Dynamic, data-driven prediction model of molten steel temperature in the second blowing stage of a converter

GU Mao-qiang; XU An-jun; LIU Xuan; WANG Hui-xian

doi:10.13374/j.issn2095-9389.2022.01.05.002

Volume 44 Issue 9

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2022 > 44(9): 1595-1606

GU Mao-qiang, XU An-jun, LIU Xuan, WANG Hui-xian. Dynamic, data-driven prediction model of molten steel temperature in the second blowing stage of a converter[J]. Chinese Journal of Engineering, 2022, 44(9): 1595-1606. doi: 10.13374/j.issn2095-9389.2022.01.05.002

Citation:

GU Mao-qiang, XU An-jun, LIU Xuan, WANG Hui-xian. Dynamic, data-driven prediction model of molten steel temperature in the second blowing stage of a converter[J]. Chinese Journal of Engineering, 2022, 44(9): 1595-1606. doi: 10.13374/j.issn2095-9389.2022.01.05.002

Citation:

PDF( 3206 KB)

Dynamic, data-driven prediction model of molten steel temperature in the second blowing stage of a converter

doi: 10.13374/j.issn2095-9389.2022.01.05.002

School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: anjunxu@126.com
Received Date: 2022-01-05
Available Online: 2022-03-07
Publish Date: 2022-09-01

Abstract

Abstract

Molten steel temperature is a parameter in converter end-point control. Accurate prediction of molten steel temperature is crucial for converter end-point control. However, most of the previous end-point prediction models are static models, which can only predict the molten steel temperature at the end-point of converter blowing and cannot realize dynamic prediction, affording a limited role for these models. To solve this challenge, a data-driven prediction model of molten steel temperature in the second blowing stage in a converter is proposed. First, the model retrieves the similar cases in the historical case base through the process parameters in the main blowing stage of the new case, such as carbon content and temperature of TSC measurement, based on the case-based reasoning (CBR) algorithm. Second, the process parameters in the second blowing stage of the similar cases, such as oxygen flow, lance position, and argon flow, are used to train the relationship between the process parameters and the molten steel temperature based on the long short-term memory (LSTM) algorithm. Third, the trained LSTM model is used to dynamically calculate the molten steel temperature in the second blowing stage of the new case. Finally, the actual production data is divided into five sets for cross-validation, and the model prediction accuracy changes are tested when the number of reuse cases ranges from 1 to 10, and the number of neurons is 5, 10, 15, and 20. The results show that, on the one hand, the prediction accuracy of the model first increases and then decreases with an increasing number of cases, and when the number of reused cases is 4, the prediction accuracy of the model is the highest, indicating that the number of cases is increased when training the model. Improving the prediction accuracy of the model is beneficial; however, the reference value of the case decreases with the similarity of the case, reducing the prediction accuracy of the model. Conversely, when the number of neurons is 10, the prediction accuracy of the model reaches it’s the highest value. The hit rate of the prediction error in the range of [?5 ℃, 5 ℃], [?10 ℃, 10 ℃], and [?15 ℃, 15 ℃] reached 40.33%, 68.92%, and 88.33%, respectively. This paper also establishes the traditional quadratic model and cubic model as well as further proves the effectiveness of the model by comparing the three indicators of these models, namely, the RMSE, MSE, and hit rate.
- converter,
- molten steel temperature,
- case-based reasoning,
- long short-term memory,
- dynamic prediction

FullText(HTML)

References(26)

