JP2005097658A - Method for predicting main raw material component ratio of sintered ore and method for controlling component ratio of sintered ore and program for predicting main raw material component ratio of sintered ore - Google Patents

Method for predicting main raw material component ratio of sintered ore and method for controlling component ratio of sintered ore and program for predicting main raw material component ratio of sintered ore Download PDF

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JP2005097658A
JP2005097658A JP2003331104A JP2003331104A JP2005097658A JP 2005097658 A JP2005097658 A JP 2005097658A JP 2003331104 A JP2003331104 A JP 2003331104A JP 2003331104 A JP2003331104 A JP 2003331104A JP 2005097658 A JP2005097658 A JP 2005097658A
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sintered ore
main raw
component ratio
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Shuichi Yamamoto
修一 山本
Masaaki Yoshino
正晃 吉野
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JFE Steel Corp
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Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for predicting the main raw material component ratios of sintered ore in the case the characteristics of compounded raw materials change dynamically and a method for controlling the main raw material component ratios of the sintered ore using the same, and a program for predicting the main raw material component ratios of the sintered ore. <P>SOLUTION: The method for predicting the main raw material component ratios of the sintered ore has (1) a data-for-identification collecting step of collecting the time series data for identification of the main raw material component ratios of the sintered ore obtained from the component analysis values of the sintered ore and the actually measured value of the component ratios of the auxiliary raw materials of the sintered ore, (2) a step for generating autoregressive model groups corresponding to frequencies of generating the respective autoregressive models by classifying respective pieces of the data to time series data of a plurality of frequency bands, (3) a frequency band judging step of performing the frequency analysis of the most recent component analysis values of the sintered ore thereby judging the strong frequency bands of power spectra, (4) an autoregressive model selecting step of selecting the autoregressive models corresponding to the judged frequency bands from the model groups, and (5) a step of predicting the component ratios of the main raw materials of the sintered ore based on the selected models. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

この発明は、時系列データ予測方法に関し、特に焼結鉱主原料成分割合予測方法および焼結鉱成分割合制御方法に関するものである。   The present invention relates to a time-series data prediction method, and more particularly, to a sintered ore main raw material component proportion prediction method and a sintered ore component proportion control method.

焼結原料は、高炉装入鉱石の予備処理で発生する粉鉱、および粉鉱として存在する鉄鉱石と製鉄所内で発生する含鉄原料（ミルスケール、高炉ダスト、転炉ダストなど）とを主原料とし、副原料として粉石灰石などの造滓材からなっている。また、最近では高炉操業上直接使用するには好ましくない熱割れ鉱石や粘着鉱石などを破砕して焼結原料とすることもある。   The main raw materials for sintering materials are fine ore generated during pretreatment of blast furnace charging ore, iron ore existing as fine ore, and iron-containing raw materials (mill scale, blast furnace dust, converter dust, etc.) generated in steelworks. It is made of ironmaking materials such as powdered limestone as an auxiliary material. In addition, recently, hot cracked ore or adhesive ore, which is not preferred for direct use in blast furnace operation, is sometimes crushed into a sintered raw material.

焼結鉱時系列データの予測方法に関する従来の技術としては、焼結鉱塩基度の予測方法に関して、特開昭５９−６４７１８号公報（特許文献１）に開示された技術がある。この焼結鉱塩基度の予測方法は、焼結鉱塩基度の実測値と計算値との差を時系列に測定し、この差の過去の情報を統計処理して偏差を求め、この偏差と計算値で求まる塩基度とから焼結鉱塩基度を予測するものである。   As a conventional technique relating to a method for predicting sinter time series data, there is a technique disclosed in Japanese Patent Application Laid-Open No. 59-64718 (Patent Document 1) relating to a method for predicting sinter basicity. This sinter basicity prediction method measures the difference between the measured value and the calculated value of sinter basicity in time series, statistically processes the past information of this difference to determine the deviation, The basicity of the sinter is predicted from the basicity obtained from the calculated value.

