JP2005230629A - Powdered activated carbon controller - Google Patents
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Abstract
Description
本発明は、粉末活性炭の注入量を精度よく制御することができる粉末活性炭制御装置に関するものである。 The present invention relates to a powdered activated carbon control device capable of accurately controlling the amount of powdered activated carbon injected.
水道水は安全で衛生的でなければならず、施設の大小にかかわらず浄水処理においては必ず消毒装置を設けなければならない。消毒には塩素が必ず使用され、消毒効果が大きく、消毒の効果が残留する特徴がある。
一方、発ガン性物質であるトリハロメタンやハロ酢酸などの消毒副生成物を生成することが知られており、活性炭注入を行うことで副生成物の濃度を抑制することが重要となる。
活性炭は木質原料を炭化処理することで製造され、マクロ孔を有した構造であることから、吸着性能が優れており、通常の浄水処置だけでは除去できないカビ臭原因物質や有機物質を除去できる。
従来、浄水処理過程での用いられる活性炭注入制御装置は、図２に示すようになっている。図２は、従来の活性炭注入制御装置のブロック図と処理工程を示す説明図である。図において、１はスラリー液槽、２は注入量調整計、３は調整弁、４は計測値、５は注入量設定装置からなる。注入量設定装置５は、５１計測値蓄積装置、５７予測値演算装置、５８注入量指令装置、５９定数入力装置である。
通常、注入方式には、粉末のまま注入する乾式と、スラリー液として注入する湿式とがあり、図２は湿式を示している。スラリー液槽１の内には、図示しないスラリー溶解槽にて水と活性炭を混合した一定濃度のスラリー液が移送され貯蔵されており、濃度を均一に保つため攪拌機が取り付けられている。スラリー液は、混和槽7に注入され、一定時間原水に接触された後、凝集沈殿池8、急速ろ過機9において、凝集・ろ過され除去される。ろ過された水は配水池10から需要家に供給される。
活性炭の注入量は、注入量調整計２によって、注入量設定装置５で設定された注入量になるように調整弁３を制御することで、除去対象物質の濃度に応じて調整され、トリハロメタン前駆物質の場合は、３０〜１００ｍｇ／ｌの範囲が多い。
一方、トリハロメタン前駆物質に代表されるような消毒副生成物量の測定には、複雑な前処理やイオンクロマトグラフ法による測定器の操作などの労力を必要とし、分析に非常に時間がかかっている。
そこで、副生成物の生成量が原水の水温や導電率などで大きく変化することから、注入量設定装置５では、除去対象物質を統計的手法で予測して、適正な注入量を決定している。
注入量測定装置５において、予測値演算装置５７は、計測値蓄積装置５１に取り込まれた水温や導電率や紫外線吸光度などのオンラインの計測値４と、定数入力装置５９で入力された定数を用いて次の式で除去対象物質の生成量を予測する（例えば、特許文献１）。
Tap water must be safe and hygienic, and a disinfection device must be provided for water purification regardless of the size of the facility. Chlorine is always used for disinfection, and it has the feature that the disinfection effect is large and the disinfection effect remains.
On the other hand, it is known to produce disinfection by-products such as carcinogenic substances such as trihalomethane and haloacetic acid, and it is important to suppress the concentration of by-products by performing activated carbon injection.
Activated carbon is produced by carbonizing a woody material and has a macropore structure. Therefore, the activated carbon has excellent adsorption performance and can remove mold odor-causing substances and organic substances that cannot be removed only by ordinary water purification treatment.
2. Description of the Related Art Conventionally, an activated carbon injection control device used in a water purification process is as shown in FIG. FIG. 2 is a block diagram of a conventional activated carbon injection control device and an explanatory diagram showing processing steps. In the figure, 1 is a slurry liquid tank, 2 is an injection amount adjusting meter, 3 is an adjustment valve, 4 is a measured value, and 5 is an injection amount setting device. The injection amount setting device 5 is a 51 measured value storage device, a 57 predicted value calculation device, a 58 injection amount command device, and a 59 constant input device.
