Resin regeneration economic evaluation and early warning method and system based on regeneration effect training

Abstract

The present invention discloses a method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training, comprising: S1, establishing a regeneration economic efficiency evaluation model based on resin regeneration effect training; S2, establishing a regeneration intelligent optimization and diagnosis model based on big data mining; S3, performing a resin regeneration economic efficiency evaluation and warning based on the regeneration economic efficiency evaluation model based on resin regeneration effect training and the regeneration intelligent optimization and diagnosis model. Also disclosed are corresponding systems, electronic devices, and computer-readable storage media, thereby providing an intelligent, soft method for rationally evaluating and warning the economic efficiency of resin regeneration, enabling in-depth optimization of the resin regeneration process in fine processing equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical condensate polishing regeneration optimization control, and in particular to a resin regeneration economic evaluation and early warning method and system based on regeneration effect training. Background Art

[0002] Resin regeneration is a key process in condensate polishing equipment (commonly referred to in power plants as "mixed beds"). The current resin regeneration process requires personnel to visit each polishing unit to observe resin delivery. If resin delivery is complete, they return to the control room to stop resin delivery. Polishing equipment is pressurized (typically low-pressure mixed beds with operating pressures <1.0 MPa, or medium-pressure mixed beds with operating pressures >3.0 MPa). During operation, the resin wears the mixed bed peepholes under pressure. Furthermore, during unit startup, the condensate is relatively dirty, significantly contaminating the peepholes, causing them to appear blurry and dirty. In practice, many mixed beds cannot effectively observe resin delivery. Instead, a rule of thumb is often used: after repeated water flushing and refilling according to the regeneration sequence, resin delivery is considered complete and the resin scrubbing phase begins. Therefore, for normal regeneration, personnel must be on-site at this point to observe the peepholes at the bottom of the yin and yang regeneration towers to confirm resin delivery. This wastes manpower and resources, and the numerous on-site pipes and confined space make observation difficult.

[0003] In addition, there is currently a lack of intelligent soft methods for reasonable evaluation and early warning of the economic feasibility of resin regeneration, making it impossible to deeply optimize the resin regeneration process of fine processing equipment. Summary of the Invention

[0004] The purpose of the present invention is to provide a resin regeneration economic evaluation and early warning method and system based on regeneration effect training, thereby overcoming the defects of the prior art.

[0005] The first aspect of the present invention is to provide a resin regeneration economic evaluation and early warning method based on regeneration effect training, comprising:

[0006] S1, establish a regeneration economic evaluation model based on resin regeneration effect training;

[0007] S2, establishes a regenerative intelligent optimization and diagnosis model based on big data mining;

[0008] S3, performing resin regeneration economic evaluation and early warning based on the regeneration economic evaluation model trained based on the resin regeneration effect and the regeneration intelligent optimization and diagnosis model.

[0009] Preferably, the S1 includes:

[0010] S11, establishing resin regeneration evaluation indicators;

[0011] S12, establishing six regeneration control nodes based on the six steps, and assigning weights to the six regeneration control nodes respectively; then evaluating the regeneration effect; wherein the six steps correspond to the six regeneration stages, namely, conveying the spent resin, resin scrubbing, resin stratification and anion resin transfer, regeneration scrubbing, conveying the anion and cation resins, and returning the regenerated resin;

[0012] S13, evaluating and re-assigning the initial regeneration weight based on the resin regeneration evaluation index, and establishing a regeneration economic evaluation model based on resin regeneration effect training;

[0013] S14, based on the water production, acid consumption, alkali consumption and regeneration process evaluation rules, establish a scoring weight intelligent adjustment model to achieve accurate evaluation of regeneration.

[0014] Preferably, the S11 includes: establishing the water production and water consumption per set of resins as evaluation indicators for each regeneration; wherein the water production is the amount of water processed by this set of resins in this regeneration cycle, in units of ten thousand tons; the water consumption per unit includes two indicators, acid consumption and alkali consumption, wherein the acid consumption refers to the amount of hydrogen chloride consumed per ton of water production by this set of resins in this regeneration cycle, in units of g / t, and the alkali consumption refers to the amount of sodium hydroxide consumed per ton of water production by this set of resins in this regeneration cycle, in units of g / t.

[0015] Preferably, the S12 includes:

[0016] (1) setting the original weight value range according to the empirical value of the regeneration process of the fine treatment system; changing the original weight value range within the weight value range according to the actual situation of each set of resin, and keeping the total score of all weight values ​​of the six regeneration control nodes set to 100 points;

[0017] (2) The initial regeneration weight is obtained after evaluating the regeneration effect according to the characteristics of the regeneration stage.

[0018] Preferably, the S13 includes:

[0019] (1) The economic indicators are set according to the actual value, average value and target value, and the economic indicators are evaluated according to the actual value level; for water production, the target value is 25t, the actual value ≥ the average value, the water production indicator is rated as excellent, the actual value < the average value, the water production is rated as poor, the average value ≥ the target value × 85%, the water production is rated as excellent, the average value < the target value × 85%, the water production is rated as poor; for acid consumption, the target value is 32g / t, the actual value ≤ the average value, the indicator is rated as excellent, the actual value > the average value, the indicator is rated as poor, the average value ≤ the target value × 1.20, the indicator If the target value is 53g / t, the actual value is ≤ the average value, the indicator is rated as excellent; if the actual value is greater than the average value, the indicator is rated as poor; if the average value is ≤ the target value × 1.20, the indicator is rated as excellent; if the average value is greater than the average value, the indicator is rated as poor; the actual value is the economic indicator of the regenerated resin operation at that time; the average value is the average value of the water production and acid and alkali consumption economic evaluation indicators of all regenerated resins in this evaluation period, which needs to be statistically analyzed; the target value is the value that should be achieved in resin design or benchmarking;

[0020] (2) Establish regeneration evaluation rules based on water production, acid consumption, alkali consumption and regeneration process scores.

