JP7083269B2 - Control method and control device for water treatment plants with agglomeration - Google Patents

Description

The present invention relates to the control of a water treatment plant in which a coagulant is added to coagulate and separate impurities, and in particular, to a control method and a control device for determining the injection amount of the coagulant in the water treatment plant.

In water treatment plants such as water purification plants and wastewater treatment plants, in order to settle and remove suspended suspended matter contained in raw water, a flocculant is added to the raw water to increase the settling speed and promote the removal of suspended suspended matter. I'm letting you. As the flocculant, for example, polyaluminum chloride (PAC) and the like are widely used. Since the optimum amount of the flocculant added to the raw water varies greatly depending on the water quality of the raw water, it is necessary to carry out a jar test to determine the optimum amount of the flocculant to be added. In the jar test, after performing a water quality analysis on the raw water, the raw water is collected and used as sample water, a flocculant is added to the sample water in a beaker, the mixture is stirred and allowed to stand, and the supernatant water after standing is collected. The turbidity and chromaticity are measured, and the sample water remaining in the beaker is filtered to measure the turbidity and chromaticity of the filtered water. In practice, for example, 3 to 10 jar testers that can stir and stand for multiple beakers at the same time are used, and different amounts of coagulant are added to each beaker containing the sample water. Perform jar tests in parallel on sample water in multiple beakers. For example, when using a 6-series jar tester, different amounts of coagulant are added to the sample water poured into each of the 6 beakers. Then, the amount of the flocculant to be added to the raw water in the water treatment plant is determined based on which of the plurality of beakers achieved the target water quality value and showed the best result. In a water treatment plant, jar tests are performed regularly, and also when a sudden change in the quality of raw water is detected or when the source of raw water is changed.

By the way, before carrying out the jar test, it is necessary to determine the conditions for carrying out the jar test, particularly the amount of the flocculant added to the sample water of each beaker and the stirring time. FIG. 1 is a flowchart showing a conventional execution procedure of a jar test. First, the water quality of raw water is analyzed in step 101. Items for water quality analysis include, for example, turbidity (or suspended solids (SS)), chromaticity, pH, TOC (total organic carbon), COD (chemical oxygen demand), etc. The analysis item is selected for. Next, in step 102, the conditions for carrying out the jar test are determined based on the results of water quality analysis (mainly turbidity and chromaticity) of raw water, literature values, and past achievements and experiences. Then, in step 103, a jar test is performed under the conditions set in step 102, and the supernatant water and the filtered water are analyzed. Then, in step 104, as a result of the jar test, it is determined whether or not the coagulant injection rate or range that is the target treated water quality is obtained. If the coagulant injection rate or range that is the target treated water quality is obtained here, the jar test is terminated. If not, the process returns to step 102 to change the condition setting and repeat the jar test. ..

Jar tests with a sol tester give results for different coagulant injection rates at once. In order to have the optimum coagulant injection rate, it is not only that the target treated water quality is satisfied, but also that the injection rate is larger than the coagulant injection rate in one test by the sol tester. The result and the result for the injection rate should be better than the result for the injection rate smaller than the coagulant injection rate. Therefore, in order to obtain the optimum coagulant injection rate, not only whether or not the target treated water quality is satisfied in step 104, but also the coagulant injection rate showing the best result is determined by the jar tester once. It is necessary to confirm that the coagulant injection rate used in the test is neither the maximum nor the minimum.

In the conventional method described above, the condition setting of the jar test is probably performed depending on the experience of the operator. If you are a veteran technician or researcher, you can set the jar test conditions to include the optimum flocculant injection rate from the raw water quality, especially from the visually obtained information such as turbidity and chromaticity. However, inexperienced technicians may not be able to obtain the optimum flocculant injection rate in a single jar test with a jar tester. In that case, the jar test will be repeated until the optimum injection rate is obtained.

Attempts have been made to determine the injection amount of a chemical such as a flocculant using AI (artificial intelligence). When a human determines the drug injection amount, the drug injection amount is determined to be excessive, so that the AI that learns such a drug injection amount also determines the drug injection amount to be excessive. Therefore, Patent Document 1 obtains a statistical injection rate by statistically processing raw water quality information, chemical information, and treated water quality information in order to avoid excessive chemical injection, while using chemicals. It is disclosed that the model injection rate using the water quality reaction model that models the reaction is obtained, and the operating conditions for controlling the chemical injection rate in the water treatment plant are generated based on the statistical injection rate and the model injection rate. is doing.