In general, there are two types of injection methods: a dry method of injecting powder as it is and a wet method of injecting as a slurry liquid. FIG. 2 shows the wet method. In the slurry liquid tank 1, a slurry liquid having a constant concentration obtained by mixing water and activated carbon in a slurry dissolving tank (not shown) is transferred and stored, and a stirrer is attached to keep the concentration uniform. The slurry liquid is poured into the mixing tank 7 and brought into contact with the raw water for a certain period of time, and then coagulated and filtered in the coagulating sedimentation basin 8 and the rapid filter 9 and removed. The filtered water is supplied from the distribution reservoir 10 to consumers.
The injection amount of the activated carbon is adjusted according to the concentration of the substance to be removed by controlling the adjustment valve 3 so that the injection amount set by the injection amount setting device 5 is adjusted by the injection amount adjusting device 2. In the case of substances, the range of 30 to 100 mg / l is large.
On the other hand, the measurement of the amount of disinfection by-products represented by trihalomethane precursors requires labor such as complicated pretreatment and operation of measuring instruments by ion chromatography, and the analysis is very time consuming. .
Therefore, since the amount of by-products generated varies greatly depending on the raw water temperature, conductivity, etc., the injection amount setting device 5 predicts the substance to be removed by a statistical method and determines an appropriate injection amount. Yes.
In the injection amount measuring device 5, the predicted value calculating device 57 uses the online measured value 4 such as the water temperature, conductivity, and ultraviolet absorbance taken into the measured value storage device 51 and the constant input by the constant input device 59. Then, the generation amount of the removal target substance is predicted by the following formula (for example, Patent Document 1).
Ｃ：トリハロメタン生成量（μｇ／ｌ）
Ａ：紫外線吸光度（ａｂｓ／５０ｍｍ）
Ｔ：試料水の液温（℃）
Ｘ：原水の液温（μ／ｓ）
ａ，ｂ，ｇ，ｈ：定数
定数ａ、ｂ、ｇ、ｈは、予備試験あるいは過去のデータにより統計的に決定された値を定数入力装置５９を通して設定する。
予測された生成量に応じて、注入量指令装置５８にて注入量設定値を決定し注入量調整計に指令値を渡す。
A: UV absorbance (abs / 50 mm)
T: Sample water temperature (° C)
X: Raw water solution temperature (μ / s)
a, b, g, h: constants The constants a, b, g, h are set through the constant input device 59 as statistically determined values based on preliminary tests or past data.
In accordance with the predicted generation amount, an injection amount setting device 58 determines an injection amount setting value and passes the command value to the injection amount adjuster.
従来の粉末活性炭制御装置において除去対象物質を予測する場合、除去対象物質と相関関係の高い計測項目について、過去のデータを統計的に処理することで定数を決定し、この定数ならびに現在の計測値と予測される対象物質との関係を表現する前述の式を用いて推定している。しかし、実際の除去対象物質の生成には、現在の計測値に加え、数分前の計測値の挙動を考慮する必要があるが、現状の予測方法では困難であった。したがって、正確な予測ができていない結果になっている。
そこで、本発明は、現時点の計測値に加え、数分前の計測値の挙動を考慮した予測を行い、高精度の予測ができる粉末活性炭制御装置を提供することを目的とする。
When the removal target substance is predicted in the conventional powdered activated carbon control device, a constant is determined by statistically processing past data for the measurement item having a high correlation with the removal target substance. It is estimated using the above-described formula that expresses the relationship with the predicted target substance. However, in order to actually generate the substance to be removed, it is necessary to consider the behavior of the measured value several minutes ago in addition to the current measured value, but it is difficult with the current prediction method. Therefore, the results are not accurate.
Therefore, an object of the present invention is to provide a powdered activated carbon control apparatus capable of performing prediction in consideration of the behavior of a measured value several minutes ago in addition to the current measured value, and performing highly accurate prediction.
