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The Effect of Silica and Reducing Agent on the Contents of Impurities in Silicon Produced

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Abstract

Petroleum coke, soft coal and silica are the main raw materials used to produce silicon, and impurities in them remarkably influence the purity of silicon products. Two different furnace types (8.5 MVA and 12.5 MVA) were used in this study, each of which has two different sub-types. The influence of the three raw materials on the contents of multiple impurities in silicon was systematically studied using a large industrial data set. The correlation between the total impurity contents in silicon products and the different amounts of raw materials used was analyzed by a linear regression method. The correlation between the percentage of impurity contents in silicon products in raw materials and the different raw material consumption was also investigated using the same method. The results showed that the total percentage of impurities in silicon products was closely associated with the amounts of raw materials used and the correlation coefficient reached ≥98%. The slopes of the linear fitting lines showed that using coal as the reducing agent significantly increased the percentage of impurities in silicon products followed by petroleum coke and silica. Comparing the 12.5 MVA and 8.5 MVA furnaces, the variation between the different raw materials and the percentage of impurities in silicon products was also represented by a linear fit. The 12.5 MVA furnaces exhibited more efficient smelting by producing the highest-quality silicon products under the same amounts of raw materials. The results may provide guidance and a theoretical basis for the future cleaner production of silicon.

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References

  1. Ramírez-Márquez C, Contreras-Zarazúa G, Martín M, Segovia-Hernández JG (2019) Safety, economic and environmental optimization applied to three processes for the production of solar grade silicon. ACS sustain Chem Eng 5355–5366

  2. Green M (2009) The path to 25% silicon solar cell efficiency: history of silicon cell evolution. Prog Photovolt 17(3):183–189

    Article  CAS  Google Scholar 

  3. Li GQ, Lu YS, Xuan QD, Akhlaghi YG, Pei G, Ji J, Zhao XD (2020) Small scale optimization in crystalline silicon solar cell on efficiency enhancement of low-concentrating photovoltaic cell. Sol Energy 202:316–325

    Article  CAS  Google Scholar 

  4. Pinto MA, Frate CA, Rodrigues TO, Caldeira-Pires A (2020) Sensitivity analysis of the carbon payback time for a brazilian photovoltaic power plant. Util Policy 63(10104):101014

    Article  Google Scholar 

  5. Ramírez-Márquez C, Martín-Hernández E, Martín M, Segovia-Hernández JG (2020) Surrogate based optimization of a process of polycrystalline silicon production. Comput Chem Eng 140(106878):106870

    Article  Google Scholar 

  6. Chigondo F (2017) From metallurgical-grade to solar-grade silicon: an overview. Silicon 10(3):1–10

    Google Scholar 

  7. Müller M, Ghosh M, Sonnenschein R, Woditsch P (2006) Silicon for photovoltaic applications. Mater Sci Eng B 134(2/3):257–262

    Article  Google Scholar 

  8. Ni ZY, Zhou S, Zhao SY, Peng WB, Yang DR, Pi XD (2019) Silicon nanocrystals: unfading silicon materials for optoelectronics. Mater Sci Eng R 138:85–117

    Article  Google Scholar 

  9. Pizzini S, Acciarri M, Binetti S, Cavalcoli D, Cavallini A, Chrastina D, Colombo L, Grilli E, Isella G, Lancin M, Le Donne A, Mattoni A, Peter K, Pichaud B, Poliani E, Rossi M, Sanguinetti S, Texier M, von Känel H (2006) Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications. Mater Sci Eng B 134(2/3):118–124

    Article  CAS  Google Scholar 

  10. Cristea D, Craciunoiu F, Caldararu M (2000) Components for optoelectronic and photonic integrated circuits — design, modelling, manufacturing and monolithic integration on silicon. Mater Sci Eng B 74(1–3):89–95

    Article  Google Scholar 

  11. An YL, Tian Y, Wei CL, Zhang YC, Xiong SL, Feng JK, Qian YT (2020) Recent advances and perspectives of 2D silicon: synthesis and application for energy storage and conversion. Energy Stor Mater 32:115–150

    Article  Google Scholar 

  12. Vercauteren R, Scheen G, Raskin JP, Francis LA (2020) Porous silicon membranes and their applications: recent advances. Sensor Actuat A-Phys 112486

