Evaluation of Viscosity of Mold Flux by Using Neural Network Computation

Hanao, Masahito; Kawamoto, Masayuki; Tanaka, Toshihiro; Nakamoto, Masashi

doi:info:doi/10.2355/isijinternational.46.346

このアイテムのアクセス数:85件（2021-09-21 21:06 集計）

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タイトル	Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
著者	Hanao, Masahito Hanao, Masahito
	Kawamoto, Masayuki Kawamoto, Masayuki
	Tanaka, Toshihiro Tanaka, Toshihiro
	Nakamoto, Masashi Nakamoto, Masashi
キーワード等	viscosity
	solidification temperature
	slag
	mold flux
	neural network computation
	regression estimation
抄録	A new estimation method of viscosity or solidification temperature of mold fluxes was proposed by applying the neural network computation. In this evaluation system, the viscosity and the solidification temperature of mold fluxes can be evaluated from the analytical compositions in multi-component systems of SiO_2–Al_2O_3–CaO–MgO–Na_2O–F–T.Fe–ZrO_2–TiO_2–BaO–MnO–B_2O_3–S–C without any conversion of S or F to sulphide or fluoride. It was found that the calculated results of the dependence of viscosity on temperature and composition agree with the experimental results more precisely than some conventional physical models for viscosity. Furthermore, viscosity of mold fluxes can be estimated precisely in the wide range of SiO_2 content.
公開者	日本鉄鋼協会
公開者の別表記	The Iron and Steel Institute of Japan
公開者 (ヨミ)	ニホン　テッコウ　キョウカイ
掲載誌名	ISIJ International
巻	46
号	3
開始ページ	346
終了ページ	351
刊行年月	2006
ISSN	09151559
NCID	AA10680712
DOI (出版社版)	info:doi/10.2355/isijinternational.46.346
URL	http://hdl.handle.net/11094/26416
権利情報	© 2006 ISIJ
言語	英語
カテゴリ	学術雑誌論文 Journal Article

著者版フラグ	publisher
NII資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
dcmi資源タイプ	text
DCTERMS.bibliographicCitation	ISIJ International.46(3) P.346-P.351
DC.title	Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
DC.creator	Hanao, Masahito
	Kawamoto, Masayuki
	Tanaka, Toshihiro
	Nakamoto, Masashi
DC.publisher	日本鉄鋼協会
DC.language" scheme="DCTERMS.RFC1766	英語
DCTERMS.issued" scheme="DCTERMS.W3CDTF	2006
DC.identifier	info:doi/10.2355/isijinternational.46.346
DC.identifier" scheme="DCTERMS.URI	http://hdl.handle.net/11094/26416
DC.subject	viscosity
	solidification temperature
	slag
	mold flux
	neural network computation
	regression estimation
DCTERMS.abstract	A new estimation method of viscosity or solidification temperature of mold fluxes was proposed by applying the neural network computation. In this evaluation system, the viscosity and the solidification temperature of mold fluxes can be evaluated from the analytical compositions in multi-component systems of SiO_2–Al_2O_3–CaO–MgO–Na_2O–F–T.Fe–ZrO_2–TiO_2–BaO–MnO–B_2O_3–S–C without any conversion of S or F to sulphide or fluoride. It was found that the calculated results of the dependence of viscosity on temperature and composition agree with the experimental results more precisely than some conventional physical models for viscosity. Furthermore, viscosity of mold fluxes can be estimated precisely in the wide range of SiO_2 content.
DC.rights	© 2006 ISIJ
citation_title	Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
citation_author	Hanao, Masahito
	Kawamoto, Masayuki
	Tanaka, Toshihiro
	Nakamoto, Masashi
citation_publisher	日本鉄鋼協会
citation_language	英語
citation_date	2006
citation_journal_title	ISIJ International
citation_volume	46
citation_issue	3
citation_firstpage	346
citation_lastpage	351
citation_issn	09151559
citation_doi	info:doi/10.2355/isijinternational.46.346
citation_public_url	http://hdl.handle.net/11094/26416
citation_keywords	viscosity
	solidification temperature
	slag
	mold flux
	neural network computation
	regression estimation

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