Advertisement

Fused magnesia manufacturing process: a survey

  • Jie YangEmail author
  • Shaowen Lu
  • Liangyong Wang
Article
  • 384 Downloads

Abstract

This paper provides an overview of the manufacturing process of fused magnesia. A brief introduction to fused magnesia and its industrial production process are presented first. In order to meet the market requirements and reduce costs, fused magnesia industrial process begins to focus on these issues: high energy consumption, serious pollution, low utilization of raw materials. So the issues related to fused magnesia are reviewed. The literature work related to the fused magnesia manufacturing process is divided into four categories: modeling, optimization, control, and experimental constraints. As can be seen, with the continuous development of intelligent manufacturing technology, fused magnesia manufacturing process begins to emerge new opportunities. Research trends and opportunities are presented in the final section, with an emphasis on future potential intelligent technologies.

Keywords

Fused magnesia Fused magnesium furnace Modelling Optimization Experimental constraints 

References

  1. Abraham, M., Butler, C., & Chen, Y. (1971). Growth of high-purity and doped alkaline earth oxides: I. MgO and CaO. The Journal of Chemical Physics, 55(8), 3752–3756.CrossRefGoogle Scholar
  2. Acha, E., Semlyen, A., & Rajakovic, N. (1990). A harmonic domain computational package for nonlinear problems and its application to electric arcs. IEEE Transactions on Power Delivery, 5(3), 1390–1397.CrossRefGoogle Scholar
  3. Agah, S. M., Hosseinian, S., Askarian Abyaneh, H., & Moaddabi, N. (2010). Parameter identification of arc furnace based on stochastic nature of arc length using two-step optimization technique. IEEE Transactions on Power Delivery, 25(4), 2859–2867.CrossRefGoogle Scholar
  4. Ahmethodzic, A., Kapetanovi, M., Sokolija, K., Smeets, R. P., & Kertsz, V. (2011). Linking a physical arc model with a black box arc model and verification. IEEE Transactions on Dielectrics and Electrical Insulation, 18(4), 1029–1037.CrossRefGoogle Scholar
  5. Allgaier, R. (1970). Interpretation of transport measurements in electronically conducting liquids. II. Hall mobility. Physical Review B, 2(6), 2257–2259.CrossRefGoogle Scholar
  6. Alonso, M. A. P., & Donsion, M. P. (2004). An improved time domain arc furnace model for harmonic analysis. IEEE Transactions on Power Delivery, 19(1), 367–373.CrossRefGoogle Scholar
  7. Alves, M. F., & Peixoto, Z. M. A. (2011). Modeling and compensation of flicker in electrical networks using chaos theory and SVC systems. In S. Banerjee, M. Mitra, & L. Rondoni (Eds.), Applications of chaos and nonlinear dynamics in engineering (Vol. 1, pp. 39–63). Berlin: Springer.CrossRefGoogle Scholar
  8. Alves, M. F., Peixoto, Z. M. A., Garcia, C. P., & Gomes, D. G. (2010). An integrated model for the study of flicker compensation in electrical networks. Electric Power Systems Research, 80(10), 1299–1305.CrossRefGoogle Scholar
  9. Amadi, A., & Wang, Z. (2012). Energy optimization of submerged arc furnace. In Proceedings of international conference on systems and informatics (ICSAI) (pp. 800–804). 19–20 May, Yantai, China, IEEE.Google Scholar
  10. Anderson, P. J., & Livey, D. T. (1961). Physical methods for investigating the properties of oxide powders in relation to sintering. Powder Metallurgy, 4(7), 189–203.CrossRefGoogle Scholar
  11. Ansys I. (2011). ANSYS FLUENT, theory guide and user’s guide (p. 15317). Canonsburg, PA: Ansys Inc.Google Scholar
  12. Anuradha, K., Muni, B., & Kumar, A. R. (2009). Modeling of electric arc furnace & control algorithms for voltage flicker mitigation using DSTATCOM. In Proceedings of 6th international power electronics and motion control conference (IPEMC) (pp. 1123–1129). 17–20 May, IEEE.Google Scholar
  13. Arkel, A. V., Flood, E., & Bright, N. F. (1953). The electrical conductivity of molten oxides. Canadian Journal of Chemistry, 31(11), 1009–1019.CrossRefGoogle Scholar
  14. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.CrossRefGoogle Scholar
  15. Baheti, R., & Gill, H. (2011). Cyber-physical systems. The Impact of Control Technology, 12(1), 161–166.Google Scholar
  16. Balamurugan, S., Ashna, L., & Parthiban, P. (2014). Synthesis of nanocrystalline MgO particles by combustion followed by annealing method using hexamine as a fuel. Journal of Nanotechnology, 2014(841803), 1–6.CrossRefGoogle Scholar
  17. Balan, R., Maties, V., Hancu, O., Stan, S., & Ciprian, L. (2007). Modeling and control of an electric arc furnace. In Proceedings of mediterranean conference on control & automation (MED) (pp. 1–6), IEEE.Google Scholar
  18. Banerjee, J. C., & Sircar, N. R. (1964). A comprehensive study of indian magnesites as refractory material. Transactions of the Indian Ceramic Society, 23(1), 49–59.CrossRefGoogle Scholar
  19. Baron, B., Świszcz, P., & Kraszewski, T. (2012). Some aspects of the analysis and the interpretation of electrical measurements of submerged arc-resistance furnace. Przeglad Elektrotechniczny, 88(7b), 211–213.Google Scholar
  20. Bekker, J. G., Craig, I., & Pistorius, P. C. (2000). Model predictive control of an electric arc furnace off-gas process. Control Engineering Practice, 8(4), 445–455.CrossRefGoogle Scholar
  21. Benilov, M. S. (2002). Theory and modelling of arc cathodes. Plasma Sources Science and Technology, 11(3A), A49.CrossRefGoogle Scholar
  22. Benilov, M. S. (2008). Understanding and modelling plasmaelectrode interaction in high-pressure arc discharges: A review. Journal of Physics D: Applied Physics, 41(14), 144,001.CrossRefGoogle Scholar
  23. Bertola, A., Lazaroiu, G. C., Roscia, M., & Zaninelli, D. (2004). A matlab-simulink flickermeter model for power quality studies. In Proceedings of 11th international conference on harmonics and quality of power (pp. 734–738). 12–15 Sept. Lake Placid, NY, USA, IEEE.Google Scholar
  24. Bhatti, A. S., Dollimore, D., & Dyer, A. (1984). Magnesia from seawater: A review. CLAY MINER Clay Miner, 19(5), 865–875.CrossRefGoogle Scholar
  25. Billings, S. A., Boland, F. M., & Nicholson, H. (1979). Electric arc furnace modelling and control. Automatica, 15(2), 137–148.CrossRefGoogle Scholar
