JP2023147019A - Chemical injection control system, chemical injection control method, and automatic coagulation treatment apparatus - Google Patents
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Abstract
Description
本発明は、薬品注入制御システム、薬品注入制御方法及び自動凝集処理装置に関する。 The present invention relates to a chemical injection control system, a chemical injection control method, and an automatic aggregation processing apparatus.
環境への影響の観点から、浄水処理装置の役割は重要である。浄水処理装置においては、被処理水に対して凝集剤が注入され、これにより懸濁物質が凝集し、そして、フロックが形成される。フロックは、固液分離され、さらにはろ過され、これにより、被処理水が浄化される。 From the perspective of environmental impact, the role of water purification equipment is important. In a water purification treatment apparatus, a flocculant is injected into the water to be treated, whereby suspended substances are flocculated and flocs are formed. The flocs are subjected to solid-liquid separation and then filtered, thereby purifying the water to be treated.
浄水処理においては、原水に対して、闇雲に凝集剤を注入するのではなく、ジャーテストと呼ばれる試験を実施して、凝集剤の適切な注入率を決定することが一般的である。しかしながら、このジャーテストの実施及び結果の判定には熟練と経験を要する。また、ジャーテストの実施及び結果の判定については、個人差も大きい。そのため、ベテラン技術者が不足すると、技術伝承が途絶えるといった問題がある。 In water purification treatment, rather than blindly injecting a flocculant into raw water, it is common to conduct a test called a jar test to determine the appropriate injection rate of the flocculant. However, skill and experience are required to conduct this jar test and judge the results. Furthermore, there are large individual differences in the implementation of jar tests and the judgment of results. Therefore, if there is a shortage of veteran engineers, there is a problem that the transmission of technology will be disrupted.
近年では、機械学習の分野の発展が目覚ましく、機械学習を用いた凝集剤の注入制御の研究が行われてきている。機械学習は、一般的に、過去のデータから、コンピューターアルゴリズムが自動で入力データのパターンやルールを解析する。この機械学習により、ベテラン技術者等の経験を要さず、迅速に誰でも凝集剤の注入率を決定することができる。 In recent years, the field of machine learning has made remarkable progress, and research has been conducted on controlling the injection of flocculants using machine learning. Machine learning generally uses computer algorithms to automatically analyze patterns and rules in input data from past data. This machine learning allows anyone to quickly determine the injection rate of the coagulant without requiring the experience of a veteran engineer.
例えば、特開2017-123088号公報(特許文献1)には、決定木学習アルゴリズムを利用して、センシングデータ及び薬品注入データから、次の時点での薬品注入データを予測する方法が開示されている。また、特開2021-146246号公報(特許文献2)には、学習済みモデルを利用して、水質データまたは運転操作条件データから、凝集処理試験の処理後の水質を予測し、凝集剤の注入率と処理後の水質の変化率の絶対値から凝集剤の注入率を予測する方法が開示されている。 For example, Japanese Patent Laid-Open No. 2017-123088 (Patent Document 1) discloses a method of predicting drug injection data at the next point in time from sensing data and drug injection data using a decision tree learning algorithm. There is. In addition, JP 2021-146246 A (Patent Document 2) discloses that a trained model is used to predict the water quality after treatment in a coagulation treatment test from water quality data or driving operation condition data, and to inject a flocculant. A method for predicting the flocculant injection rate from the absolute value of the rate and the rate of change in water quality after treatment is disclosed.
機械学習等の人工知能(AI)を用いた予測は、一般的に、学習に使ったデータ範囲の中であれば、凝集剤の注入率の制御は比較的高い精度を保つことができる。しかしながら、異常時等の範囲外のデータの演算時の予測精度は、低下する傾向にある。一方で、近年はゲリラ豪雨等の今までに経験の無い異常事態が発生することがある。このような異常事態によって流入原水の水質が急激に変化すると、特許文献1及び2に記載されるような、パターンやルールを解析する機械学習に基づく凝集剤注入率の予測だけでは対応することが難しい場合がある。 In general, predictions using artificial intelligence (AI) such as machine learning can maintain relatively high precision in controlling the flocculant injection rate within the data range used for learning. However, the prediction accuracy when calculating data outside the range, such as during abnormal times, tends to decrease. On the other hand, in recent years, unprecedented abnormal situations such as torrential rains have occurred. When the water quality of inflowing raw water changes rapidly due to such an abnormal situation, it is not possible to deal with it simply by predicting the flocculant injection rate based on machine learning that analyzes patterns and rules, as described in Patent Documents 1 and 2. It can be difficult.
上記課題を鑑み、本発明は、原水の急激な性状変動が生じても、原水に注入する薬品の注入率を適切に制御することが可能な薬品注入制御システム、薬品注入制御方法及び自動凝集処理装置を提供する。 In view of the above problems, the present invention provides a chemical injection control system, a chemical injection control method, and an automatic aggregation process that can appropriately control the injection rate of chemicals injected into raw water even if sudden changes in the properties of raw water occur. Provide equipment.
上記課題を解決するために、本発明者らが鋭意検討した結果、機械学習アルゴリズムを用いた薬品の注入率の予測結果に対し、その予測結果が適切であるか否かを確認する所定の工程を設けることが有用であるとの知見を得た。 In order to solve the above problems, the present inventors have conducted extensive studies and found that a predetermined process for checking whether or not the prediction result of the drug injection rate using a machine learning algorithm is appropriate. We found that it is useful to provide a
以上の知見を基礎として完成した本発明は一側面において、原水の水質データと、原水を処理する水処理プラントの運転操作条件データとを含む情報を取得する情報取得部と、水質データと運転操作条件データとを含む説明変数を取得し、原水へ注入する薬品の最適な注入率を含む目的変数を出力する学習済モデルを用いた機械学習アルゴリズムを用いて、原水へ注入する薬品の注入率を予測する予測部と、薬品の注入率の予測結果に基づいて、原水に薬品を注入して確認試験を行い、該確認試験により得られる処理水の水質データを基準値と比較することにより、予測結果が適正であるか否かを確認する確認部と、確認部の確認結果に基づいて、原水へ注入する薬品の注入率を制御する注入制御部とを備える薬品注入制御システムである。 One aspect of the present invention, which was completed based on the above knowledge, includes: an information acquisition unit that acquires information including water quality data of raw water and operation condition data of a water treatment plant that processes the raw water; The injection rate of chemicals to be injected into raw water is calculated using a machine learning algorithm using a trained model that acquires explanatory variables including condition data and outputs objective variables including the optimal injection rate of chemicals to be injected into raw water. Based on the predicted results of the prediction unit and the chemical injection rate, a confirmation test is performed by injecting chemicals into the raw water, and the water quality data of the treated water obtained from the confirmation test is compared with the standard value. This is a chemical injection control system that includes a confirmation section that confirms whether the results are appropriate or not, and an injection control section that controls the injection rate of chemicals to be injected into raw water based on the confirmation results of the confirmation section.
本発明に係る薬品注入制御システムは一実施態様において、確認部が、薬品の注入率の予測結果が適正である場合には、予測結果に基づく注入率を、原水へ薬品を注入して水処理を行うための薬品の注入率として決定し、薬品の注入率の予測結果が適正でない場合には、注入率を変更して再試験を行い、該再試験に基づいて、原水へ薬品を注入して水処理を行うために最適な薬品の注入率を決定する。 In one embodiment of the chemical injection control system according to the present invention, if the predicted result of the chemical injection rate is appropriate, the confirmation unit adjusts the injection rate based on the predicted result to water treatment by injecting the chemical into raw water. If the predicted result of the chemical injection rate is not appropriate, the injection rate is changed and retested, and based on the retest, the chemical is injected into the raw water. determine the optimal chemical injection rate for water treatment.
