JP2661162B2 - Water quality estimation method - Google Patents
Water quality estimation methodInfo
- Publication number
- JP2661162B2 JP2661162B2 JP63188772A JP18877288A JP2661162B2 JP 2661162 B2 JP2661162 B2 JP 2661162B2 JP 63188772 A JP63188772 A JP 63188772A JP 18877288 A JP18877288 A JP 18877288A JP 2661162 B2 JP2661162 B2 JP 2661162B2
- Authority
- JP
- Japan
- Prior art keywords
- water quality
- indicator
- microorganisms
- microorganism
- appearance
- 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.)
- Expired - Fee Related
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 82
- 244000005700 microbiome Species 0.000 claims description 69
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 13
- 229910052760 oxygen Inorganic materials 0.000 claims description 13
- 239000001301 oxygen Substances 0.000 claims description 13
- 230000005484 gravity Effects 0.000 claims description 12
- 230000000813 microbial effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000010865 sewage Substances 0.000 description 4
- 241000902900 cellular organisms Species 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 3
- 239000010802 sludge Substances 0.000 description 3
- 241000700141 Rotifera Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 239000008235 industrial water Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004065 wastewater treatment Methods 0.000 description 2
- 241000254173 Coleoptera Species 0.000 description 1
- 241000223892 Tetrahymena Species 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000012447 hatching Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000006241 metabolic reaction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
Landscapes
- Activated Sludge Processes (AREA)
Description
【発明の詳細な説明】 A.産業上の利用分野 本発明は、例えば活性汚泥法により処理した処理水の
水質を微生物データにもとづいて推定する方法に関する
ものである。The present invention relates to a method for estimating the quality of treated water treated by, for example, an activated sludge method based on microbial data.
B.発明の概要 本発明は被測定水の水質を水中に含まれる微生物デー
タにもとづいて推定する方法において、 水質に対応する例えばBOD濃度帯域を指定し、指標性
微生物毎に各BOD濃度帯域における出現可能性を示すメ
ンバーシップ値を予め定義しておき、被測定水中に出現
した微生物について各BOD濃度帯域のメンバーシップ値
を拾って、その値にもとづいてBOD濃度を推定すること
によって、 微生物に関する知識を処理水質の予測に反映させなが
ら処理水質を具体的数値で表現できるようにしたもので
ある。B. Summary of the Invention The present invention provides a method for estimating the quality of water to be measured based on microbial data contained in the water.Specifying, for example, a BOD concentration band corresponding to the water quality, the BOD concentration band in each BOD concentration band for each indicator microorganism. By defining membership values indicating the possibility of appearance in advance, picking up the membership value of each BOD concentration band for microorganisms that appeared in the water to be measured, and estimating the BOD concentration based on that value, It is intended to be able to express the treated water quality with specific numerical values while reflecting the knowledge in the prediction of the treated water quality.
C.従来の技術 都市下水の処理に広く適用されている活性汚泥法は微
生物の代謝反応を利用して下水を浄化するシステムであ
る。ここに下水処理場はその放流水質（処理水質）によ
って規制を受けており、従ってプロセス管理では処理水
質が最も重要な指標である。ところで、システムの処理
効率（処理水の水質）はシステム的に出現する微生物に
大きく依存している。従来より、処理場では操作員が顕
微鏡観察で指標性と呼ばれる微生物の出現頻度を調べ、
処理場状態の診断を行って来た。各指標性微生物は長年
の研究よりそれぞれどの様な水質で出現しやすいかが分
かっており、熟練した操作員は指標性微生物の出現頻度
よりおおまかな処理水質を予測することができる。C. Conventional technology The activated sludge method widely applied to the treatment of municipal sewage is a system that purifies sewage using the metabolic reaction of microorganisms. Here, the sewage treatment plant is regulated by its discharged water quality (treated water quality), and thus treated water quality is the most important indicator in process management. By the way, the treatment efficiency (water quality of treated water) of the system largely depends on microorganisms appearing in the system. Conventionally, operators at treatment plants have examined the frequency of appearance of microorganisms called indicator by microscopic observation,
Diagnosis of treatment plant condition has been performed. Long-term studies have shown which water quality each indicator microorganism is likely to appear in, and a skilled operator can predict rough treated water quality from the frequency of appearance of indicator microorganisms.
