A low-salt-consumption intelligent regenerated water softener and a regeneration agent dosage precise control method

Abstract

The application relates to the technical field of water treatment equipment, and discloses a low-salt-consumption intelligent regeneration soft water machine and a regeneration agent dosage precise control method, which comprises a softening unit: a tank body (the material is food-grade glass fiber reinforced plastic or stainless steel, and the volume is 5-200L), ion exchange resin (the type is strong acid cation exchange resin, and the filling volume accounts for 60%-80% of the effective volume of the tank body) filled in the tank body, and a multi-way intelligent control valve installed at the water inlet and outlet end of the tank body. The residual exchange capacity of the resin is calculated in real time (the error is less than or equal to 5%), and the regeneration agent dosage is dynamically adjusted (the actual demand is accurately matched), so that the traditional equipment fixed excessive dosage (such as 30%-50% more salt) is avoided, the salt consumption is reduced by 40%-70% (for example, the traditional soft water machine needs 0.45-0.9 kg of salt for each regeneration, while the application only needs 0.45-0.9 kg of salt), and the regeneration is triggered based on the actual residual capacity of the resin, so that the problems of early regeneration or late regeneration caused by water consumption fluctuation of the traditional equipment are solved.

Description

Technical Field

[0001] This invention relates to the field of water treatment equipment technology, specifically to a low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage. Background Technology

[0002] Low-salt-consumption intelligent regeneration water softeners are high-efficiency devices that optimize the softening process through an intelligent control system. Their core lies in accurately sensing the resin's saturation state and dynamically adjusting the regeneration cycle and the dosage of regenerant (usually table salt or specialized regeneration salt) based on actual water consumption and hardness. This significantly reduces salt consumption and water waste associated with traditional fixed-program regeneration methods. The precise control of regenerant dosage relies on multi-parameter sensors to monitor influent hardness, flow rate, and resin exchange capacity in real time. Combined with a built-in algorithm, it calculates precise regeneration needs, initiating the regeneration program only when necessary. A metering pump or intelligent valve delivers a measured amount of high-purity regenerant as needed, ensuring full resin recovery while avoiding excessive salt usage. Some advanced models also support remote monitoring and self-learning functions, continuously optimizing control strategies to achieve a 30%–60% reduction in salt consumption compared to traditional equipment, balancing softening effectiveness with operating costs.

[0003] While existing technologies such as low-salt-consumption intelligent regeneration water softeners and precise control methods for regenerative agent dosage have reduced salt consumption and regeneration water consumption to some extent, they still have the following shortcomings:

[0004] Insufficient precision in controlling regenerant dosage: Existing water softeners typically use a fixed amount of regenerant salt or a simple algorithm based on experience, which fails to fully consider the impact of fluctuations in influent hardness, changes in actual water consumption, and the real-time exchange capacity of the resin. This results in a mismatch between the amount of regenerant added and the actual demand, which can easily lead to high salt consumption or insufficient resin regeneration, thus affecting the softening effect.

[0005] The timing of regeneration depends on static thresholds: Most systems only trigger regeneration based on the cumulative water production or fixed time intervals, without combining real-time water hardness for dynamic adjustment. This may lead to regeneration that is too early (wasting salt consumption) or too late (reducing softening capacity), affecting system stability and water-saving efficiency.

[0006] Limited sensor monitoring capabilities: Existing equipment relies on single or low-precision sensors to monitor the hardness of influent water and the saturation state of resin, which makes it difficult to accurately reflect the actual exchange capacity of the resin, thus affecting the accurate calculation of regenerant dosage. Some systems are not even equipped with online hardness detection function and rely solely on estimation to control the regeneration process.

[0007] Insufficient optimization of intelligent control algorithms: Although some intelligent water softeners have introduced algorithm adjustment, they are mostly based on simple linear models or fixed rules, and have not fully considered factors such as dynamic changes in water quality, user water usage habits and long-term performance degradation of resin, resulting in poor adaptability of regeneration strategies and difficulty in maintaining the optimal salt consumption level in the long term.

[0008] The regeneration process has high energy and water consumption: Although existing intelligent regeneration technologies have reduced salt consumption, some systems still use a fixed flow rate or long-term flushing mode during the regeneration process, failing to optimize the regeneration water and energy consumption according to actual needs, which further affects the overall operating efficiency.

[0009] Therefore, we propose a low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage. Summary of the Invention

[0010] To achieve the above objectives, the present invention provides the following technical solution: a low-salt-consumption intelligent regeneration water softener, comprising:

[0011] Softening unit: includes a tank (made of food-grade fiberglass or stainless steel, with a volume of 5-200L), ion exchange resin (a strong acid cation exchange resin, filling 60%-80% of the effective volume of the tank) filled in the tank, and multi-way intelligent control valves installed at the inlet and outlet of the tank (with switching functions for softening mode, regeneration mode, forward flushing mode, backflushing mode and standby mode, and the valve core is made of corrosion-resistant engineering plastic or stainless steel);

[0012] Multi-parameter sensing module:

[0013] Online hardness sensor (installed at the inlet and outlet of the softening unit, measuring range 0-1000 mg / L (calculated as CaCO3), accuracy ±3%, response time ≤30 seconds, using ion-selective electrode method or titration method principle);

[0014] High-precision electromagnetic flow meter (installed on the main inlet pipe of the softening unit, measuring range 0.1-5m³ / h, resolution 0.01L / min, accuracy ±0.5%, used to measure cumulative water production and real-time flow).

[0015] Differential pressure sensor (installed between the inlet and outlet of the resin tank, range 0-0.5 bar, accuracy ±0.005 bar, used to monitor the pressure difference of the resin layer to assess the degree of resin blockage).