本発明に係る薬品注入制御システムは別の一実施態様において、確認部が、原水を処理する処理槽と、処理槽内に薬品を注入する薬品注入部と、処理槽内を撹拌する撹拌機と、処理槽で得られる処理水の水質データを分析する水質分析器と、処理槽内へ注入する薬品の注入率を制御する制御部とを備える。 In another embodiment of the chemical injection control system according to the present invention, the confirmation unit includes a treatment tank for treating raw water, a chemical injection unit for injecting chemicals into the treatment tank, and a stirrer for stirring the inside of the treatment tank. , a water quality analyzer that analyzes water quality data of treated water obtained in the treatment tank, and a control unit that controls the injection rate of chemicals to be injected into the treatment tank.
本発明は別の一側面において、原水の水質データと原水を処理する水処理プラントの運転操作条件データとを含む説明変数を取得し、原水へ注入する薬品の最適な注入率を含む目的変数を出力する学習済モデルを用いた機械学習アルゴリズムを用いて原水へ注入する薬品の注入率を予測する工程と、薬品の注入率の予測結果に基づいて、原水に薬品を注入して確認試験を行い、該確認試験により得られる処理水の水質データを基準値と比較することにより、薬品の注入率の予測結果が適正であるか否かを確認する工程と、確認の結果に基づいて、原水へ注入する薬品の注入率を制御する工程とを含む薬品注入制御方法である。 In another aspect of the present invention, explanatory variables including water quality data of raw water and operating condition data of a water treatment plant that treats the raw water are acquired, and objective variables including an optimal injection rate of chemicals to be injected into the raw water are determined. A process of predicting the injection rate of chemicals to be injected into raw water using a machine learning algorithm using a trained model to be output, and a confirmation test by injecting chemicals into raw water based on the predicted results of the chemical injection rate. , a process of comparing the water quality data of the treated water obtained from the confirmation test with the standard value to confirm whether the predicted results of the chemical injection rate are appropriate, and based on the confirmation results, This is a chemical injection control method including a step of controlling an injection rate of a chemical to be injected.
本発明に係る薬品注入制御方法は一実施態様において、確認する工程が、薬品の注入率の予測結果が適正である場合には、予測結果に基づく注入率を、原水へ薬品を注入して水処理を行うための薬品の注入率として決定し、薬品の注入率の予測結果が適正でない場合には、注入率を変更して再試験を行い、該再試験により得られる処理水の水質データが基準値の範囲内となるまで、再試験を繰り返し、原水へ薬品を注入して水処理を行うために最適な薬品の注入率を決定することを含む。 In one embodiment of the chemical injection control method according to the present invention, in the step of confirming, if the predicted result of the chemical injection rate is appropriate, the injection rate based on the predicted result is injected into the raw water and The injection rate of chemicals for treatment is determined, and if the predicted chemical injection rate is not appropriate, the injection rate is changed and retested, and the water quality data of the treated water obtained from the retest is This involves repeating the test until it falls within the standard value range, and determining the optimal chemical injection rate for water treatment by injecting chemicals into raw water.
本発明に係る薬品注入制御方法は別の一実施態様において、確認する工程が、原水に薬品として凝集剤を注入して凝集沈殿処理を行い、凝集沈殿処理で得られる処理水の水質データとして、濁度、色度、pH、アルカリ度、微粒子数、TOC、COD、紫外線吸光度、有機物分画の少なくともいずれかを分析することを含む。 In another embodiment of the chemical injection control method according to the present invention, the step of checking injects a flocculant as a chemical into the raw water to perform a coagulation sedimentation treatment, and as water quality data of the treated water obtained by the coagulation sedimentation treatment, This includes analyzing at least one of turbidity, chromaticity, pH, alkalinity, number of fine particles, TOC, COD, ultraviolet absorbance, and organic matter fraction.
本発明は更に別の一側面において、原水へ注入する凝集剤の最適な注入率を予測するための学習済モデルを用いた機械学習アルゴリズムにより予測された凝集剤の注入率の予測結果を取得する情報取得部と、凝集剤の注入率の予測結果と、予め設定した試験条件とに基づいて、原水採取工程、薬品注入工程、撹拌工程及び静置工程を含む凝集処理試験を実施する処理槽と、凝集処理試験で得られる処理水の水質データを分析する水質分析器と、水質分析器で分析された処理水の水質データを基準値と比較し、予測結果が適正か否かを判断する確認部と、予測結果が適正な場合は、予測結果に基づく注入率に基づいて、原水に凝集剤を注入して水処理を行うための薬品注入設備の凝集剤の注入率を制御し、予測結果が適正でない場合は、新たな凝集剤注入率で凝集処理試験を実施し、該凝集処理試験で得られる処理水の水質データが基準値を満たすまで凝集処理試験を繰り返した結果、決定される最適凝集剤注入率に基づいて、薬品注入設備の凝集剤の注入率を制御する注入制御部とを備える自動凝集処理装置である。 In still another aspect of the present invention, the present invention obtains a prediction result of a coagulant injection rate predicted by a machine learning algorithm using a trained model for predicting an optimal injection rate of a coagulant to be injected into raw water. an information acquisition unit, a treatment tank that conducts a flocculation treatment test including a raw water collection process, a chemical injection process, a stirring process, and a standing process based on the predicted results of the flocculant injection rate and preset test conditions; , a water quality analyzer that analyzes the water quality data of the treated water obtained from the flocculation treatment test, and a confirmation that compares the water quality data of the treated water analyzed by the water quality analyzer with standard values to determine whether the predicted results are appropriate. If the prediction result is appropriate, control the injection rate of the coagulant in the chemical injection equipment for water treatment by injecting the coagulant into raw water based on the injection rate based on the prediction result, and calculate the predicted result. If it is not appropriate, perform a flocculation test with a new flocculant injection rate, repeat the flocculation test until the water quality data of the treated water obtained in the flocculation test meets the standard value, and then determine the optimum The present invention is an automatic agglomeration processing apparatus including an injection control section that controls the injection rate of the flocculant of the chemical injection equipment based on the flocculant injection rate.
本発明によれば、原水の急激な性状変動が生じても、原水に注入する薬品の注入率を適切に制御することが可能な薬品注入制御システム、薬品注入制御方法及び自動凝集処理装置が提供できる。 According to the present invention, there are provided a chemical injection control system, a chemical injection control method, and an automatic aggregation treatment device that can appropriately control the injection rate of chemicals injected into raw water even if sudden changes in the properties of raw water occur. can.
以下、図面を参照しながら本発明の実施の形態を説明する。以下の図面の記載においては、同一又は類似の部分には同一又は類似の符号を付している。なお、以下に示す実施の形態は、この発明の技術的思想を具体化するための装置や方法を例示するものであって、この発明の技術的思想は構成部品の構造、配置等を下記のものに特定するものではない。 Embodiments of the present invention will be described below with reference to the drawings. In the description of the drawings below, the same or similar parts are denoted by the same or similar symbols. The embodiments shown below are illustrative of devices and methods for embodying the technical idea of this invention. It is not something specific.
(薬品注入制御システム)
本発明の実施の形態に係る薬品注入制御システム100は、図1に示すように、原水を水処理する水処理プラント20に注入される薬品の注入率を制御するための薬品注入制御装置10を備える。
(Chemical injection control system)
As shown in FIG. 1, a chemical injection control system 100 according to an embodiment of the present invention includes a chemical injection control device 10 for controlling the injection rate of chemicals injected into a water treatment plant 20 that processes raw water. Be prepared.
