轉爐吹煉過程噴濺機理及預報模型研究進展

辛同澤; 王敏; 包燕平

doi:10.13374/j.issn2095-9389.2022.08.18.002

轉爐吹煉過程噴濺機理及預報模型研究進展

doi: 10.13374/j.issn2095-9389.2022.08.18.002

辛同澤¹,
王敏^{2, 3, ,},
包燕平²

1.
北京科技大學土木與資源工程學院，北京 100083
2.
北京科技大學鋼鐵冶金新技術國家重點實驗室，北京 100083
3.
北京科技大學金屬冶煉重大事故防控技術支撐基地，北京 100083

基金項目: 國家自然科學基金資助項目(52174297)；中央高校基本科研業務費資助項目(FRF-BD-19-022A)

詳細信息

通訊作者:
E-mail: wangmin@ustb.edu.cn

中圖分類號: X938
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- 文章訪問數: 233
- HTML全文瀏覽量: 36
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- 被引次數: 0
出版歷程
- 收稿日期: 2022-08-18
- 網絡出版日期: 2022-12-14
- 刊出日期: 2023-10-25

Research progress of converter splash mechanism and prediction model technology

XIN Tongze¹,
WANG Min^{2, 3
, ,},
BAO Yanping²

1.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
3.
Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: wangmin@ustb.edu.cn

摘要

摘要: 轉爐作為一個高溫高壓、多元多相的反應容器，容易發生噴濺或溢渣事故. 良好的熔池攪拌可以增大渣–金反應面積，提高煉鋼效率；異常的熔池攪拌則會造成金屬損失，毀壞爐體及其附屬設備，甚至威脅到爐前工作人員的人身安全. 本文總結了前人對噴濺機理及影響因素的研究結果，轉爐噴濺按產生的原因可以分為爆發性噴濺、泡沫性噴濺、金屬性噴濺和其他噴濺，其中爆發性噴濺的危害最大，泡沫性噴濺的發生頻率最高. 噴濺事故的產生總體可以歸結為爐內激烈化學反應產生氣泡驅動的高溫熔體噴濺和頂底復吹為熔池提供的流動能量所產生的噴濺，且一次噴濺事故的發生常常是多種因素耦合引發，從單方面分析噴濺事故原因過于片面，研究出一套適用于轉爐噴濺的安全評價模型是當務之急. 并對現有的噴濺預報模型進行了綜述，總結了爐氣分析法、音頻分析法、圖像分析法的預測原理及部分應用結果，指出現有預測模型沒有得到廣泛應用的原因，未來噴濺預測模型會朝著更加智能化、精細化的方向發展.
- 轉爐噴濺 /
- 溢渣 /
- 噴濺機理 /
- 影響因素 /
- 預測模型
Abstract: As a high-temperature, high-pressure, multi-phase reaction vessel, the converter is vulnerable to splashing or slag overflow. Good molten pool surge can expand the slag–gold reaction area and enhance steelmaking efficiency. Abnormal molten pool surge can cause metal loss, damage the furnace body and its auxiliary equipment, and even threaten the personal safety of workers working in front of the furnace. This paper summarizes the previous research findings on splashing mechanisms and influencing factors. According to the occurrence principle, converter splashes can be classified into explosive splashes, foam splashes, metallic splashes, and other splashes, among which explosive splashes are the most dangerous and foam splashes occur most frequently. The occurrence of splashing accidents can be generally attributed to the high-temperature melt splashing caused by bubbles produced during the vigorous chemical reaction in the furnace and the splashing produced by the flow energy generated during the top–bottom combined blowing of the molten pool. The influencing factors of splashing are discussed based on six aspects: loading system, slag making system, oxygen supply system, bottom blowing system, temperature system, and safety system, and the foam of slag, oxygen lance blowing parameters, and bottom blowing parameters are thoroughly examined. It is observed that the occurrence of a splashing accident is frequently caused by the coupling of multiple factors. It is mainly one-sided, and hence the cause of the splashing accident cannot be unilaterally analyzed. Currently, no methods are present that can effectively quantify the effect of each factor on the splashing. Thus, developing a set of safety evaluation models suitable for converter splashing is imperative. Furthermore, the author summarizes the existing splash prediction models, examines the benefits and drawbacks of some splash prediction models, and summarizes the prediction principles and some application outcomes of the furnace gas analysis, audio analysis, and image analysis methods. Although preliminary progress has been made in the study of prediction models, there are still challenges that need to be overcome. It is pointed out that the reason why the existing prediction models have not been widely used is due to the low prediction accuracy, short prediction time, high cost, and low practicability. Several researchers have used a combination of several models to predict splash in converter. The findings reveal that various models can learn from each other, and the prediction accuracy of the comprehensive model is higher than that of the single model. Furthermore, the splash prediction model will become more intelligent and refined in the future.
- converter splashes /
- slag overflow /
- splash mechanism /
- influence factor /
- prediction model