[0016] Temperature sensor (installed in the inlet pipe of the softening unit, measuring range 0-80℃, accuracy ±0.5℃, used to compensate for temperature interference in hardness detection and the influence of reclaimed water temperature);

[0017] Optional turbidity sensor (installed at the regeneration rinse water drain, measuring range 0-100 NTU, accuracy ±2 NTU, used to assist in determining whether resin rinsing is complete);

[0018] The regenerant storage and dispensing unit includes a sealed regenerant container (made of polyethylene or polypropylene, with a capacity of 1-10 kg, storing table salt or special regenerant salt (NaCl purity ≥99.5%)), a precision metering pump (flow range 0.1-100 mL / min, accuracy ±1%, made of salt-resistant engineering plastic or stainless steel), and a regenerant dissolving and mixing unit (with a built-in spiral impeller, volume 50-200 mL, ensuring that the regenerant and water are fully dissolved to form a uniform solution).

[0019] The intelligent control unit integrates a 32-bit microprocessor (clock frequency ≥ 100MHz, memory ≥ 256KB), a non-volatile storage module (storage capacity ≥ 4MB, used to save resin parameters, historical operating data, and algorithm models), pre-stores the initial exchange capacity Q0 of the ion exchange resin (unit: mmol / mL, based on resin type calibration or experimental determination), and is equipped with a dynamic resin capacity model and adaptive regeneration control algorithm; the intelligent control unit is configured as follows:

[0020] The system collects in real time data from the multi-parameter sensing module, including influent hardness H_in, effluent hardness H_out, cumulative water production V_total, instantaneous flow rate Q(t), pressure difference ΔP, water temperature T, and optional turbidity. Based on the data from the online hardness sensor, the system dynamically calculates the actual exchange capacity decay rate of the resin (unit: % / h).

[0021] By combining the cumulative water production, influent hardness fluctuation, resin blockage status (corrected by the ratio of pressure difference ΔP to preset threshold ΔP_threshold) and water temperature T (corrected by the temperature-hardness compensation coefficient for online hardness measurement), the real-time remaining exchange capacity Q_remain of the resin (unit: mmol) is predicted by the dynamic resin capacity model.

[0022] When Q_remain ≤ preset minimum remaining capacity threshold Q_min (e.g., Q0×5%-20%), the regeneration process is triggered, and the optimal regenerant dosage M (unit: mmol or g), regeneration water flow rate V_regeneration (unit: m / h), regeneration time t_regeneration (unit: min) and rinse time t_rinse (unit: min) under the current operating conditions are calculated through an adaptive regeneration control algorithm.

[0023] The system controls the multi-channel intelligent control valve to switch to regeneration mode, drives the precision metering pump to add regenerant solution (concentration 5%-10%) according to the calculated amount M, and simultaneously adjusts the regeneration water flow rate V_regeneration and the rinsing strategy (dynamically adjusting t_rinse based on the hardness feedback of the effluent) to optimize water consumption and regeneration efficiency.

[0024] Preferably, the multi-channel intelligent control valve is a lead-free ceramic valve core or a corrosion-resistant engineering plastic valve core (such as PVDF material), supports at least five working modes (softening mode, regeneration mode, forward flushing mode, backflushing mode, and standby mode), and is equipped with an O-ring (made of EPDM rubber or fluororubber, with a salt corrosion resistance life of ≥5 years) at the connection between the valve body and the tank body.

[0025] Preferably, the online hardness sensor is an ion-selective electrode sensor (the measuring electrode material is a silver sulfide-copper sulfide composite film, and the reference electrode is an Ag / AgCl electrode), or a titration optical sensor (detecting the color change after hardness reaction by colorimetry); the differential pressure sensor has a differential pressure measurement range of 0-0.5 bar, an accuracy of ±0.005 bar, and the sensor probe surface is coated with an anti-scaling coating (material is Teflon or ceramic).

[0026] Preferably, the top of the regenerant container is equipped with a moisture-proof sealing cap (made of polyethylene with a built-in desiccant pack), and the bottom is connected to the metering pump inlet via a hose (the hose is made of salt-resistant polyurethane or silicone); the stirring impeller of the regenerant dissolving mixer rotates at 100-300 rpm (driven by a micro motor with an IP65 protection rating), and the mixer outlet is equipped with a filter screen (with a pore size ≤1 mm to prevent undissolved particles from entering the pipeline).

[0027] A low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage, comprising the following steps:

[0028] S1: Initialization Phase

[0029] The initial exchange capacity Q0 of the ion exchange resin is determined by calibration experiments (e.g., Q0 = 1.0-1.5 mmol / mL for strongly acidic cation exchange resin, calibrated by injecting raw water of known hardness and monitoring the maximum softening capacity after complete regeneration).

[0030] Measure and store the resin filling volume V_resin in the resin tank (calculated using the tank geometry and resin bulk density, or a factory preset value).

[0031] Set the default parameters: minimum remaining capacity threshold Q_min = Q0 × V_resin × 10% (i.e., retain 10% remaining capacity to prevent over-regeneration), regeneration efficiency correction coefficient η_regeneration initial value = 95% (based on historical data under standard water quality conditions), resin clogging correction coefficient α initial value = 1 (when there is no clogging).

[0032] S2: Real-time monitoring phase:

[0033] Hardness monitoring: Collect influent hardness H_in and effluent hardness H_out every minute, and calculate the real-time hardness removal rate η=(H_in-H_out) / H_in (unit: %).

[0034] Flow monitoring: The total water production V_total (unit: L) during this regeneration cycle is recorded cumulatively using an electromagnetic flow meter, and the instantaneous flow rate Q(t) (unit: L / min) is sampled every 10 seconds.

[0035] Differential pressure monitoring: Real-time acquisition of the pressure difference ΔP between the inlet and outlet of the resin tank. When ΔP ≥ ΔP_threshold (preset value = 0.1-0.2 bar, calibrated according to resin type), calculate the resin blockage correction coefficient α = 1 - ΔP / ΔP_max (ΔP_max = 0.3-0.5 bar, which is the critical pressure difference when the resin is completely blocked).

[0036] Temperature monitoring: Real-time acquisition of inlet water temperature T, and correction of the online hardness sensor measurement value according to the temperature-hardness compensation formula (e.g., hardness measurement value H_corrected=H_measured×(1+0.001×(T-25)), where T is the water temperature and 25℃ is the reference temperature).

[0037] Optional turbidity monitoring: During the regeneration flushing stage, the turbidity of the wastewater is collected every 30 seconds. When the turbidity is ≤10 NTU, it helps to determine that the resin flushing is complete (optional step).

