JP2000345604A - Device for estimating inflow sewage quantity - Google Patents
Device for estimating inflow sewage quantityInfo
- Publication number
- JP2000345604A JP2000345604A JP11161734A JP16173499A JP2000345604A JP 2000345604 A JP2000345604 A JP 2000345604A JP 11161734 A JP11161734 A JP 11161734A JP 16173499 A JP16173499 A JP 16173499A JP 2000345604 A JP2000345604 A JP 2000345604A
- Authority
- JP
- Japan
- Prior art keywords
- inflow
- amount
- data
- sewage
- rainfall
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Sewage (AREA)
- Feedback Control In General (AREA)
Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、下水処理場の流入
下水量予測を、精度良く行うことが出来る流入下水量予
測装置に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an inflow sewage amount prediction apparatus capable of accurately predicting an inflow sewage amount of a sewage treatment plant.
【０００２】[0002]
【従来の技術】従来、浸水防除のためには雨水排水施設
の拡大、既存施設を生かした施設運用が必要である。特
に、既存施設の運用では、降雨量からポンプ井への流入
量を予測し、その値に見合ったポンプ制御を行う方法が
種々提案されている。たとえば、降雨をもとに雨水排水
路の設計に必要な雨水流出ハイドログラフを算出する修
正RRL(Load Research Laboratory) 法を使用したものが
ある（特開8-123538）。図２はその一部を示す流入下水
量予測装置のブロック図である。１は雨量計で、雨の降
雨強度を測定する。３は、ポンプ場１３に設置された雨
水・汚水を排水する排水ポンプで、ポンプ井１４に貯留
した雨水・汚水を貯留量に応じて汲み上げ吐出する。４
は計測値蓄積装置で、雨量計で計測された降雨強度等の
計測データを蓄積する装置である。１２は修正RRL 法を
用いた流入下水量予測装置、１１はポンプ運転指令装置
である。流入下水量予測装置１２は、浸透域面積、流下
時間、貯留量・流出量曲線を求め、有効降雨量、雨水流
入水、流入下水量の演算を行う。浸透域面積は、排水面
積に対する不浸透域面積の割合を示すものである。不浸
透域面積、浸透域面積の計算方法は、対象とする排水区
域の航空写真に二つの乱数の組み合わせを適当な場所を
原点としてプロットし、全プロット数に対する不浸透域
プロット数、浸透域プロット数の割合をもって不浸透域
面積、浸透域面積を算出する方法である。有効降雨量の
演算は、有効面積演算装置で演算された浸透域と不浸透
域から管きょに流入する流入降雨量を演算する。流下時
間は、排水区域の管きょの位置、形状、管径、こう配な
どからから、満流流速を使って各マンホール間の時間か
ら求める。貯留量・流出量曲線は、管きょに流入した雨
水の管内貯留を考慮に入れた貯留量・流出量の関係を求
める。雨水流入水は、有効降雨強度と流下時間とを用い
て、単位図の手法により算出する。流入下水量は、貯留
量・流出量曲線で求められた関数から連続式を解いて求
める。ポンプ運転指令装置１１は、流入下水量予測装置
１２により演算された流入下水量から最適なポンプ運転
指令を出力する。2. Description of the Related Art Conventionally, in order to prevent flooding, it is necessary to expand rainwater drainage facilities and to operate facilities utilizing existing facilities. In particular, in the operation of existing facilities, various methods have been proposed for predicting the amount of inflow to the pump well from the amount of rainfall and performing pump control according to the value. For example, there is a method using a modified RRL (Load Research Laboratory) method for calculating a rainwater outflow hydrograph required for designing a rainwater drainage channel based on rainfall (Japanese Patent Laid-Open No. 8-123538). FIG. 2 is a block diagram of the inflow sewage amount prediction device showing a part thereof. A rain gauge 1 measures the intensity of rainfall. Reference numeral 3 denotes a drainage pump installed in the pump station 13 for draining rainwater and sewage, and pumps up and discharges rainwater and sewage stored in the pump well 14 according to the storage amount. 4
