WO2022208799A1 - Dispositif, procédé et programme de gestion de qualité de l'eau - Google Patents

Dispositif, procédé et programme de gestion de qualité de l'eau Download PDF

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Publication number
WO2022208799A1
WO2022208799A1 PCT/JP2021/014000 JP2021014000W WO2022208799A1 WO 2022208799 A1 WO2022208799 A1 WO 2022208799A1 JP 2021014000 W JP2021014000 W JP 2021014000W WO 2022208799 A1 WO2022208799 A1 WO 2022208799A1
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WIPO (PCT)
Prior art keywords
seawater
residual chlorine
water quality
concentration
unit
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PCT/JP2021/014000
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English (en)
Japanese (ja)
Inventor
敏治 柳川
圭二 尾山
一憲 小路
義文 旭
一朗 勝山
有頂 定道
勇也 鈴木
好貴 市川
Original Assignee
中国電力株式会社
日機装株式会社
日本エヌ・ユー・エス株式会社
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Application filed by 中国電力株式会社, 日機装株式会社, 日本エヌ・ユー・エス株式会社 filed Critical 中国電力株式会社
Priority to JP2021576358A priority Critical patent/JP7101912B1/ja
Priority to PCT/JP2021/014000 priority patent/WO2022208799A1/fr
Priority to TW111111376A priority patent/TW202242728A/zh
Publication of WO2022208799A1 publication Critical patent/WO2022208799A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/50—Treatment of water, waste water, or sewage by addition or application of a germicide or by oligodynamic treatment
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/70—Treatment of water, waste water, or sewage by reduction
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/72—Treatment of water, waste water, or sewage by oxidation
    • C02F1/76—Treatment of water, waste water, or sewage by oxidation with halogens or compounds of halogens
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning

Definitions

  • the present invention relates to a water quality control device, a water quality control method, and a water quality control program. More specifically, the present invention relates to a water quality control device, a water quality control method, and a water quality control program used in a seawater utilization plant such as a power plant.
  • Seawater electrolytic chlorine sodium hypochlorite
  • adherent organisms such as barnacles and mussels
  • biofilms that adhere to the seawater system of seawater plants
  • thermal power plants and nuclear power plants Therefore, the technique of injecting water into the water intake is widely practiced.
  • sodium hypochlorite is generated by electrolyzing natural seawater, and an electrolytic solution containing the sodium hypochlorite is injected into a seawater intake to prevent adhesion of marine organisms.
  • the present invention has been made in view of the above problems, and is capable of preventing the concentration of residual chlorine in seawater from exceeding a reference value in a seawater outlet, a water quality control device, a water quality control method, and Intended to provide a water quality management program.
  • the present invention provides a water quality control device for a seawater utilization plant, comprising: an attribute value acquisition unit for acquiring an attribute value of seawater flowing through a condenser installed in the seawater utilization plant; a concentration prediction unit that predicts the residual chlorine concentration at the outlet of the discharge channel that discharges the seawater from the condenser to the sea; and a residual chlorine injected into the discharge channel based on the predicted residual chlorine concentration
  • the present invention relates to a water quality control device comprising a required amount calculation unit that calculates a required amount of a chlorine neutralizer, and a neutralizer injection unit that injects the required amount of the neutralizer into the discharge channel.
  • the attribute value includes the concentration of residual chlorine in seawater at the intake of the condenser, the water temperature of seawater at the outlet of the condenser, the flow time of seawater from the intake to the discharge, and the concentration It is preferable that the prediction unit predicts the residual chlorine concentration at the water outlet by applying the attribute value to the Arrhenius equation.
  • the concentration prediction unit inputs history data of attribute values including residual chlorine concentration, salinity, pH, ORP (oxidation-reduction potential), and water temperature of seawater at the inlet of the condenser.
