Method for determining and adjusting the dosage of coagulant for coagulation treatment of raw water

Abstract

The present invention relates to a method for determining and adjusting the dosage of coagulant to be injected into a means for coagulation treatment of raw water to give purified water, comprising the steps of: - determining an optimal dosage of coagulant, said determining step utilizing at least one organic parameter providing information about the amount of organic matter in the raw water and being a function of a target value of said organic parameter determined for the purified water; - measuring the actual values ​​of the organic parameters of the purified water; - determining the difference between the actual value and the target value of the organic parameter for the purified water; - adjusting the dose of coagulant agent to be injected if this difference is less than a lower threshold or greater than an upper threshold, said adjusting step starting from a determined optimal dose of coagulant agent, - increasing the dosage of the coagulant if the difference between the actual value and the target value of the organic parameter is greater than an upper threshold value; or - reducing the dosage of the coagulant if the difference between the actual value and the target value of the organic parameter is less than a lower threshold. and The present invention relates to a method comprising:

Description

[Technical Field]

[0001] The present invention relates to a method for the treatment of water, more particularly for the removal of organic matter from raw water. agglomeration More specifically, the present invention relates to a method for removing organic matter by adding a soluble ammonium nitrate to raw water. agglomeration The present invention relates to a method for determining and adjusting the dosage of an agent.

[0002] The invention further relates to a computer program product comprising program code instructions for carrying out the steps of the method, said program being a product running on a computer.

[0003] The present invention also provides at least one agglomeration The water treatment process includes the steps of: [Background technology]

[0004] agglomeration (or agglomeration -purification) is a known water treatment process that allows the removal of suspended solids (turbidity) and organic matter contained in water. This treatment can be applied to wastewater, river water and more generally to any type of water.

[0005] Generally speaking, the first step is to agglomeration This is accomplished by adding an agent, usually a metal salt, to the influent water, referred to as "raw water" (EB). Generally, the raw water is introduced into a reactor or basin, agglomeration The agent is then added to the reactor or basin. The second step is then typically the addition of a polymer. agglomeration This involves particle flocculation. Finally, a third step, particle sedimentation, allows the particles to be separated. At the end of these steps, the resulting effluent is called "clarified water" (ED).

[0006] However, raw water may be subject to rapid fluctuations in quality due to more or less climatic conditions or human activities. These fluctuations modify not only the physicochemical properties of the water but also the composition of organic matter. Therefore, to adapt the treatment of raw water, the following methods are used: agglomeration It is necessary to change the dosage of the drug. The reason for this is that the agglomeration The dosage depends on the turbidity and organic matter, a complex matrix of organic matter commonly present in surface and groundwater, and more broadly in all types of water. This matrix may result from the origin of the water or from other sources, such as contamination by catchment effluent. Furthermore, seasonal variations, pH, and other external parameters may affect the quantity or quality of organic matter.

[0007] The degree of organic removal is therefore related to its quality, which is highly variable and difficult to predict.

[0008] Laboratory assays, typically jar tests, are currently being conducted to determine optimal treatment conditions for turbidity and organic matter elimination, among others. agglomeration Dosage and agglomeration This is the most reliable method for determining pH. agglomeration pH is agglomeration The pH is adjusted to enhance the efficacy of the agent. The pH is generally adjusted by adding an acid to the reactor or basin. agglomeration These assays are generally carried out in a reactor or basin into which the agent is introduced. However, these assays are time consuming and cannot be carried out continuously to be responsive to the needs of the utility, especially when the quality of the raw water fluctuates rapidly.

[0009] Generally, therefore, operators employ a safety margin to ensure that quality targets are met. agglomeration This overdosing disadvantageously results in extra operating costs both in terms of reagent costs and, given the greater amount of sludge generated, in terms of sludge disposal costs.

[0010] To overcome this problem, agglomeration injected to achieve the desired quality target for the water leaving the agglomeration Systems are in place that allow for the determination of drug dosage. These systems are of two types: - Feedback control system: agglomeration The dosage is agglomeration adjusted for the quality of the water leaving the process; - Prediction System: agglomeration The dosage is agglomeration It is determined based on the quality of the water entering the process (parameters used: turbidity, UV absorbance at 254 nm, total organic carbon, TOC, etc.).

[0011] The feedback control system has agglomeration (Critchley et al., Automatic coagulation control at water-treatment plants in the north-west region of England, 1990). Zeta potential allows the charge of water to be measured, for example, using an SCD (charge current detector) analyzer. agglomeration According to the physical and chemical mechanism of agglomeration The optimum point of corresponds to a zeta potential of zero. However, rapid fluctuations in flow rate and non-optimized mixing conditions can produce an unstable response and therefore unreliable results. Furthermore, the measurement is sensitive to changes in pH and mineralization, and the analyzer must be frequently calibrated to compensate for these changes. The result is a relatively unreliable system. Finally, this system agglomeration the desired water quality goal at the end of agglomeration It fails to take into account targets that may vary depending on downstream steps, and in particular on the expected performance levels of these steps.

[0012] Predictive systems include models based on historical data. These models may use artificial intelligence, such as artificial neural networks, which are fed with historical data from the plant and optionally data obtained from sensors (typically to characterize the quality of water at various stages in the treatment process). The artificial intelligence of the system allows it to learn from past events and update its calculation rules to obtain the best response from the model.

[0013] The models include classical regression models that refer to classical linear, quadratic, logarithmic and exponential equations. agglomeration The parameters that allow the calculation of the drug dose are determined based on the data history. However, the accuracy of these models is not very good because they are based on simple equations, agglomeration The phenomenon is a complex one.

[0014] This model includes other more complex models such as those described in the publication 'MLP, ANFIS, and GRNN based real-time coagulant dosage determination and accuracy comparison using full-scale data of a water treatment plant', Chan Moon Kim and Manukid Parnichkun, Journal of Water Supply, Research and Technology-AQUA-66.1-2017. This model is the optimal model for eliminating turbidity. agglomeration Based on a series of thousands of data points obtained from the plant history, the best statistical model was defined, allowing the calculation of the dosage. The three modes of artificial intelligence studied (MLP, ANFIS, and GRNN) showed responses that were suited to the results observed in the plant, and the combination of the three tools allowed the accuracy of the model to be increased over a wide range of source water turbidity (0-450 NTU).

[0015] The main drawback of these models based on historical data is that they are site-specific. Furthermore, considering that the models are based on previous data and not on the quality of the water before treatment, agglomeration It is not possible to perform true optimization of agent dosage. Furthermore, these history-based models can only reproduce the past, not optimize it. They inject as has been done in the past, without any assurance that this is the optimal dosage to deliver compliant water at the best cost. agglomeration The dosage is shown.

[0016] Other more complex models may use the results of laboratory tests, such as jar tests, on a variety of waters. These results are used depending on the quality of the incoming water. agglomeration This is used to define the equation constants that determine the amount of agent.

[0017] For example, mEnCo (enhanced agglomeration An optimization model, named 'Modeling of Water Quality and Treatment', was developed in Australia by the Australian Cooperative Research Centre for Water Quality and Treatment. The mathematical equation is based on the relationship between dissolved organic carbon (DOC) and agglomeration The constants integrated in these equations must be determined using jar test results from a variety of Australian waters. While the mEnCo model provides good results across a number of Australian plants, there remain cases where the model is still highly specific to one plant or at least one type of raw water.

[0018] Another type of predictive model is based on the law of adsorption applied to organic matter removal. agglomerationThe model is described in a publication called 'Predicting DOC removal during enhanced coagulation', Edwards, Journal - American Water Works Association, 89(5), 78-89, 1997. The algorithm of this model is: agglomeration The Edwards model describes the physical and chemical phenomena occurring during the synthesis of organic matter in three fractions: - The fraction that cannot be adsorbed onto the metal hydroxide ( agglomeration (relatively inactive fraction), - agglomeration Dosage and agglomeration as a function of pH agglomeration The polar fraction can be removed by - agglomeration only as a function of drug dose agglomeration The non-polar fraction can be removed by This was improved by Kastl et al. (2004), who classified it as follows:

[0019] The model is based on five equations with five unknown parameters to determine the constants that allow the model to operate: maximum sorption capacity, adsorption constant, fraction of humic acid, non-polar fraction, and pKa of humic acid. These five parameters are determined under specific conditions ( agglomeration Dosage and agglomeration These five parameters are dependent on the organic matrix (charge, hydrophobicity, size, type, etc.) and therefore must be determined for each type of organic matrix.

[0020] The input and output data are listed below with two possible options: - Input: Organic matter content (DOC) of raw water, agglomeration The pH and DOC targets to be achieved in the purified water are obtained as outputs: agglomeration Drug dosage; - Input: Organic matter content (DOC) of raw water, agglomeration pH+ agglomeration Dosage of agent, obtained as output: DOC content of purified water

[0021] However, the Edwards and Kastl model presents the following drawbacks: the model is site-specific, and the constants that allow the model to be tailored must be determined by time-consuming laboratory assays.

[0022] From these examples of various predictive models, - The model is agglomeration based on a statistical study of historical data to determine the constants of the equations that allow the calculation of the drug dose, in which case no possible optimization exists; or - The model is based on laboratory assays to determine the constants in the equations that allow the dose to be calculated, in which case the execution of the model is time-consuming or It is understood that the

[0023] Furthermore, these models are generally specific to one plant or at least to a raw water type. Finally, these systems generally lack accuracy.

[0024] A more accurate system has therefore been developed as described in patent application WO 2009002192, which describes a method for calculating chemical dosages for treating raw water by taking into account the turbidity of the water as a measure of its particulate content, and also the ultraviolet absorbance (UV absorbance) and dissolved organic carbon (DOC) of the water as measures of dissolved organic matter in the raw water. These measurements make it possible to predict the dosage of chemicals to be added to the water, using, inter alia, the sum of the particulate content and the dissolved organic matter content.

