Optimal scheduling of converter oxygen based on particle swarm optimization

KONG Fu-lin; TONG Li-ge; WEI Peng-cheng; ZHANG Pei-kun; WANG Li; WU Bing; CHEN En-jun

doi:10.13374/j.issn2095-9389.2020.04.02.002

Volume 43 Issue 2

Feb. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(2): 279-288

KONG Fu-lin, TONG Li-ge, WEI Peng-cheng, ZHANG Pei-kun, WANG Li, WU Bing, CHEN En-jun. Optimal scheduling of converter oxygen based on particle swarm optimization[J]. Chinese Journal of Engineering, 2021, 43(2): 279-288. doi: 10.13374/j.issn2095-9389.2020.04.02.002

Citation:

KONG Fu-lin, TONG Li-ge, WEI Peng-cheng, ZHANG Pei-kun, WANG Li, WU Bing, CHEN En-jun. Optimal scheduling of converter oxygen based on particle swarm optimization[J]. Chinese Journal of Engineering, 2021, 43(2): 279-288. doi: 10.13374/j.issn2095-9389.2020.04.02.002

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Optimal scheduling of converter oxygen based on particle swarm optimization

doi: 10.13374/j.issn2095-9389.2020.04.02.002

KONG Fu-lin¹,
TONG Li-ge^{1, 2
,
,},
WEI Peng-cheng¹,
ZHANG Pei-kun^{1, 2},
WANG Li^{1, 2},
WU Bing³,
CHEN En-jun³

1.
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Key Laboratory of Energy Saving and Emission Reduction for Metallurgical Industry, University of Science and Technology Beijing, Beijing 100083, China
3.
Shougang Jingtang Iron & Steel Co. Ltd., Tangshan 063200, China

More Information

Corresponding author: E-mail: tonglige@me.ustb.edu.cn
Received Date: 2020-04-02
Publish Date: 2021-02-26

Abstract

Abstract

The air separation plants of iron and steel enterprises are characterized by a high oxygen-emission rate and high comprehensive energy consumption. To solve this problem, a converter oxygen scheduling model was established based on particle swarm optimization (PSO) with the goal of reducing the fluctuation of the total oxygen consumption and saving system energy consumption in the converter. With the full consideration of constraints, such as the constant duration of blowing intervals, compliant starting time of each blowing interval, molten steel temperature above 1250 °C, and minimal variation before and after converter scheduling, PSO based on integer space was used to solve the hypothesis. With the air separation plant of a large domestic iron and steel enterprise as a case study, Pipeline Studio software was used to establish the oxygen transmission and distribution model, and the energy-saving performance of the converter oxygen scheduling was verified. The results show that the optimal scheduling of converter oxygen based on PSO can arrange oxygen for a single converter as much as possible during the study period; moreover, the optimal scheduling can effectively reduce the overlapping time of oxygen blowing in multiple converters, reduce the fluctuation of the total oxygen amount, and alleviate the contradiction between oxygen supply and demand. The oxygen emission of the pipeline transmission and distribution system before and after the dispatch is reduced from 1242.1 m³ to 0 within the 120 min simulation period; the corresponding air separation system oxygen production energy consumption saves 1192.42 kW·h; the compression energy consumption of the oxygen compressor increases by 41 kW·h; and the total energy saving of the system is 1151.42 kW·h. Based on comprehensive calculations, optimal scheduling of converter oxygen based on PSO is applied throughout the year. The oxygen transmission and distribution pipeline system is expected to reduce the total amount of oxygen emission by 5.44×10⁶ m³ and save the total energy consumption by 5.22×10⁶ kW·h.
- converter oxygen dispatch scheduling,
- particle swarm optimization algorithm,
- oxygen release,
- energy saving,
- oxygen transmission and distribution network,
- multiple constraints

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References(29)