[0038] S3: Dynamic Resin Capacity Calculation:

[0039] Exchanged capacity calculation: Based on the real-time hardness removal rate η and instantaneous flow rate Q(t), the cumulative exchanged calcium and magnesium ion amount after the most recent regeneration is calculated as Q_used = ∫(H_in-H_out)×Q(t)dt (the integration period is the total permeate volume V_total in this regeneration cycle, and the cumulative summation is used in the discretization calculation: Q_used≈Σ[(H_in(i)-H_out(i))×Q(t(i))×Δt], where Δt=10 seconds, and i is the sampling point number).

[0040] Remaining exchange capacity calculation: Based on the initial exchange capacity Q0 and the resin filling volume V_resin, calculate the theoretical maximum exchange capacity Q_max = Q0 × V_resin (unit: mmol), and the current remaining exchange capacity Q_remain = Q_max - Q_used;

[0041] Correcting remaining capacity: If the pressure difference ΔP ≥ ΔP_threshold, then correct the remaining capacity by α: Q_remain_actual = Q_remain × α;

[0042] Regeneration trigger judgment: When Q_remain_actual≤Q_min, the regeneration process is triggered; otherwise, the softening mode continues to run.

[0043] S4: Optimization of regenerant dosage and parameters:

[0044] Calculation of basic regenerant dosage: Based on the remaining exchange capacity (Q_need=Q_max-Q_remain_actual) and regeneration efficiency η_regeneration, calculate the theoretical regenerant dosage M_theoretical=Q_need×(1 / η_regeneration)×K (K is the regenerant equivalent coefficient, K=2 for salt, that is, 2mol NaCl can exchange 1mol Ca²⁺ / Mg²⁺).

[0045] Dynamically adjust regenerant dosage: Combining the current influent hardness H_in (corrected H_in_corrected) with historical regenerant dosage data under the same hardness conditions (optimized through the learning model stored in the intelligent control unit), fine-tune M_theoretical to obtain the final regenerant dosage M = M_theoretical × f(H_in_corrected) (f(H_in_corrected) is the hardness correction function, for example: f(H_in_corrected) = 1 + 0.0005 × (H_in_corrected - 200), where H_in_corrected is in mg / L).

[0046] Regeneration parameter calculation:

[0047] The regenerated water flow rate V_regeneration = 8-12 m / h (the optimal value is based on the resin type to avoid disordered resin layering; for example, the recommended flow rate for 001×7 strong acid resin is 10 m / h).

[0048] For simplified calculations, the empirical value is: t_regeneration = 20-40 minutes (adjusted according to the resin volume V_resin);

[0049] Optimized rinsing strategy: After regeneration, the forward rinsing phase begins, with an initial rinsing time of t_rinse_initial = 5 minutes. Subsequently, the water hardness H_out_rinse is measured every 30 seconds. The rinsing is terminated when H_out_rinse ≤ 17.1 mg / L (1 German degree, i.e., ≤ 10 mg / L CaCO3) (otherwise, the rinsing time is extended until the standard is met).

[0050] Preferably, in step S1, the initial exchange capacity Q0 is determined through a calibration experiment: raw water with a known hardness (H_cal = 300-500 mg / L) is injected into the tank and operated at a fixed flow rate (Q_cal = 1-2 L / min) until the hardness of the outlet water H_out ≤ 17.1 mg / L (1 German degree), and the cumulative water production V_cal is recorded at this time. The output water is calculated using the formula Q0 = (H_cal × Q_cal × V_cal) / V_resin (unit: mmol / mL).

[0051] Preferably, the calculation of the real-time hardness removal rate η further includes: when the effluent hardness H_out≤5mg / L (0.3 German degrees), the softening effect is deemed to be up to standard, and the upper limit of η is 98% (to prevent sensor error from causing the calculated value to be falsely high); when H_out>H_in×0.8 (i.e. softening failure), the system alarm is triggered and the system is forced to switch to regeneration mode.

[0052] Preferably, the discretized calculation formula for the cumulative exchanged calcium and magnesium ion quantity Q_used is: Q_used≈Σ[(H_in(i)-H_out(i))×Q(t(i))×10 / 60] (unit: mmol, where Δt=10 seconds=10 / 60 minutes, i is the sampling point number every 10 seconds); the correction formula for the resin blockage correction coefficient α is α=1-ΔP / ΔP_max (ΔP_max=0.3-0.5 bar, calibrated according to the resin type, for example, 001×7 resin ΔP_max=0.4 bar).

[0053] Compared with existing technologies, this invention provides a low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage, which has the following beneficial effects:

[0054] 1. This low-salt-consumption intelligent regeneration water softener and its precise regenerant dosage control method calculates the remaining resin exchange capacity in real time (error ≤5%) and dynamically adjusts the regenerant dosage (precisely matching actual needs), avoiding the fixed over-dosing of traditional equipment (such as adding 30%-50% more salt). Salt consumption is reduced by 40%-70% compared to traditional water softeners (for example, traditional regeneration uses 1.5kg of salt per cycle, while this invention only requires 0.45-0.9kg). Regeneration is triggered based on the actual remaining resin capacity (rather than a fixed water production volume or time), solving the problem of premature regeneration (wasted salt consumption) or delayed regeneration (softening failure) caused by fluctuations in water consumption in traditional equipment. The stability of the softening effect is improved by 30%-50% (the hardness of the effluent is consistently ≤17.1mg / L).

[0055] 2. This low-salt-consumption intelligent regeneration water softener and its precise method for controlling the dosage of regenerant dynamically adjust the regeneration water flow rate (8-12m / h) and time (20-40 minutes) according to the resin type, avoiding disordered layering and ineffective soaking. During the rinsing stage, the duration is controlled by feedback of the effluent hardness (20%-40% shorter than the traditional fixed 10-15 minutes), resulting in a 30%-50% reduction in overall water consumption (e.g., traditional rinsing uses 200L of water, while this invention only requires 100-140L). By storing historical operating data (such as the dosage of regenerant at different hardness levels and the resin decay rate), and combining it with a machine learning model (optional), the regeneration efficiency correction coefficient η_regeneration is dynamically optimized. After long-term operation, the control accuracy is further improved (salt consumption fluctuation ≤ ±5%).

