Electrical Conductivity Calculation of Molten Multicomponent Slag by Neural Network Analysis

被引：14

作者：

Haraguchi, Yusuke ^{[1
]}

Nakamoto, Masashi ^{[1
]}

Suzuki, Masanori ^{[1
]}

Fuji-Ta, Kiyoshi ^{[1
]}

Tanaka, Toshihiro ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Engn, 2-1 Yamadaoka, Suita, Osaka 5650871, Japan

来源：

ISIJ INTERNATIONAL | 2018年 / 58卷 / 06期

关键词：

electrical conductivity; multicomponent molten slag; neural network computation; estimation; SURFACE-TENSION; SYSTEM; TEMPERATURE; COMPUTATION; VISCOSITY; GLASSES; MELTS;

D O I：

10.2355/isijinternational.ISIJINT-2017-757

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

Only a few models for estimating electrical conductivity of molten slag have been developed, which are limited to systems with certain types of components. In this study, a new method for estimating the electrical conductivity of molten slag through neural network calculations is proposed and is compared with previous estimation approaches. The present estimation approach can reproduce the electrical conductivity of molten slag composed of SiO2, CaO, MgO, MnO, Al2O3, FeO, Fe2O3, and Na2O to an uncertainty of 30%. We found that the neural network calculation is applicable to various kinds of molten slag over wider mole fraction and temperature ranges than conventional models.

引用

页码：1007 / 1012

页数：6

共 46 条

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