Japanese Unexamined Patent Publication No. 2017-140595

The optimum injection rate when injecting a flocculant such as PAC in a water treatment plant is uniquely obtained by calculation from the result of water quality analysis at present even if the technique described in Patent Document 1, for example, is used. Is difficult, and it is essential to perform a jar test with a jar tester to determine the optimum injection rate. However, the condition setting in the jar test largely depends on the experience of the worker, and in the case of an unskilled worker, the optimum flocculant injection rate cannot be obtained unless the jar test is performed many times.

An object of the present invention is a control method and a control device for a water treatment plant that determines an optimum coagulant injection rate, and is a control that can reduce the number of repetitions in a jar test regardless of the skill level of an operator or the like. To provide methods and controls.

The control method of the present invention is a control method for determining the coagulant injection rate for a jar test using raw water as sample water in a water treatment plant in which a flocculant is added to raw water to treat the raw water. A value obtained by analyzing or measuring one or more items with respect to the sample water and the treated water after the jar test, and the coagulant injection using a plurality of coagulant injection rates used in one jar test as an injection rate group. A recording calculation device that stores data with a rate and has undergone a learning process is used, and in the recording calculation device, the relationship between the value of any one or more items and the coagulant injection rate based on the stored data. In the recording calculation device, the coagulant injection rate constituting the injection rate group is determined from the relational expression based on the input value, and the learning step is the jar test using the determined injection rate group. Based on the results, for any of the relational expressions, the coagulant injection rate calculated from the relational expression is used as the theoretical injection rate, and the coagulant injection rate in the injection rate group corresponding to the result of the jar test with respect to the theoretical injection rate. When the correlation coefficient of is greater than or equal to a predetermined multiple of the correlation coefficient with the flocculant injection rate used to generate the relational expression to the theoretical injection rate, the data corresponding to the result is added to the recording calculation device. Has a step of accumulating.

The control device of the present invention is a control device for determining the coagulant injection rate for a jar test using raw water as sample water in a water treatment plant in which a coagulant is added to raw water to treat the raw water. A value obtained by analyzing or measuring one or more items with respect to the sample water and the treated water after the jar test and the coagulant injection using a plurality of coagulant injection rates used in one jar test as an injection rate group. Based on the data stored in the data storage unit and the data storage unit that stores the data with the rate, a relational expression showing the relationship between the value of any one or more items and the coagulant injection rate is generated and the data is stored. It has a data learning unit that executes learning processing on the data stored in the unit, and a condition determination unit that determines the coagulant injection rate that constitutes the injection rate group from the relational expression based on the input value. In the treatment, based on the result of the jar test using the determined injection rate group, for any relational expression, the flocculant injection rate calculated from the relational expression is used as the theoretical injection rate, and the jar test is performed with respect to the theoretical injection rate. When the correlation coefficient with the flocculant injection rate in the injection rate group corresponding to the result is a predetermined multiple or more of the correlation coefficient with the flocculant injection rate used to generate the relational expression to the theoretical injection rate. The data corresponding to the result is added to the data storage unit and stored.

According to the present invention, the number of repetitions in the jar test can be reduced regardless of the skill level of the operator.

It is a flowchart which shows the conventional execution procedure of a jar test. It is a block diagram which shows the structure of the control device of one Embodiment of this invention. (A) and (b) are graphs showing the relationship between the PAC injection rate and the supernatant water turbidity. It is a flowchart which shows the execution procedure of the jar test in one embodiment. It is a figure explaining the determination of the coagulant injection rate based on the turbidity. It is a sequence diagram which shows the operation of a record arithmetic unit and a terminal. It is a sequence diagram which shows the operation of a record arithmetic unit and a terminal.

Next, an embodiment of the present invention will be described with reference to the drawings. FIG. 2 shows the configuration of the control device according to the embodiment of the present invention.

