Method for controlling a wastewater treatment plant and associated wastewater treatment plant

Abstract

The present invention relates to a method for controlling a wastewater treatment plant comprising a primary treatment system and a secondary treatment system, the method comprising the following steps: - measuring (110), in the primary treatment system, at least one first-type parameter characterising the treated water; - determining (120) at least one second-type parameter from the or each measured first-type parameter, the or each second-type parameter characterising the organic matter content of the treated water; - controlling (130) the carbon content in the secondary treatment system as a function of the or each second-type parameter.

Description

[0001] TITLE: Method for controlling a wastewater treatment plant and associated wastewater treatment plant

[0002] The present invention relates to a method for controlling a wastewater treatment plant.

[0003] The present invention also relates to a wastewater treatment plant implementing such a process.

[0004] As is well known, wastewater treatment takes place at several levels, the implementation of which is generally governed by the regulations in force.

[0005] Typically, in the first stage of treatment, wastewater is placed in a primary clarifier. Such a clarifier consists of a tank in which heavy solids settle to the bottom, while lighter materials such as colloids and grease rise to the surface. The material that has settled at the bottom of the tank can then be removed and is called primary sludge. The material on the surface can be removed directly. The clarified water then proceeds to a second stage of treatment.

[0006] In this second level, materials containing nitrogen and / or carbon and / or phosphorus are removed using biological treatment by bacteria.

[0007] Finally, in a third treatment stage, the treated water undergoes further filtration before being discharged into a sensitive ecosystem. This final treatment may include phosphorus removal, filtration, disinfection, and the removal of micropollutants.

[0008] The best-known biological process for removing nitrogen from wastewater in the second treatment stage involves the implementation of successive nitrification / denitrification steps.

[0009] Indeed, the nitrification stage of this process involves the oxidation of ammonia to nitrite, and then the oxidation of nitrite to nitrate. This stage is carried out by bacteria under aerobic conditions, meaning with access to oxygen. Therefore, sufficient aeration is necessary for this nitrification stage to occur.

[0010] The denitrification stage involves the reduction of nitrates to nitrite, and then the reduction of nitrite to dinitrogen, a gas that can be released into the atmosphere. This stage is also carried out by bacteria, but under anoxic conditions, meaning without access to oxygen. Depending on current regulations, the total permissible nitrogen discharge limit imposes significant limitations on the structure and design of wastewater treatment plants. Lowering this permissible limit results in a longer and more expensive nitrification stage. Furthermore, achieving low nitrogen discharge limits is difficult using only a biological process. Therefore, in most cases, the addition of carbon from an external source is necessary to implement the denitrification stage.

[0011] In the current state of the art, most methods for controlling wastewater treatment plants focus on controlling aeration during the nitrification stage. Aeration can thus account for up to 25% of the operating costs of such plants. However, this control is not optimal, and often, excess carbon is used to implement the denitrification stage. This leads to excessive operating costs for current wastewater treatment plants. These costs are primarily due to the suboptimal use of resources.

[0012] The present invention aims to solve this problem and to provide means of controlling a wastewater treatment plant that optimize resource consumption by such a plant, while respecting the permitted discharge limits for nitrogen and phosphorus. The invention thus makes it possible to optimize the operating costs of such a wastewater treatment plant.

[0013] To this end, the invention relates to a method for controlling a wastewater treatment plant comprising a primary treatment system and a secondary treatment system, the primary treatment system comprising a primary clarifier configured to separate primary sludge from treated water, the secondary treatment system comprising a nitrification device and a denitrification device for treated water.

[0014] The process includes the following steps:

[0015] - measurement in the primary treatment system of at least one first-type parameter characterizing the treated water;

[0016] - determination of at least one second type parameter from the measured first type parameter(s), the second type parameter(s) characterizing the organic matter content of the treated water;

[0017] - control of the carbon content in the secondary treatment system as a function of the second type parameter(s).

[0018] Advantageously, the second parameter(s) also characterize the concentration of ammonia nitrogen and phosphorus. Thanks to these characteristics, the invention makes it possible to observe the wastewater parameters characterizing the organic matter content and to adjust the carbon content in the secondary treatment system according to these parameters. This adjustment can be made practically in real time, thus optimizing the amount of carbon required, in particular for the denitrification stage in the wastewater treatment plant.

[0019] Unlike prior art methods, which either fail to observe the parameters characterizing the organic matter content of wastewater or require expensive, specific sensors with limited accuracy and often burdensome maintenance, the invention enables the determination of such parameters practically in real time using other types of parameters characterizing treated water, particularly physicochemical parameters, which are more readily observable in a wastewater treatment plant, especially using routine sensors that are simple to use, maintain, and inexpensive. Furthermore, by determining the organic matter content of wastewater practically in real time from the measured parameters, it is possible to optimally adjust the amount of carbon required to implement the denitrification process.

[0020] In the state of the art, specific sensors allow the measurement of parameters characterizing the organic matter content of wastewater, such as chemical oxygen demand (COD), solids, total suspended solids concentration (TSS), or ammonia nitrogen concentration (NH4). +These sensors were developed under laboratory conditions, and to date, their application to real-world, on-site, and real-time conditions has not been possible within acceptable cost, accuracy, and maintenance requirements. Therefore, the current state of the art does not allow for the characterization of the organic matter content of treated water at any given moment in situ within an industrial wastewater treatment plant, and thus does not enable the real-time adaptation of the plant's operation. Specific sensors developed in the laboratory, based on the prior art, are of very limited use in such a context.

[0021] It should be noted in particular that direct measurements of parameters characterizing the organic matter content of wastewater, for example in a sewer pipe, are generally not possible due to the complexity of the sensors required (e.g., UV sensors). Thus, only laboratory measurements of such parameters are generally available. In some embodiments, the step of determining the second parameter(s) is carried out with a repetition frequency of less than 1 hour, advantageously less than 10 minutes, and preferably less than 5 minutes.