References

[1]	李新創. 加快推進鋼鐵產業高質量發展. 冶金經濟與管理, 2020(1):1 Li X C. Accelerate the development of high-quality steel industry. Metall Economy Manage, 2020(1): 1
[2]	楊杰, 胡琦, 肖亭, 等. 基于能耗的冷軋連退機組能效建模與工藝參數優化及排序建模優化. 中國機械工程, 2020, 31(14):1724 doi: 10.3969/j.issn.1004-132X.2020.14.012 Yang J, Hu Q, Xiao T, et al. Energy efficiency modeling, process parameter optimization and sequencing modeling optimization of cold rolling continuous annealing units based on energy consumptions. Chin Mech Eng, 2020, 31(14): 1724 doi: 10.3969/j.issn.1004-132X.2020.14.012
[3]	張延平, 凌晨, 周建波, 等. 鋼鐵企業大型空分的能耗分析與經濟運行. 冶金能源, 2014, 33(5):6 doi: 10.3969/j.issn.1001-1617.2014.05.002 Zhang Y P, Ling C, Zhou J B, et al. Energy consumption and optimize circulate of air separation unit of steel corporation. Energy Metall Ind, 2014, 33(5): 6 doi: 10.3969/j.issn.1001-1617.2014.05.002
[4]	Tong L G, Zhang A J, Li Y L, et al. Exergy and energy analysis of a load regulation method of CVO of air separation unit. Appl Therm Eng, 2015, 80: 413
[5]	張乾乾, 趙珺, 盛春陽, 等. 一種兩階段鋼鐵企業氧氣系統平衡調整方法. 控制工程, 2017, 24(4):716 Zhang Q Q, Zhao J, Sheng C Y, et al. A two-stage adjustment method for the balance of the oxygen system in steel and iron enterprises. Control Eng China, 2017, 24(4): 716
[6]	盧宏, 孫鵬. 鋼鐵企業基于無線傳感網的氧氣實時優化調度. 軟件, 2018, 39(5):129 doi: 10.3969/j.issn.1003-6970.2018.05.028 Lu H, Sun P. Steel enterprises based on the wireless sensor network oxygen real-time optimization scheduling. Comput Eng Software, 2018, 39(5): 129 doi: 10.3969/j.issn.1003-6970.2018.05.028
[7]	劉青, 劉倩, 楊建平, 等. 煉鋼?連鑄生產調度的研究進展. 工程科學學報, 2020, 42(2):144 Liu Q, Liu Q, Yang J P, et al. Progress of research on steelmaking?continuous casting production scheduling. Chin J Eng, 2020, 42(2): 144
[8]	鄭鵬, 唐秋華, 張啟敏, 等. 基于離散粒子群算法的煉鋼連鑄生產調度. 機械設計與制造, 2016(7):49 doi: 10.3969/j.issn.1001-3997.2016.07.013 Zheng P, Tang Q H, Zhang Q M, et al. Discrete particle swarm optimization for steelmaking and continuous casting production. Mach Des Manuf, 2016(7): 49 doi: 10.3969/j.issn.1001-3997.2016.07.013
[9]	張培昆, 王立. 高爐休風時供氧管網壓力對氧氣調度的影響. 工程科學學報, 2017, 39(2):283 Zhang P K, Wang L. Effects of oxygen pipe-network pressure on the oxygen scheduling during blast furnace blow-down. Chin J Eng, 2017, 39(2): 283
[10]	張子陽, 孫彥廣, 馬湧. 基于改進單純形法的鋼鐵企業氧氣系統優化調度研究. 數學的實踐與認識, 2018, 48(23):189 Zhang Z Y, Sun Y G, Ma Y. Based on the improved simplex method of iron and steel enterprise oxygen system optimization scheduling research. Math Pract Theory, 2018, 48(23): 189
[11]	童莉葛, 王立, 湯學忠, 等. 降低氧氣放散率的高爐休風模型. 冶金能源, 1999, 18(3):16 Tong L G, Wang L, Tang X Z, et al. Model of blast furnace blow down for oxygen releasing rate. Energy Metall Ind, 1999, 18(3): 16
[12]	陳光, 陸鐘武, 蔡九菊, 等. 鋼鐵企業氧氣系統動態仿真. 東北大學學報(自然科學版), 2002, 23(10):940 Chen G, Lu Z W, Cai J J, et al. Dynamic simulation of oxygen supply system in iron and steel company. J Northeast Univ Nat Sci, 2002, 23(10): 940
[13]	鄭忠, 朱道飛, 高小強. 基于蟻群算法的煉鋼-連鑄作業計劃編制方法. 北京科技大學學報, 2009, 31(4):504 doi: 10.3321/j.issn:1001-053X.2009.04.019 Zheng Z, Zhu D F, Gao X Q. Production planning based on an ant colony algorithm for steel-making and continuous casting. J Univ Sci Technol Beijing, 2009, 31(4): 504 doi: 10.3321/j.issn:1001-053X.2009.04.019
[14]	王繼超, 李擎, 崔家瑞, 等. 一種改進的人工蜂群算法——粒子蜂群算法. 工程科學學報, 2018, 40(7):871 Wang J C, Li Q, Cui J R, et al. An improved artificial bee colony algorithm: particle bee colony. Chin J Eng, 2018, 40(7): 871