D.発明が解決しようとする課題 しかし、その様な指標性微生物の出現頻度と処理水質
とを結び付ける知識は非常にあいまいな概念である。例
えば、ある微生物は良い水質のとき出現することが分か
っている。ここで「良い水質」とは具体的な数値に裏付
けされたものでなく、あいまいで感覚的なものである。
人間の知識とは、一般的に上例にも示されるようにあい
まいな表現のものが多いが、実際のシステム管理にはど
うしても具体的数値が必要である。本発明は微生物に関
する知識を処理水質の予測に反映させながら、処理水質
を具体的数値で表現することができ、しかも将来的な水
質の予測をも可能にした水質の推定方法を提供すること
を目的とする。D. Problems to be solved by the Invention However, the knowledge linking the frequency of occurrence of such indicator microorganisms and the quality of treated water is a very vague concept. For example, certain microorganisms have been found to emerge when water quality is good. Here, "good water quality" is not based on concrete figures, but is vague and intuitive.
Human knowledge generally has many vague expressions as shown in the above example, but actual system management requires specific numerical values. The present invention provides a method for estimating water quality that can express treated water quality by specific numerical values while reflecting knowledge about microorganisms in the prediction of treated water quality, and that enables prediction of future water quality. Aim.
E.課題を解決するための手段 本発明は、上記目的を達成するために、水質の指標と
なる複数種の指標性微生物の単位水量当たりの出現個数
を各々複数の出現ランクに区分し、 被測定質の水質の指標である酸素要求量を複数帯域に
区分して、指標性微生物毎に全帯域中で出現可能な最大
個数を予め求め、 且つ前記各指標性微生物毎に各酸素要求量帯域におけ
る出現可能性を示すメンバーシップ値を、前記各指標性
微生物の出現可能な最大個数の逆数を因子として予め定
めるとともに、 被測定水中の単位水量当たりの指標性微生物の個数を
測定して、各指標性微生物について前記複数の出現ラン
クの中から各々の所属する出現ランクを求め、出現した
指標性微生物について各酸素要求量帯域のメンバーシッ
プ値を拾い、このメンバーシップ値を当該指標性微生物
の所属する出現ランクにより重み付けして、これらメン
バーシップ値を酸素要求量帯域毎に合計してメンバーシ
ップ値の水質方向の重心を求め、この重心位置の酸素要
求量を被測定水の酸素要求量として推定することを特徴
とするものである。E. Means for Solving the Problems In order to achieve the above object, the present invention classifies the number of appearances of a plurality of types of indicator microorganisms serving as indicators of water quality per unit water amount into a plurality of appearance ranks, The oxygen demand, which is an indicator of the water quality of the measurement quality, is divided into a plurality of bands, and the maximum number that can appear in all the bands for each indicator microorganism is determined in advance, and each oxygen demand zone is determined for each indicator microorganism. The membership value indicating the likelihood of appearance is determined in advance by using the reciprocal of the maximum number of each of the indicator microorganisms that can appear as a factor, and the number of indicator microorganisms per unit water amount in the measured water is measured. From the plurality of appearance ranks for the indicator microorganisms, the appearance rank to which each belongs is determined, and the membership value of each oxygen demand band is picked up for the appeared indicator microorganisms, and this membership value is calculated. The membership values are weighted by the appearance rank to which the indicator microorganism belongs, and these membership values are summed up for each oxygen demand band to obtain the center of gravity of the membership value in the water quality direction. It is characterized in that it is estimated as the oxygen demand.
