Condensate polishing resin regeneration intelligent control method

Abstract

The invention discloses a condensate polishing resin regeneration intelligent control method, and relates to the condensate polishing related field, and the method comprises the following steps: carrying out numbering identification on to-be-applied resin; circulation path tracking is carried out based on the resin number information and the valve opening and closing state set, and resin circulation path information is recorded; mixed bed water outlet state data flow is monitored and obtained, and regeneration period prediction is carried out in combination with resin flow path information and historical resin water outlet period data; resin conveying and regeneration control is carried out based on the resin regeneration condition parameters, a resin liquid level image is obtained, and resin volume content information is calculated; and carrying out proportion stability and regeneration energy consumption analysis, determining a resin proportion supplement parameter and a resin regeneration energy consumption parameter, and carrying out resin regeneration energy-saving control. The technical problems that in existing resin regeneration control, regeneration cycle prediction is not accurate, the resin ratio is not stable, and regeneration energy consumption is high are solved, and the technical effects of improving the accuracy and stability of resin regeneration control and reducing the regeneration energy consumption are achieved.

Description

Technical Field

[0001] This application relates to the field of condensate polishing, and particularly to an intelligent control method for the regeneration of condensate polishing resin. Background Art

[0002] The regeneration control of condensate polishing resin is crucial for ensuring the stable operation of the water treatment system and energy conservation and consumption reduction. At present, the main method to solve the problem of condensate polishing resin regeneration control is to carry out the transportation and regeneration of resin through empirical judgment and timing control. However, due to the lack of real-time monitoring data and intelligent control means, this method often leads to problems such as large differences in resin regeneration cycles, unstable resin ratios, and high regeneration energy consumption.

[0003] In the current related technologies, there are technical problems in the regeneration control of condensate polishing resin, such as large differences in regeneration cycles, unstable resin ratios, and high regeneration energy consumption. Summary of the Invention

[0004] This application provides an intelligent control method for the regeneration of condensate polishing resin. By numbering and identifying the resin, tracking the flow path, real-time monitoring the effluent state of the mixed bed, predicting the regeneration cycle by combining historical data, controlling the resin transportation and regeneration according to the prediction results, simultaneously obtaining the resin liquid level image in real time to calculate the volume content, and finally analyzing the ratio stability and regeneration energy consumption for energy-saving control and other technical means, the technical effects of improving the accuracy and stability of resin regeneration control and reducing the regeneration energy consumption are achieved.

[0005] This application provides an intelligent control method for the regeneration of condensate polishing resin, including: numbering and identifying N resins to be applied to obtain N resin numbering information; tracking the flow paths of the N resins to be applied based on the N resin numbering information and the valve opening and closing state set of the condensate polishing system, and recording N resin flow path information; real-time monitoring and obtaining the effluent state data stream of the mixed bed, predicting the regeneration cycle by combining the N resin flow path information, the effluent state data stream of the mixed bed, and historical resin effluent cycle data to obtain N resin regeneration condition parameters; controlling the resin transportation and regeneration based on the N resin regeneration condition parameters, and simultaneously using a sight glass acquisition device to obtain the resin liquid level image in real time and calculating the resin volume content information of the resin liquid level image; analyzing the ratio stability and regeneration energy consumption based on the resin volume content information to determine the resin ratio supplement parameters and resin regeneration energy consumption parameters, and performing resin regeneration energy-saving control through the resin ratio supplement parameters and resin regeneration energy consumption parameters.

[0006] In a possible implementation manner, to obtain the N resin numbering information, the following processing is performed: Obtain the resin numbering requirement target, parse the resin numbering requirement target, and determine the numbering element information, where the numbering element information includes resin type, resin application batch, and system storage location; classify the resin information of each of the N resins to be applied by using the numbering element information to obtain N resin numbering element parameters; design a numbering rule based on the numbering element information, and build a resin numbering system, where the resin numbering system includes a numbering sequence and a numbering identifier; perform numbering identification on each resin parameter in the N resin numbering element parameters according to the resin numbering system to obtain the N resin numbering information.

[0007] In a possible implementation manner, to record the N resin transfer path information, the following processing is performed: Initialize to obtain the N resin initial position information of the N resins to be applied; construct a transfer path tracking system according to the valve distribution structure information of the condensate polishing system, and input the N resin initial position information into the transfer path tracking system; perform transfer path parsing on the transfer path tracking system based on the valve switch state set to obtain a resin predicted transfer path set; perform transfer path tracking on the N resins to be applied by combining the N resin numbering information, the N resin initial position information, and the resin predicted transfer path set to obtain N resin transfer path information.

[0008] In a possible implementation manner, to obtain the N resin transfer path information, the following processing is performed: Determine the N resin transfer state information according to the N resin numbering information and the N resin initial position information; perform path tracking records on the N resins to be applied according to the N resin numbering information and the N resin transfer state information to obtain N resin transfer monitoring path information; perform intersection matching on the resin predicted transfer path set and the N resin transfer monitoring path information to determine the N resin transfer path information.

