JP2019219231A - Prediction method of activity index of fly ash - Google Patents
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本発明は、コンクリート用混和材やセメント用混合材として用いられるフライアッシュの活性度指数の予測方法に関する。 The present invention relates to a method for predicting the activity index of fly ash used as an admixture for concrete or an admixture for cement.
石炭火力発電所において、微粉炭を燃焼した際に燃焼ガスから集塵器で採取された石炭灰であるフライアッシュは、コンクリート用混和材やセメント用混合材に用いると、ポゾラン反応を生じてコンクリートの耐久性を向上することができるため、有用性の高い材料として知られる。その一方、こうしたフライアッシュの採取量は、石炭灰発生量全体の２％弱に過ぎず、品質が安定しにくい事情もあり、フライアッシュの活用率を充分に高められない要因ともなっている。 At a coal-fired power plant, fly ash, which is coal ash collected from the combustion gas when pulverized coal is burned by a dust collector, causes a pozzolanic reaction when it is used as a concrete admixture or cement admixture. It is known as a highly useful material because it can improve durability. On the other hand, the amount of fly ash collected is only less than 2% of the total amount of coal ash generated, and the quality is difficult to stabilize. This is a factor that makes it impossible to sufficiently increase the utilization rate of fly ash.
このような状況から、フライアッシュをセメント用混合材等として利用する場合、かかるフライアッシュがロット毎に要求品質を満たすか否かの確認を要する。通常、フライアッシュのポゾラン反応性は、ＪＩＳ Ａ ６２０１「コンクリート用フライアッシュ」に規定される活性度指数の試験方法を用いて評価されるが、試験結果を得るまでに２８日間または９１日間もの長期間を要することから、実用性に乏しく、新たな代替方法の開発がなされている。 Under such circumstances, when fly ash is used as a mixture for cement or the like, it is necessary to confirm whether such fly ash satisfies the required quality for each lot. Usually, the pozzolanic reactivity of fly ash is evaluated using the test method of the activity index specified in JIS A 6201 “Fly ash for concrete”, but it takes 28 days or 91 days to obtain the test result. Due to the time required, it is not practical and new alternative methods are being developed.
例えば、特許文献１では、フライアッシュ硬化体の材齢７日以内の電気抵抗値を計測し、予め求めておいたフライアッシュの活性度指数とフライアッシュ硬化体の電気抵抗値との相関関係に基づいて、フライアッシュの活性度指数を予測している。また、特許文献２では、フライアッシュのブレーン比表面積やＳＯ3の含有率、非晶質の含有率等を用いてフライアッシュの活性度指数の予測値を求めている。 For example, in Patent Literature 1, the electrical resistance value of a fly ash cured product within 7 days of age is measured, and the correlation between the previously calculated fly ash activity index and the electrical resistance value of the fly ash cured product is determined. Based on this, fly ash activity index is predicted. Further, in Patent Document 2, the predicted value of the activity index of fly ash is obtained using the Blaine specific surface area of fly ash, the content of SO 3 , the content of amorphous, and the like.
しかしながら、上記特許文献に記載の方法であっても、フライアッシュの活用率を充分に向上させるべく、精度の高い活性度指数の予測値を得るには、依然としてさらなる改善を要する状況である。 However, even with the method described in the above-mentioned patent document, further improvement is still required to obtain a highly accurate predicted value of the activity index in order to sufficiently improve the utilization rate of fly ash.
したがって、本発明の課題は、より精度の高い活性度指数の予測値を的確に得ることのできるフライアッシュの活性度指数の予測方法を提供することにある。 Therefore, an object of the present invention is to provide a fly ash activity index prediction method that can accurately obtain a more accurate activity index prediction value.
そこで本発明者らは、上記課題を解決すべく鋭意検討を行った結果、フライアッシュにおける複数の粒群を対象として、各々の球換算比表面積値とともに特性値を求め、これを活用することにより、精度の高い活性度指数の予測値が求められることを見出した。 Therefore, the present inventors have conducted intensive studies to solve the above problems, and as a result, for a plurality of particle groups in fly ash, determine a characteristic value together with each sphere-converted specific surface area value, and by utilizing this, It was found that a highly accurate predicted value of the activity index was required.
すなわち、本発明は、次の工程（Ｉ）〜（IV）：
（Ｉ）フライアッシュを分級して複数の粒群に分割した後、得られた粒群ごとに全粒中の存在比率を求めるとともに、球換算比表面積値を求めるか、或いは球換算比表面積値と、化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値とを求める工程
（II）得られた存在比率と球換算比表面積値とを用いて全粒換算し、或いは得られた存在比率と球換算比表面積値と特性値とを用いて全粒換算して、予測式用全粒換算値を求める工程
（III）得られた予測式用全粒換算値を説明変数とし、次いで活性度指数の実測値を目的変数として重回帰分析を行い、予測式を作製する工程
（IV）得られた予測式を用い、予測対象とするフライアッシュの活性度指数を求める工程
を備える、フライアッシュの活性度指数の予測方法を提供するものである。
That is, the present invention provides the following steps (I) to (IV):
(I) After classifying fly ash and dividing it into a plurality of grain groups, determine the abundance ratio in all grains for each of the obtained grain groups, and calculate the sphere-converted specific surface area value, or the sphere-converted specific surface area value And a step of obtaining one or more characteristic values obtained from the chemical composition, mineral composition and loss on ignition (II) Using the obtained abundance ratio and the sphere-converted specific surface area value, convert the whole grain, Or a step of obtaining the whole grain conversion value for the prediction formula by converting the whole grain using the obtained existence ratio, the sphere-converted specific surface area value, and the characteristic value. (III) Explaining the obtained whole grain conversion value for the prediction formula. Step of preparing a prediction equation by performing multiple regression analysis using the actual measured value of the activity index as the target variable as the variable, and (IV) Step of obtaining the activity index of the fly ash to be predicted using the obtained prediction equation Provided is a method for predicting fly ash activity index, comprising: Things.
