Method for accurately determining the end point of regeneration of an anion exchanger

Abstract

The present application relates to the technical field of resin regeneration, and particularly relates to a method for accurately judging the end point of anion exchanger regeneration, which comprises the following steps: injecting an ionic liquid membrane alkali into an alkali metering tank in an anion exchanger, and backwashing the anion exchanger; after resin particles in the anion exchanger sink, discharging the upper water in a resin tank; adding a regeneration liquid into the resin tank and opening an alkali outlet valve, and recording the real-time foam amount of the alkali outlet valve by using a camera; inputting the real-time foam amount into a trained neural network model, and determining the regeneration liquid feeding time corresponding to the maximum value of the real-time foam amount by using the neural network model; based on the regeneration liquid feeding time, closing the alkali outlet valve, and continuously feeding the regeneration liquid for a predetermined time, so as to accurately judge the end point of the anion exchanger regeneration. The method is more accurate in judging the successful end point of the anion exchanger regeneration, and is more efficient; and the method avoids the possibility that the poor quality of desalted water caused by unqualified regeneration affects the safe operation of a boiler.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resin regeneration, and particularly relates to a method for accurately judging the regeneration end point of an anion exchanger. BACKGROUND

[0002] The make-up water system of a copper smelting boiler mainly comprises a filter, a clean water tank, a strong acid cation exchanger, a carbon remover, a strong alkali anion exchanger, a desalted water tank and the like equipment of a primary desalination system. The design production capacity is 80t / h, and the water quality requirements are that the total hardness is about 0umol / L, the conductivity is less than or equal to 10us / cm, SiO2 is less than 0.1mg / L, and the pH value is 8.5-9.2.

[0003] After the failure of the strong alkali anion exchanger, the ion liquid membrane alkali can be used for regeneration. The common regeneration method at present is to use ion liquid membrane alkali with a certain concentration for a fixed time. This regeneration method with fixed time for alkali has certain deviation in actual production. If the time is set too short, the regeneration cannot be completed, which may lead to unqualified desalted water quality, and even affect the preparation of the whole desalted water. If the time is set too long, the ion liquid membrane alkali and water consumption are relatively large, which causes certain waste. Moreover, due to the fluctuation of the water quality and the difference of each equipment, the time setting as the regeneration program is not accurate enough.

[0004] Therefore, the prior art still needs to be improved and developed. SUMMARY

[0005] In view of the above problems in the prior art, the present application aims to provide a method for accurately judging the regeneration end point of an anion exchanger, so as to solve the problems of inaccurate judgment of the regeneration program of the existing anion exchanger, incomplete regeneration and material waste.

[0006] The technical scheme of the present application is as follows:

[0007] A method for accurately judging the regeneration end point of an anion exchanger comprises the following steps:

[0008] Ion liquid membrane alkali is injected into an alkali metering tank in the anion exchanger, and backwashing is performed on the anion exchanger;

[0009] After the resin particles in the anion exchanger sink, the upper water in the resin tank is discharged;

[0010] Regeneration liquid is added to the resin tank, an alkali outlet valve is opened, and a camera is used to record the real-time foam volume of the alkali outlet valve;

[0011] The real-time foam volume is input into a trained neural network model, and the time when the real-time foam volume reaches the maximum value is determined by the neural network model;

[0012] Based on the regeneration liquid feeding time, the alkali outlet valve is closed, and the regeneration liquid continues to be fed for a predetermined time, so as to accurately determine the regeneration end point of the anion exchanger.

[0013] The method for accurately determining the regeneration end point of the anion exchanger, wherein the ion liquid membrane alkali is a sodium hydroxide solution with a mass concentration of 28-32%.

[0014] The method for accurately determining the regeneration end point of the anion exchanger, wherein the backwashing flow rate is 12-14 t / h, and the backwashing time is 5-15 min.

[0015] The method for accurately determining the regeneration end point of the anion exchanger, wherein the regeneration liquid is obtained by diluting the ion liquid membrane alkali with desalted water with a flow rate of 12-14 t / h, and the mass concentration of the regeneration liquid is 2-3%.

[0016] The method for accurately determining the regeneration end point of the anion exchanger, wherein the trained neural network model is obtained by the following training steps:

[0017] A photo of the real-time foam amount discharged from the resin tank, and the corresponding regeneration liquid flow rate and regeneration liquid feeding time are obtained;

[0018] The photo is preprocessed to obtain a preprocessed photo;

[0019] The preprocessed photo is associated with the regeneration liquid flow rate and the regeneration liquid feeding time to obtain a trained neural network model.

