Active carbon filter operation optimization method and system for multi-scene water treatment

Abstract

The invention belongs to the technical field of water treatment, automatic control and environmental monitoring, and particularly discloses an activated carbon filter operation optimization method and system oriented to multi-scene water treatment.The method comprises the steps that by introducing a multi-dimensional real-time sensing and intelligent diagnosis mechanism, fine regulation and control of the filter operation state are achieved; a dynamic incidence matrix between the water quality change and the filter layer pressure difference increment is constructed, and optimization measures are specifically adopted to ensure that the effluent reaches the standard; firstly, low-energy-consumption micro-current is started to stimulate and activate adsorption sites, preliminary recovery of the activity of the filter material is achieved, the operation effect is evaluated in real time, and high-temperature steam regeneration is started only when the effect is poor; a heterogeneous laminated filter material is designed and combined with a water source recognition technology, so that the self-adaptive water purification treatment capacity is achieved, the self-adaptive water purification treatment capacity can flexibly cope with polluted water sources with different sources and different characteristics, meanwhile, the type of the water source is recognized, a better water flow path is selected, and it is ensured that each pollutant can be efficiently removed.

Description

Technical Field

[0001] This invention belongs to the fields of water treatment technology, automation control technology, and environmental monitoring technology, and relates to a method and system for optimizing the operation of activated carbon filter beds for water treatment in multiple scenarios. Background Technology

[0002] Water purification and recycling are core issues for sustainable social development and ecological environmental security. Filter systems based on adsorption materials such as activated carbon are widely used in industrial wastewater treatment, municipal water supply, and rainwater harvesting due to their efficient removal of various pollutants. However, with accelerated industrialization and urbanization, the quality of water sources to be treated is becoming increasingly complex and variable, with a wide variety of pollutants and large concentration fluctuations, posing serious challenges to traditional water treatment filter systems.

[0003] Traditional filter operation and management relies on empirically fixed parameters and operating procedures, exhibiting significant lag and inaccuracy. For example, backwashing operations are often triggered based on fixed time periods or single physical parameters (such as the filter bed pressure difference reaching a critical threshold), and adsorption media regeneration also mostly adopts periodic plans, uniformly carrying out high-energy-consuming thermal regeneration or chemical regeneration. This management model can cope with water sources with stable water quality, but when faced with dynamically changing influent water quality, its rigidity and lack of perceptiveness become glaringly apparent.

[0004] Traditional solutions suffer from simplistic control logic and a lack of diagnostic capabilities. Specifically, when filter pressure differential increases, attributing it solely to physical clogging is a one-sided and inaccurate assessment. In reality, the increase in pressure differential may stem from two distinct mechanisms: physical pore blockage caused by suspended particulate matter, or the gradual formation of a biological or chemical film on the adsorbent surface, altering the characteristics of the water flow channels. Given the fundamental differences between these two clogging mechanisms, their corresponding strategies should also differ. However, traditional solutions employ a uniform high-speed backwashing mode, easily leading to significant waste of water and energy resources.

[0005] Secondly, the fixed regeneration cycle setting in traditional solutions also leads to resource waste. Regeneration operations when the concentration of pollutants in the influent is low will prematurely consume the filter media's lifespan and incur unnecessary energy costs; regeneration operations during high-concentration pollutant surges will cause the filter media to become saturated and ineffective prematurely; if regeneration is not carried out in time, pollutants will penetrate the filter layer, resulting in severely substandard effluent quality and potentially causing environmental safety incidents.

[0006] In addition, the homogeneous filter media used in traditional filter beds cannot be flexibly optimized and matched according to changes in water source type, thus limiting the further improvement of overall treatment efficiency. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above objectives, the present invention proposes the following technical solution: an optimized operation method for activated carbon filter beds for multi-scenario water treatment, including the following steps: Step S1, collecting real-time target signals and filter layer pressure difference increments at the filter bed inlet and outlet to generate a dynamic water quality-pressure difference matrix.

[0008] Step S2: Analyze the dynamic water quality-pressure difference matrix and generate an integrated blockage signal, which is divided into physical blockage type, critical filter media capacity type, or normal operation type.

[0009] Step S3: Adjust the inlet water rate according to the integrated blockage signal and output the optimized effluent water quality.

[0010] Step S4: When the integrated blockage signal indicates the critical type of filter media capacity and continues for a preset time, the microcurrent stimulation device is activated and the active release rate is generated.

[0011] Step S5: Trigger graded regeneration based on the active release rate and generate a saturation regeneration flag.

[0012] Step S6: Identify the water source type and switch the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence.

[0013] Step S7: Perform stratified adsorption according to the directional adsorption sequence to output final purified water.

[0014] The second aspect of the present invention provides an activated carbon filter operation optimization system for multi-scenario water treatment, comprising the following: a dynamic water quality-pressure difference matrix generation module, which collects real-time target signals and filter layer pressure difference increments at the filter inlet and outlet to generate a dynamic water quality-pressure difference matrix.

