JP3823907B2 - Estimating the amount of decarburization in converter steel and converter operation using this method - Google Patents
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Description
【０００１】
【発明の属する技術分野】
本発明は、転炉の吹錬から出鋼、二次精錬処理前までの操業に関し、特に、転炉出鋼中の脱炭量推定法と、転炉吹錬制御モデルを用いた吹錬制御に際し、転炉吹錬制御モデルの精度向上のためのモデル学習に好適な転炉操業法に関する。
【０００２】
ここで、転炉操業とは、Si、Ｓ、Ｐ等の不純物を除去する溶銑予備処理後の溶銑から脱炭処理を行う場合の転炉処理、およびSi、Ｓ等の不純物を除去する溶銑予備処理後の溶銑から脱燐脱炭処理を同時に行う場合の転炉処理の各操業をいい、転炉吹錬とは、脱炭処理時の吹錬、脱燐脱炭処理を同時に行う場合の吹錬をいう。
【０００３】
【従来の技術】
従来、転炉吹錬における制御としては、例えば吹錬前の溶銑炭素濃度や吹込み酸素量等の転炉操業情報を用いて吹錬終点時の溶綱炭素濃度や溶鋼温度を制御するスタティック制御と、それらの情報にサブランスを用いて吹錬中に測定した中間実測値を加味し、吹錬終点時の溶鋼炭素濃度や溶鋼温度を制御するダイナミック制御が一般に採用されている。
【０００４】
これらスタティック制御、ダイナミック制御は、吹錬反応をモデル化した転炉吹錬制御モデルを用いた転炉の吹錬制御法である。
たとえば、転炉吹錬制御モデルは、転炉吹錬前の溶銑成分とその濃度および溶銑温度等の溶銑条件と、溶鋼成分とその濃度および溶鋼温度等の目標溶鋼条件等に対し、投入媒溶材の種類と量、吹込み酸素量、吹錬時間等の吹錬条件と、さらに、吹錬終点時の推定溶鋼成分とその濃度および推定溶鋼温度等の転炉吹錬状態をモデル化したものである。
【０００５】
上記のような転炉吹錬制御モデルを用いた吹錬制御法では、転炉炉体の溶損状況などの数値化困難な要因が多数存在することや、様々の要因の影響を完全にモデル化することが不可能であることから、制御精度を向上するために過去の吹錬実績データを用いた学習が行われている。
【０００６】
これらモデルの学習方法には様々な方法が提案されているが、どの方法においても転炉吹錬制御モデルは吹錬終点時の溶鋼炭素濃度と溶鋼温度を目標としているため、吹錬実績データを用いたモデル学習を行うためには、吹錬終点実測が不可欠であり、吹錬終点実測に要する費用や実測待ち時間ロス、および実測待ち時間ロスによる熱ロスがコスト高の要因となっていた。
【０００７】
そこで、転炉における終点実測（吹錬終点直前から出鋼終了までにおける全ての実測）を省略し、転炉の次工程である二次精錬設備で処理する前に実測し、この二次精錬処理前の実測値を次回以降の転炉吹錬制御に利用する方法が提案されている（例えば、特許文献１参照。）。
【０００８】
【特許文献１】
特開平１１−２６９５３０号（段落００１０−００１９）
【０００９】
【発明が解決しようとする課題】
ところで、転炉吹錬終点時には、スラグ中や溶鋼中に、吹錬中の成分反応に消費されなかった酸素（以下、未反応酸素と称する）が残存する。加えて、吹錬終点後の出鋼は取鍋への出鋼であり、取鍋へは溶鋼を落流させるため、溶鋼の落流攪拌が激しい。従って、吹錬終点から、溶鋼を取鍋に出鋼し二次精錬処理開始までの間に、前記未反応酸素により脱炭が生じ、溶鋼炭素濃度、溶鋼温度に変化を与える。
【００１０】
しかしながら、特許文献１にて提案されている、転炉の吹錬終点実測を省略し、二次精錬処理前の実測値を次回以降の吹錬制御に利用する転炉操業法では、出鋼時の合金添加による炭素増加分のみ考慮に入れ、上記脱炭の影響をモデル要素として捉えず、そのまま出鋼時の脱炭の影響を含めたモデル学習が行われてしまうため、制御精度を向上するような転炉吹錬制御モデルの学習には不向きである。
【００１１】
よって、特許文献１の方法では、転炉の吹錬終点実測は省略できるが、転炉吹錬制御モデルの制御精度が良くないため、結果として二次精錬処理前の溶鋼炭素濃度、溶鋼温度等が目標値から外れる場合があるという問題がある。
【００１２】
本発明は、上記したような問題を解決せんとしてなされたものであり、吹錬終点時の未反応酸素量から出鋼中の脱炭量を推定すること、及び、推定した出鋼中の脱炭量を用い、吹錬終点実測を省略した場合でも、転炉吹錬制御モデルの制御精度向上が図れる転炉操業法を提供することを目的とする。
【００１３】
【課題を解決するための手段】
上記した目的を達成するために、本発明に係る転炉出鋼中脱炭量推定法は、転炉操業情報のうち吹錬終点時の未反応酸素量を含む複数の変数を用いて、出鋼中の脱炭量を推定すること、また、吹錬終点時の溶鋼炭素濃度として転炉吹錬制御モデルを用いて推定する溶鋼炭素濃度推定値を用いることとしている。そして、このようにすることで、出鋼中の脱炭量が精度良く推定できる。
【００１４】
また、本発明に係る転炉操業法は、転炉吹錬制御モデルを用いて推定する吹錬終点時の各推定値と、出鋼中の脱炭量を含む複数の変数を用いて推定する二次精錬処理前の溶鋼炭素濃度推定値及び溶鋼温度推定値と、二次精錬処理前の各実測値により転炉吹錬制御モデルの学習を行うこと、あるいは、二次精錬処理前の各実測値と、出鋼中の脱炭量を含む複数の変数を用いて逆算推定する吹錬終点時の溶鋼炭素濃度及び溶鋼温度の逆算推定値と、転炉吹錬制御モデルを用いて推定する吹錬終点時の各推定値により転炉吹錬制御モデルの学習を行うこととしている。
【００１５】
このように学習することで、転炉の吹錬終点実測を省略した場合でも、転炉吹錬制御モデルの精度向上が可能となる。
【００１６】
【発明の実施の形態】
以下、本発明に係る転炉出鋼中脱炭量推定法、及びこの方法を用いた転炉操業法について詳細に説明する。
（１）、第一の本発明について説明する。
