JP3786261B2

JP3786261B2 - Desulfurization model creation device and its program in hot metal pretreatment, and desulfurization agent input amount calculation device and program in hot metal pretreatment

Info

Publication number: JP3786261B2
Application number: JP2002032269A
Authority: JP
Inventors: 信行友近; 浩一松田; 万希志中山; 龍平三角; 郁生星川; 泰一上山
Original assignee: Kobe Steel Ltd
Current assignee: Kobe Steel Ltd
Priority date: 2002-02-08
Filing date: 2002-02-08
Publication date: 2006-06-14
Anticipated expiration: 2022-02-08
Also published as: JP2003231908A

Description

【０００１】
【発明の属する技術分野】
本発明は、溶銑予備処理において複数種類の脱硫剤の投入量に関する変数と脱硫剤投入前後での溶銑中の硫黄濃度との関係に基づいた脱硫モデル式を作成する装置及びそのプログラム、並びに、処理後での溶銑中の硫黄濃度を最適にする脱硫剤投入量を算出する装置及びそのプログラムに関する。
【０００２】
【従来の技術】
一般に、製鉄・製鋼工程において高炉から出銑された溶銑は、含有する硫黄、燐、珪素等の成分が製品の規格濃度以下となるように、脱硫、脱燐、脱珪等の溶銑予備処理を経た後、転炉に装入される。ここで脱硫処理については、通常、転炉や溶鋼処理の段階で脱硫するのは脱硫効率（脱硫剤単位量当たりの脱硫量、即ち脱硫剤における脱硫への寄与率）の点で不利であるため、溶銑予備処理の段階で脱硫を行う。
【０００３】
溶銑予備処理における脱硫には、比較的安価であることから石灰（ＣａＯ）を主成分として脱硫反応を促進するための蛍石、アルミナ等を加えた石灰系脱硫剤が汎用されている。しかし一般に石灰は溶銑に対する濡れ性が悪く、これが石灰系脱硫剤の脱硫効率が低い要因の１つとなっている。脱硫効率が低いと多くの脱硫剤が必要となったり、脱硫時間が長くなったりという不都合が生じる。また近年製品の高級化に伴って製品中の硫黄濃度目標値が低下する傾向にあり、石灰系脱硫剤では所定の時間内に脱硫処理を終了できなくなってきている。
【０００４】
そこで脱硫効率を高めて脱硫時間を短縮するために、石灰系脱硫剤と比べて高価であるが、溶銑との濡れ性が良く脱硫効率の高いカルシウムカーバイド（ＣａＣ₂）系脱硫剤、金属マグネシウム（Ｍｇ）系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム（Ｃａ）系脱硫剤、カルシウム合金系脱硫剤、ソーダ灰（Ｎａ₂ＣＯ₃）系脱硫剤等を石灰系脱硫剤と共に用いることが行われる。
【０００５】
また脱硫を行う際は、コストを最小に抑えるため、製品の規格硫黄濃度に対して過不足ないよう脱硫剤の投入量を計算しておく必要がある。脱硫剤の投入量、投入時間等の条件は、処理後における溶銑中の硫黄濃度を目標濃度以下に抑えると共に操業コストを上昇させないよう、バランスを考慮して決定することが重要である。そこで従来は、例えば重回帰等の統計的手法により、脱硫操業における脱硫剤の投入量に関する変数と脱硫剤投入前後での溶銑中の硫黄濃度との関係を脱硫モデル式としてモデル化し、処理後の溶銑中の硫黄濃度を推定することにより脱硫剤投入量を決定している。従来用いられている脱硫モデル式の一例として、下記式（ｉ）が挙げられる。なお式（ｉ）では、脱硫剤として石灰系脱硫剤とカルシウムカーバイド系脱硫剤とを用いた場合を想定している。
ｌｎ（Ｓｉ／Ｓｆ）＝ａ＊Ｘ＋ｂ＊Ｙ＋Δ （ｉ）
（Ｓｉ：脱硫剤投入前の溶銑中の硫黄濃度、Ｓｆ：処理後の溶銑中の硫黄濃度、Ｘ：石灰系脱硫剤の原単位、Ｙ：カルシウムカーバイド系脱硫剤の原単位、ａ，ｂ：定数、Δ：その他の変数項及び／又は定数項）
【０００６】
一般にカルシウムカーバイドの方が石灰よりも脱硫効率が高いことからｂ＞ａ＞０であること、カルシウムカーバイドや石灰の投入量を増加させるほど脱硫効率は高くなるが寄与率は低くなること、パラメータの調整が容易であること等、上記式（ｉ）のような重回帰等の統計的手法による脱硫モデルは物理的に解釈しやすいという利点がある。しかしながら上記式（ｉ）右辺は、石灰系脱硫剤、及びカルシウムカーバイド系脱硫剤という異なる脱硫剤の原単位Ｘ，Ｙが夫々独立した構造、即ち各要素が互いに掛け合わされずに単に和で結合されただけの構造となっている。つまり式（ｉ）右辺から判るように、従来の脱硫モデル式は、種類の異なる脱硫剤同士の干渉については影響が小さいものと見なし、干渉項を無視したものとなっている。また、式（ｉ）のような線形モデルでは、非線形性を表現することができず、モデルの精度に限界がある。
【０００７】
そこでモデルの精度を向上させるため、例えば特開平８−２６９５１８公報に開示されているような、ニューラルネットワークを用いたものがある。ニューラルネットワークを用いると、処理後の溶銑中硫黄濃度を精度良く予測することができる。
【０００８】
【発明が解決しようとする課題】
しかしながらニューラルネットワークを用いたモデルでは、モデルの外挿性を良くするためには試行錯誤しながら学習させる必要があり、さらに、モデルの構造からは物理的な因果関係が解釈しにくい。このため、プロセスが大幅に変更された場合、パラメータ調整に手間がかかってモデルの修正が困難である。即ちモデルの保守性において問題がある。
【０００９】
本発明は以上の問題に鑑みてなされたものであり、精度がよく保守性に優れた溶銑予備処理における脱硫モデルを作成する作成装置及びそのプログラム、並びに、脱硫効率やコストを考慮した最適な脱硫剤投入量を得ることができる溶銑予備処理における脱硫剤投入量算出装置及びそのプログラムを提供することを目的とする。
【００１０】
【課題を解決するための手段】
本発明者らは、石灰（ＣａＯ）系脱硫剤の原単位Ｘとカルシウムカーバイド（ＣａＣ₂）系脱硫剤の原単位Ｙと処理後の溶銑中の硫黄濃度Ｓｆとの関係を、実操業データの解析やニューラルネットワークモデルを使用したシミュレーションにより求めた。その結果を図６に示す。図６から明らかなように、処理後の溶銑中の硫黄濃度Ｓｆは、石灰系脱硫剤の原単位Ｘとカルシウムカーバイド系脱硫剤の原単位Ｙとの両方に依存している。また、各脱硫剤の脱硫効率は他の脱硫剤の投入量に依存している。つまり、石灰系脱硫剤の投入量が一定であっても、カルシウムカーバイド系脱硫剤の投入量によって、カルシウムカーバイド系脱硫剤の脱硫効率が変動する。またこの逆も言え、種類の異なる脱硫剤を投入する場合、脱硫処理に際して互いに干渉し合うことが分かった。
【００１１】
溶銑予備処理における脱硫効率を高めるため、図４に示すような、脱硫剤を溶銑中に効果的に分散させる所謂「インジェクション脱硫方式」が汎用されている。このインジェクション脱硫方式では、耐火物性のランス２０がトピード１３内の溶銑１２に挿入され、脱硫剤１１は窒素等のガスと共に溶銑１２内に吹き込まれる。この方式においては、脱硫効率を高めるという観点から、粉体である脱硫剤１１が窒素ガスの気泡１０を脱して溶銑へ侵入することが重要である。
【００１２】
図５に拡大図示するように、ランス２０から窒素ガスの気泡１０と共に吹き出された脱硫剤１１は、窒素ガスの気泡１０に内包された脱硫剤１１ａと、溶銑へ侵入した脱硫剤１１ｂとに分けられる。本発明者らは、脱硫剤１１が窒素ガスの気泡１０から脱して溶銑１２に侵入するための因子として、溶銑１２に対する脱硫剤１１の「濡れ性」に着目し、実験を繰り返した。その結果、溶銑１２に対する濡れ性の良い脱硫剤１１ｂは容易に溶銑１２中に侵入して脱硫効率の向上に大きく寄与するが、濡れ性が悪い脱硫剤１１ａは窒素ガス１０の気泡中に留まった状態で溶銑１２中を浮上する傾向にあり、脱硫効率の向上にほとんど寄与しないことが分かった。例えば、生石灰は溶銑との濡れ性が悪く、これが生石灰の脱硫効率が低い原因の１つである。これに対して、カルシウムカーバイド、金属マグネシウム、ソーダ灰等は溶銑との濡れ性がよいため、高い脱硫効率が得られる。
【００１３】
石灰系脱硫剤のように濡れ性の悪い脱硫剤の脱硫効率を高めようとする場合、石灰系脱硫剤におけるランス２０先端から溶銑１２への突出速度を大きくするのが有効である。これは、石灰系脱硫剤が溶銑１２に侵入しやすくなると考えられるからである。しかし、例えば上述した図４に示すインジェクション脱硫方式において、脱硫処理時間を短縮するために濡れ性の異なる複数種類の脱硫剤（例えば石灰系脱硫剤とカルシウムカーバイド系脱硫剤）１１が同時にランス２０先端から溶銑１２へと吹き込まれる場合には、全脱硫剤１１の吹き込み速度を大きくしても、ガス１０の運動エネルギーが十分に脱硫剤１１に伝達されず、脱硫剤１１のランス２０先端から溶銑１２への突出速度は小さくなってしまうことが本発明者らの実験により確認された。つまり、複数種類の脱硫剤１１が同時にランス２０先端から溶銑１２へと吹き込まれる場合、カルシウムカーバイド系脱硫剤のような濡れ性の良い脱硫剤は突出速度にほとんど影響されず溶銑内に侵入するが、石灰系脱硫剤のような濡れ性の悪い脱硫剤は突出速度が小さくなることでさらに溶銑１２に侵入しにくくなってしまう。言い換えると、濡れ性の悪い石灰系脱硫剤等は、これよりも濡れ性に優れた他の脱硫剤と同時に吹き込まれると脱硫効率の向上への寄与を妨げられてしまう。このように、複数種類の脱硫剤を用いたときの脱硫効率は、同時に使用される脱硫剤の投入量に依存する。
【００１４】
以上のように、本発明者等は、濡れ性の異なる複数種類の脱硫剤が同時に用いられる場合、これら脱硫剤同士が互いに干渉し合い、この干渉が処理後の溶銑中の硫黄濃度に影響することを見出した。本発明は、この知見に基づいてなされたものであり、請求項１の溶銑予備処理によける脱硫モデル作成装置は、複数種類の脱硫剤が投入される溶銑予備処理における、複数種類の前記脱硫剤の投入量に関する変数と複数種類の前記脱硫剤の投入前後での溶銑中の硫黄濃度との関係を表す脱硫モデル式として、少なくとも１種類の前記脱硫剤の投入量に関する変数の係数が、別の１又は複数種類の前記脱硫剤の投入量に関する変数の関数になっている式を作成することを特徴とする。
【００１５】