[0022] According to some embodiments, the carbon content control step is implemented with substantially the same repetition frequency as the step for determining the second type parameter(s).

[0023] Thanks to these characteristics, it is possible to obtain measurements characterizing the organic matter content of treated water as quickly as possible and therefore to adapt the operation of the wastewater treatment plant practically in real time.

[0024] According to some embodiments, the parameter or parameters of the first type are chosen from the group comprising the following elements characterizing treated water:

[0025] - pH;

[0026] - oxygen content;

[0027] - oxidation-reduction potential;

[0028] - turbidity;

[0029] - electrical conductivity;

[0030] - temperature ;

[0031] - Speed.

[0032] The aforementioned parameters of the first type are easily measurable in real time using simple, inexpensive sensors that are easy to install on a standard treatment plant. These sensors, used alone or in combination as appropriate, allow for the indirect determination of one or more parameters of the second type, characterizing the organic matter content of treated water in situ and in real time. Furthermore, these sensors can easily be used to characterize wastewater.

[0033] According to some embodiments, the parameter or parameters of the second type are chosen from the group comprising the following elements characterizing the organic matter content of the treated water:

[0034] - concentration of suspended matter;

[0035] - biochemical oxygen demand (BOD);

[0036] - chemical oxygen demand (COD).

[0037] Optionally, at least one parameter of the second type further includes at least one parameter chosen from the concentration of ammoniacal nitrogen (N-NH4 + ) and the total phosphorus concentration (P-PO4).

[0038] Thanks to these characteristics, it is possible to accurately characterize the organic matter content of treated water using sensors that measure only the first type of parameters. The parameters of the second type are known in themselves but can currently only be measured directly under laboratory conditions or using complex sensors. As described previously, sensors for directly measuring second-type parameters are generally expensive, difficult to maintain, have limited accuracy, and are difficult to adapt for use in wastewater treatment plants.

[0039] In a particular embodiment, the measurement of at least one first-type parameter characterizing the treated water includes the measurement of an electrical conductivity of the treated water and the measurement of a temperature of the treated water, and optionally the measurement of one or more other first-type parameters.

[0040] In a particular embodiment, the measurement of at least one first-type parameter characterizing the treated water includes the measurement of the electrical conductivity of the treated water, the measurement of the temperature of the treated water and the measurement of the turbidity of the treated water, and optionally the measurement of one or more other first-type parameters.

[0041] According to some embodiments, the measurement of at least one first-type parameter is carried out upstream and / or downstream of the primary clarifier.

[0042] It should be noted that in some cases, all the first-type parameters are measured only upstream, and in other cases, all of these parameters are measured only downstream of the primary clarifier. In still other cases, at least some of these parameters are measured upstream and at least some other parameters are measured downstream of the primary clarifier.

[0043] This may, for example, depend on the nature of the first-type parameter being measured. For instance, in some cases, it is advantageous to measure the turbidity and temperature of the treated water upstream of the primary clarifier, if applicable, and to measure any other first-type parameters downstream of it.

[0044] According to some embodiments, the determination of the parameter or each parameter of the second type from the parameter or parameters of the first type measured is carried out using a mathematical model, preferably the mathematical model being determined by a machine learning technique and / or by a statistical technique and / or by a mechanistic technique, that is to say by using explicit mathematical equations, in particular by possibly using after adaptation mathematical equations known to the person skilled in the art.

[0045] Thanks to these characteristics, it is possible to establish links between the measurable first-type parameters in wastewater treatment plants and the second-type parameters, which are difficult to measure in such plants. The mathematical model can be determined using one of the aforementioned techniques or a combination thereof. Furthermore, the nature of the mathematical model can be chosen, for example, based on the nature of the measured first-type parameters.

[0046] According to some embodiments, the control of the carbon content in the secondary treatment system is achieved by controlling a redirection of the primary sludge into the secondary treatment system.

[0047] Thanks to these characteristics, it is possible to utilize the carbon naturally present in the primary sludge. This primary sludge can be redirected to the secondary treatment system via a dedicated conduit, for example, between the primary sludge outlet of the primary clarifier and the inlet of the secondary treatment system. Redirection control can be achieved, for instance, using a valve and a pump to regulate the flow rate of the fluid through this conduit.

[0048] Thus, in such a case, the need for a chemical carbon source can be minimized.

[0049] According to some embodiments, the control of the carbon content in the secondary treatment system is carried out by controlling the addition of carbon from an external source into the secondary treatment system, preferably into the denitrification device.

[0050] Thanks to these features, carbon content can be easily controlled by adjusting the addition of carbon from an external source. This requires minimal modifications to existing wastewater treatment plants, as such an addition is generally already factored into the design of these facilities.

[0051] Furthermore, it is advantageous to make such an addition directly into the denitrification device because this is the main point of carbon consumption.

[0052] According to some embodiments, the control of the carbon content in the secondary treatment system is achieved by controlling an internal recirculation between the denitrification device and the nitrification device.

[0053] Thanks to these characteristics, it is possible to reuse the biological carbon naturally contained in the internal recirculation flow between the denitrification device and the nitrification device.

[0054] It is therefore possible to minimize the need for carbon input from an external source.

[0055] In some embodiments, the carbon content in the secondary treatment system is controlled by controlling chemical reagents in the primary clarifier. This allows for influencing the production of primary sludge in the primary clarifier, thereby also controlling the carbon content in the secondary treatment system.

[0056] It should be noted that all the aforementioned techniques for controlling carbon content in the secondary treatment system can be combined with each other in any technically possible combination.