  • a label acquisition unit that acquires history data of the residual chlorine concentration in the water discharge port as a label; and a set of the input data and the label as learning data, the residual chlorine in the water discharge port
  • a learning unit that builds a learning model for estimating the concentration, and an estimated value generating unit that generates an estimated value of the residual chlorine concentration by applying a new attribute value to the learning model after building the learning model. It is preferable to have
  • the learning unit preferably constructs the learning model using a random forest.
  • the learning unit preferably constructs the learning model using generalized addition (GAM method).
  • the present invention also provides a water quality management method for a seawater utilization plant, comprising: an attribute value acquisition step of acquiring an attribute value of seawater flowing through a condenser installed in the seawater utilization plant; a concentration prediction step of predicting a residual chlorine concentration at an outlet of a discharge channel that discharges the seawater from the condenser to the sea, based on the predicted residual chlorine concentration;
  • the present invention relates to a water quality control method comprising a required amount calculation step of calculating a required amount of a neutralizer for residual chlorine, and a neutralizer injection step of injecting the required amount of the neutralizer into the discharge channel.
  • the present invention also provides a water quality management program for a seawater utilization plant, comprising: an attribute value obtaining step of obtaining an attribute value of seawater flowing through a condenser installed in the seawater utilization plant; a concentration prediction step of predicting a residual chlorine concentration at an outlet of a discharge channel that discharges the seawater from the condenser to the sea, based on the predicted residual chlorine concentration; Water quality management for causing a computer to execute a required amount calculation step of calculating a required amount of a neutralizer for residual chlorine and a neutralizer injection step of injecting the required amount of the neutralizer into the discharge channel. Regarding the program.
  • FIG. 1 is an overall configuration diagram of a seawater utilization plant according to an embodiment of the present invention
  • FIG. 1 is a functional block diagram of a water quality control device according to an embodiment of the present invention
  • FIG. It is a flowchart which shows operation
  • It is a functional block diagram of a concentration prediction part included in the water quality control device according to the embodiment of the present invention.
  • It is a flowchart which shows operation
  • It is a figure which shows the Arrhenius equation based on embodiment of this invention.
  • FIG. 1 It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. It is a figure which shows the Arrhenius equation based on embodiment of this invention. FIG.
  • FIG. 5 is a diagram showing a comparison between original data and prediction results by random forest according to the embodiment of the present invention
  • FIG. 5 is a diagram showing a comparison between original data and prediction results by random forest according to the embodiment of the present invention
  • GAM generalized addition method
  • GAM generalized addition method
  • GAM generalized addition method
  • GAM generalized addition method
  • FIG. 1 is an overall configuration diagram of a seawater utilization plant 100 including a water quality control device 1 and a condenser 2 according to this embodiment.
  • the condenser 2 takes in seawater as cooling water from the sea 3A through the water intake channel 21, and after cooling the steam turbine and the like, the cooling water is discharged from the outlet 221 of the condenser 2. The water is discharged to the sea 3B through the waterway 22.
  • a seawater electrolytic solution containing sodium hypochlorite is injected.
  • the water quality control device 1 predicts the residual chlorine concentration at the water outlet 222 of the water discharge channel 22 based on the attribute values of the seawater flowing through the condenser 2, and compares it with the residual chlorine concentration of the seawater at the condenser inlet 211. After calculating the amount of decrease, the required amount of the neutralizer is calculated based on the amount of decrease, and the required amount of the neutralizer is injected into the discharge channel 22 .
  • FIG. 1 A water quality control device 1 according to a first embodiment of the present invention will be described below with reference to FIGS. 2 and 3.
  • FIG. 2 A water quality control device 1 according to a first embodiment of the present invention will be described below with reference to FIGS. 2 and 3.
  • FIG. 2 A water quality control device 1 according to a first embodiment of the present invention will be described below with reference to FIGS. 2 and 3.
  • FIG. 2 A water quality control device 1 according to a first embodiment of the present invention will be described below with reference to FIGS. 2 and 3.
  • FIG. 2 is a functional block diagram of the water quality control device 1. As shown in FIG.