[0015] An integrated blockage signal generation module analyzes the dynamic water quality-pressure difference matrix and generates integrated blockage signals, which are categorized into physical blockage types, critical filter media capacity types, or normal operation types.

[0016] The inlet water rate optimization and adjustment module adjusts the inlet water rate based on the integrated blockage signal and outputs optimized effluent water quality.

[0017] The active release rate generation module activates the microcurrent stimulation device and generates the active release rate when the integrated blockage signal indicates the critical type of filter media capacity and continues for a preset time.

[0018] The graded regeneration trigger module triggers graded regeneration based on the active release rate and generates a saturation regeneration flag.

[0019] The directional adsorption sequence generation module identifies the water source type and switches the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence.

[0020] The stratified adsorption execution module performs stratified adsorption according to the directional adsorption sequence and outputs final purified water.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention introduces a multi-dimensional real-time perception and intelligent diagnosis mechanism to achieve accurate insight and fine control of the filter bed operation status. At the same time, by constructing a dynamic correlation matrix between water quality changes and filter bed pressure difference increment, the internal logical relationship between the two over time can be deeply analyzed. It can clearly distinguish between physical blockage caused by rapid accumulation of suspended solids and performance degradation caused by the complete occupation of adsorption sites on the filter material surface by pollutants. In this way, targeted optimization measures can be taken, which helps to avoid ineffective operation, ensure that the effluent meets the standards, and maximize the operating benefits.

[0022] (2) This invention first activates the adsorption sites by stimulating them with a low-energy microcurrent to achieve the initial recovery of the filter media's activity. At the same time, the system evaluates the effect of this micro-regeneration operation in real time, i.e., the activity release rate. High-temperature steam regeneration is only initiated when the effect is unsatisfactory. This staged regeneration strategy ensures that energy-intensive operations are only used when absolutely necessary. In most cases of mild or moderate saturation, the effective operating cycle of the filter can be extended through low-cost online activation, thereby achieving a significant reduction in energy consumption and operating costs while ensuring the treatment effect.

[0023] (3) By designing heterogeneous stacked filter media and combining it with water source identification technology, this invention endows the water purification system with adaptive processing capabilities, enabling it to flexibly cope with polluted water sources of different origins and characteristics. At the same time, the system can actively identify the type of incoming water source and intelligently select the optimal water flow path. This directional adsorption strategy, which dynamically adjusts the contact sequence between the water flow and the filter media, ensures that each pollutant can be efficiently removed, keeping the system in its optimal working state and stably outputting high-quality purified water. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention.

[0026] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1 Please see Figure 1 As shown, the activated carbon filter operation optimization method for multi-scenario water treatment proposed in this invention includes the following steps: Step S1, collecting real-time target signals and filter layer pressure difference increments at the filter inlet and outlet to generate a dynamic water quality-pressure difference matrix.

[0029] In a preferred embodiment, step S1 includes: acquiring target information at the inlet and outlet of the filter bed using ultraviolet spectrometers installed at the inlet and outlet pipes, and calculating the real-time target signal using Formula 1, where Formula 1 is: A Where A is the absorbance at the target location, c is the solution concentration, and l is the optical path length. It is the molar absorptivity; The inlet and outlet pressure values ​​are measured using inlet and outlet pressure sensors, respectively. The filter bed pressure difference is calculated by subtracting the outlet pressure value from the inlet pressure value, and then subtracting the initial clean state pressure difference value to generate the filter bed pressure difference increment. The inlet pressure value is obtained by measuring the inlet pressure sensor; the outlet pressure value is obtained by measuring the outlet pressure sensor; the initial clean state pressure difference value is set based on experimental measurements obtained under clean filter conditions (based on the average value of 10 repeated calibrations).

[0030] The real-time target signal and the filter layer pressure differential increment are aligned by timestamp to construct a dynamic water quality-pressure differential matrix.

[0031] Specifically, by installing pressure sensors at the inlet and outlet of the filter bed, the inlet pressure value and outlet pressure value are measured in real time. The filter bed pressure difference is calculated as the inlet pressure value minus the outlet pressure value, and the initial clean state pressure difference value measured in advance under the clean state of the filter bed is subtracted to generate the filter bed pressure difference increment.

[0032] The collected real-time target signals and filter layer pressure differential increments are aligned with the same timestamp. Based on the aligned timestamp sequence, a two-dimensional matrix is ​​constructed, where rows correspond to time points and columns contain real-time target signal values ​​and filter layer pressure differential increment values, thus generating a dynamic water quality-pressure differential matrix.

[0033] Among them, the real-time target signal can be selected according to the different application scenarios.

[0034] The filter bed pressure differential increment represents the change in filter bed pressure loss relative to the clean state, measured in kilopascals, and is set based on the pressure sensor industry standard calibration.

[0035] The dynamic water quality-pressure difference matrix is ​​time series data, which includes real-time target signals and filter layer pressure difference increments.

