JPH0238860A - Estimation of water quality - Google Patents
Estimation of water qualityInfo
- Publication number
- JPH0238860A JPH0238860A JP63188772A JP18877288A JPH0238860A JP H0238860 A JPH0238860 A JP H0238860A JP 63188772 A JP63188772 A JP 63188772A JP 18877288 A JP18877288 A JP 18877288A JP H0238860 A JPH0238860 A JP H0238860A
- Authority
- JP
- Japan
- Prior art keywords
- water
- oxygen demand
- microorganisms
- water quality
- 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.)
- Granted
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 72
- 244000005700 microbiome Species 0.000 claims abstract description 57
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 15
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 15
- 239000001301 oxygen Substances 0.000 claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 14
- 230000005484 gravity Effects 0.000 claims abstract description 11
- 230000014509 gene expression Effects 0.000 abstract description 3
- 241000700141 Rotifera Species 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000813 microbial effect Effects 0.000 description 4
- 239000010865 sewage Substances 0.000 description 4
- 239000010802 sludge Substances 0.000 description 3
- 241000902900 cellular organisms Species 0.000 description 2
- 210000004916 vomit Anatomy 0.000 description 2
- 230000008673 vomiting Effects 0.000 description 2
- 102100040160 Rabankyrin-5 Human genes 0.000 description 1
- 101710086049 Rabankyrin-5 Proteins 0.000 description 1
- 241000223892 Tetrahymena Species 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects 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
- 238000005457 optimization Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000004065 wastewater treatment 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)
Abstract
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らとづいて推定する方法に関するも
のである。DETAILED DESCRIPTION OF THE INVENTION A. Field of Industrial Application The present invention relates to a method for estimating the quality of treated water, for example, by an activated sludge method, based on microbial data.
Ｂ１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ＢＯ
Ｄ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′ａ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。B1 Summary of the Invention The present invention is a method for estimating the water quality of water to be measured based on data on microorganisms contained in the water.
A membership value indicating the possibility of appearance in the D concentration band is defined in advance, and the membership value of each BOD'a degree band is picked up for microorganisms that appear in the water to be measured.
By estimating the BOD concentration based on this value, it is possible to reflect the knowledge about microorganisms in predicting the quality of treated water while expressing the quality of treated water with specific numerical values.
Ｃ１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。C1 Prior Art The activated sludge method, which is widely applied to the treatment of urban sewage, is a system that purifies sewage using metabolic reactions of microorganisms. Sewage treatment plants are regulated by the quality of their effluent water (treated water quality), and therefore treated water quality is the most important indicator in process management. By the way, the treatment efficiency of the system (the quality of the treated water) largely depends on the microorganisms that appear in the system. Traditionally, operators at treatment plants have examined the frequency of appearance of microorganisms called indicator microorganisms through microscopic observation to diagnose the condition of the treatment plant. Through years of research, it has been known in what water quality each indicator microorganism is likely to appear, and skilled operators can roughly predict the quality of treated water based on the frequency of appearance of indicator microorganisms.
Ｄ８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、−船釣に上鍔にも示されるようにあいま
いな表現なものが多いが、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。D8 Problems to be Solved by the Invention However, the knowledge that links the frequency of appearance of indicator microorganisms with the quality of treated water is a very vague concept. For example, we know that certain microorganisms appear when water quality is good. Here, ``good water quality'' is not backed up by concrete numbers, but is vague and intuitive. Human knowledge is often expressed in ambiguous terms, as shown in Boat Fishing and Kamitsuba, but for actual system management, concrete numerical values are absolutely necessary. The present invention aims to provide a water quality estimation method that can express treated water quality in concrete numerical values while reflecting knowledge about microorganisms in prediction of treated water quality, and also makes it possible to predict future water quality. purpose.
９４課題を解決するための手段
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。94 Means for Solving the Problems The present invention focuses on the existence of fuzzy theory as a mathematical method that connects human knowledge (vague expressions) with concrete numerical values, and uses this fuzzy theory to solve problems based on microbial data. For example, the idea is to estimate the quality of water treated with the activated sludge method.