水処理プラント20としては、以下に限定されるものではないが、浄水処理設備が利用できる。水処理プラント20は、例えば、着水井21、急速撹拌池22、フロック形成池23、沈殿池24、原水に薬品を注入する薬品注入設備25、急速ろ過池26、浄水池27及び配水池28を備える。 As the water treatment plant 20, although not limited to the following, water purification treatment equipment can be used. The water treatment plant 20 includes, for example, a water receiving well 21, a rapid stirring tank 22, a floc formation tank 23, a settling tank 24, a chemical injection facility 25 for injecting chemicals into raw water, a rapid filtration tank 26, a water purification tank 27, and a water distribution tank 28. Be prepared.
着水井21は、取り入れた原水の量及び水位等を調整する。急速撹拌池22は、凝結剤等の薬品を注入した後、急速撹拌を行って混和処理を行う。フロック形成池23は、凝集剤を原水に注入した後、フロックを形成させ、濁りを凝集させる。薬品注入設備25は急速撹拌池22或いはフロック形成池23に薬品を注入する装置である。 The water landing well 21 adjusts the amount of raw water taken in, the water level, etc. The rapid stirring basin 22 performs rapid stirring to perform a mixing process after injecting chemicals such as a coagulant. The flocculation pond 23 injects a flocculant into raw water, forms flocs, and flocculates turbidity. The chemical injection equipment 25 is a device for injecting chemicals into the rapid stirring pond 22 or the flocculation pond 23.
沈殿池24は、フロック形成池23で形成した粗大フロックを沈降分離により沈降させる。急速ろ過池26は、沈殿池24で得られた上澄水を急速ろ過処理する。浄水池27はろ過水に所定の殺菌処理を施した後の浄水を貯留する。配水池28は、浄水池27から供給された浄水を貯留し、これを家庭、学校、工場等の各施設に自然流下等によって供給する。 The sedimentation tank 24 settles the coarse flocs formed in the floc formation tank 23 by sedimentation separation. The rapid filtration tank 26 rapidly filtrates the supernatant water obtained in the settling tank 24. The water purification pond 27 stores purified water after performing a predetermined sterilization treatment on the filtered water. The water distribution reservoir 28 stores purified water supplied from the water purification reservoir 27 and supplies it to each facility such as a home, school, or factory by gravity flow or the like.
水処理プラント20各設備には計測器(不図示)が設けられている。以下に限定されるものではないが、薬品の注入前と注入後で同一種類の水質を測定するために、薬品の注入前と注入後とで、同じ種類の計測器を備えておくことが好ましい。計測器の種類としては、温度、濁度、透明度、色度、pH、アルカリ度、微粒子数、凝集剤注入率、pH調製用薬品注入率、TOC(全有機炭素)、COD(化学的酸素要求量)、紫外線吸光度、有機物分画等を測定するための種々の計測器を備えることができる。 Each piece of equipment in the water treatment plant 20 is provided with a measuring device (not shown). Although not limited to the following, in order to measure the same type of water quality before and after chemical injection, it is preferable to have the same type of measuring instrument before and after chemical injection. . The types of measuring instruments include temperature, turbidity, transparency, chromaticity, pH, alkalinity, number of fine particles, flocculant injection rate, pH adjustment chemical injection rate, TOC (total organic carbon), COD (chemical oxygen demand). It can be equipped with various measuring instruments for measuring the amount), ultraviolet absorbance, organic matter fraction, etc.
薬品注入制御装置10は、一般的なハードウェア構成で構成されることができ、プロセッサ、メモリ(例:RAM等)、記憶媒体(例:HDD、SSD等)及び通信モジュールを備えることができる。薬品注入制御装置10は、サーバー装置として機能してもよく、又は、クライアント装置として機能してもよい。薬品注入制御装置10は、1台のハードウェアを構成してもよいし、複数台のハードウェアに分散されてもよい。 The chemical injection control device 10 can be configured with a general hardware configuration, and can include a processor, a memory (eg, RAM, etc.), a storage medium (eg, HDD, SSD, etc.), and a communication module. The drug injection control device 10 may function as a server device or a client device. The chemical injection control device 10 may constitute one piece of hardware, or may be distributed over a plurality of pieces of hardware.
薬品注入制御装置10は、情報取得部11と、予測部12と、確認部13と、注入制御部14とを備える。情報取得部11は、水処理プラント20が備える上述した計測器(不図示)と、直接又は間接的に通信可能に接続されている。情報取得部11は、計測器が測定した処理水の水質データ及び原水の水質データ等を取得する。更に、情報取得部11は、原水を処理する水処理プラント20の運転操作に関わる種々のパラメータ、例えば、各設備への原水流入量等を含む運転操作条件データを取得する。 The drug injection control device 10 includes an information acquisition section 11, a prediction section 12, a confirmation section 13, and an injection control section 14. The information acquisition unit 11 is directly or indirectly connected to the above-mentioned measuring instrument (not shown) included in the water treatment plant 20 so as to be communicable. The information acquisition unit 11 acquires water quality data of treated water, water quality data of raw water, etc. measured by a measuring device. Further, the information acquisition unit 11 acquires various parameters related to the operation of the water treatment plant 20 that processes raw water, such as operation condition data including the amount of raw water flowing into each facility.
注入制御対象とする薬品としては、水処理プラント20に利用される種々の薬品であれば任意の薬品を用いることができる。典型的には凝集剤が用いられる。凝集剤としては、ポリ硫酸第二鉄、塩化第二鉄、PAC(ポリ塩化アルミニウム)又は硫酸バンドなどが挙げられる。これ以外に、高分子凝集剤、凝集助剤、有機凝結剤、無機凝結剤、或いは凝集改良剤なども用いられ、これらは単独或いは併用して用いることもできる。 As the chemical to be injection controlled, any chemical can be used as long as it is a variety of chemicals used in the water treatment plant 20. A flocculant is typically used. Examples of the flocculant include polyferric sulfate, ferric chloride, PAC (polyaluminum chloride), and aluminum sulfate. In addition to these, a polymer flocculant, a flocculation aid, an organic coagulant, an inorganic coagulant, or a coagulation improver can also be used, and these can be used alone or in combination.
凝集剤の他に、pH調整剤、アルカリ剤、或いは、硝化脱窒処理等で添加されるメタノールや有機物、界面活性剤、殺菌処理等で用いられる塩素、次亜塩素酸等も、注入制御対象とする薬品として用いてもよい。 In addition to coagulants, pH adjusters, alkaline agents, methanol and organic substances added in nitrification and denitrification treatments, surfactants, chlorine and hypochlorous acid used in sterilization treatments, etc. are also subject to injection control. It may also be used as a drug.
予測部12は、原水の水質データと、原水を処理する水処理プラント20の運転操作条件データとを少なくとも含む所定の説明変数を取得し、原水へ注入する薬品の最適な注入率を含む所定の目的変数を出力する学習済モデルを用いた機械学習アルゴリズムを用いて、原水へ注入する薬品の注入率を予測する。 The prediction unit 12 acquires predetermined explanatory variables that include at least water quality data of the raw water and operational condition data of the water treatment plant 20 that processes the raw water, and obtains predetermined explanatory variables that include the optimal injection rate of chemicals to be injected into the raw water. A machine learning algorithm using a trained model that outputs objective variables is used to predict the injection rate of chemicals to be injected into raw water.