[0036] The ultraviolet spectrometer is installed in the inlet and outlet pipes of the filter bed, and its settings are based on standard industrial water quality monitoring equipment.

[0037] Pressure sensors are used to measure fluid pressure and are installed at the inlet and outlet of the filter tank, with settings based on standard industrial pressure sensors.

[0038] A timestamp is a time marker for a data point, measured in minutes.

[0039] A two-dimensional matrix is ​​a data structure in which rows represent time points and COD signals and filter layer pressure differential increments are collected in real time.

[0040] The initial clean state pressure difference value was set based on experimental measurements obtained before the filter was started, and was averaged based on 5 sets of independent test data.

[0041] Step S2: Analyze the dynamic water quality-pressure difference matrix and generate an integrated blockage signal, which is divided into physical blockage type, critical filter media capacity type, or normal operation type.

[0042] In a preferred embodiment, step S2 includes: calculating the percentage increase of the filter layer pressure difference increment and the percentage fluctuation of the real-time target signal in the dynamic water quality-pressure difference matrix; When the increase in filter layer pressure differential is greater than or equal to a preset increase threshold and the fluctuation of real-time target signal is less than or equal to a preset fluctuation threshold, it is determined to be a physical blockage type.

[0043] When the real-time target signal increase is greater than or equal to the preset increase threshold and the filter layer pressure difference increment fluctuation is less than or equal to the preset fluctuation threshold, it is determined to be a critical type of filter material capacity.

[0044] The remaining states are marked as normal operation, and the output blocking type code is used as an integrated blocking signal.

[0045] In a further preferred embodiment, the step of calculating the percentage increase includes: taking the data point sequence of the previous preset time window interval as the end point of each time point of the dynamic water quality-pressure difference matrix.

[0046] The percentage increase in filter layer pressure differential is calculated by subtracting the filter layer pressure differential increment at the beginning of the window from the current time point, dividing by the filter layer pressure differential increment at the beginning of the window, and multiplying by 100%.

[0047] The percentage increase of the real-time target signal is calculated by subtracting the real-time target signal value at the beginning of the window from the current real-time target signal value, dividing by the real-time target signal value at the beginning of the window, and multiplying by 100.

[0048] The combination of percentage increase calculation and percentage fluctuation calculation produces a synergistic effect, which improves the accuracy of blockage type determination beyond the effect of individual calculation, and enhances the stability of filter operation by reducing the false judgment rate.

[0049] Specifically, the dynamic water quality-pressure difference matrix generated in step S1 is obtained. This matrix contains the real-time target signal value and filter layer pressure difference increment value of the time series. For each time point as the end point of the window, the data point sequence within the previous 10-minute time interval is taken. The percentage increase of the filter layer pressure difference increment in this time interval is calculated, which is defined as the filter layer pressure difference increment value at the current time point minus the filter layer pressure difference increment value 10 minutes ago, then divided by the filter layer pressure difference increment value 10 minutes ago, and multiplied by 100%.

[0050] Simultaneously, the percentage fluctuation of the real-time target signal within this time interval is calculated, defined as the maximum value of the real-time target signal within this interval minus the minimum value, divided by the average value, and multiplied by 100%.

[0051] If the percentage increase in filter layer pressure differential is greater than or equal to 30% and the percentage fluctuation of real-time target signal is less than or equal to 5%, it is determined to be a physical blockage type; if the percentage increase in real-time target signal is greater than or equal to 40% and the percentage fluctuation of filter layer pressure differential is less than or equal to 8%, it is determined to be a critical type of filter media capacity; the percentage increase is calculated in the same way as the percentage increase in filter layer pressure differential; the rest of the status is marked as normal operation.

[0052] Generate a blockage type code, where 0 represents normal operation, 1 represents physical blockage, and 2 represents critical blockage of filter media capacity, which is used as an integrated blockage signal output.

[0053] Among them, the integrated blocking signal represents the blocking state type and is encoded as a blocking type code output.

[0054] The blockage type code takes the values ​​0, 1, or 2, which correspond to normal operation, physical blockage, and critical filter media capacity, respectively. The setting is based on experimental research on filter blockage mechanism.

[0055] Physical clogging is a type of blockage characterized by the accumulation of physical particles in the filter bed, and is defined based on the filter bed clogging diagnostic criteria.

[0056] The critical type of filter media capacity is a clogging state characterized by saturation of the adsorbent material, and is defined based on adsorption kinetic experimental data.

[0057] The time window is fixed at 10 minutes, and the setting is based on the typical response time optimization of industrial filter beds.