F.実施例 本発明は、人間の知識（あいまい表現）を具体的数値
と結び付ける数学的手法としてファジー理論が存在する
ことに着目し、このファジー理論を用いて、微生物デー
タにもとづいて例えば活性汚泥法の処理水の水質を推定
しようとする着想である。F. Embodiments The present invention focuses on the fact that fuzzy theory exists as a mathematical method for linking human knowledge (fuzzy expression) with concrete numerical values, and uses this fuzzy theory to, for example, activate sludge based on microbial data. The idea is to estimate the quality of treated water.
第１図は本発明方法におけるファージ推論の実施例の
概略を示す図である。このファジー推論は通常のファジ
ー推論とは若干異なっている。先ず処理水中の微生物種
の中から水質の指標となる指標性微生物を第３図に示す
ように選択しておく。そして水質を例えば「良い」，
「悪い」，「その中間である」の３つに分けるために、
処理水の酸素要求量例えばBOD濃度（生化学的酸素要求
量）を３つの帯域に区分して、各濃度帯域と水質とを対
応させる。指標性微生物としては説明の便宜上微生物A,
B,Cを想定し、第２図に示すように微生物Ａは良い処理
水質のとき、微生物ＢとＣとは悪い処理水質のときに出
現するものと仮定する。FIG. 1 is a diagram schematically showing an example of phage inference in the method of the present invention. This fuzzy inference is slightly different from ordinary fuzzy inference. First, indicator microorganisms serving as indicators of water quality are selected from the microorganism species in the treated water as shown in FIG. And the water quality is good,
In order to divide into "bad" and "between",
The oxygen demand of the treated water, for example, the BOD concentration (biochemical oxygen demand) is divided into three zones, and each concentration zone is associated with water quality. As indicator microorganisms, microorganism A,
Assuming B and C, as shown in FIG. 2, it is assumed that the microorganism A appears when the treated water quality is good and the microorganisms B and C appear when the treated water quality is bad.
ここで本発明の実施例では、処理水の単位水量当たり
の微生物の出現個数を複数の出現ランク、例えば次のよ
うに０〜５の６つに区分する。Here, in the embodiment of the present invention, the number of microorganisms appearing per unit water amount of the treated water is classified into a plurality of appearance ranks, for example, six (0 to 5) as follows.
一方過去のデータから微生物毎に出現可能な最大個数
を把握しておくと共に、微生物毎に各BOD濃度帯域にお
ける出現可能性を示すメンバーシップ値を、前記最大個
数の逆数を因子として予め定めておく。微生物の出現個
数はその種類によって様々である。そして、ある種の微
生物が優先化した場合の最大個数もその種類によって違
う。（“廃水処理の生物学”（須藤）産業用水調査会S.
52.6.12発行，“生物相からみた処理機能の診断”（須
藤・稲森）産業用水調査会S.58.4.21発行）本実施例で
扱う微生物は第３図に示した18種類であるが、これだけ
の種類でもそれぞれの出現個数の範囲は広い。例えば、
イ、ロという微生物の単位水量当たりの最大個数がそれ
ぞれ10,000個と500個であるとして、測定の結果イ，ロ
ともに100個出現していたとしても、イとロとではその
水質の指標としての重みが違ってくる。つまり、出現個
数だけでその水質を評価することは出来ない。そこで、
本発明では各微生物の最大個数を過去のデータなどを参
考にして決め、その逆数をメンバーシップ値の因子とす
ることによって、１個体の水質の指標としての重みを表
している。 On the other hand, while grasping the maximum number that can appear for each microorganism from past data, a membership value indicating the possibility of occurrence in each BOD concentration band for each microorganism is determined in advance using the reciprocal of the maximum number as a factor. . The number of appearing microorganisms varies depending on the type. The maximum number when a certain microorganism is prioritized also differs depending on the type. ("Biology of wastewater treatment" (Sudo) Industrial Water Research Committee S.