[0057] It should also be noted that the implementation of these control techniques or a combination of these techniques can be chosen according to the nature of the available measured parameters or according to the values ​​of at least some of these parameters or according to the values ​​of second type parameters.

[0058] The invention also relates to a wastewater treatment plant comprising a primary treatment system and a secondary treatment system, the primary treatment system comprising a primary clarifier configured to separate primary sludge from the treated water, the secondary treatment system comprising a nitrification device and a denitrification device for the treated water; the treatment plant further comprising:

[0059] - a measurement module in the primary treatment system for at least one first-type parameter characterizing the treated water;

[0060] - a first processing module configured to determine at least one second type parameter from the measured first type parameter(s), the second type parameter(s) characterizing the organic matter content of the treated water;

[0061] - means of controlling the carbon content in the secondary treatment system as a function of the second type parameter(s).

[0062] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0063] - Figures 1 to 3 are schematic views illustrating a wastewater treatment plant including means for controlling carbon content according to the first, second, and third embodiments respectively;

[0064] - Figure 4 is a flowchart of a control method according to the invention, the method being implemented by the processing installation of one of the preceding figures; and

[0065] - Figures 5 to 8 present measurement results for parameters of the second type, namely respectively chemical oxygen demand COD, suspended solids concentration TSS, ammonia nitrogen N-NH4 + and chemical oxygen demand COD, carried out on raw and / or settled wastewater using the treatment module 81 of Figure 1, prior art sensors installed in the treatment plant 81 of Figure 1 and prior art sensors installed in the laboratory.

[0066] Figure 1 illustrates a wastewater treatment plant 10 according to the invention.

[0067] Such a facility 10 can be used to treat wastewater from a wastewater collection system extending across a predetermined geographical area, such as a city, a conurbation, or a group of municipalities. The treated wastewater can therefore include municipal wastewater as well as industrial wastewater.

[0068] With reference to Figure 1, the treatment plant 10 comprises a primary treatment system 21 connected to the wastewater collection system, a secondary treatment system 22 connected downstream of the primary treatment system 21, a sludge treatment system 25 connected to the primary 21 and secondary 22 treatment systems, and a control system 28.

[0069] In some examples, the treatment facility 10 may also include a tertiary treatment system (not shown) connected downstream of the secondary treatment system 22.

[0070] The primary treatment system 21 includes a primary clarifier 31 and a measuring module 32.

[0071] The primary clarifier 31 is configured to separate primary sludge from the treated water, and the clarified water is then injected into the secondary treatment system 22. In some cases, the primary clarifier 31 is also configured to separate light materials, such as fats and sands, from the treated water. It can also sometimes be connected to pretreatment systems such as screens, grit chambers, and oil separators.

[0072] In particular, the primary clarifier 31 has a tank suitable for storing a predetermined volume of wastewater and includes an inlet 35, a first outlet 36 and a second outlet 37.

[0073] The inlet 35 of the primary clarifier 31 is connected to the wastewater collection system and thus allows this wastewater to be received directly into the primary clarifier 31.

[0074] The first outlet 36 of the primary clarifier 31 allows heavy solids formed by settling of the treated water in the primary clarifier 31 to be removed from the primary clarifier 31.

[0075] This first outlet 36 is, for example, located at the bottom of the tank forming the primary clarifier 31 and is connected to the sludge treatment system 25 by a discharge circuit 38. The solid matter formed by the settling of the treated water in the primary clarifier 31 is called primary sludge. The discharge circuit 38 then carries this primary sludge to the sludge treatment system 25.

[0076] The second outlet 37 of the primary clarifier 31 allows clarified water following decantation by the primary clarifier 31 to be transmitted to the secondary treatment system 22.

[0077] Advantageously, each of the outlets 36, 37 is equipped with a remotely controllable valve, for example by the control system 28. Thus, these outlets can be opened / closed according to business rules ensuring the proper functioning of the primary clarifier 31 and more generally of the primary treatment system 21.

[0078] The measurement module 32 allows for the direct measurement of at least certain parameters relating to the water treated by the primary treatment system 21.

[0079] In particular, the parameters measured by this measurement module 31 are subsequently called first type parameters.

[0080] Each parameter of the first type is preferably a physico-chemical parameter characterizing treated wastewater.

[0081] Each first-type parameter is chosen from the group comprising the following elements characterizing treated wastewater:

[0082] - pH; oxygen content; oxidation-reduction potential (ORP);

[0083] - turbidity; electrical conductivity;

[0084] - temperature; flow rate.

[0085] In a particular embodiment, the measuring module 32 is configured to measure an electrical conductivity of treated wastewater and a temperature of treated wastewater, as well as optionally one or more other parameters of the first type chosen from a turbidity, a pH, an oxygen content, an oxidation-reduction potential and / or a flow rate of treated wastewater.

[0086] In a particular embodiment, the measuring module 32 is configured to measure the electrical conductivity, temperature, and turbidity of treated wastewater, as well as optionally one or more other parameters of the first type selected from pH, oxygen content, redox potential, and / or flow rate of treated wastewater.

[0087] It should be noted that the wastewater flow reflects the rainfall upstream of the treatment facility 10, so that at least one parameter of the first type could include a characteristic parameter of this rainfall other than the treated wastewater flow.

[0088] To measure these first-type parameters, the measurement module 32 includes one or more sensors for measuring first-type parameter(s) known per se. For example, electrical conductivity is measured using a conductivity meter; pH is measured using a pH meter; turbidity is measured using a turbidimeter; ...

[0089] It is therefore understood that the parameter sensors of the first type can be chosen from off-the-shelf sensors, with a precision suitable for the precision required for the process according to the invention, easy to install, use and maintain, and of acceptable size and cost for the processing installation 10 on which the process is implemented.

[0090] In the example in Figure 1, the measuring module 32 is arranged entirely upstream of the primary clarifier 31.