本発明のフライアッシュの活性度指数の予測方法によれば、フライアッシュの活性度指数を実情に即して的確に予測することが可能であり、品質変動の大きいフライアッシュを迅速に選別することができる。 According to the fly ash activity index prediction method of the present invention, it is possible to accurately predict the fly ash activity index according to the actual situation, and to quickly select fly ash with large quality fluctuations. Can be.
以下、本発明について詳細に説明する。
本発明のフライアッシュの活性度指数の予測方法は、次の工程（Ｉ）〜（IV）：
（Ｉ）フライアッシュを分級して複数の粒群に分割した後、得られた粒群ごとに存在比率を求めるとともに、球換算比表面積値を求めるか、或いは球換算比表面積値と、化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値とを求める工程
（II）得られた存在比率と球換算比表面積値とを用いて全粒換算し、或いは得られた存在比率と球換算比表面積値と特性値とを用いて全粒換算して、予測式用全粒換算値を求める工程
（III）得られた予測式用全粒換算値を説明変数とし、次いで活性度指数の実測値を目的変数として重回帰分析を行い、予測式を作製する工程
（IV）得られた予測式を用い、予測対象とするフライアッシュの活性度指数を求める工程
を備える。このように、フライアッシュを分級して得られる複数の粒群ごとに求められる球換算比表面積値を含む種々の値から、全粒換算を介することにより作製した予測式を用いれば、予測精度の高いフライアッシュの活性度指数を得ることができる。
Hereinafter, the present invention will be described in detail.
The method for predicting the activity index of fly ash according to the present invention comprises the following steps (I) to (IV):
(I) After classifying fly ash and dividing it into a plurality of grain groups, determine the abundance ratio for each of the obtained grain groups, and determine the sphere-converted specific surface area value, or the sphere-converted specific surface area value and the chemical composition For determining one or more characteristic values obtained from the mineral composition and the loss on ignition (II) Using the obtained abundance ratio and the sphere-converted specific surface area value, converted to whole grains or obtained A step of obtaining a whole-grain converted value for the prediction formula by converting the whole grain using the abundance ratio, the sphere-converted specific surface area value and the characteristic value (III) using the obtained whole-grain converted value for the prediction formula as an explanatory variable, A step of performing a multiple regression analysis using the actually measured value of the activity index as an objective variable and preparing a prediction equation (IV) The method includes a step of using the obtained prediction equation to determine an activity index of a fly ash to be predicted. As described above, from various values including the sphere-converted specific surface area value obtained for each of the plurality of particle groups obtained by classifying fly ash, the prediction accuracy of the prediction accuracy can be improved by using the prediction formula prepared through whole-grain conversion. A high fly ash activity index can be obtained.
工程（Ｉ）は、フライアッシュを分級して複数の粒群に分割した後、得られた粒群ごとに存在比率を求めるとともに、球換算比表面積値を求めるか、或いは球換算比表面積値と、化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値とを求める工程である。予測式を作製するため工程（Ｉ）において用いるフライアッシュは、特にその種類について制限はないが、活性度指数がばらつく可能性が高いＪＩＳ II種〜IV種に相当するフライアッシュを用いることが好ましい。また、銘柄やロットの異なるフライアッシュを複数用いるのが好ましく、１０点以上のフライアッシュを用いることが好ましい。そして、活性度指数の実測値ができるだけ異なるフライアッシュを複数選択して用いるのが好ましく、具体的には、活性度指数の実測値の振れ幅が好ましくは２０以上であるように、より好ましくは活性度指数の実測値の振れ幅が３０以上の幅を有するように、複数のフライアッシュを選択して用いるのがよい。こうしたフライアッシュを分級して複数の粒群に分割することにより、各粒群から得られる所定の値の精度を高めつつ、予測精度の高いフライアッシュの活性度指数を得ることができる予測式を作製することが可能となる。 In the step (I), fly ash is classified and divided into a plurality of grain groups, and then, the existence ratio is determined for each of the obtained grain groups, and the sphere-converted specific surface area value is determined. And one or more characteristic values obtained from the chemical composition, the mineral composition and the ignition loss. The type of fly ash used in the step (I) for preparing the prediction formula is not particularly limited, but it is preferable to use fly ash corresponding to JIS type II to type IV having a high possibility that the activity index varies. . It is preferable to use a plurality of fly ashes of different brands and lots, and it is preferable to use 10 or more fly ashes. And it is preferable to select and use a plurality of fly ash whose actual value of the activity index is as different as possible. Specifically, as the swing width of the actual value of the activity index is preferably 20 or more, more preferably A plurality of fly ashes are preferably selected and used so that the fluctuation width of the measured value of the activity index has a width of 30 or more. By classifying such fly ash and dividing it into a plurality of grain groups, it is possible to improve the accuracy of a predetermined value obtained from each grain group while obtaining a fly ash activity index having high prediction accuracy. It can be manufactured.
