Numerical simulation of thermal insulation and longevity performance in new lightweight ladle
Ying Sun
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorCorresponding Author
Jinrong Tian
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Correspondence
Jinrong Tian, Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Email: [email protected]
Search for more papers by this authorDu Jiang
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorBo Tao
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorYing Liu
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorJuntong Yun
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorDisi Chen
School of Computing, University of Portsmouth, Portsmouth, UK
Search for more papers by this authorYing Sun
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorCorresponding Author
Jinrong Tian
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Correspondence
Jinrong Tian, Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Email: [email protected]
Search for more papers by this authorDu Jiang
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorBo Tao
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorYing Liu
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorJuntong Yun
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
Search for more papers by this authorDisi Chen
School of Computing, University of Portsmouth, Portsmouth, UK
Search for more papers by this authorFunding information: National Defense Pre-Research Foundation of Wuhan University of Science and Technology, GF201705; National Natural Science Foundation of China, 51575338; 51575407; 51575412; 61733011; The State Key Laboratory of Refractories and Metallurgy of China, 2018QN16
Summary
For meeting the comprehensive requirements of “super insulation,” “lightweight,” and “longevity” of the contemporary ladle, this article designs a new kind of lightweight ladle with heat preservation and longevity performance, and based on steady-state analysis method and numerical simulation technology, the comparison of temperature distribution between new lightweight and traditional ladle under typical operating modes is made and analyzed. The simulation results of temperature field prove that the performance of heat preservation in new kind of lightweight ladle has been improved obviously from the two aspects of ladle shell temperature and molten steel file rate. At the same time, the simulation results of stress field indicate that the stress of designed lightweight ladle reduced and distributed more evenly, which is conducive to prolonging the work time in-service of the ladle. Finally, based on the field test, the simulation is proved to be effective, and the designed ladle structure achieves the expected purpose.
REFERENCES
- 1Li G, Liu Z, Jiang G, Xiong H, Liu H. Numerical simulation of the influence factors for rotary kiln in temperature field and stress field and the structure optimization. Adv Mech Eng. 2015; 7(6):1687814015589667. https://doi.org/10.1177/1687814015589667.
- 2Qi L, Zhang X, Dou W, Hu C, Yang C, Chen J. A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Future Generat Comput Syst. 2018; 88: 636-643.
- 3Terzić A, Pezo L, Mitić VV. Optimization of drying through analytical modeling: clays as bonding agents in refractory materials. Ceram Int. 2016; 42(5): 6301-6311.
- 4Cheng W, Sun Y, Li G, et al. Simulation analysis of stress distribution and its influence factors of the new structure ladle. Appl Math Inf Sci. 2017; 11(2): 611-619.
10.18576/amis/110233 Google Scholar
- 5Cui Z, Xue F, Zhang S, et al. A hybrid blockchain-based identity authentication scheme for multi-WSN. IEEE Trans Serv Comput. 2020; 13(2): 241–251. https://doi.org/10.1109/TSC.2020.2964537.
- 6Luo B, Li G, Kong J, et al. Simulation analysis of temperature field and its influence factors of the new structure ladle [J]. Appl Math Inf Sci. 2017; 11(2): 589-599.
10.18576/amis/110231 Google Scholar
- 7Hua J, Li G, Jiang D, Zhao H, Qi J. An optimized selection method of channel numbers and electrode layouts for hand motions recognition. Int J Humanoid Robot. 2019; 16(4): 1941006.
- 8Chang W, Li G, Kong J, Sun Y, Jiang G, Liu H. Thermal mechanical stress analysis of ladle lining with integral brick joint. Arch Metallurgy Mater. 2018; 63(2): 659-666.
- 9Hassan M, Rehmani M, Chen J. DEAL: differentially private auction for Blockchain based microgrids energy trading. IEEE Trans Serv Comput. 2020; 13(2): 263–275. https://doi.org/10.1109/TSC.2019.2947471.
- 10Han W, Li X, Zhang C, Zhou J, Shi X. Heat preservation of hot metal ladle by capping. Iron Steel. 2015; 50(12): 91-94.
