Data-driven reliable prediction of production indicators in the blast furnace using TS fuzzy neural network based on bat algorithm

References

• An, J. Q., Shen, X. L., Wu, M., & She, J. H. (2019). A multi-time-scale fusion prediction model for the gas utilization rate in a blast furnace. Control Engineering Practice, 92 （2019）, 104120. https://doi.org/10.1016/j.conengprac.2019.104120
   Google Scholar
• Azadi, P., Winz, J., Leo, E., Klock, R., & Engell, S. (2022). A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace. Computers & Chemical Engineering, 156 （2022） , 107573. https://doi.org/10.1016/j.compchemeng.2021.107573
   Google Scholar
• Beskardes, A., Turkoglu, S., & Aci, C. (2016). Prediction of the hot metal silicon content in the blast furnace. Proceedings of 24th Signal Processing and Communication Application Conference (SIU)(pp. 709–712). Zonguldak, TURKEY (IEEE). https://doi.org/10.1109/SIU.2016.7495838
   Google Scholar
• Boulkaibet, I., Belarbi, K., Bououden, S., Chadli, M., & Marwala, T. (2018). An adaptive fuzzy predictive control of nonlinear processes based on multi-kernel least squares support vector regression. Applied Soft Computing, 73 （2018） , 572–590. https://doi.org/10.1016/j.asoc.2018.08.044
   Google Scholar
• Buch-Larsen, T., Nielsen, J. P., Guillen, M., & Bolance, C. (2005). Kernel density estimation for heavy-tailed distributions using the champernowne transformation. Statistics, 39(6), 503–518. https://doi.org/10.1080/02331880500439782
   Web of Science ®Google Scholar
• Chen, W., Wang, B. X., & Han, H. L. (2010). Prediction and control for silicon content in pig iron of blast furnace by integrating artificial neural network with genetic algorithm. Ironmaking & Steelmaking, 37(6), 458–463. https://doi.org/10.1179/174328109X445769
   Web of Science ®Google Scholar
• Das, S. K., Kumari, A., Bandopadhay, D., Akbar, S. A., & Mondal, G. K. (2011). A mathematical model to characterise effects of liquid hold up on bosh silicon transport in the dripping zone of a blast furnace. Applied Mathematical Modelling, 35(9), 4208–4221. https://doi.org/10.1016/j.apm.2011.02.045
   Web of Science ®Google Scholar
• Gan, C., Cao, W. H., Wu, M., & Chen, X. (2018). A new bat algorithm based on iterative local search and stochastic inertia weight. Expert Systems with Applications, 104 （2018） , 202–212. https://doi.org/10.1016/j.eswa.2018.03.015
   Google Scholar
• Gao, C. H., Jian, L., & Luo, S. (2012). Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Transactions on Industrial Electronics, 59(2), 1134–1145. https://doi.org/10.1109/TIE.2011.2159693
   Web of Science ®Google Scholar
• Gao, C. H., Ge, Q. H., & Jian, L. (2014). Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decisionmaking. IEEE Transactions on Fuzzy Systems, 22(3), 586–596. http://doi.org/10.2355/isijinternational.51.1668
   Web of Science ®Google Scholar
• Li, Y. J., Zhang, S., Yin, Y. X., Zhang, J., & Xu, Z. G. (2017). Quantitative association rule mining for blast furnace production data. Proceedings of Chinese automation congress (CAC) (pp. 2275–2280). Jinan, PEOPLES R CHINA (IEEE). https://doi.org/10.1109/CAC.2017.8243154
   Google Scholar
• Li, K. S., Ma, Z. Y., Fu, P., & Krishnaswarmy, S. (2018). Quantitative evaluation of surface crack depth with a scanning laser source based on particle swarm optimization-neural network. NDT & E International, 98 （2018） , 208–214. https://doi.org/10.1016/j.ndteint.2018.05.011
   Google Scholar
• Li, J. P., Hua, C. C., Yang, Y. N., & Guan, X. P. (2018a). Fuzzy classifier design for development tendency of hot metal silicon content in blast furnace. IEEE Transactions on Industrial Informatics, 14(3), 1115–1123. https://doi.org/10.1109/TII.2017.2770177
   Web of Science ®Google Scholar
• Li, J. P., Hua, C. C., Yang, Y. N., & Guan, X. P. (2018b). Bayesian block structure sparse based T-S fuzzy modeling for dynamic prediction of hot metal silicon content in the blast furnace. IEEE Transactions on Industrial Electronics, 65(6), 4933–4942. http://doi.org/10.1109/TIE.2017.2772141
   Web of Science ®Google Scholar
• Li, J. P., Hua, C. C., Qian, J. L., & Guan, X. P. (2021). Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace. Fuzzy Sets and Systems, 421 （2021） , 178–192. https://doi.org/10.1016/j.fss.2020.08.012
   Google Scholar
• Li, J. P., Hua, C. C., Yang, Y. N., & Guan, X. P. (2021). A novel MIMO T-S fuzzy modeling for prediction of blast furnace molten iron quality with missing outputs. IEEE Transactions on Fuzzy Systems, 29(6), 1654–1666. https://doi.org/10.1109/TFUZZ.2020.2983667
   Web of Science ®Google Scholar
• Luo, S. H., Huang, J., Zeng, J. S., & Zhang, Q. S. (2011). Identification of the optimal control center for blast furnace thermal state based on the fuzzy C-means clustering. ISIJ International, 51(10), 1668–1673. https://doi.org/10.2355/isijinternational.51.1668
   Web of Science ®Google Scholar
• Luo, S. H., Dai, Z. A., Chen, T. X., Chen, H. Y., & Jian, L. (2020). A weighted SVM ensemble predictor based on AdaBoost for blast furnace ironmaking process. Applied Intelligence, 50(7), 1997–2008. https://doi.org/10.1007/s10489-020-01662-y
   Web of Science ®Google Scholar
• LV, Z., Zhao, J., Zhai, Y. W., & Wang, W. (2018). Non-iterative T-S fuzzy modleing with random hidden-layer structure for BFG pipeline pressure prediction. Control Engineering Practice, 76 （2018） , 96–103. https://doi.org/10.1016/j.conengprac.2018.04.002
   Google Scholar
• Martin, R. D., Obeso, F., Mochon, J., Barea, R., & Jienez, J. (2007). Hot metal temperature prediction in blast furnace using advanced model based on fuzzy logic tools. Ironmaking & Steelmaking, 34(3), 241–247. https://doi.org/10.1179/174328107X155358
   Web of Science ®Google Scholar
• Nogami, H., Chu, M. S., & Yagi, J. (2005). Multidimensional transient mathematical simulator of blast furnace process based on multifluid and kinetic theories. Computers & Chemical Engineering, 29(11–12), 2438–2448. https://doi.org/10.1016/j.compchemeng.2005.05.024
   Google Scholar
• Nurkkala, A., Pettersson, F., & Saxen, H. (2011). Nonlinear modeling method applied to prediction of hot metal silicon in the ironmaking blast furnace. Industrial & Engineering Chemistry Research, 50(15), 9236–9248. https://doi.org/10.1021/ie200274q
   Web of Science ®Google Scholar
• Papoulis, A. (1984). Probability, random variables and stochastic processes. McGraw-Hill.
