JP2019019385A - Method and device for predicting molten iron temperature, operation method of blast furnace, operation guidance device, and method and device for controlling molten iron temperature - Google Patents
Method and device for predicting molten iron temperature, operation method of blast furnace, operation guidance device, and method and device for controlling molten iron temperature Download PDFInfo
- Publication number
- JP2019019385A JP2019019385A JP2017139600A JP2017139600A JP2019019385A JP 2019019385 A JP2019019385 A JP 2019019385A JP 2017139600 A JP2017139600 A JP 2017139600A JP 2017139600 A JP2017139600 A JP 2017139600A JP 2019019385 A JP2019019385 A JP 2019019385A
- Authority
- JP
- Japan
- Prior art keywords
- hot metal
- metal temperature
- calculated
- value
- blast furnace
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 title abstract description 40
- 229910052742 iron Inorganic materials 0.000 title abstract description 20
- 230000008859 change Effects 0.000 claims abstract description 47
- 239000002184 metal Substances 0.000 claims description 156
- 229910052751 metal Inorganic materials 0.000 claims description 156
- 239000003245 coal Substances 0.000 claims description 10
- 239000000571 coke Substances 0.000 claims description 10
- 230000007704 transition Effects 0.000 claims description 9
- 238000002347 injection Methods 0.000 claims description 6
- 239000007924 injection Substances 0.000 claims description 6
- 230000002123 temporal effect Effects 0.000 abstract description 7
- 238000004364 calculation method Methods 0.000 description 17
- 238000010586 diagram Methods 0.000 description 15
- 239000007789 gas Substances 0.000 description 9
- 230000004044 response Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 229910000831 Steel Inorganic materials 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 239000000498 cooling water Substances 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 230000036284 oxygen consumption Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Landscapes
- Manufacture Of Iron (AREA)
Abstract
Description
本発明は、溶銑温度予測方法、溶銑温度予測装置、高炉の操業方法、操業ガイダンス装置、溶銑温度制御方法、及び溶銑温度制御装置に関する。 The present invention relates to a hot metal temperature prediction method, a hot metal temperature prediction device, a blast furnace operation method, an operation guidance device, a hot metal temperature control method, and a hot metal temperature control device.
製鉄業における高炉プロセスにおいて、溶銑温度は重要な管理指標である。特に近年の高炉操業は、原燃料コストの合理化を追求すべく、低コークス比及び高微粉炭比の条件下で行われており、炉況が不安定化しやすい。このため、炉熱ばらつき低減のニーズが大きい。一方、高炉プロセスは、固体が充填された状態で操業を行うために、プロセス全体の熱容量が大きく、操作に対する応答の時定数が長いという特徴を有している。また、高炉の上部から装入された原料が高炉の下部に降下するまでには数時間オーダーの無駄時間が存在する。このため、炉熱制御のためには将来の炉熱予測に基づいた操作変数の操作量の適正化が必須となる。 In the blast furnace process in the steel industry, the hot metal temperature is an important management index. In particular, blast furnace operations in recent years are conducted under conditions of low coke ratio and high pulverized coal ratio in order to pursue rationalization of raw fuel costs, and the furnace conditions are likely to become unstable. For this reason, there is a great need for reducing furnace heat variation. On the other hand, since the blast furnace process is operated in a state of being filled with a solid, the heat capacity of the entire process is large and the time constant of response to the operation is long. Also, there is a dead time of several hours before the raw material charged from the upper part of the blast furnace descends to the lower part of the blast furnace. For this reason, for the furnace heat control, it is essential to optimize the manipulated variables of the manipulated variables based on the future furnace heat prediction.
このような背景から、特許文献１には、物理モデルを利用した炉熱予測方法が提案されている。具体的には、特許文献１に記載の炉熱予測方法は、現在の炉頂ガスの組成に合致するように物理モデルに含まれるガス還元速度パラメータを調整し、パラメータ調整後の物理モデルを用いて炉熱を予測する。
Against this background,
しかしながら、物理モデルを用いて溶銑温度を予測する際には、鉄鉱石の被還元性やガス偏流等のモデル化が困難な外乱の影響によって溶銑温度の予測精度が低下する場合がある。なお、特許文献１記載の炉熱予測方法は、モデル化が困難な外乱の影響を打ち消すために、現在の炉頂ガスの組成に合致するように物理モデルに含まれるガス還元速度パラメータを調整している。ところが、特許文献１記載の発明は、溶銑温度の予測精度の評価方法として溶銑温度の絶対値の予測精度の向上を確認しているのみであり、溶銑温度の変化量の予測精度については言及していない。溶銑温度を精度よく制御するためには、溶銑温度の変化量の予測精度の方が溶銑温度の絶対値の予測精度よりも重要度が高い。これは、一般的なモデル予測制御においては、将来の予測値は予測された変化量に現在の実測値を加算することによって求められるためである。
However, when predicting the hot metal temperature using a physical model, the prediction accuracy of the hot metal temperature may decrease due to the influence of disturbances that are difficult to model, such as reducibility of iron ore and gas drift. Note that the furnace heat prediction method described in
本発明は、上記課題に鑑みてなされたものであって、その目的は、溶銑温度の変化量の予測精度を向上させて溶銑温度の予測精度を向上可能な溶銑温度予測方法及び溶銑温度予測装置を提供することにある。また、本発明の他の目的は、溶銑温度を精度よく制御可能な高炉の操業方法、操業ガイダンス装置、溶銑温度制御方法、及び溶銑温度制御装置を提供することにある。 The present invention has been made in view of the above problems, and an object of the present invention is to provide a hot metal temperature prediction method and a hot metal temperature prediction apparatus capable of improving the prediction accuracy of the hot metal temperature by improving the prediction accuracy of the amount of change in the hot metal temperature. Is to provide. Another object of the present invention is to provide a blast furnace operation method, an operation guidance device, a hot metal temperature control method, and a hot metal temperature control device capable of accurately controlling the hot metal temperature.