[0020] The method for accurately determining the regeneration end point of the anion exchanger, wherein the preprocessing of the photo comprises:

[0021] The foam size data and the foam quantity in the photo are obtained;

[0022] A target image database is generated according to the foam size data and the foam quantity.

[0023] The method for accurately determining the regeneration end point of the anion exchanger, wherein the predetermined time is 28-32 min.

[0024] The method for accurately determining the regeneration end point of the anion exchanger, wherein after the predetermined time of continuously feeding the regeneration liquid, the method further comprises the following steps:

[0025] The resin particles in the anion exchanger are cleaned with clean water to wash away the regeneration waste liquid.

[0026] The method for accurately judging the regeneration end point of the anion exchanger, wherein the cleaning treatment comprises first small positive washing and second large positive washing; the time of the first small positive washing is 10 minutes; and the time of the second large positive washing is 15 minutes.

[0027] Beneficial effects: the present application provides a method for accurately judging the regeneration end point of the anion exchanger, comprising the steps of: injecting an ionic liquid membrane alkali into an alkali metering tank in the anion exchanger, and backwashing the anion exchanger; after the resin particles in the anion exchanger sink, discharging the upper water in the resin tank; adding a regeneration liquid to the resin tank and opening an alkali outlet valve, and recording the real-time foam amount of the alkali outlet valve by using a camera; inputting the real-time foam amount into a trained neural network model, and determining the regeneration liquid feeding time corresponding to the maximum value of the real-time foam amount by using the neural network model; based on the regeneration liquid feeding time, closing the alkali outlet valve, and continuing to feed the regeneration liquid for a predetermined time, to realize accurate judgment of the regeneration end point of the anion exchanger. The present application utilizes the fact that the anion exchanger will generate foam when feeding alkali, records the real-time foam amount by using a camera, combines a trained neural network model, obtains the highest point of the foam amount, and then continues to feed the regeneration liquid for a predetermined time, so that the foam amount is converted from high to low, and the anion exchanger is regenerated. The method is more accurate in judging the successful regeneration end point of the anion exchanger, has higher efficiency compared with the traditional method of analyzing SiO2 and conductivity after regeneration by time to judge whether the regeneration is qualified, and avoids the possibility of affecting the safe operation of the boiler due to poor quality of the desalted water caused by unqualified regeneration. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The program flowchart of the method for accurately judging the regeneration end point of the anion exchanger;

[0029] Figure 2 The structure of the anion exchanger. DETAILED DESCRIPTION

[0030] The present application provides a method for accurately judging the regeneration end point of the anion exchanger, in order to make the purpose, technical scheme and effects of the present application more clear and explicit, the present application is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0031] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0032] The strong basic anion exchanger can be regenerated with ionic liquid membrane base after failure. The common regeneration method is to pass 1%-5% concentration of ionic liquid membrane base at a fixed time. This regeneration method of passing base at a fixed time has certain deviation in actual production. If the time is set too short, the regeneration cannot be completed, and the desalted water quality may not meet the requirements, which may even affect the preparation of the entire desalted water. If the time is set too long, the ionic liquid membrane base and water consumption for regeneration are large, which causes waste. Moreover, due to the fluctuation of water quality and the difference between each device, the time setting as the regeneration program is not accurate enough.

[0033] In the production process, through observation, under normal circumstances, the anion exchanger will appear foam when the base is fed. At the beginning, the foam is less, and the foam will become more and more, and start to accumulate in the channel, and directly fill the channel at 400mm (this process generally takes about 60 minutes, but there is certain difference between each bed), and then with the continuous feeding of the base, the foam starts to subside.

[0034] Based on this, as shown in Figure 1 The application provides a precise judgment method for the regeneration end point of an anion exchanger, which comprises the following steps:

[0035] Step S10: injecting ionic liquid membrane base into the base metering tank in the anion exchanger to perform backwashing on the anion exchanger;

[0036] Step S20: discharging the upper water in the resin tank after the resin particles in the anion exchanger sink;

[0037] Step S30: adding regeneration liquid to the resin tank and opening the base outlet valve, and recording the real-time foam amount of the base outlet valve by using a camera;

[0038] Step S40: inputting the real-time foam amount into a trained neural network model to determine the feeding time of the regeneration liquid corresponding to the maximum value of the real-time foam amount by using the neural network model;

[0039] Step S50: based on the feeding time of the regeneration liquid, closing the base outlet valve, continuing to feed the regeneration liquid for a predetermined time, and realizing the precise judgment of the regeneration end point of the anion exchanger.