   Google Scholar
• Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3), 1065–1076. http://doi.org/10.2307/2237880
   Google Scholar
• Pravesjit, S. (2016). A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. Artificial Life and Robotics, 21(1), 112–119. https://doi.org/10.1007/s10015-015-0248-3
   Web of Science ®Google Scholar
• Saxen, H., Gao, C. H., & Gao, Z. W. (2013). Data-driven time discrete models for dynamics prediction of the hot metal silicon content in the blast furnace - A review. IEEE Transactions on Industrial Informatics, 9(4), 2213–2225. https://doi.org/10.1109/TII.2012.2226897
   Web of Science ®Google Scholar
• Sun, Z. Q., Zhang, J., Dai, L., Li, C. D., Zhou, C. E., Xin, J. L., & Li, S. Z. (2018). Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing, 329 （2018） , 447–456. https://doi.org/10.1016/j.neucom.2018.10.047
   Google Scholar
• Uchida, Y., Nabeshima, S., Kishimoto, Y., & Miki, Y. (2014). Effective desiliconization method with strong mixing of agent in swirling flow of hot metal. ISIJ International, 54(8), 1813–1822. https://doi.org/10.2355/isijinternational.54.1813
   Web of Science ®Google Scholar
• Wang, X. P., Hu, T. H., & Tang, L. X. (2021). A multiobjective evolutionary nonlinear ensemble learning with evolutionary feature selection for silicon prediction in blast furnace. IEEE Transactions on Neural Networks and Learning Systems 33 (5), 2080–2093 . https://doi.org/10.1109/TNNLS.2021.3059784
   Web of Science ®Google Scholar
• Xu, X., Hua, C. C., Tang, Y. G., & Guan, X. P. (2016). Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer. Neural Computing & Applications, 27(6), 1451–1461. https://doi.org/10.1007/s00521-015-1951-7
   Web of Science ®Google Scholar
• Zeng, J. S., Gao, C. H., & Su, H. Y. (2010). Data-driven predictive control for blast furnace ironmaking process. Computers & Chemical Engineering, 34(11), 1854–1862. https://doi.org/10.1016/j.compchemeng.2010.01.005
   Web of Science ®Google Scholar
• Zhang, H. G., Zhang, S., Yin, Y. X., & Chen, X. (2018). Prediction of the hot metal silicon content in blast furnace based on extreme learning machine. International Journal of Machine Learning and Cybernetics, 9(10), 1697–1706. https://doi.org/10.1007/s13042-017-0674-8
   Web of Science ®Google Scholar
• Zhang, S., Jiang, H. H., Yin, Y. X., Xiao, W. D., & Zhao, B. Y. (2018). The prediction of the gas utilization ratio based on TS fuzzy neural network and particle swarm optimization. Sensors, 18(2), 625. https://doi.org/10.3390/s18020625
   PubMed Web of Science ®Google Scholar
• Zhang, H. W., Shang, J., Zhang, J. X., & Yang, C. J. (2021). Nonstationary process monitoring for blast furnaces based on consistent trend feature analysis. IEEE Transactions on Control Systems Technology, 29(5), 2180–2191. https://doi.org/10.1109/TCST.2021.3105540
   PubMedGoogle Scholar
• Zhao, J., Wang, T. Y., Pedrycz, W., & Wang, W. (2021). Granular prediction and dynamic scheduling based on adaptive dynamic programming for the blast furnace gas system. IEEE Transactions on Cybernetics, 51(4), 2201–2214. https://doi.org/10.1109/TCYB.2019.2901268
   PubMed Web of Science ®Google Scholar
• Zhou, P., Song, H., Wang, H., & Chai, T. Y. (2016). Data-driven nonlinear subspace modeling for prediction and control of molten iron quality indices in blast furnace ironmaing. IEEE Transactions on Control Systems Technology, 25(5), 1761–1774. http://doi.org/10.1109/TCST.2016.2631124
   Web of Science ®Google Scholar
• Zhou, P., & Liu, J. (2018). Data-driven multi-output ARMAX modeling for online estimation of central temperatures for cross temperature measuring in blast furnace ironmaking. Acta Automatica Sinica, 44(3), 552–561. https://doi.org/10.16383/j.aas.2018.c160840
   Google Scholar