[0009] In a possible implementation manner, to obtain the N resin regeneration condition parameters, the following processing is performed: Obtain the historical water outlet cycle data of the N resins to be applied according to the historical resin water outlet cycle data, where the historical water outlet cycle data includes mixed bed water outlet state data, resin transfer path data, and corresponding resin regeneration cycle data; use a recurrent neural network structure to perform identification training on the historical water outlet cycle data of the N resins to be applied to construct a resin regeneration cycle prediction network; perform water outlet cycle prediction on the N resin transfer path information and the mixed bed water outlet state data stream based on the resin regeneration cycle prediction network, and output the N resin predicted regeneration cycles; perform regeneration condition analysis based on the N resin predicted regeneration cycles respectively to obtain the N resin regeneration condition parameters.

[0010] In a possible implementation, to obtain the N resin regeneration condition parameters, the following processing is performed: Based on the historical regeneration data mining of the N resins to be applied, N resin regeneration control spaces are constructed. The N resin regeneration control spaces include resin regeneration cycle data, regeneration condition parameters, and corresponding regeneration effect data; Based on the N resin regeneration control spaces, regeneration condition analysis is respectively performed on the N predicted resin regeneration cycles to obtain N resin regeneration parameter selection thresholds; Global comparison and optimization are performed within the N resin regeneration parameter selection thresholds to obtain the N resin regeneration condition parameters.

[0011] In a possible implementation, to calculate the resin volume content information of the resin liquid level image, the following processing is performed: Convert the resin liquid level image into a resin liquid level grayscale image, and perform filtering denoising and contrast enhancement on the resin liquid level grayscale image to obtain a standard resin liquid level grayscale image; Use the Sobel operator to perform edge detection on the standard resin liquid level grayscale image to determine the resin liquid level edge information; Extract the contour of the standard resin liquid level grayscale image according to the resin liquid level edge information, and perform region filling based on the extracted contour information to obtain a resin region information set; Calculate and obtain the total area information of the resin region information set, and perform a ratio calculation on the total area information based on the total volume information of the resin bed to determine the resin volume content information of the resin liquid level image.

[0012] In a possible implementation, to determine the resin ratio supplement parameter and the resin regeneration energy consumption parameter, the following processing is performed: Obtain a set of resin regeneration influencing factors, and perform ratio stability analysis based on the set of resin regeneration influencing factors to obtain a resin ratio volume standard; Perform ratio supplement analysis on the resin volume content information based on the resin ratio volume standard to determine the resin ratio supplement parameter; Obtain the theoretical acid-base amount and the theoretical desalination water amount for resin regeneration, and perform regeneration energy consumption calculation on the resin volume content information based on the theoretical acid-base amount and the theoretical desalination water amount for resin regeneration to determine the resin regeneration energy consumption parameter.

[0013] In a possible implementation, to determine the resin ratio supplement parameter, the following processing is performed: According to the resin ratio volume standard, obtain the standard volume V0 of the anion resin and the standard volume V2 of the cation resin; Based on the resin volume content information, determine the volume content V1 of the anion resin and the volume content V3 of the cation resin; Perform resin deviation calculations on the standard volume V0 of the anion resin and the volume content V1 of the anion resin, and the standard volume V2 of the cation resin and the volume content V3 of the cation resin respectively to determine the deviation volume of the anion resin and the deviation volume of the cation resin; Perform resin supplement warning analysis based on the deviation volume of the anion resin and the deviation volume of the cation resin to determine the resin ratio supplement parameter.

[0014] In a possible implementation, the following processing is performed: The condensate polishing treatment system consists of a polishing treatment system and a resin regeneration system. Among them, the polishing treatment system includes a high-speed mixed bed, a bypass, and a recirculation system, and the resin regeneration system includes a cation resin regeneration tower, an anion resin regeneration tower, a resin separation tower, and an acid / alkali metering tank.

[0015] A condensate polishing treatment resin regeneration intelligent control method proposed by this application first numbers and identifies N resins to be applied to obtain N resin number information, and then tracks the flow paths of the N resins to be applied based on the N resin number information and the valve switch state set of the condensate polishing treatment system, records N resin flow path information, then monitors and obtains the mixed bed effluent state data stream in real time, combines the N resin flow path information, the mixed bed effluent state data stream, and historical resin effluent cycle data to predict the regeneration cycle, obtains N resin regeneration condition parameters, then controls the resin transportation and regeneration based on the N resin regeneration condition parameters, and at the same time uses a sight glass acquisition device to obtain the resin liquid level image in real time, calculates the resin volume content information of the resin liquid level image, and finally performs ratio stability and regeneration energy consumption analysis based on the resin volume content information, determines the resin ratio supplement parameters and resin regeneration energy consumption parameters, and performs resin regeneration energy-saving control through the resin ratio supplement parameters and resin regeneration energy consumption parameters. It achieves the technical effects of improving the accuracy and stability of resin regeneration control and reducing regeneration energy consumption. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to needs, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of a condensate polishing treatment resin regeneration intelligent control method provided by an embodiment of this application.

[0018] Figure 2 It is a schematic flowchart of recording N resin flow path information in a condensate polishing treatment resin regeneration intelligent control method provided by an embodiment of this application. Detailed Embodiments

[0019] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below.

[0020] In order to make the purpose, technical solution and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that comprises a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0022] An intelligent control method for condensate polishing resin regeneration is provided in an embodiment of this application, as Figure 1 shown. The method includes:

[0023] Step S100, number and identify N resins to be applied to obtain N resin number information.

