Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Abstract

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation, a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively. This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

Keywords

BF ironmaking / intelligent BF / industrial big data / machine learning / integrated mechanism and data

References

[1] Zhang JL, Fu HY, Liu YX, et al. Review on biomass metallurgy: Pretreatment technology, metallurgical mechanism and process design. Int. J. Miner. Metall. Mater., 2022, 29(6): 1133.

[2] He HN, Wang XC, Peng GZ, et al. Intelligent logistics system of steel bar warehouse based on ubiquitous information. Int. J. Miner. Metall. Mater., 2021, 28(8): 1367.

[3] Zhou P, Dai P, Song H D, Chai T Y. Data-driven recursive subspace identification based online modelling for prediction and control of molten iron quality in blast furnace iron-making. IET Control Theory Appl., 2017, 11(14): 2343.

[4] Lin L, Zeng JQ. Consideration of green intelligent steel processes and narrow window stability control technology on steel quality. Int. J. Miner. Metall. Mater., 2021, 28(8): 1264.

[5] K. Jiang, Z.H. Jiang, Y.F. Xie, D. Pan, and W.H. Gui, Prediction of multiple molten iron quality indices in the blast furnace ironmaking process based on attention-wise deep transfer network, IEEE Trans. Instrum. Meas., 71(2022), art. No. 2512114.

[6] Liu S, Xie S, Zhang Q. Multi-energy synergistic optimization in steelmaking process based on energy hub concept. Int. J. Miner. Metall. Mater., 2021, 28(8): 1378.

[7] Li JP, Hua CC, Yang YN, Guan XP. A novel MIMO T–S fuzzy modeling for prediction of blast furnace molten iron quality with missing outputs. IEEE Trans. Fuzzy Syst., 2021, 29(6): 1654.

[8] Yan YF, Lü ZM. Multi-objective quality control method for cold-rolled products oriented to customized requirements. Int. J. Miner. Metall. Mater., 2021, 28(8): 1332.

[9] Lee J, Singh J, Azamfar M, Sun KY. Industrial AI: a systematic framework for AI in industrial applications. China Mech. Eng., 2020, 31(1): 37.

[10] Boom R. Research Fund for Coal and Steel RFCS: A European success story. Ironmaking Steelmaking., 2014, 41(9): 647.

[11] Wu SW, Yang J, Zhang RH, Ono H. Prediction of endpoint sulfur content in KR desulfurization based on the hybrid algorithm combining artificial neural network with SAPSO. IEEE Access, 2020, 8, 33778.

[12] K.Y. Shin and H.C. Park, Smart manufacturing systems engineering for designing smart product-quality monitoring system in the industry 4.0, [in] 2019 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, 2020, p. 1693.

[13] Y. Luo, Progress of POSCO Smart Factory construction, China Steel Focus, 2021, No. 12, p. 51.

[14] Wang GD, Liu ZY, Zhang DH, Chu MS. Transformation and development of materials science and technology and construction of iron and steel innovation infrastructure. J. Iron Steel Res., 2021, 33(10): 1003.

[15] C. Xiao and L.H. Lyu, Application of full stack machine learning in intelligent manufacturing of steel process, Baosteel Technol., 2021, No. 2, p. 24.

[16] Li ZN. Prediction and Optimization of Key Process Parameters of Large Blast Furnace Based on Big Data Mining, 2020, Shenyang, Northeastern University, 27 [Dissertation]

[17] Qi YS, Chu MS, Tang J, Shi Q, Wang MY, Liu ZQ. Research progress of blast furnace data governance based on big data technology. Metall. Ind. Autom., 2023, 47(1): 43.

[18] Zhang SN. Comprehensive Evaluation of Blast Furnace Conditions Based on the Combination of Expert Knowledge and Data, 2020, Baotou, Inner Mongolia University of Science and Technology, 9 [Dissertation]

[19] Chen SF, Liu XJ, Li HY, Bu XP, Lyu Q, Liu FL. Preliminary study on missing data processing of blast furnace ironmaking. China Metall., 2021, 31(2): 17.

[20] Zhao J, Chen SF, Liu XJ, Li X, Li HY, Lyu Q. Outlier screening for ironmaking data on blast furnaces. Int. J. Miner. Metall. Mater., 2021, 28(6): 1001.

[21] Zheng ZQ. Comparative Research of Data Filling Algorithms under Different Missing Mechanisms, 2022, Guizhou, Guizhou Minzu University, 39 [Dissertation]

[22] Li ZN, Chu MS, Liu ZG, Li BF. Prediction and optimization of blast furnace parameters based on machine learning and genetic algorithm. J. Northeastern Univ. Nat. Sci., 2020, 41(9): 1262.

