A water quality softening process that stabilizes pelleting of the polymerization reaction

Abstract

The application provides a water quality hardening removal process for stabilizing pelletized polymerization reaction, comprising: obtaining wastewater to be treated, wherein the wastewater to be treated contains calcium and magnesium ions; adjusting the pH value of the wastewater to be treated to a preset alkaline range and adjusting the water temperature to a preset temperature range; conveying the adjusted wastewater to the bottom inlet of a fluidized bed reactor, wherein the fluidized bed reactor is filled with porous carriers, and the surface and internal pores of the porous carriers are loaded with active components; adding polymerization reagents into the fluidized bed reactor, and controlling the upward flow rate in the fluidized bed reactor within a preset flow rate range, while controlling the aeration intensity within a preset intensity range, so that the porous carriers are in a fluidized state; performing solid-liquid separation on the mixture output from the fluidized bed reactor, obtaining supernatant as treated effluent, and discharging the pelletized solid product.

Description

Technical Field

[0001] This invention relates to the field of chemical water treatment, and more particularly to a water hardening process for stabilizing pelletizing polymerization reactions. Background Technology

[0002] In the process of treating wastewater containing calcium and magnesium ions, improper control of the fluidization state of the porous carrier in the fluidized bed reactor can lead to uneven carrier distribution, which presents a significant technical contradiction with the stability of microcrystalline nucleation and polymerization.

[0003] Specifically, in order to ensure that calcium and magnesium ions react with the polymerization agent used to induce their polymerization to form microcrystals in the liquid phase and grow through the heterogeneous nucleation mechanism, i.e., the crystal nuclei preferentially attach and grow at the solid-liquid interface of the porous carrier, it is necessary to precisely control the upward flow velocity in the fluidized bed reactor to 0.5-1.0 m / s and the aeration intensity to 0.2-0.5 m³ / h, so as to keep the porous carrier in a fluidized state.

[0004] However, adjusting the upflow velocity and aeration intensity often leads to uneven distribution of porous carriers in the reactor, especially near the bottom inlet of the reactor. The carriers may be washed to the top due to excessive flow velocity, or deposited due to insufficient flow velocity, resulting in excessively low or high carrier concentrations in local areas, which in turn affects the uniformity of microcrystal adsorption and nucleation.

[0005] Furthermore, instability in the fluidized state can interfere with the aggregation and stacking process of crystals on the carrier surface, potentially causing some crystals to detach prematurely or fail to densify, forming granular solid products with uneven particle size, thus affecting the recovery efficiency and quality of the final product. Crystals refer to fine particles with a diameter of less than 1 mm, and an example of uneven particle size is an average particle size of 0.5-2 mm.

[0006] More importantly, when refluxing the fine crystals and part of the support after solid-liquid separation, if the support concentration in the fluidized bed reactor does not reach the preset target of 10 g / L, the refluxing ratio needs to be determined based on the difference. The specific calculation process is as follows: Let C_target be the preset target concentration of 10 g / L, C_current be the current concentration, the difference ΔC = C_target - C_current, and the refluxing ratio R = ΔC / C_target. For example, if C_current is 8 g / L, then ΔC = 2 g / L, and R = 0.2. However, the introduction of refluxing material may further disrupt the fluidization state, especially forming local turbulence at the inlet, leading to uneven mixing of the support and fine crystals, which in turn affects the stability of the subsequent nucleation and grain growth processes.

[0007] In continuous flow crystallization, the instability of the crystallization environment within the reactor is the core reason why crystal growth is difficult and large amounts of reflux are required. This problem is particularly prominent in the treatment of wastewater with high calcium and magnesium ion concentrations. When the system is under dynamic fluidization conditions, uneven carrier distribution and flow rate fluctuations exacerbate crystal breakage and loss, making it difficult to maintain a stable crystal polymerization process. Therefore, how to accurately control the carrier distribution and flow rate under dynamic fluidization conditions to achieve long-term stability of crystal polymerization has become an urgent technical challenge. Summary of the Invention

[0008] This invention provides a stable pelletizing polymerization process for water hardening. The pelletizing polymerization reaction refers to the process by which calcium and magnesium ions form microcrystals under specific conditions, undergo heterogeneous nucleation on the surface of a porous carrier, and ultimately grow into a dense, pellet-shaped solid product through crystal polymerization. The main steps include: obtaining wastewater containing calcium and magnesium ions; adjusting the pH of the wastewater to a preset alkaline range of 9.0-10.5 and the water temperature to a preset temperature range of 25-40 degrees Celsius; and conveying the adjusted wastewater to the bottom inlet of a fluidized bed reactor, which is filled with a porous carrier. The surface and internal pores of the porous carrier are loaded with an active component, which is nano-sized silica or magnesium hydroxide seed crystals, used to provide heterogeneous nucleation sites and catalyze crystal polymerization. A polymerization agent, a composite agent of sodium carbonate or sodium hydroxide and a small amount of sodium polyacrylate, is added to the fluidized bed reactor. The dosage is 1.1-1.3 times the stoichiometric ratio of calcium and magnesium ions, in excess. The upward flow velocity in the fluidized bed reactor is controlled within a preset range of 30-50 m / h, while the aeration intensity is controlled within a preset range of 5-10 m³ / (m²·h), keeping the porous carrier in a fluidized state. Under these conditions, the calcium and magnesium ions react with the polymerization agent to form calcium carbonate or magnesium hydroxide microcrystals in the liquid phase. During fluidization, these microcrystals collide with and adsorb onto the active sites on the surface of the porous carrier, undergoing heterogeneous nucleation. Under the catalytic action of the active component, the nucleated crystals continue to polymerize, stack, and densify through mechanisms such as surface hydroxyl bridging, forming a uniformly sized pellet-shaped solid product. When the pellet-shaped solid product grows to a preset size greater than 2 mm, it detaches from the surface of the porous carrier due to gravity. The mixture output from the fluidized bed reactor undergoes solid-liquid separation, for example, using an inclined plate sedimentation tank, to obtain the supernatant as treated effluent, and the pellet-shaped solid product is collected. The collected solid product is sieved using a vibrating screen to separate fine crystals with a particle size less than a preset threshold of 0.2 mm and small carrier particles generated due to abrasion. The concentration of porous carriers in the fluidized bed reactor is monitored using an online turbidimeter or suspended solids concentration meter, and the difference ΔC between this concentration and a preset target concentration of 15-20% by volume is calculated. The reflux ratio R is determined based on the difference ΔC: R=0 when ΔC≤0; R=30% when 0<ΔC≤5%; and R=50% when ΔC>5%. The determined proportion of fine crystals and a portion of the carriers is then refluxed back to the inlet of the fluidized bed reactor.Furthermore, the surface and internal pores of the porous carrier are loaded with active components, which are metal oxide catalysts, including iron oxide or manganese oxide, used to promote the oxidative degradation of pollutants in wastewater. The loading method is an impregnation method: first, a 0.5 mol / L metal salt solution is prepared, the porous carrier is immersed in it for 2 hours, then dried at 120°C for 4 hours, and finally calcined at 500°C for 3 hours. Furthermore, the aeration intensity is simultaneously controlled within a preset range to keep the porous carrier in a fluidized state. Furthermore, under the action of the nickel-based catalyst active components, the nucleated grains continuously aggregate, stack, and densify through a surface diffusion mechanism, forming uniformly sized pellet-shaped solid products. When the particle size of the pellet-shaped solid products grows to a preset size range of 0.5 mm to 2 mm, they detach from the surface of the porous carrier. Furthermore, the pellet-shaped solid products are discharged.