  26. Billings, S. A., & Nicholson, H. (1977). Modelling a three-phase electric arc furnace: A comparative study of control strategies. Applied Mathematical Modelling, 1(7), 355–361.CrossRefGoogle Scholar
  27. Bisio, G., Rubatto, G., & Martini, R. (2000). Heat transfer, energy saving and pollution control in UHP electric-arc furnaces. Energy, 25(11), 1047–1066.CrossRefGoogle Scholar
  28. Bocanegra-Bernal, M. H. (2002). Agglomeration of magnesia powders precipitated from sea water and its effects on uniaxial compaction. Materials Science and Engineering: A, 333(1–2), 176–186.CrossRefGoogle Scholar
  29. Boulet, B., Lalli, G., & Ajersch, M. (2003). Modeling and control of an electric arc furnace. In Proceedings of the 2003 american control conference (pp. 3060–3064). 4–6 June, Denver, CO, USA, IEEEGoogle Scholar
  30. Boulos, M. I. (1991). Thermal plasma processing. IEEE Transactions on Plasma Science, 19(6), 1078–1089.CrossRefGoogle Scholar
  31. Bowman, B., & Edels, H. (1969). Radial temperature measurements of alternating current arcs. Journal of Physics D: Applied Physics, 2(1), 53–63.CrossRefGoogle Scholar
  32. Budnikov, P. P., Volodin, P. L., & Tresvyatskiy, S. G. (1960). Investigation of sintering and recrystallization processes of pure magnesium oxide. Refractories, 1(1), 53–56.CrossRefGoogle Scholar
  33. Çamdali, Ü., & Tunç, M. (2002). Modelling of electric energy consumption in the AC electric arc furnace. International Journal of Energy Research, 26(10), 935–947.CrossRefGoogle Scholar
  34. Çamdali, Ü., & Tunç, M. (2003). Exergy analysis and efficiency in an industrial AC electric arc furnace. Applied Thermal Engineering, 23(17), 2255–2267.CrossRefGoogle Scholar
  35. Çamdali, Ü., & Tunç, M. (2004). Thermodynamic analysis of some industrial applications with variable ambient conditions. International Journal of Thermophysics, 25(6), 1965–1979.CrossRefGoogle Scholar
  36. Çamdali, Ü., & Tunç, M. (2005). Computation of chemical exergy potential in an industrial AC electric arc furnace. Journal of energy resources technology, 127(1), 66–70.CrossRefGoogle Scholar
  37. Çamdali, Ü., Tunç, M., & Karakaş, A. (2003). Second law analysis of thermodynamics in the electric arc furnace at a steel producing company. Energy Conversion and Management, 44(6), 961–973.CrossRefGoogle Scholar
  38. Çamdali, Ü., Yetişken, Y., & Ekmekci, I. (2012). Determination of the optimum cost function for an electric arc furnace and ladle furnace system by using energy balance. Energy Sources, Part B: Economics, Planning, and Policy, 7(2), 200–212.CrossRefGoogle Scholar
  39. Cao, M., Proulx, P., Boulos, M., & Mostaghimi, J. (1994). Mathematical modeling of high-power transferred arcs. Journal of Applied Physics, 76(12), 7757–7767.CrossRefGoogle Scholar
  40. Carlos, R. C., Kahn, C. E., & Halabi, S. (2018). Data science: Big data, machine learning, and artificial intelligence. Journal of the American College of Radiology, 15(3), 497–498.CrossRefGoogle Scholar
  41. Cayla, F., Freton, P., & Gonzalez, J.-J. (2008). Arc/cathode interaction model. IEEE Transactions on Plasma Science, 36(4), 1944–1954.CrossRefGoogle Scholar
  42. Chai, T., Wu, Z., & Wang, H. (2017). A CPS based optimal operational control system for fused magnesium furnace. IFAC-PapersOnLine, 50(1), 14992–14999.CrossRefGoogle Scholar
  43. Chang, G. W., Chen, C.-I., & Liu, Y.-J. (2010). A neural-network-based method of modeling electric arc furnace load for power engineering study. IEEE Transactions on Power Systems, 25(1), 138–146.CrossRefGoogle Scholar
  44. Chen, F., Athreya, K. B., Sastry, V. V., & Venkata, S. S. (2004). Function space valued markov model for electric arc furnace. IEEE Transactions on Power Systems, 19(2), 826–833.CrossRefGoogle Scholar
  45. Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501.CrossRefGoogle Scholar
  46. Collantes-Bellido, R., & Gomez, T. (1997). Identification and modelling of a three phase arc furnace for voltage disturbance simulation. IEEE Transactions on Power Delivery, 12(4), 1812–1817.CrossRefGoogle Scholar
  47. Czapla, M., Karbowniczek, M., & Michaliszyn, A. (2008). The optimisation of electric energy consumption in the electric arc furnace. Archives of Metallurgy and Materials, 53(2), 559–565.Google Scholar
  48. Das, A., Maiti, J., & Banerjee, R. (2010). Process control strategies for a steel making furnace using ANN with bayesian regularization and ANFIS. Expert Systems with Applications, 37(2), 1075–1085.CrossRefGoogle Scholar
  49. Delgado-Álvárez, J., Ramírez-Argáez, M. A., & González-Rivera, C. (2012). Mathematical modeling of a gas jet impinging on a two phase bath. In AIP conference proceedings (vol. 1479, pp. 177–180), AIP Publishing.Google Scholar
  50. Deng, J., Li, J., & Deng, X. (2015). A network-based manufacturing model for spiral bevel gears. Journal of Intelligent Manufacturing, 29(2), 1–15.Google Scholar
  51. Di Barba, P., Dughiero, F., Dusi, M., Forzan, M., Mognaschi, M. E., Paioli, M., et al. (2012). 3D FE analysis and control of a submerged arc electric furnace. International Journal of Applied Electromagnetics and Mechanics, 39(1), 555–561.Google Scholar
  52. Dionise, T., & Johnston, S. (2015). Surge protection for ladle melt furnaces: LMF transformer terminals were equipped with primary surge protection consisting of surge arresters and RC snubbers. IEEE Industry Applications Magazine, 21(5), 43–52.CrossRefGoogle Scholar
  53. Eastman, P. F., & Cutler, I. B. (1966). Effect of water vapor on initial sintering of magnesia. Journal of the American Ceramic Society, 49(10), 526–530.CrossRefGoogle Scholar
  54. Emanuel, A. E., & Orr, J. A. (2000). An improved method of simulation of the arc voltage-current characteristic. In Proceedings of ninth international conference on harmonics and quality of power (vol.1, pp. 148–154). 1–4 Oct., Orlando, FL, USA.Google Scholar
  55. Esfahani, M. T., & Vahidi, B. (2012). A new stochastic model of electric arc furnace based on hidden markov model: A study of its effects on the power system. IEEE Transactions on Power Delivery, 27(4), 1893–1901.CrossRefGoogle Scholar
  56. Eubank, W. R. (1951). Calcination studies of magnesium oxides. Journal of the American Ceramic Society, 34(8), 225–229.CrossRefGoogle Scholar
  57. Faghihi-Sani, M.-A., & Yamaguchi, A. (2002). Oxidation kinetics of MgO-C refractory bricks. Ceramics International, 28(8), 835–839.CrossRefGoogle Scholar