[23] Liu X, Zhang WJ, Shi Q, Zhou L. Operation parameters optimization of blast furnaces based on data mining and cleaning. J. Northeastern Univ. Nat. Sci., 2020, 41(8): 1153.

[24] Deng CY, Wu KH, Tan YP, Hu J. Outlier detection method based on multivariate time series segmentation clustering. Comput. Eng. Des., 2020, 41(11): 6.

[25] Zhao CX, Xue HF, Wang L, Wan Y. Water consumption abnormal data detection method based on isolation forest. J. China Inst. Water Res. Hydropower Res., 2020, 18(1): 9.

[26] Bessec M. Revisiting the transitional dynamics of business cycle phases with mixed-frequency data. Econ. Rev., 2019, 38(7): 711.

[27] Ye G. Research on the coincident index and economic fluctuations in China with mixed-frequency data. Stat. Res., 2015, 32(08): 17.

[28] Gao CL. Blast Furnace Smelting Process Optimization of Pulverized Coal Injection Based on Data-driven, 2015, Baotou, Inner Mongolia University of Science & Technology, 22 [Dissertation]

[29] An JQ, Chen YF, Wu M. A prediction method for carbon monoxide utilization ratio of blast furnace based on improved support vector regression. CIESC J., 2015, 66(1): 206.

[30] Y.T. Wang, J.Z. Zhao, X.P. Gong, and G. Yang, A Method for Predicting Silicon Content of Molten Iron in Blast Furnace with Uncertain Information of Time Delay, Chinese Patent, Appl. 110309608A, 2019.

[31] Ming F. Research on Application of Association Rule Mining to Blast Furnace Situation Prediction, 2009, Chongqing, Chongqing University, 30 [Dissertation]

[32] Huang CL, Tang YL, Zhang XF, Chu YZ. Prediction and simulation of silicon content in blast furnace for PCA and PSO-ELM. Comput. Simul., 2020, 37(02): 398.

[33] Liu DF, Zhang J, Fu Q. Deep learning prediction modeling of blast furnace condition based on principal component analysis of temperature field. Metall. Ind. Autom., 2021, 45(3): 42.

[34] Meng CC, Zeng JS, Li WJ. Blast furnace fault detection based on KPCA. J. China Univ. Metrol., 2012, 23(04): 332.

[35] Li ZQ, Du JQ, Nin B, Xiong WP, Huang CY, Li H. Summary of feature selection methods. Comput. Eng. Appl., 2019, 55(24): 10.

[36] Wang ZY, Jiang DH, Wang XD, Zhang JL, Liu ZJ. Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine. Chin. J. Eng., 2021, 43(4): 569.

[37] Jiang DW, Wang ZY, Zhang JL, Jiang DJ, Li KJ, Liu FL. Machine learning modeling of gas utilization rate in blast furnace. JOM, 2022, 74(4): 1633.

[38] Shi QJ, Pan F, Long FH, et al. A review of feature selection methods. Microelectron. Comput., 2022, 39(03): 1.

[39] Zhang CJ, Chen BB, Zhou C, Yin XG. Feature selection algorithm based on multi-objective bare-bones particle swarm optimization. J. Comput. Appl., 2018, 38(11): 3156.

[40] B. Wutzl, K. Leibnitz, F. Rattay, M. Kronbichler, M. Murata, and S.M. Golaszewski, Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness, PLoS One, 14(2019), No. 7, art. No. e0219683.

[41] Y.R. Li and C.J. Yang, Domain knowledge based explainable feature construction method and its application in ironmaking process, Eng. Appl. Artif. Intell., 100(2021), art. No. 104197.

[42] Deng Y, Lyu Q. Establishment of evaluation and prediction system of comprehensive state based on big data technology in a commercial blast furnace. ISIJ Int., 2020, 60(5): 898.

[43] Cui GM, Sun T, Zhang Y. Application of support vector machine (SVM) in prediction of molten iron temperature in blast furnace. Control Eng. China, 2013, 20(5): 809.

[44] Li S, Chang JC, Chu MS, Li J, Yang AM. A blast furnace coke ratio prediction model based on fuzzy cluster and grid search optimized support vector regression. Appl. Intell., 2022, 52(12): 13533.

[45] Zhao J, Li HW, Liu XJ, Li X, Li HY, Lyu Q. Prediction model of permeability index based on Xgboost. China Metall., 2021, 31(03): 22.