[0091] This means that all the sensors of this measuring module 32 are arranged upstream of the primary clarifier 31, for example in a conduit connecting the inlet 35 of the primary clarifier 31 and the wastewater collection system.

[0092] According to another embodiment (not illustrated), the measuring module 32 is arranged entirely downstream of the primary clarifier 31.

[0093] Thus, for example, this measuring module 32 can be arranged between the second output 37 of the primary clarifier 31 and the secondary treatment system 22. This means that all the sensors of this measuring module 32 are arranged, for example, in a circuit connecting the second output 37 of the primary clarifier 31 to the secondary treatment system 22.

[0094] According to yet another embodiment (not illustrated), at least some of the parameter measurement sensors of the first type of the measurement module 32 are arranged upstream of the primary clarifier 31 and at least some other parameter measurement sensors of the first type are arranged downstream of this primary clarifier 31.

[0095] The upstream and downstream sensors can be chosen according to the nature of the primary parameter measured by these sensors. Thus, for example, sensors measuring turbidity or temperature can be placed upstream of the primary clarifier 31 and sensors measuring other parameters can be placed downstream of this primary clarifier 31.

[0096] It is also possible that several sensors measuring the same first-type parameter are arranged upstream and downstream of the primary clarifier 31.

[0097] In such a case, the measurements taken by such sensors make it possible, for example, to form an average between the measurements delivered upstream and downstream of the primary clarifier 31.

[0098] The measurement module 32 is also connected to the control system 28 and allows the measurements generated by its sensors to be delivered to this control system 28.

[0099] Advantageously, the measurement frequency of the measuring module 32 is less than one hour, advantageously less than ten minutes and preferably less than or equal to five minutes.

[0100] It is also possible that the measurements relating to at least some of the parameters are taken with different frequencies.

[0101] The secondary treatment system 22 allows for the implementation of a biological treatment process for treated water, in particular to remove nitrogen contained in this water.

[0102] To achieve this, the secondary treatment system 22 includes a phosphorus removal device 41, a denitrification device 42, a nitrification device 43 and a secondary clarifier 44.

[0103] In the example shown in Figure 1, the phosphorus removal device 41 is connected directly to the inlet of the secondary treatment system 22, the denitrification device 42 is connected downstream of the phosphorus removal device 41, and the nitrification device 43 is connected downstream of the denitrification device 42. Furthermore, the secondary clarifier 44 is connected downstream of these devices 41 to 43. These connections are made via a main circuit 45. Other types of connections and arrangements of these different devices are also possible, as will be explained in more detail later.

[0104] The phosphorus removal device 41 removes phosphorus from the water received by the secondary treatment system 22 using anaerobic bacteria. In some embodiments, the secondary treatment system 22 does not include the phosphorus removal device 41.

[0105] The nitrification device 43 allows the oxidization of ammonium (NH4 + ) in nitrate (NO3 _ ) under aerobic conditions using aerobic bacteria. The nitrification process implemented by this nitrification device 43 takes place in two stages. The first stage consists of the oxidation of ammonium ions (NH4 + ) into nitrite ions (NO₃⁻). The second step consists of the oxidation of nitrite ions (NO₃⁻) into nitrate ions (NO₃⁻). In other words, the nitrification process can be expressed by the following resulting chemical equation:

[0106] NH4 + + 2O2 -> NO3- + 2H + + H2O

[0107] The nitrification device 43, for example, features equipment to ensure the necessary oxygen (O2) supply by aeration.

[0108] The denitrification device 42 allows the conversion of nitrate ions (NO / ) into dinitrogen (N2) which can be released in gaseous form, for example into the atmosphere.

[0109] The reaction carried out by the denitrification device 42 is catalyzed by anaerobic bacteria, that is, by bacteria placed in anaerobic, advantageously anoxic, conditions. Denitrification also takes place in two stages.

[0110] The first step consists of the reduction of nitrate ions (NO / ) to nitrite ions (NO / ) by bacteria and the second step consists of the reduction of nitrite ions (NO ) to dinitrogen (N2) also by bacteria.

[0111] In other words, the denitrification process can be expressed by the following half-reaction redox equation:

[0112] 2 NO3- + 10 e- + 12 H + -> N2+ 6 H2O

[0113] The need for electrons e- is met by the addition of carbon, as will be explained in more detail later.

[0114] The denitrification device 42, for example, features a structure or equipment that is not ventilated to ensure the necessary anoxic conditions.

[0115] The circulation of treated water between the denitrification device 42 and the nitrification device 43 is recirculated by an internal recirculation circuit 46. In particular, the internal recirculation circuit 46 extends in the example of Figure 1 between the part of the main circuit 45 connecting the nitrification device 43 to the secondary clarifier 44, and the part of the main circuit 45 connecting the phosphorus removal device 41 to the denitrification device 42.

[0116] The circulation of treated water in this internal recirculation circuit 46 is controlled by a recirculation valve or pump 47 which allows the flow rate of treated water to be regulated between the internal recirculation circuit 46 and the part of the main circuit 45 connecting the nitrification device 43 to the secondary clarifier 44.

[0117] The control of this valve or recirculation pump 47 is carried out, for example, by the control system 28 according to business rules. In other embodiments, the denitrification devices 42 and nitrification devices 43 are arranged / connected differently. In particular, in some examples, the nitrification device 43 is located upstream of the denitrification device 42. In other examples, the denitrification devices 42 and nitrification devices 43 are combined within a single device. In these cases, the internal recirculation circuit 46 may not be necessary.

[0118] The secondary clarifier 44 includes a first inlet 55 connected to the nitrification device 43 (or the denitrification device 42) via the main circuit 45, a first outlet 44 connected to the sludge treatment system 25 and a second outlet 57 connected to the outlet of the secondary treatment system 22.

