Cite this article as: |
Mingyu Wang, Jue Tang, Mansheng Chu, Quan Shi, and Zhen Zhang, Prediction and optimization of flue pressure in sintering process based on SHAP, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-024-2955-z |
Jue Tang E-mail: tangj@smm.neu.edu.cn
[1] |
F.Q. Zheng, Y.F. Guo, J.F. Xiang, S. Wang, L.Z. Yang, and F. Chen, Improvement of iron ore sintering productivity by redistributing air volume during sintering process, ISIJ Int., 62(2022), No. 1, p. 74. doi: 10.2355/isijinternational.ISIJINT-2021-160
|
[2] |
V. Roshan, K. Kumar, R. Kumar, and G.V.S. Nageswara Rao, Preparation of iron ore micro-pellets and their effect on sinter bed permeability, Trans. Indian Inst. Met., 71(2018), No. 9, p. 2157. doi: 10.1007/s12666-018-1347-3
|
[3] |
A.M. Nyembwe, R.D. Cromarty, and A.M. Garbers-Craig, Effect of concentrate and micropellet additions on iron ore sinter bed permeability, Miner. Process. Extr. Metall., 125(2016), No. 3, p. 178. doi: 10.1080/03719553.2016.1180033
|
[4] |
H. Zhou, Z.Y. Lai, L.Q. Lv, et al., Improvement in the permeability of sintering beds by drying treatment after granulating sinter raw materials containing concentrates, Adv. Powder Technol., 31(2020), No. 8, p. 3297. doi: 10.1016/j.apt.2020.06.017
|
[5] |
H. Zhou, M.X. Zhou, D.P. O’dea, B.G. Ellis, J.Z. Chen, and M. Cheng, Influence of binder dosage on granule structure and packed bed properties in iron ore sintering process, ISIJ Int., 56(2016), No. 11, p. 1920. doi: 10.2355/isijinternational.ISIJINT-2016-298
|
[6] |
L.M. Lu, M. Adam, A. Edenton, et al., Strategies for efficient utilization of CRL magnetite pellet feed in sintering and pelletising, [in] Proceedings 5th Baosteel Biennial Academic Conference, Shanghai, 2013, p. A31.
|
[7] |
Q. Shi, J. Tang, and M.S. Chu, Evaluation, prediction, and feedback of blast furnace hearth activity based on data-driven analysis and process metallurgy, Steel Res. Int., 95(2024), No. 2, art. No. 2300385. doi: 10.1002/srin.202300385
|
[8] |
Q. Shi, J. Tang, and M.S. Chu, Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology, Int. J. Miner. Metall. Mater., 30(2023), No. 9, p. 1651. doi: 10.1007/s12613-023-2636-3
|
[9] |
X.X. Huang, X.H. Fan, X.L. Chen, G.M. Yang, and M. Gan, Bed permeability state prediction model of sintering process based on data mining technology, ISIJ Int., 56(2016), No. 12, p. 2113. doi: 10.2355/isijinternational.ISIJINT-2016-193
|
[10] |
S.H. Wang, H.F. Li, Y.J. Zhang, and Z.S. Zou, A hybrid ensemble model based on ELM and improved AdaBoost.RT algorithm for predicting the iron ore sintering characters, Comput. Intell. Neurosci., 2019(2019), art. No. 4164296.
|
[11] |
S. Liu, Q. Lyu, X.J. Liu, Y.Q. Sun, and X.S. Zhang, A prediction system of burn through point based on gradient boosting decision tree and decision rules, ISIJ Int., 59(2019), No. 12, p. 2156. doi: 10.2355/isijinternational.ISIJINT-2019-059
|
[12] |
X. Chen, J. Hu, M. Wu, and W.H. Cao, T–S fuzzy logic based modeling and robust control for burning-through point in sintering process, IEEE Trans. Ind. Electron., 64(2017), No. 12, p. 9378. doi: 10.1109/TIE.2017.2708004
|
[13] |
S. Du, M. Wu, L.F. Chen, L. Jin, W.H. Cao, and W. Pedrycz, Operating performance improvement based on prediction and grade assessment for sintering process, IEEE Trans. Cybern., 52(2022), No. 10, p. 10529. doi: 10.1109/TCYB.2021.3071665
|
[14] |
S. Du, M. Wu, L.F. Chen, and W. Pedrycz, Prediction model of burn-through point with fuzzy time series for iron ore sintering process, Eng. Appl. Artif. Intell., 102(2021), art. No. 104259. doi: 10.1016/j.engappai.2021.104259
|
[15] |
A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation, J. Comput. Graph. Stat., 24(2015), No. 1, p. 44. doi: 10.1080/10618600.2014.907095
|
[16] |
J. Kim, H.J. Lee, and H. Lee, Mining the determinants of review helpfulness: A novel approach using intelligent feature engineering and explainable AI, Data Technol. Appl., 57(2023), No. 1, p. 108.