焼結鉱の塩基度の計算値と実測値の偏差が過去の偏差と相関関係があることに着目し、この偏差を時系列に測定し、偏差の予測式を統計的手法を用いて実操業データを基に決定する。塩基度（ＣａＯ／ＳｉＯ2）の計算値は、配合原料の総ＣａＯ量と総ＳｉＯ2量との比により原料切出し時点で計算する。 そして、現時点で数時間以上前に切出された配合原料についての焼結鉱塩基度のデータから偏差を推定することにより、現時点において焼結されようとしている配合原料を焼結した後の塩基度が推定できるとしている。
特開昭５９−６４７１８号公報 Paying attention to the fact that the deviation between the calculated basic value and the measured value of sintered ore is correlated with the past deviation, measure this deviation in time series, and use the statistical method to predict the deviation. Determine based on data. The calculated value of basicity (CaO / SiO 2 ) is calculated at the time of cutting out the raw material by the ratio of the total CaO amount of the blended raw material and the total SiO 2 amount. Then, by estimating the deviation from the data of the sinter basicity for the blended raw material cut out several hours or more ago at the present time, the basicity after sintering the blended raw material to be sintered at the present time Can be estimated.
JP 59-64718

しかし、特許文献１で開示された技術では、一つの回帰式を用いて予測偏差を求めているため、配合原料の特性がダイナミックに変化する場合には、回帰係数をその都度求め直さなければならず、実操業において常に継続して高精度な予測値を得ることは困難であるという問題点がある。   However, in the technique disclosed in Patent Document 1, since the prediction deviation is obtained using one regression equation, when the characteristics of the blended material change dynamically, the regression coefficient must be obtained each time. However, there is a problem that it is difficult to always obtain a highly accurate predicted value in actual operation.

また、配合原料の特性変化に応じて、予め準備した別の回帰式を用いるとしても、その選定方法は提示されていない。   Even if another regression equation prepared in advance is used according to the change in the characteristics of the blended raw material, no selection method is presented.

この発明は、従来技術の上述のような問題点を解消するためになされたものであり、配合原料の特性がダイナミックに変化する場合においても、高精度に予測することのできる焼結鉱主原料成分割合の予測方法およびこれ用いた焼結鉱成分割合制御方法ならびに焼結鉱主原料成分割合予測プログラムを提供することを目的としている。   The present invention has been made to solve the above-described problems of the prior art, and is a main raw material for sintered ore that can be predicted with high accuracy even when the characteristics of the blended raw material change dynamically. It is an object of the present invention to provide a component ratio prediction method, a sinter component ratio control method, and a sinter main raw material component ratio prediction program.

本発明は、時系列的に変動する焼結鉱の主原料成分割合を予測する方法であって、次の（１）〜（６）の工程を有することを特徴とする焼結鉱主原料成分割合予測方法である。
（１）焼結鉱の成分分析値と焼結鉱の副原料の成分割合の実測値とから求められる、焼結鉱主原料成分割合の同定用時系列データを収集する同定用データ収集工程。
（２）収集された同定用時系列データを、複数の周波数帯の時系列データに分類して、それぞれに対応する自己回帰モデルを作成する周波数対応自己回帰モデル群作成工程。
（３）至近の焼結鉱の成分分析値の時系列データについて、その周波数解析を行い、パワースペクトルの強い周波数帯を判定する周波数帯判定工程。
（４）判定されたパワースペクトルの強い周波数帯に対応する自己回帰モデルを、前記周波数対応自己回帰モデル群の中から選択する自己回帰モデル選択工程。
（５）選択された自己回帰モデルに基づき、副原料を添加する時点での焼結鉱主原料の成分割合を予測する焼結鉱主原料成分割合の予測工程。 The present invention is a method for predicting the ratio of main raw material components of sintered ore that varies in time series, and has the following steps (1) to (6): It is a percentage prediction method.
(1) An identification data collection step for collecting time-series data for identification of the sinter ore main raw material component ratio obtained from the component analysis value of the sinter ore and the actual measured value of the component ratio of the secondary raw material of the sinter ore.
(2) A frequency-corresponding autoregressive model group creation step of classifying the collected time series data for identification into time series data of a plurality of frequency bands and creating an autoregressive model corresponding to each.
(3) A frequency band determination step of performing frequency analysis on the time series data of component analysis values of the nearest sintered ore and determining a frequency band having a strong power spectrum.
(4) An autoregressive model selection step of selecting an autoregressive model corresponding to the determined frequency band having a strong power spectrum from the group of frequency-corresponding autoregressive models.
(5) A step of predicting a sinter ore main material component ratio that predicts a component ratio of the sinter ore main material at the time of adding the auxiliary material based on the selected autoregressive model.

また本発明は、請求項１に記載の焼結鉱主原料成分割合予測方法において、前記周波数帯判定工程での周波数解析に、ウェーブレット変換を用いることを特徴とした焼結鉱主原料成分割合予測方法である。   Moreover, this invention is a sintered ore main raw material component ratio prediction method of Claim 1, Comprising: Wavelet transformation is used for the frequency analysis in the said frequency band determination process, The sintered ore main raw material component ratio prediction Is the method.