[0119] Just like the primary clarifier 31, the secondary clarifier 44 has a tank allowing the solid matter to be separated from the treated water.

[0120] Thus, the first outlet 56 of the secondary clarifier 44 is, for example, located at the bottom of the tank forming the secondary clarifier 44 and allows these solid materials to be evacuated from the secondary clarifier 44. These solid materials are called biological sludge.

[0121] The second outlet 57 allows clarified water to be evacuated from the secondary clarifier 44.

[0122] The first outlet 56 of the secondary clarifier 44 is connected to the sludge treatment system 25 by a discharge circuit 58.

[0123] Furthermore, the second outlet 56 of the secondary clarifier 44 is also connected to the inlet of the secondary treatment system 22 by a sludge recirculation circuit 59. The flow rate of the sludge circulating in the circuits 58 and 59 is regulated by a valve or a pump 60. The operation of this valve or pump 60 is, for example, controlled by the control system 28 according to business rules.

[0124] The sludge treatment system 25 allows the treatment of sludge from the primary treatment system 21 and the secondary treatment system 22.

[0125] In particular, as explained previously, the sludge treatment system 25 allows the recovery of primary sludge from primary clarifier 31 and biological sludge from secondary clarifier 44.

[0126] The treatment of this sludge is carried out, for example, by a suitable and well-known treatment device.

[0127] The control system 28 allows for the monitoring of the operation of the treatment plant 10 by, for example, controlling the operation of all the valves and / or pumps in this plant. This monitoring is self-explanatory and will not be described in detail hereafter. The control system 28 also allows for the monitoring of the carbon content in the secondary treatment system 22.

[0128] To do this, the control system 28 includes a first processing module 81 connected to the measuring module 32 and a second processing module 82 connected to the first processing module 81 and to means for controlling the carbon content which will be described in more detail later.

[0129] In particular, the first treatment module 81 processes the first-type parameters measured by the measurement module 32 to determine at least one second-type parameter characterizing the organic matter content of the treated water. Advantageously, the second-type parameter(s) also characterize the concentration of ammonia nitrogen and phosphorus.

[0130] In particular, the second type parameter or parameters is chosen from the group comprising the following elements characterizing the organic matter content of the treated water: the concentration of suspended solids (i.e. of total suspended solids, or TSS from the English Total Suspended Solids); the biochemical oxygen demand BOD (or BOD from the English Biological Oxygen Demand); the chemical oxygen demand COD (or COD from the English Chemical Oxygen Demand).

[0131] In some examples, the first processing module 81 also allows for the determination of at least certain variations of these parameters, such as variations of at least one, in particular two or three, parameter(s) chosen from among the BOD5 parameter corresponding to the 5-day biochemical oxygen demand (BOD5), the concentration of ammonia nitrogen (N-NH4) + ) and the total phosphorus concentration, including phosphates and orthophosphates (P-PO4).

[0132] In a particular embodiment, the first treatment module 81 is configured to determine at least two parameters of the second type chosen from suspended solids concentration TSS, biochemical oxygen demand BOD, in particular parameter BOD5, and chemical oxygen demand COD, and optionally at least one parameter of the second type chosen from ammonia nitrogen concentration (N-NH4 +) and the total phosphorus concentration (P-PO4).

[0133] In this case, it is particularly advantageous to control the treatment plant 10 by monitoring the carbon content in the secondary treatment system 22 based on at least one ratio of two of the measured parameters of the second type. In a particular embodiment, the first treatment module 81 is configured to determine the concentration of suspended solids, the biochemical oxygen demand (BOD5), in particular the parameter BOD5, and the chemical oxygen demand (COD), as well as optionally at least one parameter of the second type selected from the concentration of ammonia nitrogen (N-NH4). + ) and the total phosphorus concentration (P-PO4).

[0134] In this case, it is particularly advantageous to control the treatment installation 10 by controlling the carbon content in the secondary treatment system 22 as a function of a pair comprising a ratio of two of the measured second type parameters and the third measured second type parameter.

[0135] To determine for at least one of the second type parameters and possibly their variations, the first processing module 81 is capable of implementing a mathematical model which then links the or each first type parameter to this or these second type parameter(s).

[0136] Advantageously, the mathematical model is determined by a machine learning technique and / or by a statistical technique and / or by a mechanistic technique.

[0137] To determine this mathematical model, for example by a machine learning technique, it is possible to use a first database containing measurements of first-type parameters and a second database containing measurements of second-type parameters.

[0138] A learning phase can therefore be implemented using both databases. This can, for example, be implemented prior to the operation of the processing plant 10.

[0139] The mathematical model determined by a statistical technique can also be determined using two databases as defined previously, as well as statistical methods to link these two databases together.

[0140] Finally, the mathematical model determined by a mechanistic technique can, for example, be determined using business formulas known in themselves that link at least some of the second type parameters to at least some of the first type parameters.

[0141] In all cases, the mathematical model can be parameterized by configuration parameters relating to the processing installation 10.

[0142] These configuration parameters may, for example, correspond to the dimensions of this installation as well as its specific structure. In some embodiments, the mathematical model is expressly determined for the treatment plant 10.

[0143] For example, this mathematical model can be determined using data relating to the operation of this installation before automatic control of the carbon content in the secondary treatment system 22 is implemented.

[0144] Of course, any other technique that allows for the determination and optimization of the mathematical model can be used.

[0145] Furthermore, it is possible to evolve this mathematical model during the operation of the processing installation 10 by using, for example, machine learning techniques.

[0146] This provision makes it possible to improve the accuracy of the control of the treatment plant 10 as measurement data are collected in situ, and / or to adapt the control of the plant to variations, in particular sudden and / or seasonal, of the wastewater to be treated.