  • the water quality control device 1 includes a control section 11 , a neutralizing agent injection section 12 and a storage section 13 .
  • the control unit 11 is a part that controls the entire water quality management apparatus 1, and by reading and executing various programs from a storage area such as a ROM, a RAM, a flash memory, or a hard disk (HDD), the Various functions are realized.
  • the control unit 11 may be a CPU.
  • the control unit 11 includes an attribute value acquisition unit 111 , a concentration prediction unit 112 and a required amount calculation unit 113 .
  • control unit 11 includes general functional blocks such as a functional block for controlling the entire water quality management device 1 and a functional block for communication.
  • general functional blocks such as a functional block for controlling the entire water quality management device 1 and a functional block for communication.
  • these general functional blocks are well known to those skilled in the art, illustration and description are omitted.
  • the attribute value acquisition unit 111 acquires attribute values of seawater flowing through the condenser 2 . More specifically, for example, a pumping pump (not shown) installed outside the water quality control device 1 pumps up seawater from the water intake channel 21, and the pumped seawater is treated with the water quality installed outside the water quality control device 1. Analyze with an analyzer (not shown). The attribute value acquisition unit 111 acquires attribute values related to the water quality of seawater analyzed by the water quality analyzer. Furthermore, the attribute value acquisition unit 111 acquires the water temperature of seawater at the outlet 221 of the condenser 2, the flow time of seawater from the condenser inlet 211 to the water outlet 222, and the like as attribute values.
  • attribute values related to water quality for example, in addition to the residual chlorine concentration and water temperature of the seawater at the condenser inlet 211, for the purpose of improving accuracy, the concentration of organic matter, the amount of salt contained in the seawater, pH, ORP (oxidation-reduction potential).
  • the concentration prediction unit 112 predicts the residual chlorine concentration at the seawater water outlet 222 based on the attribute value acquired by the attribute value acquisition unit 111 .
  • the concentration prediction unit 112 uses, as attribute values, the residual chlorine concentration of seawater at the inlet 211 of the condenser 2, the water temperature of the seawater at the outlet 221 of the condenser 2, the inlet 211 of the condenser 2
  • the residual chlorine concentration at the seawater outlet 222 is estimated by applying the Arrheinius equation to the flow time of seawater from the outlet 222 to the outlet 222 .
  • the “Arrheinius equation” is an equation that predicts the rate of a chemical reaction at a certain temperature, and the reaction constant k that indicates the reaction rate is the temperature T is high and the activation energy E a is low.
  • A is a constant (frequency factor) independent of temperature
  • Ea is the activation energy per 1 mol
  • R is the gas constant
  • T is the absolute temperature.
  • the "frequency factor” is a factor representing the number of collisions between molecules in a bimolecular reaction.
  • the required amount calculation unit 113 calculates the necessary amount of the residual chlorine neutralizer to be injected into the water discharge channel 22. More specifically, the required amount calculation unit 113 calculates the required amount of the neutralizer based on the amount of decrease in the estimated residual chlorine concentration at the water outlet 222 compared to the residual chlorine concentration at the water intake 211 .
  • the neutralizing agent may be an existing agent capable of rapidly neutralizing residual chlorine, such as 35% hydrogen peroxide, sodium sulfite, or sodium thiosulfate.
  • 35% hydrogen peroxide solution reaction by-products are oxygen, water, and chloride ions
  • sodium sulfite reaction by-products are sulfate ions and chloride ions. Both of these are abundant in seawater.
  • the neutralizer injection unit 12 injects the necessary amount of neutralizer calculated by the necessary amount calculation unit 113 into the water discharge channel 22 .
  • the storage unit 13 stores the attribute value acquired by the attribute value acquisition unit 111, the residual chlorine concentration predicted by the concentration prediction unit 112, and the required amount of neutralizer calculated by the required amount calculation unit 113.
  • FIG. 3 is a flow chart showing the operation of the water quality control device 1. As shown in FIG.
