Prediction of end point %C of CONARC® furnace using machine learning methods

被引:0
作者
Parul, Kota [1 ]
Samiraj, Albin Rozario [1 ]
Hazra, Sujoy S. [1 ]
机构
[1] JSW Steel Ltd, Res & Dev, Dolvi 402107, Maharashtra, India
来源
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES | 2023年 / 48卷 / 03期
关键词
CONARC (R); steelmaking; endpoint prediction; machine learning; tree model; support vector machine; STEELMAKING PROCESS; PHOSPHORUS-CONTENT; FAULT-DIAGNOSIS; MOLTEN STEEL; MODEL;
D O I
10.1007/s12046-023-02163-7
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
CONARC (R) steel making process is a combination of convertor steel making and electric arc steelmaking to get the benefit of both the process and make it flexible in terms of using raw material feed mix. Raw material feed mix in this furnace on an average is 60% hot metal (HM), 38% cold direct reduced iron (CDRI) and 2% steel scrap. In this furnace operation there are two phases, namely, the oxygen blowing phase and arcing phase followed by tapping of steel into the ladle. During the oxygen blowing phase, the HM carbon content is reduced from 4.5% to 0.3%-0.5%, and further reduced to 0.025-0.03% in the arcing phase depending upon the grade of steel produced. During the arcing stage, CoJet (TM) lances are used for the oxidation of the bath and reduction of the carbon content to the desired values. The end point %C parameter is very important in CONARC (R) steel making as it determines the productivity and quality of the steel produced. Based on the analysis, mathematical and machine learning approach was adopted to predict the end point %C during the arcing stage of the furnace. The algorithms which are used and compared are the tree-based models and support vector machines. After comparing the results, the tree based model seems best fit after further optimization to get an accuracy of 83%. The model was validated with plant trials and the accuracy was found to be within +/- 0.013 %C.
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页数:13
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共 21 条
  • [1] Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System
    Bae, Juhee
    Li, Yurong
    Stahl, Niclas
    Mathiason, Gunnar
    Kojola, Niklas
    [J]. METALLURGICAL AND MATERIALS TRANSACTIONS B-PROCESS METALLURGY AND MATERIALS PROCESSING SCIENCE, 2020, 51 (04): : 1632 - 1645
  • [2] Endpoint temperature prediction of molten steel in RH using improved case-based reasoning
    Feng, Kai
    Wang, Hong-bing
    Xu, An-jun
    He, Dong-feng
    [J]. INTERNATIONAL JOURNAL OF MINERALS METALLURGY AND MATERIALS, 2013, 20 (12) : 1148 - 1154
  • [3] End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA
    Gao, Chuang
    Shen, Minggang
    Liu, Xiaoping
    Wang, Lidong
    Chen, Ming
    [J]. TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS, 2019, 72 (01) : 257 - 270
  • [4] Geron Aurelien, 2019, HANDS ON MACHINE LEA, VSecond
  • [5] Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine
    Han, Min
    Liu, Chuang
    [J]. APPLIED SOFT COMPUTING, 2014, 19 : 430 - 437
  • [6] Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network
    He, Fei
    Zhang, Lingying
    [J]. JOURNAL OF PROCESS CONTROL, 2018, 66 : 51 - 58
  • [7] Kappes H, 2000, REV METALL-PARIS, V97, P897
  • [8] Modeling of steelmaking process with effective machine learning techniques
    Laha, Dipak
    Ren, Ye
    Suganthan, P. N.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (10) : 4687 - 4696
  • [9] A novel data-based quality-related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process
    Ma, Liang
    Dong, Jie
    Peng, Kaixiang
    Zhang, Kai
    [J]. CONTROL ENGINEERING PRACTICE, 2017, 67 : 43 - 51
  • [10] STATIC + DYNAMIC CONTROL OF BASIC OXYGEN PROCESS
    MEYER, HW
    DUKELOW, DA
    FISCHER, MM
    [J]. JOM-JOURNAL OF METALS, 1964, 16 (06): : 501 - &