転炉吹錬から二次精錬処理までの溶銑・溶鋼処理過程を図示すると、図１の通りとなる。図１において、溶銑が転炉まで搬送されると共にその溶銑情報も転炉操業情報に伝達され、転炉では所定の転炉吹錬制御モデルに従って転炉吹錬され、吹錬終点に達すると合金鉄やフラックス等を添加しながら転炉から取鍋に出鋼され、二次精錬処理設備まで溶鋼が搬送されて、そこで最終製品に要求される品質となるように最終成分処理・最終温度処理が施される。
【００１７】
図１における▲１▼は、転炉吹錬開始時の溶銑情報、例えば炭素濃度およびSi、Ｐ、Ｓ等の不純物量の溶銑成分濃度や溶銑温度等、
▲２▼は、吹錬終点時の実測値、例えば溶鋼炭素濃度および他成分量、溶鋼温度等の実測値、
▲３▼は、吹錬終点時と出鋼後（二次精錬処理前）の溶鋼炭素濃度実測値の差から算出する出鋼中脱炭量、
▲４▼は、二次精錬処理前実測値、例えば溶鋼炭素濃度および他成分量、溶鋼温度等の実測値、
等の実測位置を示す。
また、▲５▼は、吹錬終点時の推定値、例えば溶鋼炭素濃度、溶鋼温度等の推定値、
▲６▼は、二次精錬処理から逆算した吹錬終点時の逆算推定値、例えば溶鋼炭素濃度、溶鋼温度等の逆算推定値、
▲７▼は、出鋼中の脱炭量推定値、
▲８▼は、二次精錬処理前推定値、例えば溶鋼炭素濃度、溶鋼温度等の推定値、
等の各推定値の算出位置を示す。
【００１８】
一般に、転炉の吹錬終点時に実測を行わないで、二次精錬処理前実測値を用いて転炉吹錬制御モデルの学習を行う場合、前述のように吹錬終点後の取鍋への出鋼中の脱炭量が問題となる。
そこで、出鋼中の脱炭量と吹錬後の未反応酸素量との関係について、その実態を調べ、この関係を炭素量が0.30質量％を超える高炭素鋼（○印）、炭素量が0.30質量％以下0.20質量％を超える中高炭素鋼（△印）、炭素量が0.20質量％以下0.10質量％を超える中炭素鋼（□印）、炭素量が0.10質量％以下の低炭素鋼（◇印）の４種類の炭素濃度区分に分けて図示したのが図２である。
【００１９】
この図２より、炭素濃度区分にかかわらず、未反応酸素量（Ｎm3/ton）が多く含有されているほど出鋼中の脱炭量（質量％）も大きくなって、それぞれには強い相関があり、吹錬終点時に炭素濃度で区分すれば未反応酸素量（Ｏnrea）から出鋼中の脱炭量（Δ［Ｃ］）を推定できることが判った。
なお、出鋼中の脱炭量（質量％）は、出鋼前の炭素含有濃度をａ（質量％）、出鋼後の炭素含有濃度をｂ（質量％）とすると、ａ−ｂ（質量％）で表すことができる。
【００２０】
ところで、出鋼中の脱炭量（Δ［Ｃ］）と吹錬後の未反応酸素量（Ｏnrea）は、下記の関係式により表される。
【００２１】
【数１】
［Ｃ］Ep ：吹錬終点時の溶鋼炭素濃度（質量％）
ＷGi ：出鋼中の合金鉄ｉ投入量（kg/ton）
ＣGi ：合金鉄ｉの炭素含有量（質量％）
［Ｃ］sec ：二次精錬処理前の溶鋼炭素濃度（質量％）
【００２２】
【数２】
Ｏg ：吹込み酸素量（Ｎm3/ton）
ＷSUBi ：吹錬中の媒溶材i 投入量（kg/ton）
ＯSUBi ：媒溶材i の酸素含有量（Ｎm3/kg）
Ｏrea ：成分反応消費酸素量（Ｎm3/ton）
【００２３】
ここで、数式１における吹錬終点時の溶鋼炭素濃度［Ｃ］Ep、及び、二次精錬処理前の溶鋼炭素濃度［Ｃ］sec は、実測により得た値である。
【００２４】
また、数式２における成分反応消費酸素量（Ｏrea ) とは、吹錬中の化学反応で消費される酸素量の合計であり、溶融金属やガスとの全ての反応を含むものである。その算出方法には様々な方法があるが、例えば、吹錬前後の各成分濃度の変化量から、各成分ごとにその反応に消費される酸素量を算出し、総和をとる方法を用いる。転炉の排出ガス分析値を用いて算出する方法などもあるが、どの様な方法を用いても本発明の効果は変わらない。
【００２５】
また、転炉操業情報のうち吹錬終点時の未反応酸素量、溶鋼炭素濃度を含む複数の変数を用いて、たとえば、下記数式３から、出鋼中の脱炭量が推定できる。この場合、数式としては、吹錬終点時の未反応酸素量を含む複数の変数と出鋼中の脱炭量との関係を表す数式であれば、どのような関係式でもよい。
【００２６】
【数３】
Ｘi ：転炉操業情報｛未反応酸素量（Ｎm3/ton）｝（連続変数）
Ｄj ：吹錬終点時の溶鋼炭素濃度区分を表す値（０，１の２進変数によって決定）
αi ：連続変数Ｘ i 用パラメータ
βj ：０，１の２進変数によって決定するＤ j 用パラメータ
α0 ：パラメータ
【００２７】
数式３における吹錬終点時の溶鋼炭素濃度区分（Ｄj ）とは、吹錬終点時の溶鋼炭素濃度を任意に区分する場合に、どの区分に属するかを表す値であり、例えば、図２における４種類の炭素濃度区分に相当するものである。同量の未反応酸素量であってもこの区分の違いにより出鋼中の脱炭量が変動する。
また、この数式３における転炉操業情報（Ｘi ）としては、未反応酸素量（Ｏnrea）のみを用いているが、吹錬終了から出鋼開始までの出鋼準備時間、出鋼開始から出鋼完了までの出鋼時間など、出鋼時の脱炭に影響を与える要因を加えればさらに良い。
【００２８】
（２）、第二の本発明について説明する。
数式２の未反応酸素量の算出において、同式中の成分反応消費酸素量（Ｏrea）の算出に用いている吹錬後の溶鋼炭素濃度は、転炉吹錬制御モデルを用いて推定する吹錬終点時の溶鋼炭素濃度推定値を用い、また、吹錬後の炭素を除く溶鋼成分濃度は転炉終点成分目標値を用いている。
【００２９】
本発明法で用いる溶鋼炭素濃度推定値は、後述のモデル学習により実測値に非常に近い値であって正確な算出が可能である。また炭素を除く溶鋼成分濃度は、一般に転炉吹錬処理前から調整され、吹錬前後における炭素以外の成分目標値との間の差も少ない。そして、これら吹錬前後の差が酸素との反応によるものとし、成分反応消費酸素量（Ｏrea）を理論的に正確に算出することができる。
【００３０】
この様にして、成分反応消費酸素量Ｏrea（Ｎm3/ton）を定め、数式２より未反応酸素量Ｏnrea（Ｎm3/ton）を求め、さらに数式３より出鋼中脱炭量推定値Δ［Ｃ］inf（質量％）を推定することができる。なお、吹錬終点時の溶鋼炭素濃度区分については、転炉吹錬制御モデルを用いて推定する吹錬終点時の溶鋼炭素濃度推定値による区分を表す値を用いる。