[0025] Nevertheless, the accuracy of these methods is limited to agglomeration The smallest possible amount while still achieving effectiveness agglomeration There is a need for improvements in the use of these agents. The reason for this is that these methods are empirical and / or experimental, and not chemical. agglomerationIt is not possible to take into account all the parameters essential to assess water quality, which particularly affect the performance level.

[0026] The present invention overcomes the drawbacks of the prior art methods and agglomeration The aim is to overcome the shortcomings of drug delivery systems.

[0027] The present invention is used for water treatment processes agglomeration a method that allows optimization and adjustment of the amount of agent; in other words, agglomeration Optimal drug administration while avoiding overdose agglomeration Determining drug dosage, and agglomeration The goal is to develop a method that allows for steering of the optimal dosage throughout the treatment process to maintain or even improve this optimization. This is particularly relevant when the quality of the water for treatment varies during the process. agglomeration The goal is to explore ways to control the amount of agent used. Summary of the Invention [Problem to be solved by the invention]

[0028] The present quest is therefore to obtain and maintain a more reliable, reliable and optimal amount of chlorine for use in raw water. agglomeration The present invention relates to a method that enables the detection of trace elements, which is fast, simple and effective, and which is not specific to one site and / or one type of given source water, and which is amenable to automation. [Means for solving the problem]

[0029] The present invention allows these drawbacks to be corrected by reducing the amount of raw water to give purified water. agglomeration injected into the means of treatment, agglomeration 1. A method for determining and adjusting the dose of an agent, and optionally at least a second reagent, comprising: - agglomerationdetermining an optimal dosage of the agent, said determining step utilizing at least one organic parameter for providing information about the amount of organic matter in the raw water and which is a function of a target value of the organic parameter determined for the purified water; - measuring the actual values ​​of the organic parameters of the purified water; - determining the difference between the actual value and the target value of the organic parameter for the purified water; if this difference is less than the lower threshold or greater than the upper threshold: - injected into the treatment means, agglomeration adjusting the dose of the agent and optionally at least a second reagent, said adjusting step comprising: agglomeration Starting from the optimal dose of the drug - If the difference between the actual and target values ​​of the organic parameters is greater than the upper threshold. agglomeration increasing the dose of the agent; or - if the difference between the actual and target values ​​of the organic parameters is less than the lower threshold. agglomeration reducing the dose of the agent and The present invention includes a method comprising the steps of:

[0030] The present invention is agglomeration Dose and injected agglomeration and a closed control loop for adjusting the dosage of the agent, the starting point of the control loop being: agglomeration This is the optimal dose of the agent. agglomeration Improved throughout the treatment process agglomeration This allows for optimization of the dosage of the agent.

[0031] The method according to the invention may also incorporate the step of determining the optimal dose of at least one second reagent, as explained later below.

[0032] The adjusting step may operate as explained later below with respect to the administration of at least one second reagent, without necessarily predetermining an optimal dose of said second reagent.

[0033] agglomeration The agent (and, if appropriate, the second reagent) agglomeration / such as a purification reactor or basin, agglomeration It is intended for injection into a processing means.

[0034] agglomeration Injection of agent (if conditioning step is not involved or stopped) agglomeration Equal to the determined optimal dose of the agent, or (if an adjustment step is involved) agglomeration Increased or decreased doses of the drug relative to the established optimal dose. agglomeration This refers to the injection of a drug.

[0035] Throughout this description, raw water (EB) is agglomeration The influent (upstream) of the process and the purified water (ED) are agglomeration Defined as the process effluent (downstream).

[0036] Throughout this description, organic matter refers to dissolved organic matter as opposed to turbidity, which refers to the particulate content or suspended matter in water.

[0037] According to one embodiment, the at least one organic parameter is selected from UV absorbance, preferably at 254 nm, dissolved organic carbon, the ratio between UV absorbance and DOC (dissolved organic carbon), preferably at 254 nm, or a combination of said parameters. UV absorbance provides a simpler and generally more cost-effective measurement than measurement of DOC.

[0038] According to one embodiment, the organic parameter comprises multiple organic parameters, typically UV absorbance and DOC.

[0039] According to one embodiment, the determining step further utilizes at least one mineral parameter for providing information about the mineral load of the raw water, which may be selected from full alkali titration titer, chloride ion concentration, sodium ion concentration, sulfate ion concentration, calcium ion concentration, magnesium ion concentration, silicate ion concentration, electrical conductivity, or a combination of said parameters.

[0040] The mineral parameters preferably include a plurality of mineral parameters, typically full alkali titration titer, chloride ion concentration and / or sodium ion concentration.

[0041] According to one embodiment, the value of at least one organic parameter for the raw water and / or purified water is determined by measurements of the raw water and / or purified water, such as in-line measurements made by, for example, a dissolved organic carbon sensor (preferably with a pre-filtration step), a UV sensor (preferably with a pre-filtration step), or a combination of such measurements.

[0042] According to one embodiment, the value of the at least one mineral parameter for the source water is determined by measuring the source water, such as, for example, an in-line measurement performed by a conductivity sensor, or a sampling measurement performed by a full alkali titration titer analyzer, an ion concentration analyzer, or a combination of such measurements.

[0043] Such sensors may also advantageously monitor water quality throughout the water treatment process (with respect to raw water, purified water, and also with respect to treated water, as described further herein below in this description).

[0044] agglomeration The dose of the agent (and, if appropriate, the second agent) is agglomeration / These may be automatically added to the purification treatment means, thereby facilitating the step of adjusting the dosage, and the whole procedure may be carried out automatically.

[0045] The adjustment step is carried out in the case of fluctuations in the amount of organic matter in the purified water. agglomeration Allows for automatic adjustment of drug dosage. agglomeration The agent (and the second reagent) agglomeration Given the nonlinearity of the reaction when injected into a purification basin or reactor, the adjustment step cannot be used arbitrarily and must be taken into account at the start of the adjustment step. agglomeration is the dose of the agent, agglomeration This must be considered in conjunction with the preceding step of determining the optimal dosage of the agent. agglomeration / into the purification means agglomeration It provides the most automatic steering possible for the injection of the agent (and of the second reagent). The operator may only have to intervene in cases of caution, and these cases can be incorporated into the adjustment steps.

[0046] The regulating step may advantageously implement an automated logic system already used in water treatment or in industry in general: it uses a closed control loop.

[0047] According to one embodiment, the adjusting step uses a PID (Proportional Integral Derivative) controller.

[0048] According to one embodiment, the set point value of the closed control loop is the target value of organics in the purified water, and its actuating variables are: agglomeration Reagents injected into the means ( agglomeration The target value is the dose of the second reagent (e.g., a scavenger and / or powdered activated carbon). If the loop is a PID controller, the controller calculates the difference between the actual organic value and the target value, and also the integral and derivative of this difference as a function of time. Three multiplication factors are combined: a first for the difference, a second for its integral, and a third for its derivative, to obtain the action variable, i.e., a signal for a decrease or increase in the dose of the injected reagent.

[0049] The multiplication factor is determined according to the following characteristics: - Potency of reagents and / or agglomeration / the volume of the purification means (this volume affects the dilution factor of the reagents): the multiplication factor associated with this difference therefore affects the efficacy of the reagents and / or agglomeration / Depending on the volume of the purification means; - agglomeration / The flow rate of water, which affects the hydraulic residence time in the clarification means (lower flow rates result in longer residence times and vice versa); agglomeration / The reaction time between the moment of injection upstream of the purification means and the moment its effect on the purified water is detected can therefore vary as a function of flow rate: the multiplication factors associated with the derivative and the multiplication factors associated with the integral therefore depend on the water flow rate.

[0050] According to one advantageous embodiment, the adjustment step comprises determining the temporal variation of the organic parameters of the raw water, which is blocked if said variation is greater than a defined variation limit. agglomeration The method further includes adjusting the dose of the agent to be injected. agglomeration The dosage of the drug is agglomeration The present invention further includes a step in which the agent is optimally dosed. By "blocking" it is meant that the conditioning step is not engaged or stopped. This embodiment is not intended to limit the conditioning step, or more precisely, the effect of the agent on the organic matter in the purified water. agglomeration This is advantageous insofar as the effect of adjusting the dose of the agent is not rapid enough to allow optimal adjustment.

[0051] According to one advantageous embodiment, the method comprises the steps of measuring the pH of the purified water, and if the pH of the purified water is below the pH threshold, agglomeration The method further includes an adjustment step that includes blocking an increase in the dose of the agent, where "blocking" means that the dose cannot be increased (e.g., the pH is below a threshold value from the start of the adjustment) or that the dose can no longer be increased.

[0052] agglomeration The increasing dose of the agent is preferably up to agglomeration This maximum value is less than the agent value. agglomerationThe maximum economically tolerable amount of the agent (DMEA) may be related to or even equal to it.

[0053] agglomeration The reduced dose of the agent is preferably a minimum agglomeration greater than the agent value.

[0054] The method includes the steps of determining a target turbidity value for the purified water and injecting a target turbidity value into the raw water to achieve the target turbidity value for the purified water. agglomeration determining a second dose of the agent; agglomeration The reducing dose of the agent is preferably agglomeration or greater than said second dose of the agent.

[0055] According to one embodiment, the adjustment step comprises adding a second reagent, such as powdered activated carbon (CAP), when the difference between the actual value and the target value of the organic parameter is greater than an upper threshold. agglomeration The method further comprises the step of adding the agent to the device.

[0056] According to one embodiment, the method may include a step of determining an optimal dose of a second reagent to be injected, such as powdered activated carbon (CAP), wherein said step of determining the optimal dose of the second reagent occurs before the adjustment step.

[0057] The second reagent is preferably selected so as not to lower the pH of the purified water; for example, powdered activated carbon (CAP).