[0024] Specifically, use barcodes, QR codes, RFID tags or digital coding, etc. to uniquely identify each resin to be applied. Paste or embed the above identification objects on the resin packaging or container, and use a dedicated scanning device or reader to record the number information of each resin. Among them, N resins to be applied refer to the total number of resins that need to be subjected to condensate polishing, and N is a natural number. The resin number information is the unique identifier of each resin, which is used for subsequent tracking and management.

[0025] In a possible implementation, to obtain the N resin numbering information, step S100 further includes step S110 of obtaining the resin numbering requirement target, parsing the resin numbering requirement target, and determining the numbering element information, where the numbering element information includes resin type, resin application batch, and system storage location. Specifically, collect the specific requirements for resin numbering, such as which information needs to be included for easy management and tracking, through system input, user interface interaction, or preset configuration files. Analyze the collected requirements in detail to clarify the essential and optional elements in the numbering. Based on the parsing result, determine the basic constituent elements of the numbering, such as resin type (cation resin, anion resin, etc.), resin application batch (production batch or usage order), and system storage location (specific storage container or area).

[0026] Step S120, classify the resin information of each of the N resins to be applied using the numbering element information to obtain N resin numbering element parameters. Specifically, classify and organize the relevant information (such as type, batch, storage location) of each resin to be applied according to the numbering element information, and generate a parameter set containing all necessary numbering elements for each resin, which are used for numbering identification.

[0027] Step S130, design a numbering rule based on the numbering element information and build a resin numbering system, where the resin numbering system includes a numbering sequence and a numbering identifier. Specifically, formulate the composition rules of the numbering according to the numbering element information, such as the length of the numbering, the arrangement order of each part of the element, the range of characters or numbers used, etc. Systematize the numbering rules to form a complete numbering system, including the generation method, interpretation method, and possible variants of the numbering.

[0028] Step S140, perform numbering identification on each resin parameter in the N resin numbering element parameters according to the resin numbering system to obtain the N resin numbering information. Specifically, number the numbering element parameters of each resin according to the established resin numbering system to generate a unique resin number. After completing the numbering of all resins, form a complete set of resin numbering information for subsequent management and tracking. This implementation method realizes the systematic generation of resin numbering information by clarifying the numbering requirements, determining the numbering elements, designing the numbering rules, and building the numbering system, and reduces the possibility of human errors, providing strong support for the effective management and tracking of resins.

[0029] Step S200, track the transfer paths of the N resins to be applied based on the N resin numbering information and the valve opening and closing state set of the condensate polishing system, and record N resin transfer path information.

[0030] Specifically, using Internet of Things technologies such as sensors, RFID readers, etc., combined with the valve switch states of the condensate polishing system, the flow path of the resin in the system is tracked in real time. The transfer path of each resin is recorded through a database or a data management system, including information such as the starting position, the valves passed through, and the end point reached. Among them, the valve switch state set refers to the set of the switch states of all valves in the condensate polishing system, which is used to judge the flow direction of the resin. The transfer path information refers to the complete record of the resin's flow path from the start to the end in the system.

[0031] In a possible implementation manner, step S200 further includes step S210. The condensate polishing system is composed of a polishing system and a resin regeneration system. Among them, the polishing system includes a high-speed mixed bed, a bypass, and a recirculation system, and the resin regeneration system includes a cation resin regeneration tower, an anion resin regeneration tower, a resin separation tower, and an acid / alkali metering tank.

[0032] Specifically, the condensate polishing system is clearly divided into two major parts: a polishing system and a resin regeneration system. The high-speed mixed bed in the polishing system is an efficient water treatment device that removes ions and impurities in water through the adsorption and exchange effects of the resin, and is the core device of the polishing process. The bypass in the polishing system is a standby or auxiliary flow path set outside the main flow path, which is used to provide a bypass flow path for the condensate when the high-speed mixed bed needs maintenance or fails, ensuring the continuity of the system. The recirculation system in the polishing system is a circulating pipeline system that returns a part of the treated condensate to the inlet of the mixed bed, which is used to improve the treatment efficiency and resin utilization rate.

[0033] The cation resin regeneration tower in the resin regeneration system is a device used to regenerate cation exchange resins, and the exchange capacity of the resin is restored through chemical methods. The anion resin regeneration tower is a device used to regenerate anion exchange resins, and its exchange capacity is also restored through chemical methods. The resin separation tower is a device that separates the cation and anion resins in the mixed bed before resin regeneration, which is used to regenerate the resins separately. The acid / alkali metering tank is a device used to store and meter the acid / alkali solution used for resin regeneration, which is used to ensure the accuracy and controllability of the regeneration process.

[0034] The above components are integrated together through devices such as pipelines, valves, and pumps to form a complete condensate polishing system. Sensors and monitoring devices are installed at key positions to monitor the operating status and parameters of the system in real time, and the data is transmitted to the control system for analysis and processing. The automation operation and coordinated cooperation of each device are realized by using a control system (such as PLC, DCS, etc.). This implementation manner determines the composition and function of the condensate polishing system by clarifying the various components of the system and their connection methods, thus determining the basis for resin transfer path tracking and regeneration control.

[0035] As shown Figure 2 In a possible implementation, as shown above, the step S200 of recording the information of the N resin flow paths further includes step S220 of initializing and obtaining the N resin initial position information of the N resins to be applied. Specifically, when the system is started or the resin is first introduced into the system, a one-time information entry process is performed. The initial storage position or state of each resin in the system is obtained by means of manual input, automatic scanning (such as using RFID tags), or system presetting, etc.