[0021] Preferably, the S2 includes:

[0022] S21, based on the economic indicators, determine the best regeneration case, including: grouping the economic indicators of each set of resins, and selecting the regeneration case with the best economic indicators and a reasonable number as the regeneration optimization case;

[0023] S22, extracting the end point picture of the regeneration stage in the best regeneration case, and adjusting the vector feature representation end point judgment in the form of picture + value, including: for the optimized case, combining its regeneration score, conducting data mining on it, and determining the best regeneration step sequence; extracting the end point picture of the regeneration stage for the best regeneration stage, and adjusting the vector feature representation end point judgment in the form of picture + value, thereby determining a regeneration intelligent optimization and diagnosis model for automatically optimizing and optimizing the regeneration end point.

[0024] Preferably, the S3 includes:

[0025] S31, performing a first evaluation on the regeneration economy based on the regeneration economy evaluation model trained based on the resin regeneration effect;

[0026] S32, collecting all current regeneration step endpoint images in real time, and extracting image features of all regeneration step endpoint images;

[0027] S33, comparing the image features with the physical features of the resin throughout its life cycle corresponding to the regeneration intelligent optimization and diagnosis model to obtain a second evaluation of the regeneration economy;

[0028] S34: Evaluate the current operating status of the resin based on the first evaluation and the second evaluation, and issue an early warning for the resin that is close to being scrapped.

[0029] The second aspect of the present invention is to provide a resin regeneration economic evaluation and early warning system based on regeneration effect training, which is used to implement the method of the first aspect, comprising:

[0030] A first model building unit (101) is used to build a regeneration economic evaluation model based on resin regeneration effect training;

[0031] A second model building unit (102) is used to build a regenerative intelligent optimization and diagnosis model based on big data mining;

[0032] A resin regeneration economic evaluation and early warning unit (103) is used for performing resin regeneration economic evaluation and early warning based on the regeneration economic evaluation model trained based on the resin regeneration effect and the regeneration intelligent optimization and diagnosis model.

[0033] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0034] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0035] Beneficial effects of the method and system of the present invention:

[0036] It provides an intelligent soft method for reasonable evaluation and early warning of the economic efficiency of resin regeneration, and can deeply optimize the resin regeneration process of fine processing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flow chart of a method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training according to an embodiment of the present invention.

[0039] Figure 2 The figure is a schematic structural diagram of a chemical condensate polishing and regeneration device according to an embodiment of the present invention.

[0040] Figure 3 The figure is a schematic structural diagram of a positive regeneration tower according to an embodiment of the present invention.

[0041] Figure 4 The schematic diagram of the structure of a cathode regeneration tower is provided according to an embodiment of the present invention.

[0042] Figure 5 This is an architecture diagram of a resin regeneration economic evaluation and early warning system based on regeneration effect training according to an embodiment of the present invention.

[0043] Figure 6 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0047] Example 1

[0048] See also Figure 1This embodiment provides a method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training, comprising:

[0049] S1, establish a regeneration economic evaluation model based on resin regeneration effect training;

[0050] As a preferred embodiment, the S1 includes:

[0051] S11, establishing resin regeneration evaluation indicators;

[0052] As a preferred embodiment, the water production and water consumption per set of resins are established as evaluation indicators for each regeneration; wherein the water production refers to the amount of water processed by this set of resins in this regeneration cycle, in units of ten thousand tons; the water consumption per set of resins includes two indicators, acid consumption and alkali consumption, wherein the acid consumption refers to the amount of hydrogen chloride consumed per ton of water production by this set of resins in this regeneration cycle, in units of g / t; the alkali consumption refers to the amount of sodium hydroxide consumed per ton of water production by this set of resins in this regeneration cycle, in units of g / t.

[0053] S12, establishing six regeneration control nodes based on the six steps, and assigning weights to the six regeneration control nodes respectively; then evaluating the regeneration effect; wherein the six steps correspond to the six regeneration stages, namely, conveying the spent resin, resin scrubbing, resin stratification and anion resin transfer, regeneration scrubbing, conveying the anion and cation resins, and returning the regenerated resin;

[0054] As a preferred embodiment, the S12 includes:

[0055] (1) The original weight value range is set according to the empirical value of the regeneration process of the fine treatment system; according to the actual situation of each set of resin, the original weight value range is changed within the weight value range, and the total score of all weight values ​​of the six regeneration control nodes is kept set to 100 points.

[0056] In this embodiment, the weight value ranges are 5-14, 20-30, 25-45, 15-25, 0-9, and 5-14 according to the six steps of failed resin transportation, resin scrubbing, resin stratification and anionic resin transfer, regeneration scrubbing, anionic and anionic resin transportation, and resin return after regeneration.

[0057] (2) Evaluate the regeneration effect according to the characteristics of the regeneration stage to obtain the initial regeneration weight;

[0058] In this embodiment, the number of levels for each stage is determined based on the weight assigned to each stage. The six steps of spent resin transfer, resin scrubbing, resin delamination and anionic resin transfer, regeneration scrubbing, anionic and anionic resin transfer, and post-regeneration resin return are graded three, four, five, three, two, and three levels, respectively. Each level is assigned a value based on the weight assigned / level. For a weight assigned / level ≥ 5, each differential score is 25% of the total score, with 0 ≤ the last level ≤ 10 points. For a weight assigned / level < 5, each differential score is 50% of the total score, with 0 ≤ the last level ≤ 5 points.