【００３１】
（３）、次いで、吹錬終点時の各推定値と、二次精錬処理前の溶鋼炭素濃度推定値及び溶鋼温度推定値と、二次精錬処理前の実測値、により転炉吹錬制御モデルの学習を行う第三の本発明について説明する。
【００３２】
【数４】
［Ｃ］MEp：炭素濃度推定モデルを用いて推定する吹錬終点時の溶鋼炭素濃度
推定値（質量％）
ＷGi ：出鋼中の合金鉄ｉ投入量（kg/ton）
ＣGi ：合金鉄ｉの炭素含有量（質量％）
Δ［Ｃ］inf：出鋼中の脱炭量推定値（質量％）
【００３３】
【数５】
ＴMEp：溶鋼温度推定モデルを用いて推定する吹錬終点時の溶鋼温度推定値（℃）
ＷGi ：出鋼中の合金鉄ｉ投入量（kg/ton）
ＴGi ：出鋼中の合金鉄ｉ温度降下量（℃・ton/kg）
Ｚj ：転炉操業情報｛出鋼中脱炭量推定値（質量％）、…等｝
γj :パラメータ
【００３４】
これら数式４の二次精錬処理前の溶鋼炭素濃度推定値（質量％）、数式５の二次精錬処理前の溶鋼温度推定値（℃）は、いずれも吹錬終点時の推定値と、出鋼中の脱炭量推定値を含む変数から導かれ、また、数式５における転炉操業情報（Ｚj ) としては、出鋼中の脱炭量推定値以外に、吹錬終了から出鋼開始までの出鋼準備時間、出鋼開始から出鋼完了までの出鋼時間、出鋼終了から二次精錬処理前実測までの二次精錬準備時間、取鍋使用状況など、出鋼時の温度降下に影響する要因を加味した。
なお、出鋼中の脱炭は、前述の通り酸素との反応によるものであり、溶鋼温度に影響を与えるものである。
【００３５】
そして、代表例として以下の数式６、７により転炉吹錬制御モデルの学習を行っている。
【００３６】
【数６】
【数７】
ここで、吹錬終点時の炭素濃度推定モデル式、および、吹錬終点時の溶鋼温度推定用モデル式とは、たとえばスタティック制御やダイナミック制御に用いられるモデル式である。ただし、他の吹錬制御のモデル式を適用することも可能である。
上記モデル学習項は、二次精錬処理前推定値と二次精錬処理前実測値との差から一般的な指数平滑法を用いて算出するモデル学習値とする。
【００３９】
（４）、次いで、吹錬終点時の逆算推定値と吹錬終点時の推定値等により転炉吹錬制御モデルの学習を行う第四の本発明について説明する。
吹錬終点時の逆算推定値としての逆算溶鋼炭素濃度推定値［Ｃ］REp は、二次精錬処理前の溶鋼炭素濃度実測値［Ｃ］sec と、出鋼中の合金鉄ｉ投入による炭素濃度増加量と、出鋼中の脱炭量推定値Δ［Ｃ］inf から下記数式８により算出する。
【００４０】
【数８】
［Ｃ］sec：二次精錬処理前の溶鋼炭素濃度実測値（質量％）
ＷGi ：出鋼中の合金鉄ｉ投入量（kg/ton）
ＣGi ：合金鉄ｉの炭素含有量（質量％）
Δ［Ｃ］inf：出鋼中の脱炭量推定値（質量％）
【００４１】
さらに、吹錬終点時の逆算推定としての逆算溶鋼温度推定値ＴREp は、二次精錬処理前の溶鋼温度実測値Ｔsec と、出鋼中の合金鉄ｉ投入による温度降下量ＴGiおよび数式５と同様の転炉操業情報を用いて下記数式９により算出する。
【００４２】
【数９】
Ｔsec ：二次精錬処理前の溶鋼温度実測値（℃）
ＷGi ：出鋼中の合金鉄ｉ投入量（kg/ton）
ＴGi ：出鋼中の合金鉄ｉ投入による温度降下量（℃/kg・ton）
Ｚj ：転炉操業情報｛出鋼中の脱炭量推定値（質量％）、…等｝
γj ：パラメータ
【００４３】
上記数式８、９は、二次精錬処理前各実測値と、出鋼中の脱炭量を含む複数の変数から成り、この逆算推定値により、たとえばスタティック制御モデルやダイナミック制御モデルから推定される吹錬終点時の推定値を学習することとしている。
学習の考え方は前記二次精錬処理前の推定値の学習の考え方と同じであり、下記の数式１０、１１式による。
【００４４】
【数１０】
【数１１】
終点溶鋼炭素濃度逆算推定値と推定値によるモデル学習値、および、同溶鋼温度のモデル学習値は、溶鋼炭素濃度と溶鋼温度のそれぞれについて、逆算終点推定値と転炉吹錬制御モデルの終点推定値との差から、一般的な指数平滑法を用いて算出するモデル学習量とする。
【００４７】
以上のように、第三、第四の本発明に係る転炉吹錬制御モデルの学習方法は、
ａ．二次精錬処理前推定値と二次精錬処理前実測値との差、あるいは、
ｂ．逆算終点推定値と転炉吹錬制御モデルの終点推定値との差から、
一般的な指数平滑法を用いて算出するモデル学習量とする。どちらの方法で算出してもモデル学習量は同じになり、モデルの正確性が一層向上する。学習量の算出方法として指数平滑法以外を用いてもその効果は変わらない。
【００４８】
【実施例】
（実施例１）
本発明に係る出鋼中の脱炭量推定値についての実施例を示す。
この例では、吹錬終点時の溶鋼炭素濃度実測値により決定する溶鋼炭素濃度区分（吹錬鋼種）と、吹錬前後の［Ｃ］、［Si］、［Mn］、［Ｓ］、［Ｐ］、［Ti］などの溶鋼中の各成分濃度を実測し、例えば数式２から算出される未反応酸素量と、前記溶鋼炭素濃度区分、未反応酸素量を変数として含む数式３から出鋼中の脱炭量推定値を算出した。同時に出鋼後の溶鋼炭素濃度も実測して脱炭量を求めて出鋼中の脱炭量とした。この比較により推定値の精度確認をした。
【００４９】
実施例１における転炉操業条件は、下記の通りである。
吹錬鋼種：炭素0.03〜1.0 質量％の普通鋼、
転炉からの出鋼量：210 ton/チャージ、
その他：出鋼中に合金鉄を投入していない。
なお、数式３の吹錬終点時の溶鋼炭素濃度区分は、図２で示したものと同様に吹錬終点時の溶鋼炭素濃度実績値が0.30質量％より大きいものを高炭素鋼、0.30質量％以下0.20質量％を超えるものを中高炭素鋼、0.20質量％以下0.10質量％を超えるものを中炭素鋼、0.10質量％以下のものを低炭素鋼とする４区分とした。図３は、出鋼中の脱炭量（実測値）と出鋼中の脱炭量推定値との関係を示すグラフであり、このグラフから出鋼中の脱炭量が精度良く推定できていることが判る。
【００５０】