Issued 52.6.12, "Diagnosis of treatment functions from the viewpoint of biota" (Sudo and Inamori), S.58.4.21 issued by the Industrial Water Research Committee. The microorganisms handled in this example are the 18 types shown in Fig. 3, Even with these types, the range of the number of appearances is wide. For example,
Assuming that the maximum number of microorganisms per unit water volume of a and b is 10,000 and 500, respectively, and as a result of the measurement, even if 100 appear in both a and b, a and b The weights are different. That is, the water quality cannot be evaluated only by the number of occurrences. Therefore,
In the present invention, the maximum number of each microorganism is determined with reference to past data and the like, and the reciprocal thereof is used as a factor of the membership value to express the weight as an index of water quality of one individual.
第１図に、横軸に処理水質（BOD濃度（mg/L））、縦
軸にメンバーシップ値をとった説明図であり、微生物Ａ
を例に取ると、微生物Ａの出現する可能性は良いと仮定
された水質（BOD）範囲では常に一定の値（この値は1/
（微生物Ａの最大個数）で定義される）であるが、それ
以外の範囲では常にゼロである。微生物ＢやＣの出現す
る可能性は、悪いと仮定された水質範囲で常に1/微生物
最大個数）であるが、それ以外の水質範囲では常にゼロ
である。メンバーシップ値は三角形や台形で定義される
ものが多いが、ここでは簡略化のため長方形のものを用
いている。FIG. 1 is an explanatory diagram showing the treated water quality (BOD concentration (mg / L)) on the horizontal axis and the membership value on the vertical axis.
For example, in the range of water quality (BOD) assumed that the possibility of the appearance of microorganism A is good, this value is always 1 / (this value is 1 /
(Defined by the maximum number of microorganisms A), but is always zero in other ranges. The probability of appearance of microorganisms B and C is always 1 / maximum number of microorganisms in a water quality range assumed to be bad, but is always zero in other water quality ranges. Although the membership value is often defined by a triangle or a trapezoid, a rectangular value is used here for simplicity.
今、処理水を観察した結果、微生物A,B,Cが含まれて
いるとすると、微生物A,B,Cの各々について単位水量当
たりの個数を測定して、各微生物について上記の表に示
した出現ランクの中から所属する出現ランクを求める。
例えば微生物A,B,Cの個数（1mL当たり）が夫々400〜999
の間、１〜99の間及び100〜399の間であれば、微生物A,
B,Cの出現ランクは夫々３、１、２となる。そして出現
した微生物（ここではA,B,C）について各BOD濃度帯域の
メンバーシップ値を拾う。Now, as a result of observing the treated water, assuming that microorganisms A, B, and C are contained, the number per unit water volume of each of microorganisms A, B, and C was measured, and the results are shown in the above table for each microorganism. The appearance rank to which the user belongs is determined from the appearance ranks.
For example, the number of microorganisms A, B, and C (per 1 mL) is 400 to 999, respectively.
Between, between 1 and 99 and between 100 and 399, the microorganism A,
The appearance ranks of B and C are 3, 1, and 2, respectively. Then, the membership value of each BOD concentration band is picked up for the appearing microorganisms (here, A, B, C).
次にこれらメンバーシップ値は微生物の出現ランクに
よって修正を受ける。微生物の出現ランクは０から５の
６段階で評価されるが、メンバーシップ値はこの出現ラ
ンクを５で割ったものを掛けることで修正される。第１
図の微生物A,B,Cでは、各々ランク３、１、２で出現し
ていることより、各々のメンバーシップ値は3/5、1/5、
2/5倍されることになる。この修正の結果が図中斜線で
示された長方形である。これら一連の操作の目的は、各
微生物の重み付けである。したがって、各微生物は出現
可能な最大個数が小さく、且つ、その出現ランクが大き
いという傾向が強いほど、その微生物が指し示す水質の
確からしさが増すことになる。These membership values are then modified by the rank of appearance of the organism. The rank of appearance of microorganisms is evaluated on a six-point scale from 0 to 5, but the membership value is modified by multiplying the rank of appearance by five. First
In the microorganisms A, B, and C in the figure, the membership values are 3/5, 1/5, since they appear at ranks 3, 1, and 2, respectively.