[0009] Furthermore, the fine crystals separated from the pellet-shaped solid product are used to determine the reflux ratio R of the fine crystals and a portion of the carrier, which is R = (fine crystal mass flow rate / total carrier flow rate) × 100%, with a typical value of 20%. The determined ratio of the fine crystals and a portion of the carrier is then transported to the inlet of the fluidized bed reactor.

[0010] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0011] This invention discloses a water hardness removal process using a stable pelletizing polymerization reaction. The pelletizing polymerization reaction refers to the process by which calcium and magnesium ions form microcrystals and undergo heterogeneous nucleation on a carrier surface, continuously polymerizing and stacking into pellet-shaped solids. By adjusting the pH and temperature of the wastewater containing calcium and magnesium ions, it is pumped into a fluidized bed reactor filled with a porous carrier loaded with an active component, nano-zinc oxide, which promotes heterogeneous nucleation. Under conditions of adding polymerization agents and controlling the upward flow rate and aeration intensity to fluidize the carrier, calcium and magnesium ions form microcrystals and undergo heterogeneous nucleation at active sites on the carrier surface, thereby utilizing the active component... The process involves the continuous aggregation and densification of crystals, forming uniformly sized pellet-shaped solid products until they detach. The polymerization agent is sodium carbonate, added at a rate of 1.2 times the molar ratio of calcium and magnesium ion concentration. The mechanism is induced crystal aggregation. The technical problem to be solved by this method is how to control the particle size of the product and maintain the activity of the carrier, and efficiently recover and utilize the fine crystals and the carrier. To this end, this invention separates the solid and liquid at the reactor outlet, and then dynamically determines the reflux ratio of small-sized fine crystals and a portion of the carrier based on the difference between the carrier concentration and the target concentration in the reactor, and pumps them back to the inlet. The reflux ratio R = (C_t - C_c) / C_t, where C_t is the target concentration of 50 g / L and C_c is the current concentration. This continuously replenishes crystal nuclei and active sites, maintains a stable nucleation and growth environment in the reactor, ensures uniform particle size of the pellet-shaped solid products, and ensures continuous and efficient treatment. Ultimately, it achieves simultaneous and efficient wastewater softening and solid product resource utilization. Detailed Implementation

[0012] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the embodiments of the present invention. The described embodiments are merely some embodiments of the present invention.

[0013] This embodiment provides a water hardness removal process using a stable pelletizing polymerization reaction. The pelletizing polymerization reaction refers to the process of agglomerating fine suspended matter in water into dense, stable pellets using a high-molecular-weight polymer. The method specifically includes the following steps:

[0014] S101, Obtain wastewater containing calcium and magnesium ions. In the wastewater treatment method provided in this application embodiment, the wastewater treatment system includes an automated sampling device and a component analysis module. The automated sampling device quantitatively collects 500 ml of wastewater sample from the wastewater discharge outlet every hour according to a preset program. After the system obtains the sample, it scans the concentration of calcium and magnesium ions in the sample using an ion chromatograph. The specific process is as follows: the sample is filtered and injected into the chromatograph, using a mixed solution of sodium carbonate and sodium bicarbonate as the eluent, with a flow rate set to 1.0 ml per minute. Qualitative analysis is performed based on retention time, and quantitative analysis is performed using the external standard method based on peak area. The calcium and magnesium ion concentration values ​​are output as basic chemical information. This step enables this application embodiment to quickly grasp the basic water quality status of the wastewater. To further clarify the specific situation of calcium and magnesium ions in the wastewater, as an optional embodiment of this application, the operation of obtaining the wastewater to be treated may include the following steps: S1011, Based on the preliminary scan results of the previous step S1020 (a rough concentration estimate obtained through a rapid ion sensor), the concentration of calcium and magnesium ions in the wastewater is accurately determined by spectrophotometry. Specific procedure: Input the preliminary concentration estimate as a reference, select 5 sampling points to collect wastewater samples, and measure the absorbance of each sample at a wavelength of 550 nm using a spectrophotometer. Calculate the calcium ion concentration C_Ca (mg / L) and magnesium ion concentration C_Mg (mg / L) using the formula C = (A - b) / k, where A is the absorbance, b is the intercept (0.01), and k is the correction factor (0.05). Repeat the measurement 3 times and take the average value to determine the ion concentration distribution range from the minimum to the maximum value, for example, 10-50 mg / L. The spectrophotometric method calculates the calcium and magnesium ion concentrations based on the Lambert-Beer law A = εcl. During the measurement, a calcium and magnesium indicator such as Chrome Black T is added to form a complex, and the absorbance A is measured at a wavelength of 540 nm. First, prepare a series of standard solutions, measure the absorbance, and plot a standard curve to obtain the linear equation A = kc + b, where k is the slope and b is the intercept. After the water sample is treated in the same way, its A value is measured. Substituting this value into the equation yields the ion concentration c. For example, using a 1 cm cuvette, with a k value of approximately 0.05 L / mg in a specific system, and a measured A value of 0.32, after blank correction, the concentration is obtained by substituting into the formula c=(Ab) / k. This method provides a more detailed ion concentration distribution range, offering reliable data support for subsequent treatment decisions. S1012: The measured ion concentration data is compared with an ion classification database established based on GB / T 14848-2017 Groundwater Quality Standard Class III. This database sets thresholds of 200 mg / L for Na+, 250 mg / L for Cl-, and 250 mg / L for SO4^2-. If any ion concentration exceeds the corresponding threshold, it is marked as high-concentration wastewater, and the wastewater is identified as requiring priority treatment.The ion classification database stores various ion concentration standards and their classification levels, such as a heavy metal ion threshold of 0.5 mg / L and a pH range of 7.0-8.5. Taking the measured ion concentration data as input, a K-means clustering algorithm is used to compare it with the threshold, outputting a list of samples exceeding the standard. If the concentration exceeds the threshold, it is marked as high-concentration wastewater. This mechanism prioritizes the processing of marked samples, optimizing resource allocation and improving treatment efficiency. In step S1013, for the marked high-concentration wastewater, its chemical characteristics data are obtained, including pH value, COD concentration, and ion species. Combined with water environment information, including temperature and flow rate, a support vector machine algorithm is used to make preliminary predictions about wastewater treatment schemes. First, features are extracted from the data, such as pH range of 5.0-9.0 and COD concentration greater than 500 mg / L. A model is trained using a historical wastewater treatment dataset containing 1000 samples. A hyperplane is constructed by maximizing the margin, with parameters C=1.0 controlling regularization and gamma=0.1 controlling the kernel width. Adapted treatment process parameters are output, such as aeration time of 2 hours and dosage of 50 mg / L. For samples marked as high-concentration wastewater, detailed chemical characteristic data are further obtained. Simultaneously, combined with specific ion types in the wastewater and current aquatic environment information, a support vector machine (SVM) algorithm is used for deep analysis. The SVM algorithm can handle high-dimensional data and find the optimal classification hyperplane, making it very suitable for preliminary prediction of wastewater treatment schemes. Through this algorithm's prediction, the system can output a set of treatment process parameters adapted to the current wastewater characteristics, ensuring the scientific validity and relevance of the treatment scheme. S1014, based on the predicted treatment process parameters, a corresponding wastewater treatment process configuration is generated, the operating mode of the treatment equipment is automatically adjusted, and key control points in the treatment process are determined. Specifically, key control points include pH monitoring points and flow valves. High-risk elements are identified as control points using the HACCP risk assessment algorithm. For example, the pH monitoring threshold is set between 6.5 and 8.5 to ensure neutralization, and the flow valve controls the flow rate between 10 and 50 cubic meters per hour to optimize mixing efficiency. These points are used for real-time monitoring and adjustment to maintain treatment stability. After obtaining suitable treatment process parameters, the system generates a specific wastewater treatment process configuration. At this point, the operating mode of the treatment equipment is automatically adjusted to adapt to the new process requirements. Simultaneously, based on the process parameters, the system automatically determines key control points in the treatment process using a rule engine, such as pH monitoring points, oxidation-reduction potential monitoring points, key dosing valves, and reflux flow valves. The set values ​​for these control points, such as pH control between 6.5 and 8.5, are synchronously sent to the corresponding instruments and actuators as a benchmark for real-time regulation and achieving the desired effect.S1015, Based on the initial wastewater concentrations of Ca²⁺ 250 mg / L and Mg²⁺ 120 mg / L, an ion exchange softener is configured: filled with 15 L of 001×7 Na-type resin, with an exchange capacity of 1.7 eq / L and a flow rate of 4 m³ / h. The wastewater is treated in stages, and water quality data is collected in real time to determine whether the decreasing trend of calcium and magnesium ions meets the expected target of a 92% reduction in total hardness. In the actual treatment process, the configured treatment equipment will treat the wastewater in stages. To ensure the treatment effect, the system will collect water quality change data in real time during the treatment process. By comparing and analyzing this real-time data, the system can accurately determine whether the decreasing trend of calcium and magnesium ion content meets the preset expected target. S1016, Based on the real-time collected water quality change data, the control parameters of the treatment process are dynamically updated, and the removal effect of the target substances is continuously monitored to obtain the final treatment result. If the decreasing trend deviates from the expectation, or to further optimize the treatment effect, this embodiment of the application will dynamically update the control parameters of the treatment process based on the real-time collected water quality change data. Through this dynamic adjustment mechanism, the system can continuously monitor the removal effect of target substances, ensuring that the wastewater treatment process is always in optimal condition and ultimately achieves treatment results that meet environmental standards. When the calcium and magnesium ion removal rate is detected to be lower than the preset threshold of 90%, the system initiates dynamic adjustment. Using real-time collected calcium ion concentration, pH value, and flow rate as inputs, a PID control algorithm is used to calculate the adjustment amount. The algorithm output is a correction value for the flocculant dosing rate and the pH adjustment valve opening; for example, the dosing rate can be increased by 0.5 to 2.0 ml per minute. Through this closed-loop control, the system maintains a stable removal rate above 92%, ensuring that the effluent hardness meets the standard. S102, the pH value of the wastewater to be treated is adjusted to the preset alkaline range, and the water temperature is adjusted to the preset temperature range.

