Real‐Time Heat Transfer Model Based on Distributed Thermophysical Property Calculation for the Continuous Casting Process

2018 ◽  
Vol 90 (5) ◽  
pp. 1800476
Author(s):  
Cheng Ji ◽  
Shimin Deng ◽  
Rui Guan ◽  
Miaoyong Zhu
Keyword(s):  
Heat Transfer ◽  
Continuous Casting ◽  
Thermophysical Property ◽  
Real Time ◽  
Casting Process ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Continuous Casting Process ◽  
Model Based

scholarly journals Mathematical Heat Transfer Model Research for the Improvement of Continuous Casting Slab Temperature

2005 ◽  
Vol 45 (9) ◽  
pp. 1291-1296 ◽  
Author(s):  
Hongming WANG ◽  
Guirong LI ◽  
Yucheng LEI ◽  
Yutao ZHAO ◽  
Qixun DAI ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Continuous Casting ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Continuous Casting Slab ◽  
Model Research ◽  
Slab Temperature

Fluid flow and heat transfer with nail dipping method in mould during continuous casting process

2020 ◽  
Vol 59 (2) ◽  
pp. 201-210 ◽  
Author(s):  
Yanzhao Luo ◽  
Chenxi Ji ◽  
Wenyuan He ◽  
Yanqiang Liu ◽  
Xiaoshan Yang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Continuous Casting ◽  
Casting Process ◽  
Continuous Casting Process ◽  
Flow And Heat Transfer

Identification for Control of Low Pressure Die Casting

2010 ◽  
Author(s):  
XinMei Shi ◽  
Daan M. Maijer ◽  
Guy Dumont
Keyword(s):  
Heat Transfer ◽  
State Space ◽  
Die Casting ◽  
State Space Model ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Process Data ◽  
Space Model ◽  
Model Based ◽  
Virtual Process

Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating defects focus on the execution of a pre-set casting cycle, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodologies may be employed to dynamically adjust the operational parameters of the process. In this work, a finite element heat transfer model, validated by comparison with experimental data, has been developed to predict the evolution of temperatures and the volume of liquid encapsulation in an experimental casting process. A virtual process, made up of the heat transfer model and a wrapper script for communication, has been employed to simulate the continuous operation of the real process. A stochastic state-space model, based on data from measurements and the virtual process, has been developed to provide a reliable representation of this virtual process. The parameters of the deterministic portion result from system identification of the virtual process, whereas the parameters of the stochastic portion arise from the analysis and comparison of measurement data with virtual process data. The resulting state-space model, which can be extended to a multi-input multi-output model, will facilitate the design of a model-based controller for this process.

Metallurgical Secondary Cooling of Continuous Casting Based on Neural Network Adaptive System Design of Water Distribution

2014 ◽  
Vol 926-930 ◽  
pp. 802-805
Author(s):  
Jun Li Jia ◽  
Jin Hong Zhang ◽  
Guo Zhen Wang
Keyword(s):  
Heat Transfer ◽  
Continuous Casting ◽  
Water Distribution ◽  
Cooling System ◽  
Control Level ◽  
Cooling Water ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Secondary Cooling ◽  
Water Control

Efficient secondary cooling water control level slab continuous casting process and quality are closely related. Casting solidification heat transfer model is the basis of process control and optimization, heat transfer model based on determining the secondary cooling system is the most widely used method for casting production process can be simulated. However, when considering the many factors affecting the production and input conditions change significantly, real-time and strain of this method is not guaranteed. Therefore, the artificial intelligence optimization algorithms such as genetic algorithms, neural networks, fuzzy controllers, introducing continuous casting secondary cooling water distribution and dynamics of optimal control methods, the rational allocation of caster secondary cooling water and dynamic control is important.

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