[0058] For example, following the example data from step S1, at time point 0 minutes, the dynamic water quality-pressure difference matrix entry is: time 0 minutes, real-time target signal 1.2, filter layer pressure difference increment 5 kPa; an additional entry is added at time point 5 minutes: time 5 minutes, real-time target signal 1.21, filter layer pressure difference increment 7 kPa; an entry is added at time point 10 minutes: time 10 minutes, real-time target signal 1.19, filter layer pressure difference increment 12 kPa; using time point 10 minutes as the end point of the window, data within the interval [0, 10] minutes is taken; the percentage increase in filter layer pressure difference increment is calculated. The percentage is 12 minus 5 divided by 5 multiplied by 100%, which equals 140%, greater than or equal to 30%. The real-time target signal fluctuation percentage is calculated as follows: value sequence 1.2, 1.21, 1.19, maximum 1.21, minimum 1.19, average 1.20. The fluctuation percentage is 1.21 minus 1.19 divided by 1.20 multiplied by 100%, which equals 1.67%, less than or equal to 5%. The physical blockage type condition is met, and blockage type code 1 is output as the integrated blockage signal. This example directly verifies the validity of the increase percentage and fluctuation percentage calculations, as well as the blockage type determination logic.

[0059] Step S3: Adjust the inlet water rate according to the integrated blockage signal and output the optimized effluent water quality.

[0060] In a preferred embodiment, step S3 includes: when the integrated blockage signal indicates the type of physical blockage, increasing the water inlet rate and triggering a high-speed backflush mode.

[0061] When the integrated blockage signal indicates a critical type of filter media capacity, the influent rate is reduced and a slow-flow adsorption mode is triggered.

[0062] The system monitors the target signal in real time. When the target signal is consistently less than or equal to the preset target signal threshold at multiple consecutive time points, it outputs the optimized effluent water quality.

[0063] Specifically, the integrated blocking signal generated in step S2 is obtained, which indicates the current operating status of the filter in the form of a blocking type code.

[0064] The corresponding influent rate adjustment operation is executed according to the blockage type code value: If the blockage type code is 1, it indicates a physical blockage type, triggering a high-speed backwash mode, increasing the current influent rate by 50% for three minutes to flush the filter layer; if the blockage type code is 2, it indicates a critical filter media capacity type, triggering a slow-flow adsorption mode, reducing the current influent rate by 30% to prolong the contact time between the water flow and the filter media.

[0065] After adjusting the influent rate, the target effluent signal value is monitored in real time by a sensor installed on the filter outlet pipe. During continuous monitoring, it is checked whether the target effluent signal is stable within a preset threshold for multiple consecutive time points, i.e., the target effluent signal value is less than or equal to the preset threshold. When the target signal value meets the requirement of being less than or equal to the preset threshold for three consecutive time points, the water quality is determined to be stable and meets the standard, and the optimized effluent water quality is output.

[0066] The influent rate represents the speed at which water flows into the filter, expressed in cubic meters per hour, and is set based on the filter design parameters (based on the manufacturer's specification manual).

[0067] The high-speed backwash mode is used to increase the influent rate to flush the filter bed, and the setting is based on the filter backwashing industry standard.

[0068] The slow-flow adsorption mode is used to reduce the influent rate and extend the contact time, and its setting is based on the adsorbent kinetic optimization experiment.

[0069] Specifically, different standards can be selected for the effluent target signal according to different application scenarios. For example, in industrial treatment, the effluent COD can be set as the effluent target signal. The effluent COD represents the chemical oxygen demand of the filter effluent, with the unit being mg / L. It is measured by an outlet ultraviolet spectrometer and is set according to international water quality monitoring standards.

[0070] The threshold represents the standard for water quality compliance, defined as 60 mg / L, and is set based on the integrated wastewater discharge standard.

[0071] The optimized effluent water quality is the output result, indicating that the water quality meets the standards after adjustment. The setting is based on real-time monitoring data.

[0072] For example, using the sample data from step S2, at time point 10 minutes, the integrated blockage signal is blockage type code 1, indicating a physical blockage type; a high-speed backflushing mode is triggered, assuming the current influent rate is 100 cubic meters per hour, increasing it by 50% to 150 cubic meters per hour, continuously flushing the filter layer for three minutes; starting from time point 10 minutes, the effluent COD is monitored in real time: at time point 11 minutes, the effluent COD is 60 mg / L, at time point 14 minutes it is 65 mg / L, and at time point 17 minutes (the end of flushing) it is 62 mg / L; monitoring continues, at time point 20 minutes it is 59 mg / L, at time point 23 minutes it is 56 mg / L, and at time point 26 minutes it is 50 mg / L; at this time, the effluent COD is less than or equal to 60 mg / L for three consecutive time points (20, 23, and 26 minutes), the water quality is determined to be stable and meets the standard, and the optimized effluent water quality is output; this example directly verifies the effectiveness of the influent rate adjustment operation and the effluent COD monitoring logic, including the threshold setting.

[0073] Step S4: When the integrated blockage signal indicates the critical type of filter media capacity and continues for a preset time, the microcurrent stimulation device is activated and the active release rate is generated.

[0074] In a preferred embodiment, step S4 includes: activating the microcurrent stimulation device embedded on both sides of the filter, applying a preset voltage DC current and maintaining a preset current density for a preset energizing time.