  58. Fan, J. R., Liang, X. H., Chen, L. H., & Cen, K. F. (1998). Modeling of \({\rm NO}_{{\rm x}}\) emissions from a w-shaped boiler furnace under different operating conditions. Energy, 23(12), 1051–1055.CrossRefGoogle Scholar
  59. Fernández, J. M. M., Cabal, V. Á., Montequin, V. R., & Balsera, J. V. (2008). Online estimation of electric arc furnace tap temperature by using fuzzy neural networks. Engineering Applications of Artificial Intelligence, 21(7), 1001–1012.CrossRefGoogle Scholar
  60. Fu, Y., Wang, N., Wang, Z., Wang, Z., Ji, B., & Wang, X. (2017). Smelting condition identification for a fused magnesium furnace based on an acoustic signal. Journal of Materials Processing Technology, 244, 231–239.CrossRefGoogle Scholar
  61. Gittler, P., Kickinger, R., Pirker, S., Fuhrmann, E., Lehner, J., & Steins, J. (2000). Application of computational fluid dynamics in the development and improvement of steelmaking processes. Scandinavian Journal of Metallurgy, 29(4), 166–176.CrossRefGoogle Scholar
  62. Golshan, M. H., & Samet, H. (2009). Updating stochastic model coefficients for prediction of arc furnace reactive power. Electric Power Systems Research, 79(7), 1114–1120.CrossRefGoogle Scholar
  63. Gonzalez, J., Lago, F., Freton, P., Masquere, M., & Franceries, X. (2005). Numerical modelling of an electric arc and its interaction with the anode: Part II. The three-dimensional modelinfluence of external forces on the arc column. Journal of Physics D: Applied Physics, 38(2), 306–318.CrossRefGoogle Scholar
  64. Gortler, G., & Jorgl, H. P. (2004). Energetically optimized control of an electric arc furnace. In Proceedings of the 2004 IEEE international conference on control applications (vol. 1, pp. 137–142). 2-4 Sept. Taipei, Taiwan, IEEE.Google Scholar
  65. Guézennec, A.-G., Huber, J.-C., Patisson, F., Sessiecq, P., Birat, J.-P., & Ablitzer, D. (2005). Dust formation in electric arc furnace: Birth of the particles. Powder Technology, 157(1–3), 2–11.CrossRefGoogle Scholar
  66. Gunnewiek, L., Oshinowo, L., Plikas, T., & Haywood, R. (2004). The application of numerical modelling to the design of electric furnaces. In Proceedings of tenth international ferroalloys congress (pp. 555–564). 1–4 Feb., Cape Town, South Africa.Google Scholar
  67. Haapala, K. R., Catalina, A. V., Johnson, M. L., & Sutherland, J. W. (2012). Development and application of models for steelmaking and casting environmental performance. Journal of Manufacturing Science and Engineering, 134(5), 051,013–051,025.CrossRefGoogle Scholar
  68. Hajidavalloo, E., & Alagheband, A. (2008). Thermal analysis of sponge iron preheating using waste energy of EAF. Journal of Materials Processing Technology, 208(1), 336–341.CrossRefGoogle Scholar
  69. Hallstedt, B. (1992). Thermodynamic assessment of the system MgO-\({\rm Al}_2{\rm O}_3\). Journal of the American Ceramic Society, 75(6), 1497–1507.CrossRefGoogle Scholar
  70. Harding, T. W., & Kim, Y. W. (1982). Direct sampling of gas and particulates from electric arc furnaces. In AIP conference proceedings (vol. 84, no. 1, pp. 362–376), AIP.Google Scholar
  71. Hasanuzzaman, M., Saidur, R., & Rahim, N. (2011). Energy, exergy and economic analysis of an annealing furnace. International Journal of Physical Sciences, 6(7), 1257–1266.Google Scholar
  72. Hasselman, D. (2013). Evidence for ductile deformation of single-crystal magnesium oxide subjected to thermal shock. Journal of Materials Science, 48(5), 1899–1901.CrossRefGoogle Scholar
  73. Hauksdóttir, A. S., Soderstrom, T., Thorfinnsson, Y., & Gestsson, A. (1995). System identification of a three-phase submerged-arc ferrosilicon furnace. IEEE Transactions on Control Systems Technology, 3(4), 377–387.CrossRefGoogle Scholar
  74. Hauksdóttir, A. S., Gestsson, A., & Vésteinsson, A. (2002). Current control of a three-phase submerged arc ferrosilicon furnace. Control Engineering Practice, 10(4), 457–463.CrossRefGoogle Scholar
  75. Haynes, W. M. (2014). CRC handbook of chemistry and physics (95th ed.). Boca Raton: CRC Press.Google Scholar
  76. He, Q., Qin, S. J., & Toprac, A. J. (2003). Computationally efficient modeling of wafer temperatures in an LPCVD furnace. In Advanced process control and automation (vol. 5044, pp. 97–109).Google Scholar
  77. Hołyńska, M., Tighe, A., & Semprimoschnig, C. (2018). Coatings and thin films for spacecraft thermo-optical and related functional applications. Advanced Materials Interfaces, 5(11), 1701,644.CrossRefGoogle Scholar
  78. Horton, R., Haskew, T. A., & Burch, R. F. (2009). A time-domain ac electric arc furnace model for flicker planning studies. IEEE Transactions on Power Delivery, 24(3), 1450–1457.CrossRefGoogle Scholar
  79. Hou, T.-H. T., Liu, W.-L., & Lin, L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing, 14(2), 239–253.CrossRefGoogle Scholar
  80. Iizuka, S., & Muraoka, T. (2012). Single-crystal MgO hollow nanospheres formed in RF impulse discharge plasmas. Journal of Nanomaterials, 2012, 1–6. (Article ID 691874).CrossRefGoogle Scholar
  81. Islam, M. M., & Chowdhury, A. H. (2012). Comparison of dynamic resistance arc furnace models for flicker study. In Proceedings of 2012 7th international conference on electrical & computer engineering (ICECE) (pp. 193–196). 20–22 Dec. Dhaka, Bangladesh.Google Scholar
  82. Janabi-Sharifi, F., & Jorjani, G. (2009). An adaptive system for modelling and simulation of electrical arc furnaces. Control Engineering Practice, 17(10), 1202–1219.CrossRefGoogle Scholar
  83. Janabi-Sharifi, F., Jorjani, G., & Hassanzadeh, I. (2005). Using adaptive neuro fuzzy inference system in developing an electrical arc furnace simulator. In Proceedings of 2005 IEEE/ASME international conference on advanced intelligent mechatronics (pp. 1210–1215). 24–28 July, Monterey, CA, USA.Google Scholar
  84. Jang, G., Wang, W., Heydt, G., Venkata, S., & Lee, B. (2001). Development of enhanced electric arc furnace models for transient analysis. Electric Power Components and Systems, 29(11), 1060–1073.CrossRefGoogle Scholar
  85. Jia, S., Tang, R., & Lv, J. (2014). Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing. Journal of Intelligent Manufacturing, 25(5), 913–931.CrossRefGoogle Scholar
  86. Jiang, Y., Xu, B., Y, L., Liu, C., & Liu, M. (2011). Experimental analysis on the variable polarity plasma arc pressure. Chinese Journal of Mechanical Engineering, 24(4), 607–611.CrossRefGoogle Scholar