Is a measurement value accumulating device for accumulating measurement data such as rainfall intensity measured by a rain gauge. 12 is an inflow sewage amount prediction device using the modified RRL method, and 11 is a pump operation command device. The inflow sewage amount prediction device 12 obtains the infiltration area, the downflow time, the storage amount / outflow amount curve, and calculates the effective rainfall amount, rainwater inflow water, and inflow sewage amount. The infiltration area indicates the ratio of the impermeable area to the drainage area. The calculation method of the impervious area area and the infiltration area area is to plot a combination of two random numbers on the aerial photograph of the target drainage area with the appropriate place as the origin, and plot the number of impervious area plots and the permeate area plot with respect to the total number of plots This is a method of calculating the area of the impervious area and the area of the permeate area using the ratio of numbers. The effective rainfall is calculated by calculating the inflow rainfall flowing into the pipe from the infiltration area and the impervious area calculated by the effective area calculation device. The flow time is determined from the position, shape, pipe diameter, gradient, etc. of the pipe in the drainage area, and the time between each manhole using the full flow velocity. The storage / outflow curve determines the relationship between the storage and outflow taking into account the in-pipe storage of rainwater that has flowed into the pipeline. The rainwater inflow is calculated using the effective rainfall intensity and the falling time by the method of the unit diagram. The inflow sewage amount is obtained by solving a continuous equation from the function obtained from the storage amount / outflow amount curve. The pump operation command device 11 outputs an optimal pump operation command from the inflow sewage amount calculated by the inflow sewage amount prediction device 12.
【０００３】[0003]
【発明が解決しようとする課題】ところが、このような
修正RRL 法による流入下水量予測では、有効降雨量、流
下時間、貯留量・流出曲線などを求めるために、浸透
域、不浸透域、流下時間、貯留量・流入量の関係など膨
大なデータの算出が必要となり、着工前に膨大な時間が
かかりコストもかかる。また、浸透域、不浸透域など
は、住宅状況の変化、道路状況の変化によって、その値
を随時修正しなければならない。しかし、これらの値を
変更するには時間と人の大きな労力を必要とし、常に更
新することは困難なため予測精度も悪くなる。そこで、
本発明は、降雨強度や流入下水量などのオンラインデー
タのみにより、高精度で安価な流入下水量予測装置を提
供することを目的としている。However, in the prediction of inflow and sewage by such a modified RRL method, in order to obtain the effective rainfall, the downflow time, the storage / runoff curve, etc. It is necessary to calculate a huge amount of data such as the relationship between the time, the storage amount and the inflow amount, and it takes an enormous amount of time and cost before the start of construction. In addition, the values of the infiltrated area and the impervious area must be corrected as needed according to changes in housing conditions and road conditions. However, changing these values requires a lot of time and human labor, and it is difficult to constantly update them, so that the prediction accuracy also deteriorates. Therefore,
An object of the present invention is to provide a highly accurate and inexpensive inflow sewage amount prediction device using only online data such as rainfall intensity and inflow sewage amount.
