A resin material-based wastewater treatment device and method

Abstract

This invention relates to the field of wastewater treatment technology, and discloses a wastewater treatment device and method based on resin materials. The method includes: calculating a cumulative load coefficient based on influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the last regeneration end time; calculating a regenerant strength coefficient based on regenerant temperature and concentration; calculating a synergy coefficient based on the cumulative load coefficient, regenerant strength coefficient, the rate of change in pollutant concentration at the regenerated waste liquid outlet, and pH value; calculating a bed health status coefficient based on the resin bed saturation gradient and bed pressure drop; and finally determining and adjusting a target regenerant flow rate based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient. The device includes at least two parallel cylindrical pressure vessels, a memory, and a processor, with the processor executing the above method steps. This invention achieves dynamic optimization control of the resin regeneration process, improves regeneration efficiency, and extends the resin's service life.

Description

Technical Field

[0001] The present invention belongs to the technical field of wastewater treatment, and particularly relates to a wastewater treatment device and method based on resin materials. Background Art

[0002] With the expansion of industrial scale and the increase in population density, the total amount of wastewater discharge shows a continuous upward trend. The harmful components such as heavy metal ions, organic pollutants and nutrient salts contained therein pose multiple threats to the water ecosystem and public health. To meet the increasingly stringent wastewater discharge regulations globally, developing advanced treatment technologies with both high efficiency and economy has become the core topic in the field of environmental engineering. As a mature technology, ion exchange resin technology has played a key role in the wastewater treatment of industries such as electroplating, chemical engineering, pharmaceutical and electronic manufacturing. Selective adsorption is achieved through the ion exchange reaction between the resin functional groups and target pollutants, thereby achieving the purpose of water purification. When the resin adsorption reaches the saturation state, chemical regeneration must be carried out to restore its exchange function. The efficiency of this process directly determines the cyclic service life of the resin, the system operation cost and the overall treatment stability.

[0003] Traditional resin regeneration processes have exposed several key defects in practical applications. In terms of regeneration efficiency, existing operations generally use preset fixed flow rates and constant concentration regenerants for elution, and cannot adaptively adjust according to the real-time adsorption load of the resin, the physicochemical properties of the regenerant and the dynamic changes of the bed layer. This static control mode causes excessive consumption of the regenerant under low-load conditions and insufficient regeneration in high-load scenarios, thereby weakening the purification ability of subsequent treatment cycles. There are obvious deficiencies in the process synergy evaluation link. There are complex non-linear interaction effects among the regenerant concentration, temperature, pH value and the pollutant elution rate. However, the existing monitoring system only focuses on single indicators such as pH value or conductivity changes, and fails to establish a quantitative model for the multi-parameter synergy effect, resulting in the difficulty of dynamically optimizing the regeneration process. The management of the bed layer health state has been neglected for a long time. Problems such as uneven saturation distribution inside the resin bed layer and abnormal increase in fluid resistance will significantly reduce the regeneration efficiency and accelerate the deterioration of the resin structure. Traditional methods lack real-time diagnosis and closed-loop feedback mechanisms for saturation gradient and pressure drop parameters, and are prone to faults such as fluid channeling, channel blockage and even resin particle breakage. In addition, the intelligent level of the system lags seriously behind. The current devices mainly rely on the subjective experience of operators or simple time program control, and cannot respond to fluctuations in influent water quality, cumulative changes in pollutants and differences in regenerant conditions, resulting in weak system adaptability and fluctuating operating states.

[0004] In view of the above problems, the existing technologies urgently need to be improved. Summary of the Invention

[0005] The purpose of this invention is to provide a wastewater treatment device and method based on resin materials, in order to solve the above-mentioned problems.

[0006] This invention is implemented as follows: a wastewater treatment method based on resin materials, comprising: calculating a cumulative load coefficient based on influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present; calculating a regenerant intensity coefficient based on regenerant temperature and regenerant concentration; calculating a synergy coefficient based on the cumulative load coefficient, regenerant intensity coefficient, pollutant concentration change rate at the outlet of the regenerated waste liquid, and pH value of the regenerated waste liquid; calculating a bed health status coefficient based on resin bed saturation gradient and resin bed pressure drop; and calculating a target regenerant flow rate based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient, and adjusting the current regenerant flow rate to the target regenerant flow rate.

[0007] A further technical solution involves calculating the target regenerant flow rate as follows: obtaining the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient; determining the target regenerant flow rate based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient; wherein the synergy coefficient is used to positively adjust the target regenerant flow rate, the bed health status coefficient is used to negatively adjust the target regenerant flow rate, and the target regenerant flow rate is not lower than a preset minimum safe flow rate.

[0008] A further technical solution involves the following steps for calculating the bed health status coefficient: obtaining the resin bed saturation gradient and resin bed pressure drop; comparing the resin bed saturation gradient and resin bed pressure drop with the maximum allowable standard deviation and the maximum allowable bed pressure drop, respectively, to obtain the resin bed saturation gradient index and the resin bed pressure drop index; and calculating the bed health status coefficient by weighted summation based on the resin bed saturation gradient index and the resin bed pressure drop index. A larger bed health status coefficient indicates a worse bed health status, and both the resin bed saturation gradient index and the resin bed pressure drop index are directly proportional to the bed health status coefficient.

[0009] A further technical solution involves the following process for calculating the synergy coefficient: obtaining the cumulative load coefficient, regenerant strength coefficient, pollutant concentration change rate at the outlet of the regenerated waste liquid, and pH value of the regenerated waste liquid; obtaining the desired pollutant concentration change rate by multiplying the cumulative load coefficient and regenerant strength coefficient by a proportionality constant; obtaining the pH deviation index of the regenerated waste liquid by ratioizing the absolute value of the difference between the pH value of the regenerated waste liquid and the optimal pH value during the regeneration process to the tolerance width of pH deviation; and substituting the pollutant concentration change rate at the outlet of the regenerated waste liquid, the desired pollutant concentration change rate, and the pH deviation index of the regenerated waste liquid into the formula. Obtain the synergy coefficient , , A value closer to 1 indicates a closer match between the actual elution process and the desired outcome, and a better pH condition. The change rate of pollutant concentration at the outlet of the recycled waste liquid. The desired rate of change in pollutant concentration, This refers to the pH deviation index of the regenerated waste liquid.

[0010] A further technical solution involves calculating the cumulative load coefficient as follows: obtaining the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present; based on the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present, integrating the product of the influent flow rate and the difference between the influent and effluent concentrations over time to obtain the total amount of pollutants cumulatively adsorbed by the resin bed, and then dividing by the maximum adsorption capacity of the resin bed to obtain the cumulative load coefficient.

[0011] A further technical solution involves the following steps for calculating the regenerant strength coefficient: obtaining the regenerant temperature and the regenerant concentration; comparing the regenerant concentration with the maximum safe concentration allowed for use to obtain a concentration normalization factor; obtaining a temperature influence factor through a nonlinear function based on the difference between the actual temperature and the optimal temperature of the regenerant; and combining the concentration factor with the temperature influence factor to obtain the regenerant strength coefficient, which characterizes the activity of the regenerant under current conditions. The higher the concentration and the closer the temperature is to the optimal temperature, the greater the regenerant strength coefficient.