  • step S1 the attribute value acquisition unit 111 acquires the attribute value of the water quality at the seawater intake 211, the water temperature of the seawater at the outlet 221 of the condenser 2, the flow time of the seawater from the inlet 211 to the water outlet 222, and the like. do.
  • step S ⁇ b>2 the concentration prediction unit 112 predicts the residual chlorine concentration at the seawater outlet 222 based on the attribute value acquired by the attribute value acquisition unit 111 .
  • step S3 the required amount calculation unit 113 calculates the required amount of the residual chlorine neutralizer to be injected into the discharge channel 22 based on the residual chlorine concentration predicted by the concentration prediction unit 112.
  • step S ⁇ b>4 the neutralizer injection unit 12 injects the necessary amount of neutralizer calculated by the necessary amount calculation unit 113 into the water discharge channel 22 .
  • a water quality control device 1 is a water quality control device 1 for a seawater utilization plant 100, and is an attribute value for acquiring an attribute value of seawater flowing through a condenser 2 installed in the seawater utilization plant 100.
  • the above attribute values are the residual chlorine concentration of seawater at the inlet 211 of the condenser 2, the water temperature of the seawater at the outlet 221 of the condenser 2, and the seawater temperature from the outlet 221 to the water discharge port 222.
  • the concentration prediction unit predicts the residual chlorine concentration at the water outlet 222 by applying the above attribute value to the Arrhenius equation.
  • a water quality control device 1A which is a second embodiment of the present invention, will be described below with reference to FIGS. 4 to 6.
  • FIG. In the following, for the sake of simplification of explanation, mainly the differences between the water quality control device 1A and the water quality control device 1 will be explained.
  • the basic configuration of the water quality control device 1A is the same as that of the water quality control device 1 shown in FIG.
  • the water quality control device 1A includes a concentration prediction unit 112A instead of the concentration prediction unit 112 included in the water quality control device 1.
  • FIG. The concentration prediction unit 112 mainly uses the Arrhenius equation to predict the residual chlorine concentration at the water discharge port 222, while the concentration prediction unit 112A predicts the residual chlorine concentration at the water discharge port 222 by machine learning.
  • FIG. 4 is a functional block diagram of the concentration prediction unit 112A.
  • the concentration prediction unit 112A includes an input data acquisition unit 114, a label acquisition unit 115, a learning unit 116, and an estimated value generation unit 117.
  • FIG. 4 is a functional block diagram of the concentration prediction unit 112A.
  • the concentration prediction unit 112A includes an input data acquisition unit 114, a label acquisition unit 115, a learning unit 116, and an estimated value generation unit 117.
  • the input data acquisition unit 114 acquires, from the storage unit 13, history data of attribute values including the residual chlorine concentration, salinity, pH, ORP (oxidation-reduction potential), and water temperature of the seawater in the water intake 211 as input data to be used for machine learning. to get as
  • the label acquisition unit 115 acquires history data of the residual chlorine concentration at the water outlet 222 from the storage unit 13 as labels used for machine learning.
  • the learning unit 116 constructs a learning model for estimating the residual chlorine concentration at the water outlet 222 by performing machine learning using pairs of input data and labels as learning data, and stores the constructed learning model in the storage unit 13. .
  • the machine learning performed by the learning unit 116 may be random forest or generalized addition (GAM method).
  • GAM method random forest
  • a decision tree is used as a weak learner.
  • generalized addition is an algorithm that uses a model in which the linear predictor in the generalized linear model is the sum of nonlinear functions. , B spline, natural spline, etc. are used. Among them, an algorithm using a smoothing spline as a non-linear function is called a "GAM method".
  • the estimated value generation unit 117 acquires the learning model from the storage unit 13 and generates new attribute values in the learning model. produces an estimate of the residual chlorine concentration at the outlet 222 by applying .
  • FIG. 5 is a flow chart showing the operation of the water quality control device 1A during machine learning.
  • step S11 the input data acquisition unit 114 acquires history data of attribute values including residual chlorine concentration, salinity, pH, ORP (oxidation-reduction potential), water temperature, and flow rate of seawater from the storage unit 13 as input data. .