[0147] The second processing module 82 allows the operation of the carbon content control means to be controlled according to the parameter or each parameter of the second type determined by the first processing module 81. For this purpose, a mathematical model associating for example the parameter or each parameter of the second type determined by the first processing module 81 with the carbon requirements of the secondary processing system 22 can then be determined.

[0148] As in the case of the first processing module 81, such a mathematical model can also be determined using a machine learning technique and / or a statistical technique and / or a mechanistic technique.

[0149] In addition, this mathematical model is also determined based on the nature of the means of controlling carbon content as they will be described later.

[0150] Each of the processing modules 81, 82, for example, includes at least part of a software program that is implemented by a computer comprising, for example, a processor and memory to perform this function. Alternatively or in addition, at least one of these modules 81, 82 is implemented at least partially as a programmable logic circuit such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0151] According to the first embodiment illustrated in Figure 1, the carbon content control means include a redirection circuit 90 extending between the first outlet 36 of the primary clarifier 31 and the inlet of the secondary treatment system 22. This redirection circuit then allows the primary sludge discharged by the primary clarifier 31 to be redirected to the secondary treatment system 22. The carbon content control means further include a first flow regulator 92 allowing the flow rate of the primary sludge in the redirection circuit 90 to be adjusted relative to the primary sludge discharge circuit 38.

[0152] The first flow regulator 92, for example, has a valve and / or a pump that can be configured to redirect part of the primary sludge from the first outlet 36 of the primary clarifier 31 or the total flow of this primary sludge within a predetermined time interval.

[0153] The operation of the first flow regulator 92 is controlled by the second processing module 82.

[0154] According to the second embodiment illustrated in Figure 2, the means for controlling the carbon content include a supply circuit 190, a second flow regulator 194 and a third flow regulator 196.

[0155] In particular, the supply circuit 190 connects an external carbon source 192 to the secondary processing system 22 and in particular, to the denitrification device 42.

[0156] The carbon-192 source includes, for example, methanol or any other product containing chemical or organic carbon that can be used in the denitrification process.

[0157] The second flow regulator 194 allows control of the flow of the product from the external source 192 into the supply circuit 190.

[0158] The third flow regulator 196 controls the flow rate in the internal recirculation circuit 46, allowing the nitrate-containing water to be reinjected into the denitrification unit 42. In some examples, the function of the third flow regulator 196 can be performed by the recirculation valve or pump 47, as described previously. In some embodiments, the carbon content control means do not include a third flow regulator 196.

[0159] Each of the second and third flow regulators 194, 196 presents for example a valve and / or a pump arranged in the corresponding circuit.

[0160] The operation of these two regulators 194 and 196 is controlled by the second processing module 82 to control the carbon content in the secondary processing system 22.

[0161] According to the third embodiment illustrated in Figure 3, the means for controlling the carbon content include the elements described in relation to the first two embodiments, namely the redirection circuit 90, the first flow regulator 92, the supply circuit 190, the second flow regulator 194 and the third flow regulator 196. According to this third embodiment, the means for controlling the carbon content further include an injection circuit 290 for injecting chemical reagents from an external source 292 into the primary clarifier 31.

[0162] The chemical reagent is for example chosen from a coagulant, for example ferric chloride FeCl3, ferric sulfate Fe2(SO4)3, aluminium sulfate Al2(SO4)3, iron chlorosulfate FeCISO4 or an anionic polymer type flocculant.

[0163] The control means also include a fourth flow regulator 298 allowing the flow rate of chemical reagents in this injection circuit 290 to be adjusted.

[0164] The operation of this fourth regulator 298 is also controlled by the second processing module 82.

[0165] The process of controlling the treatment installation 10 will now be described with reference to figure 4 representing a flowchart of its steps.

[0166] Advantageously, these steps are implemented regularly with a predetermined repetition frequency. This repetition frequency is, for example, less than one hour, advantageously less than ten minutes, and preferably less than five minutes.

[0167] During a first step 110, the measuring module 32 measures in the primary treatment system 21 at least one first type parameter characterizing the water treated by this primary treatment system 21.

[0168] In some cases, different first-type parameters can be measured at different recurrences of this first step 110. Thus, at least some first-type parameters can be measured with different frequencies.

[0169] Then, in a second step 120, the first processing module 81 determines at least one second type parameter from at least one, in particular several, or even all of the first type parameters measured by the measurement module 32.

[0170] In some cases, the second-type parameter determined in this step 120 depends on one, several, or even all of the available first-type parameter(s). Thus, at least some second-type parameters may be determined with different frequencies depending on the frequency of determination of the corresponding first-type parameters.

[0171] As previously stated, the second type parameter(s) characterize the organic matter content of the treated water.

[0172] In a third step 130, the control means driven by the second processing module 82 control the carbon content in the secondary processing system 22 according to the second parameter(s) determined in the preceding step 120. In particular, the implementation of this step 130 depends on the nature of the control means used to control the carbon content in the secondary processing system 22. Thus, when the control means are implemented according to the first embodiment, in this step, the second processing module 82 controls the operation of the first flow regulator 92 in the redistribution circuit 90.

[0173] When the control means are implemented according to the second embodiment, the second processing module 82 controls the operation of the second flow regulator 194 to regulate the flow in the supply circuit 190 and / or the third flow regulator 196 to control the flow in the internal recirculation circuit 46.

[0174] When the control means are implemented according to the third embodiment, the second processing module 82 controls the first flow regulator 92, the second flow regulator 194, and the third flow regulator 196, as explained previously. The second processing module 82 further controls the fourth flow regulator 298 to regulate the flow rate of the chemical reagents in the primary clarifier 31.