[0056] 3. This low-salt-consumption intelligent regeneration water softener and its precise regenerant dosage control method use multiple sensors (hardness / flow / pressure difference / temperature / turbidity) for collaborative monitoring. It supports resin clogging correction and water quality anomaly alarms (such as automatically switching to flow priority mode when the hardness sensor fails). It is compatible with different types of resins (such as 001×7, 201×7, etc.) and regenerants (table salt / special salt), making it highly versatile. Detailed Implementation

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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.

[0058] Example

[0059] An embodiment of a low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage.

[0060] A low-salt-consumption intelligent regeneration water softener includes:

[0061] Softening unit: includes a tank (made of food-grade fiberglass or stainless steel, with a volume of 5-200L), ion exchange resin (a strong acid cation exchange resin, filling 60%-80% of the effective volume of the tank) filled in the tank, and multi-way intelligent control valves installed at the inlet and outlet of the tank (with switching functions for softening mode, regeneration mode, forward flushing mode, backflushing mode and standby mode, and the valve core is made of corrosion-resistant engineering plastic or stainless steel);

[0062] Multi-parameter sensing module:

[0063] Online hardness sensor (installed at the inlet and outlet of the softening unit, measuring range 0-1000 mg / L (calculated as CaCO3), accuracy ±3%, response time ≤30 seconds, using ion-selective electrode method or titration method principle);

[0064] High-precision electromagnetic flow meter (installed on the main inlet pipe of the softening unit, measuring range 0.1-5m³ / h, resolution 0.01L / min, accuracy ±0.5%, used to measure cumulative water production and real-time flow).

[0065] Differential pressure sensor (installed between the inlet and outlet of the resin tank, range 0-0.5 bar, accuracy ±0.005 bar, used to monitor the pressure difference of the resin layer to assess the degree of resin blockage).

[0066] Temperature sensor (installed in the inlet pipe of the softening unit, measuring range 0-80℃, accuracy ±0.5℃, used to compensate for temperature interference in hardness detection and the influence of reclaimed water temperature);

[0067] Optional turbidity sensor (installed at the regeneration rinse water drain, measuring range 0-100 NTU, accuracy ±2 NTU, used to assist in determining whether resin rinsing is complete);

[0068] The regenerant storage and dispensing unit includes a sealed regenerant container (made of polyethylene or polypropylene, with a capacity of 1-10 kg, storing table salt or special regenerant salt (NaCl purity ≥99.5%)), a precision metering pump (flow range 0.1-100 mL / min, accuracy ±1%, made of salt-resistant engineering plastic or stainless steel), and a regenerant dissolving and mixing unit (with a built-in spiral impeller, volume 50-200 mL, ensuring that the regenerant and water are fully dissolved to form a uniform solution).

[0069] The intelligent control unit integrates a 32-bit microprocessor (clock frequency ≥ 100MHz, memory ≥ 256KB), a non-volatile storage module (storage capacity ≥ 4MB, used to save resin parameters, historical operating data, and algorithm models), pre-stores the initial exchange capacity Q0 of the ion exchange resin (unit: mmol / mL, based on resin type calibration or experimental determination), and is equipped with a dynamic resin capacity model and adaptive regeneration control algorithm; the intelligent control unit is configured as follows:

[0070] The system collects in real time data from the multi-parameter sensing module, including influent hardness H_in, effluent hardness H_out, cumulative water production V_total, instantaneous flow rate Q(t), pressure difference ΔP, water temperature T, and optional turbidity. Based on the data from the online hardness sensor, the system dynamically calculates the actual exchange capacity decay rate of the resin (unit: % / h).

[0071] By combining the cumulative water production, influent hardness fluctuation, resin blockage status (corrected by the ratio of pressure difference ΔP to preset threshold ΔP_threshold) and water temperature T (corrected by the temperature-hardness compensation coefficient for online hardness measurement), the real-time remaining exchange capacity Q_remain of the resin (unit: mmol) is predicted by the dynamic resin capacity model.

[0072] When Q_remain ≤ preset minimum remaining capacity threshold Q_min (e.g., Q0×5%-20%), the regeneration process is triggered, and the optimal regenerant dosage M (unit: mmol or g), regeneration water flow rate V_regeneration (unit: m / h), regeneration time t_regeneration (unit: min) and rinse time t_rinse (unit: min) under the current operating conditions are calculated through an adaptive regeneration control algorithm.

[0073] The system controls the multi-channel intelligent control valve to switch to regeneration mode, drives the precision metering pump to add regenerant solution (concentration 5%-10%) according to the calculated amount M, and simultaneously adjusts the regeneration water flow rate V_regeneration and the rinsing strategy (dynamically adjusting t_rinse based on the hardness feedback of the effluent) to optimize water consumption and regeneration efficiency.

[0074] Specifically, the multi-way intelligent control valve uses a lead-free ceramic valve core or a corrosion-resistant engineering plastic valve core (such as PVDF material), supports at least five working modes (softening mode, regeneration mode, forward flushing mode, backflushing mode, and standby mode), and has an O-ring (made of EPDM rubber or fluororubber, with a salt corrosion resistance life of ≥5 years) at the connection between the valve body and the tank.

[0075] Specifically, the online hardness sensor is an ion-selective electrode sensor (the measuring electrode material is a silver sulfide-copper sulfide composite film, and the reference electrode is an Ag / AgCl electrode), or a titration optical sensor (detecting the color change after the hardness reaction by colorimetry); the differential pressure sensor has a differential pressure measurement range of 0-0.5 bar, an accuracy of ±0.005 bar, and the sensor probe surface is coated with an anti-scaling coating (material is Teflon or ceramic).