フライアッシュを分級するのに用いる装置としては、特に制限されず、試験用ふるいや旋回気流式分級器等、通常の分級器を用いることができる。分級するにあたり、粒径４５μｍ以上の粒子は特に反応性が低い一方、粒径１０μｍ未満の粒子は特に反応性が高い傾向にあるフライアッシュの特性を加味し、予測精度を高める上で適切な値を得る観点から、具体的には、分級点として、少なくとも１０μｍ又は４５μｍを含むことが好ましく、例えば１０μｍを分級点とする場合は、１０μｍ未満の群と１０μｍ以上の群とに分割すればよい。また分級点として、１０μｍ及び４５μｍの双方を含むのがより好ましく、すなわち、少なくとも粒径１０μｍ未満の群、粒径１０μｍ以上粒径４５μｍ未満の群、及び粒径４５μｍ以上の群を含む複数の粒群に分割するのがよい。さらに分級点として２０μｍを含むことが最も好ましく、すなわち、粒径１０μｍ未満の群、粒径１０μｍ以上２０μｍ未満の群、粒径２０μｍ以上４５μｍ未満の群、及び粒径４５μｍ以上の群からなる４つの群とするのがよい。 The apparatus used for classifying fly ash is not particularly limited, and a normal classifier such as a test sieve or a swirling air classifier can be used. In classifying, particles having a particle size of 45 μm or more have a particularly low reactivity, while particles having a particle size of less than 10 μm take into account the characteristics of fly ash, which tends to have a particularly high reactivity, and are suitable values for increasing the prediction accuracy. From the viewpoint of obtaining the above, specifically, it is preferable that the classification point includes at least 10 μm or 45 μm. For example, when the classification point is 10 μm, it may be divided into a group of less than 10 μm and a group of 10 μm or more. Further, it is more preferable that the particles include both 10 μm and 45 μm as the classification points, that is, a plurality of particles including at least a group having a particle size of less than 10 μm, a group having a particle size of 10 μm or more and less than 45 μm, and a group having a particle size of 45 μm or more. It is good to divide into groups. Further, it is most preferable to include 20 μm as the classification point, that is, four groups consisting of a group having a particle size of less than 10 μm, a group having a particle size of 10 μm to less than 20 μm, a group having a particle size of 20 μm to less than 45 μm, and a group having a particle size of 45 μm or more. It is good to group.
次いで、分割した粒群ごとに存在比率を求めるとともに、球換算比表面積値を求めればよい。或いは、分割した粒群ごとに存在比率と球換算比表面積値を求め、さらに化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値を求めてもよい。 Next, the existence ratio may be determined for each of the divided particle groups, and the sphere-converted specific surface area value may be determined. Alternatively, the abundance ratio and the spherical equivalent specific surface area value may be determined for each of the divided particle groups, and one or more characteristic values obtained from the chemical composition, the mineral composition, and the ignition loss may be determined.
存在比率とは、フライアッシュ全粒中における各々の粒群に分割されたフライアッシュの割合（質量％）である。
また、球換算比表面積値は、粒度分布測定装置等により、得られる粒度分布から求めた体積積算分布図を元に、各々の粒群に対応する値として算出することができるものであり、具体的には、フライアッシュ単位体積あたりの表面積から算出される比表面積の値（ｍ2／ｃｃ）である。
The abundance ratio is a ratio (mass%) of fly ash divided into each grain group in all fly ash grains.
The sphere-converted specific surface area value can be calculated as a value corresponding to each particle group by a particle size distribution measuring device or the like based on a volume integrated distribution diagram obtained from the obtained particle size distribution. Specifically, it is a value (m 2 / cc) of a specific surface area calculated from a surface area per unit volume of fly ash.
本発明では、このように球換算比表面積値を用いて予測式を作製する。これは、フライアッシュの反応性が、反応するガラス粒子の表面積の大小に左右され変動するという本発明者らの知見に基づくものであり、かかる球換算比表面積値がフライアッシュの活性度に多大な影響を与えると考えられることから、分級したフライアッシュから求められる球換算比表面積値を含む種々の値を全粒換算して予測式用全粒換算値とし、これを活用するものである。 In the present invention, a prediction formula is created using the sphere-converted specific surface area value. This is based on the finding of the present inventors that the reactivity of fly ash fluctuates depending on the size of the surface area of the reacting glass particles, and such a sphere-converted specific surface area value greatly affects the activity of fly ash. Therefore, various values including a sphere-converted specific surface area value obtained from the classified fly ash are converted into whole grains to obtain a whole-grain converted value for a prediction formula, and this is utilized.
化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値は、後述する工程（III）において作製する予測式に応じ、予測式用全粒換算値を求めるための値として適宜選択して用いればよい。化学組成は、例えば蛍光Ｘ線による化学成分分析により求めることができ、鉱物組成は、例えば内部標準によるＸ線回折／リートベルト法により求めることができる。強熱減量は、ＪＩＳ Ａ ６２０１に準拠することにより求められる値である。 One or more characteristic values obtained from the chemical composition, the mineral composition, and the ignition loss are used as values for obtaining a whole-grain conversion value for the prediction formula according to the prediction formula prepared in the step (III) described later. It may be appropriately selected and used. The chemical composition can be determined by, for example, chemical component analysis using fluorescent X-rays, and the mineral composition can be determined by, for example, X-ray diffraction / Rietveld method using an internal standard. The ignition loss is a value determined by conforming to JIS A6201.
これら化学組成、鉱物組成又は強熱減量から得られる上記特性値としては、具体的には、石英の割合；ガラス相中のＡｌ2Ｏ3量、Ｆｅ2Ｏ3量、ＳｉＯ2量、ＣａＯ量、ＭｇＯ量等を得るためのガラス相の化学組成；強熱減量から選ばれる１種又は２種以上から得られる物性値が挙げられる。これらは、フライアッシュの反応性に影響を与える因子であると考えられることから、これを予測式に取り入れることで、予測精度をより高めることが可能である。
なお、ガラス相の化学組成は、全体の化学組成から酸化物である鉱物に相当する成分を除く算出により求めることができ、またＳＥＭ−ＥＤＳ等の直接分析によっても求めることができる。
As the characteristic values obtained from these chemical composition, mineral composition or ignition loss, specifically, the ratio of quartz; the amount of Al 2 O 3, the amount of Fe 2 O 3, the amount of SiO 2, and the amount of CaO in the glass phase And the chemical composition of the glass phase for obtaining the amount of MgO, etc .; and physical property values obtained from one or more kinds selected from ignition loss. Since these are considered to be factors affecting the reactivity of fly ash, it is possible to further improve the prediction accuracy by incorporating them into the prediction formula.
In addition, the chemical composition of the glass phase can be obtained by calculation from the total chemical composition excluding components corresponding to oxide minerals, and can also be obtained by direct analysis such as SEM-EDS.