[0040] In this embodiment, the real-time foam amount discharged from the resin tank is obtained by using a camera, and then the real-time foam amount is input into the trained neural network model to determine the maximum foam amount corresponding to the regeneration liquid feeding time by comparing the foam amount as a reference, and then the camera uploads the information to the automatic regeneration program, which will continue to feed the regeneration liquid for a predetermined time based on the time, at which time the foam amount starts to slowly decrease from the maximum amount, indicating that the anion exchanger regeneration success endpoint is reached.

[0041] Specifically, the method is more obvious for judging the anion exchanger regeneration success endpoint, which can improve the anion exchanger regeneration success rate, thereby reducing the consumption of ion liquid membrane alkali and desalted water for regeneration, prolonging the service life of the resin, reducing the cost of enterprises, and being more efficient; avoiding the possibility of affecting the safe operation of the boiler due to poor desalted water quality caused by unqualified regeneration.

[0042] In some embodiments, the ion liquid membrane alkali is a sodium hydroxide solution with a mass concentration of 28-32%.

[0043] In a preferred embodiment, the ion liquid membrane alkali is a sodium hydroxide solution with a mass concentration of 30%.

[0044] In some embodiments, the backwashing flow rate is 12-14 t / h; and the backwashing time is 5-15 min.

[0045] In a preferred embodiment, in step S10, backwashing the anion exchanger includes: performing 5-minute small backwashing on the anion exchanger; or, performing 15-minute large backwashing on the anion exchanger; and the backwashing flow rate is 13 t / h.

[0046] In some embodiments, in step S20, the backwashed anion exchanger is left still for 10 minutes to allow the resin particles to settle; then, the water above the intermediate drainage device in the anion exchanger is discharged, and the water above the middle drainage is drained, at which time there is no foam in the water.

[0047] Specifically, the simple structure diagram of the anion exchanger is as shown in Figure 2 which includes a resin layer 100, a resin upper layer 200 located above the resin layer 100, and a resin lower layer 300 located on the side of the resin layer 100 away from the resin upper layer 200; and further includes an upper water inlet valve 1, a water outlet valve 2, an acid inlet valve 3, a middle drainage valve 4, a positive washing drainage valve 5, an upper drainage valve 6, a backwashing water inlet valve 7, a middle drainage water inlet valve 8, an upper water inlet pneumatic valve 9, a resin outlet valve 10, and a resin inlet valve 11. The upper water in the resin tank is discharged by opening the middle drainage valve 4 in step S20.

[0048] In some embodiments, the regeneration liquid is obtained by diluting the ion liquid membrane alkali with desalted water at a flow rate of 12-14 t / h; the mass concentration of the regeneration liquid is 2-3%. When the regeneration liquid is added to the resin tank, the drainage changes from no foam to foam, until the amount of foam reaches the maximum, and then the foam gradually disappears, and the process is recorded by a camera.

[0049] In a preferred embodiment, the regeneration liquid is obtained by diluting the ion liquid membrane alkali with desalted water at a flow rate of 13 t / h; the mass concentration of the regeneration liquid is 2.5%.

[0050] In some embodiments, in the step S40, the trained neural network model is obtained by the following training steps:

[0051] Step S41: obtaining a photo of the real-time amount of foam discharged from the resin tank, and the corresponding regeneration liquid flow rate and regeneration liquid feeding time;

[0052] Step S42: preprocessing the photo to obtain a preprocessed photo;

[0053] Step S43: associating the preprocessed photo with the regeneration liquid flow rate and the regeneration liquid feeding time to obtain a trained neural network model.

[0054] Specifically, using the trained neural network model can save the subjectivity caused by human judgment of the amount of foam, making the method more accurate in judging the regeneration end point of the anion exchanger.

[0055] In some embodiments, preprocessing the photo includes: obtaining foam size data and foam quantity in the photo; and generating a target image database according to the foam size data and the foam quantity.