第１図は本発明方法におけるファジー推論の概略を示す
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選択しておく。そして水
質を例えば「良い」。FIG. 1 is a diagram showing an outline of fuzzy inference in the method of the present invention. This fuzzy inference is slightly different from normal fuzzy inference. First, indicator microorganisms that serve as indicators of water quality are selected from among the microorganism species in the treated water. And the water quality, for example, is "good".
「悪い」、「その中間である」の３つに分けるために、
処理水の酸素要求型例えばＢＯＤａ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。In order to divide it into three categories: "bad" and "in between".
The oxygen demand type of the treated water, such as BODa degree (biochemical oxygen demand), is divided into three bands, and each concentration band is associated with water quality. For convenience of explanation, we assume microorganisms A, B, and C as indicator microorganisms, and as shown in Figure 2, microorganism A appears when the treated water quality is good, and microorganisms B and C appear when the treated water quality is poor. Assume.
ここで本発明では、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 present invention, the number of microorganisms appearing per unit amount of treated water is determined by a plurality of appearance ranks, for example, 0 to 5 as follows.
It is divided into six categories.
出現ランク
５、かなり多い
４、多い
３、普通
２、やや少ない
１、少ない
０、出現せず
（Ｉ吐検体当たり）
・・・ ２０００個以上
・・・ １０００−１９９９
・・・ ４００−９９９
・・・ ＩＯｌ ３９９
・・・ １−９９
・・・ ０
（０，０５吐検体当たり）
１００個以上
一方過去のデータから微生物毎に各ＢＯＤ濃度帯域にて
出現する最大個数を把握しておくと共に、微生物毎に各
ＢＯＤｊ１度帯域における出現可能性を示すメンバーシ
ップ値を、少なくとも前記最大個数を因子として予め定
めておく。例えばこのメンバーシップ値は、対応する微
生物の前記最大個数の逆数で表される。第１図は、横軸
に処理水質（ＢＯＤ濃度（ｘｇ／ 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Ｉ
／（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。Appearance rank 5, quite a lot 4, a lot 3, average 2, a little less 1, a little 0, not appearing (per I vomit sample) ... 2000 or more ... 1000-1999 ... 400-999 ...・IOl 399 ... 1-99 ... 0 (per 0.05 vomit sample) 100 or more On the other hand, from past data, know the maximum number of microorganisms that appear in each BOD concentration band, and A membership value indicating the possibility of appearance in each BODj 1 degree band is determined in advance using at least the maximum number as a factor. For example, this membership value is expressed as the reciprocal of the maximum number of corresponding microorganisms. Figure 1 is an explanatory diagram with treated water quality (BOD concentration (xg/L)) on the horizontal axis and membership value on the vertical axis. Taking microorganism A as an example, the possibility of microorganism A appearing is In the range of water quality (BOD) assumed to be good, it is always a constant value (this value is I
/(maximum number of microorganisms A)), but is always zero in other ranges. The possibility that microorganisms B and C will appear is always 1/(
(maximum number of microorganisms), but it is always zero in other water quality ranges. Membership values are often defined as triangles or trapezoids, but here we use rectangles 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（ｌｓＬ当たり）
が夫々４００〜９９９の間、１００〜３９９の間及び１
〜９９の間であれば、微生物Ａ、Ｂ、Ｃの出現ランクは
夫々３．２、ｌ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ＢＯＤａ度帯域のメンバーシ
ップ値を拾う。Now, as a result of observing the treated water, if it is assumed that microorganisms A, B, and C are included, then the number of microorganisms A, B, and C per unit of water is measured, and each microorganism is shown in the table above. Find the appearance rank to which you belong from among the appearance ranks. For example, the number of microorganisms A, B, and C (per lsL)
are between 400 and 999, between 100 and 399, and 1, respectively.
