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Experimental and simulation assessment to mitigate the emission of sulfide toxic gases and removing main impurities from Zn + Pb + Cu recovery plants

  • Majid Saidi EMAIL logo and Hossein Kadkhodayan
Published/Copyright: January 31, 2022
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 18 Issue 2

Abstract

An integrated novel approach employing the Taguchi method and Aspen Plus software has been applied to evaluate a new configuration for the industrial process of Zn + Pb + Cu recovery from sphalerite ore, in order to minimize the toxic gas emission. The optimum operating condition achieved by the Taguchi method has been used as initial data for the process simulation. The impact of operating parameters on the process performance is considered. The optimum condition for the conversion of sulfide toxic gases to H2SO4 have been found to be: acid concentration of 0.867 mol/L, reaction temperature of 120 °C, stirring speed of 400 rpm, leaching time of 120 min, sulfide ore particle size of 0.01 mm; solid-to-liquid ratio of 30 wt%, additives amount of 50 kg/ton and oxygen pressure of 200 psi. Under optimum condition, H2SO4 production from sulfide toxic gases is 99%, the removal percentage of Fe, Co, Mn, Ni and Cd impurities is 99% and the recovery percentage of Zn + Pd + Cu is more than 97%.

Keywords: Aspen Plus software; simulation; sulfide ore; Taguchi experimental design
Corresponding author: Majid Saidi, School of Chemistry, College of Science, University of Tehran, PO Box 14155–6455, Tehran, Iran, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix A-1: Reactions network of sphalerite (ZnS) leaching process introduced to Aspen Plus software

(A-1) ZnS + 2 H 2 SO 4 → ZnSO 4 + H 2 S

(A-2) ZnS  + H 2 SO 4 + 1 / 2 O 2 → ZnSO 4 +  S  + H 2 O

(A-3) ZnS  + H 2 SO 4 + 1 / 2 O 2 → ZnSO 4 + S 0 + H 2 O

(A-4) H 2 S  + Fe 2 ( SO 4 ) 3 → 2 FeSO 4 + H 2 SO 4 + S 0

(A-5) ZnS  + Fe 2 ( SO 4 ) 3 → 2 FeSO 4 + ZnSO 4 + S 0

(A-6) 2 FeSO 4 + H 2 SO 4 + 1 / 2 O 2 → Fe 2 ( SO 4 ) 3 + H 2 O

Appendix A-2: Reactions network of galena (PbS) leaching process introduced to Aspen Plus software

(A-7) PbS  + H 2 SO 4 → PbSO 4 + H 2 S

(A-8) H 2 S  + Fe 2 ( SO 4 ) 3 → 2 FeSO 4 + H 2 SO 4 + S 0

(A-9) PbS  + Fe 2 ( SO 4 ) 3 → 2 FeSO 4 + PbSO 4 + S 0

(A-10) 2 FeSO 4 + H 2 SO 4 + 1 / 2 O 2 → Fe 2 ( SO 4 ) 3 + H 2 O

(A-11) PbS  + H 2 SO 4 + 1 / 2 O 2 → PbSO 4 + S 0 + H 2 O

Appendix A-3: Reactions network of chalcopyrite (CuFeS2) leaching process introduced to Aspen Plus software

(A-12) CuFeS 2 + 2 H 2 SO 4 + O 2 → CuSO 4 + FeSO 4 + 2 S 0 + 2 H 2 O

(A-13) 2 FeSO 4 + H 2 SO 4 + 1 / 2 O 2 → Fe 2 ( SO 4 ) 3 + H 2 O

(A-14) CuFeS 2 + 2 Fe 2 ( SO 4 ) 3 → 5 FeSO 4 + CuSO 4 + 2 S 0

(A-15) 2 CuFeS 2 + O 2 → Cu 2 S  + 2 FeS  + SO 2

(A-16) H 2 SO 4 + H 2 O → H 3 O +   + HSO 4 -

(A-17) HSO 4 - + H 2 O → H 3 O + + SO 4 2 -

(A-18) S  + O 2 → SO 2 ;  O 2 + 2 SO 2 → 2 SO 3

(A-19) FeS  + 2 O 2 → FeSO 4

(A-20) 3 FeS  + 5 O 2 → Fe 3 O 4 ↓ + 3 SO 2

(A-21) 2 FeSO 4 → Fe 2 O 3 ↓ + SO 2 + SO 3

(A-22) 2 FeSO 4 + 1 / 2 O 2 + H 2 SO 4 → Fe 2 ( SO 4 ) 3 + H 2 O

Appendix A-4: Reactions network of neutralization process introduced to Aspen Plus software