[0175] EXAMPLES

[0176] In order to demonstrate the technical effect of the invention, the inventors have developed a first processing module 81 according to the invention, configured to determine the following three parameters of the second type:

[0177] - the chemical oxygen demand (COD),

[0178] - the concentration of suspended solids TSS, and

[0179] - the concentration of ammonia nitrogen N-NH4 + , from measurements of parameters of the first type provided by a measurement module 32 according to the invention, using either a multiple regression model or a machine learning model. The measurement module 32 is configured to measure the following six parameters of the first type: electrical conductivity, temperature, turbidity, pH, oxygen content, redox potential and flow rate.

[0180] Several multiple regression models, combining different parameters of the first type actually measured, and several trained models, trained on the basis of different subsets of the measured parameters of the first type provided as input to a decision tree type machine learning model, were implemented in the first treatment module 81. The first treatment module 81 was implemented under real conditions in a water treatment plant 10 to control a quantity of methanol injected for the control of the carbon content in the secondary treatment system 22.

[0181] Wastewater treatment plant 10 is an urban wastewater treatment plant with a capacity of 300,000 population equivalent, with a maximum flow rate of 7000 m³ / s 3 / h, tested between March 2023 and March 2024.

[0182] As comparative tests, prior art sensors for direct measurement of the aforementioned second type parameters were simultaneously implemented in the treatment plant 10, namely the AMTAX® LXV 421 ammonium analyzer and the s::can ® Spectro:: lyser® V3 detector UV-VIS COD and TSS analyzer.

[0183] In addition, measurements of the aforementioned second type parameters were carried out in the laboratory, on samples taken from the treatment facility 10 at a frequency of 24h or 48h as appropriate.

[0184] Two types of water were analyzed, namely the raw water supplied at the inlet of the treatment plant 10, and the decanted water from the primary clarifier 31.

[0185] The results of this first experiment are presented in the graphs of figures 5 to 7, and corresponding in each case with the best of the models tested for the parameter of the respective second type determined.

[0186] Figure 5 shows on the x-axis the chemical oxygen demand (COD) values ​​obtained with the treatment module 81 ("COD(81)") for settled water 51 and raw water 52, using:

[0187] - for settled water 51: a trained model using as input the parameters of the first type, electrical conductivity and temperature, to provide as output the chemical oxygen demand (COD), and

[0188] - for raw water 52: a trained model using as input the parameters of the first type electrical conductivity, turbidity and temperature to provide as output the chemical oxygen demand COD, and on the ordinates the corresponding results of COD measurements carried out in the laboratory (“COD (Labo)”).

[0189] We can see that the measurements obtained with the trained models are relatively well correlated with the measurements carried out in the laboratory, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) observed with respect to the laboratory measurements being as follows:

[0190] - for regression line 53: RMSE = 109 mg / L; MAPE = 19%, and

[0191] - for regression line 54: RMSE = 101 mg / L; MAPE = 23%. It should be noted that the correlation coefficient of the regression line does not have real significance in this case, since we are analyzing a non-stationary time series, particularly due to the seasonality of the variations in composition and flow rate of the wastewater to be treated.

[0192] For comparison, measurements carried out in situ with the prior art sensor show a MAPE of 20% for raw water 52, therefore higher than for the invention, and a MAPE of 13% for decanted water, better than for the invention but only at the cost of laborious and costly maintenance operations, in particular cleaning.

[0193] Figure 6 represents: on the ordinate the values ​​of the TSS concentration obtained with the treatment module 81 (“TSS(81)”) for Figure 6A, and with the prior art sensor (“TSS(AA))”) for Figure 6B for raw water 52, using in the case of Figure 6A, a trained model using as input the parameters of the first type electrical conductivity, turbidity and temperature to provide as output the TSS concentration, and on the abscissa the corresponding results of the TSS measurements carried out in the laboratory (“TSS(Labo)”).

[0194] It can be observed that the measurements obtained with the trained model have improved accuracy compared to the prior art in situ sensor, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) observed compared to measurements carried out in the laboratory being as follows:

[0195] - for regression line 61: RMSE = 55.5 mg / L; MAPE = 26.2%, and

[0196] - for the regression line 62 correspond to: RMSE = 62.0 mg / L; MAPE = 29.6%.

[0197] The invention therefore makes it possible in this case to obtain more precise control than with the prior art sensor, at a lower cost and reduced maintenance.

[0198] The settled water samples 51 were also tested. The best trained model, using the first-type parameters (electrical conductivity and temperature) as input, provided the suspended solids concentration (TSS) as output. The results are not shown in the figures. Here again, the treatment module 81 demonstrated superior performance compared to the prior art sensor.

[0199] Figure 7 shows on the ordinate the ammonia nitrogen concentration values ​​obtained with the treatment module 81 (“N-NH4(81)”) for Figure 7A, and with the prior art sensor (“N-NH4 (AA))”) for Figure 7B for raw water 52, using in the case of Figure 7A, a trained model using the first type parameters electrical conductivity and temperature as input to provide the ammonia nitrogen concentration N-NH4 as output + , and on the x-axis the corresponding results of the measurements carried out in the laboratory (“NH4 (Labo)” on Figure 7A or equivalently “N-NH4 (Labo)” on Figure 7A).

[0200] It can be observed that the measurements obtained with the trained model are more accurate than those obtained with the prior art in situ sensor, with the root mean square error (RMSE) and the mean absolute percentage error (MAPE) observed compared to laboratory measurements being:

[0201] - for regression line 71: RMSE = 9.2 mg / L; MAPE = 20.1%, and

[0202] - for regression line 72: RMSE = 15.7 mg / L; MAPE = 39.0%.

[0203] In this case, the invention therefore makes it possible to obtain much more precise control than with the prior art sensor, at a lower cost and reduced maintenance.