（実施例２）
次に、本発明に係る出鋼中の脱炭量推定値について、 吹錬終点時の溶鋼炭素濃度として転炉吹錬制御モデルを用いて推定する溶鋼炭素濃度を用いた場合の実施例を示す。この例では、吹錬終点時の溶鋼炭素濃度の実測値のかわりに転炉吹錬制御モデルの推定値を用いる。前述の実施例１と同じ溶鋼炭素濃度区分を４区分とする方法により出鋼中の脱炭量推定値を算出した。
そして、未反応酸素量の算出に必要な吹錬前後の各成分濃度については、吹錬前の各成分濃度は実測し、吹錬後（吹錬終点時）の各成分濃度は、炭素については転炉吹錬制御モデルが推定する溶鋼炭素濃度推定値を、それ以外は目標値を実測値のかわりに用いた。
【００５１】
図４は、出鋼中脱炭量（実測値）と出鋼中の脱炭量推定値との関係を示すグラフであり、このグラフから吹錬終点時の溶鋼炭素濃度として転炉吹錬制御モデルを用いて推定する溶鋼炭素濃度を用いても出鋼中の脱炭量が精度よく推定できていることが判る。
【００５２】
（実施例３）
次いで、本発明に係るモデル学習の実施例を示す。
前述のように、第三、第四の本発明法は、どちらも同じ学習量（値）となりその効果は変わらないことから、この実施例においては第四の本発明法、つまり吹錬終点時の逆算推定値と吹錬終点時の推定値との差によりモデル学習量を決定する方法とした。
ここで、吹錬終点時の逆算推定値は、数式８、９により算出し、また、吹錬終点時の溶鋼炭素濃度推定用、および溶鋼温度推定用モデル式には、ダイナミック制御で用いられているモデル式を適用した。
一方、従来法の学習方法は、特許文献１での方法を採用した。
【００５３】
そして、実施例３における転炉操業条件は、下記の通りである。
吹錬鋼種：炭素0.03〜1.0 質量％の普通鋼、
吹錬終点時の溶鋼温度：1650〜1680℃、
転炉からの出鋼量：210 ton/チャージ。
【００５４】
図５、６は、溶鋼炭素濃度区分毎に、吹錬終点時の実測値に対する本発明法と従来法の溶鋼炭素濃度推定誤差、および溶鋼温度推定誤差の平均と偏差を比較して示したグラフである。このグラフにおいて、○印は本発明法、●印は従来法を示し、棒の範囲はその偏差、○、●印付与の位置は夫々の平均値を表す。吹錬終点時の溶鋼炭素濃度推定誤差、および溶鋼温度推定誤差とは、前記モデルの吹錬終点時の推定値と吹錬終点時の実測値との差である。
【００５５】
このグラフより、本発明に適用するモデル学習方法は、従来方法より溶鋼炭素濃度推定誤差、溶鋼温度推定誤差ともに小さいことが判る。つまり、本発明のモデル学習によって転炉吹錬制御モデルの制御精度が向上していることが判る。
なお、以上の実施例では、吹錬終点時の精度確認や誤差確認のために実測しているが、本来は不要である。
【００５６】
【発明の効果】
以上説明したように、本発明によれば、出鋼中脱炭量を精度良く推定でき、また、吹錬終点測定を省略する場合でも、転炉吹錬制御モデルの制御精度を向上させることができる。
【図面の簡単な説明】
【図１】転炉吹錬から二次精錬処理までの溶銑・溶鋼処理過程を示した図である。
【図２】出鋼中の脱炭量と未反応酸素量との関係を示した図である。
【図３】本発明による出鋼中の脱炭量推定結果を示した図である。
【図４】本発明による出鋼中の脱炭量推定において、転炉吹錬制御モデルが推定する溶鋼炭素濃度を用いた場合の推定結果を示した図である。
【図５】本発明法と従来法とによりモデル学習した場合のモデル吹錬終点の溶鋼炭素濃度推定誤差の平均と偏差の比較結果を示した図である。
【図６】本発明法と従来法とによりモデル学習した場合のモデル吹錬終点の溶鋼温度推定誤差の平均と偏差の比較結果を示した図である。[0001]
BACKGROUND OF THE INVENTION
TECHNICAL FIELD The present invention relates to operations from blowing a converter to steelmaking and before secondary refining treatment, and in particular, a decarburization estimation method in converter steelmaking and a blowing control using a converter blowing control model. In connection with this, the present invention relates to a converter operation method suitable for model learning for improving the accuracy of the converter blowing control model.
[0002]
Here, the converter operation is a converter process in the case of performing decarburization processing from hot metal after hot metal pretreatment for removing impurities such as Si, S, and P, and hot metal preparatory process for removing impurities such as Si and S. Refers to each operation of the converter process when dephosphorization and decarburization are performed simultaneously from the molten iron after treatment. Converter blowing is the blowing when decarburization and dephosphorization are performed simultaneously. Says temper.