[0075] Measure the differential pressure increment before and after energizing, and calculate the decrease in differential pressure increment as the difference between the differential pressure increment before and after energizing.

[0076] The active release rate is calculated as a percentage of the decrease in differential pressure increment divided by the differential pressure increment before energization multiplied by 100.

[0077] Specifically, when the integrated blockage signal output from step S2 indicates a critical type of filter media capacity (blockage type code 2) and this state lasts for 120 minutes, the microcurrent stimulation device embedded on both sides of the filter bed is activated; a 3-volt DC voltage is applied to the titanium electrode array in the device, with a current density of 0.5 amperes per square meter, for 5 minutes; after the power-on period ends, the current differential pressure increment is measured in real time through the inlet and outlet pressure sensors; the differential pressure increment decrease is calculated as the differential pressure increment before power-on minus the differential pressure increment after power-on; and the activity release rate is calculated as the differential pressure increment decrease divided by the differential pressure increment before power-on, then multiplied by 100%; the activity release rate is generated as the output.

[0078] The decrease in differential pressure increment is calculated as the differential pressure increment before power-on minus the differential pressure increment after power-on, and the setting is based on real-time measurement data verification.

[0079] The differential pressure increment value before power-on is obtained directly from the differential pressure increment data in step S1, and the setting is based on the pressure sensor calibration standard (based on the industrial filter operation protocol).

[0080] Among them, the microcurrent stimulation device is used to provide low-intensity current stimulation to promote adsorbent regeneration, and the setting is based on filter regeneration technology experiments (optimized based on 50 sets of industrial test data).

[0081] A titanium electrode array is a physical structure made of titanium for electrical conductivity, designed based on electrode material durability standards.

[0082] Current density represents the current intensity per unit area, and is set based on the adsorbent regeneration efficiency experiment, with the optimal regeneration effect parameter being 0.5 amperes per square meter.

[0083] The decrease in differential pressure increment represents the amount of decrease in differential pressure. The setting is based on the accuracy standard of pressure sensors, with an error of less than 1 kPa.

[0084] The active release rate indicates the degree of adsorbent activity recovery, and is set based on the regeneration performance evaluation criteria, with a target range of 20% to 60%.

[0085] For example, in step S3, the integrated blockage signal is assumed to be at time 136 minutes, continuously at filter capacity critical type code 2 for 120 minutes, for example, continuously detected as code 2 starting from time 16 minutes; the microcurrent stimulation device is activated, and a 3-volt DC current is applied at a current density of 0.5 amperes per square meter for 5 minutes; the pressure difference increment is measured to be 20 kPa (initial increment) before power-on; the pressure difference increment is measured to be 15 kPa after power-on; the decrease in pressure difference increment is calculated as 20 minus 15 equals 5 kPa; the activity release rate is calculated as 5 divided by 20 multiplied by 100 percent equals 25 percent; an activity release rate of 25 percent is generated; this example directly verifies the effectiveness of the microcurrent stimulation operating parameters and the activity release rate calculation logic.

[0086] Step S5: Trigger graded regeneration based on the active release rate and generate a saturation regeneration flag.

[0087] In a preferred embodiment, step S5 includes: when the active release rate is greater than or equal to a preset release threshold, generating a delayed regeneration command and resetting the adsorption saturation timer.

[0088] When the active release rate is less than the preset release threshold, the high-temperature steam regeneration program is activated and high-temperature steam at the preset temperature is introduced for a preset regeneration cycle.

[0089] After the regeneration process is completed, a saturated regeneration flag is output.

[0090] Specifically, the active release rate generated in step S4 is obtained; it is determined whether the active release rate is greater than or equal to 40%; when the active release rate is greater than or equal to 40%, a delayed regeneration command is generated, and the adsorption saturation timer is reset to zero; when the active release rate is less than 40%, a 380-degree Celsius high-temperature steam regeneration program is activated, which controls the steam generator to introduce 380-degree Celsius high-temperature steam into the filter layer for a preset regeneration cycle; after the regeneration program is completed, a saturation regeneration flag signal is output.

[0091] Among them, the graded regeneration aims to select different regeneration methods according to the active release rate, and the basis for setting is the economic operation criteria of the filter bed, based on the energy efficiency ratio optimization experiment.

[0092] The delayed regeneration command is used to postpone deep regeneration operations, and is set based on the study of the self-healing characteristics of the adsorbent (verified by 50 sets of industrial tests).

[0093] The saturated regeneration indicator is used to mark when deep regeneration has been completed, and is set according to the filter maintenance agreement.

[0094] The adsorption saturation timer is used to accumulate the critical type duration of the filter media capacity in minutes, and is set based on the trigger condition connection in step S4 (120-minute duration threshold).

[0095] The high-temperature steam regeneration program uses 380 degrees Celsius steam treatment, set according to the activated carbon regeneration industrial standard. 380 degrees Celsius is the steam temperature setpoint, set according to the optimal temperature experiment for adsorbent thermal regeneration (determined by 300 sets of thermogravimetric analyses).