[0012] A wastewater treatment device based on resin materials includes: at least two cylindrical pressure vessels connected in parallel, each cylindrical pressure vessel having a wastewater inlet, a regenerant inlet, and an exhaust port installed at its top; a purified water outlet, a regenerated waste liquid outlet, and a backwash port installed at its bottom; and a resin filling layer installed inside each cylindrical pressure vessel; a memory for storing a computer program; and a processor for implementing the steps of the above-described wastewater treatment method based on resin materials when executing the computer program.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves precise control of the regeneration process by introducing a synergy coefficient and a bed health status coefficient to dynamically correct the baseline regenerant flow rate, significantly improving regeneration efficiency, reducing regenerant consumption, and avoiding problems of insufficient or excessive regeneration.

[0014] 2. This invention effectively diagnoses the health status of the resin bed by real-time monitoring and evaluation of the resin bed saturation gradient and bed pressure drop, avoiding resin damage caused by channeling, blockage, etc., extending the service life of the resin material, and reducing the system operation and maintenance costs.

[0015] 3. This invention combines parallel container design with intelligent control strategy to achieve continuous operation of wastewater treatment and adaptive adjustment of the regeneration process, which significantly improves the system's operational stability and shock resistance, and ensures that the effluent quality consistently meets standards. Attached Figure Description

[0016] Figure 1 A schematic diagram of a wastewater treatment device based on resin materials provided by the present invention; Figure 2 A flowchart of a wastewater treatment method based on resin materials provided by the present invention.

[0017] In the attached diagram: 1. Cylindrical pressure vessel; 2. Wastewater inlet; 3. Regenerant inlet; 4. Vent; 5. Purified water outlet; 6. Regenerated waste liquid outlet. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In traditional resin regeneration processes, the regeneration process is set up with a fixed flow rate and a fixed concentration of regenerant for elution, failing to dynamically adjust according to the actual adsorption load of the resin, regenerant conditions, and bed state. This leads to incomplete regeneration or regenerant waste, affecting the water purification effect in subsequent treatment cycles. Furthermore, there are complex interactions between regenerant concentration, temperature, pH, and pollutant elution rates during the regeneration process. Current technologies only monitor single parameters such as pH or conductivity, lacking quantitative assessment of the synergistic effects of multiple parameters, making optimal control of the regeneration process impossible. In addition, uneven resin bed saturation distribution and increased pressure drop are not diagnosed in real time, easily leading to channeling, blockage, or even resin damage, thus reducing system operational stability.

[0020] For example, in the actual operation of electroplating wastewater treatment plants, the influent water quality fluctuates frequently with each production batch. After the resin becomes saturated, the regeneration process uses a preset constant flow rate and regenerant concentration. When the influent pollutant concentration is high, incomplete regeneration leads to excessive pollutant concentration in the effluent; conversely, when the influent concentration is low, the regenerant is excessively consumed. The monitoring system only records the pH value of the regenerated wastewater, ignoring the dynamic changes in the pollutant concentration rate, and cannot adjust the regeneration strategy in a timely manner. Simultaneously, due to long-term operation, localized saturation zones appear in the resin bed, causing a gradual increase in pressure drop, which goes undetected and eventually forms channeling, reducing regeneration efficiency.

[0021] If the above problems are not addressed, regeneration efficiency will remain low, resin lifespan will be shortened, and operating costs will increase. System reliability will decline, potentially leading to frequent maintenance operations and unexpected downtime. More seriously, the treated water quality may fail to meet discharge standards, posing a potential environmental pollution risk.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] like Figure 2 As shown, an embodiment of the present invention provides a wastewater treatment method based on resin materials, comprising: Based on the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time elapsed since the last regeneration, the cumulative load factor is calculated. The cumulative load factor represents the proportion of the total amount of pollutants currently adsorbed by the resin bed to its maximum adsorption capacity. This factor reflects the resin's saturation level and is an important basis for determining whether the resin needs regeneration.

[0024] The regenerant strength coefficient is calculated based on the regenerant temperature and its concentration. This coefficient represents the regenerant's activity or effectiveness and is influenced by both temperature and concentration. It is used to assess the regenerant's ability to elute contaminants.

[0025] A synergy coefficient is calculated based on the cumulative load coefficient, regenerant strength coefficient, pollutant concentration change rate at the outlet of the regenerated waste liquid, and pH value of the regenerated waste liquid. This synergy coefficient quantifies the degree of matching between several key parameters during the regeneration process (such as cumulative load, regenerant strength, pollutant elution rate, and pH value). This coefficient reflects the degree of agreement between the actual regeneration effect and the expected effect.

[0026] Based on the resin bed saturation gradient and resin bed pressure drop, a bed health state coefficient is calculated. This coefficient represents an indicator of the physical state of the resin bed, such as the uniformity of saturation distribution and the pressure drop as fluid flows through the bed. This coefficient is used to assess whether the resin bed experiences channeling, blockage, or compaction, which can affect the effective distribution of the regenerant and regeneration efficiency.

[0027] Based on the baseline regenerant flow rate, synergy coefficient, and bed health coefficient, the target regenerant flow rate is calculated and adjusted to the target flow rate. The target regenerant flow rate refers to the ideal flow rate of the regenerant through the resin bed calculated under specific regeneration conditions to achieve the best regeneration effect. Dynamic adjustment of this flow rate helps optimize the regeneration process and avoid under-regeneration or regenerant waste.

[0028] Resin materials typically refer to ion exchange resins, which contain exchangeable active groups that can react with specific ions in wastewater to remove pollutants. This material is the core adsorption medium in wastewater treatment. After the resin bed has been running for a period of time, its adsorption capacity gradually decreases, requiring regeneration. Existing technologies often struggle to accurately determine the actual adsorption load of the resin, leading to a lack of basis for setting the regenerant dosage and flow rate, easily resulting in incomplete regeneration or waste of regenerant. This embodiment calculates the cumulative load coefficient, which can reflect the resin's saturation level in real time and quantitatively, providing precise input for regeneration decisions.

[0029] Furthermore, traditional methods lack the ability to assess the synergistic effects of multiple parameters during the regeneration process. For example, the complex relationships between regenerant concentration, temperature, contaminant elution rate, and pH are often overlooked. This embodiment introduces a synergy coefficient to comprehensively consider these key parameters and quantify the degree of matching in the regeneration process. In the example above, when the synergy coefficient is low, the system can identify a deviation between the actual elution effect and the expected result, and adjust the regeneration strategy accordingly, which is impossible with traditional single-parameter monitoring.

[0030] Furthermore, existing technologies do not pay sufficient attention to the physical health of the resin bed, and problems such as uneven bed distribution or increased pressure drop often lead to low regeneration efficiency. This embodiment can diagnose the physical condition of the resin bed in real time by calculating the bed health status coefficient. In the example, when the bed health status coefficient indicates a problem, the system can actively adjust the flow rate, avoiding the uneven distribution of regenerant and resin damage caused by neglecting bed problems in traditional methods.

[0031] Ultimately, by comprehensively analyzing data from multiple dimensions, including cumulative load coefficient, regenerant intensity coefficient, synergy coefficient, and bed health status coefficient, and based on this analysis, dynamically calculating and adjusting the target regenerant flow rate, this embodiment achieves intelligent optimization of the regeneration process. This multi-parameter collaborative decision-making mechanism enables the regeneration process to better adapt to fluctuations in influent water quality, changes in pollutant load, and regenerant conditions, thereby significantly improving regeneration efficiency, reducing operating costs, and extending resin lifespan.