上記問題を解決するために、本発明は、次のように構成したものである。
請求項１に記載の発明は、 水処理工程にて各種の計測値を蓄積する計測値蓄積装置、除去対象物質の濃度の将来動向をオンラインの計測値から予測する予測値演算装置、および前記予測値演算装置の予測値に基づき粉末活性炭の注入量を指令する注入量指令装置とからなる注入量設定装置と、前記注入量指令装置の指令をうけて注入量を調整する注入量調整器と、前記注入量調整器の注入量に応じて開閉する調整弁とを備えた粉末活性炭制御装置において、前記注入量設定装置は、前記計測値蓄積装置の後段に設けられ予測に必要なシステム変数を選択するシステム変数選択装置と、前記除去対象物質の平均変動パターンを作成する平均変動パターン作成装置と、前記平均変動パターンから前記除去対象物質の測定値を差引いた残差部分を作成する除去対象物質データ演算装置と、前記除去対象物質データ演算装置の演算値を基に自己回帰係数の値を決定する自己回帰モデル作成装置と、前記平均変動パターンとの残差予測値を求める残差予測値演算装置とからなり、前記平均変動パターンと前記残差予測値との和から前記除去対象物質の濃度を予測するものである。
請求項２に記載の発明は、前記除去対象物質データ演算装置は、前記除去対象物質の周期的な変動を求め、周期的な変動部分以外は、前記除去対象物質の量と前記変動パターンの差である残差部分を求め、前記残差部分と除去対象物質の生成要因となる計測値を統計モデルの自己回帰係数として使用することにより前記残差部分の予測値を求めるものである。
請求項３に記載の発明は、前記除去対象物質データ演算装置での前記残差部分を求めるさいに、前記除去対象物質の周期的変動として、急激な除去対象物質の変動が見られない直近の期間の計測値を用いるものである。
請求項４に記載の発明は、前記残差予測値演算装置の前記残差部分の予測に、前記自己回帰モデルを用いるものである。
請求項５に記載の発明は、前記システム変数選択装置に入力するシステム変数を、相関レベル１と相関レベル２に層別したものである。
請求項６に記載の発明は、前記相関レベル１のシステム変数を、前記除去対象物質がトリハロメタンの場合、相関関係を有する水温としたものである。
請求項７に記載の発明は、前記相関レベル２のシステム変数を、前記除去対象物質がトリハロメタンの場合、流量、ｐＨ値、導電率、濁度、紫外線吸光度、色度、硬度としたものである。
In order to solve the above problems, the present invention is configured as follows.
The invention described in claim 1 is a measurement value storage device that stores various measurement values in a water treatment process, a prediction value calculation device that predicts a future trend of the concentration of a removal target substance from online measurement values, and the prediction An injection amount setting device comprising an injection amount command device for instructing the injection amount of powdered activated carbon based on the predicted value of the value calculation device, an injection amount adjuster for adjusting the injection amount in response to the command of the injection amount command device, In the powder activated carbon control device including an adjustment valve that opens and closes according to the injection amount of the injection amount adjuster, the injection amount setting device selects a system variable necessary for prediction provided at a subsequent stage of the measurement value storage device A system variable selection device that creates an average fluctuation pattern of the removal target substance, and a residual portion that is obtained by subtracting the measured value of the removal target substance from the average fluctuation pattern. To calculate a residual prediction value between the average fluctuation pattern and a removal target substance data calculation device to be formed, an autoregressive model creation device for determining a value of an autoregressive coefficient based on a calculation value of the removal target substance data calculation device It consists of a residual prediction value computing device, and predicts the concentration of the removal target substance from the sum of the average fluctuation pattern and the residual prediction value.
The invention according to claim 2 is characterized in that the removal target substance data calculation device obtains periodic fluctuations of the removal target substance, and the difference between the quantity of the removal target substance and the fluctuation pattern, except for the periodic fluctuation part. Is obtained, and a predicted value of the residual part is obtained by using the residual part and a measurement value that is a generation factor of the substance to be removed as an autoregressive coefficient of a statistical model.
According to a third aspect of the present invention, when the residual portion in the removal target substance data calculation device is obtained, the latest removal target substance is not observed as a periodic fluctuation of the removal target substance. The measured value of the period is used.
According to a fourth aspect of the present invention, the autoregressive model is used for the prediction of the residual portion of the residual prediction value calculation device.
According to a fifth aspect of the present invention, system variables input to the system variable selection device are stratified into correlation level 1 and correlation level 2.