【０００４】[0004]
【課題を解決するための手段】上記問題を解決するた
め、本発明は対象流域での降雨により下水処理場へ流入
する下水流入量を予測する流入下水量予測装置におい
て、前記対象流域での降雨量の計測値およびポンプ井に
流入する流入下水量の計測値を基に、非降雨日の流入下
水量の１日の平均変動パターンを予め作成し記憶する流
入量平均変動パターン作成手段と、降雨日の流入下水量
から前記予め記憶した非降雨日の流入下水量の平均変動
パターンを差し引いた残差部分のデータを作成する残差
データ作成手段と、前記残差部分のデータを降雨日と非
降雨日に層別する降雨日流入下水量データ演算手段と、
前記降雨日流入量データと降雨量の計測値に基づく降雨
強度との関係を統計モデルのシステム変数として作成す
るシステム変数作成手段と、前記システム変数から降雨
日流入量の予測値を演算する残差予測値演算手段と、前
記平均変動パターンと前記降雨日流入量の予測値との和
を演算し流入下水量を予測する予測値演算手段とからな
る構成にしている。また、前記システム変数作成手段を
自己回帰モデルとしてもよいし、前記１日の変動パター
ンを、曜日毎に層別してもよい。上記手段により、過去
数十日の最新の計測値を使用しているため、稼働後約１
ヶ月の短期間でその下水処理場に適した予測を行うこと
ができ、予測精度も高い。また、平均変動パターンや自
己回帰モデルを毎日更新するため、季節変動に自動的に
対応した予測を行うこともできる。SUMMARY OF THE INVENTION In order to solve the above-mentioned problems, the present invention relates to an inflow sewage amount predicting apparatus for predicting the amount of sewage flowing into a sewage treatment plant due to rainfall in a target basin. Means for creating and storing an average daily fluctuation pattern of the inflow sewage on a non-rainy day based on the measured value of the amount of water and the amount of inflow sewage flowing into the pump well; A residual data creating means for creating data of a residual portion obtained by subtracting the previously stored average fluctuation pattern of the inflow sewage amount on the non-rainy day from the inflow sewage amount of the day; Means for calculating inflow sewage data on rainy days stratified on rainy days;
System variable creating means for creating a relationship between the rainfall day inflow data and rainfall intensity based on the measured rainfall value as a system variable of a statistical model, and a residual for calculating a predicted value of the rainfall day inflow from the system variable It is configured to include a predicted value calculating means, and a predicted value calculating means for calculating a sum of the average fluctuation pattern and the predicted value of the inflow amount on a rainy day to predict an inflow sewage amount. The system variable creation means may be an autoregressive model, or the daily fluctuation pattern may be stratified for each day of the week. By the above means, the latest measured values of the past several dozen days are used.
The prediction suitable for the sewage treatment plant can be made in a short period of a month, and the prediction accuracy is high. In addition, since the average fluctuation pattern and the autoregressive model are updated every day, it is possible to automatically make predictions corresponding to seasonal fluctuations.
【０００５】[0005]
【発明の実施の形態】以下、本発明の実施例を図に基づ
いて詳細に説明する。図１は、本発明の一実施例を示す
流入下水量予測装置のブロック図である。本実施例で
は、システム変数の作成手段として自己回帰モデルを用
いた。図１において、２はポンプ井に流入する流量を測
定する流量計、５は流入量平均変動パターン作成装置、
６は残差データ作成装置、７は降雨日流入量データ演算
装置、８は自己回帰モデル作成装置、９は降雨日流入量
予測値演算装置、１０は予測値演算装置である。なお、
他の符号は、従来技術で述べたものと同一である。本発
明の流入下水量予測装置用いた予測方法をついて説明す
る。まず、流入量平均変動パターン作成装置５は、計測
値蓄積装置４で蓄積された計測値を、降雨のある降雨日
と降雨のない非降雨日に層別して流入量の平均変動パタ
ーンを作成する。つまり、１日の平均変動パターンは、
非降雨日の各時刻毎の流入下水量の直近数日間の平均値
から算出する。そしてこの算出され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ために毎
日行う。残差データ作成装置６は、計測値蓄積装置４に
記憶している計測値および流入量平均変動パターン作成
装置５で作成した流入下水量の平均変動パターンから、
降雨日のデータを作成する。すなわち、流入下水量から
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) を対象とするデータを作成する。残差部分のデ
ータは、つぎの(1) 式で求められる。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below in detail with reference to the drawings. FIG. 1 is a block diagram of an inflow sewage amount prediction device showing one embodiment of the present invention. In this embodiment, an autoregressive model is used as a means for creating a system variable. In FIG. 1, 2 is a flow meter for measuring the flow rate flowing into the pump well, 5 is an inflow rate average fluctuation pattern creating device,
6 is a residual data creation device, 7 is a rainy day inflow data calculation device, 8 is an autoregressive model creation device, 9 is a rainy day inflow prediction value calculation device, and 10 is a prediction value calculation device. In addition,
Other symbols are the same as those described in the prior art. A prediction method using the inflow sewage amount prediction device of the present invention will be described. First, the inflow rate average variation pattern creating device 5 stratifies the measurement values accumulated by the measurement value accumulation device 4 into rainy days with rainfall and non-rainfall days without rainfall, and creates an average variation pattern of inflow rate. In other words, the average daily fluctuation pattern is
It is calculated from the average value of the inflow sewage volume at each time on non-rainy days in the last few days. The calculated average fluctuation pattern is updated every day in order to use a measured value closer to the predicted date. The residual data creation device 6 calculates the average variation pattern of the inflow sewage amount created by the measured value stored in the measurement value accumulation device 4 and the inflow amount average variation pattern creation device 5,
Create data on a rainy day. That is, data is created for a portion obtained by subtracting the average fluctuation pattern from the inflow sewage flow (hereinafter referred to as a residual portion). The data of the residual part is obtained by the following equation (1).