[0032] This application further proposes the following process for calculating and obtaining the target regenerant flow rate: Obtaining the baseline regenerant flow rate, synergy coefficient, and bed health coefficient is a prerequisite for calculating the target regenerant flow rate. The baseline regenerant flow rate represents the flow rate required to complete one effective regeneration under ideal or standard operating conditions. It can be determined based on the resin bed's design capacity, the regenerant's stoichiometric requirements, and the preset regeneration cycle. For example, it can be set through engineering calculations or empirical formulas based on the resin bed's maximum adsorption capacity, the theoretical regenerant consumption, and the expected regeneration time. The synergy coefficient reflects the degree of matching between the actual and expected regeneration processes, as well as the effectiveness of the regenerant. This coefficient can be obtained based on a comprehensive analysis of parameters such as the rate of change in pollutant concentration at the regeneration wastewater outlet, the expected rate of change in pollutant concentration, and the pH value of the regeneration wastewater. The bed health coefficient characterizes the physical health condition of the resin bed, such as the presence of blockages, excessive pressure drop, or resin particle breakage. This coefficient can be obtained based on the monitoring and calculation of parameters such as the resin bed saturation gradient and resin bed pressure drop.

[0033] The target regenerant flow rate is determined based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient. The synergy coefficient is used to positively adjust the target regenerant flow rate, and the bed health status coefficient is used to negatively adjust the target regenerant flow rate. The target regenerant flow rate is not lower than a preset minimum safe flow rate. The specific calculation method is as follows: [Substitute into the formula...] Obtain the target regenerant quickly ,in, The baseline regenerant flow rate can be determined based on the total amount of regenerant and the expected regeneration time. The coefficient of synergy. The bed health status coefficient, The minimum safe flow rate is determined. This step is the core calculation in this scheme, transforming the obtained coefficients into specific regenerant flow rate commands through a mathematical model. This formula organically combines the baseline flow rate, synergy coefficient, bed health status coefficient, and minimum safe flow rate through multiplication and addition operations, forming a dynamically adjusted regenerant flow rate. Among these, the synergy coefficient... As a multiplier, its value is close to 1 when the regeneration effect is good, making the target flow rate close to the reference flow rate; when the regeneration effect is poor, its value is... A decrease in the value may lead to a reduction or adjustment of the target flow rate. Bed health status coefficient. by The form of the multiplier is used when the bed health condition is good. The value is close to 0, making Approaching 1; when bed health deteriorates, The value increases, leading to Reduce the flow rate to lower the target velocity and avoid further damage to the bed. Minimum safe flow rate. The introduction of this formula ensures that even under extreme conditions, the regenerant flow rate will not fall below a minimum level necessary to maintain basic regeneration efficiency and prevent bed damage. The calculation of this formula can be performed by software programs on embedded controllers, industrial PCs, or cloud servers. For example, data collected by sensors is input into the controller, which performs real-time calculations based on preset algorithms and formulas, and then outputs the calculated target regenerant flow rate as a control signal to the regenerant pump or flow regulating valve.

[0034] This application's solution, based on obtaining the cumulative load coefficient reflecting the resin bed's contaminant load, the regenerant strength coefficient characterizing the regenerant's activity, the synergy coefficient assessing the matching degree of the regeneration process, and the bed health status coefficient indicating the physical health of the resin bed, integrates these parameters into a unified flow rate control model through the aforementioned process, achieving intelligent and dynamic adjustment of the regenerant flow rate. Specifically, the baseline regenerant flow rate... As the initial setpoint for the regenerant flow rate, the synergy coefficient As a regulating factor, its value reflects the degree of matching between the actual regeneration effect and the expected effect. When the regeneration process exhibits good synergy, A higher value ensures the target flow velocity is closer to the baseline flow velocity, thus maintaining efficient regeneration. Bed health status coefficient Then The flow rate is corrected in this way when the resin bed is in good health. When the value is low, it has little impact on the flow rate; however, when the bed health deteriorates... The value increases, thereby reducing the target flow rate to avoid exacerbating bed damage or blockage due to excessive flow rate. Additionally, a minimum safe flow rate is required. The introduction of this technology ensures that even under the most unfavorable conditions, the regenerant flow rate remains at a minimum sufficient to maintain basic regeneration efficiency and protect the resin bed. This not only allows the regeneration process to be optimized in real time based on the actual condition of the resin bed and the performance of the regenerant, avoiding under- or over-regeneration problems caused by traditional fixed or empirical flow rates, but also effectively addresses changes in the resin bed's health condition. This, in turn, extends the resin's lifespan and reduces operating costs while ensuring regeneration efficiency.

[0035] The following is a concrete example. In a practical wastewater treatment system, the maximum adsorption capacity of the resin bed is 10,000 grams, and the expected regeneration time is 60 minutes. Based on experience and theoretical calculations, a baseline regenerant flow rate can be set. Minimum safe flow rate: 100 liters / hour The rate is 20 liters per hour. During a certain regeneration process, the system monitors and calculates the cumulative load factor in real time. The strength coefficient of the regenerant is 0.85. The coefficient is 0.9. The synergy coefficient can be calculated by analyzing the rate of change in pollutant concentration at the outlet of the regenerated waste liquid, the expected rate of change in pollutant concentration, and the pH value of the regenerated waste liquid. The value is 0.92. Simultaneously, by monitoring the resin bed saturation gradient and resin bed pressure drop, the bed health status coefficient can be calculated. The value is 0.1. Substitute these parameters into the formula. The calculated target regenerant flow rate The flow rate is 102.8 liters per hour. At this point, the system will instruct the regenerant pump or flow control valve to adjust the current regenerant flow rate to this target value. In this way, the regenerant flow rate can be dynamically adjusted according to the actual load on the resin bed, the activity of the regenerant, and the health condition of the bed, ensuring precise control of the regeneration process.

[0036] Through the above technical solution, this application can effectively integrate and transform complex resin bed operating status evaluation parameters, including cumulative load coefficient, regenerant strength coefficient, synergy coefficient, and bed health status coefficient, into precise control commands for regenerant flow rate. This solves the problem that traditional methods rely on experience or fixed values ​​for regenerant flow rate setting, leading to low regeneration efficiency, regenerant waste, or resin bed damage. Specifically, by introducing the synergy coefficient and bed health status coefficient to dynamically correct the baseline flow rate and setting a minimum safe flow rate, the regenerant flow rate can adapt to the actual operating conditions of the resin bed and the performance of the regenerant in real time, avoiding under-regeneration or over-regeneration, thereby significantly improving regeneration efficiency, reducing regenerant consumption, and effectively extending the service life of the resin bed.

[0037] This application further proposes the following steps for calculating and obtaining the bed health status coefficient: The resin bed saturation gradient and resin bed pressure drop are obtained. The resin bed saturation gradient refers to the difference in the distribution of adsorbed pollutants along the height of the resin bed. This gradient can reflect whether there are problems such as local overload, uneven penetration, or channel effects in the resin bed. It can be obtained by real-time monitoring using sensors (e.g., conductivity sensors or optical sensors) installed at different depths of the resin bed, or by stratified sampling and analysis of the resin bed. The resin bed pressure drop refers to the pressure loss caused by frictional resistance when fluid passes through the resin bed. Excessive pressure drop may indicate problems such as blockage, compaction, or particle breakage in the resin bed, affecting the uniform distribution of fluid and the effective utilization of regenerant. The resin bed pressure drop is usually measured in real time by installing pressure sensors at the resin bed inlet and outlet.