In the invention according to claim 6, when the removal target substance is trihalomethane, the correlation level 1 system variable is a water temperature having a correlation.
In the invention according to claim 7, when the removal target substance is trihalomethane, the system variable of the correlation level 2 is set to flow rate, pH value, conductivity, turbidity, ultraviolet absorbance, chromaticity, and hardness. .
請求項１に記載の発明によると、地域別に適した予測ができ、数時間前の計測値を考慮した予測ができ、高精度な予測ができる。
請求項２に記載の発明によると、平均変動と残差部分を層別でき、高精度な予測ができる。
請求項３に記載の発明は、平均変動パターンを正確に作成できるため、高精度な予測ができる。
請求項４に記載の発明によると、数時間前の計測値を考慮した予測ができ、高精度な予測ができる。
請求項５に記載の発明は、地域別に適したシステム変数を設定でき、地域固有の高精度な予測ができる。
請求項６に記載の発明は、相関関係の高いシステム変数を用いて予測するので、高精度な予測ができる。
請求項７に記載の発明は、除去対象物質と相関関係のある水質項目をシステム変数として任意に設定できるので、高精度な予測ができる。
According to the first aspect of the present invention, prediction suitable for each region can be performed, prediction can be performed in consideration of a measurement value several hours ago, and highly accurate prediction can be performed.
According to invention of Claim 2, an average fluctuation | variation and a residual part can be stratified, and highly accurate prediction can be performed.
According to the third aspect of the present invention, since the average variation pattern can be accurately created, highly accurate prediction can be performed.
According to the invention described in claim 4, it is possible to perform prediction in consideration of the measurement value several hours ago, and to perform highly accurate prediction.
The invention according to claim 5 can set a system variable suitable for each region, and can perform high-precision prediction specific to the region.
Since the invention according to claim 6 performs prediction using system variables having high correlation, prediction with high accuracy can be performed.
Since the water quality item correlated with the removal target substance can be arbitrarily set as the system variable, the invention according to claim 7 can perform highly accurate prediction.
以下、本発明の実施の形態について図を参照して説明する。 Hereinafter, embodiments of the present invention will be described with reference to the drawings.
図１は本発明の実施例を示す粉末活性炭制御装置のブロック図である。図において、５２はシステム変数選択装置、５３は平均変動パターン作成装置、５４は除去対象物質データ演算装置、５５は自己回帰モデル作成装置、５６は残差予測値演算装置である。従来と同じ構成要素については同じ符号を付してその説明を省略する。
本発明が従来技術と異なる構成は、注入量設定装置５として、システム変数選択装置５２と平均変動パターン作成装置５３と除去対象物質データ演算装置５４と自己回帰モデル作成装置５５と残差予測値演算装置５６を設けた点である。
次に装置の構成を具体的に説明する。
システム変数選択装置５２は、計測値蓄積装置５１で蓄積された計測値のなかから、予測式に使用するシステム変数を選択する。ここでは、地域によって原水水質が異なっており、除去対象物質の挙動を左右する水質項目も異なっていることから、対象地域に適したシステム変数を適宜決定する必要がある。
そこで、システム変数を、相関レベル１と相関レベル２に層別することを行う。相関レベル１に割り当てる水質項目は、予測を実施するために最低１つの水質項目を割り当てることとし、除去対象物質の挙動と相関関係が高いものとを選択することで、高精度な予測を実現できる。例えば、トリハロメタンの場合、水温といった生成に特に影響を与える水質項目をレベル１に設定する。相関レベル２は、除去対象物質の挙動に影響を与えると考えられる水質項目を割り当てる。相関レベル２は自由度が高く、設定しない場合もあれば、可能性のある水質項目を際限なく選択可能となっている。例えば、トリハロメタンの場合、相関レベル２には、影響を与える指標として考えられる、原水の導電率、ｐＨ値、流量、濁度、紫外線吸光度、色度、硬度などを使用する。
平均変動パターン作成装置５３は、システム変数選択装置で選択されたシステム変数の計測値から平均変動パターンを作成する。
ここでは、気温の上昇による水温の上昇や、降雨による取水源の流量増加などにより原水水質の変動が激しく起こり、結果として除去対象物質の変動を引き起こしているデータ領域と、周期的に緩やかに変化するデータ領域を層別し、平均変動パターンは後者のデータを用いて作成する。つまり平均変動パターンは、急激な除去対象物質の変化が発生した期間以外について、各時刻の直近数日間の平均値から算出する。また平均変動パターンの更新は、予測日により近い計測値を使用することを目的に毎日行う。
除去対象物質データ演算装置５４は、計測値蓄積装置５１に記憶している計測値から平均変動パターン作成装置５３で作成した平均変動パターンを引いた部分（以下、これを残差部分と呼ぶ）を作成し、この値を予測の対象とする。
残差部分のデータは、以下の式で求められる。
FIG. 1 is a block diagram of a powdered activated carbon control apparatus showing an embodiment of the present invention. In the figure, 52 is a system variable selection device, 53 is an average variation pattern creation device, 54 is a removal target substance data computation device, 55 is an autoregressive model creation device, and 56 is a residual prediction value computation device. Constituent elements that are the same as conventional ones are given the same reference numerals, and descriptions thereof are omitted.