【０００６】[0006]
【数１】 (Equation 1)
【０００７】ただし、Ｑdi(i) は時刻i における残差部
分の計測値、Ｑ(i) は時刻i における流入下水量、Ｑ
ave (j) は時刻j における平均変動パターン、i は計測
時刻、j は時刻(j=1,2,3,....,24) である。降雨日流入
量データ演算装置７は残差データ作成装置６で作成され
たデータが非降雨日の時は、データ列への追加は行わ
ず、降雨日の時は降雨強度と残差とをデータ列へ追加す
る。本実施例では、システム変数作成手段として自己回
帰モデルを用いた。自己回帰モデル作成装置８は、降雨
日流入量データ演算装置７で作成した降雨日流入量デー
タと降雨強度と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、(2) 式で表される。Here, Q di (i) is the measured value of the residual portion at time i, Q (i) is the inflow sewage amount at time i, and Q
ave (j) is the average fluctuation pattern at time j, i is the measurement time, and j is the time (j = 1, 2, 3,..., 24). The rainfall day inflow data processing device 7 does not add the data to the data sequence when the data created by the residual data creation device 6 is a non-rainy day. Add to column. In the present embodiment, an autoregressive model is used as a system variable creation unit. The autoregressive model creating device 8 creates the autoregressive model by inputting the rainy day inflow data and the rainfall intensity created by the rainy day inflow data computing device 7.
Now, the state of the process at time n is represented by k-dimensional all variable vectors X (n), and all variable vectors m times before time n are represented by
X (nm), U (n) represents the white noise vector, A (m) represents the regression coefficient of the autoregressive model, and M represents the optimal order of the autoregressive model.The autoregressive expression is expressed by equation (2). Is done.
【０００８】[0008]
【数２】 (Equation 2)
【０００９】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、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。すなわち、 Xi 、
Xjの相互分散を Rij(l) 、自己回帰係数の要素を A
ij(m) とすると、(3) 式の連立一次方程式をi=1,2,...,
kについて解けば Aij(m) が求められる。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. Autoregression coefficient A (m)
Is the element A ij (m) and the simultaneous equations are i = 1,2,3, ..., k
Is obtained by solving That is, Xi,
The cross-variance of Xj is R ij (l) and the element of the autoregressive coefficient is A
ij (m), the simultaneous linear equations in Eq. (3) are i = 1,2, ...,
Solving for k gives A ij (m).
【００１０】[0010]
【数３】 (Equation 3)
【００１１】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σi 2 は(4) 式のようになる。Assuming that the element of the white noise vector U (n) is εi (n), the residual variance σ i 2 is expressed by the following equation (4).
【００１２】[0012]
【数４】 (Equation 4)
【００１３】なお、モデルの最適次数Ｍは予測誤差を表
す(5) 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 equation (5) representing the prediction error.
