A New Analytical Method for Reduction Process of Iron Ore Based on the Power Spectrum

  • Guo-Feng Fan
  • Li-Ling Peng
  • Wei-Chiang HongEmail author
  • Hua Wang
Research Paper
Part of the following topical collections:
  1. Chemistry


A series of direct smelting reduction experiment has been implemented with different iron ore bases by thermogravimetric analyzers. The derivative thermogravimetric data have been obtained from these experiments. The data are then decomposed by the technology of empirical mode decomposition to receive its embedded characteristics of the power spectrum. Secondly, based on the obtained power spectrum, the energy transferring behavior for reduction process of iron oxide is analyzed and is compared with other methods (i.e., analytical reagent). Finally, the desired spectral characteristics of the power spectrum for the reduction process of Huimin iron ore can be determined. The result would play a significant role in strengthening the smelting process of Huimin iron ore.


Derivative thermogravimetric (DTG) Empirical mode decomposition (EMD) Power spectrum Energy transfer 


  1. Chen HT, Liu KC (2003) Effect of the potential field on non-Fickian diffusion problems in a sphere. Int J Heat Mass Transf 46:2809–2818. CrossRefzbMATHGoogle Scholar
  2. Chen Z, Pan C, Yu L (2018) Structural damage detection via adaptive dictionary learning and sparse representation of measured acceleration responses. Measurement 128:377–387. CrossRefGoogle Scholar
  3. Cross M, Croft TN, Djambazov G, Pericleous K (2006) Computational modelling of bubbles, droplets and particles in metals reduction and refining. Appl Math Model 30:1445–1458. CrossRefzbMATHGoogle Scholar
  4. Donskoi E, McElwain DLS (2001) Mathematical modeling of non-isothermal reduction in highly swelling iron ore-coal char composite pellet. Ironmak Steelmak 28:384–389. CrossRefGoogle Scholar
  5. Donskoi E, Liu F, McElwain DLS (1998) Two-dimensional modelling of nonisothermal reduction of an iron ore-coal composite pellet. In: Noye BJ, Teubner M, Gill A (eds) Computational techniques and applications: CTAC97. World Scientific Publishing Co, Adelaide, pp 193–200Google Scholar
  6. Echeverria JC, Crowe JA, Woolfson MS, Hayes-Gill BR (2001) Application of the empirical mode decomposition to heart rate variability analysis. Med Biol Eng Comput 39:471–479. CrossRefGoogle Scholar
  7. Georgiou TT (2002) The structure of state covariances and its relation to the power spectrum of the input. IEEE Trans Autom Control 47:1056–1066. MathSciNetCrossRefzbMATHGoogle Scholar
  8. Granett BR, Guzzo L, Coupon J, Arnouts S, Hudelot P, Ilbert O, Mccracken HJ, Mellier Y, Adami C, Bel J, Bolzonella M, Bottini D, Cappi A, Cucciati O, De La Torre S, Franzetti P, Fritz A, Garilli B, Iovino A, Krywult J, Le Brun V, Le Fevre O, Maccagni D, Malek K, Marulli F, Meneux B, Paioro L, Polletta M, Pollo A, Scodeggio M, Schlagenhaufer H, Tasca L, Tojeiro R, Vergani D, Zanichelli A (2011) The power spectrum from the angular distribution of galaxies in the CFHTLS-Wide fields at redshift ~ 0.7. Mon Not R Astron Soc 421:251–261. CrossRefGoogle Scholar
  9. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454:903–995. MathSciNetCrossRefzbMATHGoogle Scholar
  10. Kang HW, Chung WS (2004) Review of applicability of unreacted core model based on Ishida–Wen model. Ironmak Steelmak 31:117–124. CrossRefGoogle Scholar
  11. Karimi M, Karami H, Gholami M, Khatibzadehazad H, Moslemi N (2018) Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method. Energy 144:928–940. CrossRefGoogle Scholar
  12. Krishna EH, Sivani K, Reddy KA (2018) On the use of EMD based adaptive filtering for OFDM channel estimation. AEU Int J Electron Commun 83:492–500. CrossRefGoogle Scholar
  13. Lam BSY, Yu CKW, Choy SK, Leung JKT (2016) Jump point detection using empirical mode decomposition. Land Use Policy 58:1–8. CrossRefGoogle Scholar
  14. Li QJ, Hong X (2008) Mathematical simulation on reduction of fine iron oxide at low temperature. Miner Process Extr Metall 117:209–213. CrossRefGoogle Scholar
  15. Li QJ, Hong X (2009) Non-isothermal kinetics model for reduction of ferrous oxide with hydrogen and carbon monoxide. Ironmak Steelmak 36:24–28. CrossRefGoogle Scholar
  16. Li XG, Yang KD, Wang Y (2011) The power spectrum and correlation of flow noise for an axisymmetric body in water. Chin Phys B 20:4301–4308. CrossRefGoogle Scholar
  17. Loh CH, Wu TC, Huang NE (2001) Application of the empirical mode decomposition Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bull Seismol Soc Am 91:1339–1357. CrossRefGoogle Scholar
  18. Malek J (1992) The kinetic analysis of non-isothermal data. Thermochim Acta 200:257–269. CrossRefGoogle Scholar
  19. Martinez-Gonzalez E (2008) Cosmic microwave background anisotropies: the power spectrum and beyond. In: Martinez V, Saar E, Gonzales E, Pons-Borderia M (eds) Data analysis in cosmology, vol 665. Lecture notes in physics. Springer, Berlin, pp 79–120. CrossRefGoogle Scholar
  20. Pineau A, Kanari N, Gaballah I (2007) Kinetics of reduction of iron oxides by H2 Part II. Low temperature reduction of magnetite. Thermochim Acta 456:75–88. CrossRefGoogle Scholar
  21. Shi J, Donskoi E, Mcelwain DLS (2005) Modelling the reduction of an iron ore-coal composite pellet with conduction and convection in an axisymmetric temperature field. Math Comput Model 42:45–60. CrossRefzbMATHGoogle Scholar
  22. Sohn I, Fruehan RJ (2005) The reduction of iron oxides by volatiles in a rotary hearth furnace process: part I. The role and kinetics of volatile reduction. Metall Mater Trans B 36:605–612. CrossRefGoogle Scholar
  23. Sun S, Lu WK (1999) Building of a mathematical model for the reduction of iron ore in ore/coal composites. ISIJ Int 39:130–138. CrossRefGoogle Scholar
  24. Tadros H, Efstathiou G (1995) The power spectrum of IRAS galaxies. Mon Not R Astron Soc 276:45–50. CrossRefGoogle Scholar
  25. Wang T, Chen J, Yang T, Xiao C, Sun Z, Huang L, Dai D, Yang X, Zhang DH (2013) Dynamical resonances accessible only by reagent vibrational excitation in the F + HD-HF + D reaction. Science 342:1499–1502. CrossRefGoogle Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsPing Ding Shan UniversityPing Ding ShanChina
  2. 2.Department of Information Management, Oriental Institute of TechnologyPanchiao, New TaipeiTaiwan
  3. 3.Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of EducationKunmingChina

Personalised recommendations