Comparison of the effect of various clustering algorithms on the furnace profile management

LU Jie; YAN Bing-ji; ZHAO Wei; LI Peng; CHEN Dong; GUO Hong-wei

doi:10.13374/j.issn2095-9389.2021.05.25.005

Volume 44 Issue 12

Dec. 2022

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Article Navigation > Chinese Journal of Engineering > 2022 > 44(12): 2081-2089

LU Jie, YAN Bing-ji, ZHAO Wei, LI Peng, CHEN Dong, GUO Hong-wei. Comparison of the effect of various clustering algorithms on the furnace profile management[J]. Chinese Journal of Engineering, 2022, 44(12): 2081-2089. doi: 10.13374/j.issn2095-9389.2021.05.25.005

Citation:

LU Jie, YAN Bing-ji, ZHAO Wei, LI Peng, CHEN Dong, GUO Hong-wei. Comparison of the effect of various clustering algorithms on the furnace profile management[J]. Chinese Journal of Engineering, 2022, 44(12): 2081-2089. doi: 10.13374/j.issn2095-9389.2021.05.25.005

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Comparison of the effect of various clustering algorithms on the furnace profile management

doi: 10.13374/j.issn2095-9389.2021.05.25.005

School of Iron and Steel, Soochow University, Suzhou 215137, China

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Corresponding author: E-mail: bjyan@suda.edu.cn
Received Date: 2021-05-25
Available Online: 2021-10-08
Publish Date: 2022-12-01

Abstract

Abstract

The blast furnace operation profile is closely related to the operation, technical and economic indicators of a blast furnace. A reasonable furnace operation profile ensures high-quality hot metal, low fuel consumption, high yield, and furnace longevity. To guide the blast furnace ironmaking, cluster analysis of the stave temperature is implemented to effectively characterize the changes in the furnace operation profile. The K-Means, TwoStep, and hierarchical clustering algorithms are often used to monitor the blast furnace operation profile. The present study also shows that various clustering algorithms can help manage the blast furnace operation profile. However, the difference among the clustering results from these algorithms remains unclear. Based on the previous research, this paper compared the clustering principles and research status with various algorithms and selected two algorithms of K-Means and TwoStep, which were more applicable and compatible with the algorithm principles. The K-Means algorithm is a typical partition-based clustering algorithm with low time complexity, high clustering efficiency, and good clustering quality. It has been widely used in cluster analysis of the blast furnace operation profile. Additionally, domestic scholars had given effective improvement measures for the shortcomings of sensitivity to the initial center and requirements for data distribution. The TwoStep algorithm was an improved BIRCH (Balanced iterative reducing and clustering using hierarchies) algorithm, which reduced time complexity and can automatically determine the optimal number of clusters. The authors of this article considered the problem that indicators for evaluating the furnace operation profile were multiple and largely overlapped. Principal Component Analysis was introduced based on the TwoStep algorithm. Three new core indicators were generated from the traditional evaluation indicators for the clustering results of the furnace operation profile. For blast furnace operation profile monitoring and management, three core indicators also showed improved performance. In this paper, K-Means and TwoStep were used to cluster the data set. Based on the principles of these algorithms and combined with the Davies?Bouldin index and Dunn index, the clustering results were analyzed to judge the difference between the two clustering algorithms. The analysis based on the sample data and data characteristics selected in this article revealed that the K-Means algorithm achieved better clustering results than TwoStep. This research can provide a powerful reference for selection among various clustering algorithms in blast furnace ironmaking big data analysis.
- furnace profile management,
- K-Means,
- TwoStep,
- cluster evaluation index,
- big data

