan: Handout 10 (2012) Accessed 27 June 2018
  3. 3.
    Mdlovu NV, Chiang C-L, Lin K-S, Jeng R-C (2018) Recycling copper nanoparticles from printed circuit board waste etchants via a microemulsion process. J Clean Prod 185:781–796. CrossRefGoogle Scholar
  4. 4.
    Yu M, Zeng X, Song Q, Liu L, Li J (2016) Examining regeneration technologies for etching solutions: a critical analysis of the characteristics and potentials. J Clean Prod 113:973–980. CrossRefGoogle Scholar
  5. 5.
    Kuan Y-C, Lee I-H, Chern J-M (2010) Heavy metal extraction from PCB wastewater treatment sludge by sulfuric acid. J Hazard Mater 177:81–886. CrossRefGoogle Scholar
  6. 6.
    Wu C-H, Kuo C-Y, Lo S-L (2009) Recovery of heavy metals from industrial sludge using various acid extraction approaches. Water Sci Technol 59:289–293. CrossRefGoogle Scholar
  7. 7.
    Lee IH, Kuan Y-C, Chern J-M (2006) Factorial experimental design for recovering heavy metals from sludge with ion-exchange resin. J Harzard Mater 138:549–559. CrossRefGoogle Scholar
  8. 8.
    Ndiritu SW, Nzila C, Namango S (2017) Optimizing chemical extraction of heavy metals from anaerobically digested sewage sludge using response surface methodology. IJNRES 4:17–26Google Scholar
  9. 9.
  10. 10.
    Li C, Xie F, Ma Y, Cai T, Li H, Huang Z, Yuan G (2010) Multiple heavy metals extractions and recovery from hazardous electroplating sludge waste via ultrasonically enhanced two-stage acid leaching. J Hazard Mater 178:823–833. CrossRefGoogle Scholar
  11. 11.
    Yang C, Zhu N, Shen W, Zhang T, Wu P (2017) Bioleaching of copper from metal concentrates of wasted printed circuit boards by a newly isolated Acidithiobacillus ferroxidans strain Z1. J Mater Cycles Waste Manag 19:247–255. CrossRefGoogle Scholar
  12. 12.
    Ghodrat M, Rhamdhani MA, Khaliq A, Brooks G, Samali B (2018) Thermodynamic analysis of metals recycling out of wasted printed circuit board through secondary copper smelting. J Mater Cycles Waste Manag 20:386–401. CrossRefGoogle Scholar
  13. 13.
    Huyen PT, Dang TD, Tung MT, Huyen NTT, Green TA, Roy S (2016) Electrochemical copper recovery from galvanic sludge. Hydrometallurgy 164:295–303. CrossRefGoogle Scholar
  14. 14.
    Miskufova A, Havlik T, Laubertova M, Ukasik M (2006) Hydrometallurgical route for copper, zinc and chromium from galvanic sludge. Acta Metall Slovaca 12:293–302Google Scholar
  15. 15.
    Jadhav U, Hocheng H (2015) Hydrometallurgical recovery of metals from large printed circuit board pieces. Sci Rep 5:14574. CrossRefGoogle Scholar
  16. 16.
    Liu JC, Kao TH (2003) Extraction of Cu and Pb from printed circuit board sludge using ammonia solutions. Water Sci Technol 47:167–172CrossRefGoogle Scholar
  17. 17.
    Kordosky GA (2002) Copper recovery using leach/solovent extraction/electrowinning technology: forty years of innovation, 2.2 million tonnes of copper annually. J South Afr Inst Min Metal 102(8):445–450Google Scholar
  18. 18.
    Mohanty U, Rintala L, Halli P, Taskinen P, Lundstrom M (2018) Hydrometallurgical approach for leaching of metals from copper rich side stream originating from base metal production. Metals 8:40. CrossRefGoogle Scholar
  19. 19.