[0036] Step S230: Build a flow path tracking system according to the valve distribution structure information of the condensate polishing system, and input the N resin initial position information into the flow path tracking system. Specifically, collect detailed information such as the positions, types (such as on-off valves, regulating valves, etc.), connection methods (such as series, parallel, etc.), and their logical relationships (such as which paths are open when a certain valve is opened) of all valves in the system through means such as design drawings. Utilize the valve distribution structure information, combined with computer programming and database technology, to construct a system that can simulate and track the flow paths of resins in the system. This system has functions such as real-time updating of valve states, calculating possible flow paths of resins according to changes in valve states, and recording actual resin flow paths. After the flow path tracking system is built, input the previously obtained initial position information of the N resins into the system, and this information includes resin numbers, initial storage positions, states (such as whether in use, whether in the regeneration process, etc.).

[0037] Step S240: Based on the valve switch state set, perform flow path analysis on the flow path tracking system to obtain a resin predicted flow path set. Specifically, monitor the switch states of all valves in the system in real time and form a state data set. Utilize the flow path tracking system, combined with the valve switch state set, through logical judgment (such as judging which paths are connected according to the open and closed states of the valves) or algorithm calculation (such as using graph theory algorithms to calculate all possible paths of the resin from the starting point to the ending point), predict the possible flow paths of the resin in the system, and obtain a resin predicted flow path set.

[0038] Step S250, combining the N resin numbering information, the N resin initial position information and the resin predicted flow path set to track the flow paths of the N resins to be used, and obtain N resin flow path information. Specifically, the resin numbering information, the initial position information and the predicted flow path set are associated and integrated. Using the flow path tracking system, the actual flow path of the resin in the system is updated and recorded in real time. When the valve state changes, the system will recalculate the flow path of the resin according to the new valve state, and update the record to obtain the resin flow path information, including the starting position of the resin, the valve passed, the flow time, etc. This implementation method realizes the flow path tracking of the resin in the condensate water polishing system through the steps of initialization, system construction, path parsing and path tracking, provides strong support for the effective management and control of the resin, and improves the management accuracy.

[0039] In a possible implementation, the N resin flow path information is obtained, and step S250 further includes step S251, determining the N resin flow state information according to the N resin number information and the N resin initial position information. Specifically, in combination with the resin number and the initial position information, the current flow state of each resin, that is, the current position or state of the resin in the system is determined through system logic judgment or algorithm calculation, and the flow state includes: working in a mixed bed, regenerating in a regeneration tower, separating in a separation tower, and being on standby in a storage area.

[0040] Step S252, according to the N resin number information and the N resin flow status information, the path tracking and recording of the N resins to be used is performed to obtain N resin flow monitoring path information. Specifically, the flow path tracking system is used to record the flow path of the resin in the system in real time. The recorded content includes the valves, path nodes, timestamps, etc. that the resin passes through from the initial position to the current position.

[0041] Step S253, the predicted resin flow path set and the N resin flow monitoring path information are cross-matched to determine the N resin flow path information. Specifically, the predicted flow path set and the monitoring flow path information are compared and analyzed to find the intersection or matching part between them. According to the result of the cross-matching, the actual flow path information of each resin is determined. This implementation method realizes the accurate tracking and management of the flow path of the resin in the condensate polishing system by determining the flow state information, path tracking record and cross-matching.

[0042] Step S300, real-time monitoring and acquisition of mixed bed outlet water status data flow, combining the N resin flow path information, the mixed bed outlet water status data flow and historical resin outlet water cycle data to predict the regeneration cycle, and obtain N resin regeneration condition parameters.

[0043] Specifically, online monitoring instruments (such as conductivity meters, pH meters, etc.) are used to monitor the effluent status of the mixed bed in real time, including conductivity, pH value, etc. Based on machine learning algorithms or statistical models, combined with the data stream of the mixed bed effluent status, the resin flow path information, and the historical resin effluent cycle data, the regeneration cycle of each resin is predicted. According to the prediction results, the regeneration conditions of each resin are determined, that is, the condition parameters required to trigger resin regeneration, such as regeneration time, etc. Among them, the historical resin effluent cycle data is the recorded data of the past resin effluent cycle, which is used for model training and prediction.

[0044] In a possible implementation manner, to obtain the N resin regeneration condition parameters, step S300 further includes step S310 of obtaining the historical effluent cycle data of the N resins to be applied according to the historical resin effluent cycle data, where the historical effluent cycle data includes the mixed bed effluent status data, the resin flow path data, and the corresponding resin regeneration cycle data. Specifically, according to the historical resin effluent cycle data, the specific historical effluent cycle data of the N resins to be applied are retrieved and extracted. Among them, the historical resin effluent cycle data refers to the record of the effluent status (such as water quality, flow rate, etc.) and the regeneration cycle of the resin during use in the past period of time. The mixed bed effluent status data reflects the water quality situation after the mixed bed treatment. The resin flow path data is the record of the resin flow path in the system, including the flow information from which device to which device. The resin regeneration cycle data refers to the time or the amount of water treated that the resin has experienced from being put into use to the need for regeneration.