  • attribute values including residual chlorine concentration, salinity, pH, ORP (oxidation-reduction potential), water temperature, and flow rate of seawater.
  • step S12 the label acquisition unit 115 acquires history data of the residual chlorine concentration at the water outlet as a label.
  • step S13 the learning unit 116 treats pairs of input data and labels as learning data.
  • step S14 the learning unit 116 performs machine learning using the learning data.
  • step S15 if the machine learning has ended (S15: YES), the process proceeds to step S16. If the machine learning has not ended (S15: NO), the process proceeds to step S11.
  • step S16 the learning unit 116 stores the constructed learning model in the storage unit 13.
  • FIG. 6 is a flow chart showing the operation of the water quality control device 1A when injecting the neutralizer.
  • step S21 the estimated value generation unit 117 acquires the learning model from the storage unit 13.
  • step S22 the estimated value generation unit 117 acquires new attribute values from the attribute value acquisition unit 111.
  • step S23 the estimated value generator 117 generates an estimated value (predicted value) of the residual chlorine concentration at the water outlet 222 by applying the new attribute value to the learning model.
  • step S24 the required amount calculation unit 113 calculates the required amount of the residual chlorine neutralizer to be injected into the water discharge channel 22 based on the residual chlorine concentration predicted by the concentration prediction unit 112.
  • step S ⁇ b>25 the neutralizing agent injection unit 12 injects the necessary amount of neutralizing agent calculated by the necessary amount calculating unit 113 into the discharge channel 22 .
  • the concentration prediction unit 112A acquires history data of attribute values including the residual chlorine concentration, salinity, pH, ORP (oxidation-reduction potential), and water temperature of the seawater at the water intake 211 as input data.
  • an estimated value generation unit 117 that generates an estimated value of the residual chlorine concentration by applying a new attribute value to the learning model after building the learning model.
  • the learning unit 116 builds a learning model using a random forest.
  • the learning unit 116 constructs a learning model using generalized addition.
  • (1-1) Relationship with Condenser Inlet Concentration It is also known that the initial concentration contributes greatly to the attenuation of the residual chlorine concentration.
  • the residual chlorine concentration at the condenser inlet is 0.05 mg / L or more, 0.03 mg / L or more and less than 0.05 mg / L, less than 0.03 mg / L for each power generation output , and the Arrhenius equations for each case are shown in FIGS. 7A to 9C.
  • the highest coefficient of determination was 0.589 when the power generation output was 200 MW or more and the residual chlorine concentration was 0.05 mg/L or more.
  • the coefficient of determination is less than 0.05 in any case of power generation output.
  • the original data and prediction results show similar behavior. Although it is generally within the range of 0.01 mg/L with respect to the original data, the coefficient of determination between the original data and the prediction result is as low as 0.096 because the distribution of errors is biased.
  • Prediction result 1 As with the prediction using random forest, the data from June 29, 2018 to February 28, 2019 was used as learning data, and the prediction from March 1, 2019 to March 31, 2019 was made. 11A and 11B show a comparison between original data and prediction results.
  • the original data and prediction results show similar behavior. Compared to the original data, it is generally within the range of 0.01 mg/L or less, and there is little bias in the error distribution, so the coefficient of determination between the original data and the prediction results is 0.272, which is higher than the results from the random forest.
  • Prediction result 2 The results shown in the previous section differed in the learning period and prediction period. Forecasts were made from November 1, 2018 to November 30, 2018. 12A and 12B show a comparison between original data and prediction results.
  • the original data and the prediction result show similar behavior. It was generally within the range of 0.01 mg/L with respect to the original data, and the coefficient of determination between the original data and the prediction result was 0.303.
  • the behavior of the original data and the prediction results are almost identical, demonstrating high reproducibility. It is generally within the range of about 0.005 mg/L with respect to the original data, and the coefficient of determination between the original data and the prediction result is as high as 0.505.