本発明に係る溶銑温度予測方法は、非定常状態における高炉内の状態を計算可能な物理モデルを用いて高炉における溶銑温度を予測する溶銑温度予測方法であって、前記物理モデルを用いて高炉の操作変数の現在の操作量を保持した場合の将来の溶銑温度の予測値を算出する第１ステップと、前記物理モデルを用いて計算された過去の期間における高炉内の状態を示す変数の計算値と実績値との差の時間変化率を算出する第２ステップと、前記物理モデルを用いて計算された前記過去の期間における溶銑温度の計算値と実績値との差を溶銑温度の計算値の誤差として算出する第３ステップと、前記第２ステップにおいて算出された時間変化率を用いて前記第３ステップにおいて算出された溶銑温度の計算値の誤差を求める回帰式を構築する第４ステップと、前記物理モデルを用いて計算された現在における高炉内の状態を示す変数の計算値と実績値との差の時間変化率と前記第４ステップにおいて構築された回帰式とを用いて、現在における溶銑温度の計算値の誤差を算出する第５ステップと、前記第１ステップにおいて算出された溶銑温度の予測値に前記第５ステップにおいて算出された誤差を加算することによって、前記第１ステップにおいて算出された溶銑温度の予測値を補正する第６ステップと、を含むことを特徴とする。 The hot metal temperature prediction method according to the present invention is a hot metal temperature prediction method for predicting the hot metal temperature in a blast furnace using a physical model capable of calculating the state in the blast furnace in an unsteady state, and using the physical model, A first step of calculating a predicted value of the molten iron temperature in the case where the current manipulated variable of the manipulated variable is held, and a calculated value of a variable indicating a state in the blast furnace in the past period calculated using the physical model The second step of calculating the time change rate of the difference between the actual value and the actual value, and the difference between the calculated value of the hot metal temperature and the actual value in the past period calculated using the physical model is calculated as the calculated value of the hot metal temperature. A third step of calculating as an error and a regression equation for calculating an error of the calculated value of the hot metal temperature calculated in the third step using the time change rate calculated in the second step. Using the step, the time change rate of the difference between the calculated value of the variable indicating the current state in the blast furnace and the actual value calculated using the physical model, and the regression equation constructed in the fourth step, A fifth step of calculating an error in the calculated value of the hot metal temperature at the present time, and adding the error calculated in the fifth step to the predicted value of the hot metal temperature calculated in the first step. And a sixth step of correcting the predicted value of the hot metal temperature calculated in step (5).
本発明に係る溶銑温度予測装置は、非定常状態における高炉内の状態を計算可能な物理モデルを用いて高炉における溶銑温度を予測する溶銑温度予測装置であって、前記物理モデルを用いて高炉の操作変数の現在の操作量を保持した場合の将来の溶銑温度の予測値を算出する第１手段と、前記物理モデルを用いて計算された過去の期間における高炉内の状態を示す変数の計算値と実績値との差の時間変化率を算出する第２手段と、前記物理モデルを用いて計算された前記過去の期間における溶銑温度の計算値と実績値との差を溶銑温度の計算値の誤差として算出する第３手段と、前記第２手段によって算出された時間変化率を用いて前記第３手段によって算出された溶銑温度の計算値の誤差を求める回帰式を構築する第４手段と、前記物理モデルを用いて計算された現在における高炉内の状態を示す変数の計算値と実績値との差の時間変化率と前記第４手段によって構築された回帰式とを用いて、現在における溶銑温度の計算値の誤差を算出する第５手段と、前記第１手段によって算出された溶銑温度の予測値に前記第５手段によって算出された誤差を加算することによって、前記第１手段によって算出された溶銑温度の予測値を補正する第６手段と、を備えることを特徴とする。 The hot metal temperature predicting apparatus according to the present invention is a hot metal temperature predicting apparatus that predicts the hot metal temperature in the blast furnace using a physical model that can calculate the state in the blast furnace in an unsteady state, and using the physical model, A first means for calculating a predicted value of the future hot metal temperature when the current manipulated variable of the manipulated variable is held, and a calculated value of a variable indicating a state in the blast furnace in the past period calculated using the physical model A second means for calculating a time change rate of the difference between the actual value and the actual value, and a difference between the calculated value and the actual value of the hot metal temperature in the past period calculated using the physical model A third means for calculating as an error; a fourth means for constructing a regression equation for obtaining an error of the calculated value of the hot metal temperature calculated by the third means using the time change rate calculated by the second means; The physical mode Using the time change rate of the difference between the calculated value and the actual value of the variable indicating the current state of the blast furnace calculated using the above and the regression equation constructed by the fourth means, A fifth means for calculating an error of the calculated value and a hot metal calculated by the first means by adding the error calculated by the fifth means to the predicted value of the hot metal temperature calculated by the first means. And a sixth means for correcting the predicted temperature value.
本発明に係る高炉の操業方法は、本発明に係る溶銑温度予測方法を用いて補正された溶銑温度に従って高炉の操作変数を制御するステップを含むことを特徴とする。 A method for operating a blast furnace according to the present invention includes a step of controlling operating variables of the blast furnace according to a hot metal temperature corrected using the hot metal temperature prediction method according to the present invention.
本発明に係る操業ガイダンス装置は、本発明に係る溶銑温度予測装置によって補正された将来における溶銑温度の予測値の推移及び適正操作を実行した場合における将来における溶銑温度の予測値の推移を提示することにより、高炉の操業を支援する手段を備えることを特徴とする。 The operation guidance device according to the present invention presents the transition of the predicted value of the hot metal temperature corrected by the hot metal temperature prediction device of the present invention and the transition of the predicted value of the hot metal temperature in the future when an appropriate operation is performed. Thus, it is provided with means for supporting the operation of the blast furnace.
本発明に係る溶銑温度制御方法は、本発明に係る溶銑温度予測方法によって補正された溶銑温度の予測値に基づいて溶銑温度を制御する溶銑温度制御方法であって、補正された溶銑温度の予測値と目標溶銑温度との差を最小にするように送風湿分、微粉炭吹込み量、コークス比、及び送風温度のうちの少なくとも１つを含む高炉の操作変数の適正操作量を決定し、決定した適正操作量に従って高炉の操作変数を制御するステップを含むことを特徴とする。 The hot metal temperature control method according to the present invention is a hot metal temperature control method for controlling the hot metal temperature based on the predicted value of the hot metal temperature corrected by the hot metal temperature prediction method according to the present invention. Determining the appropriate operating amount of the operating variables of the blast furnace including at least one of the blast moisture, the pulverized coal injection amount, the coke ratio, and the blast temperature so as to minimize the difference between the value and the target hot metal temperature, The method includes a step of controlling an operating variable of the blast furnace according to the determined appropriate operating amount.