機械学習アルゴリズムとしては、特に限定されないが、SVR法(サポートベクター回帰法)、PLS法(部分最小二乗法:PartialLeastSquares)、ニューラルネットワーク法(例えば、DeepLearning法)、ランダムフォレスト法、又は決定木法、LSTM法(長短期記憶:LongShort-TermMemory)等が利用できる。中でも、本実施形態における機械学習アルゴリズムとしてLSTM法を用いることが好ましい。 Machine learning algorithms include, but are not limited to, SVR method (support vector regression method), PLS method (Partial Least Squares method), neural network method (for example, Deep Learning method), random forest method, or decision tree method, The LSTM method (Long Short-Term Memory) or the like can be used. Among these, it is preferable to use the LSTM method as the machine learning algorithm in this embodiment.
学習済モデルとしては、例えば、原水の水質データと、原水を処理する水処理プラント20の運転操作条件データと、原水へ注入する薬品の最適な注入率とが関連付けられたデータセットを学習データとし、機械学習アルゴリズムとしてLSTM法を用いて、薬品の注入率を予測するために構築された所定のモデルを利用する。 As a trained model, for example, a data set in which water quality data of raw water, operation condition data of the water treatment plant 20 that processes the raw water, and optimal injection rate of chemicals to be injected into the raw water are associated is used as the learning data. , using a predetermined model built to predict the drug injection rate using the LSTM method as a machine learning algorithm.
学習済モデルの構築に用いられる学習データに含まれる水質データとしては、例えば、浄水処理場の場合、例えば、温度、濁度、透明度、色度、pH、アルカリ度、微粒子数、TOC、COD、紫外線吸光度、有機物分画が挙げられる。それ以外に、例えば、天候、気温、雨量、台風の風力、中心気圧等の気象情報、pH調製時の薬品注入率等も含んでもよい。水質データについては、薬品の注入前のものと薬品の注入後のものとを含んでもよい。薬品が複数回注入される場合は、注入前後の水質データの情報をそれぞれ含んでいても良い。運転操作条件データとしては、各設備の処理槽の大きさ、薬品の種類、薬品注入率の定格値、流入水量、撹拌時間、撹拌速度、pH条件、静置時間、ろ過速度等の各設備の運転操作に関連する種々の情報が挙げられる。 For example, in the case of a water treatment plant, the water quality data included in the training data used to construct the trained model includes temperature, turbidity, transparency, chromaticity, pH, alkalinity, number of particles, TOC, COD, Examples include ultraviolet absorbance and organic matter fraction. In addition, it may also include, for example, meteorological information such as weather, temperature, rainfall, wind power of a typhoon, central pressure, etc., chemical injection rate during pH adjustment, and the like. The water quality data may include data before and after the injection of chemicals. If a chemical is injected multiple times, information on water quality data before and after each injection may be included. Operational condition data for each facility includes the size of the treatment tank, type of chemical, rated value of chemical injection rate, amount of inflow water, stirring time, stirring speed, pH condition, standing time, filtration speed, etc. Various information related to driving operations can be mentioned.
本実施形態における「薬品の注入率」とは、例えば、流入水量(L)に対する薬品の注入率(mg)の割合を意味し、典型的には以下の式で表される。
注入率(mg/L)=薬品注入量(mg)/流入水量(L)
薬品の注入率の制御は、薬品注入設備25の方式にもよるが、例えば薬品注入設備25が備える弁やポンプ等を制御することにより行われる。
The "chemical injection rate" in this embodiment means, for example, the ratio of the chemical injection rate (mg) to the amount of inflow water (L), and is typically expressed by the following formula.
Injection rate (mg/L) = Chemical injection amount (mg) / Inflow water amount (L)
Control of the injection rate of the medicine depends on the method of the medicine injection equipment 25, but is performed, for example, by controlling the valves, pumps, etc. included in the medicine injection equipment 25.
確認部13は、予測部12による薬品の注入率の予測結果に基づいて、原水に薬品を注入して確認試験を行い、確認試験により得られる処理水の水質データを、予め定められた基準値と比較することにより、予測結果が適正であるか否かを確認する。基準値は操作者が適宜設定することができる。例えば、浄水処理場である場合、基準値は、水道水の水質基準値等を用いることができる。 The confirmation unit 13 performs a confirmation test by injecting chemicals into the raw water based on the prediction result of the injection rate of chemicals by the prediction unit 12, and converts the water quality data of the treated water obtained from the confirmation test to a predetermined reference value. By comparing the prediction result with The reference value can be set as appropriate by the operator. For example, in the case of a water treatment plant, the standard value may be a water quality standard value for tap water.
確認部13は、確認試験の結果、予測部12による薬品の注入率の予測結果が適正であると判断した場合には、予測部12が予測した予測結果に基づく注入率を、原水へ薬品を注入して水処理を行うための薬品の注入率として決定する。一方、確認部13は、確認試験の結果、薬品の注入率の予測結果が適正でない場合には、注入率を変更して再試験を行い、該再試験に基づいて、原水へ薬品を注入して水処理を行うために最適な薬品の注入率を決定する。そして、注入制御部14は、確認部13の確認結果に基づいて、薬品注入設備25が原水へ注入する薬品の注入率を制御する。 As a result of the confirmation test, if the confirmation unit 13 determines that the prediction result of the chemical injection rate by the prediction unit 12 is appropriate, the confirmation unit 13 applies the injection rate based on the prediction result predicted by the prediction unit 12 to the raw water. It is determined as the injection rate of chemicals for water treatment. On the other hand, if the predicted result of the injection rate of the chemical is not appropriate as a result of the confirmation test, the confirmation unit 13 changes the injection rate and performs a retest, and injects the chemical into the raw water based on the retest. determine the optimal chemical injection rate for water treatment. Then, the injection control section 14 controls the injection rate of the chemical that the chemical injection equipment 25 injects into the raw water based on the confirmation result of the confirmation section 13 .
確認部13は、予測部12による薬品の注入率の予測結果に基づいて、原水に薬品を注入して確認試験を行うために、図2に示すような簡易的な試験装置を備えることができる。例えば、確認部13は、原水を処理する処理槽1と、処理槽1内に薬品を注入する薬品注入部3と、処理槽1内を撹拌する撹拌機2と、処理槽1で得られる処理水の水質データを分析する水質分析器5と、処理槽1内へ注入する薬品の注入率を制御する制御部6とを備える。確認部13は、処理槽1内にpH調整剤を注入するpH注入部4を更に備えても良い。 The confirmation unit 13 may include a simple testing device as shown in FIG. 2 in order to perform a confirmation test by injecting chemicals into raw water based on the prediction result of the injection rate of chemicals by the prediction unit 12. . For example, the confirmation unit 13 includes a treatment tank 1 that processes raw water, a chemical injection unit 3 that injects chemicals into the treatment tank 1, an agitator 2 that stirs the inside of the treatment tank 1, and the treatment obtained in the treatment tank 1. It includes a water quality analyzer 5 that analyzes water quality data, and a control unit 6 that controls the injection rate of chemicals to be injected into the treatment tank 1. The confirmation unit 13 may further include a pH injection unit 4 that injects a pH adjuster into the processing tank 1.
制御部6は、撹拌機2、流入弁7、排水弁8、薬品注入ポンプ31、pH調整ポンプ41、サンプリングポンプ51に接続されている。制御部6は、撹拌機2、流入弁7、排水弁8、薬品注入ポンプ31、pH調整ポンプ41、サンプリングポンプ51へ所定の制御信号を出力し、各装置の動作を制御するように構成されている。確認部13による確認試験の操作の詳細は後述する。 The control unit 6 is connected to the stirrer 2 , the inflow valve 7 , the drain valve 8 , the chemical injection pump 31 , the pH adjustment pump 41 , and the sampling pump 51 . The control unit 6 is configured to output predetermined control signals to the stirrer 2, inflow valve 7, drain valve 8, chemical injection pump 31, pH adjustment pump 41, and sampling pump 51 to control the operation of each device. ing. Details of the operation of the confirmation test by the confirmation unit 13 will be described later.