References

[1]	高放, 包燕平, 王敏, 等. 基于FA-ELM的轉爐終點磷含量預測模型. 鋼鐵, 2020, 55(12):24 Gao F, Bao Y P, Wang M, et al. Prediction model of end-point phosphorus content of converter based on FA-ELM. Iron Steel, 2020, 55(12): 24
[2]	Feng K, Xu A J, He D F, et al. An improved CBR model based on mechanistic model similarity for predicting end phosphorus content in dephosphorization converter. Steel Res Int, 2018, 89(6): 1800063 doi: 10.1002/srin.201800063
[3]	Gu M Q, Xu A J, Yuan F, et al. An improved CBR model using time-series data for predicting the end-point of a converter. ISIJ Int, 2021, 61(10): 2564 doi: 10.2355/isijinternational.ISIJINT-2020-687
[4]	Gao C, Shen M G, Liu X, et al. End-point static control of basic oxygen furnace (BOF) steelmaking based on wavelet transform weighted twin support vector regression. Complexity, 2019, 2019: 7408725
[5]	Sala D A, Jalalvand A, Van Yperen-De Deyne A, et al. Multivariate time series for data-driven endpoint prediction in the basic oxygen furnace // 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando, 2018: 1419
[6]	He F, Zhang L Y. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network. J Process Control, 2018, 66: 51 doi: 10.1016/j.jprocont.2018.03.005
[7]	程進, 王堅. 基于多任務學習的煉鋼終點預測方法. 計算機應用, 2017, 37(3):889 doi: 10.11772/j.issn.1001-9081.2017.03.889 Cheng J, Wang J. Endpoint prediction method for steelmaking based on multi-task learning. J Comput Appl, 2017, 37(3): 889 doi: 10.11772/j.issn.1001-9081.2017.03.889
[8]	楊曉猛, 趙陽, 鐘良才, 等. 基于XGBoost算法的轉爐吹煉終點預報. 煉鋼, 2021, 37(6):1 Yang X M, Zhao Y, Zhong L C, et al. Endpoint prediction of converter steelmaking based on XGBoost algorithm. Steelmaking, 2021, 37(6): 1
[9]	鉉明濤, 李嬌嬌, 王楠, 等. 基于FOA-GRNN模型的轉爐煉鋼終點預報. 材料與冶金學報, 2019, 18(1):31 Xuan M T, Li J J, Wang N, et al. Endpoint prediction of basic oxygen furnace steelmaking based on FOA-GRNN model. J Mater Metall, 2019, 18(1): 31
[10]	韓敏, 趙耀, 楊溪林, 等. 基于魯棒相關向量機的轉爐煉鋼終點預報模型. 控制理論與應用, 2011, 28(3):343 Han M, Zhao Y, Yang X L, et al. Endpoint prediction model of basic oxygen furnace steelmaking based on robust relevance-vector-machines. Control Theory Appl, 2011, 28(3): 343
[11]	劉闖, 韓敏, 王心哲. 基于膜算法進化極限學習機的氧氣轉爐煉鋼終點預報模型. 大連理工大學學報, 2014, 54(1):124 doi: 10.7511/dllgxb201401019 Liu C, Han M, Wang X Z. Endpoint prediction model for basic oxygen furnace steelmaking based on membrane algorithm evolving extreme learning machine. J Dalian Univ Technol, 2014, 54(1): 124 doi: 10.7511/dllgxb201401019
[12]	嚴良濤, 李鳴, 楊大勇. 基于GA-KPLSR的轉爐終點碳含量的預測研究. 控制工程, 2017, 24(5):923 doi: 10.14107/j.cnki.kzgc.150355 Yan L T, Li M, Yang D Y. Prediction of carbon content at end point based on GA-KPLSR in converters. Control Eng China, 2017, 24(5): 923 doi: 10.14107/j.cnki.kzgc.150355
[13]	張彩軍, 韓陽, 何世宇, 等. 爐口火焰光譜驅動的煉鋼終點控制. 儀器儀表學報, 2018, 39(1):24 Zhang C J, Han Y, He S Y, et al. Furnace mouse flame spectrum driven steelmaking end control. Chin J Sci Instrum, 2018, 39(1): 24