  13. Jung Y, Huh Y, Kim D (2020) Recent advances in surface engineering of porous silicon nanomaterials for biomedical applications. Microporous Mesoporous Mater 310(110673)

  14. Kumar R, Singh M, Soam A (2020) Study on electrochemical properties of silicon micro particles as electrode for supercapacitor application. Surf Interface 19(100524)

  15. Takla M, Kamfjord NE, Tveit H, Kjelstrup S (2013) Energy and exergy analysis of the silicon production process. Energy 58:138–146

    Article  CAS  Google Scholar 

  16. Benioub R, Adnane M, Boucetta A, Chahtou A (2017) Optimization of the raw material input molar ratio on the carbothermal production of solar-grade silicon. J New Technol Mater 7:90–96

    Article  CAS  Google Scholar 

  17. Ding Z, Chen ZQ, Ma TY, Lu CT, Ma WH, Shaw L (2020) Predicting the hydrogen release ability of LiBH4-based mixtures by ensemble machine learning. Energy Storage Materials 27:466–477

    Article  Google Scholar 

  18. Ding Z, Li H, Yan G, Yang WJ, Gao ZY, Ma WH, Shaw L (2020) Mechanism of hydrogen storage on Fe3B. Chem Commun 56:14235–14238

    Article  CAS  Google Scholar 

  19. Ding Z, Li H, Shaw L (2020) New insights into the solid-state hydrogen storage of nanostructured LiBH4-MgH2 system. Chem Eng J 385:123856

    Article  CAS  Google Scholar 

  20. Ding Z, Lu Y, Li L, Shaw L (2019) High reversible capacity hydrogen storage through Nano-LiBH4 + Nano-MgH2 system. Energy Storage Materials 20:24–35

    Article  Google Scholar 

  21. Ding Z, Shaw L (2019) Enhancement of hydrogen desorption from Nano-composite prepared by ball-milling MgH2 with in-situ aerosol-spraying LiBH4. ACS Sustain Chem Eng 7(17):15064–15072

    Article  CAS  Google Scholar 

  22. Xi FS, Li SY, Ma WH, Chen ZJ, Wei KX, Wu JJ (2021) A review of hydrometallurgy techniques for the removal of impurities from metallurgical-grade silicon. Hydrometallurgy 201:10553

    Article  Google Scholar 

  23. Duan WJ, Yu QB, Xie HQ, Qin Q (2017) Pyrolysis of coal by solid heat carrier-experimental study and kinetic modeling. Energy 135:317–326

    Article  CAS  Google Scholar 

  24. Duan WJ, Yu QB, Wang ZM, Liu JX, Qin Q (2018) Life cycle and economic assessment of multi-stage blast furnace slag waste heat recovery system. Energy 142:486–495

    Article  Google Scholar 

  25. Sheinbaum-Pardo C, Mora-Pérez S, Robles-Morales G (2012) Decomposition of energy consumption and CO2 emissions in Mexican manufacturing industries: trends between 1990 and 2008. Energy sustain dev 16:57–67

    Article  CAS  Google Scholar 

  26. Yang LJ, Bibby MJ, Chandel RS (1993) Linear regression equations for modeling the submerged-arc welding process. J Mater Process Technol 39(1–2):33–42

    Article  Google Scholar 

  27. He HH, Guan HJ, Zhu X, Lee HY (2017) Assessment on the energy flow and carbon emissions of integrated steelmaking plants. Energy Rep 3:29–36

    Article  Google Scholar 

  28. Sun WQ, Wang Q, Zhou Y, Wu JZ (2020) Material and energy flows of the iron and steel industry: status quo, challenges and perspectives. Appl Energy 268(114946):114946

    Article  Google Scholar 

  29. Sun WQ, Wang Q, Zheng Z, Cai JJ (2020) Material–energy–emission nexus in the integrated iron and steel industry. Energy Convers Manag 213(112828):112828

    Article  Google Scholar 

  30. Price L, Sinton J, Worrell E, Phylipsen D, Xiulian H, Ji L (2002) Energy use and carbon dioxide emissions from steel production in China. Energy 27(5):429–446

    Article  CAS  Google Scholar 

  31. Rosen MA (2001) Energy- and exergy-based comparison of coal-fired and nuclear steam power plants. Exergy Int J 1(3):180–192

    Article  Google Scholar 

  32. Chen ZJ, Ma WH, Wu JJ, Wei KX, Yang X, Lv GQ, Xie KQ, Yu J (2016) Influence of carbothermic reduction on submerged arc furnace energy efficiency during silicon production. Energy 116:687–693