本発明に係る溶銑温度制御装置は、本発明に係る溶銑温度予測装置によって補正された溶銑温度の予測値に基づいて溶銑温度を制御する溶銑温度制御方法であって、補正された溶銑温度の予測値と目標溶銑温度との差を最小にするように送風湿分、微粉炭吹込み量、コークス比、及び送風温度のうちの少なくとも１つを含む高炉の操作変数の適正操作量を決定し、決定した適正操作量に従って高炉の操作変数を制御するステップを含むことを特徴とする。 The hot metal temperature control device according to the present invention is a hot metal temperature control method for controlling the hot metal temperature based on the predicted value of the hot metal temperature corrected by the hot metal temperature prediction device according to the present invention, and the prediction of the corrected hot metal temperature. Determining the appropriate operating amount of the operating variables of the blast furnace including at least one of the blast moisture, the pulverized coal injection amount, the coke ratio, and the blast temperature so as to minimize the difference between the value and the target hot metal temperature, The method includes a step of controlling an operating variable of the blast furnace according to the determined appropriate operating amount.
本発明に係る溶銑温度予測方法及び溶銑温度予測装置によれば、溶銑温度の変化量の予測精度を向上させて溶銑温度の予測精度を向上させることができる。また、本発明に係る高炉の操業方法、操業ガイダンス装置、溶銑温度制御方法、及び溶銑温度制御装置によれば、溶銑温度を精度よく制御することができる。 According to the hot metal temperature prediction method and the hot metal temperature prediction apparatus according to the present invention, the prediction accuracy of the hot metal temperature change amount can be improved and the prediction accuracy of the hot metal temperature can be improved. Moreover, according to the operation method of the blast furnace, the operation guidance device, the hot metal temperature control method, and the hot metal temperature control device according to the present invention, the hot metal temperature can be accurately controlled.
以下、図面を参照して、本発明に係る溶銑温度予測方法、溶銑温度予測装置、高炉の操業方法、操業ガイダンス装置、溶銑温度制御方法、及び溶銑温度制御装置について説明する。 Hereinafter, a hot metal temperature prediction method, a hot metal temperature prediction device, a blast furnace operation method, an operation guidance device, a hot metal temperature control method, and a hot metal temperature control device according to the present invention will be described with reference to the drawings.
〔物理モデルの構成〕
まず、本発明において用いる物理モデルについて説明する。
[Configuration of physical model]
First, a physical model used in the present invention will be described.
本発明において用いる物理モデルは、参考文献１（羽田野道春ら：“高炉非定常モデルによる火入れ操業の検討”，鉄と鋼，vol.68，p.2369）記載の方法と同様、鉄鉱石の還元、鉄鉱石とコークスとの間の熱交換、及び鉄鉱石の融解等の複数の物理現象を考慮した偏微分方程式群から構成された、非定常状態における高炉内の状態を示す変数（出力変数）を計算可能な物理モデルである。 The physical model used in the present invention is the same as the method described in Reference 1 (Haneda Michiharu et al .: “Investigation of Fire Operation by Blast Furnace Unsteady Model”, Iron and Steel, vol.68, p.2369). Variable indicating the state in the blast furnace in the unsteady state, consisting of partial differential equations that take into account multiple physical phenomena such as reduction, heat exchange between iron ore and coke, and melting of iron ore (output variable) ) Is a physical model that can be calculated.
図１に示すように、この物理モデルに対して与える境界条件の中で時間変化する主なもの（入力変数，高炉の操作変数（操業因子ともいう））は、炉頂におけるコークス比（炉頂から投入されるコークス量に対する鉄鉱石量の比）、送風流量（高炉に送風される空気の流量）、富化酸素流量（高炉に吹き込まれる富化酸素の流量）、送風温度（高炉に送風される空気の温度）、微粉炭吹込み量（溶銑生成量１トンに対して使用される微粉炭の重量，ＰＣＩ）、及び送風湿分（高炉に送風される空気の湿度）である。 As shown in Fig. 1, the main ones that change with time in the boundary conditions given to this physical model (input variables, blast furnace operating variables (also called operating factors)) are the coke ratio at the furnace top (furnace top). Ratio of iron ore to the amount of coke charged from the air), air flow (flow rate of air blown into the blast furnace), enriched oxygen flow rate (flow rate of enriched oxygen blown into the blast furnace), air temperature (air blown into the blast furnace) Air temperature), pulverized coal injection amount (weight of pulverized coal used per 1 ton of hot metal production, PCI), and blowing moisture (humidity of air blown into the blast furnace).
また、物理モデルによって形成される主な出力変数は、炉内におけるガス利用率（ＣＯ２／（ＣＯ＋ＣＯ２），ηＣＯ）、原料及びガス温度、鉱石還元率、ソルーションロスカーボン量（ソルロスカーボン量）、酸素原単位、造銑速度（溶銑生成速度）、溶銑温度、炉体ヒートロス量（冷却水により炉体を冷却した際に冷却水が奪う熱量）、及び還元材比（溶銑１トンあたりの微粉炭吹込み量とコークス比との和，ＲＡＲ）である。 The main output variables formed by the physical model are the gas utilization rate (CO 2 / (CO + CO 2 ), ηCO) in the furnace, the raw material and gas temperature, the ore reduction rate, the amount of solution loss carbon (the amount of solution loss carbon). ), Oxygen consumption rate, ironmaking speed (hot metal production speed), hot metal temperature, furnace body heat loss amount (heat amount taken by cooling water when the furnace body is cooled by cooling water), and reducing material ratio (per ton of hot metal) Sum of pulverized coal injection amount and coke ratio, RAR).
本発明では、出力変数を計算する際のタイムステップ（時間間隔）は３０分とした。但し、タイムステップは目的に応じて可変であり、本実施形態の値に限定されることはない。本発明では、この物理モデルを用いて時々刻々変化する溶銑温度を含む出力変数を計算する。 In the present invention, the time step (time interval) for calculating the output variable is 30 minutes. However, the time step is variable according to the purpose, and is not limited to the value of this embodiment. In the present invention, an output variable including the hot metal temperature that changes every moment is calculated using this physical model.
〔溶銑温度の予測方法〕
次に、上記物理モデルを用いた溶銑温度の予測方法について説明する。
[Method of predicting hot metal temperature]
Next, a hot metal temperature prediction method using the physical model will be described.