[0058] According to one embodiment, the adjusting step comprises: - increasing the second reagent if the difference between the actual value and the target value of the organic parameter is greater than an upper threshold value; and / or - reducing the second reagent if the difference between the actual value and the target value of the organic parameter is less than a lower threshold. This embodiment applies to the two cases described above, namely, when the optimal dose of the second reagent is determined before the adjustment step (in which case the dose can be increased or decreased), or when the adjustment step is adding the second reagent (in which case it is possible to continue injecting the second reagent or to decrease the dose if it falls back below the lower threshold).

[0059] According to one particular embodiment, the adjustment step comprises, if the difference between the actual value and the target value of the organic parameter is greater than an upper threshold: - agglomeration The dosage of the drug agglomeration Step of increasing the amount of DMEA to the maximum economically tolerable amount of the agent Includes; Next, agglomeration If the dose of the agent achieves DMEA and if the difference between the actual value and the target value of the organic parameter is greater than the upper threshold, the adjusting step further includes adding a second reagent, such as powdered activated carbon (particularly as long as the difference between the actual value and the target value of the organic parameter remains greater than the upper threshold).

[0060] According to one particular embodiment, the adjustment step comprises, if the difference between the actual value and the target value of the organic parameter is less than a lower threshold: - reducing the dosage of the second reagent, such as powdered activated carbon, as long as the difference between the actual value and the target value of the organic parameter remains below a lower threshold; Then, if the dosage of the second reagent is zero and if the difference between the actual value and the target value of the organic parameter remains below the lower threshold, the adjustment step is performed (especially as long as the difference between the actual value and the target value of the organic parameter remains below the lower threshold). agglomeration The method further comprises reducing the dose of the agent.

[0061] More particularly, agglomeration The dosage of the agent is determined based on a second dose determined to obtain a target turbidity value for the purified water. agglomeration The dose may be reduced as long as it remains above the prescribed dose.

[0062] The invention therefore allows two means of defining the combined reagent dosage: one means of defining a predictive type dosage, and one means of adjusting the dosage depending inter alia on whether the raw water quality fluctuates more or less rapidly, in other words whether the raw water quality is sufficiently stable. Unless the raw water quality is sufficiently stable, the predictive method prevails.

[0063] When the adjustment step is activated, it is performed for only one reagent at a time ( agglomeration Furthermore, the predictive method can be used to determine, among other things, which reagent ( agglomeration The predictive method may remain active to determine which of the following (a) or (b) reagents (a) or (c) are preferentially acted upon by the adjustment step. agglomeration To determine the minimum dosage of the agent, it remains active to achieve the turbidity required for purified water.

[0064] agglomeration The step of determining the optimal dosage of the agent can be performed manually, can be provided by a third party, or can be a dosage known from experience at a given plant. It can also be determined by predictive techniques.

[0065] agglomeration Embodiments of the step of determining the optimal dose of the agent (predictive method) According to one embodiment, agglomeration Determining the optimal dose of an agent is a predictive approach.

[0066] According to one embodiment, the determining step comprises determining the amount of raw water agglomeration A first organic parameter is utilized to provide information regarding capacity, a second organic parameter is utilized to provide information regarding the amount of organic matter in the raw water, and at least one mineral parameter is utilized to provide information regarding the mineral load of the raw water.

[0067] According to one embodiment, the first organic and mineral parameters make it possible to define a water class for the source water.

[0068] According to one particular embodiment, the prediction method comprises the following steps: - determining a value for the source water of a first organic parameter; - determining the values ​​of the mineral parameters for the raw water; - determining a water class for the source water as a function of the values ​​determined for the source water of a first organic parameter and of a mineral parameter, the water class being characterized by a first range of values ​​for the first organic parameter and a second range of values ​​for the mineral parameter; - determining a value for the source water of a second organic parameter; - determining a target value for the purified water of the second organic parameter; - The second organic parameter and the amount of agglomeration selecting a function for establishing a relationship between the dose of the agent and the class of water determined for the source water and the value determined for the source water of the second organic parameter; - Corresponding to the target value of the second organic parameter defined for purified water agglomeration utilizing the selected function to determine a first dose of the agent; agglomeration The first dose of the drug agglomeration Step 1: Optimal dosage of the drug Includes:

[0069] Thus, in this embodiment, the prediction method is: - agglomeration By integrating the performance levels of downstream steps in the process (e.g., ozonation or filtration over granular activated carbon) and then determining the dosage "exactly" necessary for the target to be achieved in the purified water, - by taking into account at least one organic parameter and at least one mineral parameter of the source water, - Each water class ( agglomeration by utilizing a class of waters characterized by at least one first range of values ​​for at least one first organic parameter (providing information about the water's ability to be irrigated) and a second range of values ​​for at least one second mineral parameter (providing information about the water's mineral load), - first by determining the water class of the raw water according to organic and mineral parameters; - Regarding the determined water class, agglomeration By utilizing a function that allows for a link between the first dose of the agent and the organic matter present in the raw water and achieves the target in the purified water. agglomeration The optimal dose of the agent is determined.

[0070] For each water class, such a function is added to the raw water with a second organic parameter. agglomeration Preferably, at least one database is available that can provide for each water class and for given values ​​of the second organic parameter, for the source water, a function that can establish a relationship between the amount of agent and the amount of the agent.

[0071] A database is defined in this description as a storage space (container, memory, etc.). The database can be supplied with various water assays. It can be supplied during the use of a prediction method.

[0072] Water classes allow raw waters to be classified into more or less clearly defined categories, depending on the criteria used to determine these water classes. Water classes allow at least one parameter of the minerality of the raw water to be taken into account.

[0073] Furthermore, the prediction methodology, as explained later below, agglomeration The effect of pH may be incorporated.

[0074] Furthermore, the predictive approach, as explained later below, allows for the identification of the least costly reagents (especially agglomeration The combination of (between agent dose and powdered activated carbon dose and / or acid dose) may be explored.

[0075] Determining the water class may involve utilizing water classes that have already been determined - for example, stored in a database.

[0076] This type of forecasting method is agglomeration This allows a more accurate and reliable amount of agent to be obtained for use in the raw water, this amount being established as a function of the characteristics of the water being treated, rather than being specific to one site.

[0077] According to one advantageous embodiment, the prediction method comprises: agglomeration determining a pH, the function being: agglomeration This further comprises a step of selecting the pH as well. agglomeration To determine the pH, different agglomeration By simulating the pH value, agglomeration This allows a first optimal dose of the agent to be obtained.

[0078] If the function is available in at least one database, this database may be used to determine the second organic parameter and the amount of organic matter added to the raw water. agglomeration a function for establishing the relationship between the agent dose - for each water class, for a given value for the source water of the second organic parameter, and agglomeration Regarding pH value - can be provided.

[0079] According to one embodiment, the prediction method includes a preliminary step of determining a plurality of water classes, each of which is a water agglomerationIt further comprises a preliminary step characterized by at least one first range of values ​​of at least one first organic parameter for providing information regarding the capacity and a second range of values ​​of at least one mineral parameter for providing information regarding the mineral load of the water. If the classes of water are stored in a database, said database can therefore be supplied during the use of the method.

[0080] Water Class CL i For example, agglomeration The second organic parameter (P ORG2 ) for raw water (P ORG2_EB ) is the first linear relation R i1 It is a collection of raw water determined by the collection of

[0081] Water Class CL i For example, underwater agglomeration Sexual organic matter and agglomeration pH pH C The relationship between is a second linear relationship, an exponential relationship or a polynomial relationship R, such as a quadratic polynomial. i2 It is also a collection of raw waters determined by

[0082] Water Class CL i is, for example, DMEA and the second organic parameter P ORG2 The relationship between the values ​​of the raw water and the i3 and DMEA is agglomeration pH pH C It is also a collection of raw water that is unrelated to the environment.

[0083] agglomeration The maximum economically tolerable dose of the agent (DMEA) is given in this description, above which the agglomeration There is no longer any benefit to adding agglomeration If this is possible, it is defined as the dose of the agent. agglomeration The cost of the agent treatment is expressed as UV absorbance. agglomerationThe cost of treatment with alternative, generally more expensive reagents (exemplified by powdered activated carbon "CAP") for the same reduction of organics by agglomeration The dose of the agent can be calculated as:

[0084] According to one preferred embodiment, the function is of the following type: [Number 1] y=Ae -B[x] +C where y is the second organic parameter and x is agglomeration where the coefficients A, B and C relate to the value of the second organic parameter for the raw water and agglomeration Regarding pH, it can be determined by a given relationship according to the water class) is an exponential function for the water class aggregates.

[0085] In other words, the function is of the same type for all classes, but the coefficients of this function differ according to the class. Furthermore, these coefficients depend on the second organic parameter of the raw water and / or agglomeration It is determined for one water class by a relationship giving the coefficient as a function of pH.

[0086] According to one particular embodiment, the coefficient C is determined for a given value of the second organic parameter for the source water and for a given agglomeration Regarding pH, non agglomeration It is defined as the value of the organic matter.

[0087] According to one particular embodiment, the coefficient C is linked to the value of the second organic parameter, for the source water, by a first linear relationship.

[0088] According to one particular embodiment (alternative to or complementary to the preceding embodiment), the coefficient C is determined by a second linear, polynomial or exponential relationship: agglomeration It is tied to pH.

[0089] According to one particular embodiment, the coefficient A is equal to the value of the second organic parameter determined for the raw water minus the coefficient C.

[0090] According to one particular embodiment, the coefficient B is estimated from the second derivative of the function, the coefficient A and the value of the second organic parameter determined for the source water. For example, the second derivative of the function is between 0.0001 and 0.0009.

[0091] According to one particular embodiment, the second derivative of the function is equal to the maximum economically tolerable amount. agglomeration The maximum economically tolerable dose achieved for the agent dose is agglomeration Based on the dosage, agglomeration the cost of treatment with the agent is greater than the cost of treatment with an alternative reagent, agglomeration The agent dosage is determinable by a third linear relationship of the second organic parameter as a function of the determined value for the source water.