[0003]
[Prior art]
Conventionally, as control in the converter blowing, static control for controlling the molten steel carbon concentration and the molten steel temperature at the end of the blowing using the converter operation information such as the molten iron carbon concentration and the amount of blown oxygen before blowing, for example. In addition, dynamic control for controlling the molten steel carbon concentration and molten steel temperature at the end of blowing is generally adopted by adding intermediate measured values measured during blowing using a lance to such information.
[0004]
These static control and dynamic control are converter blowing control methods using a converter blowing control model that models the blowing reaction.
For example, the converter blowing control model is used for the molten steel components before the converter blowing, the hot metal conditions such as the concentration and the hot metal temperature, and the target molten steel conditions such as the molten steel components and the concentration and the molten steel temperature. This is a model of the blowing conditions such as the type and amount of oxygen, the amount of blown oxygen, the blowing time, etc., and the estimated molten steel composition and concentration at the end of blowing and the estimated molten steel temperature. is there.
[0005]
In the blowing control method using the converter blowing control model as described above, there are many factors that are difficult to quantify, such as the melting damage of the converter furnace body, and the effects of various factors are completely modeled. Therefore, learning using past blowing performance data is performed in order to improve control accuracy.
[0006]
Various methods have been proposed as learning methods for these models. In any method, the converter blowing control model targets the molten steel carbon concentration and molten steel temperature at the end of blowing. In order to perform the model learning used, it is indispensable to actually measure the blowing end point, and the cost required for the blowing end point measurement, the actual waiting time loss, and the heat loss due to the actual waiting time loss are the causes of the high cost.
[0007]
Therefore, the end point measurement in the converter (all measurements from just before the blow smelting end point to the end of steelmaking) is omitted, and measured before the secondary refining equipment that is the next process of the converter, this secondary refining treatment There has been proposed a method in which the previous actual measurement value is used for the converter blowing control after the next time (see, for example, Patent Document 1).
[0008]
[Patent Document 1]
JP-A-11-269530 (paragraphs 0010-0019)
[0009]
[Problems to be solved by the invention]
By the way, at the end of the converter blowing, oxygen (hereinafter referred to as unreacted oxygen) that has not been consumed in the component reaction during blowing remains in the slag or molten steel. In addition, the steel discharged after the end of blowing is steel discharged to the ladle, and since the molten steel flows down to the ladle, the molten steel is vigorously stirred down. Therefore, decarburization occurs due to the unreacted oxygen during the period from the end point of blowing to the time when the molten steel is taken out into the ladle and the secondary refining treatment is started, and the molten steel carbon concentration and molten steel temperature are changed.
[0010]
However, in the converter operation method proposed in Patent Document 1 in which the actual measurement value before the secondary refining process is used for the subsequent blowing control by omitting the actual blowing end point measurement of the converter, Taking into account only the amount of carbon increase due to alloy addition, the model learning including the effect of decarburization at the time of steel output is performed as it is without taking the effect of decarburization as a model element, so the control accuracy is improved. It is not suitable for learning such a converter blowing control model.
[0011]
Therefore, in the method of Patent Document 1, the actual measurement of the end point of the blowing of the converter can be omitted, but since the control accuracy of the converter blowing control model is not good, the molten steel carbon concentration, the molten steel temperature, etc. before the secondary refining treatment result. There is a problem that may deviate from the target value.
[0012]
The present invention has been made the problems as described above as a solution plugs, to estimate the decarburization amount in unreacted oxygen amount or we tapped during blowing end point, and estimated left in steel using decarburization amount, even when omitting the blowing endpoint measured, the control accuracy of the converter blowing control model is an object to provide a converter operation method that Re FIG.
[0013]
[Means for Solving the Problems]
In order to achieve the above-described object, the decarburization amount estimation method for converter steel output according to the present invention uses a plurality of variables including unreacted oxygen amount at the end of blowing in the converter operation information. Estimating the amount of decarburization in steel, and using the estimated value of molten steel carbon estimated using the converter blowing control model as the molten steel carbon concentration at the end of blowing. And by doing in this way, the amount of decarburization in tapping can be estimated accurately.
[0014]
Further, the converter operation method according to the present invention is estimated using a plurality of variables including each estimated value at the end of blowing and estimated using a converter blowing control model and the amount of decarburization during steelmaking. Learn the converter smelting control model based on the molten steel carbon concentration estimated value and molten steel temperature estimated value before the secondary refining treatment and each measured value before the secondary refining treatment, or each actual measurement before the secondary refining treatment Value, and the back-calculated estimated value of the molten steel carbon concentration and molten steel temperature at the end of blowing, which is estimated backward using a plurality of variables including the amount of decarburization in the steel output, and the blowing estimated using the converter blowing control model Learning of the converter blowing control model is performed based on the estimated values at the end of smelting.
[0015]
Learning in this way makes it possible to improve the accuracy of the converter blowing control model even when the actual measurement of the blowing endpoint of the converter is omitted.
[0016]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, the method for estimating the amount of decarburization in converter steel output according to the present invention and the converter operation method using this method will be described in detail.
(1) The first aspect of the present invention will be described.
The hot metal / molten steel treatment process from converter blowing to secondary refining is illustrated in FIG. In FIG. 1, the hot metal is conveyed to the converter and the hot metal information is also transmitted to the converter operation information. In the converter, the converter is blown in accordance with a predetermined converter blowing control model, and the alloy is reached when the end point of blowing is reached. Steel is fed from the converter to the ladle while adding iron, flux, etc., and the molten steel is transported to the secondary refining treatment facility, where final component treatment and final temperature treatment are performed to achieve the quality required for the final product. Applied.
[0017]
(1) in FIG. 1 indicates hot metal information at the start of converter blowing, for example, the concentration of the hot metal component of the carbon concentration and the amount of impurities such as Si, P, S, the hot metal temperature, etc.
(2) is an actual measurement value at the end of blowing, for example, an actual measurement value of molten steel carbon concentration and other component amounts, molten steel temperature,
(3) is the amount of decarburization during steel extraction calculated from the difference between the measured values of the molten steel carbon concentration at the end of blowing and after steel output (before secondary refining treatment)
(4) is an actual measurement value before secondary refining treatment, for example, an actual measurement value of molten steel carbon concentration and other component amounts, molten steel temperature,
The actual measurement position is shown.