[0045] Step S320: Use a recurrent neural network structure to perform identification training on the historical effluent cycle data of the N resins to be applied, and construct a resin regeneration cycle prediction network. Specifically, the extracted historical data is cleaned, normalized, or standardized to ensure data quality. Select a recurrent neural network (RNN) structure, such as a long short-term memory network (LSTM) or a gated recurrent unit (GRU), and use the preprocessed data to perform supervised training on the RNN model so that it can learn the patterns in the historical effluent cycle data.

[0046] Step S330: Based on the resin regeneration cycle prediction network, perform effluent cycle prediction on the N resin flow path information and the mixed bed effluent status data stream, and output the predicted regeneration cycles of the N resins. Specifically, based on the trained resin regeneration cycle prediction network, perform effluent cycle prediction on the N resin flow path information and the real-time mixed bed effluent status data stream. Take the resin flow path information and the mixed bed effluent status data stream obtained in real time as input data, and use the trained RNN model to predict these input data, and output the predicted regeneration cycles of the N resins.

[0047] Step S340: Based on the predicted regeneration cycles of the N resins, analyze the regeneration conditions respectively to obtain N resin regeneration condition parameters. Specifically, based on the predicted regeneration cycles of the N resins, combined with the operation rules of the system and the characteristics of the resins, determine the regeneration conditions for each resin, such as regeneration time, the amount of acid and alkali required for regeneration, etc. This implementation method accurately predicts the regeneration cycles of the resins through a machine learning model, avoiding premature or delayed regeneration and reducing resource waste. After accurately predicting the regeneration cycles, the regeneration time can be reasonably arranged, the water outlet cycle of the mixed bed can be extended, and the safety and stability of the system operation can be improved.

[0048] In a possible implementation method, for the step of obtaining the N resin regeneration condition parameters, step S340 further includes step S341: Mine the historical regeneration data of the N resins to be applied, and construct N resin regeneration control spaces. The N resin regeneration control spaces include resin regeneration cycle data, regeneration condition parameters, and corresponding regeneration effect data. Specifically, extract the regeneration cycle data, regeneration condition parameters (such as regeneration time, acid and alkali concentration, etc.), and corresponding regeneration effect data (such as the degree of resin performance recovery, water outlet quality, etc.) of each resin from the database or historical records. Clean the extracted data to remove outliers and noise to ensure the accuracy and consistency of the data. According to the cleaned data, construct a multi-dimensional control space for each resin, where each dimension represents a regeneration condition parameter or regeneration effect data, used to describe the relationship between the conditions and effects of resin regeneration.

[0049] Step S342: Based on the N resin regeneration control spaces, analyze the regeneration conditions of the predicted regeneration cycles of the N resins respectively to obtain N resin regeneration parameter selection thresholds. Specifically, map the predicted regeneration cycle of each resin into its corresponding regeneration control space. Within the regeneration control space, find the range of regeneration condition parameters that match the predicted regeneration cycle, that is, the regeneration parameter selection threshold.

[0050] Step S343: Conduct global comparison and optimization within the N resin regeneration parameter selection thresholds to obtain the N resin regeneration condition parameters. Specifically, according to the objectives and constraints of resin regeneration, design an objective function that can evaluate the regeneration effects under different regeneration condition parameters. Use a global search algorithm (such as genetic algorithm, particle swarm algorithm, etc.) to search within the regeneration parameter selection thresholds to find the regeneration condition parameters that make the objective function optimal. According to the results of the search algorithm, optimize and adjust the regeneration condition parameters to obtain the final resin regeneration condition parameters. This implementation method realizes the accurate analysis and optimization of resin regeneration condition parameters through technical means such as data mining, condition analysis, and global comparison and optimization, thereby reducing the cost and energy consumption of resin regeneration and improving the economy of the system.

[0051] Step S400, based on the N resin regeneration condition parameters, conduct resin transportation and regeneration control, and at the same time use the sight glass acquisition device to obtain the resin liquid level image in real time, and calculate the resin volume content information of the resin liquid level image.

[0052] Specifically, according to the regeneration condition parameters, automatically adjust the valve opening and closing, pump operation, etc. through the control system to realize the resin transportation and regeneration process. Install a sight glass on the resin storage tank or regeneration tower, and equip it with an image acquisition device (such as a camera) to obtain the resin liquid level image in real time. Use image processing algorithms (such as edge detection, threshold segmentation, etc.) to analyze the sight glass image and calculate the volume content of the resin in the storage tank or regeneration tower.

[0053] In a possible implementation manner, for calculating the resin volume content information of the resin liquid level image, step S400 further includes step S410, convert the resin liquid level image into a resin liquid level grayscale image, and perform filtering denoising and contrast enhancement on the resin liquid level grayscale image to obtain a standard resin liquid level grayscale image. Specifically, by calculating the weighted average of the red, green, and blue (RGB) values of each pixel point, convert the original color resin liquid level image into a grayscale image. Apply a filter (such as a Gaussian filter, median filter, etc.) to smooth the grayscale image to remove the noise and unnecessary details in the image. Through methods such as histogram equalization, linear stretching, or adaptive contrast enhancement, improve the contrast of the grayscale image to make the edge of the resin liquid level clearer. Among them, a grayscale image is an image that only contains brightness information and no color information, and each pixel point is represented by a grayscale value.