まず、本発明では、参考文献２（Jan著：“モデル予測制御”，東京電機大学出版，p.66）記載のモデル予測制御技術に基づいて、モデル予測制御の原則に則り、過去の一連の操作変数の操作量の時系列データを入力して出力変数を更新し、現在の操作変数の操作量が将来も一定に保持されたと仮定して物理モデルを加速実行することにより、溶銑温度を含む出力変数の予測値を算出する。図２（ａ）〜（ｆ）は、操作変数の操作量の時系列データの一例を示す図である。図３（ａ）〜（ｅ）は、出力変数の計算結果の一例を示す図であり、図中の実線及びプロットはそれぞれ出力変数の計算値及び実績値を示す。また、図２（ａ）〜（ｆ）及び図３（ａ）〜（ｅ）の横軸は時間（ｈｒ）を示し、本例では予測開始時点を０時間としている。また、図２（ａ）〜（ｆ）の将来区間（０〜１０時間）には一定に保持された操作変数の操作量（モデル入力の操作量）（実線）と実操作量（点線）とを図示した。 First, in the present invention, based on the model predictive control technique described in Reference 2 (Jan: “Model Predictive Control”, Tokyo Denki University Press, p. 66), The time series data of the manipulated variable of the manipulated variable is input and the output variable is updated. By assuming that the manipulated variable of the current manipulated variable is kept constant in the future, the physical model is accelerated and the hot metal temperature is included. Calculate the predicted value of the output variable. 2A to 2F are diagrams illustrating an example of time-series data of the operation amount of the operation variable. 3A to 3E are diagrams illustrating an example of the calculation result of the output variable, and the solid line and the plot in the figure indicate the calculated value and the actual value of the output variable, respectively. Moreover, the horizontal axis of Fig.2 (a)-(f) and Fig.3 (a)-(e) shows time (hr), In this example, the prediction start time is made into 0 hour. Further, in the future interval (0 to 10 hours) in FIGS. 2A to 2F, the manipulated variable of the manipulated variable (model input manipulated variable) (solid line) and actual manipulated variable (dotted line) Is illustrated.
上述した通り、この段階では、出力変数の予測値の計算には操作変数の将来の実操作量が反映されていないのにも関わらず、図３（ｅ）に示すように、予測開始時点（０時間）以後も溶銑温度の予測値と実績値との間に相応の合致がみられる。これは、炉内の熱容量が大きく、系の無駄時間が長いため、過去の操作の蓄積が将来の溶銑温度の変化に大きく影響を及ぼすことを意味する。なお、以下では、現在の操作変数の操作量が将来も一定に保持されたと仮定して物理モデルを用いて算出された時刻ｔにおける溶銑温度の予測値を自由応答ＨＭＴｆｒｅｅ（ｔ）と定義する。 As described above, at this stage, although the future actual manipulated variable of the manipulated variable is not reflected in the calculation of the predicted value of the output variable, as shown in FIG. After 0 hour), there is a corresponding agreement between the predicted value of the hot metal temperature and the actual value. This means that since the heat capacity in the furnace is large and the dead time of the system is long, accumulation of past operations greatly affects future changes in hot metal temperature. In the following, the predicted value of the hot metal temperature at time t calculated using the physical model on the assumption that the manipulated variable of the current manipulated variable is kept constant in the future is defined as free response HMT free (t). .
ところが、将来の溶銑温度の予測値が実績値から外れる場合が時折発生することがある。図４（ａ）〜（ｆ）は、過去の期間における溶銑温度以外の出力変数の計算値及び実績値と溶銑温度の計算値及び実績値とを示す図である。図４（ｆ）に示すように、将来区間における溶銑温度の予測値（計算値（補正前））と実績値とが乖離している。この場合の乖離要因としては、図４（ｃ）に示すように、実際のＲＡＲの時間変化率の方が計算上のＲＡＲの時間変化率よりも大きいため、ＲＡＲの実績値の方がＲＡＲの計算値より上昇したことが考えられる。このため、本発明では、ＲＡＲやソルロスカーボン量といった溶銑温度以外の出力変数の過去区間における誤差の時間変化率に基づいて溶銑温度の予測値を補正する。 However, sometimes the predicted value of the future hot metal temperature deviates from the actual value. 4 (a) to 4 (f) are diagrams showing calculated values and actual values of output variables other than the hot metal temperature and calculated values and actual values of the hot metal temperature in the past period. As shown in FIG. 4F, the predicted value (calculated value (before correction)) of the hot metal temperature in the future section and the actual value are deviated. As a divergence factor in this case, as shown in FIG. 4C, the actual RAR time change rate is larger than the calculated RAR time change rate, and therefore the actual RAR value is greater than the RAR value. It is possible that the value was higher than the calculated value. For this reason, in this invention, the predicted value of hot metal temperature is correct | amended based on the time change rate of the error in the past area of output variables other than hot metal temperature, such as RAR and the amount of solros carbon.
具体的には、図５（ａ）〜（ｅ）に示すように、まず、過去８時間分のガス利用率、ソルロスカーボン量、ＲＡＲ、造銑速度、及び炉体ヒートロス量の誤差（計算値−実績値）δの時間変化率を求める。なお、図５（ａ）〜（ｅ）には、図４（ａ）〜（ｄ）に示したケースと同一ケースにおける誤差をプロットしている。ここで、Ｓｌｏｐｅ（δＲＡＲ）（ｉ）は計算時点（ｉ）におけるＲＡＲの誤差の１時間あたりの時間変化率を表す。ガス利用率等の他の出力変数も同様である。次に、将来８時間先の溶銑温度の実績時間変化率と計算時間変化率との差を求める。ここで、δＨＭＴ（ｉ）は計算時点（ｉ）から８時間先の溶銑温度の実績時間変化率と計算時間変化率との誤差を示す。以上のステップを過去１ヶ月について繰返すことにより以下の数式（１），（２）に示すようなデータセットが生成される。
Specifically, as shown in FIGS. 5A to 5E, first, errors (calculations) of the gas utilization rate, the amount of sol-loss carbon, the RAR, the ironmaking speed, and the furnace heat loss amount for the past 8 hours. Value-actual value) δ is obtained as a time change rate. In FIGS. 5A to 5E, errors in the same case as the cases shown in FIGS. 4A to 4D are plotted. Here, Slope (δRAR) (i) represents the rate of change of the RAR error per hour at the time of calculation (i). The same applies to other output variables such as gas utilization. Next, the difference between the actual time change rate of the
なお、ここでの計算時間変化率とは、上記で述べたように操作変数の将来の操作量を現在値に保持したと仮定して計算された溶銑温度の時間変化率（予測時間変化率）ではなく、操作変数の将来の実操作量を反映した計算により求められた溶銑温度の時間変化率のことを意味する。以下、予測時間変化率ではなく計算時間変化率を用いる理由について説明する。なお、ここでいう「将来」とは、計算時点（ｉ）を基点とした未来のことを意味し、「過去」とは計算時点（ｉ）を基点とした過去のことを意味する。 The rate of change of the calculation time here is the rate of change of the hot metal temperature with time (predicted rate of change of time) calculated on the assumption that the future manipulated variable of the manipulated variable is held at the current value as described above. Rather, it means the rate of change over time in the hot metal temperature obtained by calculation reflecting the future actual manipulated variable of the manipulated variable. Hereinafter, the reason why the calculation time change rate is used instead of the predicted time change rate will be described. The “future” here means the future based on the calculation time point (i), and the “past” means the past based on the calculation time point (i).