Also, (5) is an estimated value at the end of blowing, for example, an estimated value of molten steel carbon concentration, molten steel temperature,
(6) is a reverse calculation estimated value at the end of blowing, calculated backward from the secondary refining treatment, for example, a reverse calculation estimated value of molten steel carbon concentration, molten steel temperature,
(7) is the estimated amount of decarburization during steel output,
(8) is an estimated value before secondary refining treatment, for example, an estimated value of molten steel carbon concentration, molten steel temperature,
The calculation position of each estimated value is shown.
[0018]
In general, when learning the converter blowing control model using the measured value before the secondary refining process without performing the actual measurement at the converter blowing end point, as mentioned above, The amount of decarburization during steel production becomes a problem.
Therefore, the relationship between the decarburization amount and unreacted oxygen amount after blowing in tapping, examine the actual situation, high carbon steel carbon content that exceeds 0.30% by mass of this relationship (○ mark), carbon content There crowned carbon steel exceeds 0.20 mass% 0.30 mass% or less (△ mark), medium carbon steel carbon content exceeds 0.10 mass% 0.20 mass% (□ mark), low-carbon carbon content below 0.10 wt% FIG. 2 shows the four carbon concentration categories of steel (◇).
[0019]
From Fig. 2, regardless of the carbon concentration category, the greater the amount of unreacted oxygen (Nm 3 / ton), the greater the amount of decarburization (% by mass) in the steel output. There are, blowing endpoint unreacted oxygen amount when divided by the carbon concentration at the time (O NREA) decarburization amount in tapping from (Δ [C]) was found to be estimated.
In addition, the amount of decarburization (% by mass ) in the steel output is ab (mass ) when the carbon content concentration before steel output is a (% by mass) and the carbon content concentration after steel output is b (% by mass). %).
[0020]
By the way, the amount of decarburization (Δ [C]) in the steel output and the amount of unreacted oxygen (O nrea ) after blowing are expressed by the following relational expression.
[0021]
[Expression 1]
[C] Ep : Molten steel carbon concentration (mass%) at the end of blowing
W Gi: alloy iron i input amount in tapping (kg / ton)
C Gi: carbon content ferroalloy i (wt%)
[C] sec : Molten steel carbon concentration (mass%) before secondary refining treatment
[0022]
[Expression 2]
O g : amount of blown oxygen (Nm 3 / ton)
W SUBi: medium welding material i input amount in blowing (kg / ton)
O SUBI: the oxygen content of the medium welding material i (Nm 3 / kg)
O rea : component reaction oxygen consumption (Nm 3 / ton)
[0023]
Here, the molten steel carbon concentration [C] Ep at the end of blowing in Formula 1 and the molten steel carbon concentration [C] sec before the secondary refining treatment are values obtained by actual measurement.
[0024]
In addition, the component reaction consumed oxygen amount (O rea ) in Formula 2 is the total amount of oxygen consumed in the chemical reaction during blowing, and includes all reactions with molten metal and gas. There are various calculation methods. For example, from the amount of change in the concentration of each component before and after blowing, the amount of oxygen consumed for the reaction is calculated for each component, and the sum is used. Although there is a method of calculating using the exhaust gas analysis value of the converter, the effect of the present invention is not changed by using any method.
[0025]
Moreover, the amount of decarburization in steel output can be estimated from, for example, the following Equation 3 using a plurality of variables including the unreacted oxygen amount at the end of blowing and the molten steel carbon concentration in the converter operation information. In this case, as a mathematical expression, any relational expression may be used as long as it is a mathematical expression representing a relationship between a plurality of variables including the amount of unreacted oxygen at the end of blowing and the amount of decarburization in the steel output.
[0026]
[Equation 3]
X i : Converter operation information {Unreacted oxygen amount (Nm 3 / ton)} (continuous variable)
D j: blowing end point when a value representative of the molten steel carbon density bins (0, determined by the first binary variable)
alpha i: Continuous variable X i parameter β j: 0, determined by the first binary variable D j for parameter alpha 0: Parameters [0027]
The molten steel carbon concentration category (D j ) at the end of blowing in Formula 3 is a value that indicates which category the molten steel carbon concentration at the end of blowing is assigned to, for example, FIG. Corresponds to the four types of carbon concentration categories. Even if the amount of unreacted oxygen is the same, the amount of decarburization in the steel output varies depending on the difference in this category.
In addition, as the converter operation information (X i ) in Formula 3, only the unreacted oxygen amount ( Onrea ) is used, but the steel preparation time from the end of blowing to the start of steel output, It is even better to add factors that affect the decarburization at the time of steel production, such as the time for steel production to be completed.
[0028]
(2) The second aspect of the present invention will be described.
In the calculation of the unreacted oxygen amount of Formula 2, the molten steel carbon concentration after blowing used for calculating the component reaction consumption oxygen amount (O rea ) in the equation is estimated using the converter blowing control model. The estimated value of molten steel carbon concentration at the end of blowing is used, and the molten steel component concentration excluding carbon after blowing is using the converter end component target value.
[0029]
The molten steel carbon concentration estimated value used in the method of the present invention is a value very close to the actually measured value by model learning described later, and can be accurately calculated. Moreover, the molten steel component density | concentration except carbon is generally adjusted before the converter blowing process, and there is also little difference between component target values other than carbon before and behind blowing. Then, a difference between these blowing before and after assumed by reaction with oxygen, calculated component reactive oxygen consumed amount (O rea) theoretically accurate.
[0030]
In this way, the component reaction consumption oxygen amount O rea (Nm 3 / ton) is determined, the unreacted oxygen amount O nrea (Nm 3 / ton) is obtained from Equation 2, and the amount of decarburization in steel is estimated from Equation 3. The value Δ [C] inf (% by mass) can be estimated. In addition, about the molten steel carbon density | concentration division | segmentation at the time of a blow ending end point, the value showing the division | segmentation by the molten steel carbon concentration estimated value at the time of a blow ending end point estimated using a converter blowing control model is used.
[0031]
(3) Next, the converter blowing control model is based on each estimated value at the end of blowing, molten steel carbon concentration estimated value and molten steel temperature estimated value before secondary refining treatment, and measured value before secondary refining treatment The third aspect of the present invention for performing the learning will be described.