[0059] Table 1 below takes the six steps of failed resin transfer, resin scrubbing, resin stratification and anion resin transfer, regeneration scrubbing, anion and cation resin transfer, and resin return after regeneration as examples with weights of 10, 20, 40, 15, 5, and 10, and sets the levels and scores respectively. See Table 1 for details.

[0060] Table 1

[0061]

[0062]

[0063]

[0064] S13, evaluating and re-assigning the initial regeneration weight based on the resin regeneration evaluation index, and establishing a regeneration economic evaluation model based on resin regeneration effect training;

[0065] As a preferred embodiment, the S13 includes:

[0066] (1) The economic indicators are set according to actual values, average values, and target values, and the economic indicators are evaluated based on the actual value levels, as shown in Table 2.

[0067] Table 2

[0068]

[0069] Note: (A) The actual value is the economic index of the regenerated resin operation at that time.

[0070] (B) The average value is the average value of the water production and acid and alkali consumption economic evaluation indicators of all regenerated resins within this evaluation period, which needs to be statistically analyzed;

[0071] (C) The target value is the value that should be achieved in resin design or benchmarking. The target value varies depending on the high-mix and resin characteristics, and the design value of the high-mix needs to be consulted. This example only selects the target value of a 330MW unit with a cation-cation resin ratio of 1.3:1 and a processing flow rate of 350t / h.

[0072] (2) Establish regeneration evaluation rules based on water production, acid and alkali consumption, and regeneration process scores;

[0073] Since the resin regeneration effect is basically affected by the six steps, the energy consumption index basically shows a strong correlation with the regeneration score. Through the resin regeneration effect and score data, data correlation confirmation is carried out to determine a reasonable regeneration score. Table 3 below shows the regeneration score set according to the existing data statistics. The actual regeneration of different resins may be different.

[0074] Table 3

[0075]

[0076] S14, based on the water production, acid consumption, alkali consumption and regeneration process evaluation rules, establish a scoring weight intelligent adjustment model to achieve accurate evaluation of regeneration.

[0077] Since the weight adjustment is based on abnormalities that occur during the regeneration process or significant changes in resin properties, this embodiment only constructs some cases for illustration, and uses the second and fourth cases as examples.

[0078] Example 1: When the economic evaluation effect is good but the regeneration score is low, the steps are arranged in descending order according to the actual score rate of the regeneration step. Steps with the same score rate are arranged in descending order according to the score of this step. The first two items (due to the tied names, or > two items) are weighted points-added steps, and the last two items (due to the tied names, or > two items) are weighted points-reduced steps, including tied items. Add 5 points to the weighted points-added steps until the weight limit of this step is reached. If the total points added are less than 10 points, add points to the third item in the ranking, and so on. Subtract 5 points from the weighted points-reduced steps until the weight limit of this step is reached. If the total points subtracted are less than 10 points, subtract points from the third item in the ranking, and so on. See Table 4 below.

[0079] Table 4

[0080]

[0081]

[0082] Example 2: If the economic evaluation results are poor but the regeneration score is high, the steps are sorted in ascending order according to their actual score rates. Steps with the same score rates are sorted in ascending order according to the scores of the current step. The first two (due to a tie in name, or > two) are weighted-point-added steps, and the last two (due to a tie in name, or > two) are weighted-point-reduced steps, including tied items. Each weighted-point-added step is given a bonus of 5 points, up to the weight limit of the current step. If the total bonus score is less than 10 points, the third item in the ranking is given a bonus, and so on. Each weighted-point-reduced step is subtracted by 5 points, up to the weight limit of the current step. If the total bonus score is less than 10 points, the third item in the ranking is subtracted, and so on.

[0083] At the same time, a manual adjustment interface is set up to intervene in the automatic adjustment of the model, and to manually repair errors when model adjustment occurs.

[0084] S2, establishes a regenerative intelligent optimization and diagnosis model based on big data mining;

[0085] As a preferred embodiment, the S2 includes:

[0086] S21, determine the best regeneration case based on economic indicators;

[0087] In this embodiment, the economic indicators of each set of resins are grouped, and the regeneration cases with better economic indicators and reasonable quantity are selected as the regeneration optimization cases;

[0088] S22, extracting the regeneration phase endpoint image in the best regeneration case, and adjusting the vector feature representation endpoint judgment in the form of image + value.

[0089] In this embodiment, data mining is carried out on the optimization cases in combination with their regeneration scores to determine the optimal regeneration sequence; the end point image of the optimal regeneration stage is extracted, and the vector feature representation end point judgment is adjusted in the form of image + value, thereby determining a regeneration intelligent optimization and diagnosis model for automatically optimizing and optimizing the regeneration end point.

[0090] S3, performing resin regeneration economic evaluation and early warning based on the regeneration economic evaluation model trained based on the resin regeneration effect and the regeneration intelligent optimization and diagnosis model.

[0091] As a preferred embodiment, the S3 includes:

[0092] S31, performing a first evaluation on the regeneration economy based on the regeneration economy evaluation model trained based on the resin regeneration effect;

[0093] S32, collecting all current regeneration step endpoint images in real time, and extracting image features of all regeneration step endpoint images;

[0094] S33, comparing the image features with the physical features of the resin throughout its life cycle corresponding to the regeneration intelligent optimization and diagnosis model to obtain a second evaluation of the regeneration economy;

[0095] S34: Evaluate the current operating status of the resin based on the first evaluation and the second evaluation, and issue an early warning for the resin that is close to being scrapped.