溶銑温度の将来の予測誤差の要因の内訳として、（ａ）操作変数の操作量が計算時点から変化したことに由来する部分と、（ｂ）過去の出力変数の誤差に由来する部分とがある。本発明の目的である溶銑温度のオンライン予測を行う際は、将来の操作量の実績値が存在しないため、（ａ）の影響を溶銑温度の予測に反映させることは難しい。このため、将来区間における操作量の実績値が判明している期間において、将来区間における操作量の実績値を反映させて溶銑温度を計算し、計算値と実績値との差分を溶銑温度の計算誤差として算出することによって（ａ）の影響を除く。そして、算出された計算誤差には、（ｂ）の過去の反応結果の情報がより明確に現れる。このため、過去の出力変数の誤差から溶銑温度の将来の予測誤差への影響度を定量化するためには、計算時間変化率の方が予測時間変化率よりも適切であると考えられる。 As a breakdown of future prediction error factors of the hot metal temperature, there are (a) a part derived from a change in the manipulated variable of the manipulated variable from the calculation time point, and (b) a part derived from an error in the past output variable. . When performing the online prediction of the hot metal temperature, which is the object of the present invention, there is no actual value of the future manipulated variable, so it is difficult to reflect the influence of (a) on the prediction of the hot metal temperature. Therefore, during the period when the actual value of the manipulated variable in the future section is known, the hot metal temperature is calculated by reflecting the actual value of the manipulated variable in the future section, and the difference between the calculated value and the actual value is calculated as the hot metal temperature. The influence of (a) is removed by calculating as an error. And the information of the past reaction result of (b) appears more clearly in the calculated calculation error. For this reason, in order to quantify the influence of the past output variable error on the future prediction error of the hot metal temperature, it is considered that the calculation time change rate is more appropriate than the prediction time change rate.
次に、これらの誤差情報を用いて溶銑温度の予測値を補正するステップについて述べる。上記で述べたデータセットにより誤差δＨＭＴ（ｉ）（ｉ＝１〜Ｎ）を目的変数、誤差の時間変化率Ｓｌｏｐｅ（δＲＡＲ）（ｉ）等を説明変数とした回帰式を構築することができる。具体的には、数式（１）に示す行列Ｘ及び数式（２）に示すベクトルｙを以下に示す数式（３）に代入することによって、未知変数ベクトルｗを求めることができる。 Next, the step of correcting the predicted value of the hot metal temperature using these error information will be described. Based on the data set described above, it is possible to construct a regression equation with the error δHMT (i) (i = 1 to N) as an objective variable and the error time change rate Slope (δRAR) (i) as an explanatory variable. Specifically, the unknown variable vector w can be obtained by substituting the matrix X shown in Equation (1) and the vector y shown in Equation (2) into Equation (3) shown below.
これにより、以下の数式（４），（５）に示すような溶銑温度の予測誤差に関する回帰式を構築できる。この回帰式に基づき溶銑温度の予測値ΔＨＭＴ（予測，モデル単体）を補正する。ここで、数式（４），（５）において、ΔＨＭＴ（予測，モデル単体）は物理モデル単体により求められた溶銑温度の時間変化量の計算値、ΔＨＭＴ(予測，補正後)は本発明により補正された溶銑温度の予測値を示す。また、（ｎｏｗ）は実際のオンライン予測時点を意味する。 Thereby, the regression formula regarding the prediction error of the hot metal temperature as shown in the following formulas (4) and (5) can be constructed. Based on this regression equation, the predicted hot metal temperature value ΔHMT (predicted, model alone) is corrected. Here, in Equations (4) and (5), ΔHMT (prediction, model alone) is a calculated value of the time variation of the hot metal temperature obtained from the physical model alone, and ΔHMT (prediction, corrected) is corrected by the present invention. The predicted value of the hot metal temperature is shown. (Now) means the actual online prediction time.
図４（ａ）〜（ｅ）に示す場合について、本発明の手法を用いて溶銑温度の予測値を補正した結果を図４（ｆ）に点線で示す。溶銑温度の予測値が、上方修正され、実績値の推移に近づいていることがわかる。また、１０日間のデータ（Ｎ＝４８０）を用いて溶銑温度の時間変化量の予測誤差を確認した結果を図６（ａ），（ｂ）に示す。図６（ａ）が物理モデル単体での溶銑温度の時間変化量の実績値及び予測値を示す散布図であり、図６（ｂ）が本発明を適用した場合の溶銑温度の時間変化量の実績値及び予測値を示す散布図である。図６（ａ）に示す散布図の根平均二乗誤差（ＲＭＳＥ）は１２．４℃であったのに対して、図６（ｂ）に示す散布図のＲＭＳＥは１０．７℃であった。このことから、本発明を適用することにより溶銑温度の時間変化量の予測精度が向上することが確認できた。 4 (a) to (e), the result of correcting the predicted value of the hot metal temperature using the method of the present invention is shown by a dotted line in FIG. 4 (f). It can be seen that the predicted value of the hot metal temperature has been corrected upward and is approaching the transition of the actual value. Moreover, the result of having confirmed the prediction error of the time change amount of hot metal temperature using the data for 10 days (N = 480) is shown to Fig.6 (a), (b). FIG. 6 (a) is a scatter diagram showing the actual value and the predicted value of the temporal change amount of the hot metal temperature in the physical model alone, and FIG. 6 (b) shows the temporal change amount of the hot metal temperature when the present invention is applied. It is a scatter diagram which shows a track record value and a predicted value. The root mean square error (RMSE) of the scatter diagram shown in FIG. 6 (a) was 12.4 ° C., whereas the RMSE of the scatter diagram shown in FIG. 6 (b) was 10.7 ° C. From this, it was confirmed that the application of the present invention improves the accuracy of predicting the amount of time variation of the hot metal temperature.
次に、溶銑温度の制御方法について述べる。高炉プロセスは熱容量が大きいため、操作変数の操作量の変更に対する応答の時定数は１２時間程度と非常に長い。このため、炉熱ばらつき低減のためには将来の炉内状態予測に基づいた制御則が有効である。そこで、本発明では、物理モデルによる将来予測に基づいたモデル予測制御系を構築した。 Next, a method for controlling the hot metal temperature will be described. Since the blast furnace process has a large heat capacity, the time constant of response to changes in the manipulated variable of the manipulated variable is as long as about 12 hours. For this reason, a control law based on future prediction of the in-furnace state is effective for reducing the furnace heat variation. Therefore, in the present invention, a model predictive control system based on a future prediction based on a physical model is constructed.