  87. Jiao, J. R., Simpson, T. W., & Siddique, Z. (2007). Product family design and platform-based product development: A state-of-the-art review. Journal of Intelligent Manufacturing, 18(1), 5–29.CrossRefGoogle Scholar
  88. Johansen, S. (2003). Mathematical modeling of metallurgical processes. In Proceedings of the 3rd international conference on CFD in the minerals and process industries (pp. 5–12). 4–7 Dec. Melbourne, Australia.Google Scholar
  89. Jones, R., Reynolds, Q., & Alport, M. (2002). Dc arc photography and modelling. Minerals Engineering, 15(11), 985–991.CrossRefGoogle Scholar
  90. Jordan, D. T. (2012). Computer vision based method for electrode slip measurement in a submerged arc-furnace. Thesis, Engineering & the Built EnvironmentGoogle Scholar
  91. Kadkhodabeigi, M., Tveit, H., & Johansen, S. T. (2009). Modeling the tapping of silicon melt from the submerged arc furnace. In Seventh international conference on CFD in the minerals and process industries (pp. 1–5). 9–11 Dec. Melbourne, Australia.Google Scholar
  92. Karakose, E., Gencoglu, M. T., Karakose, M., Yaman, O., Aydin, I., & Akin, E. (2018). A new arc detection method based on fuzzy logic using s-transform for pantograph-catenary systems. Journal of Intelligent Manufacturing, 29(4), 839–856.CrossRefGoogle Scholar
  93. Kennedy, M. W., Garcia, M., & Olesen, F. (2012). Comparison of classical tools and modern finite element modeling in the electrical design of slag resistance furnaces. In International smelting technology symposium: incorporating the 6th advances in sulfide smelting symposium (pp. 239–249). Wiley : Hoboken.Google Scholar
  94. Khoshkhoo, H., Sadeghi, S. H. H., Moini, R., & Talebi, H. A. (2011). An efficient power control scheme for electric arc furnaces using online estimation of flexible cable inductance. Computers & Mathematics with Applications, 62(12), 4391–4401.CrossRefGoogle Scholar
  95. King, P., & Nyman, M. (1996). Modeling and control of an electric arc furnace using a feedforward artificial neural network. Journal of Applied Physics, 80(3), 1872–1877.CrossRefGoogle Scholar
  96. King, P. E., Ochs, T. L., & Hartman, A. D. (1994). Chaotic responses in electric arc furnaces. Journal of Applied Physics, 76(4), 2059–2065.CrossRefGoogle Scholar
  97. Kirschen, M., Velikorodov, V., & Pfeifer, H. (2006). Mathematical modelling of heat transfer in dedusting plants and comparison to off-gas measurements at electric arc furnaces. Energy, 31(14), 2926–2939.CrossRefGoogle Scholar
  98. Kirschen, M., Risonarta, V., & Pfeifer, H. (2009). Energy efficiency and the influence of gas burners to the energy related carbon dioxide emissions of electric arc furnaces in steel industry. Energy, 34(9), 1065–1072.CrossRefGoogle Scholar
  99. Kirschen, M., Badr, K., & Pfeifer, H. (2011). Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry. Energy, 36(10), 6146–6155.CrossRefGoogle Scholar
  100. Klaasen, B., Jones, P.-T., Durinck, D., Dewulf, J., Wollants, P., & Blanpain, B. (2010). Exergy-based efficiency analysis of pyrometallurgical processes. Metallurgical and Materials Transactions B, 41(6), 1205–1219.CrossRefGoogle Scholar
  101. Kleimt, B., Köhle, S., Kühn, R., & Zisser, R. (2005). Application of models for electrical energy consumption to improve EAF operation and dynamic control. In Proceedings of 8th European electric steelmaking conference (pp. 183–197). 9–11 May, Birmingham, UK.Google Scholar
  102. Kleinschmidt, G., Degel, R., Kneke, M., & Oterdoom, H. (2010). AC-and DC-smelter technology for ferrous metal production. In Proceedings of the twelfth international ferroalloys congress (pp. 825–838). 6–9 June, Helsinki, Finland.Google Scholar
  103. Kolagar, A. D., Kiyoumarsi, A., Ataei, M., & Hooshmand, R. A. (2011). Reactive power compensation in a steel industrial plant with several operating electric arc furnaces utilizing openloop controlled TCR/FC compensators. European Transactions on Electrical Power, 21(1), 824–838.CrossRefGoogle Scholar
  104. Kong, W., Chai, T., Ding, J., & Yang, S. (2014). Multifurnace optimization in electric smelting plants by load scheduling and control. IEEE Transactions on Automation Science and Engineering, 11(3), 850–862.CrossRefGoogle Scholar
  105. Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., & Gostimirovic, M. (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 24(4), 755–762.CrossRefGoogle Scholar
  106. Kumagai, A., Liu, T.-I., & Hozian, P. (2006). Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. Journal of Intelligent Manufacturing, 17(1), 45–56.CrossRefGoogle Scholar
  107. Kunze, J., & Degel, R. (2004). New trends in submerged arc furnace technology. In Proceedings of tenth international ferroalloys congress (pp. 444–454). 1-4 Feb., Cape Town, South Africa.Google Scholar
  108. Lago, F., Gonzalez, J. J., Freton, P., & Gleizes, A. (2004). A numerical modelling of an electric arc and its interaction with the anode: Part I the two-dimensional model. Journal of Physics D: Applied Physics, 37(6), 883–897.CrossRefGoogle Scholar
  109. Ledoux, C., & Bonnard, F. (1997). Identification of the electric arc of a furnace. In Proceeedings of international conference on artificial neural networks (pp. 843–848). 8–10 Oct., Lausanne: Springer.Google Scholar
  110. Leu, A.-L., Ma, S.-M., & Eyring, H. (1975). Properties of molten magnesium oxide. Proceedings of the National Academy of Sciences, 72(3), 1026–1030.CrossRefGoogle Scholar
  111. Li, H., Zhao, H., Li, F., & Qiu, B. (2012). A hybrid simulation model of AC electric arc furnace. In 2012 24th Chinese control and decision conference (CCDC) (pp. 188–193). 23–25 May, Taiyuan, China.Google Scholar
  112. Li, H., Li, M., Wang, X., Wu, X., Liu, F., & Yang, B. (2013). Synthesis and optical properties of single-crystal MgO nanobelts. Materials Letters, 102, 80–82.Google Scholar
  113. Li, J., Guan, Z., Wang, L., Yang, H., & Zhou, J. (2012b). An experimental study of AC arc propagation over a contaminated surface. IEEE Transactions on Dielectrics and Electrical Insulation, 19(4), 1360–1368.CrossRefGoogle Scholar
  114. Li, L., & Mao, Z. (2012a). A direct adaptive controller for EAF electrode regulator system using neural networks. Neurocomputing, 82, 91–98.CrossRefGoogle Scholar