[0015] In a wastewater treatment method provided in this application embodiment, strict control of the physicochemical environment of the wastewater is required to ensure the smooth operation of subsequent treatment processes. Specifically, the system first acquires the initial pH and temperature data of the wastewater to be treated. In practical applications, these environmental parameters can be collected in real time using multiple high-precision sensors distributed within the treatment tank, thereby forming a complete basic monitoring dataset. Based on the collected data, if the pH is below 8.0, sodium hydroxide solution is added via a metering pump to gradually raise it to the preset alkalinity range of pH 8.0-9.0; if the pH is above 9.0, hydrochloric acid solution is added to lower it. Temperature regulation is achieved using a steam heater or a cooling circulating water system to control the water temperature within the preset temperature range of 35-45°C. This operation ensures the accuracy and reliability of subsequent treatment. As an optional embodiment of this application, in order to achieve precise control of the wastewater environment, the above-mentioned adjustment operation may specifically include the following steps: S1021, for the pH values ​​in the basic monitoring dataset, if the detected value deviates from the preset alkaline range of 7.5-8.5, an appropriate amount of alkaline substance is added through an automatic dosing device to determine the adjusted pH data. In the wastewater treatment process, pH is a key factor affecting the efficiency of ion precipitation and chemical reactions. The system compares the collected pH values ​​with the preset alkaline range in real time. Once a deviation from this range is detected, the control center sends a command to the automatic dosing device to precisely control and add an appropriate amount of alkaline substance. After thorough mixing and reaction, the pH is detected again to determine the adjusted pH data, ensuring that the water quality environment meets the process requirements. S1022, based on the adjusted pH data, the current water temperature T_current is obtained from the basic monitoring dataset. Considering the water temperature control requirements, if T_current deviates from the preset temperature range of 20-25°C, a PID algorithm is used to activate the heating or cooling equipment: the calculation error e = 22.5 - T_current, the control signal u = 1.5*e + 0.2*∫e dt +0.5*de / dt. Heating occurs when T_current is less than 20°C, and cooling occurs when T_current is greater than 25°C, thus determining the adjusted water temperature data T_adjusted. Besides pH, water temperature also significantly affects wastewater treatment. In this embodiment, the system further considers water temperature control requirements after adjusting the pH. If the current water temperature deviates from the preset temperature range, the system automatically activates the corresponding heating or cooling equipment for temperature compensation. By dynamically adjusting the operating power of the heat exchange system, the adjusted water temperature data is finally determined, ensuring the wastewater temperature remains stable within the optimal reaction range. After completing the initial pH and water temperature adjustments, the system will continuously monitor changes in environmental parameters using the adjusted water temperature and pH data.At this point, a preset threshold range is used as the evaluation standard to determine whether the entire water environment has reached a stable state. The threshold range is set at water temperature 20-30°C and pH 6.5-8.5, based on wastewater treatment standards. After obtaining monitoring data under stable conditions, the system will conduct in-depth analysis of fluctuations during the wastewater adjustment process: input continuous monitoring data, use the standard deviation algorithm to calculate the fluctuation value σ, if σ exceeds 0.5, it is identified as an abnormal parameter, and the abnormality type is output for subsequent optimization. For example, if σ=0.7, pH fluctuation is marked as abnormal and a timestamp is recorded. S1023, for abnormal parameters, the support vector machine algorithm is used for classification. First, the input features are extracted: temperature deviation T_dev=T_actual-T_set, concentration deviation C_dev=C_actual-C_target, pH deviation pH_dev=pH_actual-pH_target. Then, the pre-trained SVM model is input, with the radial basis function kernel penalty parameter C=1 and the kernel parameter gamma=0.1. Anomaly sources are classified, such as sensor drift or uneven concentration. Category labels are output to generate adjustment instructions, such as increasing the dosage by 10% for sensor drift or uneven concentration, automatically adjusting the parameters of the dosing device and temperature control equipment. To cope with the interference caused by complex and variable wastewater composition, this embodiment introduces an intelligent anomaly handling mechanism. For identified abnormal parameters, the system uses a support vector machine algorithm for deep classification. The support vector machine algorithm can accurately determine the specific source of abnormal fluctuations based on historical operating data, such as sensor drift or local uneven concentration. Subsequently, the system generates corresponding adjustment instructions based on the judgment results, automatically adjusting the operating parameters of the dosing device and temperature control equipment. Through this closed-loop control method, wastewater treatment results that fully conform to the preset alkalinity and temperature ranges are finally obtained, greatly improving the robustness and processing accuracy of the system. S103, wastewater with a adjusted pH of 6.5-8.0 and COD below 500 mg / L is pumped at a rate of 2 L / min to the bottom inlet of a fluidized bed reactor. The fluidized bed reactor is filled with a porous carrier, the surface and internal pores of which are loaded with active components. The active components are a mixture of nitrifying and denitrifying bacteria, used to catalyze wastewater denitrification. Loading is performed by immersion: the carrier is immersed in a bacterial suspension with a concentration of 10^8 CFU / mL for 24 hours, filtered, and then dried at 105℃ for 2 hours. After environmental parameter adjustment, before the wastewater enters the core reaction area, the system uses an online water quality sensor and flow meter system to acquire the initial state parameters of the wastewater in real time, including chemical oxygen demand (COD), ammonia nitrogen concentration, and pH value. These parameters are transmitted to the central processing module, which uses a preset multiple linear regression model to calculate a comprehensive index characterizing the treatability of the wastewater, using these parameters as input, as the initial conditions for subsequent fluid control.As an optional embodiment of this application, in order to achieve precise wastewater transport and efficient reaction, the above-mentioned transport and monitoring operation may specifically include the following steps: S1041, based on the pollutant concentration C and target reactor volume V in the preliminary analysis results, an automated control system is used to calculate the set flow rate using the formula Q = 0.1 multiplied by C and then multiplied by V, and the wastewater transport path and pump speed are adjusted accordingly. The inlet position at the bottom of the fluidized bed is precisely located to determine the optimized transport path scheme. S1042, based on the optimized transport path, the pipeline pressure P and flow rate Q are monitored in real time. If the pressure P exceeds 0.5 MPa or the flow rate Q deviates from the set value by more than 5 percent, an alarm is automatically triggered and the control valve opening is adjusted. S1031, firstly, parameters such as wastewater flow rate, pH value, and pollutant concentration are collected by sensors as input, and a genetic algorithm is used for preliminary analysis to output path optimization indicators such as minimum transport distance and energy consumption value. For example, the path length is reduced from the initial 50 meters to 40 meters. Based on this, an automated control system is used to adjust the wastewater transport path, and the inlet position at the bottom of the fluidized bed is precisely located to determine the optimized transport path scheme. In practical applications, the influent method of a fluidized bed reactor directly affects the distribution of the internal flow field. Therefore, the system uses preliminary CFD k-ε model analysis results as input, including flow field velocity and pressure distribution data, and employs a genetic algorithm for optimization: defining the objective function min(R+1-U), where R is the resistance coefficient and U is the velocity uniformity index from 0 to 1; setting the population size to 50 and the iteration to 100 generations, the system automatically calculates the optimal fluid inlet angle θ=25° and velocity v=2.5 m / s, and adjusts the pipeline valves accordingly to achieve precise positioning of the bottom inlet, thereby determining an optimized transmission path scheme with the least resistance and the most uniform distribution. S1032, through the optimized transmission path, wastewater is stably transported to the bottom inlet of the fluidized bed, and the environmental state inside the reactor is monitored in real time to obtain dynamic data within the reactor. Once the wastewater is stably injected into the reactor, the system activates its built-in monitoring matrix. This matrix consists of an array of pressure sensors, temperature sensors, and flow rate sensors. These sensors are installed at key locations within the reactor and connected to the central processing unit. The system works by using the sensors to collect raw signals as input, performing calculations using the finite volume method, and outputting real-time data. Through this matrix, the system monitors the fluid dynamics environment within the reactor in real time, including parameters such as flow velocity (0.1-5 m / s), pressure (0-10 MPa), temperature (20-80°C), and turbulence intensity. The data acquisition process involves sampling once per second and calculating dynamic data such as the Reynolds number Re = ρvd / μ, where ρ is density, v is velocity, d is diameter, and μ is viscosity, thus obtaining a wealth of quantitative indicators.S1033: For dynamic data within the reactor, input image