上記構成によると、複数種類の脱硫剤の投入量に関する変数と複数種類の脱硫剤の投入前後での溶銑中の硫黄濃度との関係を脱硫モデル式として表すので、物理的に解釈しやすく、パラメータの調整が容易であり、保守性に優れた脱硫モデルを作成することができる。なお「脱硫剤の投入量に関する変数」とは、脱硫剤投入量、脱硫剤原単位、及び脱硫剤投入速度を総称したものである。「脱硫剤の投入量に関する変数」として上記のいずれを用いるかは、モデル作成の際の解析や冶金学的知見を基にして決定される。脱硫モデル式において、少なくとも１種類の脱硫剤の投入量に関する変数の係数を別の１又は複数種類の脱硫剤の投入量に関する変数の関数とすることによって、種類の異なる脱硫剤同士の干渉を考慮した、精度の良いモデルを作成することができる。
【００１６】
本発明の請求項２に記載の溶銑予備処理における脱硫モデル作成装置は、請求項１において、投入される前記脱硫剤の中の１種類が石灰系脱硫剤であり、別の１又は複数種類の前記脱硫剤が、カルシウムカーバイド系脱硫剤、金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、及び、ソーダ灰系脱硫剤からなる群より選択されたものであることを特徴とする。
【００１７】
ここで石灰系脱硫剤、カルシウムカーバイド系脱硫剤、金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、ソーダ灰系脱硫剤とは、夫々の物質を主成分とする脱硫剤、即ち夫々の物質を最も多く含む脱硫剤のことを意味する。比較的安価であるが脱硫効率の低い石灰系脱硫剤に、カルシウムカーバイド系脱硫剤、金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、ソーダ灰系脱硫剤等の脱硫効率の高い脱硫剤を混合することで、脱硫剤全体としての脱硫効率を高めて脱硫時間を短縮することができる。
【００１８】
本発明の請求項３に記載の溶銑予備処理における脱硫モデル作成装置は、請求項１において、投入される前記脱硫剤が石灰系脱硫剤及びカルシウムカーバイド系脱硫剤の２種類であることを特徴とする。
【００１９】
カルシウムカーバイド系脱硫剤は脱硫効率が高いので、石灰系脱硫剤と混合させることで、全体としての脱硫効率の向上と脱硫時間の短縮をより確実にすることができる。
【００２０】
本発明の請求項４に記載の溶銑予備処理における脱硫モデル作成装置は、複数種類の脱硫剤が投入される溶銑予備処理における、複数種類の前記脱硫剤の投入量に関する変数と複数種類の前記脱硫剤の投入前後での溶銑中の硫黄濃度との関係を表す脱硫モデル式として、以下の式（１）を作成することを特徴とする。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ａ）＊Ａ＋（ｃ＋ｄ＊Ｂ）＊Ｂ＋ｅ＊Ａ＊Ｂ＋Δ （１）
（Ｓｉ：脱硫剤投入前の溶銑中の硫黄濃度、Ｓｆ：処理後の溶銑中の硫黄濃度、Ａ：石灰系脱硫剤の投入量に関する変数、Ｂ：カルシウムカーバイド系脱硫剤、金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、又は、ソーダ灰系脱硫剤の投入量に関する変数、ａ，ｂ，ｃ，ｄ，ｅ：定数、Δ：その他の変数項及び／又は定数項）
【００２１】
式（１）は、下記式（１’）における（ｆ＋ｇ）をｅに置換したものと同値である。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ａ＋ｆ＊Ｂ）＊Ａ＋（ｃ＋ｄ＊Ｂ＋ｇ＊Ａ）＊Ｂ＋Δ （１’）
【００２２】
上記式（１’）においてＡの係数は（ａ＋ｂ＊Ａ＋ｆ＊Ｂ）、Ｂの係数は（ｃ＋ｄ＊Ｂ＋ｇ＊Ａ）であり、夫々Ａ及びＢの関数として表されている。つまり、式（１），（１’）において、Ａ，Ｂ各々がＡ及びＢに依存しており、請求項１と同様の効果が得られる。
【００２３】
また、本発明の請求項５〜８に記載の溶銑予備処理における脱硫モデル作成プログラムは、コンピュータを請求項１〜４のようなものとして機能させることが可能なプログラムであり、請求項１〜４と夫々同様の作用効果を奏する。
【００２４】
本発明の請求項９〜１２に記載の溶銑予備処理における脱硫剤投入量算出装置は、複数種類の脱硫剤が投入される溶銑予備処理における、複数種類の前記脱硫剤の投入量に関する変数と複数種類の前記脱硫剤の投入前後での溶銑中の硫黄濃度との関係を表す脱硫モデル式として、夫々請求項１〜４に記載の脱硫モデル作成装置によって作成された式を記憶するための脱硫モデル記憶手段と、溶銑予備処理に関する評価関数を記憶するための評価関数記憶手段と、前記脱硫モデル記憶手段に記憶された前記式を満たし且つ前記評価関数記憶手段に記憶された前記評価関数を最適にするような複数種類の前記脱硫剤の投入量に関する変数の組み合わせを抽出する最適投入量抽出手段とを備えていることを特徴とする。
【００２５】
上記構成によると、例えば処理時間及び／又はコストに関する評価関数を溶銑予備処理に関する評価関数とすることで、処理時間が最小となる脱硫剤投入量やコストが最小となる脱硫剤投入量、処理時間及びコストの両方を勘案した脱硫剤投入量を求めることができる。なお、複数種類の脱硫剤の投入量に関する変数の組み合わせを抽出する際、操業上の制約条件を勘案してよく、こうすることにより、操業の実情に即した脱硫剤投入量を求めることができる。
【００２６】
また、本発明の請求項１３〜１６に記載の溶銑予備処理における脱硫剤投入量算出プログラムは、コンピュータを夫々請求項９〜１２のようなものとして機能させることが可能なプログラムであり、夫々請求項９〜１２と同様の作用効果を奏する。
【００２７】
なお、請求項５〜８、及び、請求項１３〜１６の夫々に記載されているプログラムは、ＣＤ−ＲＯＭ、ＦＤ、ＭＯ等のリムーバブル型記録媒体やハードディスク等の固定型記録媒体に記録して配布可能である他、有線又は無線の電気通信手段によってインターネット等の通信ネットワークを介して配布可能である。
【００２８】
【発明の実施の形態】
以下、本発明の一実施形態について、図１及び図２を参照しつつ説明する。なお本実施形態では、脱硫剤として石灰系脱硫剤とカルシウムカーバイド系脱硫剤とを用いた場合を想定する。
【００２９】
先ず図１には、本発明の一実施形態に係る脱硫剤投入量算出装置１の構成が示されている。脱硫剤投入量算出装置１には脱硫モデル作成装置としての脱硫モデル作成部２が含まれている。脱硫モデル作成部２では、溶銑予備処理における脱硫モデル式が作成される。ここで脱硫モデル式とは、下記式（１）のように、本実施形態で用いる２種類の脱硫剤の原単位Ａ，Ｂと脱硫剤の投入前後での溶銑中の硫黄濃度との関係を表すものである。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ａ）＊Ａ＋（ｃ＋ｄ＊Ｂ）＊Ｂ＋ｅ＊Ａ＊Ｂ＋Δ （１）
（Ｓｉ：脱硫剤投入前の溶銑中の硫黄濃度、Ｓｆ：処理後の溶銑中の硫黄濃度、Ａ：石灰系脱硫剤の原単位、Ｂ：カルシウムカーバイド系脱硫剤の原単位、ａ，ｂ，ｃ，ｄ，ｅ：定数、Δ：その他の変数項及び／又は定数項）
【００３０】
ここで、式（１）におけるｅを（ｆ＋ｇ）に置換すると、下記式（１’）が導出される。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ａ＋ｆ＊Ｂ）＊Ａ＋（ｃ＋ｄ＊Ｂ＋ｇ＊Ａ）＊Ｂ＋Δ （１’）
【００３１】
式（１’）においてＡの係数は（ａ＋ｂ＊Ａ＋ｆ＊Ｂ）、Ｂの係数は（ｃ＋ｄ＊Ｂ＋ｇ＊Ａ）であり、夫々Ａ及びＢの関数として表されている。また、式（１），（１’）は、各脱硫剤の原単位Ａ，Ｂを積算したＡ＊Ｂを説明変数として含んでいる。
【００３２】
なお、式（１），（１’）における定数ａ〜ｅは実数であり、０の場合もある。例えば定数ｂ＝０の場合は、下記式（２）に示すように、Ａの係数が（ａ＋ｆ＊Ｂ）というＢのみの関数として表される。また、定数ｄ＝０の場合は、下記式（３）に示すように、Ｂの係数が（ｃ＋ｇ＊Ａ）というＡのみの関数として表される。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｆ＊Ｂ）＊Ａ＋（ｃ＋ｄ＊Ｂ＋ｇ＊Ａ）＊Ｂ＋Δ （２）
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ａ＋ｆ＊Ｂ）＊Ａ＋（ｃ＋ｇ＊Ａ）＊Ｂ＋Δ （３）
【００３３】
次に、脱硫モデル作成部２で作成された脱硫モデル式は、脱硫モデル記憶部３に格納される。そして脱硫剤投入量関係式算出部４では、脱硫モデル記憶部３に格納された脱硫モデル式と、脱硫剤投入前の溶銑中の硫黄濃度Ｓｉ、目標とする処理後の溶銑中の硫黄濃度Ｓｆ、定数ａ〜ｅ、その他の変数項及び／又は定数項Δ等の入力データとに基づいて、各脱硫剤の原単位Ａ，Ｂの関係式ｆ（Ａ，Ｂ）＝０が算出される。なお、上記式（１）〜（３）で示されるような脱硫モデル式中の定数ａ〜ｅは例えば重回帰等の統計的手法により決定され、Δは脱硫剤吹き込み速度、処理前の溶銑中珪素濃度、溶銑温度、ランス深さ等の変数の線形結合で表される。
【００３４】
操業上制約条件式記憶部５には、操業上の制約条件式が格納されている。この操業上の制約条件式は用いる設備等に応じて適宜定められ、例えば下記式（４）のように表される。
ｈｉ（Ａ，Ｂ）≦０，ｉ＝１〜ｎ（４）
（ｎ：設備機器の総数）
【００３５】
評価関数記憶部６には、評価関数として、例えば下記式（５）で表されるようなコスト算出関数が格納されている。
Ｊ＝ｐ＊Ａ＋ｑ＊Ｂ（５）
（Ｊ：脱硫剤のコストｐ：石灰系脱硫剤の原単位当たりのコスト，ｑ：カルシウムカーバイド系脱硫剤の原単位当たりのコスト）
【００３６】
次に最適投入量抽出部７では、脱硫剤投入量関係式算出部４で算出された各脱硫剤の原単位Ａ，Ｂの関係式ｆ（Ａ，Ｂ）＝０と、操業上制約条件式記憶部５に格納されている操業上制約条件式と、評価関数記憶部６に格納されている評価関数とに基づいて、評価関数を最適にするような各脱硫剤の原単位Ａ，Ｂの組み合わせが抽出される。本実施形態では、評価関数として脱硫剤のコストに着目しており、操業上制約条件の不等式の範囲においてコストが最小となるＡ，Ｂの組み合わせが求められる。
【００３７】
さらに、各脱硫剤の原単位Ａ，Ｂの最適な組み合わせを抽出する過程の一例について、以下に説明する。先ず、脱硫剤投入量関係式算出部４に格納されている関係式ｆ（Ａ，Ｂ）＝０が、Ａ＝ｇ（Ｂ）と表される。次に、Ａ＝ｇ（Ｂ）を操業上制約条件式として操業上制約条件式記憶部５に格納されている上記式（４）に適用し、ｈ’ｉ（Ｂ）≦０，ｉ＝１〜ｎ（４’）というＢに関する不等式を得る。また、評価関数として評価関数記憶部６に格納されている上記式（５）にＡ＝ｇ（Ｂ）を適用し、Ｊ＝ｐ＊ｇ（Ｂ）＋ｑ＊Ｂ（５’）というＢに関するコスト算出関数を得る。そして最適投入量抽出部７によって、式（４’）の範囲において、式（５’）で表されるコストＪが最小値となる各脱硫剤の投入量（脱硫剤原単位Ａ，Ｂ）の組み合わせが抽出される。