本発明の実施の形態に係る薬品注入制御システム100によれば、機械学習を用いた薬品の注入率の予測結果について、確認部13が確認試験を行い、原水へ薬品を注入して水処理を行うために最適な薬品の注入率を決定することができる。これにより、仮に急な気象変動等の影響で、機械学習による予測結果が適切でない場合においても、その予測結果を図2の試験装置で簡易的に試験して確認し、適切な薬品の注入率を決定することにより、原水に対して常に適切な注入率で薬品を注入することができる。その結果、原水の急激な性状変動が生じても、原水に注入する薬品の注入率を適切に制御でき、処理水の水質低下を抑制することが可能な薬品注入制御システム100が提供できる。 According to the chemical injection control system 100 according to the embodiment of the present invention, the confirmation unit 13 performs a confirmation test on the prediction result of the chemical injection rate using machine learning, and performs water treatment by injecting the chemical into raw water. The optimal chemical injection rate for this purpose can be determined. As a result, even if the prediction results obtained by machine learning are not appropriate due to the effects of sudden weather changes, the prediction results can be easily tested and confirmed using the testing device shown in Figure 2, and the appropriate chemical injection rate can be determined. By determining this, it is possible to always inject chemicals into raw water at an appropriate injection rate. As a result, it is possible to provide a chemical injection control system 100 that can appropriately control the injection rate of chemicals injected into raw water and suppress deterioration in the quality of treated water even if a sudden change in the properties of raw water occurs.
(薬品注入制御方法)
本発明の実施の形態に係る薬品注入制御方法は、図1に示す薬品注入制御システム100を用いて図3に示すフローチャートに従って制御することができる。即ち、本発明の実施の形態に係る薬品注入制御方法は、原水の水質データと原水を処理する水処理プラント20の運転操作条件データとを取得する工程S1と、原水の水質データと運転操作条件データとを少なくとも含む所定の説明変数を取得し、原水へ注入する薬品の最適な注入率を含む所定の目的変数を出力する学習済モデルを用いた機械学習アルゴリズムを用いて原水へ注入する薬品の注入率を予測する工程S2と、薬品の注入率の予測結果に基づいて、原水に薬品を注入して確認試験を行い、確認試験により得られる処理水の水質データを基準値と比較することにより、薬品の注入率の予測結果が適正であるか否かを確認する工程(S3~S7)と、確認の結果に基づいて、原水へ注入する薬品の注入率を制御する工程S8とを含む。
(Chemical injection control method)
The drug injection control method according to the embodiment of the present invention can be controlled using the drug injection control system 100 shown in FIG. 1 according to the flowchart shown in FIG. 3. That is, the chemical injection control method according to the embodiment of the present invention includes a step S1 of acquiring water quality data of raw water and operation condition data of the water treatment plant 20 that processes the raw water, and a step S1 of acquiring water quality data of raw water and operation condition data of the water treatment plant 20 that processes the raw water. of chemicals to be injected into raw water using a machine learning algorithm using a trained model that obtains predetermined explanatory variables including at least data and outputs predetermined objective variables including the optimal injection rate of chemicals to be injected into raw water. Step S2 of predicting the injection rate; and based on the prediction result of the chemical injection rate, a confirmation test is performed by injecting the chemical into the raw water, and the water quality data of the treated water obtained from the confirmation test is compared with the standard value. , a step of confirming whether the predicted result of the chemical injection rate is appropriate (S3 to S7), and a step S8 of controlling the injection rate of the chemical to be injected into the raw water based on the confirmation result.
工程S1において、図1の情報取得部11は、水処理プラント20で処理される原水の水質データ及び運転操作条件データを取得する。工程S2において、予測部12は、情報取得部11が取得した水質データ及び運転操作条件データの中から学習済みモデルを構築する際に入力した水質データ及び運転操作条件データと同じ水質データ及び運転操作条件データを取得し、これを説明変数として学習済モデルに入力し、薬品の最適な注入率の予測値を得る。工程S3において、予測部12は、確認部13へ、学習済モデルが予測した薬品注入率の予測値を送信する。 In step S1, the information acquisition unit 11 in FIG. 1 acquires water quality data and operation condition data of raw water to be treated in the water treatment plant 20. In step S2, the prediction unit 12 uses the same water quality data and driving operation condition data as the water quality data and driving operation condition data input when constructing the learned model from the water quality data and driving operation condition data acquired by the information acquisition unit 11. Obtain condition data and input it as an explanatory variable into the trained model to obtain a predicted value for the optimal injection rate of the drug. In step S3, the prediction unit 12 transmits the predicted value of the drug injection rate predicted by the learned model to the confirmation unit 13.
図3の工程S4において、図1の確認部13は、予測部12からの薬品注入率の予測値の出力を受けて、図2の試験装置を用いて原水に薬品を注入して確認試験を実施する。工程S4における確認試験の詳細は後述する。工程S5において、確認部13は、確認試験により得られる処理水の水質データを、確認部13が備える水質分析器5を用いて分析する。 In step S4 of FIG. 3, the confirmation unit 13 of FIG. 1 receives the output of the predicted value of the chemical injection rate from the prediction unit 12, and performs a confirmation test by injecting the chemical into the raw water using the testing device of FIG. implement. Details of the confirmation test in step S4 will be described later. In step S5, the confirmation unit 13 analyzes the water quality data of the treated water obtained by the confirmation test using the water quality analyzer 5 included in the confirmation unit 13.
工程S5において用いられる水質分析器5としては、温度、濁度、透明度、色度、pH、アルカリ度、微粒子数、TOC、COD、紫外線吸光度、有機物分画、固形分(SS)、MLSS、pHなどの水質データを得るための種々の測定装置が用いられる。 The water quality analyzer 5 used in step S5 includes temperature, turbidity, transparency, chromaticity, pH, alkalinity, number of particles, TOC, COD, ultraviolet absorbance, organic matter fraction, solid content (SS), MLSS, and pH. Various measuring devices are used to obtain water quality data such as:
工程S6において、確認部13は、水質分析器5により分析された水質データを予め定められた基準値と比較することにより、薬品注入率の予測結果が適正であるか否かを確認する。水質データが基準値を満たしている場合、即ち、薬品の注入率の予測結果が適正である場合には、確認部13は、予測結果に基づく注入率を、原水へ薬品を注入して水処理を行うための薬品の注入率として決定し、工程S8へ進む。一方、水質データが基準値を満たしていない場合、即ち、薬品の注入率の予測結果が適正でない場合には、工程S7へ進む。 In step S6, the confirmation unit 13 confirms whether the predicted result of the chemical injection rate is appropriate by comparing the water quality data analyzed by the water quality analyzer 5 with a predetermined reference value. When the water quality data satisfies the standard value, that is, when the predicted result of the chemical injection rate is appropriate, the confirmation unit 13 injects the chemical into the raw water to perform water treatment using the injection rate based on the predicted result. The chemical injection rate is determined as the chemical injection rate for carrying out the process, and the process proceeds to step S8. On the other hand, if the water quality data does not meet the standard value, that is, if the prediction result of the chemical injection rate is not appropriate, the process proceeds to step S7.