[0096] like Figure 2-4 As shown, as a preferred embodiment, the chemical condensation water polishing regeneration equipment includes a high-mixed fat outlet valve (1), a high-mixed resin pipe (2), a high-mixed resin output main pipe (3), a peep window 1 (4), a resin storage tower to high-mixed resin delivery main pipe (5), a peep window 5 (6), a resin storage tower fat outlet door (7), a peep window 6 (8), a yin and yang regeneration tower to resin storage tower resin delivery main pipe (9), a high-mixed connecting valve (10), a yang regeneration tower fat inlet door (11), a yin resin delivery door (12), a mixed fat delivery door (13), a yang regeneration tower fat outlet door (14), a yin regeneration tower fat outlet door (15), a yang regeneration tower middle peep window 2 (16), a yang regeneration tower bottom peep window 3 (17) and a yin regeneration tower middle peep window 4 (18); wherein:

[0097] The transparent tubular peep window 1 (4) is installed on the resin output pipe of the condensate polishing equipment, and the transparent tubular peep window 1 (4) is installed on the high-mixed resin output main pipe (3) of the condensate polishing (hereinafter referred to as "polishing") equipment (the power plant usually calls the polishing equipment "mixed bed"). The installation position is the main pipe of the polishing resin to the cation regeneration tower, after the polishing equipment grease connection valve, the position is close to the polishing equipment, which is convenient for timely judgment of the end of resin transportation during resin transportation (mixed bed transportation to cation regeneration tower), saving desalted water consumption;

[0098] The bottom of the mixed bed is provided with a peep window 2 (16) in the middle of the positive regeneration tower, a peep window 3 (17) at the bottom of the positive regeneration tower, and a peep window 4 (18) in the middle of the negative regeneration tower for observing the resin conveying situation. Before regeneration, the staff need to go to each set of fine treatment equipment to observe the resin conveying situation. If the resin conveying is completed, return to the control room to stop the resin conveying. The fine treatment equipment is a pressurized equipment (the present invention is a low-pressure mixed bed with an operating pressure of less than 1.0 MPa, and most of them are medium-pressure mixed beds with an operating pressure of more than 3.0 MPa). During operation, the resin wears the mixed bed peep window under pressure. At the same time, during the startup phase of the unit, since the condensate is relatively dirty, it also has obvious pollution on the peep window, causing the peep window to be blurred and dirty. In practice, many mixed beds cannot effectively observe the resin conveying situation. Usually, empirical conveying is adopted. After repeated water filling and flushing several times according to the regeneration step, it is considered that the resin conveying is completed and the resin scrubbing phase is entered.

[0099] The transparent tube-type peep window 5 (6) is installed on the resin storage tower to the mixed bed resin delivery main pipe (5). The installation position of the peep window 5 (6) is the resin storage tower to the mixed bed resin delivery main pipe (5). After the resin storage tower exits the grease door, the position is close to the resin storage tower, which is convenient for timely judging the end of resin delivery during resin delivery (delivery back to the mixed bed), saving the amount of desalted water;

[0100] A peephole is installed at the bottom of the resin storage tower to observe resin transfer. Previously, the regeneration process required staff to visit the resin storage tower to observe resin transfer. Once resin transfer was complete, the staff would return to the control room to stop resin transfer. Because the resin storage tower is located in the regeneration workshop, it is generally some distance from the control room. Manual inspections took time, and multiple on-site inspections were required to ensure clean resin transfer, which was a waste of manpower.

[0101] The present invention installs a transparent tubular peephole 5 in the resin input (water supply to the mixed bed) pipeline. This single peephole monitors the transfer of condensate from six mixed beds in the first phase, totaling seven sets of resin (one set of spare resin). The peephole 5 is installed behind the resin storage tower outlet, as close as possible to the resin storage tower. This allows for prompt cessation of resin delivery after the resin has been fully transferred, thus conserving desalted water.

[0102] For normal regeneration, personnel need to be on site at this point to observe the peepholes at the bottom of the yin and yang regeneration towers to determine whether the resin is transported cleanly, which wastes manpower and material resources. In addition, there are many pipelines on site and the space is small, making it difficult to observe.

[0103] Therefore, a transparent tube-type peep window 6 (8) is installed on the resin conveying main pipe (7) from the yin and yang regeneration towers to the resin storage tower, close to the position of the yin and yang regeneration towers. By inspecting the resin particles in the peep window 6, the resin output in the yang regeneration tower and the yin regeneration tower can be supervised.

[0104] An image tracking and positioning system is installed on the peeping window 1 (4), the tree peeping window 5 (6), the resin storage tower fat outlet door (7), the peeping window 6 (8), the middle peeping window 2 (16) of the positive regeneration tower, the bottom peeping window 3 (17) of the positive regeneration tower and the middle peeping window 4 (18) of the negative regeneration tower.

[0105] As a preferred embodiment, the image tracking and positioning system includes a high-definition image acquisition device, so as to clearly track the regeneration images in the peep window 1 (4), the tree peep window 5 (6), the resin storage tower fat outlet (7), the peep window 6 (8), the middle peep window 2 (16) of the positive regeneration tower, the bottom peep window 3 (17) of the positive regeneration tower and the middle peep window 4 (18) of the negative regeneration tower. The high-definition image acquisition device has a continuous high-definition camera function and a target image capture function, so that it can combine the process image vector diagnosis technology to achieve the best judgment of the regeneration process, and based on the best regeneration end point conclusion, collect the image of the regeneration node to end the first regeneration process consisting of six steps of current failed resin conveying, resin scrubbing, resin stratification and negative resin transfer, regeneration scrubbing, positive and negative resin conveying and resin return after regeneration, and then enter the second regeneration process. The first regeneration process obtains the best regeneration effect.