The configuration in which the present invention is different from the prior art is that, as an injection amount setting device 5, a system variable selection device 52, an average variation pattern creation device 53, a removal target substance data computation device 54, an autoregressive model creation device 55, and a residual prediction value computation. The device 56 is provided.
Next, the configuration of the apparatus will be specifically described.
The system variable selection device 52 selects a system variable to be used for the prediction formula from the measurement values accumulated in the measurement value accumulation device 51. Here, since the raw water quality varies depending on the region, and the water quality items that influence the behavior of the removal target substance also differ, it is necessary to appropriately determine system variables suitable for the target region.
Therefore, system variables are classified into correlation level 1 and correlation level 2. The water quality item to be assigned to correlation level 1 is assigned at least one water quality item in order to carry out the prediction, and by selecting a substance having a high correlation with the behavior of the substance to be removed, highly accurate prediction can be realized. . For example, in the case of trihalomethane, a water quality item that particularly affects production, such as water temperature, is set to level 1. Correlation level 2 assigns water quality items that are considered to affect the behavior of the removal target substance. The correlation level 2 has a high degree of freedom, and there are cases where it is not set, and possible water quality items can be selected without limit. For example, in the case of trihalomethane, the correlation level 2 uses the conductivity, pH value, flow rate, turbidity, ultraviolet absorbance, chromaticity, hardness, and the like, which are considered as affecting indices.
The average variation pattern creation device 53 creates an average variation pattern from the measured values of the system variables selected by the system variable selection device.
Here, fluctuations in the quality of raw water occur violently due to water temperature rises due to temperature rises and increases in the flow rate of water intake due to rainfall, etc. The data area to be processed is stratified, and the average variation pattern is created using the latter data. In other words, the average variation pattern is calculated from the average value of the most recent days at each time except for the period in which the rapid change of the removal target substance occurs. The average fluctuation pattern is updated every day for the purpose of using a measured value closer to the predicted date.
The removal target substance data calculation device 54 subtracts the average fluctuation pattern created by the average fluctuation pattern creation device 53 from the measurement value stored in the measurement value storage device 51 (hereinafter referred to as the residual portion). Create this and make this value the target of prediction.
The data of the residual part is obtained by the following formula.
但し、
Ｍｄｉ（ｉ）：時刻ｉにおける残差部分の計測値
Ｍ（ｉ）：時刻ｉにおける除去対象物質量
Ｍａｖｅ（ｊ）：時刻ｊにおける平均変動パターン
ｉ：計測時刻
ｊ：時刻（ｊ＝１，２，３，．．．．．２４）
５５は自己回帰モデル作成装置で、除去対象物質データ演算装置５４で作成した残差データと相関レベル１と相関レベル２で決めた計測値を入力して自己回帰モデルを作成する。
いま、時刻ｎにおけるプロセスの状態をｋ次元の全変数ベクトルＸ（ｎ）、時刻ｎよりｍ時点前の全変数ベクトルをＸ（ｎ−ｍ）、白色ノイズベクトルをＵ（ｎ）、自己回帰モデルの回帰係数をＡ（ｍ）、自己回帰モデルの最適次数をＭで表すと、その自己回帰表現は、
However,
M di (i): measurement value of residual portion at time i M (i): removal target substance amount at time i M ave (j): average variation pattern at time j: measurement time j: time (j = 1) , 2, 3, ... 24)
Reference numeral 55 denotes an autoregressive model creation device that inputs the residual data created by the removal target substance data calculation device 54 and the measured values determined by the correlation level 1 and the correlation level 2 to create an autoregressive model.