data is used, and a threshold segmentation algorithm is employed to calculate the uniformity of the porous carrier distribution. If the standard deviation is greater than 5%, correction is performed by adjusting the fluid rate to the target value, and a load stability assessment report for the porous carrier surface is output. In this embodiment, the porous carrier is the primary site of the reaction. The system deeply analyzes dynamic data to assess the spatial distribution of these porous carriers within the fluidized bed. Once local aggregation or uneven distribution of the porous carriers is detected, the control system responds rapidly by fine-tuning the fluid rate of the inlet pump to perform hydraulic correction, allowing the carriers to regain a uniform fluidization state. Simultaneously, the standard deviation σ of the carrier surface load is calculated based on the dynamic image data. If σ ≤ 0.05, a stability flag 1 is output for use in S1044. S1034, based on the load stability data output from S1043, detects the adhesion of active components in the internal pores. A support vector machine (SVM) algorithm is used, with the load stability score and pore adhesion rate as input features. A classifier is trained using a radial basis function (RBF) kernel (C=1, gamma=0.1) to evaluate the activity of the attached components, outputting an activity efficiency score from 0 to 1. To further understand the efficiency of the reaction core, the system uses SEM (scanning electron microscopy) to image the internal pores of the porous carrier, collecting microscopic feature data, including 5-dimensional features such as pore diameter (μm), number of attached particles, surface coverage (%), particle size distribution, and edge detection intensity. Subsequently, an SVM algorithm is introduced, using these feature vectors as input. A classifier is trained using an 80% labeled training set (RBF kernel, C=1, γ=0.1), performing high-dimensional mapping and binary classification (high activity / low activity), outputting a decision function value as a quantitative indicator of the active component's efficiency (range 0-1, threshold above 0.5 for high activity). S1035 monitors the changing trends of the reaction environment through the efficiency indicators of the active components. If the changing trend exceeds a preset threshold, an environmental regulation mechanism is triggered to determine the stable state of the reaction environment. This monitoring of the reaction environment's changing trends, such as the efficiency indicator change rate exceeding 5%, involves inputting real-time monitoring data into a simple moving average algorithm to calculate the trend, outputting a regulation signal to determine the stable state of the reaction environment, and continuously recording various parameters during the wastewater treatment process. Based on the calculated efficiency indicators, the system can proactively monitor the changing trends of the entire reaction environment. If the changing trend is found to exceed the preset threshold for safe or efficient operation, the corresponding environmental regulation mechanism will be immediately triggered to intervene until the stable state of the reaction environment is re-determined. Furthermore, the system continuously records various key parameters during the wastewater treatment process and transmits them completely to the data storage module, thereby obtaining a complete record of the entire treatment process and providing a data traceability basis for subsequent process optimization.S104, add polyacrylamide polymerization agent to the fluidized bed reactor at a dosage of 1-3 mg / L to promote the flocculation and stacking of calcium and magnesium ions into crystals. Control the upward flow velocity in the fluidized bed reactor within a preset range of 0.5-1.5 m / h. Simultaneously control the aeration intensity within a preset range of 0.2-0.5 m³ / h. Monitor the bed pressure difference to maintain it at 0.5-2 kPa to confirm that the porous carrier is in a fluidized state. In the wastewater treatment method provided in this application embodiment, in order to promote sufficient contact between wastewater and porous carrier and to achieve efficient reaction, it is necessary to refine the management of hydraulic conditions and the gas-liquid-solid three-phase mixing state in the reactor. As an optional embodiment of this application, the control operation of the above-mentioned fluidization state may specifically include the following steps: S1041, using an ultrasonic flow velocity sensor network deployed on the wall of the fluidized bed reactor, the upward flow velocity in the reactor is collected in real time, data is collected once per second, dynamic flow velocity monitoring data is obtained, and when the flow velocity exceeds the preset range of 0.5 to 1.5 meters per second, an adjustment mechanism is triggered, linking the variable frequency pump and other fluid control devices in the reactor to dynamically adjust the flow velocity by adjusting the pump speed. In practical applications, the upward flow velocity directly determines the suspension height and mixing uniformity of the porous carrier. The system uses the sensor network deployed inside the reactor to continuously acquire dynamic flow velocity monitoring data and determine in real time whether the current flow velocity is within the preset flow velocity range. If the flow velocity is found to be too high or too low, the system will immediately generate a flow velocity adjustment command. According to the command, the fluid control device will automatically change the frequency of the inlet pump or adjust the opening of the valve to dynamically adjust the upward flow velocity. The final output is the adjusted flow velocity data, which is then input into the PID control algorithm to calculate the suspension height H (H = k * v, where k is an empirical coefficient of 0.8 and v is the flow velocity in m / s). If H is stable within the range of 0.5-1.0 m, the porous carrier is confirmed to be in a fluidized state. S1042, the aeration intensity within the reactor is continuously tracked using a thermal mass flow meter, an airflow monitoring device. This device measures the gas velocity v in the aeration pipeline in real time, in cubic meters per second, and calculates the aeration intensity I using the formula I=v / A, where A is the cross-sectional area of ​​the reactor. When the calculated intensity value deviates from the preset range, for example, below 0.1 or above 0.3 cubic meters per square meter per second, the system automatically generates an intensity adjustment signal, driving the pneumatic regulating valve of the aeration equipment to precisely control the opening to maintain stable aeration intensity. Besides hydraulic velocity, aeration intensity is also a key factor in maintaining a fluidized state. In this embodiment, the system acquires real-time aeration intensity data through the airflow monitoring device and determines whether the intensity meets the preset intensity range. If the intensity deviates from the preset range, a corresponding intensity adjustment signal will be generated, thus obtaining intensity correction parameters. Based on these parameters, the system can drive the aeration equipment at the bottom to perform adjustment operations, precisely controlling the aeration intensity.Subsequently, the adjusted intensity status information is output to confirm that the aeration intensity has reached the preset intensity range, thus providing sufficient agitation energy for the porous carrier. In S1043, the state of the porous carrier is scanned in real time by a state detection device, and combined with the reactor internal environmental data, the system links with the reagent dosing control system to dynamically adjust the amount of polymerization reagent added. To ensure efficient reaction, the system needs to confirm whether the porous carrier has truly reached the ideal fluidization state. After obtaining relevant data on fluidization maintenance through the state detection device, if it is determined that there is local deposition or insufficient fluidization in the porous carrier, the system will generate an environmental optimization command to obtain the basis for carrier state adjustment. Based on this adjustment basis and combined with the current reactor internal environmental data, the system will link with the reagent dosing control system. At this time, the amount of polymerization reagent added is dynamically proportioned and precisely added, utilizing the flocculation and flow-aiding effects of the reagent to improve fluid properties. Finally, the environmental status information after reagent addition is output, including confirmation that the fluidization degree is above 95%, and this information is input into the PID control algorithm in S1054 for stability evaluation, serving as the threshold basis for reaction monitoring in S106. S1044, a comprehensive monitoring platform is constructed based on the aforementioned state detection device and reagent dosing control system to integrate and analyze parameters such as temperature, pH value, and flow rate within the fluidized bed reactor. Specifically, real-time data is input to a Kalman filter algorithm (used for noise filtering and state estimation), and fused parameter values ​​are output. When parameter fluctuations exceed preset thresholds, such as a temperature exceeding 50 degrees Celsius or a pH value deviating from 7.0 by more than 0.5, a linkage control mechanism is triggered, automatically adjusting the reagent dosage to the target range. In addition to the above independent adjustments, this embodiment also introduces global-perspective monitoring. Through the comprehensive monitoring platform, the system can acquire linkage data on flow rate, aeration intensity, and carrier state, and perform in-depth integrated analysis. This is used to determine whether the overall environment within the reactor is stable. If the overall fluctuation of environmental parameters exceeds a preset threshold, the system triggers a linkage control mechanism to coordinate and fine-tune the water flow, air flow, and reagents, ultimately obtaining stable environmental data to ensure the high efficiency and stability of the wastewater treatment process. S105 includes the following steps: S1061, the calcium and magnesium ions and the polymerization agent form microcrystals in the liquid phase; S1062, the microcrystals adsorb at active sites (hydroxyl groups) on the surface of the porous support and undergo heterogeneous nucleation (pH 8-9, temperature 25-35℃); S1063, under the action of the active component (sodium polyacrylate catalyst supported on the support), the nucleated crystals continue to polymerize, stack, and densify, forming a pellet-shaped solid product with uniform particle size; S1064, the pellet-shaped solid product grows to a preset size (diameter 50-100 μm) and then detaches from the surface of the porous support. Based on the above independent adjustments, this application embodiment also introduces global perspective monitoring.Through a comprehensive monitoring platform, the system can acquire and deeply integrate data on flow rate, aeration intensity, and carrier status. This allows for the determination of the overall stability of the reactor environment. If the overall fluctuation of environmental parameters exceeds preset thresholds (flow rate fluctuation <5%, aeration intensity ±10%, carrier loading rate >80%), the system will trigger a linkage control mechanism to fine-tune the water flow, air flow, and reagents in a coordinated manner, ultimately obtaining stable environmental data and ensuring the high efficiency and stability of the wastewater treatment process. In a wastewater treatment method provided in this application embodiment, to effectively remove calcium and magnesium ions from wastewater, a series of chemical and physical processes are required to achieve ion crystallization and separation. Specifically, the system promotes the reaction of calcium and magnesium ions with polymerizing agents in a liquid phase environment, forming tiny crystals. These microcrystals then adsorb onto active sites on the surface of a porous carrier, initiating heterogeneous nucleation. Under the catalytic action of the active components, the nucleated crystals continue to polymerize and stack, gradually densifying, ultimately shaping uniformly sized pellet-shaped solid products. When these granular solid products grow to a preset size of 0.5-2 mm, they will naturally detach from the porous carrier surface, thus completing the separation process. This mechanism ensures the high efficiency of the processing and the controllability of the product. As an optional embodiment of this application, in order to achieve precise monitoring and optimization of the crystal formation process, the above-mentioned microcrystal generation and detachment operation can specifically include the following steps: S1051, acquire the binding state data of calcium and magnesium ions with the polymerization agent through a liquid phase environment monitoring system, and record the concentration change in real time during the binding process. The specific steps are: input the real-time concentration data C(t), where C is the concentration and t is the time; calculate the ion concentration gradient G=ΔC / Δt, where ΔC is the concentration difference and Δt is the time difference; then calculate the reaction rate R=k×G, where k is the rate constant, with a value of 0.05 L / mol·s; generate preliminary microcrystal generation parameters based on the R value, for example, when R is greater than 0.002 s⁻¹, the microcrystal size parameter D=10×R, and output the D value as 0.02 μm as the initial crystal nucleus size. In the initial stage of the reaction, ion-selective electrodes were used as liquid-phase monitoring sensors, with three monitoring points equidistantly arranged along the reactor axis. The system acquired calcium and magnesium ion concentration signals in real time, processed noise using a moving average filtering algorithm, and set the window width to 5. Based on the known reagent dosage, the reaction rate constant k was calculated using a pseudo-first-order kinetic model, with a typical value ranging from 0.1 to 0.3 per minute. This yielded preliminary parameters for the induction time and concentration of microcrystal formation, providing a data basis for subsequent steps. In step S1052, a porous carrier was introduced into the liquid-phase environment of S1051 as a substrate for microcrystal adhesion. Based on microcrystal formation parameters such as ion concentration and pH value, scanning electron microscopy was used to detect the distribution of active sites on the carrier surface. Active sites refer to the pore regions capable of adsorbing microcrystals.If the adsorption position deviation *d* exceeds a preset threshold of 0.5 μm, the liquid flow rate *v* is adjusted using the formula *v = v0 + k * d*, where *v0* is the initial rate of 1 mL / min and *k* is an adjustment coefficient of 0.2 μm. The adjusted rate guides the microcrystals to redistribute to the target region. Using atomic force microscopy (AFM) surface scanning technology, the system acquires images of the porous carrier surface as input. An image segmentation algorithm extracts the spatial coordinates of the highly active regions of the preset active sites, and the adsorption position coordinates of the microcrystals are detected in real time. If the adsorption position coordinates deviate from the active site coordinates by more than a 5 μm threshold, the system immediately adjusts the liquid flow rate to 2 to 5 mL / min to guide the microcrystals to redistribute to the active sites, ensuring that heterogeneous nucleation is completed at the optimal location. S1053: Based on the heterogeneous nucleation grain distribution data output from S1052, grain stacking data is acquired. Using an optical microscope combined with the ImageJ image analysis algorithm, morphological changes during the stacking process are continuously tracked, and the grain density ρ = N / A (ρ is density, N is the number of grains, and A is the stacking area) is calculated. When ρ exceeds 0.85, the grain densification progress and structural stability are determined. After nucleation, the system acquires relevant grain stacking data and evaluates the grain densification progress and structural stability by continuously tracking morphological changes. Specifically, the OpenCV image processing library is used to analyze morphological changes. The input is a sequence of grain stacking images, and the densification progress D = current density / initial density is calculated. If D > 0.95, it is considered stable. This process helps to identify potential instability factors in a timely manner. S1054: Using the densification progress data, the particle size growth of the pellet-shaped solid product is monitored, and its surface adhesion strength is recorded when the particle size approaches a preset size threshold to obtain the critical state parameters before detachment. The specific steps for monitoring particle size growth are as follows: Samples are collected every 10 minutes using a laser particle size analyzer. Particle size data is input, and a linear regression algorithm is used to calculate the growth rate, outputting a particle size change curve. The preset size threshold is 5.0 mm, set based on the target product specifications. Approaching the threshold is defined as the particle size reaching 90% of the threshold, i.e., 4.5 mm. When recording surface adhesion strength, a torque tester is used to apply a rotational torque T, calculating the adhesion strength S = T / r, where r is the particle radius, and outputting the critical strength value. Based on densification progress data, the system uses a laser particle size analyzer to monitor the particle size growth of the pellet-shaped solid product in real time. Once the particle size reaches 95% of the preset size threshold of 100 μm, i.e., approaching the threshold, the surface adhesion strength between the pellet-shaped solid product and the supporting substrate during production is recorded. This strength is measured through a tensile test to obtain critical state parameters, including the strength value (N / m²) and a timestamp. These parameters are used to calculate a model predicting the detachment timing.S1055, based on the critical state parameter (adhesion threshold 0.5N), an automated separation module dynamically monitors the adhesion between the pellet-shaped solid product and the porous carrier (the supporting structure supporting the pellet-shaped solid product): a force sensor collects adhesion data in real time, and a threshold judgment algorithm is used to complete the separation when the adhesion drops below 0.5N. Simultaneously, a laser particle size analyzer records the final particle size, and SEM imaging records the morphological characteristics to determine subsequent optimization parameters. In this embodiment, the automated separation module dynamically monitors the adhesion based on the critical state parameter. Once the pellet product is determined to have reached the detachment condition, the system performs the separation operation. Subsequently, by recording the final particle size and morphological characteristics of the separated pellet product, the system can determine the optimization parameters for subsequent batches of microcrystal generation and nucleation processes, thereby improving the continuity and efficiency of the overall processing. S106, the mixture output from the fluidized bed reactor is subjected to solid-liquid separation, for example, using a centrifuge at 3000 rpm for 10 minutes to obtain the supernatant as the treated effluent, and the pellet-shaped solid product is discharged. In a wastewater treatment method provided in this application embodiment, after deep reaction and crystal growth inside the fluidized bed reactor, calcium and magnesium ions have been effectively converted into solid form. In order to achieve final purification of water quality and recovery of solid products, the system needs to perform subsequent separation treatment on the substances output from the reactor. Specifically, the system first obtains the solid-liquid mixture output from the fluidized bed reactor and guides it to a dedicated solid-liquid separation module. As an optional embodiment of this application, in order to ensure the efficiency of the separation process and the stability of the effluent quality, the above solid-liquid separation and discharge operation specifically includes the following steps: First, add flocculant to the mixture at a dosage of 50 mg / L and stir rapidly for 2 min at a speed of 200 rpm; second, stir slowly for 10 min at a speed of 50 rpm to form flocs; then, let it settle for 30 min; next, discharge the supernatant from the top at a flow rate of 0.5 m / h; finally, pump out the bottom sludge: S1061, use sedimentation or filtration technology to separate the solid-liquid mixture, so that the separation operation produces a supernatant layer and a solid sediment layer.