- 11Chang W, Sun Y, Li G, et al. Ladle health monitoring system based on labview. Int J Comput Sci Math. 2018; 9(6): 566-576.
- 12Yang H, Li G, Zhao Y, Sun Y, Jiang G. Numerical simulation-based optimization of contact stress distribution and lubrication conditions in the straight worm drive. Strength Mater. 2018; 50(1): 157-165.
- 13Taddeo L, Gascoin N, Fedioun I, Chetehouna K, Lamoot L, Fau G. Dimensioning of automated regenerative cooling: setting of high-end experiment. Aerospace Sci Tech. 2015; 43: 350-359.
- 14Li G, Liu Z, Kong J, et al. Simulation analysis of temperature field and influencing factors of new ladle. Wuhan Univ Sci Tech. 2015; 38(6): 401-407.
- 15Hassan M, Rehmani M, Chen J. Differential privacy techniques for cyber physical systems: a survey. IEEE Commun Surv Tutor. 2019; 22: 746-789. https://doi.org/10.1109/COMST.2019.2944748.
- 16Li G, Liu J, Jiang G, Liu H. Numerical simulation of temperature field and thermal stress field in the new type of ladle with the nanometer adiabatic material. Adv Mech Eng. 2015; 7(4):1687814015575988. https://doi.org/10.1177/1687814015575988.
- 17Huang B, Tian N, Shi Z, Ma Z. Steel ladle exchange models during steelmaking and continuous casting process. J Iron Steel Res. 2017; 24(6): 617-624.
- 18Cheng W, Sun Y, Li G, Jiang G, Liu H. Jointly network: a network based on cnn and rbm for gesture recognition. Neural Comput Appl. 2019; 31(Suppl 1): 309-323.
- 19Cui Z, Xu X, Xue F, et al. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Trans Serv Comput. 2020. https://doi.org/10.1109/TSC.2020.2964552.
- 20Li G, Jiang D, Sun Y, Jiang G, Tao B. Life prediction mechanism of ladle composite structure body based on simulation technology [J]. Arch Metallurgy Mater. 2019; 64(4): 1555-1562.
- 21Cui Z, Zhang J, Wu D, et al. Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inform Sci. 2020; 518: 256-271.
- 22Hu J, Sun Y, Li G, Jiang G, Tao B. Probability analysis for grasp planning facing the field of medical robotics. Measurement. 2019; 141: 227-234.
- 23Cai X, Wang P, Lei D, Cui Z, Zhang W, Chen J. Multi-objective 3-dimensional DV-hop localization algorithm with NSGA-II. IEEE Sens J. 2019; 19(21): 10003-10015. https://doi.org/10.1109/JSEN.2019.2927733.
- 24Sun Y, Hu J, Li G, et al. Gear reducer optimal design based on computer multimedia simulation. J Supercomputing. 2018; 76: 4132–4148. https://doi.org/10.1007/s11227-018-2255-3.
- 25Huang L, Qiaobo F, Li G, Luo B, Chen D, Hui Y. Improvement of maximum variance weight partitioning particle filter in urban computing and intelligence. IEEE Access. 2019; 7: 106527-106535.
- 26Gonzalez H, Ramos-Banderas JA, Torres-Alonso E, Solorio-Diaz G, Hernández-Bocanegra CA. Multiphase modeling of fluid dynamic in ladle steel operations under non-isothermal conditions. J Iron Steel Res Int. 2017; 24(9): 888-900.
- 27Jiang D, Zheng Z, Li G, et al. Gesture recognition based on binocular vision. Cluster Comput. 2019; 22(Suppl 6): 13261-13271. https://doi.org/10.1007/s10586-018-1844-5.
- 28Cui Z, Zhang J, Wang Y, et al. A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci Chin Inf Sci. 2019; 62(7):070212.
- 29Ma R, Zhang L, Li G, Jiang D, Xu S, Chen D. Grasping force prediction based on sEMG signal. Alexandria Eng J. 2020. https://doi.org/10.1016/j.aej.2020.01.007.