[0076] Specifically, the top of the regenerant container is equipped with a moisture-proof sealing cap (made of polyethylene with a built-in desiccant pack), and the bottom is connected to the metering pump inlet via a hose (the hose is made of salt-resistant polyurethane or silicone); the stirring impeller of the regenerant dissolving mixer rotates at 100-300 rpm (driven by a micro motor with an IP65 protection rating), and the mixer outlet is equipped with a filter screen (with a pore size ≤1mm to prevent undissolved particles from entering the pipeline).

[0077] A low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage, comprising the following steps:

[0078] S1: Initialization phase:

[0079] The initial exchange capacity Q0 of the ion exchange resin is determined by calibration experiments (e.g., Q0 = 1.0-1.5 mmol / mL for strongly acidic cation exchange resin, calibrated by injecting raw water of known hardness and monitoring the maximum softening capacity after complete regeneration).

[0080] Measure and store the resin filling volume V_resin in the resin tank (calculated using the tank geometry and resin bulk density, or a factory preset value).

[0081] Set the default parameters: minimum remaining capacity threshold Q_min = Q0 × V_resin × 10% (i.e., retain 10% remaining capacity to prevent over-regeneration), regeneration efficiency correction coefficient η_regeneration initial value = 95% (based on historical data under standard water quality conditions), resin clogging correction coefficient α initial value = 1 (when there is no clogging).

[0082] S2: Real-time monitoring phase:

[0083] Hardness monitoring: Collect influent hardness H_in and effluent hardness H_out every minute, and calculate the real-time hardness removal rate η=(H_in-H_out) / H_in (unit: %).

[0084] Flow monitoring: The total water production V_total (unit: L) during this regeneration cycle is recorded cumulatively using an electromagnetic flow meter, and the instantaneous flow rate Q(t) (unit: L / min) is sampled every 10 seconds.

[0085] Differential pressure monitoring: Real-time acquisition of the pressure difference ΔP between the inlet and outlet of the resin tank. When ΔP ≥ ΔP_threshold (preset value = 0.1-0.2 bar, calibrated according to resin type), calculate the resin blockage correction coefficient α = 1 - ΔP / ΔP_max (ΔP_max = 0.3-0.5 bar, which is the critical pressure difference when the resin is completely blocked).

[0086] Temperature monitoring: Real-time acquisition of inlet water temperature T, and correction of the online hardness sensor measurement value according to the temperature-hardness compensation formula (e.g., hardness measurement value H_corrected=H_measured×(1+0.001×(T-25)), where T is the water temperature and 25℃ is the reference temperature).

[0087] Optional turbidity monitoring: During the regeneration flushing stage, the turbidity of the wastewater is collected every 30 seconds. When the turbidity is ≤10 NTU, it helps to determine that the resin flushing is complete (optional step).

[0088] S3: Dynamic Resin Capacity Calculation:

[0089] Exchanged capacity calculation: Based on the real-time hardness removal rate η and instantaneous flow rate Q(t), the cumulative exchanged calcium and magnesium ion amount after the most recent regeneration is calculated as Q_used = ∫(H_in-H_out)×Q(t)dt (the integration period is the total permeate volume V_total in this regeneration cycle, and the cumulative summation is used in the discretization calculation: Q_used≈Σ[(H_in(i)-H_out(i))×Q(t(i))×Δt], where Δt=10 seconds, and i is the sampling point number).

[0090] Remaining exchange capacity calculation: Based on the initial exchange capacity Q0 and the resin filling volume V_resin, calculate the theoretical maximum exchange capacity Q_max = Q0 × V_resin (unit: mmol), and the current remaining exchange capacity Q_remain = Q_max - Q_used;

[0091] Correcting remaining capacity: If the pressure difference ΔP ≥ ΔP_threshold, then correct the remaining capacity by α: Q_remain_actual = Q_remain × α;

[0092] Regeneration trigger judgment: When Q_remain_actual≤Q_min, the regeneration process is triggered; otherwise, the softening mode continues to run.

[0093] S4: Optimization of regenerant dosage and parameters: Calculation of basic regenerant dosage: Based on the remaining exchange capacity (Q_need=Q_max-Q_remain_actual) and regeneration efficiency η_regeneration, calculate the theoretical regenerant dosage M_theoretical=Q_need×(1 / η_regeneration)×K (K is the regenerant equivalent coefficient, K=2 for salt, that is, 2mol NaCl can exchange 1mol Ca²⁺ / Mg²⁺).

[0094] Dynamically adjust regenerant dosage: Combining the current influent hardness H_in (corrected H_in_corrected) with historical regenerant dosage data under the same hardness conditions (optimized through the learning model stored in the intelligent control unit), fine-tune M_theoretical to obtain the final regenerant dosage M = M_theoretical × f(H_in_corrected) (f(H_in_corrected) is the hardness correction function, for example: f(H_in_corrected) = 1 + 0.0005 × (H_in_corrected - 200), where H_in_corrected is in mg / L).

[0095] Regeneration parameter calculation:

[0096] The regenerated water flow rate V_regeneration = 8-12 m / h (the optimal value is based on the resin type to avoid disordered resin layering; for example, the recommended flow rate for 001×7 strong acid resin is 10 m / h).

[0097] For simplified calculations, the empirical value is: t_regeneration = 20-40 minutes (adjusted according to the resin volume V_resin);

[0098] Optimized rinsing strategy: After regeneration, the forward rinsing phase begins, with an initial rinsing time of t_rinse_initial = 5 minutes. Subsequently, the water hardness H_out_rinse is measured every 30 seconds. The rinsing is terminated when H_out_rinse ≤ 17.1 mg / L (1 German degree, i.e., ≤ 10 mg / L CaCO3) (otherwise, the rinsing time is extended until the standard is met).

[0099] Specifically, in step S1, the initial exchange capacity Q0 is determined through a calibration experiment: raw water with a known hardness (H_cal=300-500mg / L) is injected into the tank and operated at a fixed flow rate (Q_cal=1-2L / min) until the hardness of the outlet water H_out≤17.1mg / L (1 German degree). The cumulative water production V_cal is recorded at this time and calculated using the formula Q0=(H_cal×Q_cal×V_cal) / V_resin (unit: mmol / mL).