The control device shown in FIG. 2 is used to determine the conditions of the jar test performed when determining the coagulant injection rate in a water treatment plant such as a water purification plant or a wastewater treatment plant. , An input unit 21 and an output unit 22 connected to the recording arithmetic unit 10. A terminal 40 made of, for example, a smartphone may be connected to the recording arithmetic unit 10 via the network 30. The recording calculation device 10 is composed of, for example, a computer such as a personal computer or a server computer, and logically includes a data storage unit 11 for storing data and the like and data stored in the data storage unit 11. A data learning unit 12 that generates a relational expression described later based on the relational expression and performs machine learning on this data, and a condition determination unit 13 that determines a jar test condition based on the relational expression stored in the data storage unit 11. I have. The area of the data storage unit 11 is secured in the storage device of the computer, and the functions of the data learning unit 12 and the condition determination unit 13 are realized by the CPU (central processing unit) of the computer operating based on the software program. Will be done.

This controller is a multi-aggregator used in a single jar test by a jar tester based on the values entered for any of the various input items, or a combination of those values. The injection rate is determined and output. In the following description, the coagulant injection rate of a plurality (that is, the number corresponding to the number of beakers in the jar tester) used in one jar test by the jar tester is referred to as an injection rate group. At this time, the control device
(1) The injection rate group includes the coagulant injection rate that achieves the target treated water quality.
(2) The coagulant injection rate showing the best result is neither the maximum nor the minimum of the coagulant injection rates included in the injection rate group.
The injection rate group is determined so as to satisfy the above two conditions.

FIGS. 3 (a) and 3 (b) show the relationship between the PAC injection rate and the turbidity of the obtained supernatant water when a PAC is added as a flocculant to a certain sample water and a jar test is performed. There is. Six jar testers are used here, so the injection rate group in one jar test consists of six PAC injection rates. The target supernatant turbidity shall be 1 degree or less. FIG. 3A shows a case where 40, 60, 80, 100, 120, 140 mg / L are used as the PAC injection rate constituting the injection rate group. When the PAC injection rate is 120 mg / L or less, the supernatant water turbidity exceeds 1 degree, but when the PAC injection rate is 140 mg / L, the supernatant water turbidity is 0.8 degrees, which satisfies the condition (1). ing. However, from the results shown in FIG. 3 (a), it is unclear whether the PAC injection rate of 140 mg / L is optimal, and it cannot be denied that better results may be obtained at a higher injection rate. The PAC injection rate of 140 mg / L, which showed the best results, is the largest of the PAC injection rates included in the injection rate group, and does not satisfy the condition (2). Therefore, as the injection rate group used in the jar test, the one shown in FIG. 3A is not appropriate. On the other hand, FIG. 3B shows a case where 100, 120, 140, 160, 180, 200 mg / L are used as the PAC injection rates constituting the injection rate group, which are shown by black squares. The white squares in FIG. 3 (b) show the results shown in FIG. 3 (a) as they are. The results shown in black in FIG. 3 (b) satisfy both the above conditions (1) and (2), and indicate that a PAC injection rate of 160 mg / L is appropriate. That is, the control device should determine the injection rate group shown by the black square in FIG. 3 (b).

Actually, since it is difficult to know in advance the relational expression that determines the injection rate group satisfying the two conditions (1) and (2) from the values input to the control device, the control device in the present embodiment. Machine learning is performed in the above to gradually generate better relational expressions and obtain better results. Therefore, the data storage unit 11 stores data indicating the relationship between the value of the input item or a combination thereof and the corresponding injection rate group as data. The evaluation value for the data is the treated water quality. Further, in a water treatment plant, the smaller the coagulant injection rate is, the more cost effective it is. Therefore, the coagulant injection rate itself may be included in the evaluation value. The data used for learning is stored in the data storage unit 11.

Therefore, when performing a jar test using the control device of the present embodiment, first, as shown in FIG. 4, the raw water quality is analyzed in step 111. Items of water quality analysis include, for example, turbidity, SS, chromaticity, ultraviolet absorbance, particle concentration, temperature, pH, TOC, COD, iron concentration, manganese concentration, aluminum concentration, arsenic concentration, cadmium concentration, etc. One or more analysis items are selected from the list. Next, in step 112, the relational expression in the recording arithmetic unit 10 is referred to based on the value obtained by the analysis, and in step 113, the jar test conditions are set based on the referenced relational expression. Specifically, the injection rate group used in the jar test is determined. There are many analytical items available in step 111, but not all of these analytical items are analyzed, and turbidity or chromaticity is generally measured in the jar test. The injection rate group used will be determined primarily by turbidity or chromaticity.