[0054] Step S420, use the Sobel operator to perform edge detection on the standard resin liquid level grayscale image to determine the resin liquid level edge information. Specifically, the Sobel operator is a discrete differential operator for edge detection, which detects edges by calculating the gradient of the image grayscale value. The Sobel operator includes two filters in the horizontal and vertical directions, which are used to detect horizontal edges and vertical edges respectively. Apply the Sobel operator to the standard resin liquid level grayscale image, calculate the gradient amplitude and direction of each pixel point, and determine the edge position according to the magnitude of the gradient amplitude.

[0055] Step S430, extract the contour of the standard resin liquid level grayscale image according to the resin liquid level edge information, and perform region filling based on the extracted contour information to obtain a resin region information set. Specifically, according to the edge detection result, use a contour tracking algorithm (such as an eight-neighborhood search algorithm) to extract the contour of the resin liquid level. Fill the extracted contour to generate a binary image representing the resin region, where the resin region is white (or 1) and the background is black (or 0).

[0056] Step S440, calculate and obtain the total area information of the resin area information set, perform a proportional calculation on the total area information based on the total resin bed volume information, and determine the resin volume content information of the resin liquid level image. Specifically, count the number of white (or 1) pixel points in the binary image as the total area of the resin area. Obtain the total resin bed volume information through design parameters, measurement, or calibration. Calculate the actual volume unit represented by each pixel point according to the actual size of the resin bed and the physical size of the pixel points in the image. Then, multiply the total area information (number of pixels) of the resin area by the actual volume unit of each pixel point to obtain the actual volume of the resin. Finally, divide the actual volume of the resin by the total volume of the resin bed to obtain the resin volume content information. This implementation method can accurately identify the resin area through steps such as image preprocessing, edge detection, contour extraction, and region filling, thereby improving the measurement accuracy of the volume content.

[0057] Step S500, perform ratio stability and regeneration energy consumption analysis based on the resin volume content information, determine the resin ratio supplement parameters and resin regeneration energy consumption parameters, and perform resin regeneration energy-saving control through the resin ratio supplement parameters and resin regeneration energy consumption parameters.

[0058] Specifically, use the resin volume content information to compare with the preset resin ratio standard or historical optimal ratio data to analyze the stability of the current resin ratio. According to the analysis result, if the resin ratio deviates from the preset standard or historical optimal value, calculate the amount of resin that needs to be supplemented or adjusted to restore or optimize the ratio. This includes determining which type of resin to supplement (such as cation resin, anion resin), the supplement amount, and the supplement timing. Based on the resin volume content information, combine the energy consumption data (such as water consumption, chemical reagent consumption, etc.) during the regeneration process to perform energy consumption analysis. Through energy consumption analysis, determine the energy consumption indicators under different resin regeneration conditions, such as the resource consumption per unit resin regeneration. According to the resin ratio supplement parameters and resin regeneration energy consumption parameters, adjust the regeneration strategy to minimize energy consumption to the greatest extent while ensuring the resin regeneration effect. The embodiments of the present application use technical means such as numbering and identifying the resin and tracking the flow path, real-time monitoring the effluent state of the mixed bed and combining historical data for regeneration cycle prediction, controlling resin transportation and regeneration according to the prediction result, simultaneously obtaining the resin liquid level image in real time to calculate the volume content, and finally performing ratio stability and regeneration energy consumption analysis for energy-saving control, achieving the technical effects of improving the accuracy and stability of resin regeneration control and reducing regeneration energy consumption.

[0059] In a possible implementation manner, for the step of determining the resin ratio supplement parameter and the resin regeneration energy consumption parameter, step S500 further includes step S510 of obtaining a set of resin regeneration influencing factors and performing ratio stability analysis based on the set of resin regeneration influencing factors to obtain a resin ratio volume standard. Specifically, through historical data, experimental data or expert experience, various factors affecting resin regeneration are collected, such as resin type, service life, regeneration times, influent water quality, regenerant type and concentration, regeneration temperature, regeneration time, etc. The collected factors are sorted out to form a set of resin regeneration influencing factors. According to the characteristics and requirements of resin regeneration, an analysis method is selected, such as statistical analysis, regression analysis, machine learning, etc. Using the selected analysis method, the set of resin regeneration influencing factors is analyzed to find out the key factors affecting the resin ratio stability. Based on the analysis results and combined with the actual requirements of resin regeneration, the volume standard of the resin ratio is determined, that is, the optimal volume ratio that the resin should maintain during the regeneration process.

[0060] Step S520: Perform ratio supplement analysis on the resin volume content information based on the resin ratio volume standard to determine the resin ratio supplement parameter. Specifically, obtain the resin volume content information of the resin liquid level image, that is, the volume ratio occupied by the resin in the resin bed. Compare the resin volume content information with the resin ratio volume standard to find the difference. According to the difference situation, determine the resin ratio supplement parameter, that is, the resin volume amount that needs to be supplemented or adjusted.