[0038] The resin bed saturation gradient and resin bed pressure drop are obtained by ratioing them to the maximum permissible standard deviation and the maximum permissible bed pressure drop, respectively, to obtain the resin bed saturation gradient index and the resin bed pressure drop index. The maximum permissible standard deviation is a preset, acceptable upper limit for the resin bed saturation gradient; exceeding this value indicates an abnormal saturation distribution. This value can be set based on the resin type, process requirements, and historical operating data, or determined experimentally. The maximum permissible bed pressure drop is a preset, acceptable upper limit for the resin bed pressure drop; exceeding this value may lead to damage to the resin bed structure or uneven fluid distribution. This value is usually provided by the resin manufacturer or determined based on equipment design and operating experience. The ratio processing normalizes the actually measured resin bed saturation gradient and resin bed pressure drop to their corresponding maximum permissible values, thereby obtaining dimensionless resin bed saturation gradient indices and resin bed pressure drop indices. This processing method allows the influence of different physical quantities to be uniformly measured and compared. The resin bed saturation gradient index and the resin bed pressure drop index are normalized indicators that quantify the deviation of resin bed saturation distribution and hydraulic properties, respectively.

[0039] The bed health status coefficient is calculated by weighted summation based on the resin bed saturation gradient index and the resin bed pressure drop index. A higher bed health status coefficient indicates a worse bed health status. Both the resin bed saturation gradient index and the resin bed pressure drop index are directly proportional to the bed health status coefficient. The specific calculation method is as follows: substitute the resin bed saturation gradient index and the resin bed pressure drop index into the formula... Obtain the bed health status coefficient , , The larger the value, the worse the health of the bed layer, requiring a more conservative flow rate strategy during regeneration. The resin bed saturation gradient weights, ranging from 0 to 1, are used to adjust the resin bed saturation gradient index and resin bed pressure drop index in calculating the bed health status coefficient. The relative importance of time. For example, when more attention is paid to the uniformity of adsorption within the resin bed, the concentration can be appropriately increased. The value can be appropriately reduced when more attention is paid to the water flow performance and pressure drop of the resin bed. The value, The value can be assigned using a preset strategy or a dynamic algorithm; The resin bed saturation gradient index. The pressure drop index is the resin bed pressure drop index.

[0040] This application's solution ensures optimized regeneration by systematically assessing the physical health of the resin bed. First, it acquires two key physical parameters in real time: the resin bed saturation gradient and the resin bed pressure drop. These parameters reflect the uniformity of contaminant distribution within the resin bed and the resistance of fluid flow through the resin bed, respectively. Then, these raw data are compared with preset maximum permissible standard deviations and maximum permissible bed pressure drops, transforming these dimensional physical quantities into unified, dimensionless resin bed saturation gradient and pressure drop indices. This normalization process allows for effective comparison and weighting of different health indicators. Finally, a weighted linear combination formula is used to synthesize these two indices into a single bed health status coefficient. Among them, the resin bed saturation gradient weights The introduction of this feature allows for flexible adjustment of the impact of saturation distribution and pressure drop on bed health assessment, based on actual process requirements or resin characteristics. When the calculated bed health status coefficient... A higher pressure indicates poor physical conditions of the resin bed, such as severe saturation unevenness or excessive pressure drop. In this case, the system will adjust according to the formula mentioned above. The system automatically adjusts the target regenerant flow rate to a conservative level to avoid further physical damage to the resin bed and provide a more stable foundation for subsequent regeneration operations. This flow rate adjustment mechanism, based on physical health conditions, combined with a synergy coefficient based on chemical loading and regenerant activity, ensures comprehensive optimization of the regeneration process.

[0041] In one specific implementation, in a resin-based wastewater treatment device, multiple conductivity sensors can be installed along the height direction inside a cylindrical pressure vessel 1 to monitor the conductivity changes at different locations in the resin bed in real time, thereby calculating the resin bed saturation gradient. Simultaneously, pressure sensors are installed near the wastewater inlet 2 and the purified water outlet 5 to obtain the resin bed pressure drop. The data from these sensors can be collected in real time by a processor. The processor can preset the maximum permissible standard deviation to, for example, 0.08 (meaning the standard deviation of the saturation distribution should not exceed 8% of the average saturation), and the maximum permissible bed pressure drop to, for example, 0.3 MPa. The processor divides the collected resin bed saturation gradient value by 0.08 to obtain the resin bed saturation gradient index; and divides the resin bed pressure drop value by 0.3 MPa to obtain the resin bed pressure drop index. Subsequently, the processor substitutes these two indices into the formula... ,in This can be set to 0.7, indicating that when assessing bed health, the saturation gradient is given slightly more attention than the pressure drop. The final calculated bed health status coefficient... It will be used for the subsequent calculation and adjustment of the target regenerant flow rate.

[0042] Through the above technical solution, this application can accurately quantify the physical health status of the resin bed, ensuring that when calculating the target regenerant flow rate, not only the chemical load of the resin and the activity of the regenerant are considered, but also the physical integrity and hydraulic performance of the resin bed. This helps avoid problems such as resin wear, bed disorder, or excessive pressure drop caused by improper flow rate, thereby effectively extending the service life of the resin and reducing operation and maintenance costs. Simultaneously, by optimizing the flow rate, sufficient contact between the regenerant and the resin can be ensured, improving regeneration efficiency, reducing regenerant waste, and decreasing the amount of regeneration wastewater generated, thereby enhancing the economic efficiency and environmental friendliness of the entire wastewater treatment system.

[0043] This application further proposes the following process for calculating and obtaining the synergy coefficient: The cumulative load factor, regenerant strength factor, rate of change of pollutant concentration at the outlet of the regenerated wastewater, and pH value of the regenerated wastewater are obtained. The cumulative load factor reflects the saturation level of the resin bed, the regenerant strength factor reflects the activity of the regenerant, the rate of change of pollutant concentration at the outlet of the regenerated wastewater directly indicates the rate at which pollutants are eluted from the resin, and the pH value of the regenerated wastewater reflects whether the chemical environment of the regeneration process is at its optimal state. These parameters can be acquired in real time through various sensors installed in the wastewater treatment system. For example, the rate of change of pollutant concentration at the outlet of the regenerated wastewater can be monitored by an online pollutant analyzer and obtained through differential calculation; the pH value of the regenerated wastewater can be measured in real time by an online pH sensor. Alternatively, these parameters can be obtained through periodic sampling and laboratory analysis. For example, the regenerated wastewater can be sampled and analyzed to obtain pollutant concentration and pH value, and then the rate of change and pH value can be obtained through data processing.

[0044] The desired pollutant concentration change rate is obtained by multiplying the cumulative load factor and the regenerator intensity factor by a proportionality constant. Specifically, the calculation method involves substituting the cumulative load factor and the regenerator intensity factor into the formula. To obtain the desired rate of change in pollutant concentration ,in, It is a proportionality constant. This is the cumulative load factor. This is the regenerant strength coefficient; this step aims to establish a baseline, predicting the theoretically expected elution rate of contaminants under current resin load and regenerant strength conditions. The expected rate of change in contaminant concentration serves as a reference standard for measuring actual elution performance. This formula can be programmed into a controller or computing unit, which receives the cumulative load coefficient. and regenerant strength coefficient The input, and according to the preset proportional constant Multiplication is performed to calculate the desired rate of change in pollutant concentration in real time. (Proportionality constant) This can be determined through fitting experimental data or based on empirical values ​​such as resin type and contaminant characteristics. Alternatively, it can be achieved using a lookup table method or a machine learning model, by pre-establishing a table containing different... and Combination Expectation A lookup table for values; or training a machine learning model, inputting... and Output the desired rate of change in pollutant concentration.