Subsequently, in step 114, a jar test of sample water is performed by a jar tester using the determined injection rate group, and the supernatant water and the filtered water are analyzed. The analysis items for the supernatant water and the filtered water are the same as those described in step 111, but it is particularly preferable to measure at least one of the turbidity and the chromaticity. Then, in step 115, as a result of the jar test, it is determined whether or not there is a flocculant injection rate or a range that achieves the target treated water quality in the injection rate group used. That is, in step 115, it is determined whether or not the above condition (1) is satisfied. Of course, it is particularly preferable to determine whether or not the condition (2) is satisfied at the same time. If there is a coagulant injection rate that achieves the target treated water quality, the process proceeds to step 116, and learning of the data in the recording arithmetic unit 10 based on the analysis results in steps 111 and 116 and the coagulant injection rate used. And finish the jar test process. Learning will be described later. On the other hand, when the coagulant injection rate that achieves the target treated water quality does not exist in the injection rate group in step 115, the conditions are set after learning in the same manner as in step 116 in step 117. Return to step 112 and repeat the jar test to change.

Since this embodiment is premised on machine learning, initial data is required, but as the initial data, the relationship between the value of the input item and the corresponding evaluation value is analyzed by cluster analysis or the like based on an experiment or the like. It is possible to obtain the data and store it in the data storage unit 11. Alternatively, when the jar test is performed for the first time, a value arbitrarily determined by the user from the relationship between the visual turbidity or the visual chromaticity and the coagulant injection amount may be used as the injection rate group. When turbidity is used, as shown in FIG. 5, sample water is poured into a predetermined transparent container 50 and the turbidity is visually determined. In the visual determination of turbidity, a plurality of color samples (color charts), that is, turbidity samples, each of which corresponds to the turbidity, may be prepared and the turbidity may be determined by comparing with the turbidity sample. As shown in FIG. 5, when the visual turbidity is classified into 5 stages of # 1 to # 5, the injection rate group can be defined as shown in Table 1.

When determining the initial injection rate group by visual chromaticity, the same procedure as for the turbidity described here may be followed.

Next, the learning in steps 116 and 117 will be described. In the present embodiment, the data learning unit 12 stores only the data corresponding to the positive example in the data storage unit 11, and based on the data accumulated in this way, the values of one or more input items and the coagulant injection. A relational expression showing the relationship with the rate is generated, and the generated relational expression is also stored in the data storage unit 11. When generating a relational expression with a plurality of input items, weighting may be set between the input items. Whether it is a correct example or not is determined by the correlation coefficient _RA in the data already stored in the data storage unit 11 and the new relational expression corresponding to the new data when new data is generated based on the result of the jar test. Judgment is made based on the result of comparison with the correlation coefficient _RB for various data. In the case of this embodiment, since the relational expression cannot be said to be a linear expression, the correlation coefficient cannot be simply obtained. Therefore, assuming that the target relational expression is represented by the function f (x), that is, when the value of the item is x and the flocculant injection rate y is represented by y = f (x), the function f. The flocculant injection rate determined by (x) is called the theoretical injection rate. At this time, the data y'already stored in the data storage unit 11 with respect to the relational expression varies from the relation of y = f (x). Therefore, instead of finding the correlation coefficient between x and y', the correlation coefficient between y'and y is found and used as _RA . The new data is based on the results of the jar test, so if you are using 6 jar testers, it corresponds to 6 different flocculant injection rates (ie, injection rate groups). Therefore, as in the case of the correlation coefficient _RA , the correlation coefficient for the theoretical injection rate and the injection rates of a plurality of (for example, 6) coagulants constituting the injection rate group is obtained. This is referred to as the correlation coefficient _RB . Then, if _RB / _RA is a predetermined value or more, for example, if it is 0.5 or more, new data is stored in the data storage unit 11 as a positive example. The data learning unit 12 is a relational expression expressing the relationship between the value of any one or more items and the coagulant injection rate based on the data accumulated in the data storage unit 11, including the newly accumulated data. Is regenerated.