また本発明は、請求項１または請求項２のいずれかに記載の焼結鉱主原料成分割合予測方法において、前記周波数帯判定工程でのパワースペクトルの強い周波数帯の判定は、パワースペクトルがピークとなる周波数の高周波側および低周波側にそれぞれ設定幅を設けて周波数帯とする方法、またはパワースペクトルのピークで正規化した正規化パワースペクトルに対して、予め設定したパワースペクトルの閾値を越えた周波数帯とする方法のいずれかによることを特徴とした焼結鉱主原料成分割合予測方法である。   Further, the present invention provides the sintered ore main raw material component ratio prediction method according to claim 1 or 2, wherein the determination of a frequency band having a strong power spectrum in the frequency band determination step has a peak power spectrum. The preset power spectrum threshold was exceeded for the method of setting the frequency band by setting the width on the high frequency side and the low frequency side of the frequency to become, or the normalized power spectrum normalized by the peak of the power spectrum It is a sintered ore main raw material component ratio prediction method characterized by being based on any one of the methods for making a frequency band.

また本発明は、請求項１ないし請求項３のいずれかに記載の焼結鉱主原料成分割合予測方法を用いて焼結鉱主原料成分割合の変動量を求めて、該変動量に基づいて焼結鉱の副原料の添加割合を制御することを特徴とした焼結鉱成分割合制御方法である。   Moreover, this invention calculates | requires the variation | change_quantity of the sinter main raw material component ratio using the sintered ore main raw material component ratio prediction method in any one of Claims 1 thru | or 3, and based on this variation | change_quantity. It is a sinter component ratio control method characterized by controlling the addition ratio of the auxiliary raw material of sinter.

さらに本発明は、時系列的に変動する焼結鉱の主原料成分割合を予測するプログラムであって、コンピュータに次の（１）〜（６）の機能を実現させるための焼結鉱主原料成分割合予測プログラムである。
（１）焼結鉱の成分分析値と焼結鉱の副原料の成分割合の実測値とから求められる、焼結鉱主原料成分割合の同定用時系列データを収集する同定用データ収集機能。
（２）収集された同定用時系列データを、複数の周波数帯の時系列データに分類して、それぞれに対応する自己回帰モデルを作成する周波数対応自己回帰モデル群作成機能。
（３）至近の焼結鉱の成分分析値の時系列データについて、その周波数解析を行い、パワースペクトルの強い周波数帯を判定する周波数帯判定機能。
（４）判定されたパワースペクトルの強い周波数帯に対応する自己回帰モデルを、前記周波数対応自己回帰モデル群の中から選択する自己回帰モデル選択機能。
（５）選択された自己回帰モデルに基づき、副原料を添加する時点での焼結鉱主原料の成分割合を予測する焼結鉱主原料成分割合の予測機能。 Furthermore, the present invention is a program for predicting the ratio of main raw material components of sintered ore that varies in time series, and the main raw material of sintered ore for realizing the following functions (1) to (6) on a computer: This is a component ratio prediction program.
(1) An identification data collection function for collecting time-series data for identification of the sinter ore main raw material component ratio obtained from the component analysis value of the sinter ore and the measured value of the component ratio of the auxiliary raw material of the sinter ore.
(2) A frequency-corresponding autoregressive model group creation function that classifies collected time series data for identification into time series data of a plurality of frequency bands and creates an autoregressive model corresponding to each.
(3) A frequency band determination function for performing frequency analysis on the time series data of component analysis values of the nearest sintered ore and determining a frequency band with a strong power spectrum.
(4) An autoregressive model selection function for selecting an autoregressive model corresponding to a determined frequency band having a strong power spectrum from the group of frequency-corresponding autoregressive models.
(5) A function for predicting a sinter ore main material component ratio that predicts a component ratio of the sinter ore main material at the time of adding the auxiliary material based on the selected autoregressive model.

本発明によれば、配合原料のダイナミックな特性の変化に応じて、適正なモデルを自動選定することができるため、実操業において常に継続して高精度な予測値を得ることが可能である。さらに、長い無駄時間をもつ制御対象の変動の正確な予測値を用いて制御をおこなうようにしたため、単純なフイードバック制御では除去できなかった短期的な変動を除去できる効果もある。   According to the present invention, an appropriate model can be automatically selected according to a change in dynamic characteristics of a blended raw material, so that a highly accurate predicted value can be obtained continuously in actual operation. Furthermore, since the control is performed using an accurate predicted value of the fluctuation of the controlled object having a long dead time, there is an effect that a short-term fluctuation that cannot be removed by simple feedback control can be removed.