(A-23) Ca ( OH ) 2 + H 2 SO 4 + 2 H 2 O → CaSO 4 . 2 H 2 O ↓ + 2 H 2 O

(A-24) Fe 2 ( SO 4 ) 3 + 3 Ca ( OH ) 2 + 6 H 2 O → 2 Fe ( OH ) 3 ↓ + 3 CaSO 4 . 2 H 2 O ↓

(A-25) 4 ZnSO 4 + 3 Ca ( OH ) 2 + 6 H 2 O → ZnSO 4.3 Zn ( OH ) 2 . 4 H 2 O  + 3 CaSO 4 . 2 H 2 O

(A-26) 4 PbSO 4 + 3 Ca ( OH ) 2 + 6 H 2 O → PbSO 4 . 3 Pb ( OH ) 2 . 4 H 2 O  + 3 CaSO 4 . 2 H 2 O

(A-27) 4 CuSO 4 + 3 Ca ( OH ) 2 + 6 H 2 O → CuSO 4 . 3 Cu ( OH ) 2 . 4 H 2 O  + 3 CaSO 4 . 2 H 2 O

Appendix A-5: Reactions network of Purification process introduced to Aspen Plus software

Hot purification:

(A-28) 10 FeSO 4 + 2 KMnO 4 + 8 H 2 SO 4 → K 2 SO 4 + 2 MnSO 4 + 5 Fe 2 ( SO 4 ) 3 + 8 H 2 O

(A-29) 2 FeSO 4 + MnO 2 + 2 H 2 SO 4 → Fe 2 ( SO 4 ) 3 + MnSO 4 + 2 H 2 O

(A-30) Fe 2 ( SO 4 ) 3 + 3 Ca ( OH ) 2 + 6 H 2 O  → 2 Fe ( OH ) 3 ↓ + 3 CaSO 4 . 2 H 2 O ↓

(A-31) KMnO 4 + 3 MnSO 4 + 2 H 2 O  → 5 MnO 2 ↓ + 4 H 2 SO 4 + K 2 SO 4

(A-32) KMnO 4 + 3 CoSO 4 + 4 H 2 SO 4 → MnO 2 ↓ + 3 Co 2 ( SO 4 ) 3 + 2 H 2 O

(A-33) 3 Co 2 ( SO 4 ) 3 + 3 Ca ( OH ) 2 → 2 Co ( OH ) 3 ↓ + 3 CaSO 4 . 2 H 2 O

Cold purification:

(A-34) Zn 0   ( powder ) + CdSO 4 → Cd 0 ↓ + ZnSO 4

(A-35) Zn 0   ( powder ) + NiSO 4 → Ni 0 ↓ + ZnSO 4

References

1. Akcil, A. A preliminary research on acid pressure leaching of pyritic copper ore in Kure Copper Mine, Turkey. Miner Eng 2002;15:1193–7, https://doi.org/10.1016/s0892-6875(02)00268-6.Search in Google Scholar

2. Saidi, M, Kadkhodayan, H. Experimental and simulation study of copper recovery process from copper oxide ore using aspen plus software: optimization and sensitivity analysis of effective parameters. J Environ Chem Eng 2020;8:103772, https://doi.org/10.1016/j.jece.2020.103772.Search in Google Scholar

3. Saidi, M, Kadkhodayan, H. Process development for sodium permanganate production by waste management of industrial sludge of zinc oxide ores recovery plants: experimental and simulation study. Process Saf Environ Pro 2021;148:1254–63, https://doi.org/10.1016/j.psep.2021.02.038.Search in Google Scholar

4. Dutrizac, J. The leaching of sulphide minerals in chloride media. Hydrometallurgy 1992;29:1–45, https://doi.org/10.1016/0304-386x(92)90004-j.Search in Google Scholar

5. Saidi, M, Kadkhodayan, H. Toxic heavy metal removal from sulfide ores using potassium permanganate: process development and waste management. J Environ Manag 2020;276:111354, https://doi.org/10.1016/j.jenvman.2020.111354.Search in Google Scholar PubMed

6. Hadi, P, Xu, M, Lin, CS, Hui, C-W, McKay, G. Waste printed circuit board recycling techniques and product utilization. J Hazard Mater 2015;283:234–43, https://doi.org/10.1016/j.jhazmat.2014.09.032.Search in Google Scholar PubMed