【００３８】
次いで、図２を参照しつつ、図１に示す本発明の一実施形態に係る脱硫剤投入量算出装置１が実行する処理について説明する。先ず、脱硫モデル作成装置としての図１に示す脱硫モデル作成部２において、上記式（１）のような溶銑予備処理における脱硫モデルが作成される（Ｓ１）。そして作成された脱硫モデルは、図１の脱硫モデル記憶部３に格納される。
【００３９】
次に、脱硫剤投入前の溶銑中の硫黄濃度Ｓｉ、目標とする処理後の溶銑中の硫黄濃度Ｓｆ、及び定数ａ〜ｅやΔのデータが、図１の脱硫剤投入量関係式算出部４に入力される（Ｓ２）。脱硫剤投入量関係式算出部４では、ステップＳ２で入力されるデータと、脱硫モデル記憶部３に格納された脱硫モデル式とに基づいて、脱硫剤投入量関係式、本実施形態では各脱硫剤の原単位Ａ，Ｂの関係式ｆ（Ａ，Ｂ）＝０、が算出される（Ｓ３）。
【００４０】
次に、上述した脱硫剤投入量関係式算出部４で算出された脱硫剤投入量関係式と、操業上制約条件式記憶部５に格納されている操業上の制約条件式と、評価関数記憶部６に格納されている評価関数とに基づいて、最適投入量抽出部７によって、評価関数を最適にするような脱硫剤投入量の組み合わせが抽出され、そして出力される（Ｓ４）。
【００４１】
なお、図１に示されている脱硫剤投入量算出装置１は、例えば汎用のパーソナルコンピュータ等の情報処理装置によって構成されている。かかる情報処理装置には、ＣＰＵ、ＲＯＭ、ＲＡＭ、ハードディスク、ＦＤやＣＤの駆動装置等のハードウェアが収納されており、ハードディスクには、当該情報処理装置を脱硫剤投入量算出装置１として機能させるための脱硫剤投入量算出プログラム（このプログラムは、ＣＤ−ＲＯＭ、ＦＤ、ＭＯ等のリムーバブル型記録媒体に記録しておくことにより、任意のコンピュータにインストールすることが可能である。）を含む各種のソフトウェアが記憶されている。そして、これらのハードウェア及びソフトウェアが組み合わされることによって、上述の各部２〜７が構築されている。
【００４２】
以上のように、本実施形態の脱硫剤投入量算出装置１における脱硫モデル作成部２は、２種類の脱硫剤の原単位Ａ，Ｂと脱硫剤の投入前後での溶銑中の硫黄濃度との関係を脱硫モデル式として表すので、物理的に解釈しやすく、パラメータの調整が容易であり、保守性に優れた脱硫モデルを作成することができる。つまり、プロセスが大幅に変更されたときや時間経過により操業中にモデルを修正する必要があるとき等に、パラメータを調整して脱硫モデルを良好な状態に保つことができる。
【００４３】
また、脱硫モデル作成部２によって作成される脱硫モデル式は、上記式（１）に示すように、２種類の脱硫剤の原単位Ａ，Ｂ夫々についての係数がＡ，Ｂの関数となっている。これにより、種類の異なる脱硫剤同士の干渉を考慮した、精度の良いモデルを作成することができる。
【００４４】
また、投入される脱硫剤を石灰系脱硫剤及びカルシウムカーバイド系脱硫剤の２種類としてよい。これにより、脱硫効率が高いカルシウムカーバイド系脱硫剤を、比較的安価であるが脱硫効率の低い石灰系脱硫剤と混合させることで、全体としての脱硫効率の向上と脱硫時間の短縮を確実にすることができる。
【００４５】
また、脱硫剤投入量算出装置１は、脱硫モデル作成部２によって作成された脱硫モデル式を記憶するための脱硫モデル記憶部３と、溶銑予備処理に関する評価関数を記憶するための評価関数記憶部６と、脱硫モデル記憶部３に記憶された式を満たし且つ評価関数記憶部６に記憶された評価関数を最適にするような複数種類の脱硫剤の投入量に関する変数の組み合わせを抽出する最適投入量抽出部７とを備えている。これにより、例えば処理時間及び／又はコストに関する評価関数を溶銑予備処理に関する評価関数とすることで、処理時間が最小となる脱硫剤投入量やコストが最小となる脱硫剤投入量、処理時間及びコストの両方を勘案した脱硫剤投入量を求めることができる。なお、複数種類の脱硫剤の投入量に関する変数の組み合わせを抽出する際、操業上の制約条件を勘案してよく、こうすることにより、操業の実情に即した脱硫剤投入量を求めることができる。
【００４６】
なお、本実施形態では脱硫剤として石灰系脱硫剤とカルシウムカーバイド系脱硫剤とを用いた場合を想定しているが、これに限定するものではない。例えば金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、及び、ソーダ灰系脱硫剤からなる群より選択されたものを、石灰系脱硫剤と共に用いてよい。ここでマグネシウム合金としてはＦｅ−Ｍｇ合金、Ｍｇ−Ｓｉ合金、Ｆｅ−Ｓｉ−Ｍｇ合金が例示され、また、カルシウム合金としてはＦｅ−Ｃａ合金、Ｃａ−Ｓｉ合金、Ｆｅ−Ｓｉ−Ｃａ合金が例示される。
【００４７】
比較的安価であるが脱硫効率の低い石灰系脱硫剤に、上記のような脱硫効率の高い脱硫剤を混合することで、脱硫剤全体としての脱硫効率を高めて脱硫時間を短縮することができる。また、石灰系脱硫剤を用いず、その他の脱硫剤を複数種類用いてもよい。
【００４８】
また、本実施形態では脱硫剤を２種類としているが、３種類以上の場合は、複数種類の脱硫剤における互いの干渉を脱硫モデル式（１）に表すよう、少なくとも１種類の脱硫剤の投入量に関する変数の係数を別の１又は複数種類の脱硫剤の投入量に関する変数の関数とすればよい。
【００４９】
また、本実施形態では脱硫モデル式（１）中のＡ，Ｂを各脱硫剤の原単位としているが、脱硫剤の投入量に関する変数であれば、これに限定するものではない。即ち、脱硫剤の原単位以外にも、脱硫剤投入量、脱硫剤投入速度等をＡ，Ｂとして用いてよい。脱硫剤の投入量に関する変数として上記のいずれを用いるかは、モデル作成の際の解析や冶金学的知見を基にして、試行錯誤により決定されてよい。
【００５０】
また、脱硫モデル式は上記式（１）に限定されない。少なくとも１種類の脱硫剤の投入量に関する変数の係数を別の１又は複数種類の脱硫剤の投入量に関する変数の関数とすればよく、例えば以下の式（６）等を用いてよい。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊√Ｂ）＊Ａ＋（ｃ＋ｄ＊√Ａ）＊Ｂ＋ｅ＊Ａ＊Ｂ＋Δ （６）
【００５１】
また、脱硫モデル式の左辺の目的変数はｌｎ（Ｓｉ／Ｓｆ）に限定されず、例えば下記式（７）等を用いてよい。
Ｓｆ＝（ａ＋ｂ＊Ａ）＊Ａ＋（ｃ＋ｄ＊Ａ）＊Ｂ＋ｅ＊Ｓｉ＋Δ （７）
【００５２】
またさらに、溶銑温度等、脱硫操業に影響を与える他の因子を変数として脱硫モデル式中に混在させてもよい。
【００５３】
また、脱硫剤投入量算出装置１は図１に示す構成に限定されない。例えば、操業上制約条件の代わりに別の制約条件を設けたり、操業上制約条件及び別の制約条件の両方を用いたりしてよい。
【００５４】
また、評価関数はコスト算出関数に限定されない。例えば脱硫処理に要する時間を評価関数として用い、脱硫処理時間が最小となるような脱硫剤投入量を算出してよい。また上述のように処理時間及びコストの両方を勘案した評価関数を用いてもよい。
【００５５】
また、本実施形態では、脱硫剤投入前の溶銑中の硫黄濃度Ｓｉ、目標とする処理後の溶銑中の硫黄濃度Ｓｆ、及び定数ａ〜ｅやΔのデータを脱硫モデル式に入力してＡ，Ｂの関数としているが、これに限定されない。例えば先ずＡ，Ｂを仮定し、ステップＳ２において脱硫モデル式にＳｆを除くＡ，Ｂ、Ｓｉ、及び定数等を入力してＳｆを算出し、Ｓｆの値が目標値に十分近いときのＡ，Ｂを出力してよい。この場合、Ｓｆが目標値に近づくようにＡ，Ｂを適宜仮定する必要があり、Ｓｆが目標値に十分近づくまで処理が繰り返される。
【００５６】
【実施例】
重回帰等の統計的手法を用いた脱硫モデル式について、従来から用いられてきた脱硫モデルと本発明に係る脱硫モデルとにおける精度を検証した。ここで脱硫モデル式は、複数種類の脱硫剤の投入量に関する変数と、複数種類の脱硫剤の投入前後での溶銑中の硫黄濃度との関係を表す。なお、検証に際して、溶銑を脱硫するための脱硫剤としては石灰系脱硫剤とカルシウムカーバイド系脱硫剤とを用い、汎用のインジェクション脱硫方式を採用した。
【００５７】
本発明に係る脱硫モデル式としては下記式（Ａ）、従来から用いられてきた統計的手法による脱硫モデル式としては下記式（Ｂ）を用い、夫々モデルＡ，モデルＢとした。なお、式（Ａ），（Ｂ）中の定数ａ〜ｆは重回帰により決定され、Δは脱硫剤吹き込み速度、処理前の溶銑中珪素濃度、溶銑温度、ランス深さ等の変数の線形結合で表される。
ｌｎ（Ｓｉ／Ｓｆ）＝（ａ＋ｂ＊Ｘ）＊Ｘ＋（ｃ＋ｄ＊Ｘ）＊Ｙ＋Δ （Ａ）
ｌｎ（Ｓｉ／Ｓｆ）＝ｅ＊Ｘ＋ｆ＊Ｙ＋Δ （Ｂ）
（Ｓｉ：脱硫剤投入前の溶銑中の硫黄濃度、Ｓｆ：処理後の溶銑中の硫黄濃度、Ｘ：石灰系脱硫剤の原単位、Ｙ：カルシウムカーバイド系脱硫剤の原単位、ａ，ｂ，ｃ，ｄ，ｅ，ｆ：定数、Δ：その他の変数項及び／又は定数項）
【００５８】
ここで、上式（Ａ）は、上述した式（１）の定数ｄ＝０の場合の式（３）と同値である。つまり、Ｙの係数は、Ｘ及びＹの関数として表されず、（ｃ＋ｄ＊Ｘ）というＸのみの関数として表されている。
【００５９】
図３（ａ），（ｂ）は、処理後の溶銑中の硫黄濃度において上述したモデルＡ，Ｂを用いた予測値Ｓｆと実績値Ｓｆ’との関係を示す散布図である。図中には実線で関数ｙ＝ｘが示されており、散布点と実線ｙ＝ｘとの距離により精度が良好かどうかを把握することができる。従来のモデルＢと比較して、本発明に係るモデルＡの散布点は実線ｙ＝ｘ近傍により集中しており、予測値と実績値との誤差の標準偏差が小さいことから、モデルＡはモデルＢよりも精度が高いといえる。
【００６０】
表１には、モデルＡ及びモデルＢによって得られたｌｎ（Ｓｉ／Ｓｆ）及び処理後の溶銑中の硫黄濃度Ｓｆに関して実績値との誤差を標準偏差として計算した結果が示されている。これら２つの予測値と実績値との誤差の標準偏差について、共にモデルＡがモデルＢよりも低い値となっている。このことから、図３（ａ），（ｂ）に関して上述したように、モデルＡがモデルＢよりも精度が高いということが数値により実証された。より具体的に、処理後の溶銑中の硫黄濃度Ｓｆについての実績値との誤差（標準偏差）はモデルＢが２．１（×０．００１％）、モデルＡが１．６（×０．００１％）であり、モデルＡの方が誤差を略２５％も低減できた。
【００６１】
【表１】