以下、本発明を実施するための最良の形態について図面および数式を用いて説明する。   The best mode for carrying out the present invention will be described below with reference to the drawings and mathematical expressions.

図２は、焼結鉱の製造工程を模式的に示す図である。主原料１には配合部２において、副原料３が加えられる。そして、それらは焼結工程４を経ることによって、焼結鉱５が製造される。焼結工程４により焼結された焼結鉱５は、定期的にサンプリングされて、成分分析工程６において成分分析が行われる。この分析結果すなわち成分分析値７が間欠的な時系列データとして把握されるようになっている。   FIG. 2 is a diagram schematically showing a manufacturing process of sintered ore. The auxiliary raw material 3 is added to the main raw material 1 in the blending section 2. And the sintered ore 5 is manufactured by passing through the sintering process 4 of them. The sintered ore 5 sintered in the sintering step 4 is periodically sampled and component analysis is performed in the component analysis step 6. This analysis result, that is, the component analysis value 7 is grasped as intermittent time series data.

しかし、焼結鉱の成分分析値が判明するのは、副原料３を主原料１に添加した時点から数時間後と大きな時間差がある。例えば、焼結に１時間、冷却に１時間、サンプリングに１時間、分析に１時間、合計して約４時間といった具合である。このため、でてきた焼結鉱の成分分析値は数時間前の配合を反映したものであり、この分析値を直ぐ副原料３の添加量制御にフィードバックしても、すでにその時点では主原料の成分割合が変化している。このように、長いタイムラグを持つ焼結鉱の成分分析値を一定に保つようにすることはとても難しい。   However, the component analysis value of the sintered ore is found to have a large time difference from several hours after the addition of the auxiliary raw material 3 to the main raw material 1. For example, 1 hour for sintering, 1 hour for cooling, 1 hour for sampling, 1 hour for analysis, about 4 hours in total. For this reason, the component analysis value of the sintered ore that came out reflects the blending several hours ago. Even if this analysis value is immediately fed back to the addition amount control of the auxiliary raw material 3, the main raw material is already at that time. The component ratio of is changing. Thus, it is very difficult to keep the component analysis value of sintered ore with a long time lag constant.

副原料の成分値は事前に正確に把握されているため、過去の主原料の成分変動値は、焼結鉱の成分分析値から加えられた副原料の成分値を差し引くことで、次の（１）式のようにして求めることができる。   Since the component value of the auxiliary raw material is accurately grasped in advance, the component fluctuation value of the main raw material in the past can be calculated by subtracting the component value of the auxiliary raw material added from the component analysis value of the sintered ore as follows ( 1) It can obtain | require like Formula.

Figure 2005097658
Figure 2005097658

従って、成分分析値判明時点で主原料の数時間後の成分変動値を予測すれば、これは最新（配合時点）の主原料成分変動を表していることになり、この予測値を基に副原料配合量を決めれば、より精度良く焼結鉱の成分制御をすることができる。本発明では、この主原料成分の変動値を予測対象データ８とする。   Therefore, if the component fluctuation value after several hours of the main raw material is predicted when the component analysis value is known, this represents the latest main raw material component fluctuation (at the time of blending). If the raw material blending amount is determined, the components of the sintered ore can be controlled with higher accuracy. In the present invention, the fluctuation value of the main raw material component is set as the prediction target data 8.

図１は、焼結鉱主原料成分の変動値を予測するための予測フロー図である。先ず、焼結鉱の成分分析値と主原料に添加する副原料の成分割合の実測値とから、（１）式に示すような焼結鉱主原料成分割合の時系列データを成分値同定用データ１１として収集する。成分値同定用データ１１の一例を、図３に示す
この成分値同定用データ１１を、FFTなどのフィルタ１２を通して同定１３することにより、複数の周波数帯の時系列データに分類して、それぞれの自己回帰モデル群１４ｉを作成しておく。 FIG. 1 is a prediction flow chart for predicting a fluctuation value of a sintered ore main raw material component. First, the time series data of the sinter ore main material component ratio as shown in the equation (1) is used for component value identification from the component analysis value of the sinter ore and the measured value of the component ratio of the auxiliary material added to the main material. Collect as data 11. An example of the component value identification data 11 is shown in FIG. 3. This component value identification data 11 is identified 13 through a filter 12 such as an FFT to classify it into time-series data of a plurality of frequency bands. An autoregressive model group 14i is created.