[0016] In practical applications, the solid-liquid mixture contains treated water and large, detached granular solid products. The system can flexibly select gravity sedimentation or mechanical filtration techniques based on the concentration and size distribution of the solid particles in the mixture. Through physical separation, the mixture gradually separates into layers within the separation module, ultimately forming a clear supernatant layer and a solid sediment layer rich in granular solid products. S1062, the supernatant layer is extracted as the treated effluent, and the granular solid products in the solid sediment layer are collected.

[0017] After the stratification process is completed, the system smoothly extracts the supernatant layer using an overflow device or pump at the top. Since most of the calcium and magnesium ions have been removed from this supernatant, the water quality is significantly improved, and it can be directly discharged as treated effluent or sent to the next stage of the reuse system. Simultaneously, the system collects the bottom solid sediment layer, obtaining the high-density pellet-shaped solid product. In step S1063, fine crystals and a portion of the carrier are separated from the solid product after solid-liquid separation, refluxed, and the remaining pellet-shaped solid product is discharged.

[0018] To maintain the continuous operation of the separation module, the system periodically or quantitatively discharges the remaining pellet-shaped solid products. Due to their uniform particle size and dense structure, the discharged pellet-shaped solid products not only reduce the volume and cost of sludge treatment but can also be recycled as a byproduct. Through this embodiment, a complete closed-loop wastewater treatment process can be achieved, ultimately providing users with a more efficient and environmentally friendly wastewater treatment effect. S107, from the solid products after solid-liquid separation, a vibrating screen is used to separate fine crystals with a particle size less than 50 μm and a portion of the carrier using particle size sieving technology. The fine crystals and carrier are further distinguished by density differences. An online concentration sensor monitors the difference between the porous carrier concentration C_m and the preset target concentration C_t in the fluidized bed reactor, determining the reflux ratio P = k * (C_t - C_m), where k is a proportionality coefficient of 0.5, the input is the concentration difference, and the output is the reflux ratio. The fine crystals and a portion of the carrier in this ratio are then transported to the inlet of the fluidized bed reactor. In a wastewater treatment method provided in this application embodiment, in order to maintain the dynamic balance of materials inside the fluidized bed reactor and improve reagent utilization, it is necessary to perform fine classification and reflux of the separated solid products. The system first uses particle size sieving technology to accurately separate fine crystals with a particle size smaller than a preset threshold and some porous carriers from the solid products output from the solid-liquid separation module. Through this physical classification method, a preliminary fine-crystal carrier mixture can be obtained, providing a material basis for subsequent seed crystal replenishment. As an optional embodiment of this application, the above-mentioned reflux and concentration control operation can specifically include the following steps: S1071, for the preliminary separated fine-crystal carrier mixture, a laser particle size analyzer deployed at the bottom and middle outlet of the fluidized bed reactor is used to obtain the real-time concentration data C_real of the porous carrier based on the principle of laser diffraction. Based on the preset target concentration C_target determined based on historical data and simulation models (5.0 g / L to 10.0 g / L), the difference information ΔC = C_real - C_target is calculated. In actual operation, the concentration of the carrier in the reactor directly affects the efficiency of heterogeneous nucleation. The system utilizes online monitoring equipment deployed at key locations in the reactor to acquire real-time concentration distribution data of the porous carrier. By comparing the real-time concentration data with the preset target concentration, the system can accurately calculate the current concentration deviation, i.e., the difference information. This data is the core basis for determining the amount of reflux material. S1072, based on the difference ΔC between the real-time concentration data and the preset target concentration and the current reaction load L, a multiple linear regression algorithm is used to calculate the fine crystal reflux ratio Rf and the partial carrier reflux ratio Rc. First, 100 samples are selected from historical data, including ΔC (range -0.5 to 0.5), L (range 50 to 200), and the corresponding Rf and Rc.Training model: Rf = a1·ΔC + a2·L + b1, Rc = a3·ΔC + a4·L + b2, where the coefficients are obtained by least squares fitting, for example, a1 = 0.8, a2 = -0.01, b1 = 0.2, etc. The current ΔC and L are substituted into the model to predict Rf and Rc as reflux ratio parameters. In this embodiment, the system introduces a regression analysis algorithm to deeply process the concentration difference. This algorithm uses historical operating curve data collected by the front-end sensor and the current reaction load value monitored in real time as input to dynamically calculate the optimal reflux ratio of fine crystals and part of the carrier. In this way, specific reflux ratio parameters can be obtained. If the calculated reflux ratio parameters exceed the preset safety range of 0.5 to 2.0 or the process range of 0.8 to 1.5, the information processing module responsible for process parameter verification and correction in the system will automatically intervene to adjust the ratio data to the above reasonable range, thereby determining the final reflux ratio value. S1073, Based on the final reflux ratio, an automated conveying system regulates the conveying rate of fine crystals and some carriers, and monitors the composition of the input material at the inlet of the fluidized bed reactor in real time. After determining the reflux ratio, the automated conveying system precisely regulates the conveying rate of fine crystals and some carriers according to instructions. The system synchronously monitors the input material data at the inlet of the fluidized bed reactor and determines in real time whether the composition of the input material meets the preset ratio. This closed-loop monitoring mechanism ensures the accuracy of the reflux process. S1074, If the input material meets the preset ratio requirements, the conveying status is recorded through the data feedback module. This module receives the conveying data as input, calculates and outputs the material balance information in the fluidized bed reactor in real time. When it is detected that the input material fully meets the process requirements, the data feedback module records the status parameters of this conveying in detail, including flow rate Q, concentration C, and temperature T. These data are integrated and analyzed using a mass conservation algorithm, specifically calculating the equilibrium difference Δm = Σ(Q_in·C_in) - Σ(Q_out·C_out) (where Q is the flow rate in m³ / h, C is the concentration in kg / m³, and Δm is the equilibrium difference in kg / h). The system acquires real-time material balance information within the fluidized bed reactor, including the porous carrier concentration (target constant 2-5%). This helps maintain a constant porous carrier concentration within the reactor and provides more active sites through the recirculation of fine crystals, thereby further optimizing the crystallization and removal of calcium and magnesium ions and improving the overall operational stability of the wastewater treatment system.