    Article  CAS  Google Scholar 

  33. Chen ZJ, Ma WH, Wu JJ, Li SY, Wei KX, Lv GQ (2017) Artificial neural network modeling for evaluating the power consumption of silicon production in submerged arc furnaces. Appl Therm Eng

  34. Chen ZJ, Ma WH, Wu JJ, Li SY, Ding WM (2017) Effect of raw materials on the production process of the silicon furnace. J Clean Prod 158:359–366

    Article  CAS  Google Scholar 

  35. Chen ZJ, Ma WH, Wu JJ, Wei KX, Lv GQ (2018) Predicting the electricity consumption and the exergetic efficiency of a submerged arc furnace with raw materials using an artificial neural network. Silicon 10:603–608

    Article  CAS  Google Scholar 

  36. Chen ZJ, Ma WH, Wu JJ, Wei KX, Lei Y, Lv GQ (2018) A study of the performance of submerged arc furnace smelting of industrial silicon. Silicon 10(3):1121–1127

    Article  CAS  Google Scholar 

  37. Chen ZJ, Ma WH, Li SY, Wu JJ, Wei KX, Tu ZQ, Ding WM (2018) Influence of carbon material on the production process of different electric arc furnaces. J Clean Prod 174:17–25

    Article  CAS  Google Scholar 

  38. Chen ZJ, Zhou SC, Ma WH, Deng XC, Li SY, Ding WM (2018) The effect of the carbonaceous materials properties on the energy consumption of silicon production in the submerged arc furnace. J Clean Prod 191:240–247

    Article  CAS  Google Scholar 

  39. Chen ZJ, Zhou SC, Wei KX, Ma WH, Li SY (2020) Evaluating of the exergy efficiency of the silicon production process using artificial neural networks. Phosphorus Sulfur Silicon Relat Elem 195(9):756–766

    Article  CAS  Google Scholar 

  40. Kamfjord NE, Myrhaug EH, Tveit H, Wittgens B (2010) Energy balance of a 45 MW (ferro-) silicon submerged arc furnace. In: Proceedings of the Twelfth International Ferro Alloy Congress. Helsinki Finland 729–738

  41. Hoang ND (2019) Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement 137:58–70

    Article  Google Scholar 

  42. Mehmanpazir F, Khalili-Damghani K, Hafezalkotob A (2019) Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: steel industry in Iran). Resour Policy 63(101409):101409

    Article  Google Scholar 

  43. Babu KA, Mandal S (2017) Regression based novel constitutive analyses to predict high temperature flow behavior in super austenitic stainless steel. Mater Sci Eng 187-195

  44. Uma MRP, Devarasetti H, Suresh KRN (2018) Application of regression and artificial neural network analysis in modelling of surface roughness in hard turning of AISI 52100 steel. Mater Today Proc 5(2):4766–4777

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful for financial support from the National Natural Science Foundation of China (No. 51804147) and the Yunnan Province Department of Education (No. 2018JS018).

Availability of Data and Material

All data generated or analysed during this study are included in this published article.

Funding

The authors are grateful for financial support from the National Natural Science Foundation of China (No. 51804147) and the Yunnan Province Department of Education (No. 2018JS018).

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Authors

Contributions

Hongmei Zhang: Conceptualization, Resources, Writing - review & editing, Visualization, Validation, Supervision. Zhengjie Chen: Formal analysis, Validation, Data curation, Writing-original draft, Writing-review&editing. Wenhui Ma: Conceptualization, Resources, Visualization, Visualization, Supervision. Shijie Cao: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation. Kaizhi Jiang: Writing - review & editing, Yaqian Zhu: Data curation, Writing - review & editing.

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Correspondence to Zhengjie Chen or Wenhui Ma.

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“For “Ethical statement”, This article does not contain any studies with human participants or animals performed by any of the authors.In this experiment, we did not collect any samples of human and animals. The human hair sample used in this paper is only a China national certified reference standard material, which was purchased from a commercial institute in China.”

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Zhang, H., Chen, Z., Ma, W. et al. The Effect of Silica and Reducing Agent on the Contents of Impurities in Silicon Produced. Silicon 14, 2779–2792 (2022). https://doi.org/10.1007/s12633-021-01072-w

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