[0204] The settled waters 51 were also tested. The best trained model, using the first type of parameters (electrical conductivity and temperature) as input, provided the ammonia nitrogen concentration as output. The results are not shown in the figures. Here again, the treatment module 81 exhibited superior performance compared to the prior art sensor.

[0205] In order to confirm these results, the inventors carried out a second large-scale test campaign, with the treatment plant 10 receiving raw water 81 of industrial origin, including from the automotive and paper industries, as well as municipal raw water.

[0206] The capacity of treatment plant 10 in this case was 400,000 population equivalents.

[0207] Figure 8A represents on the ordinate the values ​​of chemical oxygen demand COD obtained with the treatment module 81 (“COD(81)” for raw water 81 and, using the model used for raw water 52 in Figure 5, and on the ordinate the corresponding results of the COD measurements carried out in the laboratory (“COD(Labo)”).

[0208] We can again observe that the measurements obtained with the trained model have improved accuracy, the root mean square error (RMSE) and the mean absolute error (MAPE) observed compared to the laboratory measurements being as follows: - for the regression line 82: R 2 = 0.37; RMSE = 113.2 mg / L; MAPE = 16.9%, and

[0209] - for the regression line 83 is characterized by R 2 = -1.44; RMSE = 221.9 mg / L; MAPE = 43.6%.

[0210] Retraining the model on this second installation also resulted in an RMSE of 33.2 mg / L and a MAPE of 5.9%. This second series of measurements therefore confirms that the invention enables real-time control of the treatment plant that is significantly more precise than prior art sensors. The error in the value of the second type of parameter used for this control and provided in situ in the invention is much lower than that allowed by prior art sensors, at a lower cost and with reduced maintenance compared to these sensors, for a wide variety of raw water to be treated.

[0211] Similar results, not shown in the figures, were observed for the other parameters of the second type tested, namely the concentration of ammoniacal nitrogen N-NH4, the concentration of suspended matter TSS and the total concentration of phosphorus P-PO4.

Claims

24 DEMANDS 1. A method for piloting a wastewater treatment plant (10) comprising a primary treatment system (21) and a secondary treatment system (22), the primary treatment system (21) comprising a primary clarifier (31) configured to separate primary sludge from the treated water, the secondary treatment system (22) comprising a nitrification device (43) and a denitrification device (42) for the treated water; the method comprising the following steps: - measurement (110) in the primary treatment system (21) of at least one first-type parameter characterizing the treated water; - determination (120) of at least one second type parameter from the measured first type parameter or each parameter, the second type parameter or each parameter characterizing the organic matter content of the treated water; - control (130) of the carbon content in the secondary treatment system (22) as a function of the or each parameter of the second type.

2. A method according to claim 1, wherein the determination step (120) of the parameter or each parameter of the second type is carried out with a repetition frequency of less than 1 hour, advantageously less than 10 min and preferably less than 5 min.

3. Method according to claim 1 or 2, wherein the carbon content control step (130) is carried out with substantially the same repetition frequency as the determination step (120) of the parameter or each parameter of the second type.

4. A method according to any one of the preceding claims, wherein the parameter or each parameter of the first type is chosen from the group comprising the following elements characterizing treated water: - pH; - oxygen content; - oxidation-reduction potential (ORP); - turbidity; - electrical conductivity; - temperature ; - Speed.

5. A method according to the preceding claim, wherein the measurement (100) in the primary treatment system (21) of at least one first-type parameter characterizing the treated water includes the measurement of electrical conductivity and the measurement of temperature, and optionally the measurement of turbidity.

6. A method according to any one of the preceding claims, wherein the parameter or each parameter of the second type is chosen from the group comprising the following elements characterizing the organic matter content of the treated water: - concentration of suspended solids (TSS); - biochemical oxygen demand (BOD); - chemical oxygen demand (COD).

7. A method according to the preceding claim, wherein the at least one parameter of the second type further comprises at least one parameter selected from a concentration of ammoniacal nitrogen (N-NH4 + ) and a total phosphorus concentration (P-PO4).

8. A method according to any one of the preceding claims, wherein the measurement of at least one first-type parameter is carried out upstream and / or downstream of the primary clarifier (31).

9. A method according to any one of the preceding claims, wherein the determination of the parameter or each parameter of the second type is carried out using a mathematical model, preferably the mathematical model being determined by a machine learning technique and / or by a statistical technique and / or by a mechanistic technique.

10. A method according to any one of the preceding claims, wherein the control of the carbon content in the secondary treatment system (22) is achieved by controlling a redirection of the primary sludge in the secondary treatment system (22).

11. A method according to any one of the preceding claims, wherein the control of the carbon content in the secondary treatment system (22) is achieved by controlling the addition of carbon from an external source (192) into the secondary treatment system (22), preferably in the denitrification device (42).

12. A method according to any one of the preceding claims, wherein the control of the carbon content in the secondary treatment system (22) is carried out by controlling an internal recirculation between the denitrification device (42) and the nitrification device (43).

13. A method according to any one of the preceding claims, wherein the control of the carbon content in the secondary treatment system (22) is carried out by the control of chemical reagents in the primary clarifier (31).

14. Wastewater treatment plant (10) comprising a primary treatment system (21) and a secondary treatment system (22), the primary treatment system (21) comprising a primary clarifier (31) configured to separate primary sludge from the treated water, the secondary treatment system (22) comprising a nitrification device (43) and a denitrification device (42) for the treated water; the treatment plant (10) further comprising: - a measurement module (32) in the primary treatment system (21) of at least one first-type parameter characterizing the treated water; - a first processing module (81) configured to determine at least one second type parameter from the measured first type parameter(s), the second type parameter(s) characterizing the organic matter content of the treated water; - means of controlling the carbon content in the secondary treatment system (22) as a function of the second type parameter or each parameter.

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