[0061] Step S530: Obtain the theoretical acid-base amount of the resin and the theoretical desalted water amount of the resin, and perform regeneration energy consumption calculation on the resin volume content information based on the theoretical acid-base amount of the resin and the theoretical desalted water amount of the resin to determine the resin regeneration energy consumption parameter. Specifically, according to the resin type, regeneration process and regeneration effect requirements, calculate or consult relevant materials to obtain the theoretical acid-base amount and desalted water amount required for resin regeneration. The theoretical acid-base amount of the resin refers to the amount of acid or base theoretically required during the resin regeneration process to restore the exchange capacity of the resin. The theoretical desalted water amount of the resin refers to the amount of demineralized water theoretically required during the resin regeneration process to wash the resin and remove the waste generated during the regeneration process. Based on the theoretical acid-base amount and desalted water amount of the resin and combined with the resin volume content information, calculate the resource consumption during the regeneration process. Based on the results of the regeneration energy consumption calculation, determine the energy consumption parameters of resin regeneration, such as the resource consumption amount and resource consumption cost during regeneration. This implementation manner can find out the key factors affecting the resin ratio stability by obtaining the set of resin regeneration influencing factors and performing ratio stability analysis, so as to determine the resin ratio volume standard, which helps to ensure that the resin maintains a stable volume ratio during the regeneration process and improve the effect and efficiency of resin regeneration. By obtaining the theoretical acid-base amount and desalted water amount of the resin and combining with the resin volume content information to perform regeneration energy consumption calculation, it helps to accurately control the energy consumption of resin regeneration, reduce the energy consumption cost and improve the energy utilization efficiency.

[0062] In a possible implementation, for the step S520 of determining the resin ratio supplement parameter, it further includes step S521 of obtaining the standard volume V0 of the anion resin and the standard volume V2 of the cation resin according to the resin ratio volume standard. Specifically, the standard volume amounts of the anion resin and the cation resin are extracted from the resin ratio volume standard and denoted as V0 and V2 respectively. The standard volume V0 of the anion resin refers to the volume amount that the anion resin should maintain in the ratio volume standard. The standard volume V2 of the cation resin refers to the volume amount that the cation resin should maintain in the ratio volume standard.

[0063] Step S522: Based on the resin volume content information, determine the volume content V1 of the anion resin and the volume content V3 of the cation resin. Specifically, according to the resin volume content information, calculate the volume contents of the anion resin and the cation resin respectively, denoted as V1 and V3. The volume content V1 of the anion resin is the volume ratio of the anion resin in the resin bed calculated in real time. The volume content V3 of the cation resin is the volume ratio of the cation resin in the resin bed calculated in real time.

[0064] Step S523: Perform resin deviation calculations on the standard volume V0 of the anion resin and the volume content V1 of the anion resin, and on the standard volume V2 of the cation resin and the volume content V3 of the cation resin respectively, to determine the deviation volume of the anion resin and the deviation volume of the cation resin. Specifically, calculate the difference between the standard volume V0 of the anion resin and the volume content V1 of the anion resin to obtain the deviation volume of the anion resin; similarly, calculate the difference between the standard volume V2 of the cation resin and the volume content V3 of the cation resin to obtain the deviation volume of the cation resin. The resin deviation volume is the difference between the actual volume content of the resin and the standard volume, which is used to evaluate the deviation degree of the resin ratio.

[0065] Step S524: Based on the deviation volume of the anion resin and the deviation volume of the cation resin, conduct resin supplement warning analysis to determine the resin ratio supplement parameter. Specifically, analyze whether the deviation volumes of the anion resin and the cation resin exceed the preset threshold range. If so, trigger a supplement warning. According to the results of the resin supplement warning analysis, determine the type (anion resin or cation resin), quantity, and supplement timing of the resin to be supplemented, forming the resin ratio supplement parameter, that is, the parameter of the type, quantity, and timing of the supplementary resin determined to maintain or optimize the resin ratio. This implementation method can accurately determine the type and quantity of the resin to be supplemented by real-time monitoring of the resin volume content and calculation of the deviation, avoiding waste of resources caused by inaccurate manual judgment. By optimizing the resin ratio, the frequent regeneration caused by the imbalance of the resin ratio is reduced, thereby saving the consumption of acid-base and demineralized water.

[0066] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the protection scope of this application. In some cases, the actions or steps recited in this application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An intelligent control method for the regeneration of condensate polishing resin, characterized in that, The method includes: Number and identify N resins to be applied to obtain N resin number information; Based on the N resin number information and the valve switch state set of the condensate polishing system, track the flow paths of the N resins to be applied, and record N resin flow path information; Monitor and obtain the mixed bed effluent state data stream in real time, and predict the regeneration cycle by combining the N resin flow path information, the mixed bed effluent state data stream and the historical resin effluent cycle data to obtain N resin regeneration condition parameters; Based on the N resin regeneration condition parameters, control the resin transportation and regeneration. At the same time, use the sight glass acquisition device to obtain the resin liquid level image in real time, and calculate the resin volume content information of the resin liquid level image; Based on the resin volume content information, analyze the ratio stability and regeneration energy consumption, determine the resin ratio supplement parameters and resin regeneration energy consumption parameters, and perform resin regeneration energy-saving control through the resin ratio supplement parameters and resin regeneration energy consumption parameters.

2. The intelligent control method for the regeneration of condensate polishing resin according to claim 1, wherein, The obtaining of the N resin number information includes: Obtain the resin number requirement target, analyze the resin number requirement target to determine the number element information, and the number element information includes resin type, resin application batch and system storage location; Classify the resin information of each of the N resins to be applied by using the number element information to obtain N resin number element parameters; Design the numbering rule based on the number element information, and build a resin numbering system, which includes the numbering sequence and the numbering identifier; Number and identify each resin parameter in the N resin number element parameters according to the resin numbering system to obtain the N resin number information.

