Prediction of FeO content in sinter based on time series model

doi:10.13228/j.boyuan.issn1001-0963.20240190

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Journal of Iron and Steel Research ›› 2025, Vol. 37 ›› Issue (5) : 570-578. DOI: 10.13228/j.boyuan.issn1001-0963.20240190

Smelting and Working

Prediction of FeO content in sinter based on time series model

LI Qinqin¹, SONG Baoyu¹, ZHANG Zhaoxin¹, WANG Kuiyue¹, SONG Jun¹, REN Wei²

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Abstract

The steel metallurgy industry is a crucial component of the basic industries, where the quality stability of sinter is vital to the entire production process. A novel online prediction framework, the Process Feature Serialization and Extraction Prediction model (PFSE) is proposed to predict the FeO content in the sinter accurately. The framework first serialized and differentiated the raw data to enhance its expressiveness. Subsequently, it employed feature extraction techniques such as Grey Relational Analysis (GRA) and Correlation Coefficient (CC) to identify key process characteristics. Then, a prediction model for FeO content was constructed using Recurrent Neural Networks (RNN) and its variants, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). Experiments conducted on sintering process data from a steel plant between 2022 and 2023 validated the PFSE framework, demonstrating good stability and accuracy. With an error tolerance of 0.1, the model achieved a high accuracy rate of 85.3%. which confirms the effectiveness and reliability of this method.

Key words

FeO content prediction / feature serialization / feature extraction / time differentiation / time series model

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LI Qinqin, SONG Baoyu, ZHANG Zhaoxin, WANG Kuiyue, SONG Jun, REN Wei. Prediction of FeO content in sinter based on time series model[J]. Journal of Iron and Steel Research, 2025, 37(5): 570-578 https://doi.org/10.13228/j.boyuan.issn1001-0963.20240190

