Academic literature on the topic 'Continuous casting. Steel. Mathematical optimization'
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Journal articles on the topic "Continuous casting. Steel. Mathematical optimization"
Li, Yang, Yan Jin, Hui Yu, Kang Yang, Fan Ai, Jun Wang, and Hong Bin Huang. "Numerical Calculation of Fluid Flow in a Continuous Casting Tundish." Advanced Materials Research 774-776 (September 2013): 316–20. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.316.
Full textWang, Zhao Feng, Man Yao, Xiao Bing Zhang, and Xu Dong Wang. "Optimization Control for Solidification Process of Secondary Cooling in Continuous Casting Steel." Applied Mechanics and Materials 263-266 (December 2012): 822–27. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.822.
Full textYang, Jie, Dengfu Chen, Fengting Qin, Mujun Long, and Huamei Duan. "Melting and Flowing Behavior of Mold Flux in a Continuous Casting Billet Mold for Ultra-High Speed." Metals 10, no. 9 (August 28, 2020): 1165. http://dx.doi.org/10.3390/met10091165.
Full textYang, Jie, Dengfu Chen, Mujun Long, and Huamei Duan. "An Approach for Modelling Slag Infiltration and Heat Transfer in Continuous Casting Mold for High Mn–High Al Steel." Metals 10, no. 1 (December 26, 2019): 51. http://dx.doi.org/10.3390/met10010051.
Full textSantos, C. A., J. A. Spim, and A. Garcia. "Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel." Engineering Applications of Artificial Intelligence 16, no. 5-6 (August 2003): 511–27. http://dx.doi.org/10.1016/s0952-1976(03)00072-1.
Full textWang, Sen, Yarong Shi, and Shixin Liu. "Integrated Scheduling for Steelmaking Continuous Casting— Hot Rolling Processes considering Hot Chain Logistics." Mathematical Problems in Engineering 2020 (May 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/6902934.
Full textLin, Peng, Yan Jin, Fu Yang, Ziyu Liu, Rundong Jing, Yang Cao, Yuyang Xiang, Changgui Cheng, and Yang Li. "A Simulation and Optimization Study of the Swirling Nozzle for Eccentric Flow Fields of Round Molds." Metals 10, no. 5 (May 25, 2020): 691. http://dx.doi.org/10.3390/met10050691.
Full textSantos, C. A., N. Cheung, A. Garcia, and J. A. Spim. "Application of a Solidification Mathematical Model and a Genetic Algorithm in the Optimization of Strand Thermal Profile Along the Continuous Casting of Steel." Materials and Manufacturing Processes 20, no. 3 (May 2005): 421–34. http://dx.doi.org/10.1081/amp-200053451.
Full textLUKIN, SERGEY V., NIKOLAY I. SHESTAKOV, MICHAIL V. PAVLOV, KONSTANTIN YU LEVASHEV, and ALEKSEY A. ZBRODOV. "MATHEMATICAL MODELING AND OPTIMIZATION OF HEAT PROCESSING OF SQUARE CROSS SECTION STEEL BAR ON THE TECHNOLOGICAL LINE “CONTINUOUS CASTING MACHINE - TUNNEL THERMOS - HEATING FURNACE”." Cherepovets State University Bulletin 1, no. 100 (2021): 41–55. http://dx.doi.org/10.23859/1994-0637-2021-1-100-3.
Full textMaurya, Ambrish, and Pradeep Kumar Jha. "Two-phase analysis of interface level fluctuation in continuous casting mold with electromagnetic stirring." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 9 (September 3, 2018): 2036–51. http://dx.doi.org/10.1108/hff-08-2017-0310.
Full textDissertations / Theses on the topic "Continuous casting. Steel. Mathematical optimization"
De, Wet Gideon Jacobus. "CFD modelling and mathematical optimisation of a continuous caster submerge entry nozzle." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-141026.
Full textKholmatov, Shavkat. "On some positive effects of swirling flow for the continuous cast mould billets." Licentiate thesis, Stockholm : Division of Applied Process Metallurgy, School of Industrial Engineering and Management, Royal Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4453.