工程S7において、確認部13が、薬品注入率を予測値とは異なる注入率に変更する。変更方法としては、以下に限定されるものではないが、確認部13は、薬品注入率の予測値が予め設定した基準値を上回る場合には、薬品注入率を増加させる。薬品注入率の予測値が基準値を下回る場合には、薬品注入率を減少させるように、変更後の薬品注入率を決定し、工程S4へ進む。変更時の注入率の増減量については予め設定することができる。 In step S7, the confirmation unit 13 changes the chemical injection rate to an injection rate different from the predicted value. Although the changing method is not limited to the following, the confirmation unit 13 increases the chemical injection rate when the predicted value of the chemical injection rate exceeds a preset reference value. If the predicted value of the chemical injection rate is less than the reference value, a changed chemical injection rate is determined so as to reduce the chemical injection rate, and the process proceeds to step S4. The increase or decrease in the injection rate at the time of change can be set in advance.
次に、工程S4において、工程S7で決定された変更後の薬品注入率で薬品を注入して再試験を行う。確認部13は、再試験の結果、水質データが基準値を満たすまで、工程S4~工程S7を繰り返すことにより、最適な薬品注入率を決定する。最適な薬品注入率が決定された場合には、工程S8へ進む。 Next, in step S4, a retest is performed by injecting a chemical at the changed chemical injection rate determined in step S7. The confirmation unit 13 determines the optimum chemical injection rate by repeating steps S4 to S7 until the water quality data satisfies the reference value as a result of the retest. If the optimum chemical injection rate has been determined, the process advances to step S8.
工程S8において、注入制御部14は、水処理プラント20の薬品注入設備25に、原水の処理に対して最適な薬品注入率を送信する。薬品注入設備25は、注入制御部14から出力された制御信号に基づいて、原水に最適な薬品注入率で薬品を注入する。 In step S8, the injection control unit 14 transmits the optimum chemical injection rate for raw water treatment to the chemical injection equipment 25 of the water treatment plant 20. The chemical injection equipment 25 injects a chemical into the raw water at an optimal chemical injection rate based on the control signal output from the injection control unit 14 .
(工程S4、S5の詳細フローについて)
確認部13が備える図2の試験装置を用いた試験工程S4及び水質分析工程S5の操作フローについて図4のフローチャートと図2の試験装置を例に以下に説明する。
(About the detailed flow of steps S4 and S5)
The operation flow of the test step S4 and the water quality analysis step S5 using the test device of FIG. 2 included in the confirmation unit 13 will be described below using the flowchart of FIG. 4 and the test device of FIG. 2 as examples.
図4の薬品注入率設定工程S41において、図2の制御部6に、予測部12による薬品注入率の予測結果に基づいて薬品注入率の情報が入力されると、図2の制御部6が、薬品注入部3による薬品注入率の設定を行う。 In the drug injection rate setting step S41 in FIG. 4, when information on the drug injection rate is input to the control unit 6 in FIG. 2 based on the prediction result of the drug injection rate by the prediction unit 12, the control unit 6 in FIG. , the drug injection rate by the drug injection unit 3 is set.
原水採取工程S42において、制御部6が、処理槽1に接続された流入弁7を開いて水処理プラント20が処理する原水を所定量採取し、処理槽1内へと流入させる。流入量の制御は、オーバーフロー管による水位調整、水位計による制御、流入速度と弁の開放時間による制御等によって適宜行うことができる。 In the raw water collection step S42, the control unit 6 opens the inflow valve 7 connected to the treatment tank 1 to collect a predetermined amount of raw water to be treated by the water treatment plant 20, and causes the raw water to flow into the treatment tank 1. The inflow amount can be appropriately controlled by adjusting the water level using an overflow pipe, controlling using a water level gauge, controlling the inflow speed and opening time of a valve, etc.
薬品注入工程S43において、制御部6が、薬品注入ポンプ31を制御し、薬品注入率設定工程S41で設定された薬品注入率となるように、薬品として例えば凝集剤を処理槽1内へ注入する。この際、処理槽1内のpH調整が必要な場合は、制御部6がpH調整ポンプ41を介してpH調整剤を処理槽1内へ注入させる。pHの調整は、pH調整剤を固定量注入する方法と、pH測定器(不図示)によって制御する方法などがある。 In the chemical injection step S43, the control unit 6 controls the chemical injection pump 31 to inject, for example, a flocculant as a chemical into the processing tank 1 so as to achieve the chemical injection rate set in the chemical injection rate setting step S41. . At this time, if it is necessary to adjust the pH within the processing tank 1, the control unit 6 injects the pH adjuster into the processing tank 1 via the pH adjustment pump 41. There are two methods for adjusting the pH: a method of injecting a fixed amount of a pH adjuster, and a method of controlling using a pH measuring device (not shown).
急速撹拌工程S44において、制御部6が、撹拌機2を制御し、事前に設定された撹拌速度、撹拌時間で処理水の撹拌を行う。一般的に急速撹拌の撹拌速度は100~150/minで、撹拌時間は3分程度である。 In the rapid stirring step S44, the control unit 6 controls the stirrer 2 to stir the treated water at a preset stirring speed and stirring time. Generally, the stirring speed for rapid stirring is 100 to 150/min, and the stirring time is about 3 minutes.
緩速撹拌工程S45において、制御部6が、撹拌機2を制御し、事前に設定された撹拌速度、撹拌時間で処理水の撹拌を行う。緩速撹拌は、一般的に、急速撹拌工程S44の撹拌速度より遅く、例えば30/min程度とし、撹拌時間は10分程度である。 In the slow stirring step S45, the control unit 6 controls the stirrer 2 to stir the treated water at a preset stirring speed and stirring time. The slow stirring is generally slower than the stirring speed in the rapid stirring step S44, for example, about 30/min, and the stirring time is about 10 minutes.
静置工程S46において、制御部6が、撹拌機2の動作を停止させ、事前に設定された静置時間まで静かに置く。静置時間は5分以上であることが好ましい。 In the standing step S46, the control unit 6 stops the operation of the stirrer 2, and allows the stirrer 2 to stand still until a preset standing time. The standing time is preferably 5 minutes or more.
水質分析工程S51において、制御部6が、静置工程S46後の処理槽1上部の処理水をサンプリングポンプ51にて備え付けられた水質分析器5に送り、処理水の水質データの分析を行う。 In the water quality analysis step S51, the control unit 6 sends the treated water in the upper part of the treatment tank 1 after the standing step S46 to the water quality analyzer 5 equipped with the sampling pump 51, and analyzes the water quality data of the treated water.
排水工程S52において、制御部6が、排水弁8を開け、処理槽1内の処理水を排水させる。引き続き、洗浄工程S53において、確認試験後の処理水、純水、または水道水等で、処理槽1及び水質分析器5を洗浄する。 In the draining step S52, the control unit 6 opens the drain valve 8 to drain the treated water in the treatment tank 1. Subsequently, in the cleaning step S53, the treatment tank 1 and the water quality analyzer 5 are cleaned with the treated water after the confirmation test, pure water, tap water, or the like.
このように、本発明の実施の形態に係る薬品注入制御方法によれば、機械学習を用いた薬品の注入率の予測結果について確認試験を行い、必要に応じてその予測結果を補正することで、原水へ薬品を注入して水処理を行うために最適な薬品の注入率を決定することができる。これにより、仮に急な気象変動等の影響で機械学習による予測結果が適切でない場合においても、その予測結果を適切な注入率に補正することができる。その結果、原水の急激な性状変動が生じても、原水に注入する薬品の注入率を適切に制御することが可能となる。 As described above, according to the drug injection control method according to the embodiment of the present invention, a confirmation test is performed on the predicted result of the drug injection rate using machine learning, and the predicted result is corrected as necessary. , it is possible to determine the optimal chemical injection rate for water treatment by injecting chemicals into raw water. Thereby, even if the prediction results obtained by machine learning are not appropriate due to sudden weather changes, etc., the prediction results can be corrected to an appropriate injection rate. As a result, even if a sudden change in the properties of raw water occurs, it is possible to appropriately control the injection rate of chemicals injected into raw water.