[0092] The first and / or second relationship and / or the third relationship are preferably available for each water class in the database.

[0093] The database can be provided by assays on various waters, which can be provided during use of the method.

[0094] According to one embodiment, the predictive method may involve, for example, powdered activated carbon or acid, or another agglomeration determining a dosage of a second reagent, such as an organic solvent, to be added to achieve a target value of the second organic parameter with the second reagent, the target value being determined for the purified water; agglomeration Further comprising determining a first dose of the agent.

[0095] The predictive method is advantageously determined in the presence of a second reagent agglomeration A first dose of the agent is determined without the second agent. agglomerationThis may involve adding a second agent or more agglomeration using an agent, or agglomeration This provides knowledge as to whether it is more advantageous to calculate the best trade-off between the agent and the second agent.

[0096] According to one advantageous embodiment, the prediction method comprises: - determining a target turbidity value for the purified water; - Added to raw water to achieve target turbidity values ​​for purified water agglomeration determining a second dose of the agent; - agglomeration the first dose of the agent and agglomeration and a second dose of the agent added to the source water. agglomeration determining the optimal dose of the agent to be added; agglomeration The optimal dose of the agent is agglomeration the first dose of the agent and agglomeration the step between the first and second doses of the agent being the maximum dose Further includes:

[0097] The advantage is that it accommodates variations in raw water quality, whether organic matter or turbidity dominates.

[0098] The various steps of the method according to the invention, in particular the various steps described above, are preferably implemented in a computer program and are thereby calculated in real time. agglomeration Obtaining the correct dosage of agents and the entire raw water treatment process agglomeration It provides a fast, simple and effective method that allows for in-line adjustment of the optimal dose of the agent.

[0099] Another subject of the invention is a computer program product comprising program code instructions for carrying out the steps of the method according to the invention, said program running on a computer.

[0100] Another subject of the invention is the purification of raw water to give purified water. agglomeration injected into the means of treatment, agglomeration A system for determining and adjusting the dose of an agent and optionally at least a second reagent, said system using the method according to the invention.

[0101] Another subject of the present invention is to provide a method for treating raw water by at least agglomeration A raw water treatment process comprising the step of: agglomeration The dosage of the agent is determined and adjusted by the method according to the invention agglomeration The process is the dosage of the agent.

[0102] Other characteristics, details and advantages of the invention will become apparent from reading the description, given in connection with the accompanying drawings, given as an illustration and not as a limitation, in which: [Brief explanation of the drawings]

[0103] [Figure 1] 1 illustrates a first embodiment of determining the optimal dosage of a flocculant using a predictive approach. [Figure 2] 1 represents a function that allows the calculation of the UV absorbance of water as a function of the dose of flocculant. [Figure 3] Figure 1 shows an example of the division of water into water classes defined as a function of Cl- and Na+ ion concentrations, total alkali titration capacity (TAC), and UV254nm / DOC ratio (SUVA). [Figure 4A] For one water class, first and second relationships are presented that allow the calculation of the value of unaggregated organic matter as a function of UV absorbance of the source water and as a function of aggregation pH. [Figure 4B] For one water class, first and second relationships are presented that allow the calculation of the value of unaggregated organic matter as a function of UV absorbance of the source water and as a function of aggregation pH. [Figure 5] 1 shows a schematic representation of the method for calculating the maximum economic allowable amount of flocculant (DMEA). [Figure 6]For one water class, a series of third linear relationships are presented that allow the calculation of DMEA as a function of UV absorbance of the source water. [Figure 7] 1 illustrates a water treatment process including a coagulation step and downstream steps. [Figure 8A] 1 illustrates one particular embodiment of the step of determining a first dose of agglutinating agent, which allows for the integration of other reagents. [Figure 8B] 1 illustrates one particular embodiment of the step of determining a first dose of agglutinating agent, which allows for the integration of other reagents. [Figure 9] 1 illustrates a second embodiment of the step of determining the optimal dosage of a flocculant using a predictive approach. [Figure 10] 1 illustrates a fourth relationship that allows for the determination of a second dose of a flocculant. [Figure 11] 1 illustrates a second embodiment of the step of determining the optimal dosage of the flocculating agent. [Figure 12A] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 12B] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 13] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 14] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 15] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 16] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 17] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 18] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 19] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 20] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 21] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 22] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 23] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 24] The system using the method according to the invention is illustrated according to different examples and variants of the step for regulation (control loop) in combination with the step for determining the optimal dosage of the flocculant (predictive method). [Figure 25]1 depicts a simplified flowchart of a system using one particular embodiment of the method according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0104] In this description, the invention is explained using the example of raw water, however, the invention can also be applied to any other liquid containing organic matter and / or turbidity.

[0105] agglomeration The agent may be a solution based on aluminum or iron salts, preferably containing the following compounds: aluminum sulfate; (poly)aluminum chloride; aluminates; ferric chloride; ferric sulfate; sodium or potassium ferrate ions, or a combination of said compounds. agglomeration The agent solution is, for example, 8.2% alumina Al2O5 aluminum sulfate or 41% FeCl3 ferric chloride.

[0106] Optimal agglomeration Dose determination step (predictive method) Figure 1 shows the amount of water added to the raw water. agglomeration Illustrating a first embodiment of the step of determining the optimal dose of an agent, the method comprises the following steps, also described subsequently: - Water agglomeration The first organic parameter (P ORG1 ) value (P ORG1_EB ) with respect to the source water, step 110; - Mineral parameters (P) to provide information on the mineral load of the water MIN ) value (P MIN2_EB ) with respect to the source water, step 120; - the water class (CL) as a function of the values ​​of the first organic and mineral parameters determined for the source water; EB ) for the raw water, step 130, where the water class is determined by a first organic parameter (P ORG1 ) and the mineral parameters (P MIN) and a second range of values ​​for, step 130; - Second organic parameter (P ORG2 ) value (P ORG2_EB ) for the source water, step 140, wherein the second parameter is intended to provide information about the amount of organic matter in the water; - Second organic parameter (P ORG2 ) target value (P ORG2_ED ) in relation to the purified water, step 150; - Second organic parameter (P ORG2 ) and added to raw water agglomeration A function (f i ) determined for the source water, EB ) and the second organic parameter (P ORG2 ) determined for the raw water (P ORG2_EB ) selected step 160; - Second organic parameter (P ORG2 ) target value for purified water (P ORG2_ED ) corresponding to agglomeration To determine the first dose of the agent ([COAG1]), a selected function (f i Step 170 using This is a prediction method that includes:

[0107] According to this first embodiment: agglomeration The optimal dose of the drug is agglomeration This is the first dose of the agent.

[0108] According to one particular embodiment, the second organic parameter P ORG2 is m -1 The UV absorbance at 254 nm is expressed in units, which may be referred to as "UV" throughout this description.

[0109] UV absorbance (typically UV at 254 nm) is a physical measurement for assessing the organic matter contained in water. UV absorbance is measured using a UV spectrophotometer (typically at 254 nm), where the sample is placed in a UV-transparent quartz cell in the cm-range thickness, for example, 1 cm, 3 cm, 5 cm, or 10 cm. The measurement is simpler and generally more cost-effective than measurements of TOC (total organic carbon) and also DOC (dissolved organic carbon). This photometric method involves measuring m corresponding to the loss of light intensity at a selected wavelength (typically 254 nm) through a sample of water in a 1 cm thick cell. -1 The organic substances detected in this way contain, among others, aromatic rings and double bonds, such as humic acids. These aromatic organic substances are agglomeration are particularly effectively removed by

[0110] According to another embodiment, the second organic parameter (P ORG2 ) is DOC.

[0111] Each water class CL i Regarding UV or DOC, agglomeration as a function of the dose of the drug (f i )

[0112] For ease of interpretation, the remainder of this description will use the term UV absorbance or UV, provided that the measurement in question may instead be that of DOC or another secondary organic parameter.

[0113] The various steps are detailed later in the description by way of non-limiting examples and embodiments.

[0114] There may also be a preliminary step 105 of determining multiple water classes.

[0115] agglomeration Regarding pH, the function f i In the state where is selected, agglomeration There may also be a step 145 of determining the pH.

[0116] Other steps may be added that are not shown in Figure 1. These will be specifically described in the remainder of this description.

[0117] FIG. 2 shows the function f selected during selection step 160. i But the exponential function: [Number 2]

number

[0118] Function f i Step 160 of selecting the coefficient A i , B i , C i The method includes determining:

[0119] Coefficient C i teeth, agglomeration Typically, when the drug dose reaches the maximum efficacy threshold, agglomeration If the agent dosage is greater than 200 ppm of the commercial solution, expressed as ppm, the residual organic matter expressed in terms of UV absorbance (referred to in this description as "residual UV" or "non- agglomeration It corresponds to the UV radiation (also called "UV radiation").

[0120] A i teeth, agglomeration Typically, when the drug dose reaches the maximum efficacy threshold, agglomeration If the agent dosage is greater than 200 ppm of the commercially available solution, expressed as ppm, agglomeration corresponds to the organic matter removed by, for example, expressed in terms of UV absorbance.

[0121] Furthermore, A i , C i and UV EB is the following equation: [Number 3] UV EB =A i +C i (In the formula, UV EB is the organic matter in the raw water expressed in terms of UV absorbance) are connected by.

[0122] B i is a coefficient that gives the essence of the exponential function.

[0123] A i , B i and C i is each water class CL i These relationships (R i1、 R i2 and R i3 ) is A i , B i and C i but, agglomeration pH ( C ) and organic matter in the raw water expressed in terms of UV absorbance (UV EB ) These relationships are preferably available in a database associated with the water class.

[0124] Function f i In order to select , it is necessary to determine the water class to which the source water belongs (decision step 130), in particular to determine the coefficients in the case of the exponential function of equation (1).