It will be multiplied by 2/5. The result of this modification is a rectangle indicated by hatching in the figure. The purpose of these series of operations is to weight each microorganism. Therefore, the greater the tendency that the maximum number of each microorganism that can appear is small and that the appearance rank is large, the greater the certainty of the water quality indicated by the microorganism.
次のステップとして先に修正を受けたメンバーシップ
値（斜線部）の重ね合わせ（合成）をBOD濃度帯域毎に
行う。これは単に図形的に斜線部を合成したものと考え
られる。結果は第１図の一番下の図形（斜線で塗られた
もの）となる。As a next step, superposition (synthesis) of the previously corrected membership values (hatched portions) is performed for each BOD concentration band. This is thought to be simply the combination of the hatched portions in a graphical manner. The result is the figure at the bottom of FIG. 1 (hatched).
最後に合成したメンバーシップ値より、推論される処
理水質を計算する。これには重心計算を用いる。即ち、
合成されたメンバーシップ値の横軸方向（水質）の重心
を求め、この重心位置のBOD濃度を処理水質濃度として
推定する。これによってファジー推論による水質推論は
終了する。Finally, the inferred treated water quality is calculated from the combined membership value. For this, the center of gravity calculation is used. That is,
The center of gravity of the combined membership value in the horizontal axis direction (water quality) is obtained, and the BOD concentration at this position of the center of gravity is estimated as the treated water quality concentration. This ends the water quality inference by fuzzy inference.
本実施例では、水質の指標となる指標性微生物は第３
図の18種類全てを示している。したがって、被測定水中
に18種類の全ての微生物が観測されたならば、それぞれ
の出現個数から出現ランクを決め、さらに各々の最大個
数の逆数を因子としてメンバーシップ値を計算し、その
値を合計して重心を求めることになる。一般には全ての
微生物が観測される例は少ない。数種類しか観測されな
い場合が多く、第３図で示した種類以外の微生物が多く
出現することもある。この場合は、18種類の指標性微生
物の中で出現しているものだけでメンバーシップ値を計
算し、その値を合計して重心を求める。当然、本発明の
指標性微生物は、第３図の18種類に限定されるものでは
ない。本実施例の18種類は、一般的に水質の指標となる
と言われているものを例示的に列挙したものである。In this embodiment, the indicator microorganism serving as an indicator of water quality is the third microorganism.
All 18 types in the figure are shown. Therefore, if all 18 microorganisms were observed in the water to be measured, the appearance rank was determined from the number of each occurrence, and the membership value was calculated using the reciprocal of the maximum number as a factor, and the values were summed. To find the center of gravity. Generally, there are few examples in which all microorganisms are observed. In many cases, only a few types are observed, and many types of microorganisms other than the types shown in FIG. 3 may appear. In this case, the membership value is calculated only for those appearing among the 18 types of indicator microorganisms, and the values are summed to obtain the center of gravity. Naturally, the indicator microorganism of the present invention is not limited to the 18 types shown in FIG. The 18 types of this embodiment exemplarily list those which are generally considered to be indicators of water quality.
具体的数値を用いた実施例を以下に示す。水質のファ
ジー推論には第３図に示す18種類の指標微生物を用い
る。各々の微生物がどの様な水質で出現するかは第２図
に示されている。データは下水試験法84年より取った。
図において、例えばテトラヒメナは良い−中間水質の間
から中間−悪い水質の間まで出現すると考えられてい
る。また、ヒルガタワムシなどは良い水質の時のみ出現
する。第２図の例では、良い水質はX1からX2の範囲、中
間水質はX2からX3の範囲、悪い水質はX3からX4の範囲と
定義されている。これら４点（X1,X2,X3,X4）の値は、
生物相データとそれに対応する処理水質（“廃水処理の
生物学”（須藤）、“生物相からみた処理機能の診断”
（須藤・稲森）より最適化（シンプレックス法）の手法
を用いて求めたところ、X1＝−7.1、X2＝31.6、X3＝51.
