基于PSO–VIKOR的燒結配礦成本與能耗協同優化模型

馬云飛; 李擎; 張建良; 劉征建; 郭鋒; 王耀祖

doi:10.13374/j.issn2095-9389.2022.08.30.004

基于PSO–VIKOR的燒結配礦成本與能耗協同優化模型

doi: 10.13374/j.issn2095-9389.2022.08.30.004

1.
北京科技大學自動化學院，北京 100083
2.
北京科技大學冶金與生態工程學院，北京 100083
3.
北京科技大學人工智能研究院，北京 100083

基金項目: 北京市科技新星計劃資助項目（Z211100002121115）;博士后面上基金資助項目（2021M690369）

詳細信息

通訊作者:
E-mail: yaozuwang@ustb.edu.cn

中圖分類號: TP3-05
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出版歷程
- 收稿日期: 2022-08-30
- 網絡出版日期: 2023-03-28

Synergistic optimization model of sintering ore allocation cost and energy consumption based on PSO–VIKOR

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
3.
Institute of Artificial Intelligence, University of Science and Technology Beijing Beijing 100083, China

More Information

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

摘要

摘要: 燒結作為鋼鐵生產的主要能源消耗工序之一，在鋼鐵總能耗中約占10%. 燒結工序能源主要來源于固體燃料，傳統燒結優化配礦燃料配比通常由經驗確定，未能實現原料類型與燒結過程燃耗的動態平衡. 針對燒結過程的能量平衡，首先在已有化學成分、堿度、原料配比等約束條件的基礎上，嵌入燒結能量平衡約束，構建了基于燒結能量平衡的燒結配料模型，最后采用粒子群算法進行求解，實現了燒結鐵礦石、熔劑和燃料的協同優化. 仿真結果表明，本文提出的基于PSO–VIKOR (Particle swarm optimization–multicriteria optimization and compromise solution)燒結優化配礦模型提高了燒結過程的能源利用率，在考慮燒結成本與質量的同時，實現了燒結過程的節能減排，有助于鋼鐵企業燒結低碳綠色發展.
- 燒結優化配礦 /
- 燒結能質平衡 /
- 多準則決策 /
- 冶金智能制造 /
- 數字孿生模型
Abstract: As one of the major energy-consuming processes in steel production, sintering accounts for approximately 10% of the total energy consumption of steel production. The energy consumed in the sintering process is mainly attributed to solid fuels. Additionally, in traditional sintering, optimized ore–fuel ratio is usually determined by experience, which fails to achieve a dynamic balance between raw material type and sintering process combustion consumption. In this study, we first analyze the complex physicochemical reaction processes, such as the decomposition of crystalline water, combustion of solid fuels, and oxidation and reduction of iron oxides in the sintering process, to understand the energy flow of the sintering process. We then set empirical parameters according to an actual sintering site, and we finally establish a sintering energy–mass balance model. Subsequently, the sintering energy balance constraint is embedded on the basis of the existing constraints of chemical composition, alkalinity, raw material ratio, etc. Additionally, the cost of sintering raw material is taken as the optimization target, after which a sintering batching model based on sintering energy balance is constructed; the penalty function method is used to transform the constrained problem into an unconstrained one; finally, the actual furnace charge structure of a certain steel plant is solved by using the particle swarm algorithm (PSO) to realize completely automatic dosing of sintering iron ore, flux and fuel. The simulation results show that the optimized sintering ore allocation based on the proposed PSO algorithm-led optimal sintering ore allocation model results in a suitable fuel ratio and increased energy efficiency of the sintering process. The optimal sintering ore allocation method is a compromise of various conflicting objectives; therefore, the solved ore allocation scheme is taken as the object, and the four indicators TFe, cost, S content, and solid fuel usage are integrated; additionally, the weights of each indicator are objectively obtained by using the entropy weight method according to the dispersion degree of data and information entropy of each indicator, under the principle of considering the balance of group benefit maximization and individual regret minimization. The VIKOR (Multicriteria optimization and compromise solution) method is used for compromise ranking and preference of the scheme. The final results confirm that the proposed PSO–VIKOR sintering ore allocation optimization model achieves energy saving and emission reduction in the sintering process while considering the sintering cost and quality, which is expected to help in low-carbon green development and sustainable evolution of sintering in iron and steel enterprises and achieve the double carbon target.
- sintering optimal ore allocation /
- sintering energy–mass balance /
- multi-criteria decision making /
- metallurgical smart manufacturing /
- digital twin model

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圖 1 鐵礦石燒結工藝流程示意圖

Figure 1. Flow diagram of the sintering process

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圖 2 燒結熱平衡示意圖

Figure 2. Sintering heat balance diagram

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圖 3 各方案熱量總收入

Figure 3. Total heat income by the schemes

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圖 4 各方案熱量總支出

Figure 4. Total heat expenditure by the schemes

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圖 5 迭代曲線

Figure 5. Iterative curve

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表 1 燒結點火焦爐煤氣成分 (質量分數)