[14]	李超, 劉輝. 改進MTBCD火焰圖像特征提取的轉爐煉鋼終點碳含量預測. 計算機集成制造系統,http://kns.cnki.net/kcms/detail/11.5946.TP.20210428.1806.020.html Liu C, Liu H. Carbon content prediction of converter steelmaking end-point based on improved MTBCD flame image feature extraction. Comput Integr Manuf Syst, http://kns.cnki.net/kcms/detail/11.5946.TP.20210428.1806.020.html
[15]	朱雯瓊, 周木春, 趙琦, 等. WCARS-ISPA 火焰光譜特征選擇的轉爐煉鋼終點預測. 光譜學與光譜分析, 2021, 41(8):2332 Zhu W Q, Zhou M C, Zhao Q, et al. End-point prediction of BOF steelmaking based on flame spectral feature selection using WCARS-ISPA. Spectrosc Spectr Anal, 2021, 41(8): 2332
[16]	岳峰, 包燕平, 崔衡, 等. 基于副槍控制的轉爐終點預測模型. 煉鋼, 2009, 25(1):38 Yue F, Bao Y P, Cui H, et al. Sub-lance control-based predication model for BOF end-point. Steelmaking, 2009, 25(1): 38
[17]	Wang X Z, Xing J, Dong J, et al. Data driven based endpoint carbon content real time prediction for BOF steelmaking // 2017 36th Chinese Control Conference (CCC). Dalian, 2017: 9708
[18]	胡志剛, 何平, 劉瀏, 等. 利用爐氣分析進行轉爐鋼水連續定碳. 鋼鐵研究, 2003, 31(3):12 doi: 10.3969/j.issn.1001-1447.2003.03.004 Hu Z G, He P, Liu L, et al. Continuous determination of bath carbon in BOF by off-gas analysis. Res Iron Steel, 2003, 31(3): 12 doi: 10.3969/j.issn.1001-1447.2003.03.004
[19]	胡志剛, 劉瀏, 何平, 等. 利用爐氣分析進行鋼水連續定溫 // 中國金屬學會2003中國鋼鐵年會論文集(3). 北京, 2003: 613 Hu Z G, Liu L, He P, et al. Continuous determination of bath temperature in BOF by off-gas analysis // Proceedings of 2003 China Iron & Steel Annual Meeting(3). Beijing, 2003: 613
[20]	劉錕, 劉瀏, 何平, 等. 基于煙氣分析轉爐終點碳含量控制的新算法. 煉鋼, 2009, 25(1):33 Liu K, Liu L, He P, et al. A new algorithm of endpoint carbon content of BOF based on of off-gas analysis. Steelmaking, 2009, 25(1): 33
[21]	王新華, 李金柱, 劉鳳剛. 轉型發展形勢下的轉爐煉鋼科技進步. 煉鋼, 2017, 33(1):1 Wang X H, Li J Z, Liu F G. Technological progress of BOF steelmaking in period of development mode transition. Steelmaking, 2017, 33(1): 1
[22]	Liao D S, Sun S, Waterfall S, et al. Integrated KOBM steelmaking process control // Proceedings of the 6th International Congress on the Science and Technology of Steelmaking (Ⅰ). Beijing, 2015: 107
[23]	林文輝, 焦樹強, 孫建坤, 等. 轉爐吹煉后期碳含量預報的改進指數模型. 工程科學學報, 2020, 42(7):854 Lin W H, Jiao S Q, Sun J K, et al. Modified exponential model for carbon prediction in the end blowing stage of basic oxygen furnace converter. Chin J Eng, 2020, 42(7): 854
[24]	李南, 林文輝, 曹玲玲, 等. 基于熔池混勻度的轉爐煙氣分析定碳模型. 工程科學學報, 2018, 40(10):1244 Li N, Lin W H, Cao L L, et al. Carbon prediction model for basic oxygen furnace off-gas analysis based on bath mixing degree. Chin J Eng, 2018, 40(10): 1244
[25]	Gu M Q, Xu A J, Wang H B, et al. Real-time dynamic carbon content prediction model for second blowing stage in BOF based on CBR and LSTM. Processes, 2021, 9(11): 1987 doi: 10.3390/pr9111987
[26]	侯玉梅, 許成媛. 基于案例推理法研究綜述. 燕山大學學報(哲學社會科學版), 2011, 12(4):102 doi: 10.15883/j.13-1277/c.2011.04.026 Hou Y M, Xu C Y. Review of case-based reasoning. J Yanshan Univ Philos Soc Sci Ed, 2011, 12(4): 102 doi: 10.15883/j.13-1277/c.2011.04.026