HTML全文

圖 1 轉爐吹煉不同階段的噴濺特征

Figure 1. Characteristics of splashing in different stages of converter blowing

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圖 2 轉爐噴濺事故影響因素圖

Figure 2. Influencing factors of the converter splashing accident

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圖 3 吹煉產生的表面波示意圖

Figure 3. Schematic of the surface wave produced by blowing

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圖 4 傳統氧槍和漩流氧槍示意圖. (a) 傳統氧槍噴孔示意圖; (b) 漩流氧槍噴孔示意圖

Figure 4. Schematic of conventional oxygen lance and nozzle-twisted oxygen lance: (a) schematic of the traditional oxygen lance; (b) schematic of the nozzle-twisted oxygen lance

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圖 5 不同孔數噴頭對噴濺率的影響. (a) Yang等^[25]30 t轉爐1∶4水模型實驗數據; (b) Ma等^[26]260 t轉爐1∶9水模型實驗數據

Figure 5. Effect of different hole number nozzles on spray rate: (a) 1∶4 water model experimental data of 30 t converter by Yang et al.^[25]; (b) 1∶9 water model experimental data of 260 t converter by Ma et al.^[26]

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圖 6 氧流量和槍位對噴濺率的影響. (a) Yang等^[27]300 t轉爐6孔噴頭1∶10水模型實驗結果; (b) Guo等^[18]260 t轉爐5孔噴頭1∶7水模型實驗結果

Figure 6. Effect of oxygen flow rate and gun position on splash rate: (a) 1∶10 water model experimental results of the 6-hole nozzle in 300 t converter by Yang et al.^[27]; (b) 1∶7 water model experimental results of the 5-hole nozzle in 260 t converter by Guo et al.^[18]

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圖 7 頂底復吹能量混合示意圖

Figure 7. Schematic of the top–bottom combined blowing energy mixing

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圖 8 底吹流量對噴濺現象的影響. (a) Yang等^[25]300 t轉爐1∶10水模型實驗結果; (b) Lyu等^[28]150 t轉爐1∶7水模型實驗結果

Figure 8. Effect of bottom blowing flow rate on splash phenomenon: (a) 1∶10 water model test results of 300 t converter by Yang et al.^[25]; (b) 1∶7 water model test results of 150 t converter by Lyu et al.^[28]

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圖 9 爆發性噴濺機理圖. (a) 爆發性噴濺發生流程圖; (b) 爆發性噴濺示意圖

Figure 9. Mechanism of explosive splash: (a) flow chart of explosive splashing; (b) schematic of an explosive splash

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圖 10 泡沫性噴濺機理圖. (a) 泡沫性噴濺發生流程圖; (b) 泡沫性噴濺示意圖

Figure 10. Foam splash mechanism: (a) flow chart of foam splashing; (b) schematic of a foam splash

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圖 11 金屬性噴濺機理圖. (a)金屬性噴濺發生流程圖; (b)金屬性噴濺示意圖

Figure 11. Mechanism of metallic splashing: (a) flow chart of metallic splashing; (b) schematic of a metallic splash