6、X4＝63.8mgBOD/Lであった。X1の値はマイナスとなっ
ているが、これは重心計算で水質を計算するため避けら
れないものである。例えば、ヒルガタワムシだけが出現
している場合を考えてみる。すると、合成メンバーシッ
プ値はヒルガタワムシのメンバーシップ値そのものであ
る。これで重心を計算してみると、重心はX1とX2の中間
点となる。従って例えX1がマイナスであろうと重心（推
定水質）は12.25mgBOD/Lとなる。An example using specific numerical values will be described below. The 18 types of indicator microorganisms shown in Fig. 3 are used for fuzzy inference of water quality. FIG. 2 shows the water quality of each microorganism. Data were taken from the sewage test method 1984.
In the figure, for example, it is considered that Tetrahymena appears between good-medium water quality and medium-bad water quality. Also, beetles and the like appear only when the water quality is good. In the example of FIG. 2, good water quality is defined as X1 to X2, intermediate water quality is defined as X2 to X3, and bad water quality is defined as X3 to X4. The values of these four points (X1, X2, X3, X4)
Biota data and corresponding treated water quality ("Biology of wastewater treatment" (Sudo), "Diagnosis of treatment functions from the viewpoint of biota")
(Sudo and Inamori) Using the optimization (simplex method) method, X1 = -7.1, X2 = 31.6, X3 = 51.
6, X4 = 63.8 mg BOD / L. Although the value of X1 is negative, this is inevitable because the water quality is calculated by the center of gravity calculation. For example, consider a case where only the rotifer has appeared. Then, the composite membership value is the membership value of the rotifer. When calculating the center of gravity with this, the center of gravity is the midpoint between X1 and X2. Therefore, even if X1 is minus, the center of gravity (estimated water quality) is 12.25 mgBOD / L.
G.発明の効果 本発明によれば、被測定水の水質に対して例えばBOD
濃度帯域を指定し、指標性微生物毎に各BOD濃度帯域に
おける出現可能性を示すメンバーシップ値を予め定義し
ておき、被測定水中に出現した微生物について各BOD濃
度帯域のメンバーシップ値を拾って、その値にもとづい
てBOD濃度を推定しているため、微生物に関する知識を
水質の予測に反映させながら水質を具体的数値で表現す
ることができる。また実際の水質はシステム内の微生物
変動の結果として現れ、ここに本発明では、微生物の出
現をみて、微生物変動をデータとして用いているため、
水質の変動をいち早く捉えることができ、将来的な水質
を予測することができる。これに対して実際の水質のみ
を監視する方法では、変化がみられたときには対応が追
いつかず、とり返しのつかない場合もあり得る。G. Effects of the Invention According to the present invention, for example, BOD
Specify the concentration band, define in advance the membership value indicating the possibility of appearance in each BOD concentration band for each indicator microorganism, and pick up the membership value of each BOD concentration band for the microorganisms that appeared in the measured water. Since the BOD concentration is estimated based on the value, the water quality can be represented by specific numerical values while reflecting knowledge about microorganisms in the prediction of the water quality. In addition, the actual water quality appears as a result of microbial fluctuations in the system, and in the present invention, since the microbial fluctuations are used as data in view of the appearance of microorganisms,
Water quality fluctuations can be quickly detected, and future water quality can be predicted. On the other hand, in the method of monitoring only the actual water quality, when a change is observed, the response cannot catch up, and there is a case where it cannot be recovered.