[0045] The pH deviation index of the regenerated waste liquid is obtained by ratioing the absolute value of the difference between the pH value of the regenerated waste liquid and the optimal pH value during the regeneration process to the tolerance width of pH deviation. This step aims to quantify the degree to which the pH value of the regenerated waste liquid deviates from the ideal state. pH value is a key factor affecting the regeneration efficiency of ion exchange resins; excessively high or low pH values ​​will reduce the regeneration effect. By calculating the deviation index, the pH state can be quantified into a dimensionless index, facilitating subsequent calculations. This step can be implemented in the data processing module, which receives the real-time measured pH value of the regenerated waste liquid and compares it with the preset optimal pH value for the regeneration process, calculating the absolute difference. Then, this absolute difference is divided by the preset pH deviation tolerance width to obtain the pH deviation index. The optimal pH value and tolerance width can be determined experimentally based on the resin type, pollutant properties, and regenerant type. Alternatively, it can be implemented using a software algorithm, for example, defining a function that takes the current pH value, optimal pH value, and tolerance width as input and outputs the pH deviation index.

[0046] Substitute the pollutant concentration change rate at the outlet of the regenerated waste liquid, the expected pollutant concentration change rate, and the pH deviation index of the regenerated waste liquid into the formula. Obtain the synergy coefficient , , A value closer to 1 indicates a closer match between the actual elution process and the desired outcome, and a better pH condition. The change rate of pollutant concentration at the outlet of the recycled waste liquid. The desired rate of change in pollutant concentration, This refers to the pH deviation index of the regenerated wastewater. This step comprehensively considers the degree of matching between the actual pollutant elution efficiency and the theoretical expectation, as well as the pH state of the regeneration environment. Synergy coefficient. It is a comprehensive indicator used to assess the overall synergistic effect of the regeneration process. The formula can be implemented in a central processing unit or dedicated computing chip, with the processor receiving the actual rate of change in pollutant concentration. Expected rate of change in pollutant concentration pH deviation index As input, division, exponentiation, and multiplication are performed, ultimately outputting the synergy coefficient. Alternatively, this can be achieved through software libraries or API calls. For example, the calculation logic for the formula can be constructed using exponential functions and basic arithmetic operations from a mathematical computing library, and the function can be called to perform the calculation when needed.

[0047] This application's scheme aims to more comprehensively and in real-time evaluate the efficiency and suitability of the resin regeneration process by introducing the calculation of a synergy coefficient. First, the system obtains the cumulative load coefficient of the current resin bed and the strength coefficient of the regenerant. These two parameters jointly determine the expected elution rate of pollutants under ideal conditions, i.e., the desired pollutant concentration change rate. This sets a theoretical performance benchmark for the regeneration process. Simultaneously, the system monitors the pollutant concentration change rate at the regeneration wastewater outlet in real time, which directly reflects the actual rate at which pollutants are eluted from the resin, and the pH value of the regeneration wastewater, which reflects the chemical environment in which the regenerant acts. By comparing the actual pollutant elution rate with the expected elution rate, the actual utilization efficiency of the regenerant and the pollutant removal effect can be evaluated. Furthermore, the deviation of the regeneration wastewater pH value from the optimal pH value is quantified as a pH deviation index, used to correct the elution efficiency assessment, because even with a high elution rate, a poor pH environment can lead to incomplete regeneration or resin damage. Finally, the ratio of the actual elution efficiency to the expected efficiency is weighted and combined with the pH deviation index using an exponential function to obtain the synergy coefficient. This coefficient comprehensively reflects the degree of matching between the pollutant elution effect and the chemical environment during the regeneration process. When the synergy coefficient is close to 1, it indicates that the actual regeneration effect is highly consistent with the expectation and the pH environment is ideal; conversely, it indicates that the regeneration process is inefficient or the environment is unsuitable. This comprehensive evaluation mechanism provides a more accurate and reliable basis for subsequent dynamic adjustment of the regenerant flow rate, enabling the addition of regenerant to better adapt to the actual state of the resin bed and the real-time performance of the regeneration process, thereby optimizing the regeneration effect and saving regenerant.

[0048] As a specific implementation method, the synergy coefficient can be calculated in actual operation as follows: First, the influent flow rate, influent pollutant concentration, and effluent pollutant concentration are continuously monitored using a flow meter and an online pollutant analyzer. Combined with the last regeneration end time, the calculation module in the control system calculates the cumulative load coefficient according to a preset integral algorithm. Simultaneously, the regenerant temperature and concentration are acquired in real time using a temperature sensor and a concentration meter, and the calculation module calculates the regenerant intensity coefficient according to a preset normalization and S-curve formula. Then, these two coefficients are substituted into a preset proportionality constant. formula The desired rate of change in pollutant concentration is calculated. During the regeneration process, the rate of change in pollutant concentration at the regeneration waste liquid outlet is acquired in real time using an online pollutant analyzer and pH sensor installed at the outlet. The pH value of the regenerated wastewater was then measured and compared with the preset optimal pH value. The absolute difference was calculated and divided by the preset pH deviation tolerance range to obtain the pH deviation index of the regenerated wastewater. Finally, , and Substitute into the formula The coordination coefficient is calculated in real time by the computing module in the control system. For example, when the actual elution rate matches the desired elution rate and the pH value is within the optimal range, The coefficient of synergy is close to 0. It will be close to 1. Conversely, if the actual elution rate is much lower than expected, or the pH value deviates significantly, then The value will decrease significantly.

[0049] Through the above technical solution, this application can more accurately evaluate the actual effect of the resin regeneration process. By comprehensively considering the degree of matching between the pollutant elution efficiency and the theoretical expectation, as well as the pH state of the regenerant's operating environment, the calculation of the synergy coefficient can provide a comprehensive and real-time performance index of the regeneration process. This enables the system to accurately identify whether the regenerant is effectively utilized, whether the pollutants are fully eluted, and whether the regeneration environment is suitable. When the synergy coefficient is used for the subsequent dynamic adjustment of the regenerant flow rate, it can avoid the problems of excessive use of regenerant or incomplete regeneration caused by insufficient evaluation of a single parameter, thereby significantly improving the utilization efficiency of the regenerant, reducing operating costs, and ensuring the stability of wastewater treatment effects.

[0050] This application further proposes the following process for calculating the cumulative load factor: Obtain the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present; influent flow rate. This refers to the volumetric flow rate of wastewater entering the resin bed. Its function is to quantify the total amount of wastewater passing through the resin bed per unit time. It can be monitored and data collected in real time using flow meters installed on the inlet pipe, such as electromagnetic or ultrasonic flow meters. (Inlet pollutant concentration) This refers to the concentration of target pollutants in the wastewater entering the resin bed. Its function is to reflect the pollutant load entering the resin bed. It can be continuously monitored using online analyzers, such as UV-Vis spectrometers or ion-selective electrodes, or obtained through periodic sampling and laboratory analysis. Effluent pollutant concentration. This refers to the concentration of target pollutants in the effluent after resin bed treatment. Its function is to reflect the adsorption and removal efficiency of the resin bed for pollutants, similar to the influent pollutant concentration. It can be obtained through online analyzers or periodic sampling and analysis. The time from the end of the last regeneration to the present is also considered. This refers to the time elapsed from the completion of the last regeneration of the resin bed and its commissioning to the current moment. Its function is to serve as the upper limit of the integral, defining the starting and ending points of pollutant accumulation. This time can be obtained by recording the timestamp of the last regeneration completion by the system control unit and calculating it with the current system time.