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圖 12 爐氣分析系統預測噴濺示意圖

Figure 12. Schematic of the predicted splashing of furnace gas analysis system

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圖 13 音頻分析系統預測噴濺示意圖

Figure 13. Schematic of the predicted splash of audio analysis system

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圖 14 圖像分析系統預測噴濺示意圖

Figure 14. Schematic of the splash prediction using image analysis system

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表 1 不同噴濺類型對比表

Table 1. Comparison of different splash types

Splash type	Main cause	Main occurrence period	Occurrence frequency (relative)	Main hazards
Explosive splash	Violent reaction of carbon and oxygen	Early and late stages of smelting	Moderate	Large cost loss and possible injury
Foam splash	Serious foam of slag	Middle stage of smelting	High frequency	Large cost loss, resulting in shutdown
Metallic splash	Post-drying of slag	Middle stage of smelting	Low frequency	Metal loss and possible injury

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表 2 爐渣泡沫化的影響因素

Table 2. Influence factors of the slag foam

Factor	Influence mode	Specific performance	Correlation with foam index
Si and P content of molten iron	During the blowing process, SiO₂ and P₂O₅ are produced and enter the slag	SiO₂ and P₂O₅ are surface-active substances that reduce surface tension and increase viscosity^[7]	Positive correlation
FeO content in slag	The amount of FeO in slag dynamically changes with the progress of oxidation and decarbonization reaction and affects the liquid phase ratio in slag^[15]	When the FeO content is less than 20%, the slag viscosity decreases with the increase in FeO content and tends to a fixed value when the FeO content is greater than 20%^[16?17]	Negative correlation
Basicity	Basicity is an important parameter of slag, and it changes with the addition of slag-forming materials and smelting, which has an important effect on the characteristics of slag	At the same temperature, the foam index decreases first and then increases with the increase in basicity^[12]	First negative correlation, then positive correlation
Temperature	Temperature is important to the formation rate of carbon–oxygen reaction and the physical and chemical properties of slag components	With the increase in temperature, on the one hand, the reaction rate of carbon and oxygen increases, which is conducive to foaming; However, the viscosity of molten slag reduces, and the foaming property decreases. The latter has a slightly higher impact than the former^[12]	Positive correlation

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表 3 轉爐噴濺預測模型優缺點對比

Table 3. Comparison of benefits and drawbacks of the prediction models for converter splashing

Splash prediction method	Advantage	Shortcoming
Furnace gas analysis method	Give certain guidance to the blowing conditions in the furnace (such as temperature, decarbonization, end-point carbon drawing^[40], etc.)	Real-time performance is poor, and it is primarily used for cause analysis during and after splashing
Audio analysis method	Auxiliary slagging, obtaining slag status, high hit rate of dry return prediction, and avoiding metal splashing	When splashing, the audio frequency is relatively low, making it challenging to obtain the sound signal and predict it
Image analysis method	Real-time detection of flame state and prediction through image recognition can assist in slag and end-point assessment, which is more intuitive	High cost and increased implementation requirements. The actual smelting is difficult to use
Oxygen lance vibration method	Splashing can be predicted by detecting the slagging state	The model is complex, there are several interference factors in the furnace, and the prediction accuracy is low

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表 4 音頻分析預測噴濺實際應用結果

Table 4. Application of the audio frequency analysis to predict splashing

	Splash prediction (explosive splash and foam splash)	Prediction of back drying (metallic splash)
Jiang et al.^[50] 300 t converter	Hit rate: 97.5%	Hit rate: 100%
Liu^[51] 120 t converter	Splash rate reduced by 4.4%	Post-drying rate reduced by 24%
Li^[52] 120 t converter	Hit rate: 89.47%	Hit rate: 100%

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表 5 聲音信號與圖像處理結合模型預測噴濺實際應用結果

Table 5. Application of the audio frequency analysis to predict splashing

Detection system	Percentage of false slopping/%
Image only	11
Sound only	33
Image and sound combined	6

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