[0106] In this embodiment, in order to ensure the clarity of the picture, the high-definition image acquisition device is an infrared camera device or lighting is added at the peep window 1 (4), the tree peep window 5 (6), the resin storage tower fat outlet door (7), the peep window 6 (8), the peep window 2 (16) in the middle of the positive regeneration tower, the peep window 3 (17) at the bottom of the positive regeneration tower and the peep window 4 (18) in the middle of the negative regeneration tower.

[0107] As a preferred embodiment, the six steps of spent resin transport, resin scrubbing, resin layering and anion resin transfer, regeneration scrubbing, anion and cation resin transport, and regeneration resin return are as follows:

[0108] For the transportation of failed resin, the specific operation is as follows: open the high-mix fat outlet valve (1), the high-mix connecting valve (10) and the positive regeneration tower fat inlet door (11), start the flushing water pump or compressed air, and transport the failed resin, thereby transporting the failed resin in the chemical condensate polishing equipment to the positive regeneration tower or the resin separation tower; based on the peep window 1 (4), the resin water filling height and water filling time in the water filling → water delivery operation are optimized, and the failed resin delivery is optimized according to the on-site resin delivery situation: when the residual amount is small, the mixed bed water level is lowered, and the air mixing stirring method is used to improve the resin delivery efficiency;

[0109] For resin scrubbing and resin stratification, the specific operation is: use compressed air to scrub the resin in the positive tower, and after scrubbing clean, add water in reverse flow, and use the density difference between the negative and positive resins to stratify the positive and negative resins, so as to prepare for the next step of regeneration; in this embodiment, in order to save water and reduce cross-contamination of resins, some resins are provided with a mixed fat layer, which can reduce the requirements for stratification, that is, the positive and negative resins do not have to be completely separated, as long as the boundaries between the negative resin and the mixed fat and the boundaries between the positive resin and the mixed fat are clear; a high-definition camera for obtaining the image of the regenerated resin is set on the peep window 2 (16) in the middle of the positive regeneration tower, and the specific amount of the regenerated resin each time is obtained according to the image of the regenerated resin, so as to determine the appropriate scrubbing liquid level; at the same time, the color and particle number of the scrubbing water after scrubbing are determined by the high-definition camera on the peep window 2 (16), and the resin scrubbing end point is measured. At the same time, the resin fat content of the regenerated resin is collected through the peep window 2 and recorded for analysis. The resin fat level after drainage is collected at the peep window, thereby determining the resin fat content and judging whether the fat content is appropriate; a camera is set at the peep window 3 (17) at the bottom of the positive tower resin, and the resin separation interface is judged by the camera. If the interface is clean and flat, the next regeneration step is entered, otherwise the operation is repeated and re-layered; in the case of setting a mixed fat layer, the bottom of the negative resin output pipe is marked at the peep window 3 (17) at the bottom of the positive regeneration tower, and it is judged that the highest height of the mixed fat layer is less than the bottom height of the negative resin output pipe and the lowest height of the mixed fat layer is greater than the bottom height of the mixed fat output pipe, indicating that the resin stratification is completed; otherwise, re-layer or adjust the resin ratio; at the same time, the mixed fat layer height data is collected to analyze and diagnose the resin scrubbing effect and resin agglomeration;

[0110] For the anion resin transfer, the specific operation is as follows: judging the transport interface through the peep window 1 (4), the peep window 5 (6), the peep window 2 (16) in the middle of the positive regeneration tower, the peep window 3 (17) at the bottom of the positive regeneration tower and the peep window 4 (18) in the middle of the negative regeneration tower;

[0111] For regeneration scrubbing, the specific operation is: including scrubbing and regenerating the anion resin in the anion regeneration tower and scrubbing and regenerating the cation resin in the cation regeneration tower;

[0112] The specific operation of scrubbing and regenerating the anion resin in the anion regeneration tower is as follows: opening the anion resin delivery door (12) to transfer the anion resin with a smaller density to the anion regeneration tower, repeating the resin scrubbing operation, and then regenerating the anion resin by adding alkali solution; opening the mixed resin (mixed fat) delivery door (13) to transport the mixed fat to the mixed fat tower for storage and participation in the regeneration of the next set of resin;

[0113] For the cation resin scrubbing regeneration in the cation regeneration tower, the specific operation is as follows: after the cation resin is repeatedly scrubbed in the cation regeneration tower, the cation resin is regenerated by adding acid solution;

[0114] For the transportation and regeneration of the cation and anion resins, the specific operation is: open the cation regeneration tower fat outlet door (14) and the cation regeneration tower fat outlet door (15) respectively, and transfer the regenerated cation and anion resins to the resin storage tower for standby use.

[0115] Working principle:

[0116] Targeted automated and intelligent optimization is conducted based on the steps in the condensate regeneration process that require manual on-site judgment and those that have a significant impact on the economic operation of condensate polishing. Based on these factors, the regeneration process is broken down into six evaluation steps: spent resin transfer, resin scrubbing, resin stratification and anionic resin transfer (with a viewing window to judge the transfer interface), regeneration scrubbing, anionic and ionic resin transfer, and fat mixing.

[0117] (1) Failed resin transportation

[0118] The resin that has failed in the fine treatment equipment is transported to the positive regeneration tower or resin separation tower. The present invention is a positive regeneration tower. Many condensate fine treatment regeneration systems are equipped with a resin separation tower. The failed resin is first transported to the resin separation tower.