【００６２】
なお、本実施例は脱硫剤として石灰系脱硫剤とカルシウムカーバイド系脱硫剤を用いて検証したものであるが、脱硫モデル式の構造を本発明に係るモデルＡのように種類の異なる脱硫剤同士の干渉を考慮したものとすることで、他の脱硫剤を複数種類用いた場合にも同様の結果が得られるものと考えられる。
【００６３】
【発明の効果】
本発明は以上説明したように構成されるので、以下に記載されるような効果を奏する。
【００６４】
複数種類の脱硫剤の投入量に関する変数と複数種類の脱硫剤の投入前後での溶銑中の硫黄濃度との関係を脱硫モデル式として表すので、物理的に解釈しやすく、パラメータの調整が容易であり、保守性に優れた脱硫モデルを作成することができる。
【００６５】
また、脱硫モデル式において、例えば上記式（１）に示すように、少なくとも１種類の脱硫剤の投入量に関する変数の係数を別の１又は複数種類の脱硫剤の投入量に関する変数の関数とすることによって、種類の異なる脱硫剤同士の干渉を考慮した、精度の良いモデルを作成することができる。
【００６６】
また、比較的安価であるが脱硫効率の低い石灰系脱硫剤に、カルシウムカーバイド系脱硫剤、金属マグネシウム系脱硫剤、マグネシウム合金系脱硫剤、金属カルシウム系脱硫剤、カルシウム合金系脱硫剤、ソーダ灰系脱硫剤等の脱硫効率の高い脱硫剤を混合することで、脱硫剤全体としての脱硫効率を高めて脱硫時間を短縮することができる。
【００６７】
また、カルシウムカーバイド系脱硫剤は脱硫効率が高いので、石灰系脱硫剤と混合させることで、全体としての脱硫効率の向上と脱硫時間の短縮をより確実にすることができる。
【００６８】
また、溶銑予備処理における脱硫剤投入量を算出する際、例えば処理時間及び／又はコストに関する評価関数を溶銑予備処理に関する評価関数とすることで、処理時間が最小となる脱硫剤投入量やコストが最小となる脱硫剤投入量、処理時間及びコストの両方を勘案した脱硫剤投入量を求めることができる。なお、複数種類の脱硫剤の投入量に関する変数の組み合わせを抽出する際、操業上の制約条件を勘案してよく、こうすることにより、操業の実情に即した脱硫剤投入量を求めることができる。
【図面の簡単な説明】
【図１】本発明の一実施形態に係る脱硫剤投入量算出装置の構成を示すブロック図である。
【図２】本発明の一実施形態に係る脱硫剤投入量算出装置が実行する処理を示すフローチャートである。
【図３】本発明に係る脱硫モデル式と従来の脱硫モデル式とによって算出された処理後の溶銑中の硫黄濃度Ｓｆと実績値Ｓｆ’との関係を示す散布図である。
【図４】溶銑予備処理におけるインジェクション脱硫方式を示す模式図である。
【図５】図４のランス近傍を拡大図示した模式図である。
【図６】脱硫剤原単位と処理後の溶銑中の硫黄濃度との関係についてニューラルネットワークを用いて求めた解析結果を示すグラフである。
【符号の説明】
１脱硫剤投入量算出装置
２脱硫モデル作成部（脱硫モデル作成装置）
３脱硫モデル記憶部（脱硫モデル記憶手段）
４脱硫剤投入量関係式算出部
５操業上制約条件式記憶部
６評価関数記憶部（評価関数記憶手段）
７最適投入量抽出部（最適投入量抽出手段）
１０窒素ガスの気泡
１１脱硫剤
１１ａ窒素ガスに内包された脱硫剤
１１ｂ溶銑へ侵入した脱硫剤
１２溶銑
１３トピード
２０ランス[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an apparatus for creating a desulfurization model formula based on the relationship between a variable relating to the amount of desulfurization agent input in the hot metal pretreatment and the sulfur concentration in the hot metal before and after the desulfurization agent is charged, its program, and the process The present invention relates to a device for calculating a desulfurizing agent input amount that optimizes the sulfur concentration in the hot metal later and a program thereof.
[0002]
[Prior art]
In general, hot metal discharged from a blast furnace in the iron and steel making process is subjected to hot metal pretreatment such as desulfurization, dephosphorization, and desiliconization so that the components such as sulfur, phosphorus, and silicon are below the standard concentration of the product. After that, it is charged into the converter. As for desulfurization treatment, desulfurization is usually disadvantageous in terms of desulfurization efficiency (desulfurization amount per unit amount of desulfurization agent, that is, contribution to desulfurization in the desulfurization agent) at the stage of converter or molten steel treatment. Then, desulfurization is performed at the hot metal preliminary treatment stage.
[0003]
For desulfurization in the hot metal pretreatment, a lime-based desulfurization agent containing lime (CaO) as a main component and fluorite, alumina or the like for promoting the desulfurization reaction is widely used because it is relatively inexpensive. However, in general, lime has poor wettability with hot metal, which is one of the causes of low desulfurization efficiency of lime-based desulfurization agents. If the desulfurization efficiency is low, a large amount of desulfurizing agent is required and the desulfurization time becomes long. Further, in recent years, with the upgrading of products, the target value of sulfur concentration in products tends to decrease, and lime-based desulfurization agents can no longer complete desulfurization treatment within a predetermined time.
[0004]
Therefore, in order to increase the desulfurization efficiency and shorten the desulfurization time, it is more expensive than the lime-based desulfurization agent, but it has good wettability with hot metal and high desulfurization efficiency (CaC). ₂ ) Type desulfurization agent, metal magnesium (Mg) type desulfurization agent, magnesium alloy type desulfurization agent, metal calcium (Ca) type desulfurization agent, calcium alloy type desulfurization agent, soda ash (Na ₂ CO _Three ) -Based desulfurizing agent and the like are used together with a lime-based desulfurizing agent.
[0005]
When desulfurization is performed, it is necessary to calculate the input amount of the desulfurization agent so as not to be excessive or insufficient with respect to the standard sulfur concentration of the product in order to minimize the cost. It is important to determine the conditions such as the amount of desulfurizing agent and the amount of time to be charged in consideration of balance so that the sulfur concentration in the molten iron after the treatment is kept below the target concentration and the operating cost is not increased. Therefore, conventionally, for example, by using a statistical method such as multiple regression, the relationship between the variable relating to the amount of desulfurizing agent input in the desulfurization operation and the sulfur concentration in the hot metal before and after the desulfurizing agent is added is modeled as a desulfurization model formula. The amount of desulfurization agent input is determined by estimating the sulfur concentration in the hot metal. As an example of a conventional desulfurization model formula, the following formula (i) may be mentioned. In formula (i), it is assumed that a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent are used as the desulfurizing agent.
ln (Si / Sf) = a * X + b * Y + Δ (i)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, X: basic unit of lime-based desulfurizing agent, Y: basic unit of calcium carbide-based desulfurizing agent, a, b: Constant, Δ: other variable terms and / or constant terms)
[0006]
In general, calcium carbide has higher desulfurization efficiency than lime, so that b>a> 0. Increasing the input amount of calcium carbide and lime increases the desulfurization efficiency but decreases the contribution rate. A desulfurization model by a statistical method such as multiple regression such as the above formula (i) has an advantage that it is easy to interpret physically, such as easy adjustment. However, the right side of the above formula (i) is a structure in which the basic units X and Y of different desulfurizing agents such as a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent are independent, that is, elements are not combined with each other and are simply combined in a sum. It has a simple structure. That is, as can be seen from the right side of the equation (i), the conventional desulfurization model equation is regarded as having little influence on interference between different types of desulfurization agents, and the interference term is ignored. In addition, in a linear model such as equation (i), nonlinearity cannot be expressed, and the accuracy of the model is limited.
[0007]
Therefore, in order to improve the accuracy of the model, there is one using a neural network as disclosed in, for example, Japanese Patent Laid-Open No. 8-269518. If a neural network is used, the sulfur concentration in the hot metal after the treatment can be accurately predicted.
[0008]
[Problems to be solved by the invention]
However, in a model using a neural network, it is necessary to learn by trial and error in order to improve the extrapolation of the model, and further, it is difficult to interpret the physical causal relationship from the model structure. For this reason, when the process is significantly changed, it takes time to adjust the parameters and it is difficult to correct the model. That is, there is a problem in the maintainability of the model.
[0009]
The present invention has been made in view of the above problems, and a creation apparatus and program for creating a desulfurization model in hot metal pretreatment having high accuracy and excellent maintainability, and optimum desulfurization in consideration of desulfurization efficiency and cost. It is an object of the present invention to provide a desulfurization agent input amount calculation device and its program in hot metal pretreatment capable of obtaining an agent input amount.
[0010]
[Means for Solving the Problems]
The present inventors have made basic unit X of lime (CaO) desulfurization agent and calcium carbide (CaC). ₂ ) The relationship between the basic unit Y of the desulfurizing agent and the sulfur concentration Sf in the hot metal after the treatment was obtained by analysis of actual operation data and simulation using a neural network model. The result is shown in FIG. As is apparent from FIG. 6, the sulfur concentration Sf in the hot metal after the treatment depends on both the basic unit X of the lime-based desulfurizing agent and the basic unit Y of the calcium carbide-based desulfurizing agent. Further, the desulfurization efficiency of each desulfurization agent depends on the input amount of other desulfurization agents. That is, even if the input amount of the lime-based desulfurizing agent is constant, the desulfurization efficiency of the calcium carbide-based desulfurizing agent varies depending on the input amount of the calcium carbide-based desulfurizing agent. The reverse was also true, and it was found that when different types of desulfurizing agents were added, they interfered with each other during the desulfurization treatment.
[0011]
In order to increase the desulfurization efficiency in the hot metal pretreatment, a so-called “injection desulfurization method” in which the desulfurizing agent is effectively dispersed in the hot metal as shown in FIG. 4 is widely used. In this injection desulfurization method, the refractory lance 20 is inserted into the hot metal 12 in the topped 13 and the desulfurizing agent 11 is blown into the hot metal 12 together with a gas such as nitrogen. In this system, it is important that the desulfurizing agent 11 as a powder removes the nitrogen gas bubbles 10 and enters the molten iron from the viewpoint of increasing the desulfurization efficiency.
[0012]
As shown in an enlarged view in FIG. 5, the desulfurizing agent 11 blown out together with the nitrogen gas bubbles 10 from the lance 20 is divided into a desulfurizing agent 11 a included in the nitrogen gas bubbles 10 and a desulfurizing agent 11 b that has entered the hot metal. It is done. The inventors of the present invention repeated the experiment, focusing on the “wetting property” of the desulfurizing agent 11 with respect to the molten iron 12 as a factor for the desulfurizing agent 11 to escape from the nitrogen gas bubbles 10 and enter the molten iron 12. As a result, the desulfurization agent 11b having good wettability with respect to the hot metal 12 easily penetrates into the hot metal 12 and greatly contributes to the improvement of the desulfurization efficiency, but the desulfurization agent 11a with poor wettability remains in the bubbles of the nitrogen gas 10. It was found that the molten iron 12 tends to float in the state, and hardly contributes to the improvement of the desulfurization efficiency. For example, quick lime has poor wettability with hot metal, which is one of the causes of the low desulfurization efficiency of quick lime. On the other hand, calcium carbide, metallic magnesium, soda ash, and the like have good wettability with hot metal, so that high desulfurization efficiency is obtained.
[0013]
In order to increase the desulfurization efficiency of a desulfurization agent having poor wettability such as a lime-based desulfurization agent, it is effective to increase the protruding speed from the tip of the lance 20 to the hot metal 12 in the lime-based desulfurization agent. This is because it is considered that the lime-based desulfurization agent easily enters the hot metal 12. However, in the injection desulfurization system shown in FIG. 4 described above, for example, a plurality of types of desulfurizing agents (for example, lime-based desulfurizing agent and calcium carbide-based desulfurizing agent) 11 having different wettability are simultaneously applied to the tip of the lance 20 in order to shorten the desulfurization treatment time. , The kinetic energy of the gas 10 is not sufficiently transmitted to the desulfurizing agent 11 even if the blowing speed of the total desulfurizing agent 11 is increased, and the hot metal 12 is introduced from the tip of the lance 20 of the desulfurizing agent 11. It has been confirmed by experiments of the present inventors that the protrusion speed to the surface becomes small. That is, when a plurality of types of desulfurization agents 11 are simultaneously blown from the tip of the lance 20 into the hot metal 12, desulfurization agents with good wettability such as calcium carbide desulfurization agents penetrate into the hot metal almost without being influenced by the protruding speed. In addition, a desulfurization agent having poor wettability such as a lime-based desulfurization agent becomes more difficult to enter the hot metal 12 due to a decrease in the protruding speed. In other words, if a lime-based desulfurization agent having poor wettability or the like is blown simultaneously with another desulfurization agent having better wettability, contribution to improvement of desulfurization efficiency is hindered. Thus, the desulfurization efficiency when using a plurality of types of desulfurization agents depends on the input amount of the desulfurization agent used at the same time.
[0014]
As described above, when a plurality of types of desulfurization agents having different wettability are used at the same time, the present inventors interfere with each other, and this interference affects the sulfur concentration in the hot metal after the treatment. I found out. The present invention has been made on the basis of this finding, and the desulfurization model creation apparatus according to claim 1 is a plurality of types of desulfurization in hot metal pretreatment in which a plurality of types of desulfurizing agents are introduced. As a desulfurization model equation representing the relationship between the variable relating to the amount of the additive and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agents, the coefficient of the variable relating to the amount of at least one type of desulfurizing agent is different. The formula which becomes the function of the variable regarding the input amount of the said 1 or multiple types of said desulfurization agent is produced.