この図１の左側に示す一連の準備処理を、以下に詳説する。
図３で示した成分値同定用の時系列データを、（２）式で示すフーリエ変換により周波数データ（図４参照）に変換する。 A series of preparation processes shown on the left side of FIG. 1 will be described in detail below.
The time-series data for component value identification shown in FIG. 3 is converted into frequency data (see FIG. 4) by Fourier transform expressed by equation (2).

Figure 2005097658
Figure 2005097658

次に、図４の周波数データの内、特徴的な周波数帯を複数残すようにする。図５は、低周波帯を残すようにしたものであり、高周波帯のパワースペクトルにマスクをかけている。図６は、元の周波数データでパワースペクトルが一番強い中間周波帯を残すようにしたものであり、対象帯域外の低・高周波帯領域にそれぞれマスクをかけている。このような処理を、特徴的な周波数帯を残すべく適宜複数回行う。   Next, a plurality of characteristic frequency bands are left in the frequency data of FIG. FIG. 5 shows that the low frequency band is left, and the power spectrum in the high frequency band is masked. FIG. 6 shows that the intermediate frequency band having the strongest power spectrum is left in the original frequency data, and the low and high frequency band regions outside the target band are respectively masked. Such processing is appropriately performed a plurality of times to leave a characteristic frequency band.

これらマスクをかけた周波数データについて、（３）式で示すフーリエ逆変換を行い、時系列データに戻す。   The frequency data subjected to these masks is subjected to Fourier inverse transform represented by equation (3), and returned to time series data.

Figure 2005097658
Figure 2005097658

図７および図８は、それぞれ図５および図６に示した周波数データを逆変換した時系列データである。図３の元の時系列データに比べると、図７は高周波帯が除かれた低周波帯を強調した時系列データになっていることが見て取れる。同様に、図８は中間周波帯を強調した時系列データになっていることが見て取れる。   7 and 8 are time series data obtained by inversely converting the frequency data shown in FIGS. 5 and 6, respectively. Compared to the original time-series data in FIG. 3, it can be seen that FIG. 7 is time-series data in which the low frequency band excluding the high frequency band is emphasized. Similarly, it can be seen that FIG. 8 is time-series data in which the intermediate frequency band is emphasized.

次に、このように周波帯域毎に分類されたデータそれぞれに、（４）式で示す自己回帰モデル（ＡＲモデル）を当てはめて、モデルの同定を行う。   Next, the model is identified by applying the autoregressive model (AR model) represented by the equation (4) to each of the data thus classified for each frequency band.

Figure 2005097658
Figure 2005097658

以上で、図１の１４ｉとして示すｎ個の周波数対応ＡＲモデルが、準備できたわけである。 The n frequency-corresponding AR models indicated as 14i in FIG. 1 have been prepared.

次に、図１の右側の処理フローを説明する。焼結鉱の成分分析値の至近時系列データ（成分値至近データ１５）を準備する。この成分値至近データ１５に、（５）式で示すウェーブレット変換を施す（ウェーブレット変換１６）。   Next, the processing flow on the right side of FIG. 1 will be described. Prepare the latest time series data (component value nearest data 15) of the component analysis value of the sintered ore. The component value nearest data 15 is subjected to wavelet transformation expressed by the equation (5) (wavelet transformation 16).

Figure 2005097658
Figure 2005097658

このウェーブレット変換されたパワースペクトルの強い周波数帯を判定し、判定された周波数帯に対応する自己回帰モデル１８を、複数の周波数対応ＡＲモデル１４ｉの中から選択する（モデル選定１７）。 すなわち、至近データの周波数帯を常に監視し、この変化を捕らえ（判定）て、その周波数帯に対応するモデルを選択するというものである。   A frequency band having a strong power spectrum subjected to the wavelet transform is determined, and an autoregressive model 18 corresponding to the determined frequency band is selected from a plurality of frequency-corresponding AR models 14i (model selection 17). That is, the frequency band of the nearest data is always monitored, and this change is captured (determined), and a model corresponding to the frequency band is selected.

