煉鋼合金減量化智能控制模型及其應用

鄭瑞軒; 包燕平; 王仲亮

doi:10.13374/j.issn2095-9389.2021.10.07.004

煉鋼合金減量化智能控制模型及其應用

doi: 10.13374/j.issn2095-9389.2021.10.07.004

北京科技大學鋼鐵冶金新技術國家重點實驗室，北京 100083

基金項目: 國家自然科學基金資助項目（51874021）；鋼鐵冶金新技術國家重點實驗室基金資助項目（41620020）

詳細信息

通訊作者:
E-mail: baoyp@ustb.edu.cn

中圖分類號: TF741
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- 文章訪問數: 1525
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-10-07
- 網絡出版日期: 2021-11-05
- 刊出日期: 2021-12-24

Intelligent control model of steelmaking using ferroalloy reduction and its application

State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China

More Information

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

摘要

摘要: 基于K均值聚類法對轉爐出鋼過程的合金損耗進行了研究，分析了影響合金損耗的關鍵因素，并將其分為3個聚類，得到轉爐出鋼合金損耗最低的工藝模式。在此基礎上，開發了基于PCA-BP神經網絡和混合整數線性規劃的合金減量化智能控制系統，并以某煉鋼廠為例進行了實際應用。通過對模型進行在線運行，驗證了模型的準確性和實用性。使用該模型后，提高了合金化鋼液成分準確度，減少由傳統人工經驗計算配料造成的成本浪費和成分超標等情況，優化了合金配料方案，降低了煉鋼合金化成本，不同鋼種鐵合金加入總成本降低5.95%～14.74%，平均降幅11.72%。
- 煉鋼 /
- 鐵合金 /
- 減量化 /
- 智能控制 /
- 成本
Abstract: The steel industry is a major energy consumer in China. As an effective measure for energy saving, cost and emission reduction, and higher efficiency among enterprises, ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality. In the steelmaking process, the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties. The chemical composition of molten steel is mainly adjusted using ferroalloys. With the development of ferroalloy smelting technology, ferroalloys of various types are developed. These ferroalloys show major gaps in cost performance and composition. Before ferroalloy addition, it is essential to determine an appropriate and cost-effective type and its amount for cost-saving purposes. However, the traditional method of offering a manually determined amount cannot meet the above requirement. Therefore, it is necessary to explore an intelligent ferroalloy addition method without human intervention. Based on the K-means clustering algorithm, this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process. The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process. Using this model, an intelligent control system for alloy reduction was developed. The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming. This system was implemented in a steelmaking plant, in which the accuracy and practicability of this model were verified by running it online. This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition, which are frequently seen in traditional calculations with a manual experience. The ferroalloy dosing scheme is also optimized, and the alloying cost of steelmaking is reduced. The total cost of adding ferroalloys of various types is reduced by 5.95% to 14.74%, with an average reduction of 11.72%.
- steelmaking /
- ferroalloy /
- reduction /
- intelligent control /
- cost

HTML全文

圖 1 42CrMo轉爐出鋼工序工藝參數聚類分布圖(3個聚類)(a)及噸鋼硅錳耗量(b)和噸鋼中碳錳鐵耗量(c)

Figure 1. Cluster distribution map of process parameters in the 42CrMo converter steel discharge process (3 clusters) (a), silicomanganese consumption per ton of steel (b), and medium carbon ferromanganese consumption per ton of steel (c)

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圖 2 模型計算流程圖

Figure 2. Model calculation flowchart

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圖 3 合金減量化智能控制模型主界面

Figure 3. Main interface of the intelligent control steelmaking ferroalloy reduction model

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圖 4 合金收得率PCA-BP神經網絡建立流程圖

Figure 4. Flowchart of the alloy element yield PCA-BP neural network

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圖 5 PCA-BP神經網絡結構圖

Figure 5. Structure diagram of the PCA-BP neural network

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圖 6 合金收得率預測誤差頻率分布

Figure 6. Frequency distribution of the alloy yield prediction error

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圖 7 模型計算成本與實際成本對比

Figure 7. Comparison of the model calculation alloy cost and the actual alloy cost

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表 1 3聚類爐次占比及錳收得率

Table 1. 3 Clustering proportion and the manganese yield in each cluster

Cluster	Manganese yield/%	Proportion /%
Cluster 1	88.46	23.57
Cluster2	86.56	20.71
Cluster3	87.11	55.71

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表 2 合金收得率預測結果和實際收得率對比

Table 2. Comparison of the actual alloy yield and the forecast alloy yield

Number	Actual alloy yield/%	Forecast alloy yield/%	Error/%
1	94.40	95.89	1.48
2	95.68	95.65	–0.02
3	95.08	93.83	–1.24
4	95.82	95.05	–0.76
5	95.88	95.96	0.08
6	95.66	96.28	0.62
7	96.51	95.23	–1.27
8	97.07	95.44	–1.62

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表 3 工業試驗結果

Table 3. Results of the industrial test

Furnace number	O₂ consumption/ (m³?t^–1)	Tapping temperature/℃	Hot metal ratio	Mn yield/%	Past average cost/(￥·t^–1)	Current cost/ (￥·t^–1)	Reduce costs /(￥·t^–1)	Decrease rate /%	Whether the ingredients are qualified
Control group	52.20	1641	0.92	86.52	198.4	—	—	—	—
1	56.46	1585	0.77	86.60	198.4	167.5	30.9	11.68	√
2	50.48	1581	0.77	87.03	198.4	170.1	28.3	11.67	√
3	56.77	1589	0.78	89.31	198.4	175.2	23.2	14.24	√
4	53.37	1581	0.78	89.37	198.4	159.9	38.4	5.95	√
5	59.45	1531	0.91	89.65	198.4	160.5	37.9	9.27	√
6	45.97	1582	0.78	89.66	198.4	166.3	32.1	14.03	√
7	46.54	1571	0.76	89.80	198.4	161.8	36.6	9.82	√
8	47.62	1584	0.92	90.25	198.4	175.1	23.3	9.24	√
9	49.83	1583	0.92	90.26	198.4	168.4	30.0	14.74	√
10	50.84	1575	0.77	90.35	198.4	161.1	37.3	12.70	√
11	51.48	1582	0.78	90.58	198.4	170.8	27.6	11.46	√
12	55.76	1571	0.78	90.64	198.4	161.1	37.3	14.06	√
13	51.35	1569	0.8	91.10	198.4	166.5	31.8	13.52	√

下載: 導出CSV

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