[0015]
According to the above configuration, the relationship between the variables related to the amount of desulfurization agent input and the sulfur concentration in the hot metal before and after the addition of multiple types of desulfurization agent is expressed as a desulfurization model equation. Therefore, a desulfurization model having excellent maintainability can be created. The “variable relating to the input amount of the desulfurizing agent” is a general term for the input amount of the desulfurizing agent, the basic unit of the desulfurizing agent, and the input speed of the desulfurizing agent. Which of the above is used as the “variable relating to the input amount of the desulfurizing agent” is determined based on the analysis and metallurgical knowledge at the time of creating the model. In the desulfurization model formula, interference between different types of desulfurization agents is taken into account by making the coefficient of the variable related to the input amount of at least one desulfurization agent a function of the variable related to the input amount of one or more other desulfurization agents. A highly accurate model can be created.
[0016]
The desulfurization model creation apparatus in the hot metal pretreatment according to claim 2 of the present invention is the desulfurization model creation apparatus according to claim 1, wherein one of the desulfurization agents to be added is a lime-based desulfurization agent, and The desulfurization agent is selected from the group consisting of a calcium carbide desulfurization agent, a metal magnesium desulfurization agent, a magnesium alloy desulfurization agent, a metal calcium desulfurization agent, a calcium alloy desulfurization agent, and a soda ash desulfurization agent. It is characterized by being.
[0017]
The lime-based desulfurizing agent, calcium carbide-based desulfurizing agent, magnesium-based desulfurizing agent, magnesium-alloy-based desulfurizing agent, metallic calcium-based desulfurizing agent, calcium-alloy-based desulfurizing agent, and soda ash-based desulfurizing agent are the main substances. It means a desulfurizing agent as a component, that is, a desulfurizing agent containing the largest amount of each substance. Calcium desulfurization agent, metal carbide desulfurization agent, magnesium alloy desulfurization agent, metal calcium desulfurization agent, calcium alloy desulfurization agent, soda ash desulfurization By mixing a desulfurization agent having a high desulfurization efficiency such as an agent, the desulfurization efficiency of the entire desulfurization agent can be increased and the desulfurization time can be shortened.
[0018]
The apparatus for creating a desulfurization model in hot metal pretreatment according to claim 3 of the present invention is characterized in that, in claim 1, the desulfurization agent to be added is a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent. To do.
[0019]
Since the calcium carbide-based desulfurization agent has a high desulfurization efficiency, mixing with the lime-based desulfurization agent can further improve the overall desulfurization efficiency and shorten the desulfurization time.
[0020]
According to a fourth aspect of the present invention, there is provided a desulfurization model creation apparatus for hot metal pretreatment in which a plurality of types of desulfurization variables and variables related to the amount of the desulfurization agent introduced in the hot metal pretreatment in which a plurality of types of desulfurization agents are charged. The following formula (1) is created as a desulfurization model formula representing the relationship with the sulfur concentration in the hot metal before and after the introduction of the agent.
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: Sulfur concentration in hot metal before adding desulfurizing agent, Sf: Sulfur concentration in hot metal after treatment, A: Variable relating to input amount of lime-based desulfurizing agent, B: Calcium carbide-based desulfurizing agent, metallic magnesium-based desulfurizing agent , Magnesium alloy-based desulfurizing agent, metallic calcium-based desulfurizing agent, calcium alloy-based desulfurizing agent, or soda ash-based desulfurizing agent, variables relating to input, a, b, c, d, e: constants, Δ: other variable terms And / or constant term)
[0021]
Formula (1) is equivalent to the value obtained by replacing (f + g) with e in the following formula (1 ′).
ln (Si / Sf) = (a + b * A + f * B) * A + (c + d * B + g * A) * B + Δ (1 ′)
[0022]
In the above formula (1 ′), the coefficient of A is (a + b * A + f * B) and the coefficient of B is (c + d * B + g * A), which are expressed as functions of A and B, respectively. In other words, in the formulas (1) and (1 ′), A and B depend on A and B, respectively, and the same effect as in claim 1 can be obtained.
[0023]
Moreover, the desulfurization model creation program in the hot metal pretreatment according to claims 5 to 8 of the present invention is a program that allows a computer to function as in claims 1 to 4. And each has the same effect.
[0024]
The desulfurization agent input amount calculation device for hot metal pretreatment according to claims 9 to 12 of the present invention includes a variable and a plurality of variables relating to the input amounts of the plurality of types of desulfurization agents in the hot metal pretreatment in which a plurality of types of desulfurization agents are input. The desulfurization model for memorize | storing the formula created by the desulfurization model preparation apparatus of Claims 1-4 as a desulfurization model formula showing the relationship with the sulfur concentration in the hot metal before and behind injection | throwing-in of the said kind of said desulfurization agent, respectively. Optimizing the evaluation function that satisfies the formula stored in the desulfurization model storage means and that is stored in the evaluation function storage means and that stores storage means, evaluation function storage means for storing the evaluation function related to the hot metal pretreatment And an optimum input amount extracting means for extracting a combination of variables relating to the input amounts of the plurality of types of desulfurizing agents.
[0025]
According to the above configuration, for example, by setting the evaluation function related to the processing time and / or cost as the evaluation function related to the hot metal preliminary processing, the desulfurizing agent input amount and the processing time that minimize the processing time and the cost are minimized. In addition, the desulfurization agent input amount can be obtained in consideration of both the cost and the cost. It should be noted that when extracting a combination of variables relating to the input amounts of a plurality of types of desulfurizing agents, operational constraints may be taken into account, and in this way, the desulfurizing agent input amounts in accordance with the actual circumstances of operation can be obtained. .
[0026]
In addition, the desulfurization agent input amount calculation program in the hot metal pretreatment according to claims 13 to 16 of the present invention is a program capable of causing a computer to function as in claims 9 to 12, respectively. There exists an effect similar to item 9-12.
[0027]
The program described in each of claims 5 to 8 and claims 13 to 16 is recorded on a removable recording medium such as a CD-ROM, FD, or MO, or a fixed recording medium such as a hard disk. Besides being distributable, it can also be distributed via a communication network such as the Internet by wired or wireless telecommunication means.
[0028]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, an embodiment of the present invention will be described with reference to FIGS. 1 and 2. In the present embodiment, it is assumed that a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent are used as the desulfurizing agent.
[0029]
First, FIG. 1 shows a configuration of a desulfurization agent input amount calculation apparatus 1 according to an embodiment of the present invention. The desulfurization agent input amount calculation device 1 includes a desulfurization model creation unit 2 as a desulfurization model creation device. In the desulfurization model creation unit 2, a desulfurization model formula in the hot metal pretreatment is created. Here, the desulfurization model equation is the relationship between the basic units A and B of the two types of desulfurization agents used in the present embodiment and the sulfur concentration in the hot metal before and after the desulfurization agent is charged, as in the following equation (1). It represents.
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, A: basic unit of lime-based desulfurizing agent, B: basic unit of calcium carbide-based desulfurizing agent, a, b, c, d, e: constant, Δ: other variable term and / or constant term)
[0030]
Here, when e in Expression (1) is replaced with (f + g), the following Expression (1 ′) is derived.
ln (Si / Sf) = (a + b * A + f * B) * A + (c + d * B + g * A) * B + Δ (1 ′)
[0031]
In the equation (1 ′), the coefficient of A is (a + b * A + f * B) and the coefficient of B is (c + d * B + g * A), which are expressed as functions of A and B, respectively. Moreover, Formula (1), (1 ') contains A * B which integrated | accumulated the basic units A and B of each desulfurization agent as an explanatory variable.
[0032]
The constants a to e in the expressions (1) and (1 ′) are real numbers and may be 0. For example, when the constant b = 0, as shown in the following formula (2), the coefficient of A is expressed as a function of B only (a + f * B). When the constant d = 0, as shown in the following formula (3), the coefficient of B is expressed as a function of only A with (c + g * A).
ln (Si / Sf) = (a + f * B) * A + (c + d * B + g * A) * B + Δ (2)
ln (Si / Sf) = (a + b * A + f * B) * A + (c + g * A) * B + Δ (3)
[0033]
Next, the desulfurization model formula created by the desulfurization model creation unit 2 is stored in the desulfurization model storage unit 3. Then, the desulfurizing agent input amount relational expression calculating unit 4 includes the desulfurization model formula stored in the desulfurizing model storage unit 3, the sulfur concentration Si in the hot metal before the desulfurizing agent is added, and the sulfur concentration Sf in the hot metal after the target treatment. , Constants a to e, other variable terms, and / or input data such as a constant term Δ, a relational expression f (A, B) = 0 of the basic units A and B of each desulfurizing agent is calculated. In addition, the constants a to e in the desulfurization model formulas represented by the above formulas (1) to (3) are determined by a statistical method such as multiple regression, and Δ is the desulfurization agent blowing speed and the hot metal before treatment. It is expressed by a linear combination of variables such as silicon concentration, hot metal temperature, and lance depth.
[0034]
The operational constraint expression storage 5 stores operational constraint expressions. This operational constraint equation is appropriately determined according to the equipment to be used and is represented, for example, by the following equation (4).
hi (A, B) ≦ 0, i = 1 to n (4)
(N: total number of equipment)
[0035]
The evaluation function storage unit 6 stores a cost calculation function represented by the following formula (5), for example, as an evaluation function.
J = p * A + q * B (5)
(J: Cost of desulfurizing agent p: Cost per basic unit of lime-based desulfurizing agent, q: Cost per basic unit of calcium carbide-based desulfurizing agent)
[0036]
Next, in the optimum input amount extraction unit 7, the relational expression f (A, B) = 0 of the basic units A and B of each desulfurization agent calculated by the desulfurization agent input amount relational expression calculation unit 4 and the operational constraint condition formula Based on the operational constraint condition expression stored in the storage unit 5 and the evaluation function stored in the evaluation function storage unit 6, the basic units A and B of the respective desulfurization agents that optimize the evaluation function Combinations are extracted. In the present embodiment, attention is paid to the cost of the desulfurizing agent as the evaluation function, and a combination of A and B that minimizes the cost within the range of the inequality of the operational constraint is obtained.
[0037]
Further, an example of a process for extracting the optimum combination of the basic units A and B of each desulfurizing agent will be described below. First, the relational expression f (A, B) = 0 stored in the desulfurizing agent input amount relational expression calculating unit 4 is expressed as A = g (B). Next, A = g (B) is applied to the above equation (4) stored in the operation constraint equation storage unit 5 as an operation constraint equation, and h′i (B) ≦ 0, i = 1. Obtain the inequality for B of ~ n (4 '). Further, A = g (B) is applied to the above equation (5) stored in the evaluation function storage unit 6 as an evaluation function, and the cost for B of J = p * g (B) + q * B (5 ′). Get the calculation function. Then, by the optimum input amount extraction unit 7, in the range of the formula (4 ′), the input amounts of the desulfurization agents (desulfurization agent basic units A and B) at which the cost J represented by the formula (5 ′) becomes the minimum value are obtained. Combinations are extracted.