Now, the state of the process at time n is a k-dimensional all variable vector X (n), all variable vectors m times before time n are X (nm), white noise vector is U (n), autoregressive model When the regression coefficient of A (m) and the optimal order of the autoregressive model are represented by M, the autoregressive expression is
で表される。
従って自己回帰モデルの作成とは、自己回帰係数、白色ノイズベクトルの分散および自己回帰モデルの最適次数の決定に帰結される。
自己回帰係数Ａ（ｍ）は、要素をＡｉｊ（ｍ）とし、次の連立方程式をｉ＝１，２，３，・・・ｋについて解くことにより求められる。
但し、Ｘｉ、Ｘｊの相互分散をＲｉｊ（ｌ）、自己回帰係数の要素をＡｉｊ（ｍ）とすると
It is represented by
Therefore, the creation of the autoregressive model results in the determination of the autoregressive coefficient, the variance of the white noise vector, and the optimal order of the autoregressive model.
The autoregressive coefficient A (m) is obtained by solving the following simultaneous equations for i = 1, 2, 3,... K with the element Aij (m).
However, if the mutual variance of Xi and Xj is Rij (l) and the element of the autoregressive coefficient is Aij (m)
という連立一次方程式をｉ＝１，２，．．．，ｋについて解けばＡｉｊ（ｍ）が求められる。
白色ノイズベクトルＵ（ｎ）の要素をεｉ（ｎ）とすると、その残差分散値σｉ２は次のようになる。
The simultaneous linear equations i = 1, 2,. . . , K, Aij (m) is obtained.
When the element of the white noise vector U (n) is εi (n), the residual variance value σi 2 is as follows.
なお、モデルの最適次数Ｍは予測誤差を表す（６）式のＭＦＰＥ（Ｍ）を最小にする値である。 Note that the optimal order M of the model is a value that minimizes MFPE (M) in the equation (6) representing the prediction error.
但し、Ｎはデータ数、‖ｄＭ‖はＵ（ｎ）の分散共分散行列推定値である。またＭＦＰＥはMultiple Final Prediction Errorの頭文字である。
このようにして自己回帰係数、白色ノイズの分散および最適モデル次数が求められ、自己回帰モデルが作成される。従って、残差部分の予測を行うために必要な、残差部分と計測値との関係式を自己回帰モデルから求めることができる。自己回帰モデルの更新は、直近の計測値を使用することを目的に直近数十日の計測値を使用して１日１回行う。
５６は残差予測値演算装置で、自己回帰モデル作成装置５５で作成した自己回帰モデルによる予測値から統計的に類推可能な残差部分の数時間先の予測値を演算する。自己回帰モデルを用いた時の数時間先の予測は次のように表される。
Here, N is the number of data, and ‖d M ‖ is the variance / covariance matrix estimate of U (n). MFPE is an acronym for Multiple Final Prediction Error.
In this way, the autoregressive coefficient, the variance of white noise, and the optimal model order are obtained, and an autoregressive model is created. Therefore, a relational expression between the residual part and the measurement value necessary for predicting the residual part can be obtained from the autoregressive model. The autoregressive model is updated once a day using the measured values of the last several tens of days for the purpose of using the latest measured values.
Reference numeral 56 denotes a residual prediction value calculation device, which calculates a prediction value several hours ahead of the residual portion that can be statistically estimated from the prediction value based on the autoregressive model created by the autoregressive model creation device 55. The prediction several hours ahead when using the autoregressive model is expressed as follows.