[0056] Specifically, after preprocessing the photo, a target image database is generated according to the foam size data and the foam quantity, and then the regeneration liquid flow rate and the regeneration liquid feeding time are associated to generate a neural network model for judging whether the amount of foam discharged from the resin tank reaches the maximum value. In each analysis of the real-time amount of foam (video), the analysis is performed according to the set frame rate, and then imported into the trained neural network model, and by matching the characteristics such as foam size data and foam quantity, the regeneration liquid feeding time corresponding to the maximum value of the real-time amount of foam is determined.

[0057] Further, the camera is used to observe the foam generated by the anion exchanger regeneration. When the foam reaches the highest point and starts to slowly disappear, the feedback is given to the automatic regeneration program to start closing the alkali outlet valve and continue to feed the regeneration liquid, so as to accurately determine the regeneration end point of the anion exchanger. The method can improve the regeneration success rate of the anion exchange resin, reduce the regeneration frequency, thereby reducing the consumption of ion liquid membrane alkali and desalted water, and effectively reducing the production cost of the enterprise.

[0058] In some embodiments, the predetermined time is 28-32 minutes, based on the regeneration liquid feeding time, the alkali outlet valve is closed, and the regeneration liquid is continuously fed for 28-32 minutes, so as to achieve the regeneration effect of the anion exchanger.

[0059] In a preferred real-time manner, the predetermined time is 30 minutes. It is found that the regeneration effect is better when the maximum amount of foam appears and then the alkali is continuously fed for 30 minutes.

[0060] In some embodiments, after the predetermined time of continuously feeding the regeneration liquid, the method further comprises the step of: using clean water to wash the resin particles in the anion exchanger to wash away the regeneration waste liquid.

[0061] In some embodiments, the washing process comprises a first small positive washing and a second large positive washing; the time of the first small positive washing is 10 minutes; and the time of the second large positive washing is 15 minutes.

[0062] In some embodiments, the regeneration process of the anion exchanger is simply explained as follows: backwashing → static → water drainage → alkali feeding → displacement → first small positive washing → second large positive washing → regeneration completion.

[0063] The following examples are further used to illustrate the present application. It should also be understood that the following examples are only used to further illustrate the present application, and cannot be understood as limiting the protection scope of the present application. Some non-essential improvements and adjustments made by those skilled in the art based on the above content of the present application all belong to the protection scope of the present application.

[0064] Example 1

[0065] In this embodiment, the strong acid 201x7 anion exchange resin is placed in the anion exchange resin tank. The design uses 30% ion liquid membrane alkali as the regeneration reagent, the regeneration concentration is 2.5%, the ion liquid membrane alkali for regeneration is 1.5 tons, and the water for regeneration is desalted water, about 60 tons. The resin is filled to about 200mm in the middle, about 6 tons in total. The drainage pipeline is a carbon steel pipeline, and the drainage channel is a 400mmx300mm channel.

[0066] ​The anion exchanger of the embodiment is regenerated by using the accurate judgment method of the regeneration end point of the anion exchanger, and the judgment of the regeneration end point of the anion exchanger comprises the following steps:

[0067] Step 1: Ion liquid alkali film (30% NaOH) is injected into the alkali metering tank in advance;

[0068] Step 2: The anion exchanger is subjected to 5-minute small backwashing, and the backwashing flow is 13 t / h;

[0069] Step 3: Rest for 10 minutes, and wait for the resin particles to drop;

[0070] Step 4, water drainage: The water in the upper part of the intermediate drainage device in the exchanger is drained, and the water above the middle row is drained dry. At this time, there is no foam in the water;

[0071] Step 5, regeneration liquid: Ion liquid film alkali is diluted with desalted water with a flow rate of 13 t / h, and the concentration of the alkali solution during regeneration is controlled to be 2.5%. With the progress of time, the drainage changes from no foam to foam, until the amount of foam reaches the maximum, the highest point, and then the foam slowly disappears. This process uses a camera machine to input the real-time foam amount (real-time video) during the regeneration of the regeneration liquid into a trained neural network model for analysis according to the set frame rate. When the highest point of foam appears, the camera sends an on-off signal to close the alkali outlet valve, and the regeneration liquid is fed for 30 minutes to realize the displacement process;

[0072] The displacement process can further regenerate the upper exchanger by using the regenerated liquid sinking in the exchanger, and the regenerated waste liquid is preliminarily washed away;

[0073] Step 6, first small forward washing: The regenerated waste liquid in the pressure layer is washed away, and the residual regenerated liquid is basically washed clean;

[0074] Second large forward washing: The regenerated waste liquid is thoroughly washed away until the water is clear. At this time, through water sample analysis, it is detected that SiO2≤50 μg / L and the conductivity is below 30 μS / cm, and the regeneration is successfully completed.