本発明は上記の実施の形態によって記載したが、この開示の一部をなす論述及び図面はこの発明を限定するものであると理解すべきではない。即ち、本開示は、上述の実施形態に限定されるものではなく、その要旨を逸脱しない範囲で構成要素を相互に組み合わせ、変形して具体化できることは勿論である。 Although the present invention has been described by the above-described embodiments, it should not be understood that the statements and drawings that form part of this disclosure limit the present invention. That is, the present disclosure is not limited to the above-described embodiments, and it goes without saying that components can be combined and modified with each other without departing from the spirit of the disclosure.
(水処理プラント20)
図1の例では、水処理プラント20として、浄水処理プラントの例を示している。しかしながら、本発明の実施の形態に係る水処理プラント20は、図1の構成には限定されず、所定の薬品を注入して、所定の水処理を行う種々の設備であれば利用できることは勿論である。例えば、水処理プラント20として、浄水処理プラント、純水製造プラント、廃水処理プラント、下水処理プラント、し尿処理(汚泥再生)プラント、浸出水処理プラント、海水淡水化プラント、汚泥処理プラント等の種々の水処理プラントに利用できることは勿論である。
(Water treatment plant 20)
In the example of FIG. 1, the water treatment plant 20 is a water purification treatment plant. However, the water treatment plant 20 according to the embodiment of the present invention is not limited to the configuration shown in FIG. 1, and can of course be used with various types of equipment that inject predetermined chemicals and perform predetermined water treatment. It is. For example, the water treatment plant 20 may be a water treatment plant, a pure water production plant, a wastewater treatment plant, a sewage treatment plant, a human waste treatment (sludge regeneration) plant, a leachate treatment plant, a seawater desalination plant, a sludge treatment plant, etc. Of course, it can be used in water treatment plants.
(自動凝集処理装置)
図1の薬品注入制御装置10は、自動凝集処理装置として単独で構成され、上述の水処理プラント20だけでなく、薬品注入制御を必要とする薬品注入設備25を備える種々の設備に接続することができる。
(Automatic flocculation processing equipment)
The chemical injection control device 10 in FIG. 1 is configured independently as an automatic coagulation treatment device, and can be connected not only to the water treatment plant 20 described above but also to various equipment including chemical injection equipment 25 that requires chemical injection control. Can be done.
即ち、本発明の実施の形態に係る薬品注入制御装置10は、原水へ注入する凝集剤の最適な注入率を予測するための学習済モデルを用いた機械学習アルゴリズムにより予測された凝集剤の注入率の予測結果を取得する情報取得部11と、凝集剤の注入率の予測結果と、予め設定した試験条件とに基づいて、原水採取工程S42、薬品注入工程S43、撹拌工程S44、S45及び静置工程S46を含む凝集処理試験を実施する処理槽1と、凝集処理試験で得られる処理水の水質データを分析する水質分析器5と、水質分析器5で分析された処理水の水質データを基準値と比較し、予測結果が適正か否かを判断する確認部13と、予測結果が適正な場合は、予測結果に基づく注入率に基づいて、原水に凝集剤を注入して水処理を行うための薬品注入設備25の凝集剤の注入率を制御し、予測結果が適正でない場合は、新たな凝集剤注入率で凝集処理試験を実施し、凝集処理試験で得られる処理水の水質データが基準値を満たすまで凝集処理試験を繰り返した結果、決定される最適凝集剤注入率に基づいて、薬品注入設備25の凝集剤の注入率を制御する注入制御部14を備える。 That is, the chemical injection control device 10 according to the embodiment of the present invention performs injection of the flocculant predicted by a machine learning algorithm using a trained model for predicting the optimal injection rate of the flocculant to be injected into raw water. Based on the information acquisition unit 11 that acquires the prediction result of the coagulant injection rate, and the test conditions set in advance, the raw water collection step S42, the chemical injection step S43, the stirring steps S44, S45, and the static A treatment tank 1 that carries out a flocculation treatment test including a coagulation treatment step S46, a water quality analyzer 5 that analyzes water quality data of treated water obtained in the flocculation treatment test, and a water quality analyzer 5 that analyzes water quality data of treated water analyzed by the water quality analyzer 5. A confirmation unit 13 compares it with a reference value and determines whether the predicted result is appropriate, and if the predicted result is appropriate, a coagulant is injected into the raw water based on the injection rate based on the predicted result to perform water treatment. The injection rate of the flocculant in the chemical injection equipment 25 is controlled, and if the predicted result is not appropriate, a flocculation treatment test is carried out with a new flocculant injection rate, and the water quality data of the treated water obtained from the flocculation treatment test is The apparatus includes an injection control unit 14 that controls the injection rate of the flocculant of the chemical injection equipment 25 based on the optimum flocculant injection rate determined as a result of repeating the flocculant treatment test until the aggregation treatment test satisfies the reference value.
本発明の実施の形態に係る薬品注入制御装置10によれば、機械学習による薬品注入率の予測結果を簡易な試験装置を用いて検証することができるため、ゲリラ豪雨等の異常な気象事象が発生し、予測結果が大きくずれるおそれがあったとしても、予測結果を適切に補正して、原水に適切な注入率で薬品を注入することができる。その結果、凝集剤使用量を最適化でき、効率の良い凝集沈殿処理を行うことができ、且つ水質の良好な浄水を得ることができる。 According to the chemical injection control device 10 according to the embodiment of the present invention, the prediction result of the chemical injection rate based on machine learning can be verified using a simple test device, so that abnormal weather events such as torrential rain can be avoided. Even if this occurs and there is a risk that the predicted result will deviate significantly, the predicted result can be appropriately corrected and chemicals can be injected into the raw water at an appropriate injection rate. As a result, the amount of flocculant used can be optimized, efficient coagulation-sedimentation treatment can be performed, and purified water with good quality can be obtained.
1…処理槽
2…撹拌機
3…薬品注入部
4…pH注入部
5…水質分析器
6…制御部
7…流入弁
8…排水弁
10…薬品注入制御装置
11…情報取得部
12…予測部
13…確認部
14…注入制御部
20…水処理プラント
21…着水井
22…急速撹拌池
23…フロック形成池
24…沈殿池
25…薬品注入設備
26…急速ろ過池
27…浄水池
28…配水池
31…薬品注入ポンプ
41…pH調整ポンプ
51…サンプリングポンプ
100…薬品注入制御システム
1... Treatment tank 2... Stirrer 3... Chemical injection part 4... pH injection part 5... Water quality analyzer 6... Control part 7... Inflow valve 8... Drain valve 10... Chemical injection control device 11... Information acquisition part 12... Prediction part 13... Confirmation unit 14... Injection control unit 20... Water treatment plant 21... Water receiving well 22... Rapid stirring pond 23... Floc formation pond 24... Sedimentation basin 25... Chemical injection equipment 26... Rapid filtration basin 27... Water purification basin 28... Water distribution basin 31... Chemical injection pump 41... pH adjustment pump 51... Sampling pump 100... Chemical injection control system
Claims (7)
前記水質データと前記運転操作条件データとを含む説明変数を取得し、前記原水へ注入する薬品の最適な注入率を含む目的変数を出力する学習済モデルを用いた機械学習アルゴリズムを用いて、前記原水へ注入する前記薬品の注入率を予測する予測部と、
前記薬品の注入率の予測結果に基づいて、前記原水に前記薬品を注入して確認試験を行い、該確認試験により得られる処理水の水質データを基準値と比較することにより、前記予測結果が適正であるか否かを確認する確認部と、
前記確認部の確認結果に基づいて、前記原水へ注入する前記薬品の注入率を制御する注入制御部と
を備えることを特徴とする薬品注入制御システム。 an information acquisition unit that acquires information including water quality data of raw water and operation condition data of a water treatment plant that processes the raw water;
Using a machine learning algorithm using a trained model that acquires explanatory variables including the water quality data and the operation condition data and outputs objective variables including the optimal injection rate of chemicals to be injected into the raw water, a prediction unit that predicts the injection rate of the chemical to be injected into raw water;
Based on the prediction result of the injection rate of the chemical, the chemical is injected into the raw water and a confirmation test is performed, and the water quality data of the treated water obtained from the confirmation test is compared with the reference value, so that the predicted result is confirmed. a confirmation section that confirms whether the
A chemical injection control system comprising: an injection control section that controls an injection rate of the chemical to be injected into the raw water based on a confirmation result of the confirmation section.