Full textSpinelli, Jose Eduardo. "Simulação do lingotamento continuo de tiras finas de aços." [s.n.], 2000. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264701.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica
Made available in DSpace on 2018-07-27T19:18:36Z (GMT). No. of bitstreams: 1 Spinelli_JoseEduardo_M.pdf: 6965190 bytes, checksum: 39afbc7a3acd510a9c00a458e1f8ba9a (MD5) Previous issue date: 2000
Resumo: Entende-se por modelagem de processo, o desenvolvimento de uma representação quantitativa ou qualitativa dos fenômenos físicos associados ao processo. Neste trabalho são realizadas simulações do processo twin roll de lingotamento contínuo de tiras de aços, utilizando como referência tecnológica o equipamento piloto instalado nas dependências do Instituto de Pesquisas Tecnológicas do Estado de São Paulo. São construídos dois simuladores para o processo: um simulador da solidificação unidirecional, com molde refrigerado de aço e paredes laterais de material refratário, utilizando-se o aço inoxidável 304 como material de simulação; e variando-se as temperaturas de vazamento; e outro simulador a frio, com componentes feitos de acrílico, água como fluido de simulação e permanganato de potássio como corante. Um modelo matemático previamente desenvolvido é utilizado para confrontar perfis térmicos teóricos com perfis experimentais, para a determinação do coeficiente de transferência de calor metal/molde. O levantamento de valores de espaçamento dendrítico secundário é realizado nas amostras, além da observação simultânea das estruturas de solidificação, o que comprovou a eficiência do simulador em caracterizar o processo de solidificação do equipamento piloto. O uso do modelo frio permitiu a caracterização do posicionamento de barreira a 15 cm ou a 20 cm da lateral esquerda do distribuidor como a melhor configuração
Abstract: It can be understood by process modeling the development of a quantitative or qualitative representation of the physical phenomena associated to the process. In this work, simulations concerning the twin roll continuous caster of steels process at IPT (Instituto de Pesquisas Tecnológicas do Estado de São Paulo) are performed. Two simulators were developed: (i) a simulator of unidirectional solidification with cooled steel mold and refractory lateral walls, by using a stainless steel as the reference metal (AISI 304), and varying superheat temperatures; (ii) a physical model, with components made of acrylic, water as the simulation fluid and potassium permanganate as a pigment. A previously developed mathematical model has been used to determine the metal/mold heat transfer coefficient by a method that compares experimenta1ltheoretical temperature curves. Measurement of secondary dendrite arm spacings is performed by microestructural examination of the samples, confirming the simulator efficiency in characterizing the solidification process in the pilot equipment. The use of the physical model has permitted to attain the best configuration for the tundish, by positioning the dam 15 cm or 20 cm from the left side of tundish
Mestrado
Materiais e Processos de Fabricação
Mestre em Engenharia Mecânica
Pustějovský, Michal. "Optimalizace teplotního pole s fázovou přeměnou." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232173.
Full textFan, Chih-Ming, and 范志銘. "Application of Mathematical Model and Physical Model in Ladle Refining and Continuous Casting Processes of Steel." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/96636553685563930792.
Full text國立成功大學
材料科學及工程學系碩博士班
91
The purpose of this study is to develop two mathematical models, which are based on a computational fluid dynamics technique, named SOLA, and the k-ε two-equation turbulence model, to analyze the fluid flow phenomena of molten steel in the ladle during the secondary refining process and the tundish operation in the continuous casting process of steel. The first mathematical model is used to analyze the fluid flow phenomena and the corresponding diffusion of the injected Ca-Si under various design and operating conditions to find the optimal Ca-Si injection position. The second mathematical model is used to analyze fluid flow phenomena and design the optimal flow control device during initial casting operation, steady state operation and ladle-interchange operation. Several physical models are also constructed in this study. Water model experiments are conducted to verify the accuracy and reliability of the mathematical models. Good agreements are observed between the simulations and measurements. As the mathematical model is verified to be a trustworthy tool, it is then applied to the actual operation to simulate the fluid flow; heat transfer and mass transfer phenomena of the secondary refining and continuous casting processes. The flow pattern of two-phase flow in the stirred ladle for the secondary refining is simulated. The results show that the injection position of D, which has the coordinate of , has the shortest mixing time. The simulated results for the actual ladle operation show the similar trend to the water model. For the injection positions on the tuyere/circle center plane, the additives prematurely float to the top as the injection positions are near the plume zone. It helps the additives to transport to the bottom as the injection positions are away from the plume zone. The simulated results for the actual ladle operation show that the optimal positions for injection are located on the tuyere/circle-center plane, opposite side of the tuyere, and 0.5-0.8 r away from the circle center. For these injection positions, the mixing time is shorter and the flow pattern favors the transport of the additive to the bottom of the ladle. The fluid flow and heat transfer phenomena of the molten steel in the tundish during its filling stage and subsequent initial casting operation are studied. The results show that the temperature field in LP-ND operation is not uniform Inclusion distribution and the extents of inclusion contamination among the outlets of the various strands in the tundish are also analyzed. The left half of tundish, inclusion contamination in the #2 strand is significantly more severe then that in the #1 strand. This is confirmed by the actual experience on the shop floor of that particular billet caster that the #2 strand experiences more difficulty in clogging problem during the initial casting operation than the #1 strand does. The LP-SD has no eddy near outlet strand and has more uniform temperature field. It shows that LP-SD lowers nozzle clogging and makes distribution of steel more uniform. The fluid flow and mass transfer phenomena of the molten steel in the three different tundish caster during steady state are studied. Three tundish caster include A-shaped four strands billet tundish, twin strand billet tundish, and single strands slab tundish. In A-shaped four strands billet tundish, HP-SB reduces the difference of residence times between two strands, and makes distribution of steel more uniform. In twin strands billet tundish, the billet tundish with the HLPP design has the longest min-RT and it can inhibit turbulence as well as guide flow upward along slag/metal interface in the tundish. It is thus considered that HLPP arrangement is the optimal SEFCD design for twin strands billet tundish. In the single strand slab tundish, the result shows that the plain tundish has short circuit and the lowest inclusion removal ratio. The tundish with PP can reduce short circuit and has the highest inclusion removal ratio, even for inclusions, which is smaller than 50 μm. PP is the optimal SEFCD design for the single strand slab tundish. The fluid flow and mass transfer phenomena of the molten steel in the tundish caster during ladle-change period are also studied. The results show that the weir and slanting dam reduce the velocity of steel and lower the chance for slag particles to flow into the outlet. WSLD40-F120 has the smallest number of slag particles to flow into the outlet and is considered the optimal flow control design in the T-shaped single strand slab tundish caster.