[0119] 1. Add a tubular peep window① to realize timely judgment of the transportation process, reduce the workload of multiple manual visits to the site for confirmation, waste of desalted water when returning to the control room for the next step after transportation, and inaccurate observation due to contamination and wear of the peep window.

[0120] 2. Incomplete resin transportation. During the resin transportation process, after water transportation and air transportation, the water filling → water transportation operation is repeated. At this time, the water is filled to 1 / 3 of the high-mix bed. Since the water level in the high-mix bed is relatively high, the resin remaining around the water cap at the bottom of the high-mix bed and at the edge of the bed cannot be effectively rinsed and stirred, resulting in incomplete resin transportation. The optimization of the present invention is to optimize the resin filling height and filling time in the water filling → water transportation operation by observing the on-site resin transportation situation. According to the on-site resin transportation situation, this step is optimized: when the residual amount is small, the mixed bed water level is lowered, and air mixing and stirring are adopted to improve the resin transportation efficiency and achieve a failed resin residual rate of ≤0.1%.

[0121] (2) Resin scrubbing

[0122] The resin is scrubbed in the cation regeneration tower or resin separation tower to prepare for the separation of the anion and cation resins. Resin scrubbing not only cleans the resin and removes any coverings, improving regeneration efficiency, but also separates clumped resins, facilitating separation. Scrubbing can cause some wear and tear on the resin, so excessive scrubbing is recommended.

[0123] 1. Add a high-definition camera to the peep window 2 (16) in the middle of the positive regeneration tower to determine the appropriate scrubbing liquid level based on the specific amount of regenerated resin each time.

[0124] Controlling the water level before resin scrubbing is crucial for effective scrubbing. The water level should be approximately 250mm above the resin. This optimization optimizes the scrubbing water level for different resin amounts, maintaining an appropriate water level and ensuring sufficient disturbance during scrubbing. Water level control should be correlated with the resin amount and scrubbing air pressure. If necessary, adjustments to the scrubbing air pressure should be considered and optimized, and then integrated into the scrubbing process.

[0125] 2. Through the high-definition camera of the peep window 2 (16), the color and particle count of the scrubbing water after scrubbing are used to determine the resin scrubbing end point to ensure that the scrubbing is clean and the resin is fully washed away while avoiding over-rinsing.

[0126] 3. Collect the amount of resin in this set of regenerated resin through the peep window 2 (16) and record and analyze. After collecting the drainage, the resin fat level is at the position of the peep window 2 (16). Record the amount of resin in this set and judge whether the amount of resin is appropriate.

[0127] (1) Count the actual average resin amount of 7 sets of resin. If the resin loss rate is 5% or greater than 2%, an early warning is issued to remind you to add resin. If the resin loss rate is greater than 5%, an alarm is issued for resin loss. The calculation formula of the resin loss rate is shown in the following formula (1):

[0128]

[0129] (2) If the current amount of regenerated resin is greater than the actual average amount of resin × 1.05, the mixed resin is fed into the mixed resin tower at a specific time to extend the resin transfer time. If the current amount of regenerated resin is less than the actual average amount of resin × 0.95, the mixed resin is fed into the mixed resin tower at a specific time to shorten the resin transfer time. The specific time requires the current amount of regenerated resin data. After on-site debugging and measuring the parameters, optimize them into the corresponding optimization control program.

[0130] (3) Resin stratification and negative resin transfer

[0131] During regeneration, the anionic and cationic resins in the mixed bed need to be separated and transferred to the anionic and cationic resin regeneration towers for regeneration respectively. Resin stratification is an important factor affecting the resin regeneration effect. Poor stratification quality will cause some resins to fail, waste acid and alkali consumption, and reduce the water production of the mixed bed.

[0132] Since the color of the anionic and cation resins changes during use, and the resin stratification interface is greater than 2.5 meters from the ground, it is difficult to accurately judge due to the influence of distance and light on the ground. If personnel climb up the ladder to the peephole position to observe the resin separation, there is a certain safety hazard.

[0133] 1. A camera is installed at the viewing window 3 (17) at the bottom of the resin tower. The separation interface of the resin is judged by the camera. If the interface is clean and smooth, the next regeneration step is entered. Otherwise, the operation is repeated to re-layer the resin.

[0134] (1) In this embodiment, a mixed resin layer is designed. The bottom of the negative resin output tube is marked in the viewing window. When the highest level of the mixed resin layer (h1) is less than the bottom level of the negative resin output tube (h2), and the lowest level of the mixed resin layer (h3) is greater than the bottom level of the mixed resin output tube (h4), the resin layering is complete. Otherwise, the layering needs to be re-done or the resin ratio needs to be adjusted.

[0135] (2) At the same time, the height data of the mixed fat layer is collected to analyze and diagnose the resin scrubbing effect and resin "clustering".

[0136] 2. Collect the ratio of cation and anion resins of this set of regenerated resins through the peep window 3 and record and analyze. Collect the position of the cation and anion resin interface at the peep window, and analyze and diagnose the ratio of cation and anion resins of this set of mixed bed resins in combination with the fat content of the resin. Make sure that the cation and anion resin ratio of this set of resins will not affect the transfer of anion resins. If the resin interface is too high, higher than the height of the anion resin delivery door, part of the cation resin will be delivered to the anion regeneration tower, and after NaOH regeneration, it will become Na-type cation resin, lose the water production effect, and affect the water production of the resin. If the resin interface is too high, if the overall resin content is normal, it means that the ratio of cation and anion resins is unbalanced, there is too little anion resin, and the mixed bed will leak silicon early, which will greatly reduce the water production.