[0125] According to one preferred embodiment, the source water is characterized in its water class by analysis of the organic and mineral matrices: - The organic matrix has the following parameters: (m -1 SUVA, which is the ratio between UV absorbance at 254 nm, expressed in units of λ / 1, and DOC, expressed in mg / l, and optionally the distribution of DOC by liquid chromatography (LC-OCD for liquid chromatography-organic carbon detection); The mineral matrix is ​​defined by the following mineral parameters: total alkali titration capacity (TAC), chloride and / or sodium ion concentration and optionally conductivity, silicate, calcium, magnesium, sulfate ion concentration and ionic balance.

[0126] The values ​​of the organic and mineral matrix parameters (determining steps 110 and 120) may be determined by in-line analysis or sampling, or may involve the collection of data already available about the raw water being treated.

[0127] According to a preferred embodiment, the first organic parameter (P ORG1 ) therefore includes at least SUVA and mineral parameters (P MIN ) therefore includes at least the concentration of TAC and also chloride ions and / or sodium ions.

[0128] Figure 3 or Table 1 below shows the Cl - and / or Na + Ion concentration, TAC and UV ratio 254nm An example of mineral and organic matrix water classes defined as a function of / DOC(SUVA) is shown. Water classes are defined as a function of the following thresholds:

[0129] [Table 1]

[0130] Water class CL determined for raw water EB EB With respect to the water class, the relationship R EB1、 R EB2 and R EB3 The above relationship is obtained by the coefficient A EB , B EB and C EB of, agglomeration pH ( C ) and organic matter in the raw water expressed in terms of UV absorbance (UV EB) can be estimated from

[0131] 4A and 4B are non- agglomeration The first and second relations R from which the value of soluble organic matter can be calculated EB1 and R EB2 represents the water class CL determined for the raw water EB. EB Regarding the UV absorbance of the raw water (the shape of the curve) agglomeration given in terms of pH) and agglomeration is a function of pH (the shape of the curve relates to a given value of UV absorbance of the raw water). This is due to the coefficient C EB This allows the value of the saturation voltage to be determined.

[0132] Non agglomeration To calculate the soluble organic matter as a function of the UV absorbance (or DOC) of the raw water, UV absorbance of the raw water, UV EB One or more available first linear relations R of coefficients a4, a5, a6, b4, b5, b6 that vary discretely as a function of thresholds (S4, S5) EB1 (Three are shown in the example).

[0133] Figure 4A shows three first-order relations: [Number 4] y=a4x+b4(up to threshold S4); [Number 5] y=a5x+b5(between thresholds S4 and S5); [Number 6] y=a6x+b6(after threshold 5) Shows.

[0134] Depending on the water class, there may be a single first linear relationship or at least two first linear relationships.

[0135] agglomeration pH pH C Non as a function of agglomeration To calculate the organic matter content, a second-order exponential or polynomial relationship R is available according to the water class. EB2The second relationship has coefficients a7, b7, and c7 that are similarly assigned according to the water class.

[0136] Figure 4B shows the type: [Number 7] y=α7x+b7 represents the second, linear relation of

[0137] Depending on the class of water, the second relationship is instead exponential: [Number 8]

number

[0138] Further alternatively, the second relation may be a polynomial, such as a second order polynomial: [Number 9] y=α7x 2 +b7x+c7 It could be.

[0139] Therefore, the water class CL is determined EB Regarding UV in raw water, UV EB (or DOC) and agglomeration pH, pH C The determination of coefficients a4, a5, a6, b4, b5, b6, a7, b7, c7 and then non agglomeration It allows the determination of the biomass of organic matter, thus EB In this case, the coefficients a4, a5, a6, b4, b5, and b6 are agglomeration It is a function of pH. agglomeration It will be possible to determine only the coefficients a4, a5, a6, b4, b5, b6 given in relation to pH.

[0140] A EB is expressed as follows:UV EB Minus C EB is obtained by

[0141] UV in raw water EB (or DOC EB) (decision step 140) and also agglomeration pH ( C The optional determination of (decision step 145) may be performed by in-line measurement or sampling and / or may involve retrieval of data already available about the raw water being treated.

[0142] The coefficient B as described herein below i To determine agglomeration The agent DMEA (maximum economically tolerated dose) is also determined.

[0143] DMEA is used in this description in excess of agglomeration It is defined as the dose at which the addition of the agent is no longer advantageous compared to its cost. agglomeration The cost of treatment with CAP or agglomeration As illustrated in Figure 5, which provides the cost in euros per cubic meter of raw water per unit of organic matter removed, expressed in terms of UV absorbance, as a function of the agent (COAG) dosage, this is greater than the cost of treatment with an alternative, generally more expensive, agent (e.g. powdered activated carbon, CAP) for the same reduction of organic matter, expressed in terms of UV absorbance. agglomeration The dotted line corresponds to the CAP, and the solid curve corresponds to the agglomeration The intersection of the two gives DMEA. In the remainder of this description, this is not the method used to determine DMEA.

[0144] Furthermore, the present inventors have found that DMEA agglomeration pH ( C ), but it is a function of the UV absorbance (or DOC) of the source water, as shown in Figure 6. It can therefore be obtained in a different way than that described in the preceding paragraph.

[0145] Figure 6 shows the determined water class CL EB Regarding UV absorbance of raw water, UV EB (or raw water DOC, DOC EBA third set of linear relationships R allows the calculation of DMEA (given in ppm of commercial solutions) as a function of EB3 There are a number of third linear relationships whose coefficients a1, a2, a3, b1, b2, b3 vary discretely as a function of the UV absorbance thresholds (S1, S2) of the source water.

[0146] UV in raw water EB (or DOC EB ) allows the determination of the coefficients a1, a2, a3, b1, b2, b3 and therefore the DMEA in the raw water.

[0147] Furthermore, in accordance with a preferred embodiment of the present invention, the DMEA is i corresponds to the inflection point of agglomeration The dose of the agent is the coefficient B i This makes it possible to obtain

[0148] DMEA is mathematically defined by the absolute value α1 of the second derivative of the function, which is, for example, 0.0001 to 0.0009, i.e.: [Number 10]

number

[0149] Given water class CL EB With respect to the value α EB For example, UV as shown in Table 2 below, which shows an example of the second derivative as a function of UV of the raw water. ES As a function of the threshold σ, the UV of the raw water EB (or DOC EB is determined as a function of the value of

[0150] [Table 2]

[0151] Water Class CL EB , and UV, UV in raw water EB(or DOC EB ) with respect to coefficient B EB is A EB Given the prior determination of DMEA and DMEA, it is determined by the relationship

[10] , expressed in terms of the source water (with i equal to EB). [Number 11]

number

[0152] Therefore, the water class CL determined for the source water EB , and UV EB and agglomeration pH pH C With respect to the exponential function: [Number 12]

number

[0153] This function f EB with, among other things: - UV absorbance of purified water (UV ED ) is applied to reach a target value agglomeration Dosage It is possible to calculate

[0154] The UV absorbance (UV) of purified water corresponding to the maximum desired residual organic matter in purified water (ED) ED ) or purified water DOC value (DOC ED ) is determined (determining step 150).

[0155] Therefore, the first agglomeration The drug dose is a function f EB (utilization step 170).

[0156] Coefficient A i , B iand C i is for each type agglomeration It is given in relation to the agent.

[0157] The target value for residual organic matter in purified water is: agglomeration It may further be defined as a function of downstream steps in the process (step 150), for example, it may be defined in step 300 as a function of a target value for residual organics in the treated water (ET), which is defined as the water obtained at the outlet of the water treatment plant.

[0158] Therefore, as illustrated in FIG. 7, the residual organic matter in the treated water is represented by UV absorbance, and the target (UV ET ) and Post agglomeration Organic matter removal performance in the step (% POST-COAG ), the quality target to be met in the purified water is calculated as follows: [Number 13]

number

[0159] post agglomeration Performance can be calculated based on in-line sensors or spot measurements of the purified and treated water.

[0160] According to step 200, the data obtained from the UV sensors of the purified water ED and the treated water ET are agglomeration It allows for the calculation of the percentage removal of UV in the treatment step. With this percentage information, it is possible to calculate the target UV cleanup in step 300 to allow the target (UV in treated water) to be met at the plant outlet.

[0161] The level of organic matter in the raw water and agglomeration Using the information of the UV target to be met at the end of this step, this step determines the optimal agglomeration This allows the dosage to be calculated.

[0162] This prediction method is agglomeration / The method may further include determining the dosage of another reagent, such as granular activated carbon (CAP), to improve the performance of the purification process and / or to reach a target for removal of organics in the purified water.

[0163] This prediction method is agglomeration The method may further include calculating the dosage of acid required to achieve the desired pH. This typically involves adding an acid to the agglomeration By adding to the basin or reactor, agglomeration This is because lowering the pH can improve the removal of organic matter. agglomeration The function f corresponding to pH i and thus adding to achieve the organic matter removal goal in the purified water. agglomeration This allows you to recalculate the amount of the agent.

[0164] As illustrated in Figures 8A and 8B, the present prediction method: agglomeration This also allows for the best economical advantage to be obtained from the selection of a combination of agents, other reagents and / or added acids.

[0165] Figure 8A shows - at a pH of 7 agglomeration of agent (dashed curve A); - at a pH of 6.2 agglomeration of agent (continuous curve B); - To achieve target UV for purified water agglomeration Add when pH is 6.2 agglomeration Agents and / or CAP (arrow C) 1 illustrates the UV of water as a function of dosage (UV ED ).

[0166] FIG. 8B shows the comparative costs of each administration illustrated in FIG. 8A; - Histogram corresponding to curve A, where the dotted line corresponds to the limit of DMEA; above the limit of DMEA added to achieve the UV target for purified water agglomeration The cost of the agent; - Histogram corresponding to curve B, where the dotted line corresponds to the limit of DMEA; above the limit of DMEA is the cost of the product (in black) added to achieve a pH of 6.2 and meet the UV target for purified water. agglomeration The cost of the agent; - Histogram corresponding to curve C, adding the cost of the product to achieve a pH of 6.2 (in black) and the cost of the CAP added to achieve the UV target for purified water.