[0032]
[Expression 4]
[C] MEp : Molten steel carbon concentration at the end of blowing, estimated using a carbon concentration estimation model
Estimated value (mass%)
W Gi: alloy iron i input amount in tapping (kg / ton)
C Gi: carbon content ferroalloy i (wt%)
Δ [C] inf : Estimated decarburization amount in steel output (mass%)
[0033]
[Equation 5]
T MEp : Estimated molten steel temperature at the end of blowing (C) estimated using a molten steel temperature estimation model
W Gi: alloy iron i input amount in tapping (kg / ton)
T Gi : Temperature drop of alloy iron in steel output (℃ ・ ton / kg)
Z j : Converter operation information {Estimated decarburization amount in steel (mass%), etc.}
γ j : parameter
The estimated value (mass%) of the molten steel carbon before the secondary refining treatment in Equation 4 and the estimated molten steel temperature (° C.) before the secondary refining treatment in Equation 5 are both the estimated value at the end of blowing and the output. Derived from variables including estimated decarburization amount in steel, and the converter operation information (Z j ) in Equation 5 includes the estimated decarburization amount in steel output and the start of steel extraction after the end of blowing Temperature drop at the time of steel production, such as steel preparation time to start, steel production time from the start of steel production to completion of steel production, secondary refining preparation time from the end of steel production to actual measurement before secondary refining treatment, use condition of ladle, etc. Including factors that affect
In addition, decarburization in the steel output is due to the reaction with oxygen as described above, and affects the molten steel temperature.
[0035]
As a representative example, learning of the converter blowing control model is performed by the following formulas 6 and 7.
[0036]
[Formula 6]
[Expression 7]
Here, the carbon concentration estimation model formula at the end of blowing and the model formula for estimating the molten steel temperature at the end of blowing are model formulas used for static control and dynamic control, for example. However, it is also possible to apply other model formulas for blowing control.
The model learning term is a model learning value that is calculated using a general exponential smoothing method from the difference between the estimated value before the secondary refining process and the actually measured value before the secondary refining process.
[0039]
(4) Next, the fourth aspect of the present invention that learns the converter blowing control model from the back-calculated estimated value at the end of blowing and the estimated value at the end of blowing will be described.
Estimated value of molten steel carbon concentration [C] REp as an estimated reverse value at the end of blow smelting is the measured value of molten steel carbon concentration before secondary refining treatment [C] sec and the carbon concentration due to the introduction of alloy iron i in the steel. It is calculated by the following formula 8 from the increase amount and the estimated decarburization amount Δ [C] inf in the steel output.
[0040]
[Equation 8]
[C] sec : Measured value (mol%) of molten steel carbon concentration before secondary refining treatment
W Gi: alloy iron i input amount in tapping (kg / ton)
C Gi: carbon content ferroalloy i (wt%)
Δ [C] inf : Estimated decarburization amount in steel output (mass%)
[0041]
Further, the reverse calculation molten steel temperature estimated value T REp as the reverse calculation at the end of the blow smelting is obtained by measuring the molten steel temperature T sec before the secondary refining treatment, the temperature drop T Gi due to the introduction of the alloy iron i in the steel output, and the formula Using the same converter operation information as in FIG.
[0042]
[Equation 9]
T sec : Actual measured value of molten steel before secondary refining treatment (° C)
W Gi: alloy iron i input amount in tapping (kg / ton)
T Gi : Temperature drop due to the introduction of alloy iron i in steel output (℃ / kg ・ ton)
Z j : Converter operation information {Estimated decarburization amount (% by mass) in steel output, etc.}
γ j : parameter
The above formulas 8 and 9 are made up of a plurality of variables including the actual measurement values before the secondary refining treatment and the amount of decarburization in the steel output, and are estimated from, for example, a static control model or a dynamic control model by this back calculation estimated value. The estimated value at the end of blowing is to be learned.
The concept of learning is the same as the concept of learning the estimated value before the secondary refining process, and is based on the following formulas 10 and 11.
[0044]
[Expression 10]
[Expression 11]
Estimated end point of molten steel carbon concentration, model learned value based on estimated value, and model learned value of molten steel temperature are estimated back end point estimated value and end point of converter blowing control model for molten steel carbon concentration and molten steel temperature, respectively. The model learning amount calculated using a general exponential smoothing method is calculated from the difference from the value.
[0047]
As described above, the learning method of the converter blowing control model according to the third and fourth aspects of the present invention is as follows.
a. The difference between the estimated value before secondary refining treatment and the actual measurement value before secondary refining treatment, or
b. From the difference between the reverse calculation end point estimate and the end point estimate of the converter blowing control model,
The model learning amount is calculated using a general exponential smoothing method. Whichever method is used, the model learning amount is the same, and the accuracy of the model is further improved. Even if a method other than the exponential smoothing method is used as the learning amount calculation method, the effect does not change.
[0048]
【Example】
Example 1
The Example about the decarburization amount estimated value in the steel output concerning this invention is shown.
In this example, the molten steel carbon concentration classification (blowing steel type) determined by the actual measured value of molten steel carbon at the end of blowing, and [C], [Si], [Mn], [S], [P] before and after blowing ], [Ti], etc., each component concentration in the molten steel is actually measured, for example, from the unreacted oxygen amount calculated from Equation 2, the molten steel carbon concentration classification, and from the Equation 3 including the unreacted oxygen amount as variables The estimated decarburization amount was calculated. At the same time, the molten steel carbon concentration after steel output was also measured to determine the amount of decarburization, which was taken as the amount of decarburization in steel output. The accuracy of the estimated value was confirmed by this comparison.
[0049]
The converter operating conditions in Example 1 are as follows.
Blowing steel grade: Carbon 0.03-1.0 mass% ordinary steel,
Steel output from converter: 210 ton / charge,
Others: Alloy iron is not thrown into steel.
In addition, the molten steel carbon concentration classification at the end of blowing in Formula 3 is the same as that shown in FIG. 2, the actual value of the molten steel carbon concentration at the end of blowing is higher than 0.30 mass%, high carbon steel, 0.30 mass% medium and high carbon steel and shall exceed 0.20 wt% or less, 0.20 wt% 0.10 wt%, greater medium carbon steel shall, was 4 sections and low carbon steel ones 0.10 mass% or less. FIG. 3 is a graph showing the relationship between the decarburization amount (actually measured value) in the steel output and the estimated decarburization amount in the steel output. From this graph, the decarburization amount in the steel output can be accurately estimated. I know that.
[0050]
(Example 2)
Next, with respect to the estimated amount of decarburization in the steel output according to the present invention, an example in the case of using the molten steel carbon concentration estimated using the converter blowing control model as the molten steel carbon concentration at the end of blowing is shown. . In this example, the estimated value of the converter blowing control model is used instead of the actual measured value of the molten steel carbon concentration at the end of blowing. The estimated decarburization amount in the steel output was calculated by a method in which the same molten steel carbon concentration category as in Example 1 was set to 4 categories.