|
[17] |
L.S. Shapley and M. Shubik, A method for evaluating the distribution of power in a committee system, Am. Polit. Sci. Rev., 48(1954), No. 3, p. 787. doi: 10.2307/1951053
|
[18] |
Z. Zhang, J. Tang, M.S. Chu, et al., The amount prediction and optimization of the returned ore generated from sintering process based on SHAP value and ensemble learning, Steel Res. Int., 94(2023), No. 9, art. No. 2300114. doi: 10.1002/srin.202300114
|
[19] |
D.W. Jiang, Z.Y. Wang, K.J. Li, and J.L. Zhang, Analysis of blast furnace permeability regulation strategy based on machine learning, Steel Res. Int., 95(2024), No. 3, art. No. 2300590. doi: 10.1002/srin.202300590
|
[20] |
S.J. Zhang, H.G. Lei, Z.C. Zhou, G.Q. Wang, and B. Qiu, Fatigue life analysis of high-strength bolts based on machine learning method and SHapley Additive exPlanations (SHAP) approach, Structures, 51(2023), p. 275. doi: 10.1016/j.istruc.2023.03.060
|
[21] |
J. Takalo-Mattila, M. Heiskanen, V. Kyllönen, L. Määttä, and A. Bogdanoff, Explainable steel quality prediction system based on gradient boosting decision trees, IEEE Access, 10(2022), p. 68099. doi: 10.1109/ACCESS.2022.3185607
|
[22] |
M.X. Zhou, H. Zhou, D.P. O’dea, B.G. Ellis, T. Honeyands, and X.T. Guo, Characterization of granule structure and packed bed properties of iron ore sinter feeds that contain concentrate, ISIJ Int., 57(2017), No. 6, p. 1004. doi: 10.2355/isijinternational.ISIJINT-2016-734
|
[23] |
Y. Chabchoub, M.U. Togbe, A. Boly, and R. Chiky, An In-depth study and improvement of isolation forest, IEEE Access, 10(2022), p. 10219. doi: 10.1109/ACCESS.2022.3144425
|
[24] |
H.X. Ma, T. Peng, C. Zhang, C.L. Ji, Y.M. Li, and M.S. Nazir, Developing an evolutionary deep learning framework with random forest feature selection and improved flow direction algorithm for NOx concentration prediction, Eng. Appl. Artif. Intell., 123(2023), art. No. 106367. doi: 10.1016/j.engappai.2023.106367
|
[25] |
R.I. Hamilton and P.N. Papadopoulos, Using SHAP values and machine learning to understand trends in the transient stability limit, IEEE Trans. Power Syst., 39(2024), No. 1, p. 1384. doi: 10.1109/TPWRS.2023.3248941
|
[26] |
M. Jamei, M. Ali, M. Karbasi, et al., Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF–EMD–VMD, Boruta–SHAP, and eXplainable GPR, Expert Syst. Appl., 237(2024), art. No. 121512. doi: 10.1016/j.eswa.2023.121512
|
[27] |
A. Aminifar, M. Shokri, F. Rabbi, V.K.I. Pun, and Y. Lamo, Extremely randomized trees with privacy preservation for distributed structured health data, IEEE Access, 10(2022), p. 6010. doi: 10.1109/ACCESS.2022.3141709
|
[28] |
K. Uçak and G.Ö. Günel, Adaptive stable backstepping controller based on support vector regression for nonlinear systems, Eng. Appl. Artif. Intell., 129(2024), art. No. 107533. doi: 10.1016/j.engappai.2023.107533
|
[29] |
S. Koley, T. Ray, I. Mohanty, S. Chatterjee, and M. Shome, Prediction of electrical resistivity of steel using artificial neural network, Ironmaking Steelmaking, 46(2019), No. 4, p. 383. doi: 10.1080/03019233.2017.1403109
|
[30] |
X.P. Wang, T.H. Hu, and L.X. Tang, A multiobjective evolutionary nonlinear ensemble learning with evolutionary feature selection for silicon prediction in blast furnace, IEEE Trans. Neural Networks Learn. Syst., 33(2022), No. 5, p. 2080. doi: 10.1109/TNNLS.2021.3059784
|
[31] |
S.L. Wu, J. Zhu, J.X. Fan, G.L. Zhang, and S.G. Chen, Sintering behavior of return fines and their effective utilization method, ISIJ Int., 53(2013), No. 9, p. 1561. doi: 10.2355/isijinternational.53.1561
|
[32] |
X. Zhang, Q. Zhong, C. Liu, et al., Partial substitution of anthracite for coke breeze in iron ore sintering, Sci. Rep., 11(2021), No. 1, art. No. 1540. doi: 10.1038/s41598-021-80992-4
|