    Ochromowicz K, Chmielewski T (2011) Solvent extraction in hydrometallurgical processing of polish copper concentrates. Physicochem Probl Miner Process 46:207–218Google Scholar
  20. 20.
    Huyen NTT, Dung DT, Giang NH, Thanh NDB, Tung MT, Thuy HTB (2017) Optimization of the leaching process of the printed circuit boards production’s sludge for copper recovery via electrolysis. Vietnam J Chem 55:254–258Google Scholar
  21. 21.
    Deng J, Feng X, Qiu X (2009) Extraction of heavy metal from sewage sludge using ultrasound-assisted nitric acid. Chem Eng J 152:177–182. CrossRefGoogle Scholar
  22. 22.
    Silva JE, Soares D, Paiva AP, Labrincha JA, Castro F (2005) Leaching behaviour of a galvanic sludge in sulphuric acid and ammonia media. J Hazard Mater 121:195–202. CrossRefGoogle Scholar
  23. 23.
    Liu Y, Lin Q, Li L, Fu J, Zhu Z, Wang C, Qian D (2014) Study on hydrometallurgical process and kinetics of manganese extraction from low-grade manganese carbonate ores. Int J Min Sci Technol 24:567–571CrossRefGoogle Scholar
  24. 24.
    Tu Y-J, Chang C-K, You C-F, Lou J-C (2010) Recycling of Cu powder from industrial sludge by combined acid leaching, chemical exchange, and ferrite process. J Hazard Mater 181:981–985. CrossRefGoogle Scholar
  25. 25.
    Ҫopur M, Özmetin C, Özmetin E, Kocakerim MM (2004) Optimization study of the leaching of roasted zinc sulphide concentrate with sulphuric acid solutions. Chem Eng Processing 43:1007–1014. CrossRefGoogle Scholar
  26. 26.
    Ҫoruh S, Elevli S, Geeyikçi F (2012) Statistical evaluation and optimization of factors affecting the leaching performance of copper floatation waste. World J, Sci. Google Scholar
  27. 27.
    Liang G, Tang J, Liu W, Zhou Q (2013) Optimizing mixed culture of two acidophiles to improve copper recovery from printed circuit boards (PCBs). J Hazard Mater 250–251:238–245. CrossRefGoogle Scholar
  28. 28.
    Saadat S, Karimi-Jashni A (2011) Optimization of Pb(II) adsorption onto modified walnut shells using factorial design and simplex methodologies. Chem Eng J 173:743–749. CrossRefGoogle Scholar
  29. 29.
    Elhalil A, Tounsadi H, Elmoubarki R, Mahjoubi FZ, Farnane M, Sadiq M, Abdennouri M, Qourzal S, Barka N (2016) Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process. Water Resour Ind 15:41–48. CrossRefGoogle Scholar
  30. 30.
    Rice EW, Baird RB, Eaton AD, Clesceri LS (2012) Standard method for the examination of water and wastewater, 22nd edn. American Public Health Association, American Water Work Association and Water Environmental Federation, WashingtonGoogle Scholar
  31. 31.
    CEN/TC (2005) Determination of pH in soil, sewage sludge and biowaste. Working document—horizontal standards in the field of sludge, Biowaste and Soil for EU Directive(s). Accessed 27 June 2018
  32. 32.
    NIST/SEMATECH e-Handbook of Statistical Methods (2012) Adding centerpoints. Accessed 27 June 2018
  33. 33.
    Al-Abed SR, Jegadeesan G, Purandare J, Allen D (2008) Leaching behavior of mineral processing waste: comparison of batch and column investigations. J Hazard Mater 153:1088–1092. CrossRefGoogle Scholar
  34. 34.
    Akteke-Öztürk B, Weber GW, Köksal G (2015) Desirability functions in multiresponse optimization. In: Plakhov A, Tchemisova T, Freitas A (eds) Optimization in the natural sciences. EmC–ONS 2014. Communications in computer and information science, vol 499. Springer, Cham, pp 129–146Google Scholar

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© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Chemistry, Faculty of ScienceKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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