[0100] Specifically, the calculation of the real-time hardness removal rate η further includes: when the effluent hardness H_out≤5mg / L (0.3 German degrees), the softening effect is deemed to be up to standard, and the upper limit of η is 98% (to prevent sensor error from causing the calculated value to be too high); when H_out>H_in×0.8 (i.e. softening failure), the system alarm is triggered and the system is forced to switch to regeneration mode.

[0101] Specifically, the discretized calculation formula for the cumulative exchanged calcium and magnesium ions Q_used is: Q_used≈Σ[(H_in(i)-H_out(i))×Q(t(i))×10 / 60] (unit: mmol, where Δt=10 seconds=10 / 60 minutes, and i is the sampling point number every 10 seconds); the correction formula for the resin blockage correction coefficient α is α=1-ΔP / ΔP_max (ΔP_max=0.3-0.5 bar, calibrated according to the resin type, for example, 001×7 resin ΔP_max=0.4 bar).

[0102] Through the above technical solution, this invention calculates the remaining resin exchange capacity in real time (error ≤ 5%) and dynamically adjusts the amount of regenerant added (precisely matching actual needs), avoiding the fixed excessive addition of salt in traditional equipment (such as adding 30%-50% more salt). Salt consumption is reduced by 40%-70% compared to traditional water softeners (for example, traditional equipment uses 1.5kg of salt per regeneration, while this invention only requires 0.45-0.9kg). Regeneration is triggered based on the actual remaining resin capacity (rather than a fixed water production volume or time), solving the problem of premature regeneration (wasted salt consumption) or late regeneration (softening failure) caused by fluctuations in water consumption in traditional equipment. The stability of the softening effect is improved by 30%-50% (the hardness of the effluent is consistently ≤ 17.1mg / L). The regenerated water flow rate (8-12 m / h) and time (20-40 minutes) are dynamically adjusted according to the resin type to avoid disordered stratification and ineffective soaking. The rinsing stage uses effluent hardness feedback to control the duration (20%-40% shorter than the traditional fixed 10-15 minutes), reducing overall water consumption by 30%-50% (e.g., traditional rinsing uses 200L, this invention only requires 100-140L). By storing historical operating data (such as regenerator dosage and resin decay rate at different hardness levels) and combining it with a machine learning model (optional), the regeneration efficiency correction coefficient η_regeneration is dynamically optimized, further improving control accuracy after long-term operation (salt consumption fluctuation ≤ ±5%). Multi-parameter sensors (hardness / flow rate / pressure difference / temperature / turbidity) provide collaborative monitoring, supporting resin clogging correction and water quality anomaly alarms (e.g., automatically switching to flow priority mode when the hardness sensor malfunctions). It is compatible with different types of resins (such as 001×7, 201×7, etc.) and regenerators (table salt / special salt), offering strong versatility.

[0103] Example 1 (Household scenario, influent water hardness 200-300 mg / L)

[0104] Equipment parameters:

[0105] Softening unit: 80L tank volume (food-grade fiberglass), 50L resin filling volume (strong acid cation exchange resin 001×7, Q0=1.3mmol / mL), multi-way intelligent valve (engineering plastic valve core).

[0106] Multi-parameter sensing module: online hardness sensor (1 each for inlet and outlet water, measurement range 0-500mg / L, accuracy ±3%), electromagnetic flow meter (range 0.5-2m³ / h, resolution 0.01L / min), differential pressure sensor (range 0-0.3bar, accuracy ±0.003bar), temperature sensor (range 5-50℃, accuracy ±0.5℃).

[0107] Regenerant Unit: Salt container (capacity 3kg, NaCl purity 99.8%), metering pump (flow range 1-50mL / min, accuracy ±1%), dissolving mixer (volume 100mL, impeller speed 200rpm).

[0108] Intelligent control unit: STM32F407 microprocessor (168MHz clock speed, 512KB memory), storage module (4MB Flash, pre-stored resin Q0=1.3mmol / mL, V_resin=50L→Q_max=65mmol).

[0109] Control flow example:

[0110] Initialization: The calibration experiment showed that Q0 = 1.3 mmol / mL, V_resin = 50 L → Q_max = 65 mmol, Q_min = 65 × 10% = 6.5 mmol (retaining 10% of the remaining volume), η_regeneration initial value = 95%, α initial value = 1.

[0111] Daily operation:

[0112] Influent hardness H_in = 250 mg / L, effluent hardness H_out = 15 mg / L → η = (250-15) / 250 = 94%; cumulative water production V_total = 800 L (within 24 hours), pressure difference ΔP = 0.08 bar (α = 1 - 0.08 / 0.3 ≈ 0.73); calculate Q_used ≈ Σ[(250-15) × Q(t) × 10 seconds] (discrete accumulation, Q(t) average 1.5 L / min) → Q_used ≈ 18 mmol; Q_remain = 65 - 18 = 47 mmol; Q_remain_actual = 47 × 0.73 ≈ 34.3 mmol (>Q_min = 6.5 mmol, continue operation).

[0113] Regeneration triggering: When V_total accumulates to 1200L, Q_used increases to 30mmol, Q_remain=65-30=35mmol, Q_remain_actual=35×0.73≈25.5mmol (still > 6.5mmol); when the pressure difference ΔP rises to 0.2bar (α=1-0.2 / 0.3≈0.33), Q_remain_actual=35×0.33≈11.5mmol (still > 6.5mmol); finally, when Q_used increases to 58.5mmol (Q_remain=6.5mmol, Q_remain_actual=6.5×0.33≈2.1mmol<6.5mmol), regeneration is triggered. Regeneration execution: Calculate Q_need = 65 - 2.1 ≈ 62.9 mmol, M_theoretical = 62.9 × (1 / 0.95) × 1 ≈ 66.2 mmol (corresponding to salt mass ≈ 66.2 × 58.5 mg ≈ 3870 mg ≈ 3.9 g); Considering the correction of H_in = 250 mg / L, f(H_in) = 1 + 0.0005 × (250 - 200) = 1.025 → M = 66.2 × 1.025 ≈ 67.9 mmol (actual salt added is approximately 3950 mg ≈ 4 g);

[0114] The regenerated water flow rate V_regeneration = 10 m / h, and the regeneration time t_regeneration = 25 minutes (empirical value).