  115. Li, L., & Mao, Z.-Z. (2012b). A novel robust adaptive controller for EAF electrode regulator system based on approximate model method. Journal of Central South University, 19(8), 2158–2166.CrossRefGoogle Scholar
  116. Li, T., Wang, Z., & Wang, N. (2011a). Temperature field analysis and process control strategies for MgO single crystal production using adaptive neuro-fuzzy inference system. Open Materials Science Journal, 5(1), 162–169.CrossRefGoogle Scholar
  117. Li, Y., Mao, Z.-Z., Wang, Y., Yuan, P., & Jia, M.-X. (2011b). Model predictive control synthesis approach of electrode regulator system for electric arc furnace. International Journal of Iron and Steel Research, 18(11), 20–25.CrossRefGoogle Scholar
  118. Liu, H.-B. (2011). The research of multi-modality control strategy of arc furnace electrode regulation. In 2011 international conference on mechatronic science, electric engineering and computer (MEC) (pp. 2518–2521). 19–22 Aug., Jilin, China.Google Scholar
  119. Liu, X., Cui, D., Li, J., & Wang, L. (2001). Simulation on adaptive control of electrode regulator systems of arc furnace. In Proceedings of the fifth international conference on electrical machines and systems (vol. 2, pp. 687–690). 18–20 Aug., Shenyang, China, IEEE.Google Scholar
  120. Liu, Y., Xu, X., Zhang, L., Wang, L., & Zhong, R. Y. (2017). Workload-based multi-task scheduling in cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 45(2017), 3–20.CrossRefGoogle Scholar
  121. Liu, Y.-J., Chang, G. W., & Hong, R.-C. (2010). Curve-fitting-based method for modeling voltagecurrent characteristic of an ac electric arc furnace. Electric Power Systems Research, 80(5), 572–581.CrossRefGoogle Scholar
  122. Logar, V., Dovzan, D., & Skrjanc, I. (2011). Mathematical modeling and experimental validation of an electric arc furnace. ISIJ International, 51(3), 382–391.CrossRefGoogle Scholar
  123. Logar, V., Dovžan, D., & Škrjanc, I. (2012a). Modeling and validation of an electric arc furnace: Part 1, heat and mass transfer. ISIJ International, 52(3), 402–412.CrossRefGoogle Scholar
  124. Logar, V., Dovžan, D., & Škrjanc, I. (2012b). Modeling and validation of an electric arc furnace: Part 2, thermo-chemistry. ISIJ International, 52(3), 413–423.CrossRefGoogle Scholar
  125. Logar, V., & Škrjanc, I. (2012a). Development of an electric arc furnace simulator considering thermal, chemical and electrical aspects. ISIJ International, 52(10), 1924–1926.CrossRefGoogle Scholar
  126. Logar, V., & Škrjanc, I. (2012b). Modeling and validation of the radiative heat transfer in an electric arc furnace. ISIJ International, 52(7), 1225–1232.CrossRefGoogle Scholar
  127. Lombardo, L., & Kapitulnik, A. (1992). Growth of \({\rm Bi}_2{\rm Sr}_2{\rm CaCu}_2\text{ O }_8\) single crystals using MgO crucibles. Journal of Crystal Growth, 118(3–4), 483–489.CrossRefGoogle Scholar
  128. Malfliet, A., Lotfian, S., Scheunis, L., Petkov, V., Pandelaers, L., Jones, P. T., et al. (2014). Degradation mechanisms and use of refractory linings in copper production processes: A critical review. Journal of the European Ceramic Society, 34(3), 849–876.CrossRefGoogle Scholar
  129. Martell, F., Deschamps, A., Mendoza, R., Melendez, M., Llamas, A., & Micheloud, O. (2011). Virtual neutral to ground voltage as stability index for electric arc furnaces. ISIJ International, 51(11), 1846–1851.CrossRefGoogle Scholar
  130. Masoumi, M., Sadrameli, S., Towfighi, J., & Niaei, A. (2006). Simulation, optimization and control of a thermal cracking furnace. Energy, 31(4), 516–527.CrossRefGoogle Scholar
  131. Mehrabi, M. G., Ulsoy, A. G., & Koren, Y. (2000). Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 11(4), 403–419.CrossRefGoogle Scholar
  132. Mesa, J. M., Menendez, C., Ortega, F. A., & Garcia, P. J. (2009). A smart modelling for the casting temperature prediction in an electric arc furnace. International Journal of Computer Mathematics, 86(7), 1182–1193.CrossRefGoogle Scholar
  133. Moghadasian, M., & Alenasser, E. (2011). Modelling and artificial intelligence-based control of electrode system for an electric arc furnace. Journal of Electromagnetic Analysis and Applications, 2011(3), 47–55.CrossRefGoogle Scholar
  134. Montanari, G., Loggini, M., Cavallini, A., Pitti, L., & Zaninelli, D. (1994). Arc-furnace model for the study of flicker compensation in electrical networks. IEEE Transactions on Power Delivery, 9(4), 2026–2036.CrossRefGoogle Scholar
  135. Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037–1048.CrossRefGoogle Scholar
  136. Mulholland, A., Brereton-Stiles, P., & Hockaday, C. (2009). The effectiveness of current control of submerged arc furnace electrode penetration in selected scenarios. Journal of the South African Institute of Mining & Metallurgy, 109(10), 601–607.Google Scholar
  137. Nogami, H., Chu, M., & Yagi, J.-I. (2005). Multi-dimensional transient mathematical simulator of blast furnace process based on multi-fluid and kinetic theories. Computers & Chemical Engineering, 29(11), 2438–2448.CrossRefGoogle Scholar
  138. O.J.P., G., Ramírez-Argáez, M. A., & AN, C., (2010). Mathematical modeling of the melting rate of metallic particles in the electric arc furnace. The Iron and Steel Institute of Japan International, 50(1), 9–16.Google Scholar
  139. Okada, I., Utsunomiya, Y., Uchida, H., & Aizawa, M. (2002). Md simulation of crystal growth from MgO melt. Journal of Molecular Liquids, 98, 191–200.CrossRefGoogle Scholar
  140. Ollila, J., Niemel, P., Rousu, A., & Mattila, O. (2010). Preliminary characterization of the samples taken from a submerged arc ferrochrome furnace during operation. In Proceedings of the twelfth international ferroalloys congress (pp. 317–326). 6–9 June, Helsinki, Finland.Google Scholar
  141. O’Neill-Carrillo, Bánfai, B., Heydt, G., & J. Si, E. (2001). Emtp implementation and analysis of nonlinear load models. Electric Power Components and Systems, 29(9), 809–820.CrossRefGoogle Scholar
  142. Oosthuizen, D. J., Craig, I., & Pistorius, P. (2004). Economic evaluation and design of an electric arc furnace controller based on economic objectives. Control Engineering Practice, 12(3), 253–265.CrossRefGoogle Scholar
  143. Ozgun, O., & Abur, A. (2002). Flicker study using a novel arc furnace model. IEEE Transactions on Power Delivery, 17(4), 1158–1163.CrossRefGoogle Scholar
