Model Study of Blast Furnace Operation with Central Coke Charging
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Abstract
Blast furnace (BF) remains the dominant ironmaking process worldwide. Central coke charging (CCC) operation is a promising technology for stabilizing BF operations, but it needs reliable and quantified process design and control. In this work, a multi-fluid BF model is further developed for quantitatively investigating flow-thermal-chemical phenomena of a BF under CCC operation. This model features the respective chemical reactions in the respective coke and ore layers, and a specific sub-model of layer profile for the burden structure for the CCC operation. The simulation results confirm that the gas flow patterns and cohesive zone’s shape and location under the CCC operation are quite different from the non-CCC operation. Under the CCC operation, the heat is overloaded at the furnace center while the reduction load is much heavier at the periphery regions; the profiles of top gas temperature and gas utilization show bell-shape and inverse-bell-shape patterns, respectively. More importantly, these differences are characterized quantitatively. In this given case, when the CCC opening radius at the throat is 0.35 m, the cohesive zone top opening radius is around 0.50 m, and the isotherms of CCC operation become much steeper (~ 80 deg) than those of non-CCC operation (~ 60 deg) near BF central regions. In addition, it is confirmed that carbon solution-loss reaction rate can be decreased significantly at BF central regions under CCC operation. The model helps to understand CCC operation and provides a cost-effective method for optimizing BF practice.
Introduction
The ironmaking blast furnace (BF) is one of the most important but complex industrial reactors. In this process, coke, as the main fuel and reducing agent in BF ironmaking, together with iron ore are charged in alternate layers through a rotating charging chute from furnace top, resulting in a layer-structured coke and ore burden distribution. The burden will then descend slowly and affect furnace performance predominantly, including gas flow pattern, temperature distribution and species distribution of various phases. However, ironmaking BF is facing many new challenges, including decreased quality of raw materials, leading to lowered permeability, increased pressure drop and worse BF stability, and increased social pressure of environmental protection. Many innovative operations have been adopted in modern ironmaking BFs for improving BF stability and efficiency, for example, oxygen-enriched blast,[1,2] pulverized coal injection,[3, 4, 5, 6] and central coke charging (CCC) operations.[7, 8, 9, 10] In the process, modeling has played an important role in the investigation and optimization of BF internal states.[11, 12, 13, 14, 15, 16, 17]
The CCC operation has been studied using plant test and mathematical models. For example, Toshiyuki et al. reported the effectiveness of the CCC operation in a commercial BF in Japan.[7] Wang reported the influence of CCC operation on the smelting practice of iron-fluorine bearing ore.[19] Feng reported a case in which the CCC operation was successfully used to improve the activity of BF central regions.[23] It was found that CCC operation can indeed increase the permeability of gas and liquid flow, stabilize BF operation and help to improve the coal injection rate and furnace performance. However, BF is a huge black box, and more detailed phenomena under the CCC operation cannot be directly indicated or measured in practice. Therefore, the mathematical modeling approach has been used for understanding CCC operation. For example, Kiichi et al. studied the behavior of charged burden concerning the formation of a central coke column.[24] It was found that the CCC operation has little influence on gas flow in BF lower parts. Their work was based on aerodynamics analysis and no thermal–chemical phenomenon was considered. Teng et al. studied the relationship between the CCC operation and top gas utilization efficiency considering the gas flow resistance inside a BF.[25] However, the structure of solid bed was simplified to a large degree and in-furnace thermal–chemical behaviors were not considered in their work. To the best of our knowledge, so far, few CCC modeling works concerning multi-phase flow and quantified thermal–chemical distributions has been systematically reported in the open literature.
In this paper, a recent BF mathematical model[26] based on multi-fluid theory is further developed to study the inner phenomena of a BF under CCC operation, where respective thermochemical behaviors are considered in the coke and ore layers. Then, the typical internal states, including flow patterns, thermal–chemical behavior, reducing gas evolution and ferrous oxide distribution are investigated systematically and quantitatively, and compared with the non-CCC operation where necessary. This work might provide an insight into the fundamentals of CCC operation.
Model Description
The present model is based on a recent multi-fluid BF model, which has been validated by comparing the top gas information with those measured in BF ironmaking practice.[26] In the recent model, one important feature is the consideration of chemical reactions in respective coke- and ore- layers, compared to the previous BF models in the literature. In this paper, the recent model is further developed considering the specific burden profiles of CCC operation and used to investigate the related BF performance. In this part, the governing equations and expressions such as the interphase momentum transfer and chemical reaction rates are not included here for brevity. The model basics and new developments are introduced here for completeness.
Model Basics
- (a)
State I (Shrinking Index equals to 1), 0.7 < Sh < 1.0 corresponded to the portion with molten state and liquid source in which the ore layer voidage is occupied fully by the liquid phase.