【００１４】[0014]
【数５】 (Equation 5)
【００１５】ただし、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、降雨
日流入量の予測を行うために必要な、降雨日流入量と降
雨強度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。降雨日流入量予測値演算装置９は、自己回帰モデ
ル作成装置８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降雨日流入量の数10分先の予測値
を演算する。自己回帰モデルを用いた時の数10分先の予
測は(6) 式のように表される。Here, N is the number of data, and {dM} is the variance-covariance matrix estimated value of U (n). MFPE is Multiple Final
Initial for Prediction Error. In this way, the auto-regression coefficient, the variance of the white noise, and the optimal model order are obtained, and an auto-regression model is created. Therefore, a relational expression between the rainfall day inflow amount and the rainfall intensity required for predicting the rainy day inflow amount can be obtained from the autoregressive model. The update of the autoregressive model is performed once a day using the measurement values of the last several tens of days for the purpose of using the latest measurement values. The rainy day inflow predicted value calculating device 9 calculates a predicted value of the inflow of rainy day several tens minutes ahead which can be statistically inferred from the predicted value by the autoregressive model created by the autoregressive model creating device 8. The prediction several tens of minutes ahead when using the autoregressive model is expressed as in equation (6).
【００１６】[0016]
【数６】 (Equation 6)
【００１７】ただし、Ｑdi(i) p は時刻i における降雨
日流入量の予測値、Ｑdi(i) は時刻i における降雨日流
入量の計測値、Ｒrain(i) は時刻i におけるの降雨強
度、A1 1(m)は降雨日流入量の予測値に対する降雨日流入
量の自己回帰係数、A12(m)は降雨日流入量の予測値に対
する降雨強度の自己回帰係数である。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、降雨日
流入量は予測値を使用し、降雨強度は前回の降雨強度を
使用する。このようにして得られた降雨日流入量の予測
値Ｑdi(0) p , Ｑdi(1) p ・・・を予測値演算装置９に
出力する。予測値演算装置１０は、流入量平均変動パタ
ーン作成装置５の平均変動パターンと降雨日流入量残差
予測値演算装置９の降雨日流入量残差予測値との和を流
入下水量予測値とする。流入下水量予測式は、(7) 式の
ように決定される。However, Qdi(i)pIs the rainfall at time i
Daily inflow forecast, Qdi(i) is the daily rainfall at time i
Measured amount of input, Rrain(i) is the rainfall intensity at time i
Degree, A1 1(m) is the inflow of rainy day against the predicted value of inflow of rainy day
A12 (m) is the auto-regression coefficient of
Is the auto-regression coefficient of rainfall intensity. However, one point ahead
Because the above forecast is required, forecasts beyond one point
For the inflow, use the predicted value, and for the rainfall intensity, use the previous rainfall intensity.
use. Prediction of rainfall inflows obtained in this way
Value Qdi(0)p, Qdi(1)p... in the prediction value calculation device 9
Output. The predicted value calculation device 10 calculates the inflow rate average fluctuation pattern.
Fluctuation pattern and residual inflow on rainy day
The sum of the predicted value calculation unit 9 and the predicted value of the residual rainfall inflow
It will be the predicted value of incoming and outgoing water. The equation for predicting the amount of inflow and sewage is given by
Is determined as follows.