7. Siavashi, F, Saidi, M, Rahimpour, MR. Purge gas recovery of ammonia synthesis plant by integrated configuration of catalytic hydrogen-permselective membrane reactor and solid oxide fuel cell as a novel technology. J Power Sources 2014;267:104–16, https://doi.org/10.1016/j.jpowsour.2014.05.072.Search in Google Scholar

8. Behnajady, B, Balesini, A, Moghaddam, J. A new approach to the optimisation of zinc electrolyte cold purification process by Taguchi’s method. Can Metall Q 2014;53:333–9, https://doi.org/10.1179/1879139513y.0000000107.Search in Google Scholar

9. Ober, JA, Apodaca, LE, Crangle, RD. Industrial minerals and sustainability: by-products from SO2 mitigation as substitutes for mined mineral commodities. Geol Soc Am Spec Pap 2016;520:79–87.10.1130/2016.2520(09)Search in Google Scholar

10. Yang, Y, Qian, C, Shi, X, Tian, B, Chu, H, Wang, J, et al.. Efficient and industrial production of H2SO4 from sulfur sludge by acidophilic cells in a membrane bioreactor via optimizing process. J Clean Prod 2019;250:119444.10.1016/j.jclepro.2019.119444Search in Google Scholar

11. Bangga, G, Novita, FJ, Lee, H-Y. Evolutional computational fluid dynamics analyses of reactive distillation columns for methyl acetate production process. Chem Eng Process 2019;135:42–52, https://doi.org/10.1016/j.cep.2018.11.015.Search in Google Scholar

12. Sharifian, S, Harasek, M, Haddadi, B. Simulation of membrane gas separation process using aspen Plus® V8.6. Chem Prod Process Model 2016;11:67–72, https://doi.org/10.1515/cppm-2015-0067.Search in Google Scholar

13. Santoso, H, Rojas, OJ, Bao, J, Lee, P. Nonlinear process operability analysis based on steady-state simulation: a case study. Chem Prod Process Model 2007;2. https://doi.org/10.2202/1934-2659.1071.Search in Google Scholar

14. Aimiuwu, G, Osagie, E, Omoregbe, O. Process simulation for the production of methanol via CO2 reforming of methane route. Chem Prod Process Model 2022;17:69–79. https://doi.org/10.1515/cppm-2020-0049.Search in Google Scholar

15. Babatabar, MA, Saidi, M. Hydrogen production via integrated configuration of steam gasification process of biomass and water-gas shift reaction: process simulation and optimization. Int J Energy Res 2021;45:19378–94, https://doi.org/10.1002/er.7087.Search in Google Scholar

16. Faraji, M, Saidi, M. Hydrogen-rich syngas production via integrated configuration of pyrolysis and air gasification processes of various algal biomass: process simulation and evaluation using Aspen Plus software. Int J Hydrogen Energy 2021;46:18844–56, https://doi.org/10.1016/j.ijhydene.2021.03.047.Search in Google Scholar

17. Kadkhodayan, H, Seyed Dorraji, M, Rasoulifard, M, Amani-Ghadim, A. Enhanced microwave absorption property of FeO/CaCuMgTiSnO/graphene oxide nanocomposites in epoxy vinyl ester resin. J Mater Sci Mater Electron 2017;28. https://doi.org/10.1007/s10854-017-7076-2.Search in Google Scholar

18. Barrera, R, Salazar, C, Pérez, JF. Thermochemical equilibrium model of synthetic natural gas production from coal gasification using Aspen Plus. Int J Chem Eng 2014;2014. https://doi.org/10.1155/2014/192057.Search in Google Scholar

19. Liu, G, Zhang, J, Bao, J. Cost evaluation of cellulase enzyme for industrial-scale cellulosic ethanol production based on rigorous Aspen Plus modeling. Bioprocess Biosyst Eng 2016;39:133–40, https://doi.org/10.1007/s00449-015-1497-1.Search in Google Scholar PubMed

20. Liu, G, Sun, J, Zhang, J, Tu, Y, Bao, J. High titer l-lactic acid production from corn stover with minimum wastewater generation and techno-economic evaluation based on Aspen plus modeling. Bioresour Technol 2015;198:803–10, https://doi.org/10.1016/j.biortech.2015.09.098.Search in Google Scholar PubMed