[0137] A regeneration system for the mixed lipid layer is set up, with the negative resin above the upper interface of the mixed lipid layer and the positive resin below the lower interface of the mixed lipid layer.

[0138] 3. The anion resin transfer is controlled through the peep window 3 (17) at the bottom of the yang regeneration tower. The peep window 3 (17) at the bottom of the yang regeneration tower is used to determine whether the transferred resin has reached the bottom position of the anion resin delivery pipe. If it has reached the bottom position, it is determined that the anion resin delivery is completed and the next regeneration step is entered. Otherwise, the layered transfer operation is repeated.

[0139] Note: Step 1 determines whether the resin stratification meets the conveying requirements (clear stratification and all the anion resin is transferred to the anion bed), and step 3 determines whether the anion resin is transferred cleanly.

[0140] (4) Regeneration scrubbing

[0141] The regeneration process includes scrubbing the anion and cation resins in the anion regeneration tower and the cation regeneration tower respectively and adding alkali / acid. The scrubbing further removes the pollutants outside the resin to ensure the regeneration effect.

[0142] 1. Control the scrubbing height of the positive resin through the sight glass ③ to ensure that the scrubbing water level is 150mm above the resin layer.

[0143] The appropriate scrubbing liquid level is determined according to the specific amount of resin regenerated each time, and the scrubbing step parameters are optimized according to the scrubbing liquid level and fixed in the regeneration program.

[0144] 2. Install a camera at the viewing window 4 (18) in the middle of the cathode regeneration tower to control the scrubbing of the cathode resin.

[0145] The scrubbing height of the negative resin is controlled through the peep window 4 (18) in the middle of the negative regeneration tower to ensure that the scrubbing water level is 150mm above the resin layer.

[0146] The appropriate scrubbing liquid level is determined according to the specific amount of resin regenerated each time, and the scrubbing step parameters are optimized according to the scrubbing liquid level and fixed in the regeneration program.

[0147] 3. Optimize the concentration of regeneration liquid. The higher the regeneration speed, the greater the resin wear; the slower the regeneration speed, the better the regeneration effect. This needs to be considered in practice.

[0148] (5) Transfer of cation and cation resins

[0149] 1. A transparent tubular peep window 6 (8) is installed on the resin delivery main pipe from the yin and yang regeneration towers to the resin storage tower, close to the yin and yang regeneration towers, to monitor the resin output in the yang regeneration tower and the yin regeneration tower.

[0150] 2. Optimize the anion resin transfer process. Stop the process after the anion resin is regenerated and replaced. Perform the post-alkali scrubbing step before transferring the anion resin to the resin storage tower. This scrubbing step fully agitates the anion resin and improves the anion resin transfer efficiency.

[0151] 3. Refer to the optimized negative resin transfer procedure to optimize the positive resin transfer procedure, and similarly perform the resin transfer step immediately after scrubbing.

[0152] 4. The bottom water inlet device affects the thorough delivery of resin. A high-definition camera is set up in the middle peep window 2 (16) of the positive regeneration tower. The appropriate scrubbing liquid level is determined on site according to the specific amount of regenerated resin each time to determine whether it needs to be optimized.

[0153] (6) Return of recycled resin

[0154] A transparent tubular peephole 5 is installed on the resin delivery main pipe from the resin storage tower to the mixed bed. The installation position is the resin delivery main pipe from the resin storage tower to the mixed bed, after the resin outlet of the storage tower, and the position is as close to the resin storage tower as possible, so as to facilitate timely judgment of the end of resin delivery during resin delivery (delivery back to the mixed bed) and save desalted water consumption.

[0155] (7) Fat Mixing

[0156] 1. Optimize the process and consider using a mixing and draining method when mixing fat, instead of the current static mixing method. This will speed up the sedimentation and prevent the recurrence of stratification during slow sedimentation.

[0157] 2. The mixed fat water level control is carried out through on-site tests to obtain the best water level, and the optimal liquid level control is achieved through photoelectric switches and optimized according to on-site conditions. This is because a camera needs to be installed for each mixed bed, which requires a lot of equipment and has high construction and maintenance costs. In addition, there is a possibility that the peep window will be dirty and worn, and the camera image may be distorted.

[0158] (8) Others

[0159] All peep windows are made of anti-pollution and wear-resistant materials to prevent the resin from contaminating the peep window and the wear of the peep window during the transmission process, which will cause the peep window to be unclear and unable to be effectively identified.

[0160] Example 2

[0161] like Figure 5 As shown, this embodiment provides a resin regeneration economic evaluation and early warning system based on regeneration effect training, which is used to implement the method of the first aspect, including:

[0162] The first model building unit 101 is used to build a regeneration economic evaluation model based on resin regeneration effect training;

[0163] The second model building unit 102 is used to build a regenerative intelligent optimization and diagnosis model based on big data mining;

[0164] The resin regeneration economic evaluation and early warning unit 103 is used to perform resin regeneration economic evaluation and early warning based on the regeneration economic evaluation model trained based on the resin regeneration effect and the regeneration intelligent optimization and diagnosis model.

[0165] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method described in the first embodiment.

[0166] like Figure 6 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, wherein the memory 302 stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the method described in Example 1.

