IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i2d10.1007_s10845-018-1448-1.html
   My bibliography  Save this article

Fused magnesia manufacturing process: a survey

Author

Listed:
  • Jie Yang

    (Northeastern University)

  • Shaowen Lu

    (Northeastern University)

  • Liangyong Wang

    (Northeastern University)

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.

Suggested Citation

  • Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1448-1
    DOI: 10.1007/s10845-018-1448-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1448-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-018-1448-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Fan, J.R. & Liang, X.H. & Chen, L.H. & Cen, K.F., 1998. "Modeling of NOx emissions from a W-shaped boiler furnace under different operating conditions," Energy, Elsevier, vol. 23(12), pages 1051-1055.
    3. Kirschen, Marcus & Badr, Karim & Pfeifer, Herbert, 2011. "Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry," Energy, Elsevier, vol. 36(10), pages 6146-6155.
    4. Kirschen, Marcus & Velikorodov, Viktor & Pfeifer, Herbert, 2006. "Mathematical modelling of heat transfer in dedusting plants and comparison to off-gas measurements at electric arc furnaces," Energy, Elsevier, vol. 31(14), pages 2926-2939.
    5. Rusinowski, Henryk & Szega, Marcin, 2001. "The influence of the operational parameters of chamber furnaces on the consumption of the chemical energy of fuels," Energy, Elsevier, vol. 26(12), pages 1121-1133.
    6. Trejo, Eder & Martell, Fernando & Micheloud, Osvaldo & Teng, Lidong & Llamas, Armando & Montesinos-Castellanos, Alejandro, 2012. "A novel estimation of electrical and cooling losses in electric arc furnaces," Energy, Elsevier, vol. 42(1), pages 446-456.
    7. Yasser Shaban & Mouhab Meshreki & Soumaya Yacout & Marek Balazinski & Helmi Attia, 2017. "Process control based on pattern recognition for routing carbon fiber reinforced polymer," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 165-179, January.
    8. Ebru Karakose & Muhsin Tunay Gencoglu & Mehmet Karakose & Orhan Yaman & Ilhan Aydin & Erhan Akin, 2018. "A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 839-856, April.
    9. Masoumi, M.E. & Sadrameli, S.M. & Towfighi, J. & Niaei, A., 2006. "Simulation, optimization and control of a thermal cracking furnace," Energy, Elsevier, vol. 31(4), pages 516-527.
    10. Kirschen, Marcus & Risonarta, Victor & Pfeifer, Herbert, 2009. "Energy efficiency and the influence of gas burners to the energy related carbon dioxide emissions of electric arc furnaces in steel industry," Energy, Elsevier, vol. 34(9), pages 1065-1072.
    11. Bisio, G & Rubatto, G & Martini, R, 2000. "Heat transfer, energy saving and pollution control in UHP electric-arc furnaces," Energy, Elsevier, vol. 25(11), pages 1047-1066.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wiesberg, Igor Lapenda & de Medeiros, José Luiz & Paes de Mello, Raphael V. & Santos Maia, Jeiveison G.S. & Bastos, João Bruno V. & Araújo, Ofélia de Queiroz F., 2021. "Bioenergy production from sugarcane bagasse with carbon capture and storage: Surrogate models for techno-economic decisions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Hussein A. Taha & Soumaya Yacout & Yasser Shaban, 2023. "Autonomous self-healing mechanism for a CNC milling machine based on pattern recognition," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2185-2205, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trejo, Eder & Martell, Fernando & Micheloud, Osvaldo & Teng, Lidong & Llamas, Armando & Montesinos-Castellanos, Alejandro, 2012. "A novel estimation of electrical and cooling losses in electric arc furnaces," Energy, Elsevier, vol. 42(1), pages 446-456.
    2. Gajic, Dragoljub & Savic-Gajic, Ivana & Savic, Ivan & Georgieva, Olga & Di Gennaro, Stefano, 2016. "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks," Energy, Elsevier, vol. 108(C), pages 132-139.
    3. Chen, Zhengjie & Ma, Wenhui & Wu, Jijun & Wei, Kuixian & Yang, Xi & Lv, Guoqiang & Xie, Keqiang & Yu, Jie, 2016. "Influence of carbothermic reduction on submerged arc furnace energy efficiency during silicon production," Energy, Elsevier, vol. 116(P1), pages 687-693.
    4. Raul Garcia-Segura & Javier Vázquez Castillo & Fernando Martell-Chavez & Omar Longoria-Gandara & Jaime Ortegón Aguilar, 2017. "Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient," Energies, MDPI, vol. 10(9), pages 1-11, September.
    5. Manojlović, Vaso & Kamberović, Željko & Korać, Marija & Dotlić, Milan, 2022. "Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters," Applied Energy, Elsevier, vol. 307(C).
    6. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    7. Zhang, Xin & Chen, Zhichao & Hou, Jian & Liu, Zheng & Zeng, Lingyan & Li, Zhengqi, 2022. "Evaluation of wide-range coal combustion performance of a novel down-fired combustion technology based on gas–solid two-phase flow characteristics," Energy, Elsevier, vol. 248(C).
    8. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    9. Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
    10. Shang, Tongle & Zhan, Hao & Gong, Qinfei & Zeng, Tao & Li, Pengcheng & Zeng, Zhiyong, 2024. "Insights into the thermal and electric field distribution and the structural optimization in the graphitization furnace," Energy, Elsevier, vol. 297(C).
    11. Guo, Cui & Ryoo, Hong Seo, 2021. "On Pareto-Optimal Boolean Logical Patterns for Numerical Data," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    12. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    13. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    14. Samet, Haidar & Ghanbari, Teymoor & Ghaisari, Jafar, 2014. "Maximizing the transferred power to electric arc furnace for having maximum production," Energy, Elsevier, vol. 72(C), pages 752-759.
    15. Kirschen, Marcus & Velikorodov, Viktor & Pfeifer, Herbert, 2006. "Mathematical modelling of heat transfer in dedusting plants and comparison to off-gas measurements at electric arc furnaces," Energy, Elsevier, vol. 31(14), pages 2926-2939.
    16. Nardin, Gioacchino & Meneghetti, Antonella & Dal Magro, Fabio & Benedetti, Nicole, 2014. "PCM-based energy recovery from electric arc furnaces," Applied Energy, Elsevier, vol. 136(C), pages 947-955.
    17. Haendel, Michael & Hirzel, Simon & Süß, Marlene, 2022. "Economic optima for buffers in direct reduction steelmaking under increasing shares of renewable hydrogen," Renewable Energy, Elsevier, vol. 190(C), pages 1100-1111.
    18. Hua-Xi Zhou & Chang-Guang Zhou & Hu-Tian Feng, 2023. "An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode," Journal of Risk and Reliability, , vol. 237(6), pages 1248-1258, December.
    19. Haikarainen, Carl & Shao, Lei & Pettersson, Frank & Saxén, Henrik, 2024. "Mathematical optimization modeling for scenario analysis of integrated steelworks transitioning towards hydrogen-based reduction," Energy, Elsevier, vol. 305(C).
    20. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1448-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.