一般的な高炉プロセスでは、炉下部より吹込まれる高温送風の温度及び湿分（送風温度及び送風湿分）、微粉炭吹込み量、コークス比等を操作することにより、溶銑温度は一定に制御されている。以下では送風湿分を操作変数として選択したが、同様のロジックを他の操作変数についても構築可能である。 In a typical blast furnace process, the hot metal temperature is kept constant by manipulating the temperature and humidity of the high-temperature air blown from the lower part of the furnace, the amount of blown coal and the amount of pulverized coal, and the coke ratio. Has been. In the following, blast moisture is selected as an operation variable, but the same logic can be constructed for other operation variables.
次に、操作変数の最適操作量の決定方法について述べる。一般的なモデル予測制御には、予測区間（どこまで先までの区間を評価関数とするか）及び制御区間（何手先までの操作量を最適化するか）という２つの調整パラメータが存在する。本実施形態では、予測区間は１０時間、制御区間は１ステップとした。但し、これらは調整可能なパラメータであり、本実施形態の値に限定されるものではない。 Next, a method for determining the optimum manipulated variable for the manipulated variable will be described. In general model predictive control, there are two adjustment parameters: a prediction section (how far the section is used as an evaluation function) and a control section (how far the operation amount is optimized). In this embodiment, the prediction interval is 10 hours and the control interval is 1 step. However, these are adjustable parameters and are not limited to the values in the present embodiment.
本実施形態では、以下に示す数式（６），（７）を用いて、１０時間先までの溶銑温度目標値ＨＭＴｒｅｆからの偏差の積分値と送風湿分の操作量ΔＢＭとから成る評価関数Ｊを最小化するための送風湿分の操作量ΔＢＭを求める。 In this embodiment, using the following formulas (6) and (7), an evaluation function composed of an integrated value of deviation from the hot metal temperature target value HMT ref up to 10 hours ahead and an operation amount ΔBM of the blast moisture An operation amount ΔBM of the blast moisture for minimizing J is obtained.
ここで、ＨＭＴｐｒｅ（ｔ）は、送風湿分変更時の溶銑温度の予測値を示し、数式（７）に示すように自由応答ＨＭＴｆｒｅｅ（ｔ）に送風湿分の効果を重ね合わせたものである。また、数式（７）において、ＳｔｐＢＭ（ｔ）は、送風湿分を単位量だけ変化させた際のステップ応答を示す。ステップ応答ＳｔｐＢＭ（ｔ）は、実機実験により求めた値であってもよいし、数値シミュレーションの計算結果であってもよい。 Here, HMT pre (t) indicates a predicted value of the hot metal temperature when the blast moisture is changed, and the effect of the blast moisture is superimposed on the free response HMT free (t) as shown in Equation (7). It is. Further, in Equation (7), Stp BM (t) indicates a step response when the blast moisture is changed by a unit amount. The step response Stp BM (t) may be a value obtained by an actual machine experiment or may be a calculation result of a numerical simulation.
本発明により求められた最適な送風湿分の操作量及び送風湿分操作時の溶銑温度の予測推移を図７（ａ），（ｂ）に示す。図７（ａ），（ｂ）に示すように、目標値（＝１５００℃）に対して過剰な溶銑温度を予測できた時点でガイダンスに従って送風湿分を先行させて上昇させることにより、過剰な溶銑温度を緩和できることがわかる。これにより、無操作時及びガイダンス操作（適正操作）時の溶銑温度の予測推移を提示することによって、ガイダンス操作の影響を直観的に把握可能な操業ガイダンス装置を構築できる。 7 (a) and 7 (b) show the optimum operation amount of the blast moisture determined by the present invention and the predicted transition of the hot metal temperature during the blast moisture operation. As shown in FIGS. 7 (a) and 7 (b), when the hot metal temperature can be predicted with respect to the target value (= 1500 ° C.), the blast moisture is increased in advance according to the guidance. It can be seen that the hot metal temperature can be relaxed. Thereby, the operation guidance apparatus which can grasp | ascertain the influence of guidance operation intuitively can be constructed | presented by showing the prediction transition of the hot metal temperature at the time of no operation and guidance operation (proper operation).
Claims (6)
前記物理モデルを用いて高炉の操作変数の現在の操作量を保持した場合の将来の溶銑温度の予測値を算出する第１ステップと、
前記物理モデルを用いて計算された過去の期間における高炉内の状態を示す変数の計算値と実績値との差の時間変化率を算出する第２ステップと、
前記物理モデルを用いて計算された前記過去の期間における溶銑温度の計算値と実績値との差を溶銑温度の計算値の誤差として算出する第３ステップと、
前記第２ステップにおいて算出された時間変化率を用いて前記第３ステップにおいて算出された溶銑温度の計算値の誤差を求める回帰式を構築する第４ステップと、
前記物理モデルを用いて計算された現在における高炉内の状態を示す変数の計算値と実績値との差の時間変化率と前記第４ステップにおいて構築された回帰式とを用いて、現在における溶銑温度の計算値の誤差を算出する第５ステップと、
前記第１ステップにおいて算出された溶銑温度の予測値に前記第５ステップにおいて算出された誤差を加算することによって、前記第１ステップにおいて算出された溶銑温度の予測値を補正する第６ステップと、
を含むことを特徴とする溶銑温度予測方法。 A hot metal temperature prediction method for predicting the hot metal temperature in a blast furnace using a physical model capable of calculating the state in the blast furnace in an unsteady state,
A first step of calculating a predicted value of the hot metal temperature in the future when the current operating amount of the operating variable of the blast furnace is maintained using the physical model;
A second step of calculating a time change rate of a difference between the calculated value of the variable indicating the state in the blast furnace and the actual value in the past period calculated using the physical model;
A third step of calculating a difference between the calculated value and the actual value of the hot metal temperature in the past period calculated using the physical model as an error of the calculated value of the hot metal temperature;
A fourth step of constructing a regression equation for obtaining an error of the calculated value of the hot metal temperature calculated in the third step using the time change rate calculated in the second step;
Using the time change rate of the difference between the calculated value and the actual value of the variable indicating the current state of the blast furnace calculated using the physical model and the regression equation constructed in the fourth step, A fifth step of calculating an error in the calculated temperature value;
A sixth step of correcting the predicted value of the hot metal temperature calculated in the first step by adding the error calculated in the fifth step to the predicted value of the hot metal temperature calculated in the first step;
A hot metal temperature prediction method comprising:
前記物理モデルを用いて高炉の操作変数の現在の操作量を保持した場合の将来の溶銑温度の予測値を算出する第１手段と、
前記物理モデルを用いて計算された過去の期間における高炉内の状態を示す変数の計算値と実績値との差の時間変化率を算出する第２手段と、
前記物理モデルを用いて計算された前記過去の期間における溶銑温度の計算値と実績値との差を溶銑温度の計算値の誤差として算出する第３手段と、
前記第２手段によって算出された時間変化率を用いて前記第３手段によって算出された溶銑温度の計算値の誤差を求める回帰式を構築する第４手段と、
前記物理モデルを用いて計算された現在における高炉内の状態を示す変数の計算値と実績値との差の時間変化率と前記第４手段によって構築された回帰式とを用いて、現在における溶銑温度の計算値の誤差を算出する第５手段と、
前記第１手段によって算出された溶銑温度の予測値に前記第５手段によって算出された誤差を加算することによって、前記第１手段によって算出された溶銑温度の予測値を補正する第６手段と、
を備えることを特徴とする溶銑温度予測装置。 A hot metal temperature prediction device for predicting the hot metal temperature in a blast furnace using a physical model capable of calculating the state in the blast furnace in an unsteady state,
A first means for calculating a predicted value of the hot metal temperature in the future when the current manipulated variable of the operating variable of the blast furnace is maintained using the physical model;
A second means for calculating a time change rate of a difference between the calculated value of the variable indicating the state in the blast furnace and the actual value in the past period calculated using the physical model;