-99, the appearance ranks of microorganisms A, B, and C are 3.2 and 1, respectively. Then, the membership value of each BODa degree band is picked up for the microorganisms that have appeared (here, A, B, and 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、■、２で出現
していることより、各々のメンバーシップ値は３１５．
Ｉ１５．２１５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。各微生物はその出
現ランクが大きければ大きいほど、また出現可能な最大
個数が小さければ小さいほど、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 appearance rank of the microorganism. The appearance rank of microorganisms is 6 on a scale of 0 to 5.
It is evaluated in stages, but the membership value is modified by multiplying this appearance rank divided by 5. Microorganisms A, B, and C in Figure 1 appear at ranks 3, ■, and 2, respectively, so their membership values are 315.
It will be multiplied by I15.215. The result of this modification is the diagonally shaded rectangle in the figure. The purpose of these series of operations is to weight each microorganism. The higher the appearance rank of each microorganism, and the smaller the maximum number of microorganisms that can appear, the greater the certainty of the water quality indicated by that 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 the next step, the previously corrected membership values (shaded areas) are superimposed (synthesized) for each BOD density band. This is considered to be simply a graphical synthesis of the hatched areas. The result is the bottom figure (shaded with diagonal lines) in Figure 1.
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。The inferred treated water quality is calculated from the finally synthesized membership value. This uses center of gravity calculation. That is, the center of gravity in the horizontal axis direction (water quality) of the combined membership values is determined, and the BOD concentration at this center of gravity is estimated as the treated water quality concentration. This ends the water quality inference based on fuzzy inference.
Ｆ、実施例
水質のファジー推論には例えば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一中間水
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。F. Example For example, 18 types of indicator microorganisms are used for fuzzy inference of water quality. Figure 2 shows the water quality in which each microorganism appears. Data was taken from the Sewage Testing Method 1984. In the figure, for example, Tetrahymena is thought to appear from between good and medium water quality to between medium and poor water quality. Additionally, rotifers such as the boulder rotifer only appear when the water quality is good.
第２図の例では、良い水質はＸｌからＸｌの範囲、中間
水質はＸｌ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ＣＸＩ、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。In the example of FIG. 2, good water quality is defined as a range from Xl to Xl, intermediate water quality is defined as a range from Xl to X3, and poor water quality is defined as a range from X3 to X4. These four points CXI, X2. X3
．． The value of X4) is based on biota data and the corresponding treated water quality (“Biology of Wastewater Treatment” (Sudo), “Diagnosis of treatment function from the perspective of biota.
（須藤・稲森））より最適化（シンプレツクス法）の手
法を用いて求めたところ、ＸＩ＝−７，１、Ｘ２＝３１
．６、Ｘ３＝５１．６、Ｘ４＝６３．８ｎＢＯＤ／Ｌで
あった。ＸＩ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。(Sudo and Inamori)) using the optimization (simplex method) method, XI = -7,1, X2 = 31
．． 6, X3=51.6, X4=63.8nBOD/L. Although the value of XI is negative, this is unavoidable because the water quality is calculated using the center of gravity.
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ＸｌとＸｌの中間点となる。For example, consider a case in which only the boulder rotifer appears. Then, the composite membership value is the membership value of the rotifer itself. If you calculate the center of gravity using this, the center of gravity will be the midpoint between Xl and Xl.
従って例えＸｌがマイナスであろうと重心（推定水質）
は＋２゜２５ｕＢＯＤ／Ｌ となる。Therefore, even if Xl is negative, the center of gravity (estimated water quality)
becomes +2°25uBOD/L.
Ｇ１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Ｂ、ＯＤ濃度帯
域における出現可能性を示すメンバーノツプ値を予め定
義しておき、被測定水中に出現した微生物について各Ｂ
ＯＤ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
。G1 Effect of the invention According to the invention, for example, BOD
Specify a concentration band, define in advance a member nop value that indicates the possibility of appearance in each B and OD concentration band for each indicator microorganism, and set each B for microorganisms that appear in the water to be measured.
Since the membership value of the OD concentration band is collected and the BOD concentration is estimated based on that value, water quality can be expressed in concrete numerical values while reflecting knowledge about microorganisms in water quality prediction. In addition, actual water quality appears as a result of microbial fluctuations within the system, and in this invention, since the appearance of microorganisms is observed and microbial fluctuations are used as data, changes in water quality can be quickly captured and future Water quality can be predicted. In contrast, with a method that only monitors actual water quality, it may not be possible to keep up with changes when changes occur, and the problem may be irreparable.