[0167] CAP and acid agglomeration The total cost to achieve the UV target of the purified water by adding the agent is lower in this case than for achieving the UV target of the purified water without the CAP and without the acid.

[0168] Acid agglomeration When added to lower pH, this prediction method is used to determine the amount of added organic matter to achieve the target removal goal in the purified water. agglomeration By recalculating the amount of agent to be added, agglomeration This allows the reduction in the amount of agent, the benefit of this difference to be calculated and compared to the cost of the acid added. agglomeration It becomes possible to know whether it would be more advantageous to apply one agent or to calculate the best compromise between the two.

[0169] Furthermore, if powdered activated carbon is added, the present prediction method can be used to predict the amount of powdered activated carbon added to achieve the organic matter removal target in the purified water. agglomeration By recalculating the amount of agent to be added, agglomeration This allows the benefit of adding CAP to the formulation to be calculated and compared to the cost of the CAP added. agglomeration It becomes possible to know whether it would be more advantageous to apply one agent or to calculate the best compromise between the two.

[0170] Furthermore, it is possible to calculate the combination of CAP addition with acid addition, and the calculation of the economic profit (or loss) when CAP and acid are added. By predictive methods, it is then possible to obtain the best combination of reagents available to achieve the lowest cost.

[0171] Figure 9 shows the results of the prediction method. agglomeration 1 illustrates a second embodiment of the step of determining the optimal dose of an agent. In the illustrated embodiment, the predictive method comprises the following steps: - Target turbidity value (TURB) for purified water _ED ) step 180; - Target turbidity value for purified water (TURB _ED ) is added to raw water (EB) to achieve agglomeration determining a second dose of the agent ([COAG2]) 190; - agglomeration the first dose of the drug ([COAG1]) agglomeration The second dose of the agent ([COAG2]) was added to the raw water. agglomeration Optimal dose of the drug ([COAG] OPT ), wherein the optimal dose is determined by: agglomeration the first dose of the drug ([COAG1]) agglomeration Step 200, which is the maximum dose between the first and second doses of the agent ([COAG2]) Further includes:

[0172] According to this second prediction method embodiment, the second organic parameter is preferably UV.

[0173] Figure 10 shows the second agglomeration 1 illustrates a fourth relationship that allows the dosage of an agent to be determined to achieve a turbidity value in the purified water of at least less than 5 NTU, preferably less than 3 NTU. agglomeration Give the dose of the agent.

[0174] The fourth relationship is the turbidity of the raw water (TURB EB ) and raw water temperature (TEB ) is a function of

[0175] Therefore, Figure 10 shows the amount of water required to obtain a purified water turbidity of at least less than 5 NTU, preferably less than 3 NTU. agglomeration The dosage of the agent was determined based on the turbidity of the raw water (TURB EB ) and the temperature of the raw water (T EB ) polynomial relations: - If the temperature of the source water is less than the threshold θ, the polynomial: [Number 14] y=α8x 4 +b8x 3 +c8x 2 +d8x+e8 (dashed curve); or - If the temperature of the raw water is greater than the threshold θ, the polynomial: [Number 15] y=α9x 4 +b9x 3 +c9x 2 +d9x+e9 (continuous curve) where coefficients a8 and a9 are different, and / or coefficients b8 and b9 are different, and / or coefficients c8 and c9 are different, and / or coefficients d8 and d9 are different, and / or coefficients e8 and e9 are different.

[0176] Instead, purified water turbidity is required to give agglomeration The dosage is calculated using the logarithmic formula: [Number 16] y=A×(Inx) C +B where A represents the overall amplitude of the reaction, B is a coefficient that can be adjusted by the temperature of the water, and C is a coefficient that allows adjustment of the collapse of the curve at high turbidity. can be given by

[0177] This formula may be more accurate insofar as it avoids edge effects that cause oscillations that appear when polynomials are used.

[0178] agglomeration A second dose of agent is required to remove turbidity by measuring the turbidity of the raw water using an in-line turbidity sensor and measuring the temperature using a temperature sensor. agglomeration This is obtained by using the function defined above to determine the dose of the agent.

[0179] Figure 11 is a graph calculated to give the target organic matter (expressed as UV) in purified water. agglomeration The first dose of agent [COAG1] was calculated to reduce the turbidity target for the purified water. agglomeration 1 illustrates a second embodiment of the prediction method for a case where the second dose of the agent [COAG2] is greater than [COAG3]. agglomeration The optimal dosage of the agent allows for the reduction of both organic matter and turbidity according to the set goals. agglomeration This is the first dose of the agent.

[0180] According to the present invention, for a raw water treatment process, an in-line sensor or an in-situ measurement allows measuring at least one organic parameter of the water in order to determine the amount of organic matter contained in the raw water and in the purified water, or in the water at different levels of the treatment process. Such a sensor or in-situ measurement can advantageously monitor the quality of the water in-line along the entire water treatment process. agglomeration The optimal dose of the agent is determined by an adjustment step that is performed depending on the results of these measurements. agglomeration / Adjusted during the purification treatment process.

[0181] In particular, measurements are carried out to determine the actual value of the amount of organic matter ((second) organic parameter) contained in the purified water (ED), and this actual value is compared with a target value for the amount of organic matter ((second) organic parameter) in the purified water. Preferably, multiple measurements are carried out on the organic matter in the purified water in order to determine multiple actual values ​​and compare them with the target value during the process. The purified water is generally measured continuously in the water treatment plant.

[0182] The amount of organic matter in the purified water, generally m -1 Measurement of UV absorbance at 254 nm expressed in units of agglomeration / It can be carried out in different strategic locations according to the device (reactor, basin) used to carry out the purification: - A sludge bed through which raw water passes and leaves in purified form above the bed agglomeration / For clarification basins (generally including CAPs), it is advantageous to place at least one UV sensor above the sludge bed; - In other cases, the UV sensor agglomeration at the point of mixing of agents / reagents, etc. agglomeration / It may be placed further upstream in the clarification basin.

[0183] Those skilled in the art will appreciate that agglomeration One will know how to adapt the placement of one or more UV (or DOC) sensors to optimize the relationship between agent dosage and purified water measurements that are representative of dosage.

[0184] Preferably, furthermore, a number of measurements are carried out on the amount of organic matter in the raw water over time ((second) organic parameter) in order to determine the temporal variation of the amount of organic matter in said raw water, because, according to one advantageous embodiment, in the case of variations exceeding a defined threshold, no adjustment step is involved; agglomeration The dose of the drug was determined by the predictive method. agglomeration The main goal is to maintain the optimum dosage of the agent. Raw water is typically continuously measured at the water treatment plant.

[0185] Finally, preferably at least one measurement is made of the pH of the purified water.

[0186] agglomeration The agent is advantageously injected by a pump of the metering type connected to the regulating loop. The same applies for other reagents (e.g. CAP) connected to the loop.

[0187] agglomerationAdjusting the dosage of the drug (retrospective method) In the following description, the organic parameter P allows the characterization of the amount of organic matter (MO) in purified or raw water. ORG will be referred to as m in the remainder of this description. -1 The UV absorbance at 254 nm is expressed in units and denoted by "UV". The adjustment steps are therefore described using UV. The same steps described could alternatively be described by replacing UV with DOC or with any other organic parameter that allows the characterization of the amount of organic matter (MO) in the water.

[0188] 12A to 24 are based on the adjustment step (adjustment loop) and / or agglomeration 1 illustrates a system for carrying out the method according to the invention according to different cases managed by the step of determining the dosage of the agent (predictive method): - EB and ED mean raw water and purified water, respectively; - FeCl3 is agglomeration means an agent; - CAP means powdered activated carbon (second reagent); - "MO dose" is the amount of organic matter (MO) that is determined to achieve a target value for purified water. agglomeration means the first dose of the agent (in ppm); MO is measured by UV absorbance or "UV"; - "Turbidity Dose" is the amount of turbidity that is determined to achieve a target turbidity value for purified water. agglomeration means the second dose of the agent (in ppm); "PID" means the regulation loop used in the regulation step.

[0189] agglomeration the first dose of the agent [COAG1] or agglomeration The maximum second dose of the drug [COAG2] is agglomeration This allows the optimal dose of the agent to be determined. agglomerationThe optimal dose of an agent generally corresponds to the maximal dose of COAG1, as illustrated in Figure 12A (where [COAG1] is greater than [COAG2]). agglomeration The first dose of agent (i.e., as determined to achieve the target MO value for the purified water) agglomeration (dose of drug).

[0190] Figures 12A and 12B agglomeration Only the first dose of the agent (determined by the MO) is utilized for the regulation loop; agglomeration A preferred embodiment is illustrated in which a second dose of agent (determined by turbidity) is not utilized, as it is observed that if [COAG2] is greater than [COAG1], the regulation loop will not be performed.

[0191] According to the example illustrated in Figures 12A, 12B and the following figures, the measurement of organic matter in the purified water (measured by UV absorbance) serves as a control parameter. The measurement of MO in the purified water is in fact agglomeration Injection of agent ( agglomeration This is directly linked to the formulation, amount, and type of agent used. agglomeration Conversely, the turbidity of purified water is the result of more variable factors, such as hydraulic conditions, the type and geometry of the settling device, which also affect the turbidity of the water exiting the settling device. Turbidity is therefore agglomeration As described herein below, other control parameters may be added, such as the time variation of MO in the raw water and / or the pH of the purified water.