[0115] Rinsing phase: Initial rinsing for 5 minutes, the water hardness drops from 120mg / L to 8mg / L (meets the standard in the 8th minute), then rinsing is terminated.

[0116] Results: The salt consumption per regeneration cycle is approximately 4g × 58.5 (calculated from mg / mmol to actual mass, approximately 2340mg ≈ 2.34kg of salt). The amount of salt added after dissolving is approximately 2.3kg (actual amount calculated based on solution concentration). This is 30%-50% lower than the actual effective salt consumption of traditional equipment (1.5kg of salt, but over-added to 3kg). Water consumption is approximately 120L (traditional 200L), a reduction of 40%.

[0117] Example 2 (Commercial high hardness scenario, influent water hardness 500-800 mg / L)

[0118] By increasing the sampling frequency of the hardness sensor (once every 5 seconds), optimizing the dynamic resin capacity model (introducing the hardness weighting coefficient η_weight=1+0.001×(H_in-300)), and using the LSTM model to predict η_regeneration (trained based on regeneration data of the same hardness range over the past 7 days), salt consumption can be further reduced to 40%-60% of that of traditional equipment, and rinsing water consumption can be reduced by more than 50%.

[0119] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-salt-consumption intelligent regeneration water softener, characterized in that: include: Softening unit: includes a tank, ion exchange resin filled in the tank, the ion exchange resin is a strong acid cation exchange resin, the filling volume accounts for 60%-80% of the effective volume of the tank, and multi-way intelligent control valves installed at the inlet and outlet of the tank. Multi-parameter sensing module: An online hardness sensor is installed at the inlet and outlet of the softening unit. A high-precision electromagnetic flow meter is installed in the main inlet pipe of the softening unit to measure the cumulative water production and real-time flow rate. A differential pressure sensor is installed between the inlet and outlet of the resin tank to monitor the pressure difference in the resin layer to assess the degree of resin blockage. A temperature sensor is installed in the inlet pipe of the softening unit to compensate for temperature interference from hardness detection and the influence of reclaimed water temperature. A turbidity sensor is installed at the regeneration rinse water drain to help determine whether resin rinsing is complete. Regenerant storage and dispensing unit: includes a sealed regenerant container made of polyethylene or polypropylene, for storing table salt or special regenerant salt with NaCl purity ≥99.5%; a precision metering pump made of salt-resistant engineering plastic or stainless steel; and a regenerant dissolving and mixing unit with a built-in spiral impeller to ensure that the regenerant and water are fully dissolved to form a uniform solution. Intelligent control unit: integrates a 32-bit microprocessor and a non-volatile memory module, pre-stores the initial exchange capacity Q0 of the ion exchange resin, and is equipped with a dynamic resin capacity model and an adaptive regeneration control algorithm; the intelligent control unit is configured as follows: The system collects data in real time from the multi-parameter sensing module, including influent hardness H_in, effluent hardness H_out, cumulative water production V_total, instantaneous flow rate Q(t), pressure difference ΔP, water temperature T, and turbidity. Based on the data from the online hardness sensor, the system dynamically calculates the actual exchange capacity decay rate of the resin. Based on the cumulative water production, influent hardness fluctuation, resin blockage status, and water temperature T, the real-time remaining exchange capacity Q_remain of the resin is predicted using a dynamic resin capacity model. The resin blockage status is corrected by the ratio of the pressure difference ΔP to a preset threshold ΔP_threshold, and the water temperature T is corrected by a temperature-hardness compensation coefficient for the online hardness measurement. When Q_remain ≤ preset minimum remaining capacity threshold Q_min, the regeneration process is triggered; the optimal regeneration parameters under the current operating conditions are determined by an adaptive regeneration control algorithm. The regeneration parameters include the amount of regenerant M, the regeneration water flow rate V_regeneration, the regeneration time t_regeneration, and the rinsing time t_rinse; wherein, Q_min is 5%–20% of Q0, M is in mmol or g, V_regeneration is in m / h, and t_regeneration and t_rinse are in min. The system controls the multi-channel intelligent control valve to switch to regeneration mode; drives the precision metering pump to add regenerant solution according to the calculated amount M, the concentration of which is 5%–10%; and synchronously adjusts the regeneration water flow rate V_regeneration and the rinsing strategy, wherein the rinsing time t_rinse is dynamically adjusted according to the feedback of the effluent hardness to optimize water consumption and regeneration efficiency.

2. The low-salt-consumption intelligent regeneration water softener according to claim 1, characterized in that: The multi-channel intelligent control valve is a lead-free ceramic valve core or a corrosion-resistant engineering plastic valve core, and supports at least five working modes. The specific working modes include softening mode, regeneration mode, forward flushing mode, backflushing mode, and standby mode. An O-ring is provided at the connection between the valve body and the tank body.

3. The low-salt-consumption intelligent regeneration water softener according to claim 1, characterized in that: The online hardness sensor is an ion-selective electrode sensor or a titration optical sensor; the measuring electrode of the ion-selective electrode sensor is made of a silver sulfide-copper sulfide composite film, and the reference electrode is an Ag / AgCl electrode; the titration optical sensor detects the color change after the hardness reaction by colorimetry; the differential pressure sensor has a differential pressure measurement range of 0–0.5 bar and an accuracy of ±0.005 bar, and the probe surface of the differential pressure sensor is coated with an anti-scaling coating, the material of which is Teflon or ceramic.

4. A low-salt-consumption intelligent regeneration water softener according to claim 1, characterized in that: The top of the regenerant container is equipped with a moisture-proof sealing cap, which contains a desiccant pack, and the bottom is connected to the metering pump inlet via a hose; the stirring impeller of the regenerant dissolving mixer rotates at 100-300 rpm, and the mixer outlet is equipped with a filter screen.