[0038]
Next, the process executed by the desulfurization agent input amount calculation apparatus 1 according to the embodiment of the present invention shown in FIG. 1 will be described with reference to FIG. First, in the desulfurization model creation unit 2 shown in FIG. 1 as a desulfurization model creation device, a desulfurization model in the hot metal pretreatment such as the above formula (1) is created (S1). The created desulfurization model is stored in the desulfurization model storage unit 3 of FIG.
[0039]
Next, the sulfur concentration Si in the hot metal before introducing the desulfurizing agent, the sulfur concentration Sf in the hot metal after the target treatment, and the data of the constants a to e and Δ are the desulfurizing agent input amount relational expression calculating unit in FIG. 4 is input (S2). In the desulfurizing agent input amount relational expression calculating unit 4, based on the data input in step S 2 and the desulfurizing model formula stored in the desulfurizing model storage unit 3, the desulfurizing agent input amount relational expression, each desulfurization in this embodiment, is calculated. The relational expression f (A, B) = 0 between the basic units A and B of the agent is calculated (S3).
[0040]
Next, the desulfurizing agent input amount relational expression calculated by the desulfurizing agent input amount relational expression calculating unit 4 described above, the operational restriction condition equation stored in the operational restriction condition expression storage unit 5, and the evaluation function storage Based on the evaluation function stored in the unit 6, the optimum input amount extraction unit 7 extracts and outputs a combination of desulfurizing agent input amounts that optimize the evaluation function (S4).
[0041]
Note that the desulfurizing agent input amount calculation device 1 shown in FIG. 1 is configured by an information processing device such as a general-purpose personal computer. Such an information processing apparatus accommodates hardware such as a CPU, ROM, RAM, hard disk, FD and CD drive device, and the hard disk causes the information processing apparatus to function as the desulfurization agent input amount calculation device 1. Various desulfurization agent input amount calculation programs for this purpose (this program can be installed on any computer by recording it on a removable recording medium such as a CD-ROM, FD, MO, etc.). Software is stored. And these parts 2-7 are constructed | assembled by combining these hardware and software.
[0042]
As described above, the desulfurization model creation unit 2 in the desulfurization agent input amount calculation device 1 of the present embodiment calculates the basic units A and B of the two types of desulfurization agents and the sulfur concentration in the hot metal before and after the desulfurization agent is charged. Since the relationship is expressed as a desulfurization model formula, it is easy to interpret physically, the parameters can be easily adjusted, and a desulfurization model excellent in maintainability can be created. That is, the parameters can be adjusted to keep the desulfurization model in good condition when the process has changed significantly or when it is necessary to modify the model during operation over time.
[0043]
Further, the desulfurization model formula created by the desulfurization model creation section 2 is a function of A and B, with the coefficients for the basic units A and B of the two types of desulfurization agents as shown in the above formula (1). Yes. This makes it possible to create a highly accurate model that considers interference between different types of desulfurization agents.
[0044]
Moreover, it is good also considering the desulfurization agent thrown in as two types, a lime type desulfurization agent and a calcium carbide type desulfurization agent. As a result, the calcium carbide desulfurization agent having high desulfurization efficiency is mixed with the lime-based desulfurization agent having relatively low cost but low desulfurization efficiency, thereby ensuring the improvement of the desulfurization efficiency and the shortening of the desulfurization time as a whole. be able to.
[0045]
The desulfurization agent input amount calculation device 1 includes a desulfurization model storage unit 3 for storing the desulfurization model formula created by the desulfurization model creation unit 2, and an evaluation function storage unit for storing an evaluation function related to the hot metal pretreatment. 6 and an optimum input for extracting a combination of variables relating to input amounts of a plurality of types of desulfurization agents that satisfy the formula stored in the desulfurization model storage unit 3 and optimize the evaluation function stored in the evaluation function storage unit 6 A quantity extraction unit 7. Thus, for example, by setting the evaluation function related to the processing time and / or cost as the evaluation function related to the hot metal preliminary processing, the desulfurizing agent input amount, the processing time, and the cost that minimize the processing time and the cost are minimized. Thus, the desulfurization agent input amount can be determined in consideration of both. It should be noted that when extracting a combination of variables relating to the input amounts of a plurality of types of desulfurizing agents, operational constraints may be taken into account, and in this way, the desulfurizing agent input amounts in accordance with the actual circumstances of operation can be obtained. .
[0046]
In the present embodiment, it is assumed that a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent are used as the desulfurizing agent, but the present invention is not limited to this. For example, a metal magnesium-based desulfurizing agent, a magnesium alloy-based desulfurizing agent, a metal calcium-based desulfurizing agent, a calcium alloy-based desulfurizing agent, and a soda ash-based desulfurizing agent may be used together with a lime-based desulfurizing agent. . Here, examples of the magnesium alloy include Fe-Mg alloy, Mg-Si alloy, and Fe-Si-Mg alloy, and examples of the calcium alloy include Fe-Ca alloy, Ca-Si alloy, and Fe-Si-Ca alloy. Is done.
[0047]
By mixing a desulfurization agent having a high desulfurization efficiency as described above with a lime-based desulfurization agent that is relatively inexpensive but has a low desulfurization efficiency, the desulfurization efficiency of the entire desulfurization agent can be increased and the desulfurization time can be shortened. . Further, a plurality of other desulfurization agents may be used without using the lime-based desulfurization agent.
[0048]
Further, in this embodiment, two types of desulfurizing agents are used. However, in the case of three or more types, at least one type of desulfurizing agent is added so that mutual interference among a plurality of types of desulfurizing agents is expressed in the desulfurization model equation (1). The coefficient of the variable related to the amount may be a function of the variable related to the input amount of one or more kinds of desulfurization agents.
[0049]
Further, in this embodiment, A and B in the desulfurization model formula (1) are used as basic units of each desulfurization agent, but the present invention is not limited to this as long as it is a variable related to the input amount of the desulfurization agent. That is, in addition to the basic unit of the desulfurizing agent, the desulfurizing agent input amount, the desulfurizing agent input speed, and the like may be used as A and B. Which of the above is used as a variable relating to the input amount of the desulfurizing agent may be determined by trial and error based on analysis and metallurgical knowledge at the time of model creation.
[0050]
Further, the desulfurization model formula is not limited to the above formula (1). The coefficient of the variable related to the input amount of at least one type of desulfurizing agent may be a function of the variable related to the input amount of one or more other types of desulfurizing agents. For example, the following equation (6) may be used.
ln (Si / Sf) = (a + b * √B) * A + (c + d * √A) * B + e * A * B + Δ (6)
[0051]
Further, the objective variable on the left side of the desulfurization model equation is not limited to ln (Si / Sf), and for example, the following equation (7) may be used.
Sf = (a + b * A) * A + (c + d * A) * B + e * Si + Δ (7)
[0052]
Furthermore, other factors that affect the desulfurization operation such as the hot metal temperature may be mixed in the desulfurization model formula as variables.
[0053]
Further, the desulfurizing agent input amount calculation device 1 is not limited to the configuration shown in FIG. For example, another constraint condition may be provided instead of the operation constraint condition, or both the operation constraint condition and another constraint condition may be used.
[0054]
The evaluation function is not limited to the cost calculation function. For example, the time required for the desulfurization treatment may be used as an evaluation function to calculate the desulfurization agent input amount that minimizes the desulfurization treatment time. Further, as described above, an evaluation function that takes into account both processing time and cost may be used.
[0055]
In this embodiment, the sulfur concentration Si in the hot metal before the desulfurization agent is added, the sulfur concentration Sf in the hot metal after the target treatment, and the constants a to e and Δ data are input to the desulfurization model equation. , B, but is not limited to this. For example, assuming A and B first, Sf is calculated by inputting A, B, Si, and constants etc. excluding Sf into the desulfurization model equation in step S2, and A when the value of Sf is sufficiently close to the target value, B may be output. In this case, it is necessary to appropriately assume A and B so that Sf approaches the target value, and the process is repeated until Sf sufficiently approaches the target value.
[0056]
【Example】
About the desulfurization model formula using statistical methods, such as multiple regression, the precision in the desulfurization model used conventionally and the desulfurization model concerning this invention was verified. Here, the desulfurization model equation represents a relationship between a variable related to the amount of the plurality of types of desulfurizing agents and the sulfur concentration in the hot metal before and after the plurality of types of desulfurizing agents are charged. In the verification, a lime-based desulfurizing agent and a calcium carbide-based desulfurizing agent were used as desulfurizing agents for desulfurizing the hot metal, and a general-purpose injection desulfurizing method was adopted.
[0057]
The following formula (A) is used as a desulfurization model formula according to the present invention, and the following formula (B) is used as a desulfurization model formula based on a statistical method that has been used in the past. The constants a to f in the formulas (A) and (B) are determined by multiple regression, and Δ is a linear combination of variables such as the desulfurization agent blowing speed, the silicon concentration in the hot metal before treatment, the hot metal temperature, and the lance depth. It is represented by
ln (Si / Sf) = (a + b * X) * X + (c + d * X) * Y + Δ (A)
ln (Si / Sf) = e * X + f * Y + Δ (B)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, X: basic unit of lime-based desulfurizing agent, Y: basic unit of calcium carbide-based desulfurizing agent, a, b, c, d, e, f: constant, Δ: other variable terms and / or constant terms)
[0058]
Here, the above equation (A) is the same value as the equation (3) in the case where the constant d = 0 in the above equation (1). That is, the coefficient of Y is not expressed as a function of X and Y, but is expressed as a function of only X as (c + d * X).
[0059]
FIGS. 3A and 3B are scatter diagrams showing the relationship between the predicted value Sf and the actual value Sf ′ using the models A and B described above in the sulfur concentration in the hot metal after the treatment. In the figure, the function y = x is shown by a solid line, and it is possible to grasp whether the accuracy is good or not by the distance between the scattered point and the solid line y = x. Compared with the conventional model B, the scattered points of the model A according to the present invention are concentrated near the solid line y = x, and the standard deviation of the error between the predicted value and the actual value is small. It can be said that the accuracy is higher than B.
[0060]
Table 1 shows the results of calculating the standard deviation as the error from the actual value for the ln (Si / Sf) obtained by the model A and the model B and the sulfur concentration Sf in the hot metal after the treatment. Regarding the standard deviation of the error between the two predicted values and the actual value, the model A is a lower value than the model B. From this, it was proved numerically that the model A is more accurate than the model B, as described above with reference to FIGS. More specifically, the error (standard deviation) from the actual value of the sulfur concentration Sf in the hot metal after the treatment is 2.1 (× 0.001%) for model B and 1.6 (× 0. (001%), and Model A was able to reduce the error by about 25%.
[0061]
[Table 1]