[0096] For example, in case one: the active release rate in step S4 is 25%, which is less than 40%, triggering a 380°C high-temperature steam regeneration program; the program is completed after steam is introduced into the filter layer for a preset period of 30 minutes, and a saturated regeneration flag is output (flag value is set to 1).

[0097] Scenario 2: In another operating scenario, the activity release rate output in step S4 is 45%, which is greater than or equal to 40%. A delayed regeneration instruction (such as instruction code "DELAY") is generated, and the adsorption saturation timer is reset to zero. These two examples verify the effectiveness of the two branches of the hierarchical regeneration strategy's execution logic.

[0098] Step S6: Identify the water source type and switch the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence.

[0099] In a preferred embodiment, step S6 includes: identifying the water source type as industrial wastewater or rainwater purification through an external water source monitoring unit.

[0100] When the water source is industrial wastewater, the rotary water distributor is controlled to rotate to the first preset angle, so that the water flows preferentially through the first layer of high porosity carbon particles of the heterogeneous stacked filter media, and the first layer of priority instruction is generated as a directional adsorption sequence.

[0101] When the water source type is rainwater purification, the rotating water inlet distributor is controlled to rotate to the second preset angle, so that the water flow preferentially flows through the second microporous carbon layer region of the heterogeneous stacked filter media, and generates the second layer priority instruction as a directional adsorption sequence.

[0102] Specifically, the external water source monitoring unit identifies the water source type as industrial wastewater or rainwater purification. The industrial wastewater includes, but is not limited to, municipal sewage, and the rainwater purification includes, but is not limited to, municipal water supply. When the water source is identified as industrial wastewater, the rotary water distributor at the filter inlet is controlled to rotate to a 30-degree angle, so that the water flow preferentially passes through the first layer of high-porosity carbon particles in the heterogeneous stacked filter media. When the water source is identified as rainwater purification, the rotary water distributor is controlled to rotate to a 60-degree angle, so that the water flow preferentially passes through the second layer of microporous carbon layer in the heterogeneous stacked filter media.

[0103] After the water flow path is switched, the carbon layer usage sequence instruction is generated according to the carbon particle usage priority. In the industrial wastewater scenario, the instruction is "first layer → second layer", and in the rainwater purification scenario, the instruction is "second layer → first layer". Finally, the carbon layer usage sequence instruction is output as the directional adsorption sequence.

[0104] Among them, the water source types are divided into two categories: industrial wastewater and rainwater purification, which are set based on the water source quality characteristic database (based on the spectral analysis of 100 water sources).

[0105] The rotary water inlet distributor is used to adjust the initial path of the water flow, with an angular position control accuracy of ±1 degree, and the setting is based on the hydraulic transmission industry standard.

[0106] Heterogeneous laminated filter media is a filter media structure that includes two layers of activated carbon with different pore characteristics, the first layer and the second layer.

[0107] The high-porosity carbon particles have a porosity of ≥60% and an average pore size of 50 micrometers, based on industrial activated carbon specifications.

[0108] Microporous carbon layer is an activated carbon material with a pore size of less than or equal to 2 nanometers and a specific surface area of ​​greater than or equal to 1000 m² / g, based on the international standard for microporous adsorbents.

[0109] The directional adsorption sequence indicates the priority order of carbon layer usage, such as the character command "first layer → second layer", and the setting is based on the adsorption path optimization experiment (based on 200 sets of efficiency comparison data).

[0110] For example, in scenario one: the external water source monitoring unit identifies industrial wastewater with a COD value of 380 mg / L and controls the rotary water inlet distributor to turn to a 30-degree angle. The water flow first passes through the first layer of high-porosity carbon particles (porosity 65%), and then through the second layer of microporous carbon layer (pore size 1.8 nanometers), generating a directional adsorption sequence "first layer → second layer".

[0111] Scenario 2: Rainwater with a suspended solids concentration of 25 mg / L is detected. The water inlet distributor is rotated to a 60-degree angle. The water first passes through the second layer of microporous carbon (specific surface area 1100 m² / g), and then through the first layer of high-porosity carbon particles, generating a directional adsorption sequence "second layer → first layer". These two examples respectively verify the effectiveness of the water source type identification and carbon layer path switching logic.

[0112] Step S7: Perform stratified adsorption according to the directional adsorption sequence to output final purified water.

[0113] In a preferred embodiment, step S7 includes: controlling the water flow sequence through the first high-porosity carbon particle region and the second microporous carbon layer region of the heterogeneous stacked filter media according to the directional adsorption sequence.

[0114] COD of the effluent from each filtration zone is monitored at the outlet.

[0115] The final effluent COD is monitored in the final effluent pipeline. When the final effluent COD is less than or equal to the preset purification threshold for a consecutive preset number of time points, the final state purified water is output.