21. de Riva, J, Ferro, V, Moya, C, Stadtherr, MA, Brennecke, JF, Palomar, J. Aspen Plus supported analysis of the post-combustion CO2 capture by chemical absorption using the [P2228][CNPyr] and [P66614][CNPyr]AHA Ionic Liquids. Int J Greenh Gas Control 2018;78:94–102, https://doi.org/10.1016/j.ijggc.2018.07.016.Search in Google Scholar

22. Hejazi, B, Grace, JR. Simulation of tar-free biomass syngas enhancement in a calcium looping operation using Aspen Plus built-in fluidized bed model. Int J Greenh Gas Control 2020;99:103096, https://doi.org/10.1016/j.ijggc.2020.103096.Search in Google Scholar

23. Liu, J, Gao, H-C, Peng, C-C, Wong, DS-H, Jang, S-S, Shen, J-F. Aspen Plus rate-based modeling for reconciling laboratory scale and pilot scale CO2 absorption using aqueous ammonia. Int J Greenh Gas Control 2015;34:117–28, https://doi.org/10.1016/j.ijggc.2015.01.009.Search in Google Scholar

24. Ghasemi, S, Seyed, M, Azizi, A. Investigation of leaching kinetics of zinc from a low-grade ore in organic and inorganic acids. J Min Environ 2017;8:579–91.Search in Google Scholar

25. Azizi, A, Bayati, B, Karamoozian, M. A comprehensive study of the leaching behavior and dissolution kinetics of copper oxide ore in sulfuric acid lixiviant. Sci Iran 2018;25:1412–22, https://doi.org/10.24200/sci.2018.5226.1154.Search in Google Scholar

26. Kim, Y, Lee, J. Leaching kinetics of zinc from metal oxide Varistors (MOVs) with sulfuric acid. Metals 2016;6:192, https://doi.org/10.3390/met6080192.Search in Google Scholar

27. Sharififard, H, Soleimani, M. Performance comparison of activated carbon and ferric oxide-hydroxide–activated carbon nanocomposite as vanadium (V) ion adsorbents. RSC Adv 2015;5:80650–60, https://doi.org/10.1039/c5ra14493k.Search in Google Scholar

28. Özdemir, U, Özbay, B, Özbay, İ, Veli, S. Application of Taguchi L32 orthogonal array design to optimize copper biosorption by using Spaghnum moss. Ecotoxicol Environ Saf 2014;107:229–35.10.1016/j.ecoenv.2014.06.018Search in Google Scholar PubMed

Received: 2021-10-07
Accepted: 2021-12-31
Published Online: 2022-01-31

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Experimental and simulation assessment to mitigate the emission of sulfide toxic gases and removing main impurities from Zn + Pb + Cu recovery plants
  4. Dynamic behavior of CO2 adsorption from CH4 mixture in a packed bed of SAPO-34 by CFD-based modeling
  5. Competitive adsorption of heavy metals in a quaternary solution by sugarcane bagasse – LDPE hybrid biochar: equilibrium isotherm and kinetics modelling
  6. Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches
  7. Design of a new synthetic nanocatalyst resulting high fuel quality based on multiple supports: experimental investigation and modeling
  8. Numerical study on thermal-hydraulic characteristics of flattened microfin tubes
  9. Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
  10. A mathematical model for the activated sludge process with a sludge disintegration unit
  11. Process design and economic assessment of large-scale production of molybdenum disulfide nanomaterials
  12. Review
  13. Modified optimal series cascade control for non-minimum phase system
https://doi.org/10.1515/cppm-2021-0062
Keywords for this article
Aspen Plus software; simulation; sulfide ore; Taguchi experimental design

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Experimental and simulation assessment to mitigate the emission of sulfide toxic gases and removing main impurities from Zn + Pb + Cu recovery plants
  4. Dynamic behavior of CO2 adsorption from CH4 mixture in a packed bed of SAPO-34 by CFD-based modeling
  5. Competitive adsorption of heavy metals in a quaternary solution by sugarcane bagasse – LDPE hybrid biochar: equilibrium isotherm and kinetics modelling
  6. Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches
  7. Design of a new synthetic nanocatalyst resulting high fuel quality based on multiple supports: experimental investigation and modeling
  8. Numerical study on thermal-hydraulic characteristics of flattened microfin tubes
  9. Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
  10. A mathematical model for the activated sludge process with a sludge disintegration unit
  11. Process design and economic assessment of large-scale production of molybdenum disulfide nanomaterials
  12. Review
  13. Modified optimal series cascade control for non-minimum phase system
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