[0051] Based on the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present, the total amount of pollutants cumulatively adsorbed by the resin bed is obtained by integrating the product of the influent flow rate and the difference between the influent and effluent concentrations over time. This total amount is then divided by the maximum adsorption capacity of the resin bed to obtain the cumulative load factor. The specific calculation method is as follows: substituting the values ​​into the formula... Obtain the cumulative load factor , Cumulative load factor This indicates the proportion of the current pollutant load on the resin bed to its maximum adsorption capacity, while the integral term represents the total amount of pollutants adsorbed by the resin bed since the last regeneration. The closer the value is to 1, the more saturated the resin bed is, and the more urgent the need for regeneration; among them, This refers to the influent flow rate; This refers to the concentration of pollutants in the influent. The concentration of pollutants in the effluent; The time from the end of the last regeneration to the present; The maximum adsorption capacity of the resin bed (unit: g or mol) is determined by the resin type and loading amount and is a known constant. This step dynamically calculates the cumulative pollutant load on the resin bed based on real-time monitoring data and preset parameters. This can be implemented within an industrial control system, such as a programmable logic controller (PLC) or a distributed control system (DCS), or programmed into a dedicated computing unit. The system continuously collects data on influent flow rate, influent pollutant concentration, and effluent pollutant concentration, and performs real-time or periodic integration processing on this data. Integration term Indicates the time period The total amount of pollutants actually adsorbed by the resin bed. The maximum adsorption capacity of a resin bed refers to the total amount of pollutants that the resin bed can adsorb under fully saturated conditions. This is a known constant determined by the type of resin, the packing volume, and the characteristics of the target pollutant. For example, for ion exchange resins, the maximum adsorption capacity is usually expressed as equivalents per liter of resin or grams per liter of resin, and is calculated based on the actual volume of resin packed. This parameter is calibrated and set during the initial design or operation of the system. Cumulative Load Factor It is a dimensionless parameter with a value between 0 and 1. It visually represents the proportion of the current contaminant load on the resin bed to its maximum adsorption capacity. When When the value is close to 1, it indicates that the resin bed is nearing saturation and regeneration is urgently needed; when When the value is close to 0, it indicates that the resin bed has just completed regeneration and has a strong adsorption capacity.

[0052] The solution proposed in this application involves continuous monitoring of the influent flow rate. Concentration of pollutants in influent and the concentration of pollutants in the effluent And combined with the time from the end of the last regeneration to the present. The system dynamically calculates the total amount of pollutants actually adsorbed by the resin bed. Specifically, the system collects this data in real time and uses integral calculations to accurately quantify the pollutants accumulated in the resin bed during operation. Then, the calculated total amount of adsorbed pollutants is compared with the preset maximum adsorption capacity of the resin bed. The cumulative load factor is obtained by performing ratio processing. This coefficient accurately reflects the current saturation level of the resin bed, providing a crucial input parameter for subsequent calculations of the synergy coefficient. Through this dynamic, real-time load assessment mechanism, the system avoids problems such as untimely or excessive regeneration caused by fixed cycles or experience-based judgments, thereby optimizing the use of regenerant, extending resin life, and improving the overall efficiency of wastewater treatment.

[0053] The following is a concrete example. Suppose a wastewater treatment system uses an ion exchange resin bed to remove heavy metal ions from the wastewater. What is the maximum adsorption capacity of this resin bed? It was set to 1000 grams. The system will set the time after the last regeneration. Reset to 0. During operation, the flow meter on the inlet pipe continuously measures the inlet water flow rate. For example, 10 cubic meters per hour. Simultaneously, the online analyzer continuously monitors the concentration of pollutants in the influent. For example, heavy metal ions at a concentration of 50 mg / L, and the concentration of pollutants in the effluent. For example, the concentration of heavy metal ions is 1 mg / L. An industrial control system, such as a PLC, collects this data every minute (or other preset time intervals) and calculates the mass of heavy metals adsorbed by the resin bed during that time interval. The PLC then integrates these instantaneous adsorption amounts to obtain the cumulative adsorption amount from the end of the last regeneration to the current moment. For example, if the system has run for 10 hours, the total cumulative adsorption of heavy metals is 750 grams. At this point, the cumulative loading coefficient... The calculated value is 0.75. This cumulative load factor of 0.75 will be used as input for the subsequent calculation of the synergy factor, which in turn affects the determination of the target regenerant flow rate.

[0054] The above technical solution enables real-time and accurate assessment of the pollutant load on the resin bed, avoiding the problems of waste of regenerant or incomplete regeneration caused by inaccurate estimation in traditional methods. This accurate load assessment provides a reliable basis for subsequent calculation of the synergy coefficient, making the adjustment of the regenerant flow rate more precise, thereby optimizing the regeneration process, improving regeneration efficiency, extending the service life of the resin, and ensuring the continuous and stable compliance of wastewater treatment.

[0055] This application further proposes the following procedure for calculating and obtaining the strength coefficient of the regenerant: The process involves obtaining the temperature and concentration of the regenerant. Obtaining the regenerant temperature refers to real-time monitoring or setting the actual temperature of the regenerant when it enters the resin bed for regeneration. The regenerant temperature is a key factor affecting the activity and diffusion rate of regenerant molecules, thus directly impacting regeneration efficiency. This temperature can be obtained in real-time by installing a high-precision temperature sensor (e.g., a thermocouple or platinum resistance thermometer) in the regenerant delivery line, or by controlling the regenerant temperature within a specific range using a preset heating or cooling system and recording this set value as the obtained value. Obtaining the concentration of the regenerant itself refers to determining the content of the effective regenerative component in the chemical solution used for resin regeneration. The regenerant concentration directly determines its chemical potential and regeneration capacity. This concentration can be measured in real-time using an online concentration meter (e.g., a conductivity meter, refractometer, or densitometer), or by periodically sampling and performing laboratory analysis (e.g., titration analysis, spectroscopic analysis). The concentration of the regenerant itself is compared with its maximum permissible safe concentration to obtain a concentration normalization factor. This step aims to standardize the actual concentration of the regenerant into a dimensionless parameter within the range of 0 to 1, facilitating uniform processing in mathematical models. This step is achieved by dividing the real-time acquired regenerant concentration by a pre-set maximum permissible regenerant concentration value based on considerations of equipment safety and process effectiveness. This maximum safe concentration can be determined based on the equipment manufacturer's recommendations, the regenerant supplier's technical specifications, or through historical operating data and experimental verification.

[0056] Based on the difference between the actual temperature and the optimal temperature of the regenerant, a temperature influence factor is obtained through a nonlinear function. The concentration factor and the temperature influence factor are combined to obtain the regenerant strength coefficient, which characterizes the activity of the regenerant under current conditions. The higher the concentration and the closer the temperature is to the optimal temperature, the larger the regenerant strength coefficient. Specifically, the calculation method involves substituting the concentration normalization factor and the regenerant temperature into the formula. Obtain the strength coefficient of the regenerant. Regenerant strength coefficient It is a comprehensive indicator used to quantify the effective regeneration capacity of a regenerant under current concentration and temperature conditions. , The closer a value is to 1, the more ideal the regenerant conditions (concentration and temperature) and the stronger the regeneration ability; among which, For concentration normalization factor, For the regenerant temperature, The optimal temperature for the regeneration process. This refers to the ideal temperature at which the regenerant achieves its best regeneration efficiency under specific resin types and contaminant systems. Its value can be determined through experimental data fitting, consulting relevant literature, or based on the resin manufacturer's recommended operating conditions. The temperature influence coefficient controls the steepness of the S-curve, reflecting the sensitivity of regenerant strength to temperature changes. Its value can be adjusted through regression analysis of experimental data or based on empirical values. This formula cleverly combines the effects of concentration normalization factor and temperature on regenerant strength. The effect of temperature is described by an S-curve function, which reflects the promoting effect of temperature on regeneration within a certain range, as well as the potential efficiency plateau or negative impact beyond the optimal temperature. This calculation process is typically performed by a controller or computing unit that receives the concentration normalization factor and regenerant temperature as inputs, performs calculations according to a preset formula, and outputs the regenerant strength coefficient. .