[0062]
In addition, although a present Example verified using the lime type | system | group desulfurization agent and the calcium carbide type | system | group desulfurization agent as a desulfurization agent, the structure of a desulfurization model type | formula is different between different desulfurization agents like the model A which concerns on this invention. By considering this interference, it is considered that the same result can be obtained even when a plurality of other desulfurization agents are used.
[0063]
【The invention's effect】
Since the present invention is configured as described above, the following effects can be obtained.
[0064]
The relationship between the variables related to the amount of desulfurization agent input and the sulfur concentration in the hot metal before and after the addition of multiple types of desulfurization agent is expressed as a desulfurization model equation, which is easy to interpret physically and parameter adjustment is easy. Yes, a desulfurization model with excellent maintainability can be created.
[0065]
In the desulfurization model formula, for example, as shown in the above formula (1), the coefficient of the variable related to the input amount of at least one type of desulfurizing agent is a function of the variable related to the input amount of one or more types of desulfurizing agents. Accordingly, it is possible to create a highly accurate model in consideration of interference between different types of desulfurization agents.
[0066]
In addition, lime-based desulfurizing agents that are relatively inexpensive but have low desulfurization efficiency include calcium carbide-based desulfurizing agents, metallic magnesium-based desulfurizing agents, magnesium alloy-based desulfurizing agents, metallic calcium-based desulfurizing agents, calcium alloy-based desulfurizing agents, and soda ash. By mixing a desulfurization agent having a high desulfurization efficiency such as a system desulfurization agent, the desulfurization efficiency of the entire desulfurization agent can be increased and the desulfurization time can be shortened.
[0067]
Moreover, since the calcium carbide type | system | group desulfurization agent has high desulfurization efficiency, the improvement of the desulfurization efficiency as a whole and shortening of desulfurization time can be made more reliable by making it mix with a lime type | system | group desulfurization agent.
[0068]
Further, when calculating the desulfurization agent input amount in the hot metal pretreatment, for example, by setting the evaluation function related to the processing time and / or cost as the evaluation function related to the hot metal pretreatment, the desulfurization agent input amount and cost that minimize the processing time can be reduced. It is possible to obtain the desulfurization agent input amount taking into consideration both the minimum desulfurization agent input amount, the processing time and the cost. It should be noted that when extracting a combination of variables relating to the input amounts of a plurality of types of desulfurizing agents, operational constraints may be taken into account, and in this way, the desulfurizing agent input amounts in accordance with the actual circumstances of operation can be obtained. .
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration of a desulfurizing agent input amount calculating apparatus according to an embodiment of the present invention.
FIG. 2 is a flowchart showing processing executed by a desulfurizing agent input amount calculating apparatus according to an embodiment of the present invention.
FIG. 3 is a scatter diagram showing the relationship between the sulfur concentration Sf in the hot metal after the treatment calculated by the desulfurization model formula according to the present invention and the conventional desulfurization model formula and the actual value Sf ′.
FIG. 4 is a schematic diagram showing an injection desulfurization method in hot metal preliminary treatment.
5 is an enlarged schematic view of the vicinity of the lance of FIG. 4. FIG.
FIG. 6 is a graph showing an analysis result obtained using a neural network for a relationship between a desulfurizing agent basic unit and a sulfur concentration in the molten iron after the treatment.
[Explanation of symbols]
1 Desulfurization agent input amount calculation device
2 Desulfurization model creation unit (desulfurization model creation device)
3 Desulfurization model storage unit (desulfurization model storage means)
4 Desulfurizing agent input relational expression calculation section
5 Operational constraints storage unit
6 Evaluation Function Storage Unit (Evaluation Function Storage Unit)
7 Optimum input amount extraction unit (Optimum input amount extraction means)
10 Nitrogen gas bubbles
11 Desulfurization agent
11a Desulfurization agent encapsulated in nitrogen gas
11b Desulfurizing agent that penetrates hot metal
12 Hot metal
13 Toped
20 Lance