- (b)
State II (Shrinking Index equals to 2), 0.5 < Sh < 0.7 corresponded to the combined portion with softening and melting of ore particles.
- (c)
State III (Shrinking Index equals to 3), 0.0 < Sh < 0.5 corresponded to the softening stage in which the ore-melting process is limited.
New Developments for CCC Operation
Simulation Conditions
Simulation Conditions of this Model
Parameters | Values |
---|---|
Gas | |
Blast Volume Flux (m^{3}/tHM) | 1140 |
Blast Temperature (K) | 1473 |
Oxygen Enrichment (pct) | 1.7 |
Humidity (g/m^{3}) | 8.036 |
Top Gas Pressure (atm) | 2.0 |
Flame Temperature (K) | 2269 |
Reducing Gas Volume Flux (m^{3}/tHM) | 1437 |
Reducing Gas Components (pct) | CO 35.60; N_{2} 59.47; H_{2} 2.0; H_{2}O 0.0; CO_{2} 0.0 |
Solid | |
Ore Rate (t/tHM) | 1.597 |
Average Ore Components (pct) | TFe 59.93 |
Coke Main Components (pct) | C 86.794; Ash 12.162; S 0.594 |
Coal Rate (t/tHM) | 0.17 |
Coal Main Components (pct) | C 75.3; Ash 14.78; S 0.36 |
Flux Rate (t/tHM) | 0.089 |
Flux Main Components (pct) | gangue SiO_{2} 92.37 |
limestone CaO 54.93; CO_{2} 43.06 | |
dolomite CaO 32.38; MgO 19.95; CO_{2} 45.42 | |
Solid Inlet Temperature (K) | 300 |
Coke Volume Fraction | 0.153logd_{coke} + 0.724 |
Ore Volume Fraction | 0.403(100d_{ore})^{0.14} |
Average Coke Particle Diameter (m) | 0.045 |
Average Ore Particle Diameter (m) | 0.03 |
Coke Batch Weight (kg) | 28771 |
Ore Batch Weight (kg) | 140,000 |
Hot Metal | |
Main Components (pct) | Fe 95.369; C 3.805 |
Density (kg/m^{3}) | 6600 |
Viscosity (kg/m s) | 0.005 |
Conductivity (W/m K) | 28.44 |
Surface Tension (N/m) | 1.1 |
Slag | |
Basicity (−) | R_{2} 1.178; R_{3} 1.412; R_{4} 0.982 |
Density (kg/m^{3}) | 2600 |
Viscosity (kg/m s) | 1.0 |
Conductivity (W/m K) | 0.57 |
Surface Tension (N/m) | 0.47 |
Results and Discussion
In this section, the in-furnace phenomena with and without CCC operation (in which both coke and ore are charged into the furnace central regions) are compared, in terms of flow fields, temperature fields, species distributions and chemical reactions. Moreover, the CCC simulation results using this model are also compared with results using the CCC model without considering chemical reaction switch between coke and ore layers.
Comparison of CCC Operation with Non-CCC Operation
Gas flow
Thermal behaviors
Distributions of gas and solid species
Carbon solution-loss reaction
Comparison of Simulation Results Using Two CCC Models
This BF model considers “respective reacting layers” where different chemical reactions are considered in coke and ore layers, respectively,[26] by contrast to the BF models where a “mixture” of coke and ore burden was simply considered and thus did not consider the different chemical reactions in the respective layers of ore and coke. The former method is more realistic as the chemical reactions occurring in coke and ore layers are different in BF practice. Thus, phase concentration should show fluctuating or zig-zag profiles. However, if a mixture model is used, this feature cannot be captured.
Conclusions
- 1.
Both gas permeability and cohesive zone position are high at BF central regions. In this given case, when CCC opening at the throat is 0.35 m, cohesive zone top opening is around 0.50 m.
- 2.
The temperature curve of top gas shows a bell-shape trend with a narrow region of high temperature, close to 1000 K at the furnace center. In addition, the isotherms of CCC operation become much steeper (~ 80 deg) than those of non-CCC operation (~ 60 deg).
- 3.
It is found that the reducing gas utilization efficiency declines from ~ 50 to ~ 43 pct. Also, CRZ profiles of iron oxides are quite different from those of non-CCC operation. Besides, CCC operation faces with fuel rate increase though it can stabilize BF performance.
- 4.
It shows that carbon solution-loss reaction rate can be effectively suppressed at the furnace center by ~ 92 pct. This confirms the objective of good permeability at BF central regions using CCC operation.