【００１８】[0018]
【数７】 (Equation 7)
【００１９】ただし、Ｑ(i) p は予測流入下水量であ
る。以上述べた流入下水量予測装置による流入下水量の
予測方法をまとめるとつぎのようになる。 雨量計１、流量計２からオンラインで計測された値を
計測値蓄積装置４に蓄積する。 流入量平均変動パターン作成装置５により、非降雨日
の流入量の平均変動パターンを作成する。 残差データ作成装置６により、降雨日の流入下水量デ
ータから平均変動パターンを差し引いた残差部分すなわ
ち、降雨のみの流入下水量データを作成する。 降雨日流入量データ演算装置７により降雨日のみのデ
ータから残差データと降雨強度とのデータ列を作成す
る。 自己回帰モデル作成装置８により、自己回帰係数、白
色ノイズの分散および最適モデル次数を求め、降雨日流
入量と降雨強度との関係式を求める。 降雨日流入量予測値演算装置９により統計的に類推可
能な降雨日流入量の数10分先の予測値を演算する。 予測値演算装置１０により平均変動パターンと降雨日
流入量予測値との和を流入下水量予測値として演算す
る。 ポンプ運転指令装置１１により、流入下水量の予測値
データを基にした最適なポンプ運転指令データとして排
水ポンプ３に出力する。つぎに、この流入下水量予測方
法を実際に適用して、シミュレーションを行った結果
を、図３および図４のグラフに示す。図３は流入下水量
の実測値( 実線)とシミュレーションによる予測値( 点
線) を示している。図４は降雨量の時間変化を示す。図
３から、流入下水量の実測値と予測値がよく一致してい
ることが分かる。また、図４に示す降雨と図３の流入下
水量との対応もよくとれている。シミュレーションの実
施に際しては、直近数日のデータを使用しただけである
が、予測精度は極めて高いことが分かった。すなわち、
稼働開始までの期間が極めて短くできる上、予測のため
の種々のデータ蓄積も少なくてむので、コストも安価に
できる。Here, Q (i) p is the predicted inflow sewage amount. The method of estimating the inflow sewage amount by the inflow sewage amount prediction device described above is summarized as follows. The values measured online from the rain gauge 1 and the flow meter 2 are stored in the measured value storage device 4. The average variation pattern of the inflow amount on the non-rainy day is created by the inflow amount average variation pattern creating device 5. The residual data creating device 6 creates a residual part obtained by subtracting the average fluctuation pattern from the inflow sewage data on the rainy day, that is, creates inflow sewage data only for rainfall. A data string of residual data and rainfall intensity is created from the data on only the rainy day by the rainy day inflow data processor 7. The autoregressive model creation device 8 obtains an autoregressive coefficient, a variance of white noise, and an optimal model order, and obtains a relational expression between rainfall day inflow and rainfall intensity. The rainy day inflow amount prediction value calculating device 9 calculates a prediction value of the rainy day inflow amount which can be statistically analogized several tens of minutes ahead. The predicted value calculating device 10 calculates the sum of the average fluctuation pattern and the predicted value of inflow on rainy day as a predicted value of inflow sewage. The pump operation command device 11 outputs the optimum pump operation command data to the drain pump 3 based on the predicted value data of the inflow sewage amount. Next, the simulation results obtained by actually applying the inflow sewage amount prediction method are shown in the graphs of FIGS. 3 and 4. FIG. 3 shows the measured value (solid line) of the inflow sewage and the predicted value (dotted line) by simulation. FIG. 4 shows the time change of the rainfall. From FIG. 3, it can be seen that the measured value and the predicted value of the inflow sewage volume are in good agreement. Also, the correspondence between the rainfall shown in FIG. 4 and the inflow sewage amount in FIG. 3 is well taken. When performing the simulation, only the data of the last several days was used, but it was found that the prediction accuracy was extremely high. That is,
The period until the start of operation can be extremely short, and the accumulation of various data for prediction can be reduced, so that the cost can be reduced.