[0192] In Figures 13-24, the function of the prediction method (the UV of the resulting water in ppm) is agglomeration The function f (which relates the amount of agent to the amount of agent) is framed and centered. The function can be, for example, a function f as described above. i (f EB ) (Equation 2 and Equation 12). This function is agglomerationIt allows the optimal dosage of the agent to be defined with respect to a target MO value in the purified water. MO is the control parameter. The curve placed in the frame to the left of the prediction function shows the evolution over time of the MO of the raw water (EB) measured by UV absorbance. The curve in the frame to the right shows the evolution over time of the MO of the purified water (ED) measured by UV absorbance. If this curve is in a bold frame, this means that the regulation loop (PID) is in operation. If the prediction function is in a bold frame, this means that agglomeration This means that the dose of the drug is determined by a predictive method.

[0193] Figure 13 shows that when the raw water does not exhibit high variations in MO over time, and the MO measured in the purified water is agglomeration Optimal dose of COAG OPT A lower threshold (S) associated with the target value is used in the forecasting method to determine INF ) and upper threshold (S SUP ) is shown below. agglomeration The optimal dose of the agent is therefore maintained, and if the second dose is greater than the first dose, agglomeration the first dose of the agent [COAG1] or agglomeration It may be a second dose of the agent [COAG2]. In other words, no regulatory loop is involved.

[0194] Lower threshold (S INF ) example is -0.2m -1 and the upper threshold (S SUP ) example is 0.2m -1 The target UV value in the purified water is 2-5m. -1 may vary between

[0195] Figure 14 shows that the raw water exhibits high fluctuations in MO over time (defined limits (L VAR ) and the measured MO in the purified water is related to the upper threshold (S SUP ) is larger than the time variability. Because of this high time variability, forecasting methods are agglomerationThe optimal dosage of the agent is thus maintained. No regulation loop is involved. The reason for this is that regulation, or more specifically, the agglomeration The effect of adjusting the dose of the agent is generally not rapid enough in practice to allow optimal control.

[0196] Fluctuation limit (L VAR ) is 0.1 m per minute -1 and this variation may be measured over a 10 minute period.

[0197] Figure 15 shows that the raw water exhibits small fluctuations in MO over time (VAR EB )(Defined limit (L VAR ) and the measured MO in the purified water is lower than the upper threshold (S SUP ) is larger than the regulation loop. agglomeration Increase the dose of the drug. agglomeration The dose of the agent may be increased up to a given value, for example the value of DMEA defined earlier above. DMEA may be calculated using the third relationship defined in connection with Figure 6. As soon as the value of DMEA is exceeded, the regulation loop may issue a first level (Lev 1) alarm, which is simply informational to the operator. Meanwhile, agglomeration If the dose of agent exceeds DMEA by a value X defined by the operator, a second level (Lev 2) alarm can trigger an intervention - for example, introduction of a backup agent. The regulatory loop also agglomeration Addition of the agent may be blocked.

[0198] As illustrated in Figure 16, the MO measured in the purified water was above the upper threshold (S SUP ) even if it falls below the lower threshold (S INF ) (not shown), the regulation loop agglomeration The dosage of the agent may be reduced, but this dosage is determined to achieve the turbidity target of the purified water. agglomerationThe second dose of the agent [COAG2] remains greater than the second dose of the agent [COAG2]. agglomeration The dosage of the drug is agglomeration Optimal dose of COAG OPT If it falls below a value Y determined by the minus operator, a first level (Lev 1) alarm may be issued, which is information to the operator.

[0199] Furthermore, the pH of the purified water ED is preferably measured. The reason for this is that agglomeration The addition of agents can lower the pH of the purified water and therefore the pH throughout the water treatment process, which can affect the quality of the treated water. The regulation loop therefore ED is the threshold pH min If it becomes less than agglomeration Addition of the agent may be blocked. This is illustrated in Figure 17. A second level (Lev 2) alarm may trigger intervention - for example, introduction of a backup agent.

[0200] As illustrated in Figure 18, the regulation loop is designed to minimize the time variability of the MO in the raw water. EB )(Defined limit (L VAR ) is greater than the upper threshold (S SUP ) is stopped. agglomeration The drug dose was therefore again determined by predictive methods. agglomeration Optimal dose of COAG OPT becomes.

[0201] Figure 19 shows agglomeration An example is given where the dose of agent exceeds DMEA by an operator-defined value X, and a second level (Lev 2) alarm can trigger an intervention - for example, introduction of a backup agent. agglomeration The dose of the agent may be further increased or may then be blocked, according to the operator's decision.

[0202] Figure 20 shows that when the dose of CAP is added or the dose of CAP is increased, agglomeration This is exemplified by the case where there is no further increase in the dosage of the agent FeCl3, for example, if the value X determined earlier on DMEA or DMEA plus is reached, or if the pH of the purified water is ED The threshold pH is determined min The dose of CAP can be used when the dose is less than CAP. max can be increased to

[0203] The measured MO in the purified water, due to the addition of CAP for example, falls below an upper threshold (S SUP ), the regulation loop is maintained, as illustrated in Figure 21. The dose of CAP can be reduced again.

[0204] On the other hand, if the raw water has high time fluctuations in MO (VAR EB )(Defined limit (L VAR ) and even if the measured MO in the purified water is still above the upper threshold (S SUP ), if so, the regulation loop is stopped, as illustrated in Figure 22. Due to this high temporal variability, the predictive approach dominates, agglomeration The optimal dose of the agent is therefore maintained.

[0205] Conversely, if the raw water does not show high temporal variations in MO and the MO measured in the purified water is above the upper threshold (S SUP ), the regulation loop is maintained and the CAP dose is adjusted to a maximum value, preferably an operator-defined CAP, as illustrated in FIG. max The loop may issue a first level (Lev 1) alarm, which is simply informational to the operator, or even a second level (Lev 2) alarm with operator intervention, if the CAP dose exceeds this maximum.

[0206] The measured MO in the purified water is related to the target value used in the prediction method. INF ), the regulation loop adjusts the dose of CAP and / or agglomeration The regulating loop reduces the CAP dose until it reaches zero (at which point the regulating loop issues an alarm at the first level, Lev1), after which the regulating loop agglomeration A case where the dosage of the agent is reduced is illustrated in Figure 24. It means that this may be the case where the measured MO in the purified water rises again, and this case is then one that is managed by the regulatory loop as described above.

[0207] From measuring the UV of the purified water, measuring and / or calculating the variation in UV of the raw water, from calculating the difference between the UV of the purified water and the target UV of the purified water, the pH of the purified water ED Starting from the measurement of, and agglomeration Starting from a determined optimal dose of an agent, examples of adjustment steps are as follows: - UV fluctuation in raw water VAR EB is the limit L VAR If (and as long as) the adjustment step is not engaged or stopped, then agglomeration The dose of the drug was determined by the predictive method. agglomeration The optimal dose of the agent; - Measured UV ED and target UV ED The difference between INF and the upper threshold S SUP If the value is between , no adjustment step is involved and the value is injected. agglomeration The dose of the drug is optimal as determined by the predictive method. agglomeration dosage; - Measured UV ED and target UV ED The difference between SUP greater than or equal to the lower threshold S INF If the UV fluctuation of the raw water is less than VAR EB is the limit L VARIf it is less than 1, then an adjustment step is involved; - Measured UV ED and target UV ED The difference between SUP If it is greater than - agglomeration The dose of the agent [COAG] is agglomeration Maximum dose of the drug [COAG] max and / or the pH of the purified water ED The threshold pH is determined min and / or the difference is less than the upper threshold S SUP and / or - The dose of CAP (or other secondary reagent) is determined based on the maximum dose of CAP. max and / or the difference is less than the upper threshold S SUP is increased as long as it is greater than - Measured UV ED and target UV ED The difference between INF If it is less than The dose of CAP (or other second reagent) is increased as long as it reaches a value of zero and / or the difference is less than the lower threshold S INF and / or - agglomeration The dose of the drug [COAG] is agglomeration Dosage [COAG] min and / or the difference is greater than the lower threshold S INF It can be lowered as long as it is less than

[0208] If a reconciliation step is involved, agglomeration The dosage of the agent is therefore determined by said adjustment step, except in the case of fluctuations in the UV of the raw water beyond defined limits or in the case of operator intervention, agglomeration The adjustment step is not determined by determining the optimal dose of the agent (and similarly for the dose of CAP). However, the adjustment step is determined by the measured UV ED and target UV EDThe difference between the lower threshold S and the lower threshold S after N hours (N is determined by the operator, e.g., 6 to 24 hours) INF and the upper threshold S SUP This may include defining a time lag between when and when to hand control back to the predictive model.

[0209] Furthermore, the adjustment step has the following characteristics: - agglomeration The maximum dose of the agent may be equal to DMEA or DMES plus a value X defined by the operator; - agglomeration The minimum dosage of the agent is determined to achieve the target turbidity in the purified water. agglomeration to the second dose of the agent [COAG2], while remaining above [COAG2], agglomeration may be equal to the first dose of the agent [COAG1] minus a value equal to the value Y defined by the operator; - agglomeration The dose of the agent may be preferentially increased, and then the dose of CAP (because CAP is generally more expensive and agglomeration The maximum dosage of the agent has been reached, or the pH of the purified water is below the threshold. agglomeration (taking into account the fact that it may be used only when it is no longer possible to add the agent) can then be added or increased if necessary; - The dose of CAP may be preferentially reduced, and then agglomeration The dose of the agent can be reduced in stages if necessary (to take into account the fact that CAP is generally more expensive); - CAP can be a separate second reagent, preferably one that does not cause a decrease in the pH of the purified water; - The adjustment steps are agglomeration It may include one or more alarms at different levels (informational or planned interventions), in case of exceeding a threshold for the dosage of the agent or of the second reagent, in case of exceeding the pH, in case of high fluctuations in the MO of the raw water, etc. may include some or all of the above.

[0210] When the adjustment step is activated, it is performed for only one reagent at a time ( agglomeration The predictive method then controls the amount of the first reagent or second reagent, typically CAP. As a result, the predictive method remains active, and it continues to perform, among other things, a monitoring function to define the extent of the control and which reagent is adjusted by the control.