This model has provided a cost-effective way to systematically investigate CCC operation. This model is relatively high in calculation efficiency and is feasible to study the influence of, such as CCC opening radius, batch weight and furnace throat radius on BF performance. However, the detailed flow behavior at particle scale, such as particle percolation, friction and segregation cannot be captured. A large-scale DEM-CFD simulation is a promising method to capture those particle scale reacting flow phenomena but it is not computationally feasible so far considering the huge particle number in BF operations.
Notes
Acknowledgments
The authors acknowledge the financial support from the Australian Research Council (LP150100112 and LP160101100), Baosteel and Clean Energy Australia. The first author wishes to acknowledge the financial support from China Scholarship Council.
References
- 1.P. R. Austin, H. Nogami and J. Yagi: ISIJ international, 1998, vol. 38, pp. 239-45.CrossRefGoogle Scholar
- 2.H. Helle, M. Helle, H. Saxén and F. Pettersson: ISIJ international, 2010, vol. 50, pp. 931-38.CrossRefGoogle Scholar
- 3.Y. S. Shen, B. Y. Guo, A. B. Yu, and P. Zulli: Fuel, 2009, vol. 88 pp. 255-63.CrossRefGoogle Scholar
- 4.Y. S. Shen, B. Y. Guo, A. B. Yu, D. Maldonado and P. Austin: ISIJ international, 2008, vol. 48, pp. 777-86.CrossRefGoogle Scholar
- 5.Y. S. Shen, A. B. Yu and P. Zulli: Steel Res. Int., 2011, vol. 82, pp. 532-42.CrossRefGoogle Scholar
- 6.Y. S. Shen, B. Y. Guo, A. B. Yu, P. R. Austin, and P. Zulli: Fuel, 2011, vol. 90, pp. 728-38.CrossRefGoogle Scholar
- 7.T. Kamen, Y. Yasumasa, R. Hori, Y. Miyakawa, Y. Matsui and F. Nomauma: Tetsu-to-Hagané, 1987, vol. 73, pp. s756.Google Scholar
- 8.Y. Kimura, M. Isobe, M. Shimizu, S. Inaba and C. R. Che: Tetsu-to-Hagané, 1987, vol. 73, pp. s755.Google Scholar
- 9.H. Cai and M. M. Zhang: Research on Iron & Steel, 2015, vol. 43, pp. 56-8.Google Scholar
- 10.Z. Y. Ping: Ironmaking, 2016, vol. 35, pp. 48-52.Google Scholar
- 11.J. Haapakangas, H. Suopajärvi, M. Iljana, A. Kemppainen, O. Mattila, E.-P. Heikkinen, C. Samuelsson and T. Fabritius: Metallurgical and Materials Transactions B, 2016, vol. 47, pp. 2357-70.CrossRefGoogle Scholar
- 12.X. Ma, J. Zhu, H. Xu, G. Wang, H.-G. Lee and B. Zhao: Metallurgical and Materials Transactions B, 2017, vol. 49, pp. 190-99.Google Scholar
- 13.C. Yilmaz and T. Turek: J Clean Prod, 2017, vol. 164, pp. 1519-30.CrossRefGoogle Scholar
- 14.F. Bambauer, S. Wirtz, V. Scherer and H. Bartusch: Powder Technol., 2018, vol. 334, pp. 53-64.CrossRefGoogle Scholar
- 15.Y. Hashimoto, Y. Sawa, Y. Kitamura, T. Nishino and M. Kano: ISIJ Int., 2018, 58, 2210-2218CrossRefGoogle Scholar
- 16.Z. Li, S. Kuang, A. Yu, J. Gao, Y. Qi, D. Yan, Y. Li and X. Mao: Metallurgical and Materials Transactions B, 2018, vol. 49, pp. 1995-2010.CrossRefGoogle Scholar
- 17.Z. Y. Li, S. B. Kuang, D. L. Yan, Y. H. Qi and A. B. Yu: Metallurgical and Materials Transactions B, 2016, vol. 48, pp. 602-18.CrossRefGoogle Scholar
- 18.C. R. Che: Ironmaking, 1992, vol. 2, pp. 14-7.Google Scholar
- 19.D. Wang: Iron and Steel, 1992, vol. 27, pp. 5-7.Google Scholar
- 20.Y. S. Shen, B. Y. Guo, S. Chew, P. Austin, and A. B. Yu: Metallurgical and Materials Transactions B, 2016, vol. 47, pp. 1052-62.CrossRefGoogle Scholar