References

[1] 项钟庸,王筱留,顾向涛.再论落实高炉低碳炼铁生产方针[J].中国冶金,2021,31(9):9.
(XIANG Z Y,WANG X L,GU X T.Revisiting the implementation of low carbon ironmaking production policy for blast furnace[J].China Metallurgy,2021,31(9):9.)
[2] 沈峰满,郑海燕,姜鑫,等.高炉炼铁工艺中Al₂O₃的影响及适宜w(MgO)/w(Al₂O₃)的探讨[J].钢铁,2014,49(1):1.
(SHEN F M,ZHENG H Y,JIANG X,et al.Influence of Al₂O₃ in blast furnace smelting and discussions on properw(MgO)/w(Al₂O₃) ratio[J].Iron and Steel,2014,49(1):1.)
[3] 张华,黄昌先,赵刚,等.基于辅料运行特性的烧结矿质量预测模型[J].钢铁研究学报,2012,24(4):14.
(ZHANG H,HUANG C X,ZHAO G,et al.A prediction model for sinter quality based on the characteristic of auxiliary material′s movement[J].Journal of Iron and Steel Research,2012,24(4):14.)
[4] 陈小敏,湛文龙,杜晓东,等.烧结矿在高炉内熔滴性能新方法表征与预测的研究[J].冶金能源,2022,41(2):29.
(CHEN X M,ZHAN W L,DU X D,et al.Investigation on a new method for predicting and characterizing the melting property of sinter in blast furnace[J].Energy for Metallurgical Industry,2022,41(2):29.)
[5] 杨涛,龙红明,丁成义,等.铝硅比对烧结成矿特性及冶金性能的影响[J].钢铁,2022,57(4):43.
(YANG T,LONG H M,DING C Y,et al.Effect of w(Al₂O₃)/w(SiO₂) on sintered ore mineralization charac-teristics and metallurgical properties[J].Iron and Steel,2022,57(4):43.)
[6] 李温鹏,周平.高炉铁水质量鲁棒正则化随机权神经网络建模[J].自动化学报,2020,46(4):721.
(LI W P,ZHOU P.Robust regularized RVFLNs modeling of molten iron quality in blast furnace ironmaking[J].Acta Automatica Sinica,2020,46(4):721.)
[7] 牛乐乐,刘征建,张建良,等.铁矿粉矿物组成对烧结矿冶金性能的影响[J].钢铁,2019,54(9):27.
(NIU L L,LIU Z J,ZHANG J L,et al.Effect of mineral composition of iron ore on metallurgical properties of sinter[J].Iron and Steel,2019,54(9):27.)
[8] 郄亚娜,李宇壮,白诗皓,等.碱度对富矿粉厚料层烧结固相反应及矿物生成的影响[J].中国冶金,2023,33(2):38.
(QIE Y N,LI Y Z,BAI S H,et al.Influence of basicity on solid phase reaction and mineral formation in high feed sintering of ore-rich[J].China Metallurgy,2023,33(2):38.)
[9] 单长冬,张建良,王耀祖,等.富氧条件下高精粉配比对烧结指标及冶金性能的影响[J].钢铁,2023,58(1):22.
(SHAN C D,ZHANG J L,WANG Y Z,et al.Effect of high magnetite concentrates ratio on sintering index and metallurgical properties under oxygen-enriched conditions[J].Iron and Steel,2023,58(1):22.)
[10] 陈小龙,高立华.富氢还原对烧结矿低温还原粉化的影响[J].冶金能源,2022,41(4):27.
(CHEN X L,GAO L H.Effect of rich hydrogen reduction on low temperature reduction disintegration of sinters[J].Energy for Metallurgical Industry,2022,41(4):27.)
[11] FAN X H,LI Y,CHEN X L.Prediction of iron ore sintering characters on the basis of regression analysis and artificial neural network[J].Energy Procedia,2012,16(16):769.
[12] WU M,CHEN X X,CAO W H,et al.An intelligent integrated optimization system for the proportioning of iron ore in a sintering process[J].Journal of Process Control,2014,24(1):182.
[13] 向婕.铁矿石烧结过程智能集成优化控制技术及其应用研究[D].长沙:中南大学信息科学与工程学院,2010.
(XIANG J.Research on intelligent integrated optimal control technology of iron ore sintering process and application[D].Changsha:School of Information Science and Engineering,Central South University,2010.).
[14] FAN X H,HUANG X X,CHEN X L,et al.Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion[J].Ironmaking & Steelmaking,2016,43(7):488.
[15] SAXÉN H,GAO C H,GAO Z W.Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace:A review[J].IEEE Transactions on Industrial Informatics,2013,9(4):2213.
[16] ÖMER FARUK D.A hybrid neural network and ARIMA model for water quality time series prediction[J].Engineering Applications of Artificial Intelligence,2010,23(4):586.
[17] 黄玉龙,刘明波,陈迅.基于多元线性回归的动态负荷模型参数实时选择[J].华南理工大学学报(自然科学版),2016,44(2):107.
(HUANG Y L,LIU M B,CHEN X.Real-time selection of dynamic load model parameters based on multiple linear regression[J].Journal of South China University of Technology (Natural Science Edition),2016,44(2):107.)
[18] 邵慧君.基于组合模型的烧结矿质量预测系统研究与应用[D].武汉:武汉科技大学,2019.
(SHAO H J.Research and application of quality prediction system for sinter based on composite model[D].Wuhan:Wuhan University of Science and Technology,2019.)
[19] 张军红,谢安国,沈峰满.烧结矿质量预测的BP网络模型[J].鞍山钢铁学院学报,2001,24(6):406.
(ZHANG J H,XIE A G,SHEN F M.BP network model for forcasting sinter quality[J].Journal of Anshan Institute of Iron and Steel Technology,2001,24(6):406.)
[20] 刘颂,吕庆,刘小杰,等.基于混匀矿烧结基础特性配矿专家系统的开发与应用[J].烧结球团,2017,42(2):10.
(LIU S,LU Q,LIU X J,et al.Development and application of blending expert system based on basic characteristics of blended ore powder[J].Sintering and Pelletizing,2017,42(2):10.)
[21] 牛江峰,周静.基于灰色理论的烧结矿质量预测智能优化研究[J].黑龙江冶金,2016,8(1):23.
(NIU J F,ZHOU J.Intelligent study of sinter quality based on grey theory[J].Heilongjiang Metallurgy,2016,8(1):23.)
[22] 汪清瑶,刘琼.基于Elman神经网络的烧结矿质量在线预测研究[J].仪表技术与传感器,2017(10):98.
(WANG Q Y,LIU Q.Research on online prediction of sinter quality based on Elman neural network[J].Instrument Technique and Sensor,2017(10):98.)
[23] 袁致强,王斌,张良力,等.冶炼高炉烧结矿化学原料配比准确预测仿真[J].计算机仿真,2019,36(1):453.
(YUAN Z Q,WANG B,ZHANG L L,et al.Simulation of accuracy prediction for chemical raw material ratio for smelt blast furnace sinter ore[J].Computer Simulation,2019,36(1):453.)
[24] SHEN Z P,ZHANG Y M,LU J W,et al.A novel time series forecasting model with deep learning[J].Neurocomputing,2020,396:302.
[25] YUAN X F,LI L,WANG Y L.Nonlinear dynamic soft sensor modeling with supervised long short-term memory network[J].IEEE Transactions on Industrial Informatics,2020,16(5):3168.
[26] SHEN B B,GE Z Q.Weighted nonlinear dynamic system for deep extraction of nonlinear dynamic latent variables and industrial application[J].IEEE Transactions on Industrial Informatics,2021,17(5):3090.
[27] LI J P,HUA C C,YANG Y N,et al.Bayesian block structure sparse based T-S fuzzy modeling for dynamic prediction of hot metal silicon content in the blast furnace[J].IEEE Transactions on Industrial Electronics,2018,65(6):4933.
[28] DENG J L.Control problems of grey systems[J].Systems & Control Letters,1982,1(5):288.
[29] YAO L,JIANG X Y,HUANG G P,et al.Virtual sensing f-CaO content of cement clinker based on incremental deep dynamic features extracting and transferring model[J].IEEE Transactions on Instrumentation and Measurement,2020,70:2500610.
[30] KINGMA D P,JIMMY B A.A method for stochastic optimization[J].Ithaca,NYarXiv.org,2014.