Claims

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As
In the hot metal preliminary treatment, the coefficient of the variable relating to the input amount of at least one kind of desulfurizing agent is a function of the variable relating to the input amount of another one or more kinds of desulfurizing agents. Desulfurization model creation device.

One of the desulfurization agents to be charged is lime (CaO) desulfurization agent,
Another one or more kinds of the desulfurization agents are calcium carbide (CaC ₂ ) desulfurization agent, metal magnesium (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium _2. The desulfurization model creation apparatus for hot metal pretreatment according to claim 1, wherein the apparatus is selected from the group consisting of (Ca) alloy-based desulfurizing agents and soda ash (Na ₂ CO ₃ ) -based desulfurizing agents. .

2. The desulfurization model creation apparatus in hot metal pretreatment according to claim 1, wherein the desulfurization agent to be added is two types of lime (CaO) desulfurization agent and calcium carbide (CaC ₂ ) desulfurization agent.

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As
An apparatus for creating a desulfurization model in hot metal pretreatment, wherein the following formula (1) is created.
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, A: variable relating to input amount of lime (CaO) desulfurizing agent, B: calcium carbide (CaC ₂ ) type desulfurization Agent, magnesium metal (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium (Ca) alloy desulfurization agent, or soda ash (Na ₂ CO ₃ ) desulfurization (Variables related to the amount of agent added, a, b, c, d, e: constant, Δ: other variable terms and / or constant terms)

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As
A computer is made to function so as to create an expression in which a coefficient of a variable relating to an input amount of at least one kind of desulfurizing agent is a function of a variable relating to an input amount of another one or more kinds of desulfurizing agents. A desulfurization model creation program in hot metal pretreatment.

One of the desulfurization agents to be charged is lime (CaO) desulfurization agent,
Another one or more kinds of the desulfurization agents are calcium carbide (CaC ₂ ) desulfurization agent, metal magnesium (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium 6. The desulfurization model creation program for hot metal pretreatment according to claim 5, wherein the desulfurization model is selected from the group consisting of (Ca) alloy desulfurization agent and soda ash (Na ₂ CO ₃ ) desulfurization agent. .

Desulfurization model creation program in molten iron pretreatment according to claim 5, wherein the desulfurizing agent is characterized in that it is a two lime (CaO) based desulfurizing agent and calcium carbide (CaC ₂₎ based desulfurizing agent input.

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As
A desulfurization model creation program in hot metal pretreatment characterized by causing a computer to function to create the following formula (1).
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, A: variable relating to input amount of lime (CaO) desulfurizing agent, B: calcium carbide (CaC ₂ ) type desulfurization Agent, magnesium metal (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium (Ca) alloy desulfurization agent, or soda ash (Na ₂ CO ₃ ) desulfurization (Variables related to the amount of agent added, a, b, c, d, e: constant, Δ: other variable terms and / or constant terms)

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. And a desulfurization model storage means for storing an expression in which a coefficient of a variable relating to the input amount of at least one kind of desulfurizing agent is a function of a variable relating to the input amount of another one or more kinds of desulfurizing agents. ,
An evaluation function storage means for storing an evaluation function related to hot metal pretreatment;
Optimum input for extracting a combination of variables relating to the input amounts of the plurality of types of desulfurization agents so as to satisfy the equation stored in the desulfurization model storage unit and optimize the evaluation function stored in the evaluation function storage unit A desulfurization agent input amount calculation device in hot metal pretreatment characterized by comprising an amount extraction means.

One of the desulfurization agents to be charged is lime (CaO) desulfurization agent,
Another one or more kinds of the desulfurization agents are calcium carbide (CaC ₂ ) desulfurization agent, metal magnesium (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium The amount of desulfurization agent input in hot metal pretreatment according to claim 9, wherein the desulfurization agent is selected from the group consisting of (Ca) alloy-based desulfurization agent and soda ash (Na ₂ CO ₃ ) -based desulfurization agent. apparatus.

10. The desulfurization agent input amount calculation device for hot metal pretreatment according to claim 9, wherein the desulfurization agent to be added is two types of lime (CaO) desulfurization agent and calcium carbide (CaC ₂ ) desulfurization agent. .

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As a desulfurization model storage means for storing the following formula (1):
An evaluation function storage means for storing an evaluation function related to hot metal pretreatment;
Optimum input for extracting a combination of variables relating to the input amounts of the plurality of types of desulfurization agents so as to satisfy the equation stored in the desulfurization model storage unit and optimize the evaluation function stored in the evaluation function storage unit A desulfurization agent input amount calculation device in hot metal pretreatment characterized by comprising an amount extraction means.
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, A: variable relating to input amount of lime (CaO) desulfurizing agent, B: calcium carbide (CaC ₂ ) type desulfurization Agent, magnesium metal (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium (Ca) alloy desulfurization agent, or soda ash (Na ₂ CO ₃ ) desulfurization (Variables related to the amount of agent added, a, b, c, d, e: constant, Δ: other variable terms and / or constant terms)

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As a desulfurization model storage means for storing an equation in which a coefficient of a variable relating to the input amount of at least one kind of desulfurizing agent is a function of a variable relating to the input amount of another one or more kinds of desulfurizing agents,
Evaluation function storage means for storing an evaluation function related to hot metal pretreatment, and
Optimum input for extracting a combination of variables relating to the input amounts of the plurality of types of desulfurization agents so as to satisfy the equation stored in the desulfurization model storage unit and optimize the evaluation function stored in the evaluation function storage unit A desulfurization agent input amount calculation program in hot metal pretreatment for causing a computer to function as a quantity extraction means.

One of the desulfurization agents to be charged is lime (CaO) desulfurization agent,
Another one or more kinds of the desulfurization agents are calcium carbide (CaC ₂ ) desulfurization agent, metal magnesium (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium The amount of desulfurization agent input in the hot metal pretreatment according to claim 13, wherein the desulfurization agent is selected from the group consisting of (Ca) alloy-based desulfurization agent and soda ash (Na ₂ CO ₃ ) -based desulfurization agent. Calculation program.

14. The desulfurization agent input amount calculation program for hot metal pretreatment according to claim 13, wherein the desulfurization agent to be added is two types of lime (CaO) desulfurization agent and calcium carbide (CaC ₂ ) desulfurization agent. .

A desulfurization model equation representing the relationship between the variables relating to the amount of the plurality of types of desulfurizing agent introduced and the sulfur concentration in the hot metal before and after the introduction of the plurality of types of desulfurizing agent in the hot metal pretreatment in which a plurality of types of desulfurizing agent are introduced. As a desulfurization model storage means for storing the following formula (1), and
Evaluation function storage means for storing an evaluation function related to hot metal pretreatment, and
Optimum input for extracting a combination of variables relating to the input amounts of the plurality of types of desulfurization agents so as to satisfy the equation stored in the desulfurization model storage unit and optimize the evaluation function stored in the evaluation function storage unit A desulfurization agent input amount calculation program in hot metal pretreatment for causing a computer to function as a quantity extraction means.
ln (Si / Sf) = (a + b * A) * A + (c + d * B) * B + e * A * B + Δ (1)
(Si: sulfur concentration in hot metal before adding desulfurizing agent, Sf: sulfur concentration in hot metal after treatment, A: variable relating to input amount of lime (CaO) desulfurizing agent, B: calcium carbide (CaC ₂ ) type desulfurization Agent, magnesium metal (Mg) desulfurization agent, magnesium (Mg) alloy desulfurization agent, metal calcium (Ca) desulfurization agent, calcium (Ca) alloy desulfurization agent, or soda ash (Na ₂ CO ₃ ) desulfurization (Variables related to the amount of agent added, a, b, c, d, e: constant, Δ: other variable terms and / or constant terms)