- 30Cui Z, Lei D, Wang P, Cai X, Zhang W. Malicious code detection based on CNNs and multi-objective algorithm. J Parall Distrib Comput. 2019; 129: 50-58.
- 31Tian J, Cheng W, Sun Y, et al. Gesture recognition based on multilevel multimodal feature fusion. J Intell Fuzzy Syst. 2020; 38(3): 2539-2550.
- 32Chen X, Jiang G, Yang L, Li G, Xiang F. Redesign of enterprise lean production system based on environmental dynamism. Concurr Comput Pract Exp. 2020. https://doi.org/10.1002/cpe.5706.
- 33Li G, Zhang L, Sun Y, Kong J. Towards the semg hand: internet of things sensors and haptic feedback application. Multimed Tools Appl. 2019; 78(21): 29765-29782.
- 34Ning L, Xie Y Ma R. Fatigue analysis of ladle shell and prediction of lining thickness. Paper presented at: Proceedings of the IOP Conference Series Materials Science and Engineering; 2019.
- 35Sun Y, Xu C, Li G, et al. Intelligent human computer interaction based on non redundant EMG signal. Alex Eng J. 2020. https://doi.org/10.1016/j.aej.2020.01.015.
- 36Sun Y, Li C, Li G, et al. Gesture recognition based on kinect and semg signal fusion. Mob Netw Appl. 2018; 23(4): 797-805.
- 37Liao S, Li G, Li J, et al. Multi-object intergroup gesture recognition combined with fusion feature and KNN algorithm. J Intell Fuzzy Syst. 2020; 38(3): 2725-2735.
- 38Li G, Li J, Zhaojie J, Sun Y, Kong J. A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Comput Appl. 2019; 31(12): 9013-9022.
- 39Chen T, Wu S, Yang J, Cong G, Li G. Modeling of emergency supply scheduling problem based on reliability and its solution algorithm under variable road network after sudden-onset disasters. Complexity. 2020; 2020:7501891. https://doi.org/10.1155/2020/7501891.
- 40Jiang D, Li G, Sun Y, Kong J, Tao B. Gesture recognition based on skeletonization algorithm and cnn with asl database. Multimed Tools Appl. 2019; 78(21): 29953-29970.
- 41Chen T, Wu S, Yang J, Cong G. Risk propagation model and its simulation of emergency logistics network based on material reliability. Int J Environ Res Public Health. 2019; 16(23): 4677. https://doi.org/10.3390/ijerph16234677.
- 42Li G, Jiang D, Zhou Y, Jiang G, Kong J, Manogaran G. Human lesion detection method based on image information and brain signal. IEEE Access. 2019; 7: 11533-11542.
- 43Chen T, Li Q, Yang J, Cong G, Li G. Modeling of the public opinion polarization process with the considerations of individual heterogeneity and dynamic conformity. Mathematics. 2019; 7(10): 917. https://doi.org/10.3390/math7100917.
- 44Li G, Wu H, Jiang G, Xu S, Liu H. Dynamic gesture recognition in the Internet of Things. IEEE Access. 2019; 7: 23713-23724.
- 45Li J, Chen Z, Yang H, et al. Tunable broadband solar energy absorber based on monolayer transition metal Dichalcogenides materials using au Nanocubes. Nanomaterials. 2020; 10(2): 257.
- 46Qi J, Jiang G, Li G, Sun Y, Tao B. Intelligent human-computer interaction based on surface emg gesture recognition. IEEE Access. 2019; 7: 61378-61387.
- 47Chen T, Li Q, Fu P, et al. Public opinion polarization by individual revenue from the social preference theory. Int J Environ Res Public Health. 2020; 17(3): 946. https://doi.org/10.3390/ijerph17030946.
- 48Li G, Tang H, Sun Y, et al. Hand gesture recognition based on convolution neural network. Cluster Comput. 2019; 22(Suppl 2): 2719-2729.