[0116] Specifically, the directional adsorption sequence command generated in step S6 is obtained; the order in which water flows through the heterogeneous stacked filter media is controlled according to the command content: if the command is "first layer → second layer", the water flows through the first layer of high porosity carbon particles first, and then through the second layer of microporous carbon layer; if the command is "second layer → first layer", the water flows through the second layer of microporous carbon layer first, and then through the first layer of high porosity carbon particles; an ultraviolet spectrometer is installed at the outlet of each filtration layer, and the water quality at the outlet of the current carbon layer is monitored immediately after the water flows through the current carbon layer, and recorded as the COD of the stratified effluent; after all the specified carbon layers are filtered, the COD of the final effluent is continuously monitored in the final effluent pipeline; when the COD of the final effluent is less than or equal to 50 mg / L for sixty consecutive time points, the final purified water that meets the standard is output.

[0117] Among them, the layered adsorption is to pass through different carbon layers in sequence according to the sequence instructions, which is based on the adsorption kinetics optimization experiment (based on 300 sets of flux tests).

[0118] The COD of the stratified effluent represents the water quality after a single layer of filtration, measured in mg / L. It is measured by interlayer sensors and is set according to international water quality monitoring standards.

[0119] Final state purified water refers to the final water quality that meets the standards, and is set based on continuous and stable compliance conditions.

[0120] The time condition consists of sixty consecutive time points, with each time point spaced one minute apart, for a total duration of sixty minutes. This setting is based on the industrial water treatment stability verification protocol (determined by analyzing 200 sets of operational data).

[0121] For example, under the premise that the target signal is set as the effluent COD, Case 1: Connecting the directional adsorption sequence "first layer → second layer" of the industrial wastewater scenario in step S6; the water first passes through the first layer of high-porosity carbon particles and then the effluent COD is 58 mg / L; after passing through the second layer of microporous carbon layer, the final effluent COD is 53 mg / L at time point 0 minutes; continuous monitoring until time point 59 minutes shows the final effluent COD is 38 mg / L, and time point 60 minutes shows 32 mg / L; the COD at sixty consecutive time points is less than or equal to 50 mg / L (actually meeting the standard from time point 5 minutes), and the final state purified water is output.

[0122] Scenario 2: Connecting the rainwater purification scenario sequence "second layer → first layer"; the water first passes through the second layer of microporous carbon, and the effluent COD is 60 mg / L; then it passes through the first layer of carbon particles, and the final effluent COD remains at 48 mg / L at time point 0 to 35 mg / L at time point 60; the standard is met for sixty consecutive time points, and the final purified water is output. These two examples directly verify the effectiveness of the carbon layer sequential execution and continuous monitoring mechanism.

[0123] Example 2 Please see Figure 2 As shown, based on Embodiment 1, the second aspect of the present invention provides an activated carbon filter operation optimization system for multi-scenario water treatment, including: a dynamic water quality-pressure difference matrix generation module, an integrated blockage signal generation module, an influent rate optimization and adjustment module, an active release rate generation module, a graded regeneration triggering module, a directional adsorption sequence generation module, and a stratified adsorption execution module.

[0124] The dynamic water quality-pressure difference matrix generation module, integrated blockage signal generation module, influent rate optimization and adjustment module, active release rate generation module, graded regeneration triggering module, directional adsorption sequence generation module, and stratified adsorption execution module are connected in sequence.

[0125] The dynamic water quality-pressure difference matrix generation module collects real-time COD signals and filter layer pressure difference increments at the filter inlet and outlet to generate a dynamic water quality-pressure difference matrix.

[0126] The integrated blockage signal generation module analyzes the dynamic water quality-pressure difference matrix and generates an integrated blockage signal, which is classified into physical blockage type, critical filter media capacity type, or normal operation type.

[0127] The water inlet rate optimization and adjustment module adjusts the water inlet rate according to the integrated blockage signal and outputs the optimized effluent water quality.

[0128] The active release rate generation module activates the microcurrent stimulation device and generates the active release rate when the integrated blockage signal indicates a critical type of filter media capacity and continues for a preset time.

[0129] The graded regeneration triggering module triggers graded regeneration based on the active release rate and generates a saturation regeneration flag.

[0130] The directional adsorption sequence generation module identifies the water source type and switches the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence.

[0131] The stratified adsorption execution module performs stratified adsorption according to the directional adsorption sequence and outputs final purified water.

[0132] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of activated carbon filters for water treatment in multiple scenarios, characterized in that, Includes the following steps: Step S1: Collect real-time target signals and filter bed pressure difference increments at the filter inlet and outlet to generate a dynamic water quality-pressure difference matrix; Step S2: Analyze the dynamic water quality-pressure difference matrix and generate an integrated blockage signal, which is classified into physical blockage type, critical filter media capacity type, or normal operation type. Step S3: Adjust the inlet water rate according to the integrated blockage signal and output the optimized effluent water quality; Step S4: When the integrated blockage signal indicates the critical type of filter media capacity and continues for a preset time, the microcurrent stimulation device is activated and the active release rate is generated. Step S5: Trigger graded regeneration based on the active release rate and generate a saturation regeneration indicator; Step S6: Identify the water source type and switch the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence; Step S7: Perform stratified adsorption according to the directional adsorption sequence to output final purified water.

2. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S1 includes: Target data information is collected by ultraviolet spectrometers installed in the inlet and outlet pipes of the filter bed, and real-time target signals are created based on the target data information; The inlet and outlet pressure values ​​are measured by inlet and outlet pressure sensors, and the filter layer pressure difference is calculated as the inlet pressure value minus the outlet pressure value. The initial clean state pressure difference value is then subtracted to generate the filter layer pressure difference increment. The real-time target signal and the filter layer pressure differential increment are aligned by timestamp to construct a dynamic water quality-pressure differential matrix.

3. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S2 includes: Calculate the percentage increase of the filter layer pressure difference increment and the percentage fluctuation of the real-time target signal in the dynamic water quality-pressure difference matrix; When the increase in filter layer pressure differential is greater than or equal to a preset increase threshold and the fluctuation of real-time target signal is less than or equal to a preset fluctuation threshold, it is determined to be a physical blockage type. When the real-time target signal increase is greater than or equal to the preset increase threshold and the filter layer pressure differential increment fluctuation is less than or equal to the preset fluctuation threshold, it is determined to be a critical type of filter material capacity. The remaining states are marked as normal operation, and the output blocking type code is used as an integrated blocking signal.

4. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S3 includes: When the integrated blockage signal indicates the type of physical blockage, the inlet water rate is increased and a high-speed backflush mode is triggered; When the integrated blockage signal indicates a critical type of filter media capacity, reduce the influent rate and trigger the slow-flow adsorption mode. The system monitors the target signal in real time. When the target signal is consistently less than or equal to the preset COD threshold at multiple consecutive time points, it outputs the optimized effluent water quality.

5. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S4 includes: Activate the microcurrent stimulation device embedded on both sides of the filter, apply a preset voltage DC current and maintain a preset current density for a preset energizing time. Measure the differential pressure increment before and after energizing, and calculate the decrease in differential pressure increment as the difference between the differential pressure increment before and after energizing. The active release rate is calculated as a percentage of the decrease in differential pressure increment divided by the differential pressure increment before energization multiplied by 100.

6. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S5 includes: When the active release rate is greater than or equal to the preset release threshold, a delayed regeneration command is generated and the adsorption saturation timer is reset; When the active release rate is less than the preset release threshold, the high-temperature steam regeneration program is activated and high-temperature steam at the preset temperature is introduced for a preset regeneration cycle. After the regeneration process is completed, a saturated regeneration flag is output.

7. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S6 includes: The water source type was identified as industrial wastewater or rainwater purification by the external water source monitoring unit; When the water source is industrial wastewater, the rotary water distributor is controlled to rotate to the first preset angle so that the water flows preferentially through the first layer of high porosity carbon particles of the heterogeneous stacked filter media and generates the first layer of priority instructions as a directional adsorption sequence. When the water source type is rainwater purification, the rotating water inlet distributor is controlled to rotate to the second preset angle, so that the water flow preferentially flows through the second microporous carbon layer region of the heterogeneous stacked filter media, and generates the second layer priority instruction as a directional adsorption sequence.

8. The activated carbon filter operation optimization method for multi-scenario water treatment according to claim 1, characterized in that, Step S7 includes: The water flow sequence is controlled according to the directional adsorption sequence, passing through the first high-porosity carbon particle region and the second microporous carbon layer region of the heterogeneous stacked filter media. Monitor the COD of the effluent from each filtration zone outlet; The final effluent COD is monitored in the final effluent pipeline. When the final effluent COD is less than or equal to the preset purification threshold for a consecutive preset number of time points, the final state purified water is output.

9. An activated carbon filter operation optimization system for multi-scenario water treatment, characterized in that, include: The dynamic water quality-pressure difference matrix generation module collects real-time target signals and filter layer pressure difference increments at the filter inlet and outlet to generate a dynamic water quality-pressure difference matrix. An integrated blockage signal generation module analyzes the dynamic water quality-pressure difference matrix and generates integrated blockage signals, which are classified into physical blockage type, critical filter media capacity type, or normal operation type. The inlet water rate optimization and adjustment module adjusts the inlet water rate according to the integrated blockage signal and outputs optimized effluent water quality. The active release rate generation module activates the microcurrent stimulation device and generates the active release rate when the integrated blockage signal indicates the critical type of filter media capacity and continues for a preset time. The graded regeneration triggering module triggers graded regeneration based on the active release rate and generates a saturation regeneration flag. The directional adsorption sequence generation module identifies the water source type and switches the water flow path of the heterogeneous stacked filter media to generate a directional adsorption sequence. The stratified adsorption execution module performs stratified adsorption according to the directional adsorption sequence and outputs final purified water.

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