5. A low-salt-consumption intelligent regeneration water softener and a method for precise control of regenerant dosage, characterized in that: Includes the following steps: S1: Initialization phase: The initial exchange capacity Q0 of the ion exchange resin was determined by calibration experiments. The calibration experiments included: injecting raw water of known hardness and monitoring the maximum softening capacity of the resin after complete regeneration, in order to determine the initial exchange capacity Q0. Measure and store the resin filling volume V_resin in the resin tank; Set default parameters, which include: minimum remaining capacity threshold Q_min, initial value of regeneration efficiency correction coefficient η_regeneration, and initial value of resin blockage correction coefficient α; wherein, Q_min=Q0×V_resin×10%, initial value of η_regeneration is 95%, and initial value of α is 1. S2: Real-time monitoring phase: Hardness monitoring: Collect influent hardness H_in and effluent hardness H_out every minute, and calculate the real-time hardness removal rate η=(H_in-H_out) / H_in; Flow monitoring: The total water production V_total during this regeneration cycle is recorded cumulatively using an electromagnetic flow meter, and the instantaneous flow rate Q(t) is sampled every 10 seconds; The pressure difference ΔP between the inlet and outlet of the resin tank is acquired in real time. When ΔP ≥ ΔP_threshold, the resin blockage correction coefficient α = 1 − ΔP / ΔP_max is calculated. Wherein, ΔP_threshold is a preset value, taken as 0.1–0.2 bar, calibrated according to the resin type; ΔP_max is the critical pressure difference when the resin is completely blocked, taken as 0.3–0.5 bar. Temperature monitoring: The inlet water temperature T is acquired in real time, and the measured value of the online hardness sensor is corrected according to the temperature-hardness compensation formula: H_corrected=H_measured×(1+0.001×(T−25)), where H_corrected is the corrected hardness measurement value, H_measured is the measurement value of the online hardness sensor, T is the water temperature, and 25℃ is the reference temperature; Turbidity monitoring: During the regeneration rinsing stage, the turbidity of the wastewater is collected every 30 seconds. When the turbidity is ≤10 NTU, it helps to determine that the resin rinsing is complete. S3: Dynamic Resin Capacity Calculation: Exchanged capacity calculation: Based on the real-time hardness removal rate η and instantaneous flow rate Q(t), the cumulative amount of calcium and magnesium ions exchanged after the most recent regeneration is calculated as Q_used=∫(H_in-H_out)×Q(t)dt, where the integration period is the total water production V_total within this regeneration cycle. The discretization calculation uses cumulative summation: Q_used≈Σ[(H_in(i)-H_out(i))×Q(t(i))×Δt], where Δt=10 seconds and i is the sampling point number; Remaining exchange capacity calculation: Based on the initial exchange capacity Q0 and the resin filling volume V_resin, calculate the theoretical maximum exchange capacity Q_max = Q0 × V_resin, and the current remaining exchange capacity Q_remain = Q_max - Q_used; Correcting remaining capacity: If the pressure difference ΔP ≥ ΔP_threshold, then correct the remaining capacity by α: Q_remain_actual = Q_remain × α; Regeneration trigger judgment: When Q_remain_actual ≤ Q_min, the regeneration process is triggered; otherwise, the softening mode continues to run. S4: Optimization of regenerant dosage and parameters, including calculation of basic regenerant dosage: Calculate the theoretical regenerant dosage M_theoretical based on the remaining exchange capacity Q_need and the regeneration efficiency η_regeneration; Where Q_need=Q_max−Q_remain_actual, M_theoretical=Q_need×(1 / η_regeneration)×K, K is the regenerator equivalent coefficient; when the regenerator is salt, K=2, that is, 2mol NaCl can exchange 1mol Ca²⁺ / Mg²⁺; By combining the current influent hardness H_in (corrected) with historical data on actual regenerant usage under the same hardness conditions, the theoretical regenerant dosage M_theoretical is corrected to obtain the final regenerant dosage M; where M = M_theoretical × f(H_in_corrected), f(H_in_corrected) is the hardness correction function, and the unit of H_in_corrected is mg / L; the historical data on actual regenerant usage under the same hardness conditions is optimized using a learning model stored in the intelligent control unit. Regeneration parameter calculation: The regenerated water flow rate V_regeneration = 8-12 m / h; For simplified calculations, the empirical value is used: t_regeneration = 20-40 minutes, adjusted according to the resin volume V_resin; Optimized flushing strategy: After regeneration, the positive flushing stage begins, with an initial flushing time of t_rinse_initial=5 minutes. Subsequently, the hardness of the effluent, H_out_rinse, is detected every 30 seconds. The flushing is terminated when H_out_rinse≤17.1mg / L; otherwise, the flushing time is extended until the standard is met.

6. The low-salt-consumption intelligent regeneration water softener and the method for precise control of regenerant dosage according to claim 5, characterized in that: In step S1, the initial exchange capacity Q0 is determined through a calibration experiment, which includes: injecting raw water of known hardness into the tank, wherein the hardness H_cal of the raw water is 300–500 mg / L; operating at a fixed flow rate Q_cal, wherein Q_cal is 1–2 L / min; until the hardness of the effluent H_out ≤ 17.1 mg / L, recording the cumulative water production V_cal; and calculating Q0 according to Q0 = (H_cal × Q_cal × V_cal) / V_resin, wherein the unit is mmol / mL.

7. The low-salt-consumption intelligent regeneration water softener and the method for precise control of regenerant dosage according to claim 5, characterized in that: The calculation of the real-time hardness removal rate η further includes: when the effluent hardness H_out≤5mg / L, the softening effect is deemed to meet the standard, and the upper limit of η is 98%; when H_out>H_in×0.8, the softening is deemed to have failed, triggering a system alarm and forcibly switching to regeneration mode.

8. The low-salt-consumption intelligent regeneration water softener and the method for precise control of regenerant dosage according to claim 5, characterized in that: The cumulative amount of exchanged calcium and magnesium ions, Q_used, is calculated using the discretized formula: Q_used≈Σ[(H_in(i)−H_out(i))×Q(t(i))×10 / 60], with units of mmol; i is the sampling point number every 10 seconds; the correction formula for the resin blockage correction coefficient α is α=1−ΔP / ΔP_max; where ΔP_max is the critical pressure difference when the resin is completely blocked, taken as 0.3–0.5 bar, calibrated according to the resin type.