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 the range of water quality in which indicator microorganisms appear.
Claims (1)
の出現個数を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
性微生物毎に各酸素要求量帯域にて出現する最大個数を
予め求めておくと共に、 前記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、少なくとも前記最大個
数を因子として予め定めておき、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、 横軸に酸素要求量、縦軸に合計したメンバーシップ値を
とって、横軸方向におけるメンバーシップ値の重心を求
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 number of indicator microorganisms that appear per unit water volume, which is an indicator of water quality, is divided into multiple appearance ranks, and the oxygen demand, which is an indicator of the water quality of the water to be measured, is divided into multiple bands. The maximum number of microorganisms that appear in each oxygen demand band is determined in advance, and a membership value indicating the possibility of appearance in each oxygen demand band is determined in advance for each indicator microorganism, using at least the maximum number as a factor. The number of indicator microorganisms per unit amount of water in the water to be measured is determined, the appearance rank to which each indicator microorganism belongs from among the plurality of appearance ranks is determined, and each oxygen Pick up the membership value of the demand band, weight this membership value by the appearance rank to which the indicator microorganism belongs, and sum these membership values for each oxygen demand band, and plot the oxygen demand on the horizontal axis. Water quality estimation characterized by taking the total membership value on the vertical axis, determining the center of gravity of the membership value in the horizontal axis direction, and estimating the oxygen demand at this center of gravity as the oxygen demand of the water to be measured. 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 true JPH0238860A (en) | 1990-02-08 |
| JP2661162B2 JP2661162B2 (en) | 1997-10-08 |
Family
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP63188772A Expired - Fee Related JP2661162B2 (en) | 1988-07-28 | 1988-07-28 | Water quality estimation method |
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| Country | Link |
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| JP (1) | JP2661162B2 (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0669614U (en) * | 1993-02-25 | 1994-09-30 | 憲人 須藤 | Heating floor panel |
| US5370416A (en) * | 1992-03-17 | 1994-12-06 | Ikeda Bussan Co., Ltd. | Airbag system for automotive vehicle including airbag mounting structure with high degree of airtightness |
| US5772687A (en) * | 1993-03-12 | 1998-06-30 | Saito; Yoshikuni | Hub for syringe, connecting structure of hub, syringe, syringe assembly and method of assembling syringe assembly |
| JP2008008752A (en) * | 2006-06-29 | 2008-01-17 | Fuji Electric Systems Co Ltd | Abnormality detection method for water quality |
| CN111125607A (en) * | 2019-12-16 | 2020-05-08 | 中国石油天然气股份有限公司 | Method and device for controlling emission of volatile organic compounds of oil storage |
| CN115754199A (en) * | 2022-11-10 | 2023-03-07 | 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
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5370416A (en) * | 1992-03-17 | 1994-12-06 | Ikeda Bussan Co., Ltd. | Airbag system for automotive vehicle including airbag mounting structure with high degree of airtightness |
| JPH0669614U (en) * | 1993-02-25 | 1994-09-30 | 憲人 須藤 | Heating floor panel |
| US5772687A (en) * | 1993-03-12 | 1998-06-30 | Saito; Yoshikuni | Hub for syringe, connecting structure of hub, syringe, syringe assembly and method of assembling syringe assembly |
| JP2008008752A (en) * | 2006-06-29 | 2008-01-17 | Fuji Electric Systems Co Ltd | Abnormality detection method for water quality |
| CN111125607A (en) * | 2019-12-16 | 2020-05-08 | 中国石油天然气股份有限公司 | Method and device for controlling emission of volatile organic compounds of oil storage |
| 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 |
| CN115754199A (en) * | 2022-11-10 | 2023-03-07 | 河南大学 | Water quality detection method based on membership function and principal component analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2661162B2 (en) | 1997-10-08 |
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