工程（II）は、工程（Ｉ）で得られた存在比率と球換算比表面積値とを用いて全粒換算し、或いは工程（Ｉ）で得られた存在比率と球換算比表面積値と特性値を用いて全粒換算して、予測式用全粒換算値を求める工程である。かかる工程（II）を経ることにより、粒群ごとの特性を加味した全粒のフライアッシュの特性を表す値を得ることができる。全粒換算するにあたっては、具体的には、下記式を用い算出することができる。
Ｑ＝Σ（ｙ×ｚ）
（上記式中、Ｑは予測式用全粒換算値であり、ｙは工程（Ｉ）で得られた各粒群の球換算比表面積値又は特性値であり、ｚは工程（Ｉ）で得られた各粒群の存在比率である。）。
In the step (II), whole grains are converted using the abundance ratio and the sphere-converted specific surface area value obtained in the step (I), or the abundance ratio, the sphere-converted specific surface area value and the property obtained in the step (I) are used. In this step, the whole grain is converted using the values to obtain a whole grain converted value for the prediction formula. Through the step (II), it is possible to obtain a value representing the characteristics of the whole fly ash in consideration of the characteristics of each particle group. Specifically, when converting to whole grains, it can be calculated using the following equation.
Q = Σ (y × z)
(In the above formula, Q is the whole-grain conversion value for the prediction formula, y is the sphere-converted specific surface area value or characteristic value of each particle group obtained in step (I), and z is the value obtained in step (I). The abundance ratio of each grain group obtained.)
工程（III）は、工程（II）で得られた予測式用全粒換算値を説明変数とし、次いで活性度指数の実測値を目的変数として重回帰分析を行って予測式を作製する工程である。このように、フライアッシュにおける各々の粒群ごとに求めた球換算比表面積値や特性値から算出した予測式用全粒換算値を用いて重回帰分析を行うことにより、フライアッシュの活性度指数の予測精度を有効に高めることができる。 Step (III) is a step of preparing a prediction equation by performing a multiple regression analysis using the whole-grain converted value for the prediction equation obtained in the step (II) as an explanatory variable, and then using the actually measured value of the activity index as an objective variable. is there. As described above, by performing the multiple regression analysis using the whole-grain converted value for the prediction formula calculated from the sphere-converted specific surface area value and the characteristic value obtained for each particle group in fly ash, the activity index of fly ash is obtained. Can be effectively improved in prediction accuracy.
具体的には、例えば、工程（Ｉ）において、得られた粒群ごとに存在比率を求めるとともに、球換算比表面積値のみを求める場合であって、工程（II）において、かかる存在比率と球換算比表面積値とを用いて全粒換算することにより、予測式用全粒換算値を求め、次いで工程（III）が、工程（II）で得られた予測式用全粒換算値を説明変数とし、実測した活性度指数を目的変数として重回帰分析を行い、予測式を作製する工程である場合、かかる予測式は、下記式（Ｘ）
ｙx＝αx×（球換算比表面積値）＋ａx・・・（Ｘ）
（式（Ｘ）中、ｙxは活性度指数の予測値を示し、αxは偏回帰係数を示し、ａxは定数を示す。）
で表される。
球換算比表面積値は、フライアッシュの反応性に多大な影響を与える因子であると考えられることから、より予測精度を高めるべく、本発明では、一旦フライアッシュを分級し、分割した粒群ごとに球換算比表面積値を求め、これを活用して全粒換算することにより予測値を求めるものである。
Specifically, for example, in the step (I), the abundance ratio is calculated for each of the obtained particle groups, and only the sphere-converted specific surface area value is calculated. By converting the whole grain using the converted specific surface area value, the whole grain converted value for the prediction formula is obtained, and then, in the step (III), the whole grain converted value for the prediction formula obtained in the step (II) is used as an explanatory variable. In the case where the multiple regression analysis is performed using the actually measured activity index as an objective variable to generate a prediction formula, the prediction formula is represented by the following formula (X)
y x = α x × (sphere-specific surface area) + a x (X)
(In the formula (X), y x indicates a predicted value of the activity index, α x indicates a partial regression coefficient, and a x indicates a constant.)
Is represented by
Since the spherical equivalent surface area value is considered to be a factor that greatly affects the reactivity of fly ash, in order to further improve the prediction accuracy, in the present invention, fly ash is once classified, and each of the divided particle groups is classified. Then, a sphere-converted specific surface area value is obtained, and the predicted value is obtained by performing a whole-grain conversion using this value.
また、工程（Ｉ）において、得られた粒群ごとに存在比率を求めるとともに、球換算比表面積値のみならず、球換算比表面積値と、化学組成、鉱物組成及び強熱減量から得られる１種又は２種以上の特性値とを求める場合、工程（II）は、かかる存在比率と球換算比表面積値と特性値とを用いて予測式用全粒換算値を求める工程とし、予測式を作製すればよい。
これにより、上記球換算比表面積値とともに、その他フライアッシュの反応性に多大な影響を与えると考えられる因子も説明変数に組み入れることにより、より精度の高い予測値を得ることができる。
In addition, in the step (I), the abundance ratio is determined for each of the obtained particle groups, and not only the sphere-converted specific surface area value, but also the sphere-converted specific surface area value, the chemical composition, the mineral composition, and the 1 When the seed or two or more characteristic values are obtained, the step (II) is a step of obtaining a whole-grain conversion value for a prediction formula using the abundance ratio, the sphere-converted specific surface area value, and the characteristic value. What is necessary is just to manufacture.
Thereby, a more accurate predicted value can be obtained by incorporating, into the explanatory variables, factors that are thought to have a great effect on the reactivity of fly ash, in addition to the sphere-converted specific surface area value.