  • the management method by the water quality management device 1 or 1A is realized by software.
  • a program that constitutes this software is installed in the computer (water quality management device 1 or 1A).
  • These programs may be recorded on removable media and distributed to users, or may be distributed by being downloaded to users' computers via a network. Furthermore, these programs may be provided to the user's computer (water quality control device 1 or 1A) as a web service via a network without being downloaded.

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Abstract

L'invention concerne un dispositif, un procédé et un programme de gestion de la qualité de l'eau avec lesquels il est possible d'empêcher le dépassement d'une valeur de référence de la concentration de chlore résiduel dans l'eau de mer dans un orifice de refoulement d'eau de mer. Ce dispositif de gestion de la qualité de l'eau destiné à être employé dans une usine utilisant de l'eau de mer comprend : une unité d'acquisition de valeur d'attribut qui acquiert une valeur d'attribut se rapportant à l'eau de mer s'écoulant à travers un condenseur installé dans l'usine utilisant de l'eau de mer ; une unité de prédiction de concentration qui, sur la base de la valeur d'attribut, prédit la concentration de chlore résiduel à un orifice de refoulement d'un trajet de refoulement à travers lequel l'eau de mer est refoulée du condenseur vers la mer ; une unité de calcul de la quantité requise qui, sur la base de la concentration de chlore résiduel prédite, calcule la quantité requise d'un neutralisant de chlore résiduel à injecter dans le trajet de refoulement ; et une unité d'injection de neutralisant qui injecte le neutralisant dans le trajet de refoulement dans la quantité requise.
PCT/JP2021/014000 2021-03-31 2021-03-31 Dispositif, procédé et programme de gestion de qualité de l'eau WO2022208799A1 (fr)

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JP2021576358A JP7101912B1 (ja) 2021-03-31 2021-03-31 水質管理装置、水質管理方法、及び水質管理プログラム
PCT/JP2021/014000 WO2022208799A1 (fr) 2021-03-31 2021-03-31 Dispositif, procédé et programme de gestion de qualité de l'eau
TW111111376A TW202242728A (zh) 2021-03-31 2022-03-25 水質管理裝置、水質管理方法及水質管理程式

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS54104638A (en) * 1978-02-02 1979-08-17 Mitsubishi Heavy Ind Ltd Device for removing residual chlorine from cooling water
JP2011528982A (ja) * 2008-07-24 2011-12-01 サムスン ヘビー インダストリーズ カンパニー リミテッド バラスト水処理装置および方法
JP2012106224A (ja) * 2010-10-22 2012-06-07 Panasonic Corp バラスト水の制御方法及びバラスト水処理システム
JP2016022458A (ja) * 2014-07-24 2016-02-08 株式会社日立製作所 圧入水生産システム
JP2016209855A (ja) * 2015-04-28 2016-12-15 三菱瓦斯化学株式会社 海水冷却水の処理方法
WO2021053757A1 (fr) * 2019-09-18 2021-03-25 中国電力株式会社 Dispositif, procédé et programme de gestion de concentration d'injection de chlore

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS54104638A (en) * 1978-02-02 1979-08-17 Mitsubishi Heavy Ind Ltd Device for removing residual chlorine from cooling water
JP2011528982A (ja) * 2008-07-24 2011-12-01 サムスン ヘビー インダストリーズ カンパニー リミテッド バラスト水処理装置および方法
JP2012106224A (ja) * 2010-10-22 2012-06-07 Panasonic Corp バラスト水の制御方法及びバラスト水処理システム
JP2016022458A (ja) * 2014-07-24 2016-02-08 株式会社日立製作所 圧入水生産システム
JP2016209855A (ja) * 2015-04-28 2016-12-15 三菱瓦斯化学株式会社 海水冷却水の処理方法
WO2021053757A1 (fr) * 2019-09-18 2021-03-25 中国電力株式会社 Dispositif, procédé et programme de gestion de concentration d'injection de chlore

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