第１図は本発明方法の概念を示す概略図、第２図及び第
３図は指標性微生物出現水質範囲を示す説明図である。FIG. 1 is a schematic diagram showing the concept of the method of the present invention, and FIGS. 2 and 3 are explanatory diagrams showing water quality ranges in which indicator microorganisms appear.
Claims (1)
単位水量当たりの出現個数を各々複数の出現ランクに区
分し、 被測定水の水質の指標である酸素要求量を複数帯域に区
分して、指標性微生物毎に全帯域中で出現可能な最大個
数を予め求め、 且つ前記各指標性微生物毎に各酸素要求量帯域における
出現可能性を示すメンバーシップ値を、前記各指標性微
生物の出現可能な最大個数の逆数を因子として予め定め
るとともに、 被測定水中の単位水量当たりの指標性微生物の個数を測
定して、各指標性微生物について前記複数の出現ランク
の中から各々の所属する出現ランクを求め、出現した指
標性微生物について各酸素要求量帯域のメンバーシップ
値を拾い、このメンバーシップ値を当該指標性微生物の
所属する出現ランクにより重み付けして、これらメンバ
ーシップ値を酸素要求量帯域毎に合計してメンバーシッ
プ値の水質方向の重心を求め、この重心位置の酸素要求
量を被測定水の酸素要求量として推定することを特徴と
する水質の推定方法。1. The method according to claim 1, wherein the number of appearances of a plurality of types of indicator microorganisms serving as an indicator of water quality per unit water amount is divided into a plurality of appearance ranks, and the oxygen demand as an indicator of the water quality of the measured water is divided into a plurality of bands. Then, for each indicator microorganism, the maximum number that can appear in the entire band is determined in advance, and for each indicator microorganism, a membership value indicating the appearance probability in each oxygen demand zone is determined by the indicator microorganism. With the reciprocal of the maximum number of occurrences being predetermined as a factor, the number of indicator microorganisms per unit water amount in the measured water is measured, and each indicator microorganism belongs to each of the plurality of occurrence ranks. Find the appearance rank, pick up the membership value of each oxygen demand band for the appeared indicator microorganism, and weight this membership value by the appearance rank to which the indicator microorganism belongs. And summing up these membership values for each oxygen demand band to determine the center of gravity of the membership value in the water quality direction, and estimating the oxygen demand at the center of gravity as the oxygen demand of the measured water. Water quality estimation method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63188772A JP2661162B2 (en) | 1988-07-28 | 1988-07-28 | Water quality estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63188772A JP2661162B2 (en) | 1988-07-28 | 1988-07-28 | Water quality estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH0238860A JPH0238860A (en) | 1990-02-08 |
| JP2661162B2 true JP2661162B2 (en) | 1997-10-08 |
Family
ID=16229510
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP63188772A Expired - Fee Related JP2661162B2 (en) | 1988-07-28 | 1988-07-28 | Water quality estimation method |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP2661162B2 (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2636628B2 (en) * | 1992-03-17 | 1997-07-30 | 池田物産株式会社 | Airbag device |
| JPH0669614U (en) * | 1993-02-25 | 1994-09-30 | 憲人 須藤 | Heating floor panel |
| ES2137274T3 (en) * | 1993-03-12 | 1999-12-16 | Yoshikuni Saito | RETRACTILE NEEDLE SYRINGE. |
| JP5129463B2 (en) * | 2006-06-29 | 2013-01-30 | メタウォーター株式会社 | Water quality abnormality detection method |
| CN111125607B (en) * | 2019-12-16 | 2024-03-01 | 中国石油天然气股份有限公司 | Emission control method and device for volatile organic compounds in oil storage warehouse |
| CN115754199B (en) * | 2022-11-10 | 2024-09-20 | 河南大学 | Water quality detection method based on membership function and principal component analysis |
-
1988
- 1988-07-28 JP JP63188772A patent/JP2661162B2/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0238860A (en) | 1990-02-08 |
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