[46] Cheng ZK, Yan XL, Cheng WS, Yuan ZX. Study on coke quality prediction model based on gradient lifting decision tree. J. Chongqing Technol. Bus. Univ. Nat. Sci. Ed., 2021, 38(5): 55.

[47] Liu S, Lyu Q, Liu XJ, Sun YQ, Zhang XS. A prediction system of burn through point based on gradient boosting decision tree and decision rules. ISIJ Int., 2019, 59(12): 2156.

[48] Liu XJ, Deng Y, Li X, Hao LY, Liu EH. Prediction of silicon content in hot metal of blast furnace based on bid data technology. China Metall., 2021, 31(2): 10.

[49] Cui ZQ, Han Y, Yang AM, Zhang YZ, Zhang S. Intelligent prediction of silicon content in hot metal of blast furnace based on neural network time series model. Metall. Ind. Autom., 2021, 45(3): 51.

[50] Bao XJ, Weng SH, Chen G, Wang J, Chen X, Xie JC. Comparison on multi-step prediction of blast furnace gas generation based on LSTM/SARIMA time series model. Iron Steel, 2022, 57(9): 166.

[51] Zhu J. Research of Selective Ensemble Learning and Its Application, 2016, Nanchang, East China Jiaotong University, 6 [Dissertation]

[52] Shi L, Liu WH, Cao FJ, Wang JJ. Combined forecast of blast furnace gas utilization rate based on CEEMDAN-SVM-LSTM. China Meas. Test, 2023, 49(1): 86.

[53] Zhao DT. Research on Prediction of Key Parameters of Blast Furnace Smelting and Classification Method of Furnace Condition, 2018, Tianjin, Tianjin University of Technology, 23 [Dissertation]

[54] F.Y. Huang, L. Huang, M. Fu, et al., A New Anterograde Evaluation Method for Blast Furnace, Chinese Patent, Appl. 109063358A, 2018.

[55] Li HY, Bu XP, Liu XJ, et al. Evaluation and prediction of blast furnace status based on big data platform of ironmaking and data mining. ISIJ Int., 2021, 61(1): 108.

[56] X.C. Zhang, Z. Song, D.F. Zhou, and C. Fan, Research on index ranking of blast furnace parameter rules based on PCA algorithms, [in] Metallurgical Automation and Intelligence, Proceedings of the 12th China Iron and Steel Annual Conference, Beijing, 2019, p. 34.

[57] Jiang XX. Research on the Stability Evaluation for Blast Furnace Condition with Data Mining Method, 2020, Shenyang, Northeastern University, 33 [Dissertation]

[58] Ren XD. Research on Stability Evaluation Method of Blast Furnace Condition, 2019, Shenyang, Northeastern University, 43 [Dissertation]

[59] Li JP, Hua CC, Guan XP. Modeling research for smelting mechanism blast furnace smelting process based on operation data and expert knowledge. J. Shanghai Jiaotong Univ., 2018, 52(10): 1142.

[60] J.Q. An, H.C. Chen, M. Wu, W.Y. He, and J.H. She, Two-layer fault diagnosis method for blast furnace based on evidence-conflict reduction on multiple time scales, Contr. Eng. Pract., 101(2020), art. No. 104474.

[61] B. Wang, Y.M. Chen, W.G. Song, and S.B. Wang, Practice of furnace working status stabilization in Baosteel’s No.1 BF, [in] Ironmaking and Raw Materials, Proceedings of the 12th China Iron and Steel Annual Conference, Beijing, 2019, p. 31.

[62] Zheng YP. Measures to manage frequent fluctuations in furnace conditions at Jingtang’s No. 1 blast furnace. Ironmaking, 2019, 38(6): 36.

[63] Cheng Y, Wang ZY, Zhou GM. Causes and counter-measures for the fluctuating furnace conditions of Xianggang’s new No. 3 blast furnace. Ironmaking, 2020, 39(1): 38.

[64] Tian Y, Wang G, Su JQ, Bai H. Research on optimization and regulation model of blast furnace parameters based on big data mining. Metall. Ind. Autom., 2022, 46(5): 65.

[65] Zhang ZW, Che XR, Zhang HB. Establishment and validation of multi-objective optimization model of blast furnace. Chin. J. Process Eng., 2017, 17(1): 178.

[66] Dong A. Study on Prediction of Parameters and Optimization Control Model of Blast Furnace Ironmaking, 2017, Tianjin, Tianjin University of Technology, 37 [Dissertation]

[67] Zhang Q, Yao TH, Cai JJ, Shen FM. On the multi-objective optimal model of blast furnace ironmaking process and its application. J. Northeastern Univ. Nat. Sci., 2011, 32(2): 270.