KINETICS OF AN ALLOY LAYER GROWTH ON THE SURFACE OF GRAPHITE ELECTRODE IN A MOLTEN MIXTURE

戴星; 刘洪勤

您当前的位置：

首页 >

文章列表页 >

KINETICS OF AN ALLOY LAYER GROWTH ON THE SURFACE OF GRAPHITE ELECTRODE IN A MOLTEN MIXTURE

更新时间：2025-12-16

- KINETICS OF AN ALLOY LAYER GROWTH ON THE SURFACE OF GRAPHITE ELECTRODE IN A MOLTEN MIXTURE
- CIESC Journal Issue 2, (1994)
- 作者机构：
  
  北京科技大学冶金系,北京科技大学冶金系北京 100083,北京,100083
- 作者简介：
- 基金信息：
- DOI：
  CLC： 646
- Online First：25 April 1994，
  
  Published：25 April 1994
- 稿件说明：
移动端阅览
戴星, 刘洪勤. KINETICS OF AN ALLOY LAYER GROWTH ON THE SURFACE OF GRAPHITE ELECTRODE IN A MOLTEN MIXTURE[J]. CIESC Journal, 1994, (2).
DOI：

戴星, 刘洪勤. KINETICS OF AN ALLOY LAYER GROWTH ON THE SURFACE OF GRAPHITE ELECTRODE IN A MOLTEN MIXTURE[J]. CIESC Journal, 1994, (2). DOI：

摘要

熔融体系中采用表面合金化方法可在石墨电极表面形成一结构致密且具有良好导电性的抗氧化碳化物合金保护层

在电弧炉炼钢生产中能有效地降低电极消耗。本文研究了这一过程中表面合金层的形成和生长机制

提出碳原子在合金层中的非稳态固相扩散为该过程的控制步骤

并由此导出合金层的生长动力学数学模型。模型计算值与实验结果能很好吻合。

Abstract

A conducting oxidation-resistant carbide alloy layer can be formed on the surface of graphite electrode by means of the surface alloying method in a molden mixture. This compact layer can effectively reduce the consumption of graphite electrode due to carbon oxidation during arc furnace steelmaking. The process of alloy layer growth is found to be controlled by the non-steady state solid phase diffusion of carbon atom. A kinetic model was established thereupon. The model prediction is in good agreement with experimental data.

关键词

Keywords

references

Views

142

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi objective optimization of flow and heat transfer characteristics of modified manifold microchannel heat sink based on machine learning

Recent advances and perspectives on sulfide-based photocatalysts for hydrogen production

Thermodynamic performance of tower-type solar-assisted supercritical compression CO2 energy storage system

Effect of belt structure design on the vulcanization characteristics of all-steel radial tires

Recent advances in machine learning for modeling and control of crystallization processes

Related Author

No data

Related Institution

No data

⁰