[0075] In summary, the application provides a precise determination method for the regeneration end point of an anion exchanger, comprising the steps of: injecting an ionic liquid membrane alkali into an alkali metering tank in the anion exchanger to perform backwashing on the anion exchanger; after the resin particles in the anion exchanger sink, discharging the upper water in the resin tank; adding a regeneration liquid to the resin tank and opening an alkali outlet valve, and recording the real-time foam amount of the alkali outlet valve by using a camera; inputting the real-time foam amount into a trained neural network model to determine the regeneration liquid feeding time corresponding to the maximum value of the real-time foam amount by using the neural network model; based on the regeneration liquid feeding time, closing the alkali outlet valve and continuing to feed the regeneration liquid for a predetermined time to realize the precise determination of the regeneration end point of the anion exchanger. The application utilizes the fact that the anion exchanger will generate foam when feeding alkali, records the real-time foam amount by using a camera, combines a trained neural network model, obtains the highest point of the foam amount, and then continues to feed the regeneration liquid for a predetermined time, so that the foam amount is changed from high to low, and the anion exchanger is regenerated. The method is more accurate in determining the successful regeneration end point of the anion exchanger, has higher efficiency compared with the traditional method of analyzing SiO2 and conductivity to determine whether the regeneration is qualified after the regeneration is completed in time, avoids the possibility that the poor quality of desalted water caused by unqualified regeneration affects the safe operation of the boiler, and the like.

[0076] It should be understood that the application of the application is not limited to the above examples, and can be improved or changed according to the above description for those skilled in the art, and all these improvements and changes should belong to the protection scope of the appended claims of the application.

Claims

1. A method for accurately determining the end point of regeneration of an anion exchanger, characterized by, The method comprises the steps of: injecting an ionic liquid membrane alkali into an alkali metering tank of an anion exchanger, and backwashing the anion exchanger; after the resin particles in the anion exchanger sink, discharging the upper water in the resin tank; adding a regeneration liquid to the resin tank and opening an alkali outlet valve, and recording the real-time foam amount of the alkali outlet valve by using a camera; inputting the real-time foam amount into a trained neural network model, and determining, by using the neural network model, a regeneration liquid feeding time corresponding to a maximum value of the real-time foam amount; based on the regeneration liquid feeding time, closing the alkali outlet valve, continuing to feed the regeneration liquid for a predetermined time, and realizing accurate judgment of the regeneration end point of the anion exchanger; the trained neural network model is obtained by using the following training steps: obtaining a photo of the real-time foam amount discharged from the resin tank, and corresponding regeneration liquid flow rate and regeneration liquid feeding time; preprocessing the photo to obtain a pretreated photo; associating the pretreated photo with the regeneration liquid flow rate and the regeneration liquid feeding time to obtain the trained neural network model; the preprocessing of the photo comprises: obtaining foam size data and foam quantity in the photo; generating a target image database according to the foam size data and the foam quantity; the predetermined time is 28-32 minutes.

2. The method for accurately determining the end point of regeneration of an anion exchanger according to claim 1, characterized by, The ionic liquid membrane alkali is a sodium hydroxide solution with a mass concentration of 28-32%.

3. The method for accurately determining the end point of regeneration of an anion exchanger according to claim 1, characterized by, The backwashing flow rate of the backwashing is 12-14 t / h, and the backwashing time is 5-15 min.

4. The method for accurately determining the end point of regeneration of an anion exchanger according to claim 1, characterized by, The regeneration liquid is obtained by diluting the ionic liquid membrane alkali with desalted water with a flow rate of 12-14 t / h, and the mass concentration of the regeneration liquid is 2-3%.

5. The method for accurate determination of the end point of regeneration of an anion exchanger according to claim 1, characterized in that, After the predetermined time of continuing to feed the regeneration liquid, the method further comprises the steps of: cleaning the resin particles in the anion exchanger by using clean water to wash away the regeneration waste liquid.

6. The method for accurately determining the end point of regeneration of an anion exchanger according to claim 5, characterized by, The cleaning process comprises a first small positive washing and a second large positive washing; the time of the first small positive washing is 10 minutes; and the time of the second large positive washing is 15 minutes.

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