より具体的には、上記特性値を石英の割合と強熱減量とし、これらの値と球換算比表面積値から得られる値Ａ（＝（１００−石英の割合−強熱減量）×（球換算比表面積値）を求める。また、特性値をガラス相中のＣａＯ量、Ａｌ2Ｏ3量、ＭｇＯ量及びＳｉＯ2量とし、これらの値と球換算比表面積値とから得られる値Ｂ（＝ガラス相中の｛（ＣａＯ量＋Ａｌ2Ｏ3量＋ＭｇＯ量）／ＳｉＯ2量｝×（球換算比表面積値））を求める。そして、工程（III）において作製する予測式を、下記式（Ｙ）：
ｙy＝αy×Ａ＋βy×Ｂ＋ａy・・・（Ｙ）
但し、Ａ＝（１００−石英の割合−強熱減量）×（球換算比表面積値）
Ｂ＝ガラス相中の｛（ＣａＯ量＋Ａｌ2Ｏ3量＋ＭｇＯ量）／ＳｉＯ2量｝
×（球換算比表面積値）
（式（Ｙ）中、ｙyは活性度指数の予測値を示し、αy及びβyは偏回帰係数を示し、ａyは定数を示す。）
で表されるものとすればよい。
フライアッシュの反応性に最も大きな影響を与える因子は球換算比表面積値ではあるものの、反応するガラス粒子（ムライトと酸化鉄はガラス相と混在するので、反応するガラス粒子とする）の比表面積の大小でも変動し得ることを見出し、これを加味するものである。
More specifically, the above characteristic values are defined as the ratio of quartz and the loss on ignition, and a value A (= (100−ratio of quartz−loss on ignition)) × (sphere conversion) The characteristic values are CaO amount, Al 2 O 3 amount, MgO amount, and SiO 2 amount in the glass phase, and a value B (=) obtained from these values and the spherical equivalent specific surface area value is obtained. {(CaO content + Al 2 O 3 content + MgO content) / SiO 2 content} × (spherical specific surface area value) in the glass phase is determined, and the prediction formula to be produced in the step (III) is expressed by the following formula (Y ):
y y = α y × A + β y × B + a y (Y)
However, A = (100-quartz ratio-ignition loss) × (sphere-converted specific surface area value)
B = {(CaO amount + Al 2 O 3 amount + MgO amount) / SiO 2 amount} in glass phase
× (sphere equivalent specific surface area value)
(In the formula (Y), y y indicates a predicted value of the activity index, α y and β y indicate partial regression coefficients, and a y indicates a constant.)
It may be represented by
Although the factor that has the greatest influence on the reactivity of fly ash is the specific surface area in terms of sphere, the specific surface area of the glass particles that react (since mullite and iron oxide are mixed with the glass phase, they are the glass particles that react) They find that they can fluctuate even in large and small, and take this into account.
或いは、上記特性値をガラス相中のＡｌ2Ｏ3量、Ｆｅ2Ｏ3量、ＣａＯ量、ＭｇＯ量及びＳｉＯ2量とし、Ａｌ2Ｏ3量及びＦｅ2Ｏ3量の合計量とＳｉＯ2量から得られる比率Ｄ（＝ガラス相中の（Ａｌ2Ｏ3＋Ｆｅ2Ｏ3）／ＳｉＯ2）、並びにガラス相中のＣａＯ量及びＭｇＯ量の合計量とＳｉＯ2量との比率Ｅ（＝ガラス相中の（ＣａＯ＋ＭｇＯ）／ＳｉＯ2）を求める。そして、工程（III）において作製する予測式を、下記式（Ｚ）：
ｙz＝αz×（球換算比表面積値）＋βz×Ｄ＋γz×Ｅ＋ａz・・・（Ｚ）
但し、Ｄ＝ガラス相中の（Ａｌ2Ｏ3＋Ｆｅ2Ｏ3）／ＳｉＯ2
Ｅ＝ガラス相中の（ＣａＯ＋ＭｇＯ）／ＳｉＯ2
（式（Ｚ）中、ｙzは活性度指数の予測値を示し、αz、βz及びγzは偏回帰係数を示し、ａzは定数を示す。）
で表されるものとしてもよい。
これにより、材齢２８日又は材齢９１日の活性度指数を高い精度で予測することが可能である。
Alternatively, the above characteristic values are the amount of Al 2 O 3, the amount of Fe 2 O 3, the amount of CaO, the amount of MgO, and the amount of SiO 2 in the glass phase, and the total amount of the Al 2 O 3 and the amount of Fe 2 O 3 and SiO 2 ratio resulting from the amount D (= glass phase of (Al 2 O 3 + Fe 2 O 3) / SiO 2), and the ratio of the total amount and the amount of SiO 2 CaO amount and MgO of the glass phase E (= (CaO + MgO) / SiO 2 in the glass phase is determined. Then, the prediction formula produced in the step (III) is expressed by the following formula (Z):
y z = α z × (spherical equivalent specific surface area value) + β z × D + γ z × E + a z ··· (Z)
Where D = (Al 2 O 3 + Fe 2 O 3 ) / SiO 2 in the glass phase
E = (CaO + MgO) / SiO 2 in glass phase
(In the formula (Z), yz indicates a predicted value of the activity index, α z , β z and γ z indicate partial regression coefficients, and a z indicates a constant.)
May be represented by
This makes it possible to predict the activity index with a material age of 28 days or 91 days with high accuracy.
工程（IV）は、工程（III）で得られた予測式を用い、予測対象とするフライアッシュの活性度指数を求める工程である。すなわち、かかる工程（IV）では、工程（Ｉ）〜（III）を経ることにより、種々の値を元に求めた予測式用全粒換算値に基づき作製された予測式を用い、新たに入手した活性度指数を予測したいフライアッシュに対して、活性度指数の予測を行う。また、ここで予測対象としたフライアッシュの活性度指数の実測値が後日得られた場合には、その数値を予測式の重回帰分析に反映することにより、さらに予測精度の高い予測式を構築することができる。 Step (IV) is a step of obtaining an activity index of fly ash to be predicted using the prediction formula obtained in step (III). In other words, in the step (IV), after the steps (I) to (III) are performed, a new prediction formula is obtained by using a prediction formula prepared based on the whole-grain converted value for the prediction formula obtained based on various values. For the fly ash whose activity index is to be predicted, the activity index is predicted. In addition, if the measured value of the fly ash activity index that was the target of the prediction was obtained at a later date, the numerical value was reflected in the multiple regression analysis of the prediction formula to build a prediction formula with higher prediction accuracy. can do.