A third means for calculating a difference between the calculated value and the actual value of the hot metal temperature in the past period calculated using the physical model as an error of the calculated value of the hot metal temperature;
A fourth means for constructing a regression equation for obtaining an error of the calculated value of the hot metal temperature calculated by the third means using the time change rate calculated by the second means;
Using the time change rate of the difference between the calculated value and actual value of the variable indicating the current state of the blast furnace calculated using the physical model and the regression equation constructed by the fourth means, A fifth means for calculating an error in the calculated temperature value;
Sixth means for correcting the predicted value of the hot metal temperature calculated by the first means by adding the error calculated by the fifth means to the predicted value of the hot metal temperature calculated by the first means;
A hot metal temperature prediction apparatus comprising:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2017139600A JP6729514B2 (en) | 2017-07-19 | 2017-07-19 | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operating method, operation guidance device, hot metal temperature control method, and hot metal temperature control device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2017139600A JP6729514B2 (en) | 2017-07-19 | 2017-07-19 | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operating method, operation guidance device, hot metal temperature control method, and hot metal temperature control device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JP2019019385A true JP2019019385A (en) | 2019-02-07 |
| JP6729514B2 JP6729514B2 (en) | 2020-07-22 |
Family
ID=65353758
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2017139600A Active JP6729514B2 (en) | 2017-07-19 | 2017-07-19 | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operating method, operation guidance device, hot metal temperature control method, and hot metal temperature control device |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP6729514B2 (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109935280A (en) * | 2019-03-05 | 2019-06-25 | 东北大学 | A kind of blast-melted quality prediction system and method based on integrated study |
| JP2021017607A (en) * | 2019-07-17 | 2021-02-15 | Ｊｆｅスチール株式会社 | METHOD FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON, OPERATION GUIDANCE METHOD, METHOD FOR OPERATING BLAST FURNACE, METHOD FOR PRODUCING MOLTEN STEEL, AND APPARATUS FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON |
| JP2021130860A (en) * | 2020-02-21 | 2021-09-09 | 株式会社神戸製鋼所 | Furnace heat prediction device and furnace heat prediction method thereof, and furnace heat control guiding device and furnace heat control guiding method thereof |
| CN114144532A (en) * | 2019-07-23 | 2022-03-04 | 杰富意钢铁株式会社 | Process control method, operation guidance method, blast furnace operation method, molten iron manufacturing method, and process control device |
| JP2022108041A (en) * | 2021-01-12 | 2022-07-25 | Ｊｆｅスチール株式会社 | Learning model creation method, learning model creation device, blast furnace control guidance method and molten iron production method |
| WO2023008242A1 (en) * | 2021-07-27 | 2023-02-02 | Ｊｆｅスチール株式会社 | Molten iron temperature prediction method, operation guidance method, molten iron production method, molten iron temperature prediction device, operation guidance device, blast furnace operation guidance system, blast furnace operation guidance server, and terminal device |
| EP4155421A4 (en) * | 2020-07-06 | 2023-10-25 | JFE Steel Corporation | Method for controlling hot metal temperature, operation guidance method, method for operating blast furnace, method for producing hot metal, device for controlling hot metal temperature, and operation guidance device |
| WO2024172094A1 (en) * | 2023-02-16 | 2024-08-22 | Ｊｆｅスチール株式会社 | Silicon concentration prediction method, operation guidance method, silicon concentration prediction device, operation guidance device, operation guidance system, and terminal device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS61508A (en) * | 1984-06-14 | 1986-01-06 | Kawasaki Steel Corp | Operating method of blast furnace |
| JPH10147804A (en) * | 1996-11-18 | 1998-06-02 | Sumitomo Metal Ind Ltd | Method for predicting furnace heat in blast furnace |
| JP2017066510A (en) * | 2015-09-30 | 2017-04-06 | Ｊｆｅスチール株式会社 | Molten iron temperature calculation method in blast furnace |
-
2017
- 2017-07-19 JP JP2017139600A patent/JP6729514B2/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS61508A (en) * | 1984-06-14 | 1986-01-06 | Kawasaki Steel Corp | Operating method of blast furnace |
| JPH10147804A (en) * | 1996-11-18 | 1998-06-02 | Sumitomo Metal Ind Ltd | Method for predicting furnace heat in blast furnace |
| JP2017066510A (en) * | 2015-09-30 | 2017-04-06 | Ｊｆｅスチール株式会社 | Molten iron temperature calculation method in blast furnace |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109935280B (en) * | 2019-03-05 | 2022-11-15 | 东北大学 | Blast furnace molten iron quality prediction system and method based on ensemble learning |
| CN109935280A (en) * | 2019-03-05 | 2019-06-25 | 东北大学 | A kind of blast-melted quality prediction system and method based on integrated study |
| JP7067533B2 (en) | 2019-07-17 | 2022-05-16 | Ｊｆｅスチール株式会社 | Si concentration prediction method for hot metal, operation guidance method, blast furnace operation method, molten steel manufacturing method and Si concentration prediction device for hot metal |
| JP2021017607A (en) * | 2019-07-17 | 2021-02-15 | Ｊｆｅスチール株式会社 | METHOD FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON, OPERATION GUIDANCE METHOD, METHOD FOR OPERATING BLAST FURNACE, METHOD FOR PRODUCING MOLTEN STEEL, AND APPARATUS FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON |
| CN114144532A (en) * | 2019-07-23 | 2022-03-04 | 杰富意钢铁株式会社 | Process control method, operation guidance method, blast furnace operation method, molten iron manufacturing method, and process control device |
| JP2021130860A (en) * | 2020-02-21 | 2021-09-09 | 株式会社神戸製鋼所 | Furnace heat prediction device and furnace heat prediction method thereof, and furnace heat control guiding device and furnace heat control guiding method thereof |
| JP7420587B2 (en) | 2020-02-21 | 2024-01-23 | 株式会社神戸製鋼所 | Furnace heat prediction device and method, and furnace heat control guide device and method |
| EP4155421A4 (en) * | 2020-07-06 | 2023-10-25 | JFE Steel Corporation | Method for controlling hot metal temperature, operation guidance method, method for operating blast furnace, method for producing hot metal, device for controlling hot metal temperature, and operation guidance device |