[0019] It should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should be considered within the scope of protection of this invention.

Claims

1. A water hardening process for stabilizing pelletizing polymerization reaction, characterized in that, include: Obtain wastewater to be treated, the wastewater containing calcium and magnesium ions, and determine the content and concentration distribution of the calcium and magnesium ions. Based on the concentration data, determine whether the wastewater is high-concentration wastewater, thereby determining the preliminary prediction parameters of the wastewater treatment scheme, and configuring the wastewater treatment process. Adjust the pH value of the wastewater to be treated to a preset alkaline range, and adjust the water temperature of the wastewater to a preset temperature range. Continuously monitor the changes in the environmental parameters, determine whether the wastewater has reached a stable state, and generate adjustment instructions for abnormal parameters to adjust the operating parameters of the dosing device and temperature control equipment. The regulated wastewater is transported to the bottom inlet of a fluidized bed reactor, which is filled with a porous carrier. The surface and internal pores of the porous carrier are loaded with active components. The distribution of the carrier is analyzed, and the adhesion of the active components in the internal pores is detected. The efficiency index of the active components is evaluated, and the changing trend of the reaction environment is monitored. Polymerizing agent is added to the fluidized bed reactor, and the upward flow velocity in the fluidized bed reactor is controlled within a preset flow velocity range. At the same time, the aeration intensity is controlled within a preset intensity range, so that the porous carrier is in a fluidized state. It is determined whether the flow velocity and aeration intensity meet the preset range, and the fluid control device and aeration equipment are adjusted according to the state data to keep the porous carrier in a fluidized state. The calcium and magnesium ions are reacted with the polymerizing agent to form microcrystals in the liquid phase. The microcrystals are adsorbed at active sites on the surface of the porous carrier and undergo heterogeneous nucleation. Under the action of the active component, the nucleated crystals continue to polymerize, stack, and densify to form a pellet-shaped solid product with uniform particle size. After the pellet-shaped solid product grows to a preset size, it falls off the surface of the porous carrier, and the final particle size and morphological characteristics of the pellet product are recorded. The mixture output from the fluidized bed reactor is subjected to solid-liquid separation to obtain the supernatant as the treated effluent, and the pellet-shaped solid product is discharged. Fine crystals with a particle size smaller than a preset threshold and a portion of the carrier are separated from the solid product after solid-liquid separation. Based on the difference between the monitored porous carrier concentration and the preset target concentration in the fluidized bed reactor, the reflux ratio of the fine crystals and a portion of the carrier is determined, and the determined ratio of the fine crystals and a portion of the carrier is transported to the inlet of the fluidized bed reactor.