但し、
Ｍｄｉ（ｉ）ｐ：時刻ｉにおける残差部分の予測値
Ｍｄｉ（ｉ）：時刻ｉにおける残差部分の計測値
Ｌｅｖｅｌ１（ｉ）：時刻ｉにおけるのＬｅｖｅｌ１計測値
Ｌｅｖｅｌ２（ｉ）：時刻ｉにおけるのＬｅｖｅｌ２計測値
Ａ１１（ｍ）：残差部分の予測値に対する残差部分の自己回帰係数
Ａ１２（ｍ）：残差部分の予測値に対するＬｅｖｅｌ１計測値の自己回帰係数
Ａ１３（ｍ）：残差部分の予測値に対するＬｅｖｅｌ２計測値の自己回帰係数
しかし、１点先以上の予測が必要なため、１点先以上の予測には、残差部分は予測値を使用し、Ｌｅｖｅｌ１計測値とＬｅｖｅｌ２計測値は前回の計測値を使用する。このようにして得られた残差部分の予測値Ｍｄｉ（０）ｐ，Ｍｄｉ（１）ｐ・・・を予測値演算装置５７に出力する。
５７は予測値演算装置で、平均変動パターン作成装置５３の平均変動パターンと残差予測値演算装置５６の残差予測値との和を除去対象物質量予測値とする。除去対象物質予測式は、次のように決定される。
However,
M di (i) p : Prediction value of residual part at time i M di (i): Measurement value of residual part at time i Level 1 (i): Level 1 measurement value at time i Level 2 (i): Time i Level 2 measured value A 11 (m) of: autoregressive coefficient A 12 (m) of the residual part relative to the predicted value of the residual part A m (m): autoregressive coefficient A 13 (m) of the Level 1 measured value relative to the predicted value of the residual part : Autoregressive coefficient of Level 2 measurement value with respect to the prediction value of the residual part However, since prediction of one point or more is required, the prediction part uses the prediction value for the prediction of one point or more, and Level 1 measurement value The Level2 measurement value uses the previous measurement value. The prediction values M di (0) p , M di (1) p ... Of the residual portions obtained in this way are output to the prediction value calculation device 57.
57 is a predicted value calculation device, and the sum of the average fluctuation pattern of the average fluctuation pattern creation device 53 and the residual prediction value of the residual prediction value calculation device 56 is set as the removal target substance amount predicted value. The removal target substance prediction formula is determined as follows.
但し、
Ｍ（ｉ）ｐ：予測除去対象物質量
５８は注入量指令装置で、５７の予測値演算装置で演算された除去対象物質から最適な注入量指令を２の注入量調整計に出力する。
However,
M (i) p : Predicted removal target substance amount 58 is an injection amount command device, which outputs an optimal injection amount command from the removal target material calculated by the predicted value calculation device 57 to the injection amount adjuster 2.
このように、粉末活性炭制御装置は、地域に応じたシステム変数を使用して予測でき、予測を数時間前の計測値を考慮して行うので、地域別に高精度な予測ができる。また、平均変動パターンは除去対象物質の急激な変動のあった期間以外の平均値を使用し、自己回帰モデルは、過去数十日の計測値を使用していることから、稼動後数週間で浄水処理施設に適した予測を行うことができること、平均変動パターン、自己回帰モデルを毎日更新することで、年間を通して雨量や気温の影響に自動的に対応した予測を行うことができる。 As described above, the powdered activated carbon control device can predict using the system variable corresponding to the region, and performs the prediction in consideration of the measurement value several hours ago, and therefore can perform highly accurate prediction for each region. In addition, the average fluctuation pattern uses the average value for the period other than when there was a sudden change in the removal target substance, and the autoregressive model uses the measured values for the past several tens of days. Predictions suitable for water treatment facilities can be made, and average fluctuation patterns and autoregressive models can be updated daily to make predictions that automatically correspond to the effects of rainfall and temperature throughout the year.