[0167] Through the description of the above embodiments, those skilled in the art will clearly understand that the above embodiments can be implemented through software or by using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for causing a computer device (such as a personal computer, a server, or a network device) to execute the methods described in the various embodiments of the present invention.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resin regeneration economic evaluation and early warning method based on regeneration effect training, characterized in that: include: S1, establish a regeneration economic evaluation model based on resin regeneration effect training; S2, establishes a regenerative intelligent optimization and diagnosis model based on big data mining; S3, performing a resin regeneration economic evaluation and early warning based on the resin regeneration effect training-based regeneration economic evaluation model and the regeneration intelligent optimization and diagnosis model; Said S1 comprises: S11, establishing resin regeneration evaluation indicators; S12, establishing six regeneration control nodes based on the six steps, and assigning weights to the six regeneration control nodes respectively; then evaluating the regeneration effect; wherein the six steps correspond to the six regeneration stages, namely, conveying the spent resin, resin scrubbing, resin stratification and anion resin transfer, regeneration scrubbing, conveying the anion and cation resins, and returning the regenerated resin; S13, evaluating and re-assigning the initial regeneration weight based on the resin regeneration evaluation index, and establishing a regeneration economic evaluation model based on resin regeneration effect training; S14, based on the water production, acid consumption, alkali consumption and regeneration process evaluation rules, establish a scoring weight intelligent adjustment model to achieve accurate evaluation of regeneration; The S2 includes: S21, based on the economic indicators, determine the best regeneration case, including: grouping the economic indicators of each set of resins, and selecting the regeneration cases with the best economic indicators and a reasonable number as the regeneration optimization cases; S22, extracting the end point picture of the regeneration stage in the best regeneration case, and adjusting the end point judgment represented by the vector features in the form of picture + value, including: conducting data mining on the regeneration optimization case in combination with its regeneration score to determine the best regeneration step sequence; extracting the end point picture of the regeneration stage for the best regeneration stage, and adjusting the end point judgment represented by the vector features in the form of picture + value, thereby determining a regeneration intelligent optimization and diagnosis model for automatically optimizing and optimizing the regeneration end point.

2. The method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training according to claim 1, characterized in that: The S11 includes: establishing the water production and water consumption per set of resins as evaluation indicators for each regeneration; wherein the water production refers to the amount of water processed by this set of resins in this regeneration cycle, in units of ten thousand tons; the water consumption per unit includes two indicators: acid consumption and alkali consumption, wherein the acid consumption refers to the amount of hydrogen chloride consumed per ton of water production of this set of resins in this regeneration cycle, in units of g / t, and the alkali consumption refers to the amount of sodium hydroxide consumed per ton of water production of this set of resins in this regeneration cycle, in units of g / t.

3. The method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training according to claim 2, characterized in that: The S12 includes: (1) Setting the original weight value range according to the experience value of the regeneration process of the fine treatment system; changing the original weight value range within the weight value range according to the actual situation of each set of resin, and keeping the total score of all weight values ​​of the six regeneration control nodes set to 100 points; (2) The initial regeneration weight is obtained after evaluating the regeneration effect according to the characteristics of the regeneration stage.

4. The method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training according to claim 3, characterized in that: The S13 includes: (1) The economic indicators are set according to the actual value, average value and target value, and the economic indicators are evaluated according to the actual value level; for water production, the target value is 25t, the actual value ≥ the average value, the water production indicator is rated as excellent, the actual value < the average value, the water production is rated as poor, the average value ≥ the target value × 85%, the water production is rated as excellent, the average value < the target value × 85%, the water production is rated as poor; for acid consumption, the target value is 32g / t, the actual value ≤ the average value, the indicator is rated as excellent, the actual value > the average value, the indicator is rated as poor, the average value ≤ the target value × 1.20, the indicator If the target value is 53g / t, the actual value is ≤ the average value, the indicator is rated as excellent; if the actual value is greater than the average value, the indicator is rated as poor; if the average value is ≤ the target value × 1.20, the indicator is rated as excellent; if the average value is greater than the target value × 1.20, the indicator is rated as poor; the actual value is the economic indicator of the regenerated resin operation at that time; the average value is the average value of the water production and acid and alkali consumption economic evaluation indicators of all regenerated resins within this evaluation period, which needs to be counted in real time; the target value is the value that should be achieved in resin design or benchmarking; (2) Establish regeneration evaluation rules based on water production, acid consumption, alkali consumption and regeneration process scores.

5. The method for evaluating and warning the economic efficiency of resin regeneration based on regeneration effect training according to claim 4, characterized in that: The S3 includes: S31, performing a first evaluation on the regeneration economy based on the regeneration economy evaluation model trained based on the resin regeneration effect; S32, collecting all current regeneration step endpoint images in real time, and extracting image features of all regeneration step endpoint images; S33, comparing the image features with the physical features of the resin throughout its life cycle corresponding to the regeneration intelligent optimization and diagnosis model to obtain a second evaluation of the regeneration economy; S34: Evaluate the current operating status of the resin based on the first evaluation and the second evaluation, and issue an early warning for the resin that is close to being scrapped.

6. A resin regeneration economic evaluation and early warning system based on regeneration effect training, used to implement the method according to any one of claims 1 to 5, characterized in that: include: A first model building unit (101) is used to build a regeneration economic evaluation model based on resin regeneration effect training; A second model building unit (102) is used to build a regenerative intelligent optimization and diagnosis model based on big data mining; The resin regeneration economic evaluation and early warning unit (103) is used for performing resin regeneration economic evaluation and early warning based on the regeneration economic evaluation model trained based on the resin regeneration effect and the regeneration intelligent optimization and diagnosis model.

7. An electronic device comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a plurality of instructions, wherein the plurality of instructions can be read by a processor and executed by a method according to any one of claims 1 to 5.

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