  144. Pacchioni, G., Cogliandro, G., & Bagus, P. S. (1991). Characterization of oxide surfaces by infrared spectroscopy of adsorbed carbon monoxide: A theoretical investigation of the frequency shift of co on mgo and nio. Surface Science, 255(3), 344–354.CrossRefGoogle Scholar
  145. Panoiu, M., Panoiu, C., & Ghiormez, L. (2013). Modeling of the electric arc behavior of the electric arc furnace. In V. E. Balas, J. Fodor, A. R. Várkonyi-Kóczy, J. Dombi, & L. C. Jain (Eds.), Soft computing applications (pp. 261–271). Springer.Google Scholar
  146. Pathak, C. M., & Moorthy, V. K. (1976). Influence of calcination treatments on the development of morphology in magnesia powders. Transactions of the Indian Ceramic Society, 35(5), 89–98.CrossRefGoogle Scholar
  147. Peens, M. (2006). Modelling and control of an electrode system for a three-phase electric arc furnace. Thesis, Department of Electrical, Electronic and Computer Engineering.Google Scholar
  148. Pellicer, N., Ciurana, J., & Delgado, J. (2011). Tool electrode geometry and process parameters influence on different feature geometry and surface quality in electrical discharge machining of AISI H13 steel. Journal of Intelligent Manufacturing, 22(4), 575–584.CrossRefGoogle Scholar
  149. Peng, B., Peng, J., Kozinski, J. A., Jonathan, L., Chai, L.-Y., Zhang, C.-F., et al. (2003). Thermodynamic calculation on the smelting slag of direct recycling of electric arc furnace stainless steelmaking dust. Journal of Central South University of Technology, 10(1), 20–26.CrossRefGoogle Scholar
  150. Phillips, R. L. (1967). Theory of the non-stationary arc column. British Journal of Applied Physics, 18(1), 65–78.CrossRefGoogle Scholar
  151. Pickles, C. A. (2009). Thermodynamic modelling of the multiphase pyrometallurgical processing of electric arc furnace dust. Minerals Engineering, 22(11), 977–985.CrossRefGoogle Scholar
  152. Pickles, C. A. (2010). Thermodynamic modelling of the formation of zincmanganese ferrite spinel in electric arc furnace dust. Journal of Hazardous Materials, 179(1), 309–317.CrossRefGoogle Scholar
  153. Porter, J. R., Goldstein, J. I., & Kim, Y. W. (1982). Characterization of directly sampled electric arc furnace dust. In AIP conference proceedings (vol. 84, no. 1, pp. 377–393), AIP Publishing.Google Scholar
  154. Prasad, T. V., & Radovanovich, S. (1962). Studies on sintering of some natural magnesites and crystal growth of periclase at high temperatures. Transactions of the Indian Ceramic Society, 21(2), 37–48.CrossRefGoogle Scholar
  155. Purushothaman, S. (2010). Tool wear monitoring using artificial neural network based on extended kalman filter weight updation with transformed input patterns. Journal of Intelligent Manufacturing, 21(6), 717–730.CrossRefGoogle Scholar
  156. Qiu, D., & Zhang, D.-j. (2010). The research of energy balance dynamic model on electric arc furnace. In Proceedings of international conference on information networking and automation (ICINA) (pp. 507–511), IEEE.Google Scholar
  157. Ranganathan, S., & Godiwalla, K. M. (2001). Effect of preheat, bed porosity, and charge control on thermal response of submerged arc furnace producing ferrochromium. Ironmaking & Steelmaking, 28(3), 273–278.CrossRefGoogle Scholar
  158. Ranganathan, S., & Godiwalla, K. M. (2011). Influence of process parameters on reduction contours during production of ferrochromium in submerged arc furnace. Canadian Metallurgical Quarterly, 50(1), 37–44.CrossRefGoogle Scholar
  159. Rangnathan, S., Godiwalla, K. M., Satyanarayana, N. V., Kumar, P., Rao, V., Roy, A. K., et al. (2010). Simulation of the production of ferro-chromium in submerged-arc furnace. Ferrochromium Production, 2010, 401–410.Google Scholar
  160. Rathaba, P. L., Craig, I. K., & Pistorius, P. C. (2003). Identification of an electric arc furnace model. In Proceedings of the First African Control Conference (AFCON 2003), South African Council for Automation and Computation (SACAC) (pp. 145–150).Google Scholar
  161. Rau, S. H., & Lee, W. J. (2016). Dc arc model based on 3-D DC arc simulation. IEEE Transactions on Industry Applications, 52(6), 5255–5261.CrossRefGoogle Scholar
  162. Reynolds, Q. G. (2011). The dual-electrode DC arc furnace-modelling insights. Journal of the Southern African Institute of Mining and Metallurgy, 111(10), 697–704.Google Scholar
  163. Reynolds, Q. G., Jones, R. J., & Reddy, B. D. (2010). Mathematical and computational modelling of the dynamic behaviour of direct current plasma arcs. Journal of the South African Institute of Mining & Metallurgy, 110(12), 733–742.Google Scholar
  164. Rousu, A., Mattila, O., & Tanskanen, P. (2010). A laboratory investigation of the influence of electric current on the burden reactions in a submerged arc furnace. In Proceedings of the 12th international ferroalloys congress (pp. 303–310). 6–9 June, Helsinki, Finland.Google Scholar
  165. Rusinowski, H., & Szega, M. (2001). The influence of the operational parameters of chamber furnaces on the consumption of the chemical energy of fuels. Energy, 26(12), 1121–1133.CrossRefGoogle Scholar
  166. Sadeghian, A., & Lavers, J. (2000). Recurrent neuro-fuzzy predictors for multi-step prediction of vi characteristics of electric arc furnaces. In Proceedings of the ninth IEEE international conference on fuzzy systems (vol. 1, pp. 110–115). 7–10 May, San Antonio, TX, USA.Google Scholar
  167. Sadeghian, A., & Lavers, J. D. (2011). Dynamic reconstruction of nonlinear vi characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks. Applied Soft Computing, 11(1), 1448–1456.CrossRefGoogle Scholar
  168. Sadeghian, A. R., & Lavers, J. D. (2001). On the use of recurrent neuro-fuzzy networks for predictive control. In IFSA world congress and 20th NAFIPS international conference, 2001. Joint 9th (vol. 1, pp. 233–238).Google Scholar
  169. Sævarsdóttir, G., & Bakken, J. (2010). Current distribution in submerged arc furnaces for silicon metal/ferrosilicon production. In Proceedings of the 12th international ferroalloys congress (pp. 717–728).Google Scholar
  170. Sævarsdóttir, G., Jonsson, M. T., & Bakken, J. A. (2004). Arc-electrode interactions in silicon and ferrosilicon furnaces. In Proceedings of tenth international ferroalloys congress (vol. 1, pp. 593–604). 1–4 Feb. Cape Town, South AfricaGoogle Scholar