Table 1. Composition of coke oven gas %

CO	CO₂	H₂	N₂	CH₄	C₂H₄	C₂H₆	C₃H₆	O₂
6	2.3	59	3	26	2.3	0.9	0.2	0.2

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表 2 焦爐煤氣氣體密度

Table 2. Density of gas kg·m^–3

CO	CO₂	H₂	N₂	CH₄	C₂H₄	C₂H₆	C₃H₆	O₂
1.25	1.96	0.09	1.25	0.71	1.25	1.34	1.96	1.43

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表 3 燒結原料及化學成分

Table 3. Chemical composition of all the sintering materials

Mineral powder	Chemical composition(Mass fraction)									Price/(￥·t^–1)
Mineral powder	TFe/%	FeO/%	CaO/%	MgO/%	SiO₂/%	Al₂O₃/%	S/%	Ig/%	H₂O/%	Price/(￥·t^–1)
Ore1	68.79	28.81	0.18	0.26	1.94	0.84	0.02	–2.78	8.00	1317.93
Ore2	64.37	25.18	0.41	0.33	6.44	0.80	0.26	–1.29	7.00	1049.24
Ore3	57.26	0.48	0.05	0.06	5.70	2.82	0.03	7.95	6.00	896.00
Ore4	61.16	0.38	0.03	0.04	4.40	2.46	0.03	4.94	4.00	1149.56
Ore5	62.85	0.83	0.05	0.03	4.50	1.22	0.02	3.73	6.00	823.60
Ore6	60.24	0.31	0.24	0.02	3.70	2.88	0.02	6.41	10.08	774.60
Ore7	56.48	0.27	0.11	0.02	6.14	6.47	0.02	5.38	1.00	910.62
Return mine	55.00	9.50	11.3	2.55	5.80	2.50	0.06	0.09	1.00	350.00
Aux1	0.66	0.76	73.13	1.10	1.66	1.13	0.06	7.72	0.00	336.28
Flux1	0.31	0.18	31.52	20.4	0.92	0.34	0.01	45.89	1.34	72.60
Flux2	0.78	0.25	50.01	3.24	2.56	0.82	0.08	41.61	0.82	65.49
Fuel	1.72	1.65	0.85	0.13	5.48	4.02	0.07	85.72	10	860.18

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表 4 目標燒結礦化學成分約束

Table 4. Constraints of raw material

Chemical composition(Mass fraction)					R₂
TFe/%	SiO₂/%	Al₂O₃/%	MgO/%	S/%	R₂
54.0–55.5	4.8–5.5	1.0–3.0	1.5–2.0	0–0.09	1.9–2.2

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表 5 燒結原料配比約束(質量分數)

Table 5. Constraints of raw material %

Ore 1	Ore2	Ore3	Ore4	Ore5	Ore6	Ore7	Return mine	Aux1	Flux1	Flux2	Fuel
0～40	0～40	0～40	0～40	0～40	0～40	0～40	15	5	0～10	0～10	4～6

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表 6 優化后各原料配比(質量分數)

Table 6. Ratio of raw materials after optimization %

No.	Ore1	Ore2	Ore3	Ore4	Ore5	Ore6	Ore7	Return mine	Aux1	Flux1	Flux2	Fuel
No.1	3.7855	8.4815	0.04	10.2551	12.5118	20.9130	10.0088	15	5	5.0214	4.4543	4.53229
No.2	0.0507	18.6777	0.03	8.06014	13.4140	24.1340	3.41291	15	5	2.2216	5.8078	4.20222
No.3	3.3255	5.7537	0.09	0.47703	13.2724	18.1142	24.2245	15	5	7.3050	2.9765	4.46282
No.4	2.4800	11.2786	0.00	8.28373	11.6059	18.4789	13.1094	15	5	6.7806	3.6180	4.36329
No.5	0.3784	9.07963	0	11.7859	17.3902	20.1759	6.34414	15	5	6.2795	4.1559	4.42249
No.6	4.3888	13.0007	0.02	7.24491	29.3447	10.1253	1.70495	15	5	4.4295	5.4162	4.33107

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表 7 方案指標

Table 7. Program indicators

No.	TFe/%	SiO₂/%	Al₂O₃/%	MgO/%	S/%	R₂	Price/(￥·t^–1)	Flue/kg
No.1	54.97	4.93	2.72	1.87	0.060	2.123	818.90	48.24258
No.2	55.78	5.12	2.31	1.29	0.087	1.979	808.28	44.38450
No.3	53.95	5.16	3.39	2.34	0.051	2.038	787.28	47.43627
No.4	54.41	5.07	2.81	2.25	0.067	2.102	809.82	46.42388
No.5	54.70	4.96	2.53	2.15	0.062	2.177	799.86	47.32552
No.6	55.46	4.92	1.94	1.77	0.072	2.175	810.10	45.74686

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表 8 基于VIKOR的多屬性決策優化方案

Table 8. Multiattribute decision-making optimization scheme based on VIKOR

No.	TFe/%	S/%	Price/(￥·t^–1)	Flue/kg	Qv=0.5	Rank
No.1	54.98	0.0605	818.90	48.24258	0.9630	6
No.2	55.78	0.0876	808.28	44.38450	0.0720	1
No.3	53.96	0.0510	787.29	47.43627	0.7610	5
No.4	54.41	0.0670	809.82	46.42388	0.6200	4
No.5	54.70	0.0620	799.86	47.32552	0.2580	2
No.6	55.46	0.0720	810.11	45.74686	0.3640	3

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