By going through the above-mentioned processing, the learned data and the relational expression are accumulated in the data storage unit 11 of the recording arithmetic unit 10. This accumulated relational expression can also be used from the outside of the recording arithmetic unit 10, whereby it becomes possible to show the optimum jar test to the outside. In the case of the present embodiment, the terminal 40 connected to the recording arithmetic unit 10 via the network 30 can also obtain the jar test conditions determined based on the relational expression. Hereinafter, a mode in which the terminal 40 is used will be described. FIG. 6 shows the processing in the recording arithmetic unit 10 and the terminal 40 when the terminal 40 is used.

A smartphone is assumed as the terminal 40. Smartphones are generally equipped with a camera in addition to the communication function. Therefore, it is assumed that the terminal 40 is operated by the user in the water treatment plant away from the installation location of the recording arithmetic unit 10.
In step 121, the sample water injected into the predetermined container is photographed by the terminal 40. The container is specified so that the turbidity and chromaticity can be accurately determined. Since the shooting result may differ depending on the shooting light source and ambient light, shoot the container together with the test chart printed in standard colors, etc., and correct the shooting result based on the color tone and brightness of the test chart in the shooting result. Is preferable. Correction methods based on light sources and ambient light are well known. From the shooting results, for example, information such as turbidity, chromaticity, particle density, and transparency can be extracted and quantified. In step 122, if there is auxiliary data such as temperature, pH, TOC, and various metal concentrations of the sample water, the auxiliary data of the terminal 40 is also input. By using the auxiliary data, a more favorable injection rate group can be obtained. Then, in step 123, the photographing result (or data based on it) and auxiliary data are sent from the terminal 40 to the recording arithmetic unit 10 via the network 30. Correction based on the light source and ambient light, extraction and digitization of information from the shooting result may be performed on the terminal 40 side or on the recording arithmetic unit 10 side.

In step 124, the recording arithmetic unit 10 that receives the data from the terminal 40 generates a jar test condition using the relational expression in the data storage unit 11 based on the value of the received data, and in step 125, the jar test condition is generated. The test condition is transmitted to the terminal 40. The terminal 40 that has received the jar test condition displays the jar test condition on the screen of the terminal 40 in step 126. The user who confirmed the jar test conditions displayed on the screen, especially the extraction rate group, can perform the jar test of the sample water according to the jar test conditions.

Even when the terminal 40 is used, the data stored in the data storage unit 11 can be learned. In that case, as shown in FIG. 7, in the terminal 40, the jar test result is input in the step 131. Since it is possible that there is only a jar tester and a smartphone, the jar test results are, for example, the photographed image of the sample water immediately after the end of stirring in the jar tester and the photographed image after standing for 10 minutes. It may be a combination, and turbidity and chromaticity may be derived from these captured images. In step 132, the data of the jar test result is transmitted from the terminal 40 to the recording arithmetic unit 10. The recording arithmetic unit 10 that has received the jar test result learns the data in step 133 as described above.

According to the present embodiment described above, by using machine learning, even an inexperienced worker can efficiently perform a jar test, and a reduction in working time can be achieved. In addition, since the coagulant injection rate can be determined from the image data taken by a smartphone or the like, water quality analysis and the like can be omitted.

The water treatment plant targeted by the present invention is, for example, a water purification plant or a wastewater treatment plant. In the case of a water purification plant, it is known that the behavior shown by the relationship between the value obtained by analysis or measurement and the preferable flocculant injection rate differs depending on which river water or groundwater in which area the raw water is. There is. Similarly, in a wastewater treatment plant, the behavior differs depending on what the wastewater source is. Therefore, in the case of a water purification plant, the data and relationships are stored in the data storage unit 11 according to the water system such as river A or river B, and in the case of a wastewater treatment plant, according to the process from which the wastewater is discharged. It is preferable to manage the formula and perform learning by water system and process. By implementing the control method based on the present invention by distinguishing the water system and the process in this way, a more appropriate flocculant injection rate can be obtained in a short period of learning.