| JP2022108041A (en) * | 2021-01-12 | 2022-07-25 | Ｊｆｅスチール株式会社 | Learning model creation method, learning model creation device, blast furnace control guidance method and molten iron production method |
| JP7380604B2 (en) | 2021-01-12 | 2023-11-15 | Ｊｆｅスチール株式会社 | Learning model generation method, learning model generation device, blast furnace control guidance method, and hot metal manufacturing method |
| WO2023008242A1 (en) * | 2021-07-27 | 2023-02-02 | Ｊｆｅスチール株式会社 | Molten iron temperature prediction method, operation guidance method, molten iron production method, molten iron temperature prediction device, operation guidance device, blast furnace operation guidance system, blast furnace operation guidance server, and terminal device |
| JP7264321B1 (en) * | 2021-07-27 | 2023-04-25 | Ｊｆｅスチール株式会社 | Hot metal temperature prediction method, operation guidance method, hot metal production method, hot metal temperature prediction device, operation guidance device, blast furnace operation guidance system, blast furnace operation guidance server and terminal device |
| WO2024172094A1 (en) * | 2023-02-16 | 2024-08-22 | Ｊｆｅスチール株式会社 | Silicon concentration prediction method, operation guidance method, silicon concentration prediction device, operation guidance device, operation guidance system, and terminal device |
Also Published As
| Publication number | Publication date |
|---|---|
| JP6729514B2 (en) | 2020-07-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2019019385A (en) | Method and device for predicting molten iron temperature, operation method of blast furnace, operation guidance device, and method and device for controlling molten iron temperature | |
| JP6531782B2 (en) | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operation method, operation guidance device, hot metal temperature control method, and hot metal temperature control device | |
| JP6493447B2 (en) | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operation method, operation guidance device, hot metal temperature control method, and hot metal temperature control device | |
| JP6930507B2 (en) | Hot metal temperature prediction method, hot metal temperature prediction device, blast furnace operation method, operation guidance device, hot metal temperature control method, and hot metal temperature control device | |
| JP7272326B2 (en) | Operation Guidance Method, Blast Furnace Operation Method, Hot Metal Production Method, Operation Guidance Device | |
| JP4948304B2 (en) | Blast furnace hot metal temperature prediction method | |
| EP3989013B1 (en) | Method for controlling process, operation guidance method, method for operating blast furnace, method for producing hot metal, and device for controlling process | |
| CN107368125B (en) | A kind of blast furnace temperature control system and method based on CBR Yu the parallel mixed inference of RBR | |
| JP7107444B2 (en) | Hot metal temperature control method, operation guidance method, blast furnace operation method, hot metal manufacturing method, hot metal temperature control device, and operation guidance device | |
| US20150337404A1 (en) | Method and device for predicting, controlling and/or regulating steelworks processes | |
| JP2023025646A (en) | Controller, method, and program | |
| JP2021018569A (en) | Learning model generation method, learning model generation device, blast furnace molten iron temperature control method, blast furnace molten iron temperature control guidance method, and molten iron manufacturing method | |
| Botnikov et al. | Development of the metal temperature prediction model for steel-pouring and tundish ladles used at the casting and rolling complex | |
| JP2017066510A (en) | Molten iron temperature calculation method in blast furnace | |
| Agrawal et al. | Advances in thermal level measurement techniques using mathematical models, statistical models and decision support systems in blast furnace | |
| JP6098553B2 (en) | Rejuvenated phosphorus amount prediction device, recovered phosphorus amount prediction method, and converter dephosphorization control method | |
| KR101246436B1 (en) | Prediction method for product measuring of pig iron | |
| JP7173115B2 (en) | Static Blow Control Method, Temperature Correction Term Estimation Device, and Converter Control Device | |
| JP2014118599A (en) | Method for controlling furnace heat in blast furnace | |
| WO2023171501A1 (en) | Method for predicting molten iron temperature in blast furnace, method for training molten iron temperature prediction model for blast furnace, method for operating blast furnace, molten iron temperature prediction device for blast furnace, molten iron temperature prediction system, and terminal device | |
| JP2021017607A (en) | METHOD FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON, OPERATION GUIDANCE METHOD, METHOD FOR OPERATING BLAST FURNACE, METHOD FOR PRODUCING MOLTEN STEEL, AND APPARATUS FOR PREDICTING Si CONCENTRATION IN MOLTEN IRON | |
| KR102335753B1 (en) | Cooling process control method of cooling facility for melting furnace and cooling facility using the same | |
| JP7524861B2 (en) | Static blowing control method, temperature correction term/oxygen correction term estimation device, and converter control device | |
| TWI853478B (en) | Method for predicting molten iron temperature of blast furnace, method for learning molten iron temperature prediction model of blast furnace, method for operating blast furnace, device for predicting molten iron temperature of blast furnace, system for predicting molten iron temperature and terminal device | |
| KR20240144969A (en) | Method for predicting molten iron temperature in a blast furnace, learning method of a model for predicting molten iron temperature in a blast furnace, method for operating a blast furnace, device for predicting molten iron temperature in a blast furnace, system for predicting molten iron temperature and terminal device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20190221 |
|
| A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20191224 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20200107 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20200218 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20200602 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20200615 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 6729514 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |
|
| R250 | Receipt of annual fees |
Free format text: JAPANESE INTERMEDIATE CODE: R250 |
|
| R250 | Receipt of annual fees |
Free format text: JAPANESE INTERMEDIATE CODE: R250 |