3. The intelligent control method for the regeneration of condensate polishing resin according to claim 1, wherein The recording of the N resin flow path information includes: Initialize to obtain N resin initial position information of the N resins to be applied; According to the valve distribution structure information of the condensate polishing system, build a flow path tracking system, and input the N resin initial position information into the flow path tracking system; Based on the valve switch state set, analyze the flow path of the flow path tracking system to obtain a resin predicted flow path set; Combine the N resin number information, the N resin initial position information and the resin predicted flow path set to track the flow paths of the N resins to be applied to obtain N resin flow path information.

4. The intelligent control method for condensate polishing resin regeneration according to claim 3, characterized in that The obtaining of the N resin flow path information includes: Determine N resin flow state information according to the N resin number information and the N resin initial position information; Track and record the paths of the N resins to be applied according to the N resin number information and the N resin flow state information to obtain N resin flow monitoring path information; Intersect and match the resin predicted flow path set and the N resin flow monitoring path information to determine the N resin flow path information.

5. The intelligent control method for the regeneration of condensate polishing resin according to claim 1, wherein The obtaining of the N resin regeneration condition parameters includes: Based on the historical resin effluent cycle data, obtain the historical effluent cycle data of the N resins to be applied, where the historical effluent cycle data includes mixed bed effluent state data, resin flow path data, and corresponding resin regeneration cycle data; Use a recurrent neural network structure to perform identification training on the historical effluent cycle data of the N resins to be applied, and construct a resin regeneration cycle prediction network; Based on the resin regeneration cycle prediction network, predict the effluent cycle for the N resin flow path information and the mixed bed effluent state data stream, and output the predicted regeneration cycles of the N resins; Based on the predicted regeneration cycles of the N resins, perform regeneration condition analysis respectively to obtain the regeneration condition parameters of the N resins.

6. The intelligent control method for condensate polishing resin regeneration according to claim 5, wherein The obtaining of the regeneration condition parameters of the N resins includes: Based on the historical regeneration data mining of the N resins to be applied, construct N resin regeneration control spaces, where the N resin regeneration control spaces include resin regeneration cycle data, regeneration condition parameters, and corresponding regeneration effect data; Based on the N resin regeneration control spaces, perform regeneration condition analysis on the predicted regeneration cycles of the N resins respectively to obtain the selection thresholds of the regeneration parameters of the N resins; Perform global comparison and optimization within the selection thresholds of the regeneration parameters of the N resins to obtain the regeneration condition parameters of the N resins.

7. The intelligent control method for condensate polishing resin regeneration according to claim 1, characterized in that The calculation of the resin volume content information of the resin liquid level image includes: Convert the resin liquid level image into a resin liquid level grayscale image, and perform filtering denoising and contrast enhancement on the resin liquid level grayscale image to obtain a standard resin liquid level grayscale image; Use the Sobel operator to perform edge detection on the standard resin liquid level grayscale image to determine the resin liquid level edge information; Extract the contour of the standard resin liquid level grayscale image according to the resin liquid level edge information, and perform region filling based on the extracted contour information to obtain a resin region information set; Calculate and obtain the total area information of the resin region information set, and perform a ratio calculation on the total area information based on the total volume information of the resin bed to determine the resin volume content information of the resin liquid level image.

8. The intelligent control method for condensate polishing resin regeneration according to claim 1, characterized in that, The determination of the resin ratio supplement parameter and the resin regeneration energy consumption parameter includes: Obtain a set of resin regeneration influencing factors, and perform ratio stability analysis based on the set of resin regeneration influencing factors to obtain the resin ratio volume standard; Based on the resin ratio volume standard, perform ratio supplement analysis on the resin volume content information to determine the resin ratio supplement parameter; Obtain the theoretical acid-base amount for resin regeneration and the theoretical desalination water amount of the resin, and perform regeneration energy consumption calculation on the resin volume content information based on the theoretical acid-base amount for resin regeneration and the theoretical desalination water amount of the resin to determine the resin regeneration energy consumption parameter.

9. The intelligent control method for the regeneration of condensate polishing resin according to claim 8, wherein The determination of the resin ratio supplement parameter includes: According to the resin ratio volume standard, obtain the standard volume V0 of the anion resin and the standard volume V2 of the cation resin; Based on the resin volume content information, determine the volume content V1 of the anion resin and the volume content V3 of the cation resin; Resin deviation calculations are respectively performed on the standard volume V0 of the anion resin, the volume content V1 of the anion resin, the standard volume V2 of the cation resin, and the volume content V3 of the cation resin to determine the deviation volume of the anion resin and the deviation volume of the cation resin; Based on the deviation volume of the anion resin and the deviation volume of the cation resin, resin replenishment early warning analysis is carried out to determine the resin ratio replenishment parameters.

10. The intelligent control method for condensate polishing resin regeneration according to claim 1, wherein The condensate polishing system consists of a polishing system and a resin regeneration system. Among them, the polishing system includes a high-speed mixed bed, a bypass and a recirculation system, and the resin regeneration system includes a cation resin regeneration tower, an anion resin regeneration tower, a resin separation tower, and an acid / alkali metering tank.

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