- 21.Q. H. Yu, L. Chen and J. C. Song: Ironmaking, 1996, vol. 15, pp. 44-7.Google Scholar
- 22.Y. Matsui, K. Shibata, Y. Yoshida and R. Ono: Kobelco Technol. Rev., 2005, 26, 12-20.Google Scholar
- 23.B. H. Feng: Iron and Steel, 1993, vol. 28, pp. 6-11.Google Scholar
- 24.K. Narita, S. I. Inaba, M. Shimizu, A. Yamaguchi, I. Kobayashi and K. I. Okimoto: Transactions of the Iron and Steel Institute of Japan, 1981, vol. 21, pp. 405-13.CrossRefGoogle Scholar
- 25.Z. J. Teng, S. S. Cheng and G. L. Zhao: Journal of Iron and Steel Research, 2014, vol. 26, pp. 9-14.Google Scholar
- 26.X. Yu and Y. Shen: Metallurgical and Materials Transactions B, 2018, vol. 49, pp. 2370-88.CrossRefGoogle Scholar
- 27.P. R. Austin, H. Nogami and J. Yagi: ISIJ International, 1997, vol. 37, pp. 748-55.CrossRefGoogle Scholar
- 28.J. A. D. Castro, H. Nogami and J. Yagi: ISIJ Int., 2002, 42, 44-52.CrossRefGoogle Scholar
- 29.M. S. Chu, H. Nogami and J. Yagi: ISIJ International, 2004, vol. 44, pp. 510-7.CrossRefGoogle Scholar
- 30.X. F. Dong, A. B. Yu, S. J. Chew and P. Zulli: Metall. Mater. Trans. B-Proc. Metall. Mater. Proc. Sci., 2010, vol. 41, pp. 330-49.CrossRefGoogle Scholar
- 31.K. Yang, S. Choi, J. Chung and J. Yagi: ISIJ international, 2010, vol. 50, pp. 972-80.CrossRefGoogle Scholar
- 32.D. Fu, Y. Chen, Y. F. Zhao, J. D’Alessio, K. J. Ferron and C. Q. Zhou: Appl Therm Eng, 2014, vol. 66, pp. 298-308.CrossRefGoogle Scholar
- 33.S. B. Kuang, Z. Y. Li, D. L. Yan, Y. H. Qi and A. B. Yu: Miner Eng, 2014, vol. 63, pp. 45-56.CrossRefGoogle Scholar
- 34.Y. S. Shen, B. Y. Guo, S. Chew, P. Austin and A. B. Yu: Metallurgical and Materials Transactions B, 2015, vol. 46, pp. 432-48.CrossRefGoogle Scholar
- 35.Z. L. Zhang, J. L. Meng, L. Guo and Z. C. Guo: Metallurgical and Materials Transactions B, 2016, vol. 47, pp. 467-84.CrossRefGoogle Scholar
- 36.J. Chen, T. Akiyama, H. Nogami, J. Yagi and H. Takahashi: ISIJ International, 1993, vol. 33, pp. 664-71.CrossRefGoogle Scholar
- 37.S. J. Zhang, A. B. Yu, P. Zulli, B. Wright and P. Austin: Appl. Math. Model., 2002, vol. 26, pp. 141-54.CrossRefGoogle Scholar
- 38.G. X. Wang, S. J. Chew, A. B. Yu and P. Zulli: Metallurgical and Materials Transactions B, 1997, vol. 28, pp. 333-43.CrossRefGoogle Scholar
- 39.S. Ergun: Chem. Eng. Prog., 1952, vol. 48, pp. 89-94.Google Scholar
- 40.W. E. Ranz and W. R. Marshall: Chemical Enginerring Progress, 1952, vol. 48, pp. 141-6.Google Scholar
- 41.E. R. G. Eckert and R. M. Drake: Heat and mass transfer, 2nd ed., McGrawHill, New York, 1959, p.173.Google Scholar
- 42.P. J. Mackey and N. A. Warner: Metallurgical transactions, 1972, vol. 3, pp. 1807-16.CrossRefGoogle Scholar
- 43.D. Maldonado, Ph.D. thesis, UNSW, 2003.Google Scholar
- 44.I. Muchi: Transactions of the Iron and Steel Institute of Japan, 1967, vol. 7, pp. 223-37.Google Scholar
- 45.J. Yagi, K. Taakeda and Y. Omori: Transactions of the Iron and Steel Institute of Japan, 1982, vol. 22, pp. 884-92.CrossRefGoogle Scholar
- 46.H. S. Zhang, X. P. Wang, Y. L. Wang, G. Y. Wang and H. W. Li: Ironmaking, 2012, vol. 31, pp. 7-10.Google Scholar
- 47.J. Liao, A. B. Yu, and Y. Shen: Powder Technology, 2017, vol. 314, pp. 550-56.CrossRefGoogle Scholar
- 48.D. Rangarajan, T. Shiozawa, Y. Shen, J. S. Curtis, and A. Yu: Industrial & Engineering Chemistry Research, 2014, vol. 53, pp. 4983-90.CrossRefGoogle Scholar