Publication Cover
Ironmaking & Steelmaking
Processes, Products and Applications
Volume 47, 2020 - Issue 7
465
Views
16
CrossRef citations to date
0
Altmetric
Research Articles

Synthetically predicting the quality index of sinter using machine learning model

, , &
Pages 828-836 | Received 24 Dec 2018, Accepted 07 May 2019, Published online: 22 May 2019

References

  • Fan XH. Mathematical models and expert systems of iron ore agglomeration. Beijing: Science Press; 2013.
  • Kawaguchi T, Sato S, Takata K. Development and Application of an Integrated Simulation Model for Iron Ore Sintering. Iron Steel Inst Jpn. 1987;73:1940–1947. doi: 10.2355/tetsutohagane1955.73.15_1940
  • Fan XH, Wang HD, Chen J, et al. Expert system for sinter chemical composition control based on adaptive prediction. T NONFERR METAL SOC. 1996;6:47–50.
  • Kanjilal PP, Rose E. Application of adaptive prediction and control method for improved operation of the sintering process. Ironmak Steelmak. 1986;13:289–293.
  • Zhou H, Zhao JP, Loo CE, et al. Numerical modeling of the iron ore sintering process. ISIJ Int. 2012;52:1550–1558. doi: 10.2355/isijinternational.52.1550
  • Arbeithuber C, Jorgl HP, Raml H. Fuzzy control of an iron ore sintering plant. Control Eng. Pract. 1995;3:1669–1674. doi: 10.1016/0967-0661(95)00179-X
  • Hu JQ, Rose E. Predictive fuzzy control applied to the sinter strand process. Control Eng. Pract. 1997;5:247–252. doi: 10.1016/S0967-0661(97)00232-3
  • Er MJ, Liao J, Lin JY. Fuzzy neural networks-based quality prediction system for sintering process. IEEE T. Fuzzy Syst. 2000;8:314–324. doi: 10.1109/91.855919
  • Shang XQ, Lu JG, Sun YX, et al. Data-driven prediction of sintering burn-through point based on novel genetic programming. J Iron Steel Res Int. 2010;10:1–5. doi: 10.1016/S1006-706X(10)60188-4
  • Li ZP, Fan XH, Chen G, et al. Optimization of iron ore sintering process based on ELM model and multi-criteria evaluation. Neural Comput Appl 2017;28:2247–2253. doi: 10.1007/s00521-016-2195-x
  • Giri BK, Roy GG. Mathematical modelling of iron ore sintering process using genetic algorithm. Ironmak Steelmak. 2012;39:59–66. doi: 10.1179/1743281211Y.0000000037
  • Chen XX, Chen X, She JH, et al. Hybrid multistep modeling for calculation of carbon efficiency of iron ore sintering process based on yield prediction. J. Process Contr. 2017;28:1193–1207.
  • Zhang JH, Shen FM, Xie AG. The Application of G-BP in FeO Content Prediction During Sintering. J Northeast Univ(Nat Sci Ed). 2002;23:1073–1075.
  • Zhang JH, Xie AG, Shen FM. Multi-Objective optimization and analysis model of sintering process based on BP neural network. J Iron Steel Res. Int. 2007;14:1–5. doi: 10.1016/S1006-706X(07)60018-1
  • Harrington P. Machine learning practice, people’s post and telecommunications press, Beijing, 2013.
  • Chen W, Shen ZQ, Tao YB. Big data series: data visualization. Beijing: Electronic Industry Press; 2013.
  • Moslehi K, Kumar R. A reliability perspective of the smart grid. IEEE Trans Smart Grid. 2010;1:57–64. doi: 10.1109/TSG.2010.2046346
  • Yi J. Exploration of hospital information data mining and its implementation technology . Chongqing: Chongqing Medical University; 2007.
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011;12:2825–2830.
  • Guyon I, Weston J, Barnhill S, et al. Gene Selection for Cancer Classification using Support Vector Machines. Mach Learn. 2002;46:389–422. doi: 10.1023/A:1012487302797
  • Meinshausen N, Bühlmann P. Stability selection. J. Roy. Statist. Soc. Ser. B. 2010;72:417–473. doi: 10.1111/j.1467-9868.2010.00740.x
  • Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York: Springer; 2009.
  • Breiman L, Friedman J, Olshen R, et al. Classification and regression trees. Boca Raton: FL: CRC press ; 1984.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–297.
  • Dietterich TG. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Mach Learn. 2000;40:139–157. doi: 10.1023/A:1007607513941
  • Huang XX, Fan XH, Chen XL, et al. Soft-measuring models of thermal state in iron ore sintering process. Measurement. 2018;130:145–150. doi: 10.1016/j.measurement.2018.07.095
  • Laitinen PJ, Saxén H. A neural network based model of sinter quality and sinter plant performance indices. Ironmak Steelmak. 2007;34:109–119. doi: 10.1179/174328107X155312
  • Torregrossa D, Leopold U, Hernández-Sancho F, et al. Machine learning for energy cost modelling in wastewater treatment plants. J Environ Manage. 2018;223:1061–1067. doi: 10.1016/j.jenvman.2018.06.092

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.