[15]	張壯, 曹玲玲, 林文輝, 等. 基于IPSO-RELM轉爐冶煉終點錳含量預測模型. 工程科學學報, 2019, 41(8):1052 Zhang Z, Cao L L, Lin W H, et al. Improved prediction model for BOF end-point manganese content based on IPSO-RELM method. Chin J Eng, 2019, 41(8): 1052
[16]	張培昆, 王立. 空分短期停車時間閾值對氧氣生產調度的影響. 化工學報, 2017, 68(6):2423 Zhang P K, Wang L. Effects of temporary shutdown time-threshold on oxygen production schedule in air separation unit. CIESC J, 2017, 68(6): 2423
[17]	李松, 鄭雪梅, 馬愛元, 等. CaO?SiO₂?Al₂O₃?TiO₂渣系活度計算模型的建立及應用. 稀有金屬, 2020, 44(5):540 Li S, Zheng X M, Ma A Y, et al. Construction and application of activity models for CaO?SiO₂?Al₂O₃?TiO₂ slag system. Rare Mrtals, 2020, 44(5): 540
[18]	徐鋼, 黎敏, 徐金梧, 等. 基于函數型數字孿生模型的轉爐煉鋼終點碳控制技術. 工程科學學報, 2019, 41(4):52 Xu G, Li M, Xu J W, et al. Control technology of end-point carbon in converter steelmaking based on functional digital twin model. Chin J Eng, 2019, 41(4): 52
[19]	龐新富, 高亮, 潘全科, 等. 某一轉爐或精煉爐故障下煉鋼?連鑄生產重調度方法及應用. 控制與決策, 2015, 30(11):1921 Pang X F, Gao L, Pan Q K, et al. Rescheduling method of steelmaking and continuous casting with breakdown of converter or refining furnace. Control Decis, 2015, 30(11): 1921
[20]	中華人民共和國住房和城鄉建設. GB50439—2015 煉鋼工程設計規范. 北京: 中國計劃出版社, 2015 Ministry of Housing and Urban-Rural Development, People’s Republic of China. GB50439—2015 Code for Design of Steelmaking Engineering. Beijing: China Planning Press, 2015
[21]	Kennedy J, Eberhart R. Particle swarm optimization//Proceeding of ICNN’95-IEEE International Conference on Neural Networks. Perth, 1995: 1942
[22]	劉云, 易松. 基于雙參數最小二乘支持向量機(TPA-LSSVM)的風電時間序列預測模型的優化研究. 北京化工大學學報(自然科學版), 2019, 46(02):97 Liu Y, Yi S. Optimization of a wind power time series prediction model based on a two-parameter least squares support vector machine. J Beijing Univ Chem Technol, 2019, 46(02): 97
[23]	熊文濤, 禹寶軍, 孫林. 改進粒子群算法對1420 mm五機架冷連軋機軋制規程的優化. 鋼鐵研究學報, 2014, 26(11):25 Xiong W T, Yu B J, Sun L. Improved particle swarm optimization of rolling schedule on 1420 mm 5-stand tandem cold strip mill. J Iron Steel Res, 2014, 26(11): 25
[24]	Khare A, Rangekar S. A review of particle swarm optimization and its applications in Solar Photovoltaic system. Appl Soft Comput, 2013, 13(5): 2997
[25]	楊桓, 張理. PIPELINE STUDIO軟件在管網模擬分析中的應用. 天然氣勘探與開發, 2015, 38(3):91 doi: 10.3969/j.issn.1673-3177.2015.03.021 Yang H, Zhang L. Application of PIPELINE STUDIO to simulation and analysis of pepe network. Nat Gas Explor Dev, 2015, 38(3): 91 doi: 10.3969/j.issn.1673-3177.2015.03.021
[26]	蘭宇劍, 章磊, 宋代詩雨, 等. Pipeline Studio與SPS計算長輸管道儲氣調峰探討. 石化技術, 2015, 22(5):22 doi: 10.3969/j.issn.1006-0235.2015.05.013 Lan Y J, Zhang L, Song D S Y, et al. The discussion of gas storage and peak shaving methods between Pipeline Studio and SPS. Petrochem Ind Technol, 2015, 22(5): 22 doi: 10.3969/j.issn.1006-0235.2015.05.013
[27]	李宏陽, 玉建軍, 靳新迪. PL Studio for Gas 軟件在燃氣管網模擬的應用. 煤氣與熱力, 2016, 36(7):75 Li H Y, Yu J J, Jin X D. Application of PL studio for gas software to simulation of gas pipeline network. Gas Heat, 2016, 36(7): 75
[28]	陳芳, 李瑞珍, 魏蘭波, 等. 空分設備節能潛力及改造效果分析. 深冷技術, 2014(5):1 Chen F, Li R Z, Wei L B, et al. Analysis of the energy-saving potential and the reform effects of air separation plant. Cryogenic Technol, 2014(5): 1
[29]	王輝, 宿彥通, 呂義超. 壓縮機性能曲線優化. 壓縮機技術, 2019(6):13 Wang H, Su Y T, Lü Y C. Optimization for performance curves of compressor. Compressor Technol, 2019(6): 13