前記薬品の注入率の予測結果が適正である場合には、前記予測結果に基づく注入率を、前記原水へ前記薬品を注入して水処理を行うための前記薬品の注入率として決定し、
前記薬品の注入率の予測結果が適正でない場合には、前記注入率を変更して再試験を行い、該再試験に基づいて、前記原水へ前記薬品を注入して水処理を行うために最適な前記薬品の注入率を決定することを特徴とする請求項1に記載の薬品注入制御システム。 The confirmation section
If the predicted result of the injection rate of the chemical is appropriate, determine the injection rate based on the predicted result as the injection rate of the chemical for injecting the chemical into the raw water to perform water treatment,
If the prediction result of the injection rate of the chemical is not appropriate, change the injection rate and retest, and based on the retest, determine the optimum method for injecting the chemical into the raw water for water treatment. The chemical injection control system according to claim 1, further comprising: determining an injection rate of the chemical.
前記原水を処理する処理槽と、
前記処理槽内に前記薬品を注入する薬品注入部と、
前記処理槽内を撹拌する撹拌機と、
前記処理槽で得られる前記処理水の水質データを分析する水質分析器と、
前記処理槽内へ注入する前記薬品の注入率を制御する制御部と
を備えることを特徴とする請求項1又は2に記載の薬品注入制御システム。 The confirmation section
a treatment tank for treating the raw water;
a chemical injection unit that injects the chemical into the processing tank;
a stirrer that stirs the inside of the processing tank;
a water quality analyzer that analyzes water quality data of the treated water obtained in the treatment tank;
The chemical injection control system according to claim 1 or 2, further comprising: a control unit that controls an injection rate of the chemical to be injected into the processing tank.
前記薬品の注入率の予測結果に基づいて、前記原水に前記薬品を注入して確認試験を行い、該確認試験により得られる処理水の水質データを基準値と比較することにより、前記薬品の注入率の予測結果が適正であるか否かを確認する工程と、
前記確認の結果に基づいて、前記原水へ注入する前記薬品の注入率を制御する工程と
を含むことを特徴とする薬品注入制御方法。 A trained model is used that acquires explanatory variables including water quality data of raw water and operational condition data of a water treatment plant that processes the raw water, and outputs objective variables including the optimal injection rate of chemicals to be injected into the raw water. predicting the injection rate of the chemical to be injected into the raw water using a machine learning algorithm;
Based on the prediction result of the injection rate of the chemical, the chemical is injected into the raw water and a confirmation test is performed, and the water quality data of the treated water obtained from the confirmation test is compared with the standard value. a step of confirming whether the prediction result of the rate is appropriate;
A chemical injection control method comprising: controlling an injection rate of the chemical to be injected into the raw water based on the result of the confirmation.
前記薬品の注入率の予測結果が適正である場合には、前記予測結果に基づく注入率を、前記原水へ前記薬品を注入して水処理を行うための前記薬品の注入率として決定し、
前記薬品の注入率の予測結果が適正でない場合には、前記注入率を変更して再試験を行い、該再試験により得られる前記処理水の水質データが基準値の範囲内となるまで、前記再試験を繰り返し、前記原水へ前記薬品を注入して水処理を行うために最適な前記薬品の注入率を決定することを含む請求項4に記載の薬品注入制御方法。 The step of confirming is
If the predicted result of the injection rate of the chemical is appropriate, determine the injection rate based on the predicted result as the injection rate of the chemical for injecting the chemical into the raw water to perform water treatment,
If the predicted result of the injection rate of the chemical is not appropriate, the injection rate is changed and the test is performed again. 5. The chemical injection control method according to claim 4, further comprising repeating retesting to determine an optimum injection rate of the chemical for injecting the chemical into the raw water to perform water treatment.
前記原水に前記薬品として凝集剤を注入して凝集沈殿処理を行い、
前記凝集沈殿処理で得られる前記処理水の前記水質データとして、濁度、色度、pH、アルカリ度、微粒子数、TOC、COD、紫外線吸光度、有機物分画の少なくともいずれかを分析することを含む請求項4又は5に記載の薬品注入制御方法。 The step of confirming is
Injecting a flocculant as the chemical into the raw water to perform a coagulation sedimentation treatment,
The water quality data of the treated water obtained by the coagulation-sedimentation treatment includes analyzing at least one of turbidity, chromaticity, pH, alkalinity, number of fine particles, TOC, COD, ultraviolet absorbance, and organic matter fraction. The drug injection control method according to claim 4 or 5.
前記凝集剤の注入率の予測結果と、予め設定した試験条件とに基づいて、原水採取工程、薬品注入工程、撹拌工程及び静置工程を含む凝集処理試験を実施する処理槽と、
前記凝集処理試験で得られる処理水の水質データを分析する水質分析器と、
前記水質分析器で分析された前記処理水の水質データを基準値と比較し、前記予測結果が適正か否かを判断する確認部と、
前記予測結果が適正な場合は、前記予測結果に基づく注入率に基づいて、前記原水に凝集剤を注入して水処理を行うための薬品注入設備の凝集剤の注入率を制御し、前記予測結果が適正でない場合は、新たな凝集剤注入率で前記凝集処理試験を実施し、該凝集処理試験で得られる処理水の水質データが前記基準値を満たすまで前記凝集処理試験を繰り返した結果、決定される最適凝集剤注入率に基づいて、前記薬品注入設備の凝集剤の注入率を制御する注入制御部と
を備えることを特徴とする自動凝集処理装置。 an information acquisition unit that acquires a prediction result of the injection rate of the flocculant predicted by a machine learning algorithm using a learned model for predicting the optimal injection rate of the flocculant to be injected into raw water;
A treatment tank for conducting a flocculation treatment test including a raw water collection step, a chemical injection step, a stirring step, and a standing step based on the predicted result of the injection rate of the flocculant and preset test conditions;
a water quality analyzer that analyzes water quality data of the treated water obtained in the flocculation treatment test;
a confirmation unit that compares the water quality data of the treated water analyzed by the water quality analyzer with a reference value and determines whether the predicted result is appropriate;
If the prediction result is appropriate, the injection rate of the flocculant of the chemical injection equipment for injecting the flocculant into the raw water to perform water treatment is controlled based on the injection rate based on the prediction result, and the injection rate of the flocculant is controlled based on the injection rate based on the prediction result. If the result is not appropriate, carry out the flocculation treatment test with a new flocculant injection rate, and repeat the flocculation treatment test until the water quality data of the treated water obtained in the flocculation treatment test satisfies the standard value. An automatic flocculation processing apparatus comprising: an injection control unit that controls the injection rate of the flocculant of the chemical injection equipment based on the determined optimal flocculant injection rate.
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