[0057] This application's method, by accurately acquiring the temperature and concentration of the regenerator and quantifying them into a regenerator strength coefficient, can more accurately assess the actual activity and regeneration capacity of the regenerator. The method first monitors or acquires the temperature and concentration of the regenerator in real time, as these are the most direct physicochemical parameters affecting regeneration efficiency. Subsequently, a concentration normalization factor is obtained by comparing the regenerator concentration with the maximum safe concentration. This not only standardizes the concentration, making it comparable in the model, but also ensures that the regeneration operation is carried out within a safe range. Finally, this concentration normalization factor and the regenerator temperature are substituted into a mathematical model containing an S-shaped curve to calculate the regenerator strength coefficient. This model can intelligently capture the nonlinear effect of temperature on regenerator strength; that is, within a certain temperature range, the regenerator strength increases with increasing temperature, but beyond the optimal temperature, its enhancing effect may tend to level off or even decrease. In this way, the regenerator strength coefficient comprehensively reflects the true performance of the regenerator under current operating conditions, providing a more reliable and refined input for the subsequent calculation of the synergy coefficient. This precise strength assessment avoids the problems of excessive use of regenerant or incomplete regeneration caused by inaccurate regenerant strength assessment, thereby optimizing regenerant consumption, improving regeneration efficiency, and laying the foundation for intelligent control of the entire wastewater treatment process.

[0058] The following is a specific example. As a concrete implementation, suppose that in a wastewater treatment process based on resin materials, the cation exchange resin needs to be regenerated, and a dilute sulfuric acid solution is used as the regenerator. First, the current temperature of the regenerator is acquired in real time using an online temperature sensor installed on the regenerator delivery pipeline. For example, the measured temperature is 30°C. Simultaneously, the concentration of the regenerant itself is monitored in real time using an online conductivity sensor; for example, the measured sulfuric acid concentration is 4%. Assume that, based on process requirements and equipment limitations, the maximum safe concentration of the regenerant is 8%. In this case, the concentration normalization factor... The value was calculated to be 0.5. Next, this concentration normalization factor of 0.5 and the obtained regenerator temperature of 30°C were substituted into the preset regenerator strength coefficient calculation formula. The optimal temperature for this regeneration process was assumed. The temperature is 35℃, and the temperature influence coefficient is... The value is 0.15. Therefore, the strength coefficient of the regenerant is... It is calculated to be 0.1595. This calculated regenerant strength coefficient of 0.1595 will be used as input for subsequent synergy coefficient calculations, thereby guiding the precise adjustment of the regenerant flow rate.

[0059] By accurately obtaining the temperature and concentration of the regenerant and quantifying them into a regenerant strength coefficient, this application can more accurately assess the actual activity and regeneration capacity of the regenerant. This avoids the problems of overuse of the regenerant or incomplete regeneration caused by inaccurate regenerant strength assessment. In particular, the introduction of a concentration normalization factor and an S-shaped curve model considering the effect of temperature in the calculation of the regenerant strength coefficient allows the regenerant strength coefficient to comprehensively reflect the true performance of the regenerant, thus providing a more reliable basis for the subsequent calculation of the synergy coefficient. This ultimately helps to achieve fine control of the regenerant flow rate, ensuring that the resin bed is regenerated under optimal conditions, significantly improving the efficiency and economy of wastewater treatment, while extending the service life of the resin.

[0060] like Figure 1 As shown, this application further proposes a wastewater treatment device based on resin materials, comprising: at least two cylindrical pressure vessels 1 connected in parallel, wherein the top of each cylindrical pressure vessel 1 is equipped with a wastewater inlet 2, a regenerator inlet 3, and an exhaust port 4, and the bottom of each cylindrical pressure vessel 1 is equipped with a purified water outlet 5, a regenerated waste liquid outlet 6, and a backwash port, and a resin filling layer is installed inside each cylindrical pressure vessel 1; a memory for storing a computer program; and a processor for implementing the steps of the above-described wastewater treatment method based on resin materials when executing the computer program.

[0061] The at least two parallel cylindrical pressure vessels 1 form the main structure for containing the resin material and withstanding the pressure during wastewater treatment and regeneration. The parallel design allows the system to regenerate resin without interrupting wastewater treatment, thus enabling continuous operation. These vessels can be made of corrosion-resistant materials (e.g., fiberglass, stainless steel, or carbon steel lined with an anti-corrosion coating), and their shape can be vertical or horizontal, with dimensions designed according to the actual treatment capacity and site conditions.

[0062] The cylindrical pressure vessel 1 has a wastewater inlet 2, a regenerant inlet 3, and an exhaust port 4 installed on its top. These are used to introduce the wastewater to be treated and the regenerant into the vessel, and to discharge gas from the vessel during filling or operation. These interfaces are typically equipped with corresponding valves to achieve precise flow control and flow direction switching. The exhaust port 4 can integrate an automatic exhaust valve to ensure effective gas discharge even when the system is unattended, preventing gas blockage from affecting the treatment effect.

[0063] The cylindrical pressure vessel 1 has a purified water outlet 5, a regenerated waste liquid outlet 6, and a backwash port installed at its bottom. These are used to discharge treated purified water, waste liquid generated during regeneration, and backwash water, respectively. These outlets are also equipped with valves to control liquid discharge. The purified water outlet 5 is typically connected to a downstream treatment unit or discharge system, the regenerated waste liquid outlet 6 is connected to a waste liquid collection or treatment system, and the backwash port 7 is used to introduce backwash water to clean the resin bed.

[0064] The resin packing layer installed inside the cylindrical pressure vessel 1 is the core area where the resin material actually performs its adsorption and exchange functions. This packing layer is typically formed by installing support plates, water distributors, and water collectors inside the vessel to ensure uniform distribution of resin particles and prevent resin loss under the action of water flow. The resin packing layer can be filled with a single type of resin or layered with different types of resin to deal with complex pollutant components, depending on the treatment requirements.

[0065] The memory is a hardware component used to store the instructions and data required to execute the wastewater treatment method. It can be a non-volatile storage medium integrated within the controller, such as flash memory or EEPROM, or it can be a separate storage module. The memory stores not only the operating system and applications, but also configuration parameters, historical operating data, and various calculation models.

[0066] The processor is the core logic unit that executes instructions, performs calculations, and controls system operations. It can be a microcontroller (MCU), microprocessor (MPU), programmable logic controller (PLC), or distributed control system (DCS). The processor runs a computer program stored in memory, receives input data in real time from various sensors (such as flow meters, pollutant concentration sensors, temperature sensors, pH meters, and differential pressure sensors), and performs complex calculations based on the steps of the aforementioned resin-based wastewater treatment method. These calculations include factors such as cumulative load coefficient, regenerant strength coefficient, synergy coefficient, and bed health status coefficient, ultimately determining the target regenerant flow rate. Subsequently, based on the calculation results, the processor sends control commands to actuators (such as electric valves and variable frequency pumps) to precisely adjust the regenerant flow rate and other operating parameters.