【００２０】[0020]
【発明の効果】以上述べたように本発明によれば、流入
下水量予測装置を、ポンプ井への流入下水量を基に、非
降雨日の平均変動パターンを作成し記憶する流入量平均
変動パターン作成手段と、降雨日の降雨量のみによる流
入下水量、すなわち、残差部分のデータを作成する残差
データ作成手段と、降雨時のみデータ列を作成する降雨
日流入量データ演算手段と、降雨日流入量のデータと降
雨強度との関係を統計モデルのシステム変数として作成
するシステム変数作成手段（自己回帰モデル）と、降雨
日流入量の予測値を演算する降雨日流入量予測値演算手
段と、平均変動パターンと降雨日流入量の予測値との和
を演算し流入下水量を予測する予測値演算手段とからな
る構成にしたので、流入下水量の予測精度が高く分流
式、合流式の設備にも適用でき安価な流入下水量予測装
置を得る効果がある。。また、平均変動パターンや自己
回帰モデルを毎日更新するため、季節変動に自動的に対
応した予測を行うこともできる。As described above, according to the present invention, the inflow sewage amount predicting apparatus creates and stores an average fluctuation pattern on a non-rainy day based on the inflow sewage amount into the pump well, and stores the average fluctuation pattern. A pattern creation unit, an inflow sewage amount based only on the rainfall amount on a rainy day, that is, a residual data creation unit for creating data of a residual portion, and a rainy day inflow amount data calculation unit for creating a data string only during rainfall; System variable creation means (autoregressive model) for creating the relationship between rainfall day inflow data and rainfall intensity as a system variable of a statistical model, and rainy day inflow forecast value calculation means for calculating a predicted value of rainday inflow And the predicted value calculation means for calculating the sum of the average fluctuation pattern and the predicted value of the inflow amount on rainy day and predicting the inflow sewage amount. Equipment Applied can be effective to obtain an inexpensive inflow sewage quantity prediction apparatus. . In addition, since the average fluctuation pattern and the autoregressive model are updated every day, it is possible to automatically make predictions corresponding to seasonal fluctuations.
【図１】本発明の一実施例を示す流入下水量予測装置の
ブロック図である。FIG. 1 is a block diagram of an inflow sewage amount prediction device showing one embodiment of the present invention.
【図２】従来の流入下水量予測装置を示すブロック図で
ある。FIG. 2 is a block diagram showing a conventional inflow sewage amount prediction device.
【図３】本発明の流入下水量の実測値と予測値を示すグ
ラフである。FIG. 3 is a graph showing an actually measured value and a predicted value of the inflow sewage amount according to the present invention.
【図４】降雨量の時間変化を示すグラフである。FIG. 4 is a graph showing a temporal change of a rainfall amount.
１ 雨量計 ２ 流量計 ３ 排水ポンプ ４ 計測値蓄積装置 ５ 流入量平均変動パターン作成装置 ６ 残差データ作成装置 ７ 降雨日流入量データ演算装置 ８ 自己回帰モデル作成装置 ９ 降雨日流入量予測値演算装置 １０予測値演算装置 １１ ポンプ運転指令装置 １２ 修正RRL 法流入量予測装置 １３ ポンプ場 １４ ポンプ井 REFERENCE SIGNS LIST 1 rain gauge 2 flow meter 3 drain pump 4 measured value accumulator 5 inflow rate average fluctuation pattern creation device 6 residual data creation device 7 rainy day inflow data calculation device 8 autoregressive model creation device 9 rainy day inflow forecast value calculation Apparatus 10 Predicted value calculation device 11 Pump operation command device 12 Modified RRL method inflow prediction device 13 Pump station 14 Pump well
Claims (3)
する下水流入量を予測する流入下水量予測装置におい
て、 前記対象流域での降雨量の計測値およびポンプ井に流入
する流入下水量の計測値を基に、非降雨日の流入下水量
の１日の平均変動パターンを予め作成し記憶する流入量
平均変動パターン作成手段と、 降雨日の流入下水量から前記予め記憶した非降雨日の流
入下水量の平均変動パターンを差し引いた残差部分のデ
ータを作成する残差データ作成手段と、 前記残差部分のデータを降雨日と非降雨日に層別する降
雨日流入下水量データ演算手段と、 前記降雨日流入量データと降雨量の計測値に基づく降雨
強度との関係を統計モデルのシステム変数として作成す
るシステム変数作成手段と、 前記システム変数から降雨日流入量の予測値を演算する
残差予測値演算手段と、 前記平均変動パターンと前記降雨日流入量の予測値との
和を演算し流入下水量を予測する予測値演算手段とから
なることを特徴とする流入下水量予測装置。1. An inflow sewage amount prediction device for predicting an inflow amount of sewage flowing into a sewage treatment plant due to rainfall in a target basin, wherein the measured value of the amount of rainfall in the target basin and the amount of inflow sewage flowing into a pump well are provided. An inflow rate average variation pattern creating means for creating and storing in advance a daily average variation pattern of the inflow sewage volume on a non-rainy day based on the measured value; Residual data creating means for creating data of a residual portion obtained by subtracting an average variation pattern of the inflow sewage amount; and rainfall day inflow sewage data calculating means for stratifying the data of the residual portion on rainy days and non-rainy days. And a system variable creating means for creating the relationship between the rainfall day inflow data and the rainfall intensity based on the measured rainfall value as a system variable of a statistical model; and predicting the rainfall inflow from the system variables And predictive value calculating means for calculating the sum of the average fluctuation pattern and the predicted value of the inflow on rainy day and predicting the amount of inflow sewage. Water volume prediction device.