  171. Sævarsdóttir, G., Pálsson, H., Jónsson, M., & Bakken, J. (2010). Electrode erosion in submerged arc furnaces. Indian Ferro Alloys Producers Association, 2010, 752–761.Google Scholar
  172. Samet, H., & Golshan, M. E. H. (2012). A wide nonlinear analysis of reactive power time series related to electric arc furnaces. International Journal of Electrical Power & Energy Systems, 36(1), 127–134.CrossRefGoogle Scholar
  173. Samet, H., & Mojallal, A. (2014). Enhancement of electric arc furnace reactive power compensation using Grey-Markov prediction method. Generation, Transmission & Distribution, IET, 8(9), 1626–1636.CrossRefGoogle Scholar
  174. Samet, H., Farjah, E., & Sharifi, Z. (2014). A dynamic, nonlinear and time varying model for electric arc furnace. International Transactions on Electrical Energy Systems, 25(10), 2165–2180.CrossRefGoogle Scholar
  175. Sanchez, J. L. G., RamirezArgaez, M. A., & Conejo, A. N. (2009). Power delivery from the arc in AC electric arc furnaces with different gas atmospheres. Steel Research International, 80(2), 113–120.Google Scholar
  176. Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). A multi-performance prediction model based on ANFIS and new modified-GA for machining processes. Journal of Intelligent Manufacturing, 26(4), 703–716.CrossRefGoogle Scholar
  177. Scheepers, E., Yang, Y., Adema, A. T., Boom, R., & Reuter, M. A. (2010). Process modeling and optimization of a submerged arc furnace for phosphorus production. Metallurgical and Materials Transactions B, 41(5), 990–1005.CrossRefGoogle Scholar
  178. Shaban, Y., Meshreki, M., Yacout, S., Balazinski, M., & Attia, H. (2017). Process control based on pattern recognition for routing carbon fiber reinforced polymer. Journal of Intelligent Manufacturing, 28(1), 165–179.CrossRefGoogle Scholar
  179. Shand, M. A. (2006). The chemistry and technology of magnesia. Hoboken: Wiley.CrossRefGoogle Scholar
  180. Shiohara, Y., & Endo, A. (1997). Crystal growth of bulk high-\({T}_c\) superconducting oxide materials. Materials Science and Engineering: R: Reports, 19(1–2), 1–86.CrossRefGoogle Scholar
  181. Slabinski, V. J., & Smith, R. L. (1971). Lithium vapor cell and discharge lamp using MgO windows. Review of Scientific Instruments, 42(9), 1334–1338.CrossRefGoogle Scholar
  182. Staib, W. E., & Staib, R. B. (1992). The intelligent arc furnace controller: a neural network electrode position optimization system for the electric arc furnace. In Proceedings of international joint conference on neural networks (vol. 3, pp. 1–9). 7–11 June, Baltimore, MD, USA.Google Scholar
  183. Taurian, O. E., Springborg, M., & Christensen, N. E. (1985). Self-consistent electronic structures of MgO and SrO. Solid State Communications, 55(4), 351–355.CrossRefGoogle Scholar
  184. Terzija, V. V., & Koglin, H.-J. (2004). On the modeling of long arc in still air and arc resistance calculation. IEEE Transactions on Power Delivery, 19(3), 1012–1017.CrossRefGoogle Scholar
  185. Tian, G. Y., Yin, G., & Taylor, D. (2002). Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing. Journal of Intelligent Manufacturing, 13(5), 323–338.CrossRefGoogle Scholar
  186. Trejo, E., Martell, F., Micheloud, O., Teng, L., Llamas, A., & Montesinos-Castellanos, A. (2012). A novel estimation of electrical and cooling losses in electric arc furnaces. Energy, 42(1), 446–456.CrossRefGoogle Scholar
  187. Tunc, M., Camdali, U., & Arasil, G. (2012). Mass analysis of an electric arc furnace (EAF) at a steel company in Turkey. Metallurgist, 56(3–4), 253–261.CrossRefGoogle Scholar
  188. Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing, 25(4), 647–659.CrossRefGoogle Scholar
  189. Ueda, S., Natsui, S., Nogami, H., Yagi, J.-I., & Ariyama, T. (2010). Recent progress and future perspective on mathematical modeling of blast furnace. ISIJ International, 50(7), 914–923.CrossRefGoogle Scholar
  190. Vanderstaay, E. C., Swinbourne, D. R., & Monteiro, M. (2004). A computational thermodynamics model of submerged arc electric furnace ferromanganese smelting. Mineral Processing and Extractive Metallurgy, 113(1), 38–44.CrossRefGoogle Scholar
  191. Varadan, S., Makram, E. B., & Girgis, A. A. (1996). A new time domain voltage source model for an arc furnace using emtp. IEEE Transactions on Power Delivery, 11(3), 1685–1691.CrossRefGoogle Scholar
  192. Vazdirvanidis, A., Pantazopoulos, G., & Louvaris, A. (2008). Overheat induced failure of a steel tube in an electric arc furnace (EAF) cooling system. Engineering Failure Analysis, 15(7), 931–937.CrossRefGoogle Scholar
  193. Vervenne, I., Van Reuse, K., & Belmans, R. (2007). Electric arc furnace modelling from a power quality point of view. In Proceedings of 9th international conference on electrical power quality and utilisation, (pp. 1–6). 9–11 Oct., Barcelona, Spain.Google Scholar
  194. Walter, M., & Franck, C. (2014). Improved method for direct black-box arc parameter determination and model validation. IEEE Transactions on Power Delivery, 29(2), 580–588.CrossRefGoogle Scholar
  195. Wang, F., Jin, Z., Zhu, Z., & Wang, X. (2005). Application of extended Kalman filter to the modeling of electric arc furnace for power quality issues. In Proceedings of international conference on neural networks and brain (vol. 2, pp. 991–996). 13–15 Oct., Beijing, China.Google Scholar
  196. Wang, Q., Tarn, D., & Wang, Y. (2000). Event-based intelligent control system of carbide electric arc furnace (CEAF). In Proceedings of the 3rd world congress on intelligent control and automation (vol. 1, pp. 471–476). Hefei, China, IEEE.Google Scholar
  197. Wang, X., & Li, R. (2014). Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters. Journal of Intelligent Manufacturing, 25(6), 1301–1313.CrossRefGoogle Scholar
  198. Wang, Y., Mao, Z., Li, Y., Tian, H., & Feng, L. (2008). Modeling and parameter identification of an electric arc for the arc furnace. In Proceedings of IEEE international conference on automation and logistics (pp. 740–743). 1–3 Sept., Qingdao, China.Google Scholar
  199. Wang, Y., Mao, Z.-Z., Tian, H.-X., Li, Y., & Yuan, P. (2010). Modeling of electrode system for three-phase electric arc furnace. Journal of Central South University of Technology, 17(3), 560–565.CrossRefGoogle Scholar