- 49Sheshukov OY, Nekrasov IV, Mikheenkov MA, et al. Effect of refining slag phase composition on ladle furnace unit lining life. Refract Ind Ceram. 2016; 57(2): 109-116.
- 50Li C, Li G, Li G, Chen D, Liu H. Surface emg data aggregation processing for intelligent prosthetic action recognition. Neural Comput Appl. 2018. https://doi.org/10.1007/s00521-018-3909-z.
- 51Zhou J, Jiang X, Xie J, et al. Thermodynamic analysis of improvement of converter gas by injecting pulverized coal into vaporization cooling flue. J Iron Steel Res Int. 2018; 25(1): 65-71.
- 52Tan C, Sun Y, Li G, Jiang G, Chen D, Liu H. Research on gesture recognition of smart data fusion features in the IoT. Neural Comput Appl. 2019. https://doi.org/10.1007/s00521-019-04023-0.
- 53Jiang C, Chen T, Li R, et al. Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection. Concurr Comput Pract Exp. 2020; 32(7):e5592.
- 54Yu M, Li G, Jiang D, Jiang G, Tao B, Chen D. Hand medical monitoring system based on machine learning and optimal emg feature set. Personal Ubiquitous Comput. 2019. https://doi.org/10.1007/s00779-019-01285-2.
10.1007/s00779-019-01285-2 Google Scholar
- 55Sarkar S, Sahoo MR, Kujur MK, Das UK, Das SK, Reddy TS. Development of metallic fibre-reinforced MgO-C brick and its application at the SAIL Rourkela steel plant. InterCeram Int Ceram Rev. 2017; 66(7): 38-40.
10.1007/BF03401227 Google Scholar
- 56Luo B, Sun Y, Li G, Chen D, Zhaojie J. Decomposition algorithm for depth image of human health posture based on brain health. Neural Comput Appl. 2019; 32: 6327-6342. https://doi.org/10.1007/s00521-019-04141-9.
- 57Qi J, Jiang G, Li G, Sun Y, Tao B. Surface EMG hand gesture recognition system based on PCA and GRNN. Neural Comput Appl. 2019; 32: 6343-6351. https://doi.org/10.1007/s00521-019-04142-8.
- 58Yu M, Li G, Jiang D, et al. Application of PSO-RBF neural network in gesture recognition of continuous surface EMG signals. J Intell Fuzzy Syst. 2020; 38(3): 2469-2480.
- 59Garcia-Hernandez S, Morales RD, de Jesus-Barreto J, Calderon-Ramos I, Gutierrez E. Modeling study of slag emulsification during ladle change-over using a dissipative ladle shroud. Steel Res Int. 2016; 87(9): 1154-1167.
- 60Li B, Sun Y, Li G, et al. Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm. Cluster Comput. 2019; 22(Suppl 1): 503-512.
- 61Jiang D, Li G, Sun Y, Kong J, Tao B, Chen D. Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using SEMG. Personal Ubiquitous Comput. 2019. https://doi.org/10.1007/s00779-019-01268-3.
10.1007/s00779-019-01268-3 Google Scholar
- 62Fawkhry MKEI. Feasibility of new ladle-treated Hadfield steel for mining purposes. Int J Minerals Metallurgy Mater. 2018; 025(003): 300-309.
- 63Cheng Y, Li G, Li J, et al. Visualization of activated muscle area based on SEMG. J Intell Fuzzy Syst. 2020; 38(3): 2623-2634.
- 64Li G, Peixin Q, Kong J, et al. Influence of working lining parameters on temperature and stress field of ladle. Appl Math Inf Sci. 2013; 7(2): 439-448.
10.12785/amis/070203 Google Scholar
- 65Yang H, Li G, Liao Y, et al. Gesture recognition based on an improved local sparse representation classification algorithm. Cluster Comput. 2019; 22(Suppl 5): 10935-10946.
- 66Ushakov SN, Bigeev VA, Stolyarov AM, Potapova MV. Ladle desulfurization of converter low-sulfur pipe steel. Metallurgist. 2018; 62(7–8): 667-673.