10 Arithmetic logic unit 11 Data storage unit 12 Data learning unit 13 Condition determination unit 30 Network 40 Terminal

Claims (12)

It is a control method for determining the coagulant injection rate for a jar test using the raw water as a sample water in a water treatment plant in which a coagulant is added to the raw water to treat the raw water.
Multiple coagulant injection rates used in one jar test by a jar tester are used as the injection rate group.
A record of accumulating data of values obtained by analyzing or measuring one or more items with respect to the sample water and the treated water after the jar test and the coagulant injection rate, which have undergone the learning step. Using an arithmetic unit,
Based on the accumulated data in the recording arithmetic unit, a relational expression is generated by a function in which the value obtained by analyzing or measuring any one or more items is used as the independent variable and the coagulant injection rate is used as the dependent variable. death,
In the recording arithmetic unit, the flocculant injection rate constituting the injection rate group is determined from the relational expression based on the value input as the value obtained by analysis or measurement .
In the learning step, based on the input value and the result of the jar test using the determined injection rate group, for any relational expression, the flocculant injection rate calculated from the relational expression is referred to as the theoretical injection rate. Then , the correlation coefficient between the theoretical injection rate and the flocculant injection rate in the injection rate group corresponding to the result is defined as RB , and the theoretical injection rate and the _flocculant injection rate used to generate the relational expression are used. _A control method comprising a step of adding and accumulating data corresponding to the result to the recording calculation device when RB / _RA is equal to or more than a predetermined value , where _RA is the correlation coefficient of . The predetermined value is 0. 5. The control method according to claim 1. The above-mentioned one or more items include turbidity, amount of suspended matter, chromaticity, ultraviolet absorbance, particle concentration, temperature, pH, total organic carbon, chemical oxygen requirement, iron concentration, manganese concentration, aluminum concentration, arsenic concentration, and The control method according to claim 1 or 2, which comprises at least one of the cadmium concentrations. The control method according to any one of claims 1 to 3, wherein a predetermined injection rate group for visual turbidity or chromaticity is used when the jar test is performed for the first time. Take a picture of the sample water with a terminal and
In the recording arithmetic unit, the injection rate group is determined by applying the value obtained from the imaging result to the relational expression relating to the item obtained from the imaging result sent from the terminal.
The control method according to any one of claims 1 to 4, wherein the determined injection rate group is notified from the recording arithmetic unit to the terminal. The control method according to claim 5, wherein the learning step is performed in the recording arithmetic unit based on the data sent from the terminal. A control device for determining the coagulant injection rate for a jar test using the raw water as a sample water in a water treatment plant that treats the raw water by adding a coagulant to the raw water.
Multiple coagulant injection rates used in one jar test by a jar tester are used as the injection rate group.
A data storage unit that stores data of values obtained by analyzing or measuring one or more items with respect to the sample water and the treated water after the jar test and the coagulant injection rate.
Based on the data accumulated in the data storage unit, a relational expression is generated by a function in which the value obtained by analyzing or measuring any one or more items is used as the independent variable and the coagulant injection rate is used as the dependent variable. At the same time, a data learning unit that executes learning processing on the data stored in the data storage unit, and
A condition determination unit that determines the coagulant injection rate constituting the injection rate group from the relational expression based on the value input as the value obtained by analysis or measurement .
Have,
In the learning process, based on the result of the jar test using the determined injection rate group, the coagulant injection rate calculated from the relational expression is used as the theoretical injection rate for any of the relational expressions, and the theoretical injection rate is defined as the theoretical injection rate. The correlation coefficient between the above and the flocculant injection rate in the injection rate group corresponding to the result is RB , and the correlation coefficient between the theoretical injection rate and the _flocculant injection rate used to generate the relational expression is R. _{As A} , when RB / _RA is equal to or greater than a predetermined value , a control device for adding and accumulating data corresponding to the result to the data storage unit. The predetermined value is 0. 5. The control device according to claim 7. The above-mentioned one or more items include turbidity, amount of suspended matter, chromaticity, ultraviolet absorbance, particle concentration, temperature, pH, total organic carbon, chemical oxygen requirement, iron concentration, manganese concentration, aluminum concentration, arsenic concentration, and The control device according to claim 7 or 8, which comprises at least one of the cadmium concentrations. The control device according to any one of claims 7 to 9, wherein a predetermined injection rate group for visual turbidity or chromaticity is used when the jar test is performed for the first time. A terminal is connected to the control device,
When the imaging result is sent from the terminal that captured the sample water, the condition determination unit applies the value obtained from the imaging result to the relational expression regarding the item obtained from the imaging result, and the injection rate. The control device according to any one of claims 7 to 10, wherein a group is determined and the determined injection rate group is notified to the terminal. The control device according to claim 11, wherein the data learning unit performs the learning process in the recording arithmetic unit based on the data sent from the terminal.

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