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References

[1]	萬利德, 李熠, 宋釗, 等. 武鋼8號高爐穩定順行高產操作特點. 煉鐵, 2020, 39(4):12 Wan L D, Li Y, Song Z, et al. Operation techniques for keeping stable smooth & high output production of WISCO’s No 8 BF. Ironmaking, 2020, 39(4): 12
[2]	王俊, 徐輝, 張培峰, 等. 寶鋼4號高爐長期低耗生產管理. 煉鐵, 2020, 39(4):1 Wang J, Xu H, Zhang P F, et al. Management for maintaining low consumption production in Baosteel’s No 4 BF. Ironmaking, 2020, 39(4): 1
[3]	Li X L, Liu D X, Jia C, et al. Multi-model control of blast furnace burden surface based on fuzzy SVM. Neurocomputing, 2015, 148: 209 doi: 10.1016/j.neucom.2013.09.067
[4]	Jin F, Zhao J, Sheng C Y, et al. Causality diagram-based scheduling approach for blast furnace gas system. IEEE/CAA J Autom Sin, 2018, 5(2): 587 doi: 10.1109/JAS.2017.7510715
[5]	王晨露, 陳先中, 侯慶文, 等. 基于高爐料線的RCS測量及SAR成像驗證. 工程科學學報, 2018, 40(8):979 Wang C L, Chen X Z, Hou Q W, et al. RCS measurement and SAR imaging verification based on blast furnace stock line. Chin J Eng, 2018, 40(8): 979
[6]	Shi L, Wen Y B, Zhao G S, et al. Recognition of blast furnace gas flow center distribution based on infrared image processing. J Iron Steel Res Int, 2016, 23(3): 203 doi: 10.1016/S1006-706X(16)30035-8
[7]	Hua C C, Wu J H, Li J P, et al. Silicon content prediction and industrial analysis on blast furnace using support vector regression combined with clustering algorithms. Neural Comput Appl, 2017, 28(12): 4111 doi: 10.1007/s00521-016-2292-x
[8]	Chen K, Liang Y, Gao Z L, et al. Just-in-time correntropy soft sensor with noisy data for industrial silicon content prediction. Sensors, 2017, 17(8): 1830 doi: 10.3390/s17081830
[9]	劉代飛, 張吉, 付強. 基于溫度場主元分析的高爐爐況深度學習預測建模. 冶金自動化, 2021, 45(3):42 doi: 10.3969/j.issn.1000-7059.2021.03.006 Liu D F, 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 doi: 10.3969/j.issn.1000-7059.2021.03.006
[10]	王振陽, 江德文, 王新東, 等. 基于支持向量回歸與極限學習機的高爐鐵水溫度預測. 工程科學學報, 2021, 43(4):569 Wang Z Y, Jiang D W, Wang X D, et al. Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine. Chin J Eng, 2021, 43(4): 569
[11]	Fontes D O L, Vasconcelos L G S, Brito R P. Blast furnace hot metal temperature and silicon content prediction using soft sensor based on fuzzy C-means and exogenous nonlinear autoregressive models. Comput Chem Eng, 2020, 141: 107028 doi: 10.1016/j.compchemeng.2020.107028
[12]	Zhao J, Li X, Liu S, et al. Prediction of hot metal temperature based on data mining. High Temp Mater Process, 2021, 40(1): 87 doi: 10.1515/htmp-2021-0020
[13]	陳令坤. 基于高效冶煉的高爐智能控制系統分析. 冶金自動化, 2021, 45(3):2 Chen L K. Analysis of intelligent control system used for high efficiency smelting in blast furnace. Metall Ind Autom, 2021, 45(3): 2
[14]	閆炳基, 張建良, 國宏偉, 等. 基于主成分分析的高爐操作爐型評價. 東北大學學報(自然科學版), 2015, 36(7):952 doi: 10.3969/j.issn.1005-3026.2015.07.009 Yan B J, Zhang J L, Guo H W, et al. Evaluation of blast furnace operation profile based on principal component analysis(PCA). J Northeast Univ Nat Sci, 2015, 36(7): 952 doi: 10.3969/j.issn.1005-3026.2015.07.009
[15]	曹英杰, 張建良, 國宏偉, 等. 基于TwoStep算法的國豐1號高爐操作爐型聚類分析與應用. 鋼鐵, 2013, 48(10):17 doi: 10.13228/j.boyuan.issn0449-749x.2013.10.011 Cao Y J, Zhang J L, Guo H W, et al. Clustering analysis and application of operative profile in Guofeng No 1 blast furnace based furance based on the algorithm of TwoStep. Iron Steel, 2013, 48(10): 17 doi: 10.13228/j.boyuan.issn0449-749x.2013.10.011
[16]	陳令坤, 李佳. 基于模式識別的自學習型高爐冶煉專家系統的開發與應用. 東南大學學報(自然科學版), 2012, 42(增刊1): 117 Chen L K, Li J. Development and application of blast furnace expert system with self-learning function based on pattern recognition. J Southeast Univ Nat Sci, 2012, 42(Suppl 1): 117