  200. Wang, Z. (2012). Temperature field analysis and adaptive neuro-fuzzy inference system for mgo single crystal production. Journal of Wuhan University of Technology-Mater Sci Ed, 27(6), 1089–1095.CrossRefGoogle Scholar
  201. Wang, Z., Wang, N. H., & Li, T. (2011). Computational analysis of a twin-electrode dc submerged arc furnace for MgO crystal production. Journal of Materials Processing Technology, 211(3), 388–395.CrossRefGoogle Scholar
  202. Wang, Z., Fu, Y., Wang, N., & Feng, L. (2014). 3D numerical simulation of electrical arc furnaces for the MgO production. Journal of Materials Processing Technology, 214(11), 2284–2291.CrossRefGoogle Scholar
  203. Wenger, A., Farouk, B., & Wittle, K. (1996). Modeling of thermal treatment of hazardous solid wastes in a DC arc melter. Journal of the Air & Waste Management Association, 46(12), 1162–1170.CrossRefGoogle Scholar
  204. White, H. E. (1938). Electrically fused magnesia. Journal of the American Ceramic Society, 21(6), 216–229.CrossRefGoogle Scholar
  205. Wilhelmi, H., Lyhs, W., & Pfender, E. (1984). Calculation of thermodynamic and transport properties of a typical arc furnace plasma. Plasma Chemistry and Plasma Processing, 4(4), 315–323.CrossRefGoogle Scholar
  206. Wilson, I. (1981). Magnesium oxide as a high-temperature insulant. IEE Proceedings A (Physical Science, Measurement and Instrumentation, Management and Education, Reviews), 128(3), 159–164.CrossRefGoogle Scholar
  207. Wolff, E. G., & Coskren, T. D. (1965). Growth and morphology of magnesium oxide whiskers. Journal of the American Ceramic Society, 48(6), 279–285.CrossRefGoogle Scholar
  208. Worrell, E., Bernstein, L., Roy, J., Price, L., & Harnisch, J. (2009). Industrial energy efficiency and climate change mitigation. Energy Efficiency, 2(2), 109–123.CrossRefGoogle Scholar
  209. Wriedt, H. A. (1987). The Mg-O (magnesium-oxygen) system. Bulletin of Alloy Phase Diagrams, 8(3), 227–233.CrossRefGoogle Scholar
  210. Wu, H.-M., & Carey, G. F. (1992). Nonlinear convective effects on moving boundary ac plasma arcs. IEEE Transactions on Plasma Science, 20(6), 1041–1046.CrossRefGoogle Scholar
  211. Wu, H. M., Carey, G. F., & Oakes, M. E. (1994). Numerical simulation of AC plasma arc thermodynamics. Journal of Computational Physics, 112(1), 24–30.CrossRefGoogle Scholar
  212. Wu, Z., Wu, Y., Chai, T., & Sun, J. (2015). Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace. IEEE Transactions on Industrial Electronics, 62(3), 1703–1715.CrossRefGoogle Scholar
  213. Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.CrossRefGoogle Scholar
  214. X, Xu. (2017). Machine tool 4.0 for the new era of manufacturing. The International Journal of Advanced Manufacturing Technology, 92(5–8), 1893–1900.Google Scholar
  215. Xu, Y., & Ge, M. (2004). Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing, 15(3), 337–350.CrossRefGoogle Scholar
  216. Yang, W.-A., & Zhou, W. (2015). Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble. Journal of Intelligent Manufacturing, 26(6), 1161–1180.CrossRefGoogle Scholar
  217. Yetisken, Y., Camdali, U., & Ekmekci, I. (2012). Optimum charging materials for electric arc furnace (EAF) and ladle furnace (LF) system: A sample case. Engineering Science & Technology, an International Journal, 15(2), 77–83.Google Scholar
  218. Ying, S., & Hongxia, Y. (2010). The forecasting method for the furnace bottom temperature and carbon content of submerged arc furnace based on improved bp neural network. In Proceedings of international conference on computer, mechatronics, control and electronic engineering (CMCE) (vol. 3, pp. 238–240). 24–26 Aug., Changchun, China, IEEE.Google Scholar
  219. Ying, S., Niaona, Z., Xiuhe, L., Hongxia, Y., & Zhiyan, Y. (2010). Power consumption prediction of submerged arc furnace based on multi-input layer wavelet neural network. In Proceedings of international conference on mechanic automation and control engineering (pp. 3586–3589). 26–28 June, Wuhan, China.Google Scholar
  220. Zhang, S., Cao, H., Lei, W., & Zhang, Y. (2014). A logistic-interpolation-based fuzzy controller for electrode regulation of submerged arc furnace. In Proceedings of the 26th Chinese control and decision conference (pp. 2388–2392).Google Scholar
  221. Zhang, X., Xue, D., Xu, D., Feng, X., & Wang, J. (2005). Growth of large MgO single crystals by an arc-fusion method. Journal of Crystal Growth, 280(1), 234–238.CrossRefGoogle Scholar
  222. Zhang, X., Xue, D., Wang, J., & Feng, X. (2006). Improved growth technology of large MgO single crystals. Journal of Crystal Growth, 292(2), 505–509.CrossRefGoogle Scholar
  223. Zhang, X., Zheng, Y., Feng, X., Han, X., Bai, Z., & Zhang, Z. (2015a). Calcination temperature-dependent surface structure and physicochemical properties of magnesium oxide. RSC Advances, 5(105), 86102–86112.CrossRefGoogle Scholar
  224. Zhang, Y., Wang, C., & Lu, R. (2013a). Modeling and monitoring of multimode process based on subspace separation. Chemical Engineering Research and Design, 91(5), 831–842.CrossRefGoogle Scholar
  225. Zhang, Z., Wang, Y., & Wang, K. (2013b). Fault diagnosis and prognosis using wavelet packet decomposition, fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 24(6), 1213–1227.CrossRefGoogle Scholar
  226. Zhang, Z., Provis, J. L., Reid, A., & Wang, H. (2015). Mechanical, thermal insulation, thermal resistance and acoustic absorption properties of geopolymer foam concrete. Cement and Concrete Composites, 62(2015), 97–105.CrossRefGoogle Scholar
  227. Zhen, W., Ninghui, W., Tie, L., & Yong, C. (2012). 3D numerical analysis of the arc plasma behavior in a submerged DC electric arc furnace for the production of fused MgO. Plasma Science and Technology, 14(4), 321–326.CrossRefGoogle Scholar
  228. Zheng, T., & Makram, E. B. (2000). An adaptive arc furnace model. IEEE Transactions on Power Delivery, 15(3), 931–939.CrossRefGoogle Scholar
  229. Zheng, T., Makram, E. B., & Girgis, A. A. (1998). Effect of different arc furnace models on voltage distortion. In Proceedings of 8th international conference on harmonics and quality of power (vol. 2, pp. 1079–1085). 14–16 Oct., Athens, Greece.Google Scholar
  230. Zuperl, U., Cus, F., & Reibenschuh, M. (2012). Modeling and adaptive force control of milling by using artificial techniques. Journal of Intelligent Manufacturing, 23(5), 1805–1815.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

Personalised recommendations