Yeh, Jiunn-Lin, and 葉俊麟. "The Development of A Mathematical Model and Its Application in the Design/Operation of Tundishes for the Continuous Casting of Steel Slab." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/19515081379711814706.
Full textBook chapters on the topic "Continuous casting. Steel. Mathematical optimization"
Wu, Y. H., M. Chuedoung, and G. Zhang. "A Robust Numerical Algorithm for the Optimal Control of Heat Transfer in the Continuous Casting of Steel." In Optimization Methods and Applications, 399–412. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3333-4_22.
Full textXu, Pei, Dengfu Chen, Shixin Wu, Hengsong Yu, MuJun Long, Sheng Yu, and Huamei Duan. "Optimization of the Flow Behavior of Molten Steel in Ultrahigh-Speed Billet Continuous Casting Mold." In Materials Processing Fundamentals 2019, 59–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05728-2_6.
Full textK., Arya, and Debjani Sambasivam. "Optimization of Oscillation Parameters in Continuous Casting Process of Steel Manufacturing: Genetic Algorithms versus Differential Evolution." In Evolutionary Computation. InTech, 2009. http://dx.doi.org/10.5772/9616.
Full textConference papers on the topic "Continuous casting. Steel. Mathematical optimization"
Lăscuţoni, Alina, Erika Ardelean, and Teodor Hepuţ. "Thermal optimization of steel at continuous casting." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912910.
Full textArzola Ruiz, Jose, Yusdel Diaz Hernandez, Umer Asgher, Alberto Fiol Zulueta, and Thomas Hanne. "Mathematical Modeling and Process Optimization of the Radial Continuous Casting of Steel." In 2018 6th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2018. http://dx.doi.org/10.1109/iscbi.2018.00017.
Full textGalkin, Alexander, Vladimer Pimenov, Pavel Saraev, and Dmitry Tyrin. "Integrated Simulation of Process of Steel Casting on the Continuous Steel Casting Unit." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280653.
Full textGalkin, Alexander, Vladimer Pimenov, Pavel Saraev, and Dmitry Tyrin. "Computer Simulation of Steel Ingot Cooling in Continuous Steel Casting Unit." In 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). IEEE, 2019. http://dx.doi.org/10.1109/summa48161.2019.8947558.
Full textMlakar, Miha, Tea Tušar, and Bogdan Filipić. "Discrete vs. continuous multiobjective optimization of continuous casting of steel." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330879.
Full textAssunção, Charles Sóstenes, Roberto Parreiras Tavares, and Guilherme Dias Oliveira. "WATER DISTRIBUTION ASSESSMENT APPLIED TO MATHEMATICAL MODEL OF CONTINUOUS CASTING OF STEEL." In 46º Seminário de Aciaria - Internacional. São Paulo: Editora Blucher, 2017. http://dx.doi.org/10.5151/1982-9345-26252.
Full textPutta, Ramesh N., and Malur N. Srinivasan. "Mathematical Model of Solidification in Continuous Cast Low Carbon Steel Billets." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12303.
Full textFan Yang, Gui-rong Wang, and Qi-qiang Li. "Self-organizing optimization algorithm for cast planning of steel making-continuous casting." In 2014 International Conference on System Science and Engineering (ICSSE). IEEE, 2014. http://dx.doi.org/10.1109/icsse.2014.6887936.
Full textAlexander, Minayev. "Mathematical Modeling Of Process Crystallization Continuous Casting Bloom From Shipbuilding Steel By Finite Element Method." In MATERIALS PROCESSING AND DESIGN: Modeling, Simulation and Applications - NUMIFORM 2004 - Proceedings of the 8th International Conference on Numerical Methods in Industrial Forming Processes. AIP, 2004. http://dx.doi.org/10.1063/1.1766692.
Full textBhattacharya, Arya K., S. Debjani, Abhik Roychowdhury, and Jadav Das. "Optimization of continuous casting mould oscillation parameters in steel manufacturing process using genetic algorithms." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424992.
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