And for each component concentration before and after blowing necessary for calculation of unreacted oxygen amount, each component concentration before blowing is measured, and each component concentration after blowing (at the end of blowing) is about carbon The molten steel carbon concentration estimated value estimated by the converter blowing control model was used, and the target value was used in place of the actual measured value otherwise.
[0051]
FIG. 4 is a graph showing the relationship between the amount of decarburization during the steel output (actual value) and the estimated value of the amount of decarburization during the steel output. From this graph, the converter blowing control is performed as the molten steel carbon concentration at the end of blowing. It can be seen that the amount of decarburization in steel output can be estimated with high accuracy using the molten steel carbon concentration estimated using the model.
[0052]
Example 3
Next, an example of model learning according to the present invention will be described.
As described above, the third and fourth invention methods both have the same learning amount (value) and the effect thereof does not change. Therefore, in this embodiment, the fourth invention method, that is, at the time of blowing end point. The model learning amount was determined based on the difference between the back-calculated estimated value and the estimated value at the end of blowing.
Here, the back-calculated estimated value at the end of blowing is calculated by Equations 8 and 9, and the model formula for estimating the molten steel carbon concentration and molten steel temperature at the end of blowing is used in dynamic control. The model formula is applied.
On the other hand, the method of patent document 1 was employ | adopted for the learning method of the conventional method.
[0053]
And the converter operating conditions in Example 3 are as follows.
Blowing steel grade: Carbon 0.03-1.0 mass% ordinary steel,
Molten steel temperature at the end of blowing: 1650-1680 ° C,
Steel output from converter: 210 ton / charge.
[0054]
FIGS. 5 and 6 are graphs showing the average and deviation of the molten steel carbon concentration estimation error and the molten steel temperature estimation error of the method of the present invention and the conventional method with respect to the actual measured value at the end of blowing, for each molten carbon concentration category. It is. In this graph, ◯ indicates the method of the present invention, ● indicates the conventional method, the range of the bar indicates the deviation, and the positions where the ◯ and ● indicate are average values. The molten steel carbon concentration estimation error and the molten steel temperature estimation error at the end of blowing are the difference between the estimated value at the end of blowing and the measured value at the end of blowing.
[0055]
From this graph, it can be seen that the model learning method applied to the present invention is smaller in both molten steel carbon concentration estimation error and molten steel temperature estimation error than the conventional method. That is, it can be seen that the control accuracy of the converter blowing control model is improved by the model learning of the present invention.
In the above embodiment, actual measurement is performed for accuracy confirmation and error confirmation at the end of blowing, but it is not necessary.
[0056]
【The invention's effect】
As described above, according to the present invention, the amount of decarburization during steel output can be accurately estimated, and the control accuracy of the converter blowing control model can be improved even when the blowing end point measurement is omitted. it can.
[Brief description of the drawings]
FIG. 1 is a view showing a hot metal / molten steel treatment process from converter blowing to secondary refining treatment.
FIG. 2 is a diagram showing the relationship between the amount of decarburization and the amount of unreacted oxygen in steel output.
FIG. 3 is a diagram showing the estimation result of the amount of decarburization during steel output according to the present invention.
FIG. 4 is a diagram showing an estimation result in a case where a molten steel carbon concentration estimated by a converter blowing control model is used in estimation of a decarburization amount in steel output according to the present invention.
FIG. 5 is a diagram showing a comparison result between the average and deviation of molten steel carbon concentration estimation errors at the model blowing end point when model learning is performed according to the method of the present invention and the conventional method.
FIG. 6 is a diagram showing a comparison result of the average and deviation of molten steel temperature estimation errors at the model blowing end point when model learning is performed by the method of the present invention and the conventional method.
Claims (4)
転炉操業情報のうち吹錬終点時の未反応酸素量を含む複数の変数を用いて、出鋼中の脱炭量を推定することを特徴とする転炉出鋼中脱炭量推定法。In the converter operation method that performs blowing control using the converter blowing control model,
A decarburization amount estimation method in converter steel, characterized by estimating a decarburization amount in steel output using a plurality of variables including unreacted oxygen amount at the end of blowing in the converter operation information.
吹錬終点時の溶鋼炭素濃度として転炉吹錬制御モデルを用いて推定する溶鋼炭素濃度推定値を用いることを特徴とする請求項１記載の転炉出鋼中脱炭量推定法。 When calculating the unreacted oxygen amount using the component reaction consumption oxygen amount during blowing, and calculating the component reaction consumption oxygen amount from the component difference before and after blowing,
The method for estimating the amount of decarburization in converter steel according to claim 1, wherein an estimated value of molten steel carbon concentration estimated using a converter blowing control model is used as the molten steel carbon concentration at the end of blowing.
前記転炉吹錬制御モデルの学習を、
転炉吹錬制御モデルを用いて推定する吹錬終点時の各推定値と、
請求項１又は２記載の方法で推定した出鋼中の脱炭量を含む複数の変数を用いて推定する二次精錬処理前の溶鋼炭素濃度推定値及び溶鋼温度推定値と、
二次精錬処理前の各実測値により行うことを特徴とする転炉操業法。In the converter operation method that performs blowing control using the converter blowing control model,
Learning the converter blowing control model,
Estimated values at the end of blowing, estimated using the converter blowing control model,
A molten steel carbon concentration estimated value and a molten steel temperature estimated value before secondary refining treatment, which are estimated using a plurality of variables including the amount of decarburization in steel output estimated by the method according to claim 1 or 2;
Converter operation method which is characterized in that the respective actual measured value before secondary refining process.
前記転炉吹錬制御モデルの学習を、
二次精錬処理前の各実測値と、
請求項１又は２記載の方法で推定した出鋼中の脱炭量を含む複数の変数を用いて逆算推定する吹錬終点時の溶鋼炭素濃度及び溶鋼温度の逆算推定値と、
転炉吹錬制御モデルを用いて推定する吹錬終点時の各推定値により行うことを特徴とする転炉操業法。In the converter operation method that performs blowing control using the converter blowing control model,
Learning the converter blowing control model,
Each measured value before secondary refining process,
A back calculation estimated value of the molten steel carbon concentration and the molten steel temperature at the end of the blowing operation, which is estimated backward using a plurality of variables including the amount of decarburization in the steel output estimated by the method according to claim 1 or 2;
A converter operation method, which is performed by each estimated value at the end of blowing, which is estimated using a converter blowing control model.
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