パワースペクトルの強い周波数帯の判定方法としては、例えば、図９または図１０に示す方法がある。 図９で示す方法は、パワースペクトルがピークとなる周波数ｆ２の高周波側および低周波側に上下設定幅を設けて、周波数帯（ｆ１〜ｆ３）とするものである。これに対して図１０で示す方法は、パワースペクトルのピークで正規化した正規化パワースペクトルに対して、予め設定したパワースペクトルの閾値を越えた周波数帯（ｆ１〜ｆ３）とするものである。   As a method for determining a frequency band with a strong power spectrum, for example, there is a method shown in FIG. 9 or FIG. In the method shown in FIG. 9, the upper and lower set widths are provided on the high frequency side and the low frequency side of the frequency f2 at which the power spectrum reaches a peak, and the frequency band (f1 to f3) is obtained. On the other hand, the method shown in FIG. 10 uses a frequency band (f1 to f3) that exceeds a preset power spectrum threshold with respect to the normalized power spectrum normalized at the peak of the power spectrum.

選択された時系列データの自己回帰モデル（ＡＲモデル１８）に基づき、副原料を添加する時点での焼結鉱主原料の成分割合の予測値１９を予測する。選択されたＡＲモデル１８が複数の場合は、それぞれのＡＲモデルを用いた予測値を加算して最終的な主原料変動量の予測値とする。また、図１の成分値至近データ１５からＡＲモデル１８への矢印は、最終的な主原料変動量の予測値として、全周波数帯を含む生データ（成分値至近データ１５）から同定されたＡＲモデルの予測値を基本として、それに前記の選択されたＡＲモデルの予測値を補償量として加算する、もう一つの最終的な主原料変動量の予測値算出方法を示している。   Based on the autoregressive model (AR model 18) of the selected time series data, the predicted value 19 of the component ratio of the sintered ore main raw material at the time of adding the auxiliary raw material is predicted. When there are a plurality of selected AR models 18, the predicted values using the respective AR models are added to obtain the final predicted value of the main raw material fluctuation amount. In addition, the arrow from the component value closest data 15 to the AR model 18 in FIG. 1 indicates the AR identified from the raw data (component value closest data 15) including all frequency bands as the final predicted value of the main raw material fluctuation amount. Another prediction method for calculating the predicted value of the main raw material fluctuation amount is shown, in which the predicted value of the selected AR model is added as a compensation amount based on the predicted value of the model.

図１１は、焼結鉱成分のＳｉＯ2成分割合の変動の実績値と、本発明に基づく予測値とを比較したグラフである。これによると、本発明に基づく予測値は、実績値の時系列変動をよく予測できており、プラントの特性がダイナミックに変化する場合にも、本発明の焼結鉱主原料成分割合の予測方法が有効であることが分かる。 FIG. 11 is a graph comparing the actual value of the fluctuation of the SiO 2 component ratio of the sintered ore component with the predicted value based on the present invention. According to this, the predicted value based on the present invention can predict the time-series fluctuation of the actual value well, and even when the characteristics of the plant change dynamically, the method for predicting the ratio of main raw material components of the sinter according to the present invention It can be seen that is effective.

焼結鉱主原料成分の変動値を予測するための予測フロー図である。It is a prediction flow figure for estimating the fluctuation value of a sinter ore main raw material component. 焼結鉱の製造工程を模式的に示す図である。It is a figure which shows typically the manufacturing process of a sintered ore. 成分値同定用データ１１の一例を示す図である。It is a figure which shows an example of the data 11 for component value identification. 図３の時系列データをフーリエ変換した結果を示す図である。It is a figure which shows the result of having carried out the Fourier-transform of the time series data of FIG. 特定周波数帯のマスキングを説明する図である。It is a figure explaining the masking of a specific frequency band. 特定周波数帯の他のマスキングを説明する図である。It is a figure explaining other masking of a specific frequency band. 図５を逆変換した時系列データを示す図である。It is a figure which shows the time series data which carried out reverse conversion of FIG. 図６を逆変換した時系列データを示す図である。It is a figure which shows the time series data which carried out reverse conversion of FIG. パワースペクトルの強い周波数帯の判定方法の一例を示す図である。It is a figure which shows an example of the determination method of a frequency band with a strong power spectrum. パワースペクトルの強い周波数帯の判定方法の他の一例を示す図である。。It is a figure which shows another example of the determination method of a frequency band with a strong power spectrum. . 焼結鉱成分のＳｉＯ2成分割合の変動の実績値と、本発明に基づく予測値とを比較したグラフである。And actual value of the SiO 2 component ratio of the variation of the sintered ore component is a graph comparing the predicted value based on the present invention.

