The Slagging Model Research of Refining-Slag for High Aluminum Steel

2012 ◽  
Vol 602-604 ◽  
pp. 90-95
Author(s):  
Min Zhang ◽  
Jian Hua Zeng ◽  
Xing Di Yang ◽  
Yao Xian Zeng
Keyword(s):  
Prediction Model ◽  
Reduction Reaction ◽  
Refining Slag ◽  
Activity Prediction ◽  
Slag Refining ◽  
Coexistence Theory ◽  
Model Research ◽  
High Aluminum Steel ◽  
Si Content ◽  
High Aluminum

To accurately control the Si content and ensure the function of slag refining in high aluminum steel, the direction and limits of reduction reaction for SiO2 were analyzed based on the thermodynamics. In addition, the thermodynamic prediction model of multi-element slag was studied. Then, a slag activity prediction model was established based on the coexistence theory. Using the model, the suitable ladle slag composition for high aluminum steel was calculated.

Study on the Mold Slag with High Al2O3Content for High Aluminum Steel

PRICM ◽  
2013 ◽  
pp. 2231-2239
Author(s):  
Qiang Wang ◽  
Shengtao Qiu ◽  
Zhiling Tian ◽  
Guoling Zhu ◽  
Pei Zhao
Keyword(s):  
Mold Slag ◽  
High Aluminum Steel ◽  
High Aluminum

Effects of the cooling rate on the crystallization behaviors of the CaO–Al2O3–B2O3–CaF2-based mold flux

CrystEngComm ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 2158-2165
Author(s):  
Jiangling Li ◽  
Feifei Lai ◽  
Wen Yao ◽  
Liwen Hu ◽  
Qingcai Liu
Keyword(s):  
High Temperature ◽  
Cooling Rate ◽  
Steel Casting ◽  
Mold Flux ◽  
Tube Furnace ◽  
Crystallization Behaviors ◽  
High Aluminum Steel ◽  
New Type ◽  
High Aluminum

The effects of the cooling rate on a new type CaO–Al2O3–B2O3–CaF2-based mold flux for high aluminum steel casting were investigated using CSLM and a high temperature tube furnace.

The Prediction Model Research for Network Traffic Based on ARMA

2014 ◽  
Vol 530-531 ◽  
pp. 760-763
Author(s):  
Zhao Ji Zhang
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Detection System ◽  
Moving Average ◽  
Third Party ◽  
Testing System ◽  
Network Resources ◽  
Network Intrusion ◽  
Model Research ◽  
Auto Regressive

This paper presents a new WIA-PA network intrusion detection system -- Auto Regressive and Moving Average (ARMA) network traffic prediction model. This model can predict the network traffic quickly and accurately, and because this is a third party testing system, it does not need to take network resources, the security of the WIA-PA network design is of vital importance. The simulation results show that our proposed system can effectively detect intrusion attack, improve the performance of the entire network, prolonging the life of the network.

Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus : Optimization and activity prediction model development

2015 ◽  
Vol 65 ◽  
pp. 7-13 ◽  
Author(s):  
Katarina Rajkovic ◽  
Marina Pekmezovic ◽  
Aleksandra Barac ◽  
Jasmina Nikodinovic-Runic ◽  
Valentina Arsić Arsenijević
Keyword(s):  
Prediction Model ◽  
Essential Oils ◽  
Aspergillus Flavus ◽  
Model Development ◽  
Inhibitory Effect ◽  
Activity Prediction
Sign in / Sign up

Export Citation Format

Share Document