[17]	武森, 鄭錫村, 國宏偉. 數據挖掘技術在高爐爐型管理中的應用 // 全國第十屆企業信息化與工業工程學術年會論文集. 杭州, 2006:268 Wu S, Zhen X C, Guo H W. Application of data mining technique in blast furnace profile management // The 10th National Annual Conference on Enterprise Informatization and Industrial Engineering. Hangzhou, 2006: 268
[18]	García F A, Campoy P, Mochón J, et al. A new “user-friendly” blast furnace advisory control system using a neural network temperature profile classifier. ISIJ Int, 2010, 50(5): 730 doi: 10.2355/isijinternational.50.730
[19]	Saxena C, Prasad S, Lavanya A, et al. Classification of above burden profile using SOM and k-means. Ironmak Steelmak, 2007, 34(1): 5 doi: 10.1179/174328106X149851
[20]	Velmurugan T, Santhanam T. A survey of partition based clustering algorithms in data mining: An experimental approach. Inf Technol J, 2011, 10(3): 478 doi: 10.3923/itj.2011.478.484
[21]	Carlsson G, Mémoli F. Characterization, stability and convergence of hierarchical clusteringmethods. J Mach Learn Res, 2010, 11: 1425
[22]	Jiang B, Pei J, Tao Y F, et al. Clustering uncertain data based on probability distribution similarity. IEEE Trans Knowl Data Eng, 2013, 25(4): 751 doi: 10.1109/TKDE.2011.221
[23]	Al-Shammary D, Khalil I, Tari Z. A distributed aggregation and fast fractal clustering approach for SOAP traffic. J Netw Comput Appl, 2014, 41: 1 doi: 10.1016/j.jnca.2013.10.001
[24]	McNicholas P D, Murphy T B. Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinformatics, 2010, 26(21): 2705 doi: 10.1093/bioinformatics/btq498
[25]	Xu D K, Tian Y J. A comprehensive survey of clustering algorithms. Ann Data Sci, 2015, 2(2): 165 doi: 10.1007/s40745-015-0040-1
[26]	McKim C, Wu C R, Bovaird J A. Using graphical techniques from discriminant analysis to understand and interpret cluster solutions. Int J Data Anal Tech Strateg, 2017, 9(3): 189 doi: 10.1504/IJDATS.2017.086633
[27]	張鴻雁, 杜文鋒, 武麗芬. 基于密度加權的分裂式K均值聚類算法. 計算機仿真, 2021, 38(4):254 doi: 10.3969/j.issn.1006-9348.2021.04.051 Zhang H Y, Du W F, Wu L F. Split K-menas clustering algorithm based on density weighting. Comput Simul, 2021, 38(4): 254 doi: 10.3969/j.issn.1006-9348.2021.04.051
[28]	劉葉, 吳晟, 周海河, 等. 基于K-means聚類算法優化方法的研究. 信息技術, 2019, 43(1):66 Liu Y, Wu S, Zhou H H, et al. Research on optimization method based on K-means clustering algorithm. Inf Technol, 2019, 43(1): 66
[29]	于祥. 基于外部驗證指標的一致性聚類算法研究[學位論文]. 南京: 南京財經大學, 2020 Yu X. Research on Consensus Clustering Algorithm Based on External Validation Measures [Dissertation]. Nanjing: Nanjing University of Finance & Economics, 2020
[30]	Estivill-Castro V. Why so many clustering algorithms: A position paper. Acm Sigkdd Explor Newsl, 2002, 4(1): 65 doi: 10.1145/568574.568575
[31]	郭靖, 侯蘇. K-means算法最佳聚類數評價指標研究. 軟件導刊, 2017, 16(11):5 Guo J, Hou S. Study on the index of determining the optimal clustering number of K-means algorithm. Softw Guide, 2017, 16(11): 5
[32]	高新. 一種改進K-means聚類算法與新的聚類有效性指標研究[學位論文]. 合肥: 安徽大學, 2020 Gao X. Research on Improved K-Mens Algorithm and New Cluster Validity Index [Dissertation]. Hefei: Anhui University, 2020
[33]	朱連江, 馬炳先, 趙學泉. 基于輪廓系數的聚類有效性分析. 計算機應用, 2010, 30(增刊2): 139 Zhu L J, Ma B X, Zhao X Q. Clustering validity analysis based on silhouette coefficient. J Comput Appl, 2010, 30(Suppl 2): 139
[34]	呂正萍, 紀漢霖. 數種基于SPSS統計工具的聚類算法效率對比. 軟件導刊, 2018, 17(11):81 Lv Z P, Ji H L. Three clustering validity analysis based on SPSS. Softw Guide, 2018, 17(11): 81
[35]	Liu Q, Zhang P, Cheng S S, et al. Heat transfer and thermo-elastic analysis of copper steel composite stave. Int J Heat Mass Transf, 2016, 103: 341 doi: 10.1016/j.ijheatmasstransfer.2016.05.100
[36]	Zhang H, Jiao K X, Zhang J L, et al. A new method for evaluating cooling capacity of blast furnace cooling stave. Ironmak Steelmak, 2019, 46(7): 671 doi: 10.1080/03019233.2018.1454388