以下、本発明について、実施例に基づき具体的に説明する。 Hereinafter, the present invention will be specifically described based on examples.
《実測値の測定》
ＪＩＳ Ａ ６２０１に規定されるフライアッシュII種〜IV種に相当する１２種類のフライアッシュ（Ｎｏ．１〜Ｎｏ．１２）について、ＪＩＳ Ａ ６２０１「コンクリート用フライアッシュ」に準拠した材齢９１日の活性度指数を測定した。
結果を表１に示す。
《Measurement of actual measurement value》
Twelve types of fly ash (No. 1 to No. 12) corresponding to fly ash type II to type IV specified in JIS A 6201, 91 days old according to JIS A 6201 "Fly ash for concrete" The activity index was measured.
Table 1 shows the results.
［実施例１］
旋回式スピンエアシーブ（株式会社セイシン企業 SAR-75）を用い、目開きが１０μｍ、２０μｍ、４５μｍの３つのふるいにより、各フライアッシュを４つの粒群に分割し、粒群ごとに質量を測定し、存在比率を求めた。
次いで、レーザー回折式粒度分布測定装置（マイクロトラック・ベル株式会社、MT-3300EX-II）により粒群ごとに球換算比表面積値を求め、全粒換算した。具体的には、例えば、Ｎｏ．１のフライアッシュの場合、球換算比表面積値の予測式用全粒換算値ｘは以下のように求めた。
ｘ＝33.2/100×870+27.1/100×2142+19.7/100×4027+20.1/100×11274
この全粒換算値ｘを説明変数とし、材齢９１日活性度指数を目的変数として重回帰分析を行い、下記式のとおり式（Ｘ）の予測式を作製した。
ｙ＝０．００２９ｘ＋７６．９７７
（式（Ｘ）中、αx＝０．００２９、ａx＝７６．９７７、ｙ＝材齢９１日の活性度指数（％））
かかる予測式により得られた予測値を表１に示すとともに、実測値と予測値との関係を図１に示し、フライアッシュについて得られた各所定の値を表２に示す。
得られた予測式の決定係数は０．８１０であった。
[Example 1]
Using a rotating spin air sieve (SAR-75, Seishin Co., Ltd.), each fly ash is divided into four particle groups by three sieves with openings of 10 μm, 20 μm, and 45 μm, and the mass of each particle group is measured. Then, the existence ratio was determined.
Next, a sphere-converted specific surface area value was determined for each particle group using a laser diffraction particle size distribution analyzer (Microtrac Bell Co., Ltd., MT-3300EX-II), and the whole particles were converted. Specifically, for example, In the case of fly ash No. 1, the whole-grain converted value x for the prediction formula of the sphere-converted specific surface area value was obtained as follows.
x = 33.2 / 100 × 870 + 27.1 / 100 × 2142 + 19.7 / 100 × 4027 + 20.1 / 100 × 11274
Multiple regression analysis was performed using the whole-grain converted value x as an explanatory variable and the 91-day-old activity index as an objective variable, and a prediction formula of the formula (X) was prepared as shown below.
y = 0.0029x + 76.977
(In the formula (X), α x = 0.0029, a x = 76.977, y = activity index (%) at 91 days of age)
Table 1 shows the predicted values obtained by the prediction formula, FIG. 1 shows the relationship between the actually measured values and the predicted values, and Table 2 shows the predetermined values obtained for fly ash.
The coefficient of determination of the obtained prediction formula was 0.810.
［比較例１］
実施例１で用いた各フライアッシュについて、分級することなく全粒を対象として球換算比表面積値を求め、これを説明変数とした以外、実施例１と同様にして予測値を得た。この際、作製した予測式は下記式（Ｘ）'のとおりであった。
ｙ＝０．００２６ｘ＋７８．６５１
かかる予測式により得られた予測値を表１に示すとともに、実測値と予測式により得られた予測値との関係を図２に示す。
得られた予測式の決定係数は０．７８６であった。
[Comparative Example 1]
For each fly ash used in Example 1, a sphere-converted specific surface area value was obtained for all grains without classification, and a predicted value was obtained in the same manner as in Example 1 except that this was used as an explanatory variable. At this time, the prepared prediction formula was as shown in the following formula (X) ′.
y = 0.0026x + 78.651
Table 1 shows the predicted values obtained by the prediction formula, and FIG. 2 shows the relationship between the actually measured values and the predicted values obtained by the prediction formula.
The coefficient of determination of the obtained prediction formula was 0.786.