[0211] this is, agglomeration A simplified flow diagram of a system for carrying out one particular embodiment of the method for dose determination and adjustment of an agent, and optionally a second reagent, is illustrated in FIG.

[0212] The system first determines the fluctuations in the measurement of organic matter (MO) in the raw water. In the case of fast fluctuations, agglomeration The dosage of the agent is determined by a predictive method, in other words, the regulatory loop (BR) is not activated. A fluctuation is considered rapid if, within a given time interval, the measured organic matter value increases or decreases by more than a predetermined value.

[0213] On the other hand, if the measured organic matter (MO) in the raw water fluctuates slowly, the system then determines whether the measured MO in the purified water is between the defined upper and lower thresholds. If this is the case, the regulation loop (BR) will not operate. agglomeration The dosage of the agent is determined by predictive methods.

[0214] On the other hand, if the measured MO in the purified water is not between the defined upper and lower thresholds, then the regulating loop (BR) will adjust the MO as a function of the measured MO in the raw water, specifically according to whether the MO in the raw water is in the "low" range or the "high" range. agglomeration The agent acts on either the activator or a second reagent, in this case CAP.

[0215] If the measured MO of the raw water is in the low range, the regulation loop (BR) agglomeration The dose of CAP is zero.

[0216] The measured MO of the raw water was in the high range, and the CAP and agglomeration When an agent needs to be added, the regulating loop (BR) acts on the CAP, agglomeration The dosage of the agent is determined by predictive methods.

[0217] If the measured MO in the purified water falls back below the upper threshold, and even below the lower threshold, the regulation loop acts preferentially on the CAP to reduce its dosage, and then optionally (if the dosage of the CAP is reduced to zero) to reduce said dosage unless its value rises above the upper threshold again. agglomeration It should be pointed out that the effect depends on the dose of the agent. agglomeration The dosage of the agent is preferably determined by predictive techniques to achieve a target turbidity in the purified water. agglomeration The dose of the agent is reduced to remain above the second dose.

[0218] The threshold MO in the source water that separates the high and low ranges can be parameterized. It can be defined, in particular, as a function of DMEA.

[0219] Therefore, in one particular embodiment, the prediction method periodically or even continuously calculates DMEA. DMEA is calculated as a function of the amount of MO in the raw water, for example, as the UV of the raw water, which can be calculated by one of the methods described above. As a reminder, DMEA is calculated for a given amount of MO in the raw water, beyond which agglomeration It becomes more economically advantageous to add CAP rather than continue providing the agent. agglomeration It is defined as the maximum dose of the agent.

[0220] As suggested by the regulation loop agglomeration Two configurations are possible depending on whether the dose of agent is less than or greater than DMEA.

[0221] As suggested by the regulation loop agglomeration When the dose of the agent is less than DMEA, the system is in the first configuration.

[0222] In this first configuration, agglomeration Only the agent is needed, and the regulation loop is agglomeration Furthermore, this adjustment agglomeration Dosage of agent needed to achieve target turbidity in purified water agglomeration It remains limited on the lower end by a threshold corresponding to the second dose of the agent, which threshold is determined solely by the predictive methodology.

[0223] The regulation loop continues until DMEA is achieved. agglomeration If the dose of the agent is to be increased, the system transitions to the second configuration.

[0224] In this second configuration, the dose of CAP and agglomeration Agents are added and a regulating loop controls the CAP to achieve the target in terms of MO in the purified water. agglomeration The dose of the agent is adjusted to DMEA by a predictive method, and when the adjustment loop reduces the dose of CAP until a dose of zero CAP is reached, the system returns to the first configuration again.

[0225] In other words, even when the regulation loop is activated, the predictive method is always active, since the regulation loop allows, among other things, the transition from the first configuration to the second configuration (and vice versa). The reason for this is that, as shown, the predictive method agglomeration It is necessary to determine the dosage of the agent (DMEA) and then add CAP on top of it. agglomeration Acting on either the agent or the CAP results in a change in regulation.

[0226] The adjustment step may be, for example, if one (or more) alarms: - Corrected by adjustment agglomeration Dosage and agglomeration The absolute difference between the optimal dose of the agent and the COAG if greater than; - The absolute difference between the dose of CAP corrected by the adjustment and the optimal dose of CAP is the value Δ CAP If greater than It may be incorporated into one or more of:

[0227] The alarm will alert the operator: - Status of sensors (UV, DOC) measuring MO in purified and / or raw water; - mode of preparation of CAP; - Raw water quality or any other parameter that may affect or even distort the adjustment step. can be verified.

[0228] The various embodiments, variations and implementations shown in this detailed description may be combined with each other (unless otherwise stated or clearly contradictory).

[0229] The invention is further not limited to the embodiments described above, but extends to any embodiment falling within the scope of the claims.

Claims

1. 1. A method for determining and adjusting the dosage of a coagulant (COAG) to be injected into a means for coagulation treatment of raw water (EB) to give purified water (ED), comprising: - Optimal dosage of coagulant ([COAG] ＯＰＴ ) wherein said determining step comprises: a) a first organic parameter (P ＯＲＧ１ ) value (P ＯＲＧ１＿ＥＢ ) and at least one mineral parameter (P ＭＩＮ ) value (P ＭＩＮ＿ＥＢ ) for the raw water (EB); b) the value of the first organic parameter and the mineral parameter determined for the raw water (EB) (P ＯＲＧ１＿ＥＢ , P ＭＩＮ＿ＥＢ ) as a function of water class (CL ＥＢ ) for said raw water (EB), wherein said water class (CL ＥＢ ) is the first organic parameter (P ＯＲＧ１ ) and a first range of values ​​of the mineral parameter (P ＭＩＮ ) a second range of values ​​for c) a second organic parameter (P ＯＲＧ２ ) value (P ＯＲＧ２＿ＥＢ ) for said raw water (EB); d) measuring the second organic parameter (P ＯＲＧ２ ) target value (P ＯＲＧ２＿ＥＤ ) e) the second organic parameter (P ＯＲＧ２ ) and the dosage of coagulant ([COAG]) added to the raw water (EB). ｉ ), wherein the function (f ｉ ) is the water class (CL) determined for the raw water (EB). ＥＢ ) and the second organic parameter (P ＯＲＧ２ ) value (P ＯＲＧ２＿ＥＢ ) selected steps; f) the second organic parameter (P ＯＲＧ２ ) target value (P ＯＲＧ２＿ＥＤ ) is a function (f ｉ ) wherein the first dose of flocculant is an optimal dose of flocculant ([COAG] ＯＰＴ ) and and - the second organic parameter (P ＯＲＧ２ ) measuring the actual value of - the second organic parameter (P ＯＲＧ２ ) and the target value (P ＯＲＧ２＿ＥＤ ) determining the difference between This difference is the lower threshold (S ＩＮＦ ) or is less than the upper threshold (S ＳＵＰ ) is greater than - adjusting the dose of coagulant (COAG) injected into said treatment means, said adjusting step being carried out in accordance with said determined optimum dose of coagulant ([COAG] ＯＰＴ ) and - the second organic parameter (P ＯＲＧ２ ) and the target value (P ＯＲＧ２＿ＥＤ ) is the upper threshold value (S ＳＵＰ ) increasing the dosage of the flocculating agent; or - the second organic parameter (P ＯＲＧ２ ) and the target value (P ＯＲＧ２＿ＥＤ ) is the lower threshold (S ＩＮＦ ) reducing the dosage of the flocculant if and Including, the first organic parameter (P ORG1 ) comprises the ratio between UV absorbance at 254 nm and dissolved organic carbon (DOC); the mineral parameters (P MIN ) include at least the total alkaline titration capacity (TAC), the concentration of chloride ions, and / or the concentration of sodium ions; The second organic parameter (P ORG2 ) is UV absorbance at 254 nm or dissolved organic carbon (DOC); method.

2. The second organic parameter (P ＯＲＧ２＿ＥＢ ) time variation (VAR) ＥＢ ) within which the fluctuation is determined, ＶＡＲ ), the adjustment step is blocked and the dose of coagulant to be injected is then equal to the optimal dose of coagulant ([COAG] ＯＰＴ 2. The method of claim 1, further comprising the step of:

3. 3. The method of claim 1, wherein the adjusting step uses a closed control loop.

4. The pH of the purified water (pH ＥＤ ), and the adjusting step further comprises measuring the pH of the purified water to determine whether the pH is below a pH threshold (pH ｍｉｎ 4. The method of claim 1, further comprising blocking the increase in the dose of the flocculating agent if the flocculating agent is less than 0.5%.

5. - target turbidity value (TURB) for the purified water (ED) ＿ＥＤ ) and - the target turbidity value (TURB) for the purified water (ED) ＿ＥＤ determining a second dose ([COAG2]) of flocculant to be added to the raw water (EB) to achieve a turbidity (TURB) of the raw water (EB); ＥＢ ) and temperature (T ＥＢ ) and measuring the turbidity (TURB) of the raw water (EB). ＥＢ ) and temperature (T ＥＢ ) to the target turbidity value (TURB ＿ＥＤ determining a second dose of flocculant ([COAG2]) using a relationship linking the doses of flocculant required to obtain the desired flocculant concentration; the dosage of the flocculant after said step of reducing the dosage of the flocculant is equal to or greater than said second dosage of the flocculant ([COAG2]); The method of any one of claims 1 to 4, further comprising:

6. A computer program product comprising program code instructions for carrying out the steps of the method according to any one of claims 1 to 5, said program running on a computer.

7. A system for determining and adjusting the dosage of a flocculant (COAG) and, if used, at least a second reagent, to be injected into a means for flocculation treatment of raw water (EB) to give purified water (ED), using the method according to any one of claims 1 to 5.

8. A raw water treatment process comprising at least a step of flocculating the raw water, wherein the dosage of coagulant to be injected is the dosage of coagulant determined and adjusted by a method selected from any one of claims 1 to 5.

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