１ スラリー液槽
２ 注入量調整計
３ 調整弁
４ 計測値
５ 注入量設定装置
５１ 計測値蓄積装置
５２ システム変数選択装置
５３ 平均変動パターン作成装置
５４ 除去対象物質データ演算装置
５５ 自己回帰モデル作成装置
５６ 残差予測値演算装置
５７ 予測値演算装置
５８ 注入量指令装置
６ 取水源
７ 混和槽
８ 凝集沈殿池
９ 急速ろ過機
１０ 配水池
DESCRIPTION OF SYMBOLS 1 Slurry liquid tank 2 Injection quantity regulator 3 Adjustment valve 4 Measurement value 5 Injection quantity setting apparatus 51 Measurement value storage apparatus 52 System variable selection apparatus 53 Average fluctuation pattern creation apparatus 54 Removal target substance data calculation apparatus 55 Autoregressive model creation apparatus 56 Residual predicted value calculation device 57 Predicted value calculation device 58 Injection amount command device 6 Intake source 7 Mixing tank 8 Coagulation sedimentation tank 9 Rapid filter 10 Distribution reservoir
Claims (7)
前記注入量設定装置は、前記計測値蓄積装置の後段に設けられ予測に必要なシステム変数を選択するシステム変数選択装置と、前記除去対象物質の平均変動パターンを作成する平均変動パターン作成装置と、前記平均変動パターンから前記除去対象物質の測定値を差引いた残差部分を作成する除去対象物質データ演算装置と、前記除去対象物質データ演算装置の演算値を基に自己回帰係数の値を決定する自己回帰モデル作成装置と、前記平均変動パターンとの残差予測値を求める残差予測値演算装置とからなり、前記平均変動パターンと前記残差予測値との和から前記除去対象物質の濃度を予測することを特徴とする粉末活性炭制御装置。 Measurement value accumulation device for accumulating various measurement values in the water treatment process, prediction value calculation device for predicting the future trend of the concentration of the substance to be removed from online measurement values, and powder based on the prediction value of the prediction value calculation device An injection amount setting device comprising an injection amount command device for instructing an injection amount of activated carbon, an injection amount adjuster for adjusting the injection amount in response to a command of the injection amount command device, and an injection amount of the injection amount adjuster In the powdered activated carbon control device equipped with an adjustment valve that opens and closes in response,
The injection amount setting device is provided at a subsequent stage of the measurement value storage device, a system variable selection device that selects a system variable necessary for prediction, an average variation pattern creation device that creates an average variation pattern of the removal target substance, A removal target substance data calculation device that creates a residual part obtained by subtracting the measured value of the removal target substance from the average variation pattern, and a value of the autoregressive coefficient is determined based on the calculation value of the removal target substance data calculation device An autoregressive model creation device and a residual prediction value calculation device for obtaining a residual prediction value with respect to the average fluctuation pattern, and the concentration of the removal target substance is determined from the sum of the average fluctuation pattern and the residual prediction value. A powder activated carbon control device characterized by predicting.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| KR20140053344A (en) | 2011-10-28 | 2014-05-07 | 아사히 가세이 케미칼즈 가부시키가이샤 | Chemical injection control method and chemical injection controller |
| JP2019148482A (en) * | 2018-02-27 | 2019-09-05 | 株式会社日立製作所 | Aqueous environment sensing device |
| JP2021079310A (en) * | 2019-11-14 | 2021-05-27 | 学校法人 中央大学 | Information processing device, information processing method, and program |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140053344A (en) | 2011-10-28 | 2014-05-07 | 아사히 가세이 케미칼즈 가부시키가이샤 | Chemical injection control method and chemical injection controller |
| US9517954B2 (en) | 2011-10-28 | 2016-12-13 | Asahi Kasei Chemicals Corporation | Chemical injection control method and chemical injection controller |
| JP2019148482A (en) * | 2018-02-27 | 2019-09-05 | 株式会社日立製作所 | Aqueous environment sensing device |
| JP7021976B2 (en) | 2018-02-27 | 2022-02-17 | 株式会社日立製作所 | Water environment sensing device |
| JP2021079310A (en) * | 2019-11-14 | 2021-05-27 | 学校法人 中央大学 | Information processing device, information processing method, and program |
| JP7460267B2 (en) | 2019-11-14 | 2024-04-02 | 学校法人 中央大学 | Information processing device, information processing method, and program |
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