［実施例２］
実施例１で求めた各フライアッシュの粒群ごとにおける存在比率及び球換算比表面積値を用い、蛍光Ｘ線分析の定量分析により化学組成を求めるとともに、粉末Ｘ線回折／リートベルト法により鉱物組成を求めた。また、蛍光Ｘ線の定量分析により求めた各粒群全体の化学組成から酸化物（ムライト、石英、酸化鉄）の鉱物分の成分を計算により除き、ガラス相中の化学組成（ＣａＯ量、Ａｌ2Ｏ3量、ＭｇＯ量及びＳｉＯ2量）を得た。さらにＪＩＳ Ａ ６２０１に準拠して強熱減量も求めた。
これらの値から得られる特性値、存在比率及び球換算比表面積値を用いて上記工程（II）を経ることにより予測式用全粒換算値を求め、これを説明変数として重回帰分析を行い、下記式のとおり式（Ｙ）の予測式を作製した。
ｙy＝０．２２９×Ａ＋１３．３５７×Ｂ＋７９．０４
但し、Ａ＝（１００−石英の割合−強熱減量）×（球換算比表面積値）
Ｂ＝ガラス相中の｛（ＣａＯ量＋Ａｌ2Ｏ3量＋ＭｇＯ量）／ＳｉＯ2量｝
×（球換算比表面積値）
かかる予測式により得られた予測値を表１に示し、フライアッシュについて得られた各所定の値を表２に示す。
また、実測値と予測式により得られた予測値との関係から、得られた予測式の決定係数を求めたところ、０．８２８であった。
[Example 2]
Using the abundance ratio and the spherical equivalent specific surface area value of each fly ash obtained in Example 1 for each particle group, the chemical composition is determined by quantitative analysis of fluorescent X-ray analysis, and the mineral composition is determined by powder X-ray diffraction / Rietveld method. I asked. In addition, the components of the mineral components of oxides (mullite, quartz, iron oxide) were removed by calculation from the chemical composition of each particle group obtained by quantitative analysis of X-ray fluorescence, and the chemical composition (CaO content, Al 2 O 3 amount, MgO amount and SiO 2 amount). Further, the ignition loss was determined in accordance with JIS A6201.
Using the characteristic values, abundance ratios, and sphere-converted specific surface area values obtained from these values, a whole-grain converted value for the prediction formula is obtained through the above-described step (II), and a multiple regression analysis is performed using this as an explanatory variable. A prediction formula of the formula (Y) was prepared as shown below.
y y = 0.229 × A + 13.357 × B + 79.04
However, A = (100-quartz ratio-ignition loss) × (sphere-converted specific surface area value)
B = {(CaO amount + Al 2 O 3 amount + MgO amount) / SiO 2 amount} in glass phase
× (sphere equivalent specific surface area value)
Table 1 shows the predicted values obtained by such a prediction formula, and Table 2 shows each predetermined value obtained for fly ash.
Further, the coefficient of determination of the obtained prediction formula was calculated from the relationship between the actually measured value and the prediction value obtained by the prediction formula, and was 0.828.
［実施例３］
実施例１で求めた各フライアッシュの粒群ごとにおける存在比率及び球換算比表面積値を用い、Ｘ線回折／リートベルト法により化学組成及び鉱物組成を求め、実施例２と同様にして、ガラス相中のＡｌ2Ｏ3量、Ｆｅ2Ｏ3量、ＳｉＯ2量、ＣａＯ量及びＭｇＯ量を得た後、さらにＪＩＳ Ａ ６２０１に準拠して強熱減量も求めた。これらの値から得られる特性値、存在比率及び球換算比表面積値を用いて、各々の予測式用全粒換算値を求めた。
次いで、これを説明変数として重回帰分析を行い、下記式のとおり式（Ｚ）の予測式を作製した。
ｙz＝０．００２９×（球換算比表面積値）
＋２．６３×Ｄ＋２２．１×Ｅ＋７３．８５
但し、Ｄ＝ガラス相中の（Ａｌ2Ｏ3＋Ｆｅ2Ｏ3）／ＳｉＯ2
Ｅ＝ガラス相中の（ＣａＯ＋ＭｇＯ）／ＳｉＯ2
かかる予測式により得られた予測値を表１に示す。
また、実測値と上記予測式により得られた予測値との関係から、得られた予測式の決定係数を求めたところ、０．８４１であった。
[Example 3]
Using the abundance ratio and the spherical equivalent surface area value of each fly ash obtained in Example 1 for each particle group, the chemical composition and the mineral composition were determined by the X-ray diffraction / Rietveld method. After obtaining the amount of Al 2 O 3, the amount of Fe 2 O 3, the amount of SiO 2, the amount of CaO and the amount of MgO in the phase, the ignition loss was further determined in accordance with JIS A6201. Using the characteristic value, the abundance ratio and the sphere-converted specific surface area value obtained from these values, the whole-grain converted value for each prediction formula was determined.
Next, multiple regression analysis was performed using this as an explanatory variable, and a prediction formula of the formula (Z) was prepared as shown below.
yz = 0.0029 x (sphere specific surface area value)
+ 2.63 × D + 22.1 × E + 73.85
Where D = (Al 2 O 3 + Fe 2 O 3 ) / SiO 2 in the glass phase
E = (CaO + MgO) / SiO 2 in glass phase
Table 1 shows the predicted values obtained by the prediction formula.
Further, the coefficient of determination of the obtained prediction equation was calculated from the relationship between the actually measured value and the prediction value obtained by the above-mentioned prediction equation, and was 0.841.
Claims (6)
（Ｉ）フライアッシュを分級して複数の粒群に分割した後、得られた粒群ごとに存在比率を求めるとともに、球換算比表面積値を求めるか、或いは球換算比表面積値と、化学組成、鉱物組成又は強熱減量から得られる１種又は２種以上の特性値とを求める工程
（II）得られた存在比率と球換算比表面積値とを用いて全粒換算し、或いは得られた存在比率と球換算比表面積値と特性値とを用いて全粒換算して、予測式用全粒換算値を求める工程
（III）得られた予測式用全粒換算値を説明変数とし、次いで活性度指数の実測値を目的変数として重回帰分析を行い、予測式を作製する工程
（IV）得られた予測式を用い、予測対象とするフライアッシュの活性度指数を求める工程
を備える、フライアッシュの活性度指数の予測方法。 The following steps (I) to (IV):
(I) After classifying fly ash and dividing it into a plurality of grain groups, determine the abundance ratio for each of the obtained grain groups, and determine the sphere-converted specific surface area value, or the sphere-converted specific surface area value and the chemical composition Calculating one or more characteristic values obtained from mineral composition or loss on ignition (II) Using the obtained abundance ratio and sphere-converted specific surface area value, whole grain conversion or obtained A step of obtaining a whole-grain converted value for the prediction formula by converting the whole grain using the abundance ratio, the sphere-converted specific surface area value and the characteristic value (III) using the obtained whole-grain converted value for the prediction formula as an explanatory variable, A step of performing a multiple regression analysis using the actually measured value of the activity index as an objective variable and preparing a prediction equation (IV) a step of obtaining an activity index of a fly ash to be predicted using the obtained prediction equation; How to predict the ash activity index.