符号の説明Explanation of symbols

１ 主原料
２ 配合部
３ 副原料
４ 焼結工程
５ 焼結鉱
６ 成分分析工程
１１ 焼結鉱主原料の成分値同定用データ
１２ フィルタ
１３ 同定
１４ｉ 自己回帰モデル群
１５ 焼結鉱の成分分析値の至近時系列データ
１６ ウェーブレット変換
１７ モデル選定
１８ ＡＲモデル
１９ 副原料を添加する時点での焼結鉱主原料の成分割合の予測値 DESCRIPTION OF SYMBOLS 1 Main raw material 2 Compounding part 3 Sub raw material 4 Sintering process 5 Sintered ore 6 Component analysis process 11 Data for component value identification of sinter main raw material 12 Filter 13 Identification 14i Autoregressive model group 15 Component analysis value of sintered ore Time series data 16 Wavelet transform 17 Model selection 18 AR model 19 Predicted component ratio of sintered ore main raw material at the time of adding auxiliary raw material

Claims (5)

時系列的に変動する焼結鉱の主原料成分割合を予測する方法であって、次の（１）〜（６）の工程を有することを特徴とする焼結鉱主原料成分割合予測方法。
（１）焼結鉱の成分分析値と焼結鉱の副原料の成分割合の実測値とから求められる、焼結鉱主原料成分割合の同定用時系列データを収集する同定用データ収集工程。
（２）収集された同定用時系列データを、複数の周波数帯の時系列データに分類して、それぞれに対応する自己回帰モデルを作成する周波数対応自己回帰モデル群作成工程。
（３）至近の焼結鉱の成分分析値の時系列データについて、その周波数解析を行い、パワースペクトルの強い周波数帯を判定する周波数帯判定工程。
（４）判定されたパワースペクトルの強い周波数帯に対応する自己回帰モデルを、前記周波数対応自己回帰モデル群の中から選択する自己回帰モデル選択工程。
（５）選択された自己回帰モデルに基づき、副原料を添加する時点での焼結鉱主原料の成分割合を予測する焼結鉱主原料成分割合の予測工程。 A method for predicting a main raw material component ratio of a sintered ore that varies in a time series, the method comprising the following steps (1) to (6):
(1) An identification data collection step for collecting time-series data for identification of the sinter ore main raw material component ratio obtained from the component analysis value of the sinter ore and the actual measured value of the component ratio of the secondary raw material of the sinter ore.
(2) A frequency-corresponding autoregressive model group creation step of classifying the collected time series data for identification into time series data of a plurality of frequency bands and creating an autoregressive model corresponding to each.
(3) A frequency band determination step of performing frequency analysis on the time series data of component analysis values of the nearest sintered ore and determining a frequency band having a strong power spectrum.
(4) An autoregressive model selection step of selecting an autoregressive model corresponding to the determined frequency band having a strong power spectrum from the group of frequency-corresponding autoregressive models.
(5) A step of predicting a sinter ore main material component ratio that predicts a component ratio of the sinter ore main material at the time of adding the auxiliary material based on the selected autoregressive model. 請求項１に記載の焼結鉱主原料成分割合予測方法において、前記周波数帯判定工程での周波数解析に、ウェーブレット変換を用いることを特徴とした焼結鉱主原料成分割合予測方法。 The sintered ore main raw material component ratio prediction method according to claim 1, wherein wavelet transform is used for frequency analysis in the frequency band determination step. 請求項１または請求項２のいずれかに記載の焼結鉱主原料成分割合予測方法において、前記周波数帯判定工程でのパワースペクトルの強い周波数帯の判定は、パワースペクトルがピークとなる周波数の高周波側および低周波側にそれぞれ設定幅を設けて周波数帯とする方法、またはパワースペクトルのピークで正規化した正規化パワースペクトルに対して、予め設定したパワースペクトルの閾値を越えた周波数帯とする方法のいずれかによることを特徴とした焼結鉱主原料成分割合予測方法。 The sintered ore main raw material component ratio prediction method according to claim 1 or 2, wherein the determination of the frequency band having a strong power spectrum in the frequency band determination step is performed at a high frequency at a frequency at which the power spectrum reaches a peak. A method of providing a frequency band with a set width on each of the side and the low frequency side, or a method of setting a frequency band that exceeds a preset power spectrum threshold with respect to a normalized power spectrum normalized by the peak of the power spectrum A method for predicting the ratio of raw material components of sintered ore, characterized by being based on any of the above. 請求項１ないし請求項３のいずれかに記載の焼結鉱主原料成分割合予測方法を用いて焼結鉱主原料成分割合の変動量を求めて、該変動量に基づいて焼結鉱の副原料の添加割合を制御することを特徴とした焼結鉱成分割合制御方法。 A variation amount of the sintered ore main raw material component ratio is obtained using the method for predicting a sintered ore main raw material component proportion according to any one of claims 1 to 3, and the secondary amount of the sintered ore is calculated based on the variation amount. A method for controlling a sinter component ratio, characterized by controlling an addition ratio of a raw material. 時系列的に変動する焼結鉱の主原料成分割合を予測するプログラムであって、コンピュータに次の（１）〜（６）の機能を実現させるための焼結鉱主原料成分割合予測プログラム。