[0067] This application's solution combines advanced wastewater treatment methods with a specially designed physical device to achieve intelligent and automated wastewater treatment. At least two parallel cylindrical pressure vessels 1 ensure continuous wastewater treatment; while one vessel is treating wastewater, the other can be regenerating or backwashing, thus avoiding system downtime due to regeneration and significantly improving treatment efficiency and system uptime. The memory and processor work together to precisely execute the complex real-time calculations and dynamic adjustment strategies in the aforementioned resin-based wastewater treatment method. The processor can dynamically calculate and adjust the regenerant flow rate based on real-time data such as influent flow rate, pollutant concentration, regenerant status, and resin bed health, thereby optimizing resin regeneration. This dynamic and intelligent control method overcomes the limitations of traditional fixed-cycle or empirical regeneration strategies, avoiding under-regeneration or over-regeneration, effectively extending resin lifespan, reducing regenerant consumption, and ensuring stable effluent quality compliance. Through this device, the wastewater treatment system can adaptively adjust according to actual operating conditions, significantly improving operational economy and environmental benefits.

[0068] The following is a specific example. This wastewater treatment device can be configured as two parallel cylindrical pressure vessels 1. Each vessel uses a carbon steel tank lined with polyethylene, with a design pressure of 0.8 MPa and a volume of 5 cubic meters. The wastewater inlet 2, regenerant inlet 3, vent 4, purified water outlet 5, regenerated waste liquid outlet 6, and backwash port can all be connected using DN80 flanges and equipped with electric ball valves for automated control. Inside each cylindrical pressure vessel 1, the resin filling layer can consist of a 2.5-meter-high layer of strongly basic anion exchange resin, with a stainless steel wedge-shaped wire filter plate at the bottom serving as resin support and a water collection device. The memory can be the 32MB flash memory and 16MB RAM built into an industrial-grade programmable logic controller (PLC) for storing control programs, historical data, and configuration parameters. The processor can be the CPU module of the PLC, such as the Schneider M580 series, which internally runs control programs written based on IEC61131-3 standards (such as structured text or function block diagrams). The program collects data in real time from electromagnetic flowmeters, online pH meters, conductivity meters, temperature sensors, and differential pressure transmitters, and executes the calculation logic of the aforementioned wastewater treatment method based on resin materials. The processor controls the speed of the variable frequency pump and the opening of the regulating valve via an analog output module to precisely adjust the regenerant flow rate. Two cylindrical pressure vessels 1 operate alternately via an electric three-way valve on the main pipeline and electric valves at the inlet and outlet of each vessel, ensuring that while one vessel is regenerating or backwashing, the other vessel can continuously perform wastewater adsorption treatment.

[0069] Through the above technical solution, this application provides a physical carrier capable of effectively implementing advanced wastewater treatment methods. The device, through a parallel container design, ensures the continuity of wastewater treatment and avoids system downtime caused by regeneration operations. Simultaneously, by integrating a memory and processor, it achieves automated execution of complex calculations and dynamic control strategies, enabling intelligent optimization of the resin regeneration process based on real-time operating conditions. This not only improves resin utilization efficiency and service life, and reduces regenerant consumption, but also significantly enhances the stability of effluent quality and the overall system reliability.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wastewater treatment method based on resin materials, characterized in that, include: The cumulative load factor is calculated based on the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present. The regenerant strength coefficient is calculated based on the regenerant temperature and the regenerant concentration. The synergy coefficient is calculated based on the cumulative load coefficient, regenerant strength coefficient, change rate of pollutant concentration at the outlet of regenerated waste liquid, and pH value of regenerated waste liquid. Based on the resin bed saturation gradient and resin bed pressure drop, the bed health status coefficient is calculated and obtained. Based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient, the target regenerant flow rate is calculated and obtained, and the current regenerant flow rate is adjusted to the target regenerant flow rate.

2. The wastewater treatment method based on resin materials according to claim 1, characterized in that, The process for calculating and obtaining the target regenerant flow rate is as follows: Obtain the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient; The target regenerant flow rate is determined based on the baseline regenerant flow rate, synergy coefficient, and bed health status coefficient. The synergy coefficient is used to positively adjust the target regenerant flow rate, and the bed health status coefficient is used to negatively adjust the target regenerant flow rate. The target regenerant flow rate is not lower than a preset minimum safe flow rate.

3. The wastewater treatment method based on resin materials according to claim 2, characterized in that, The steps for calculating and obtaining the bed health status coefficient are as follows: Obtain the resin bed saturation gradient and resin bed pressure drop; The resin bed saturation gradient and resin bed pressure drop are respectively compared with the maximum allowable standard deviation and the maximum allowable bed pressure drop to obtain the resin bed saturation gradient index and the resin bed pressure drop index. The bed health status coefficient is calculated by weighted summation based on the resin bed saturation gradient index and the resin bed pressure drop index. The larger the bed health status coefficient, the worse the bed health status. Both the resin bed saturation gradient index and the resin bed pressure drop index are directly proportional to the bed health status coefficient.

4. The wastewater treatment method based on resin materials according to claim 2, characterized in that, The process for calculating and obtaining the synergy coefficient is as follows: Obtain the cumulative load coefficient, regenerant strength coefficient, pollutant concentration change rate at the outlet of the regenerated waste liquid, and pH value of the regenerated waste liquid; The desired pollutant concentration change rate is obtained by multiplying the cumulative load factor and the regenerator intensity factor by a proportionality constant. The pH deviation index of the regenerated waste liquid is obtained by dividing the absolute value of the difference between the pH value of the regenerated waste liquid and the optimal pH value of the regeneration process by the tolerance width of the pH deviation. Substitute the pollutant concentration change rate at the outlet of the regenerated waste liquid, the expected pollutant concentration change rate, and the pH deviation index of the regenerated waste liquid into the formula. Obtain the synergy coefficient , , A value closer to 1 indicates a closer match between the actual elution process and the desired outcome, and a better pH condition. The change rate of pollutant concentration at the outlet of the recycled waste liquid. The desired rate of change in pollutant concentration, This refers to the pH deviation index of the regenerated waste liquid.

5. The wastewater treatment method based on resin materials according to claim 4, characterized in that, The process for calculating and obtaining the cumulative load coefficient is as follows: Obtain the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present. Based on the influent flow rate, influent pollutant concentration, effluent pollutant concentration, and the time from the end of the last regeneration to the present, the total amount of pollutants cumulatively adsorbed by the resin bed is obtained by integrating the product of the influent flow rate and the difference between the influent and effluent concentrations over time. This total amount of pollutants is then divided by the maximum adsorption capacity of the resin bed to obtain the cumulative load factor.

6. The wastewater treatment method based on resin materials according to claim 4, characterized in that, The process for calculating and obtaining the strength coefficient of the regenerant is as follows: Obtain the temperature of the regenerant and the concentration of the regenerant itself; The concentration of the regenerant itself is compared with the maximum safe concentration of the regenerant to obtain the concentration normalization factor. Based on the difference between the actual temperature and the optimal temperature of the regenerator, a temperature influence factor is obtained through a nonlinear function; the concentration factor and the temperature influence factor are combined to obtain the regenerator strength coefficient, which is used to characterize the activity of the regenerator under the current conditions; wherein, the higher the concentration and the closer the temperature is to the optimal temperature, the greater the regenerator strength coefficient.

7. A wastewater treatment device based on resin materials, characterized in that, include: At least two cylindrical pressure vessels connected in parallel, wherein the top of the cylindrical pressure vessels is equipped with a wastewater inlet, a regenerator inlet and an exhaust port, the bottom of the cylindrical pressure vessels is equipped with a purified water outlet, a regenerated waste liquid outlet and a backwash port, and a resin filling layer is installed inside the cylindrical pressure vessels; Memory, used to store computer programs; A processor, configured to, when executing a computer program, implement the steps of the wastewater treatment method based on resin materials as described in any one of claims 1-6.

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