ｙx＝αx×（球換算比表面積値）＋ａx・・・（Ｘ）
（式（Ｘ）中、ｙxは活性度指数の予測値を示し、αxは偏回帰係数を示し、ａxは定数を示す。）
で表される予測式を作製する、請求項１〜３のいずれか１項に記載のフライアッシュの活性度指数の予測方法。 In the step (I), the abundance ratio and the sphere-converted specific surface area value are obtained for each of the obtained particle groups, and in the step (II), the whole grains are converted using the abundance ratio and the sphere-converted specific surface area value. In (III), the following formula (X)
y x = α x × (sphere-specific surface area) + a x (X)
(In the formula (X), y x indicates a predicted value of the activity index, α x indicates a partial regression coefficient, and a x indicates a constant.)
The method for predicting the activity index of fly ash according to any one of claims 1 to 3, wherein a prediction formula represented by the following formula is prepared.
ｙy＝αy×Ａ＋βy×Ｂ＋ａy・・・（Ｙ）
但し、Ａ＝（１００−石英の割合−強熱減量）×（球換算比表面積値）
Ｂ＝｛ガラス相中の（ＣａＯ量＋Ａｌ2Ｏ3量＋ＭｇＯ量）／ＳｉＯ2量｝
×（球換算比表面積値）
（式（Ｙ）中、ｙyは活性度指数の予測値を示し、αy及びβyは偏回帰係数を示し、ａyは定数を示す。）
で表される予測式を作製する、請求項１〜３のいずれか１項に記載のフライアッシュの活性度指数の予測方法。 In the step (I), the spherical specific surface area value is obtained for each of the obtained particle groups, and the ratio of quartz, the amounts of CaO, Al 2 O 3 , MgO and SiO 2 in the glass phase, and the ignition The weight loss is determined as a characteristic value, and in step (II), whole grains are converted using the obtained abundance ratio, sphere-converted specific surface area value, and characteristic value, and in step (III), the following formula (Y):
y y = α y × A + β y × B + a y (Y)
However, A = (100-quartz ratio-ignition loss) × (sphere-converted specific surface area value)
B = {(CaO content + Al 2 O 3 content + MgO content) / SiO 2 content in glass phase}
× (sphere equivalent specific surface area value)
(In the formula (Y), y y indicates a predicted value of the activity index, α y and β y indicate partial regression coefficients, and a y indicates a constant.)
The method for predicting the activity index of fly ash according to any one of claims 1 to 3, wherein a prediction formula represented by the following formula is prepared.
ｙz＝αz×（球換算比表面積値）＋βz×Ｄ＋γz×Ｅ＋ａz・・・（Ｚ）
但し、Ｄ＝ガラス相中の（Ａｌ2Ｏ3＋Ｆｅ2Ｏ3）／ＳｉＯ2
Ｅ＝ガラス相中の（ＣａＯ＋ＭｇＯ）／ＳｉＯ2
（式（Ｚ）中、ｙzは活性度指数の予測値を示し、αz、βz及びγzは偏回帰係数を示し、ａzは定数を示す。）
で表される予測式を作製する、請求項１〜３のいずれか１項に記載のフライアッシュの活性度指数の予測方法。 In the step (I), the sphere-converted specific surface area value is obtained for each of the obtained particle groups, and the Al 2 O 3 amount, Fe 2 O 3 amount, CaO amount, MgO amount, and SiO 2 amount in the glass phase are determined as characteristic values. And in step (II), the whole grains are converted using the obtained abundance ratio, sphere-converted specific surface area value and characteristic value, and in step (III), the following formula (Z):
y z = α z × (spherical equivalent specific surface area value) + β z × D + γ z × E + a z ··· (Z)
Where D = (Al 2 O 3 + Fe 2 O 3 ) / SiO 2 in the glass phase
E = (CaO + MgO) / SiO 2 in glass phase
(In the formula (Z), yz indicates a predicted value of the activity index, α z , β z and γ z indicate partial regression coefficients, and a z indicates a constant.)
The method for predicting the activity index of fly ash according to any one of claims 1 to 3, wherein a prediction formula represented by the following formula is prepared.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112051125A (en) * | 2020-09-16 | 2020-12-08 | 浙江省农业科学院 | Pretreatment method for determining multiple pesticide residues in white paeony root |
| CN112345414A (en) * | 2020-10-21 | 2021-02-09 | 武汉理工大学 | Method for determining surface energy of particle aggregate, storage medium and system |
| KR20230011127A (en) * | 2021-07-13 | 2023-01-20 | 삼성물산 주식회사 | Method for real-time selecting fly ash |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112051125A (en) * | 2020-09-16 | 2020-12-08 | 浙江省农业科学院 | Pretreatment method for determining multiple pesticide residues in white paeony root |
| CN112051125B (en) * | 2020-09-16 | 2022-04-26 | 浙江省农业科学院 | Pretreatment method for determining multiple pesticide residues in white paeony root |
| CN112345414A (en) * | 2020-10-21 | 2021-02-09 | 武汉理工大学 | Method for determining surface energy of particle aggregate, storage medium and system |
| KR20230011127A (en) * | 2021-07-13 | 2023-01-20 | 삼성물산 주식회사 | Method for real-time selecting fly ash |
| KR102526795B1 (en) | 2021-07-13 | 2023-04-28 | 삼성물산 주식회사 | Method for real-time selecting fly ash |
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