（１）焼結鉱の成分分析値と焼結鉱の副原料の成分割合の実測値とから求められる、焼結鉱主原料成分割合の同定用時系列データを収集する同定用データ収集機能。
（２）収集された同定用時系列データを、複数の周波数帯の時系列データに分類して、それぞれに対応する自己回帰モデルを作成する周波数対応自己回帰モデル群作成機能。
（３）至近の焼結鉱の成分分析値の時系列データについて、その周波数解析を行い、パワースペクトルの強い周波数帯を判定する周波数帯判定機能。
（４）判定されたパワースペクトルの強い周波数帯に対応する自己回帰モデルを、前記周波数対応自己回帰モデル群の中から選択する自己回帰モデル選択機能。
（５）選択された自己回帰モデルに基づき、副原料を添加する時点での焼結鉱主原料の成分割合を予測する焼結鉱主原料成分割合の予測機能。 A program for predicting a ratio of main raw material components of a sintered ore that varies in time series, and a program for predicting a ratio of main raw material components of a sintered ore for causing a computer to realize the following functions (1) to (6).
(1) An identification data collection function for collecting time-series data for identification of the sinter ore main raw material component ratio obtained from the component analysis value of the sinter ore and the measured value of the component ratio of the auxiliary raw material of the sinter ore.
(2) A frequency-corresponding autoregressive model group creation function that classifies collected time series data for identification into time series data of a plurality of frequency bands and creates an autoregressive model corresponding to each.
(3) A frequency band determination function for performing frequency analysis on the time series data of component analysis values of the nearest sintered ore and determining a frequency band with a strong power spectrum.
(4) An autoregressive model selection function for selecting an autoregressive model corresponding to a determined frequency band having a strong power spectrum from the group of frequency-corresponding autoregressive models.
(5) A function for predicting a sinter ore main material component ratio that predicts a component ratio of the sinter ore main material at the time of adding the auxiliary material based on the selected autoregressive model.
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CN102156405A (en) * 2011-01-24 2011-08-17 王爱民 Sintered ore chemical component prediction and intelligent control system under small sample poor information
KR20190036024A (en) * 2017-09-27 2019-04-04 현대제철 주식회사 Apparatus and method for manufacturing sintered ore
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CN102156405A (en) * 2011-01-24 2011-08-17 王爱民 Sintered ore chemical component prediction and intelligent control system under small sample poor information
CN110073006A (en) * 2016-12-16 2019-07-30 杰富意钢铁株式会社 Method for operating blast furnace
KR20190036024A (en) * 2017-09-27 2019-04-04 현대제철 주식회사 Apparatus and method for manufacturing sintered ore
KR101978053B1 (en) 2017-09-27 2019-05-13 현대제철 주식회사 Apparatus and method for manufacturing sintered ore
CN109943711A (en) * 2019-03-14 2019-06-28 冶金工业规划研究院 A kind of matching method and system of sinter material
KR102360397B1 (en) * 2020-10-26 2022-02-08 현대제철 주식회사 Apparatus and method for guidance of sintered ore
CN112099460A (en) * 2020-11-02 2020-12-18 常州微亿智造科技有限公司 Quality characteristic processing method suitable for deep learning metal injection molding sintering product
WO2023189336A1 (en) * 2022-03-28 2023-10-05 Ｊｆｅスチール株式会社 Method for managing operation of sintering machine, method for manufacturing sintered ore, and control device
WO2023189251A1 (en) * 2022-03-28 2023-10-05 Ｊｆｅスチール株式会社 Granulated particle prediction method, granulated particle production method, granulated particle production device, and sintered ore production method
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