ルとした請求項１記載の流入下水量予測装置。2. The inflow / sewage amount predicting apparatus according to claim 1, wherein said system variable creating means is an autoregressive model.
した請求項１または２に記載の流入下水量予測装置。3. The inflow / sewage volume prediction device according to claim 1, wherein the daily fluctuation pattern is stratified for each day of the week.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP16173499A JP4182460B2 (en) | 1999-06-08 | 1999-06-08 | Inflow sewage prediction device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP16173499A JP4182460B2 (en) | 1999-06-08 | 1999-06-08 | Inflow sewage prediction device |
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| Publication Number | Publication Date |
|---|---|
| JP2000345604A true JP2000345604A (en) | 2000-12-12 |
| JP4182460B2 JP4182460B2 (en) | 2008-11-19 |
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ID=15740881
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP16173499A Expired - Fee Related JP4182460B2 (en) | 1999-06-08 | 1999-06-08 | Inflow sewage prediction device |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101387127B (en) * | 2004-12-14 | 2011-03-09 | 株式会社东芝 | Rainwater drain control system and method |
| JP2011257149A (en) * | 2010-06-04 | 2011-12-22 | Chugoku Electric Power Co Inc:The | Air temperature prediction system, air temperature prediction method, and program |
| JP2014234675A (en) * | 2013-06-04 | 2014-12-15 | 株式会社東芝 | Flow rate prediction device, flow rate prediction method, flow rate prediction program and flow rate prediction system |
| JP2018111977A (en) * | 2017-01-11 | 2018-07-19 | 株式会社日立製作所 | Monitoring controller of sewerage facility and operation control method of sewage pump station |
| JPWO2018043252A1 (en) * | 2016-08-31 | 2019-06-24 | 日本電気株式会社 | Rainfall forecasting device, rainfall forecasting method, and recording medium |
| JP2019148482A (en) * | 2018-02-27 | 2019-09-05 | 株式会社日立製作所 | Aqueous environment sensing device |
-
1999
- 1999-06-08 JP JP16173499A patent/JP4182460B2/en not_active Expired - Fee Related
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101387127B (en) * | 2004-12-14 | 2011-03-09 | 株式会社东芝 | Rainwater drain control system and method |
| JP2011257149A (en) * | 2010-06-04 | 2011-12-22 | Chugoku Electric Power Co Inc:The | Air temperature prediction system, air temperature prediction method, and program |
| JP2014234675A (en) * | 2013-06-04 | 2014-12-15 | 株式会社東芝 | Flow rate prediction device, flow rate prediction method, flow rate prediction program and flow rate prediction system |
| JPWO2018043252A1 (en) * | 2016-08-31 | 2019-06-24 | 日本電気株式会社 | Rainfall forecasting device, rainfall forecasting method, and recording medium |
| US11150380B2 (en) | 2016-08-31 | 2021-10-19 | Nec Corporation | Prediction device rainfall amount prediction method, and recording medium, slope collapse prediction system and dangerous water level prediction system |
| JP2018111977A (en) * | 2017-01-11 | 2018-07-19 | 株式会社日立製作所 | Monitoring controller of sewerage facility and operation control method of sewage pump station |
| 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 |
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