2. The process according to claim 1, characterized in that, The process of obtaining wastewater to be treated, wherein the wastewater contains calcium and magnesium ions, and determining the content and concentration distribution of the calcium and magnesium ions, includes: Obtain the wastewater sample to be treated; The specific content of the calcium and magnesium ions is determined to ascertain the distribution range of the ion concentration. Based on the ion concentration data, the data is compared with an ion classification database. If the concentration exceeds a preset threshold, it is marked as high-concentration wastewater. Obtain chemical characteristic data of the high-concentration wastewater and predict preliminary parameters of the wastewater treatment scheme; Based on the predicted parameters, configure the wastewater treatment process and adjust the operating mode of the treatment equipment.

3. The process according to claim 1, characterized in that, Adjusting the pH value of the wastewater to be treated to a preset alkaline range and adjusting the water temperature to a preset temperature range includes: Obtain the initial pH value and water temperature data of the wastewater to be treated; If the pH value deviates from the preset alkaline range, an alkaline substance is added; If the water temperature deviates from the preset temperature range, the heating or cooling equipment will be activated; Continuously monitor the changes in the environmental parameters to determine whether the wastewater has reached a stable state; Acquire monitoring data under the steady state, analyze fluctuations during the adjustment process, and generate adjustment instructions for abnormal parameters; Adjust the operating parameters of the dosing device and the temperature control equipment according to the adjustment command.

4. The process according to claim 1, characterized in that, The adjusted wastewater is transported to the bottom inlet of a fluidized bed reactor, which is filled with a porous carrier. The surface and internal pores of the porous carrier are loaded with active components, including: Obtain the adjusted wastewater data; Adjust the wastewater transport path and locate the inlet position at the bottom of the fluidized bed; The wastewater is transported to the inlet at the bottom of the fluidized bed; Analyze the distribution of the filling carrier; if the distribution of the porous carrier is uneven, adjust the fluid rate. The adhesion of the active components in the internal pores is detected, and the performance indicators of the active components are evaluated. The system monitors the changing trend of the reaction environment. If the changing trend exceeds a preset threshold, an environmental adjustment mechanism is triggered.

5. The process according to claim 1, characterized in that, The step of adding a polymerization agent to the fluidized bed reactor and controlling the upward flow velocity within the fluidized bed reactor within a preset flow velocity range, while simultaneously controlling the aeration intensity within a preset intensity range, so that the porous carrier is in a fluidized state, includes: The system acquires dynamic monitoring data of the upward flow velocity within the fluidized bed reactor, determines whether the flow velocity is within a preset flow velocity range, and if the flow velocity exceeds the preset range, triggers an adjustment mechanism, outputs the flow velocity adjustment command, and links the fluid control device to perform dynamic adjustment to stabilize the flow velocity within the preset flow velocity range. The aeration intensity data in the reactor is continuously tracked in real time to determine whether the intensity meets the preset intensity range. If the intensity deviates from the preset range, the intensity adjustment signal is generated to drive the aeration equipment to perform adjustment operation so that the intensity reaches the preset intensity range. The state of the porous carrier is scanned in real time to obtain relevant data on the maintenance of the fluidization state. It is determined whether the porous carrier is in a fluidized state. If the porous carrier has not reached the fluidized state, the environmental optimization command is generated and linked to the reagent dosing control system to dynamically adjust the amount of polymerizing agent added to make the porous carrier in a fluidized state. By integrating and analyzing various parameters within the fluidized bed reactor, it is determined whether the environment within the reactor is stable. If the fluctuation of the environmental parameters exceeds a preset threshold, a linkage control mechanism is triggered.

6. The process according to claim 1, characterized in that, The process involves forming microcrystals of calcium and magnesium ions with the polymerization agent in the liquid phase. These microcrystals adsorb onto active sites on the surface of the porous carrier and undergo heterogeneous nucleation. Under the action of the active component, the nucleated crystals continuously polymerize, stack, and densify, forming uniformly sized pellet-shaped solid products. These pellet-shaped solid products, after growing to a predetermined size, detach from the surface of the porous carrier. The process includes: Data on the binding state of the calcium and magnesium ions with the polymerizing agent are obtained, and the concentration changes during the binding process are recorded to obtain the microcrystal formation parameters. The distribution of active sites on the porous carrier surface is detected to determine the adsorption position and initial nucleation point of the microcrystals on the carrier surface. If the adsorption position deviates from the preset active site range, the liquid flow rate is adjusted to guide the redistribution of the microcrystals so that the heterogeneous nucleation is completed in the target area. Acquire the grain stacking data after heterogeneous nucleation, track the morphological changes during the stacking process, and determine the progress of grain densification and structural stability; Monitor the particle size growth of the pellet-shaped solid product. If the particle size is close to a preset size threshold, record the surface adhesion strength of the pellet-shaped solid product to obtain the critical state parameters before the size falls off. Dynamically monitor the adhesion between the pellet-shaped solid product and the porous carrier to determine whether the pellet product has met the detachment conditions and completed the separation. Record the final particle size and morphological characteristics of the separated pellet products.

7. The process according to claim 1, characterized in that, The process of separating the mixture output from the fluidized bed reactor to obtain the supernatant as treated effluent and discharging the pellet-shaped solid product includes: Obtain the solid-liquid mixture output from the fluidized bed reactor; The solid-liquid mixture is separated using sedimentation or filtration techniques to produce the supernatant layer and the solid sediment layer; The supernatant layer is extracted as the treated effluent; Collect and remove the pellet-shaped solid products from the solid deposition layer.

8. The process according to claim 1, characterized in that, The process of separating fine crystals with a particle size smaller than a preset threshold and a portion of the carrier from the solid product after solid-liquid separation, determining the reflux ratio of the fine crystals and a portion of the carrier based on the difference between the monitored porous carrier concentration and the preset target concentration in the fluidized bed reactor, and conveying the determined ratio of the fine crystals and a portion of the carrier to the inlet of the fluidized bed reactor includes: From the solid product after solid-liquid separation, fine crystals with a particle size smaller than a preset threshold and a portion of the carrier are separated using particle size sieving technology. Obtain real-time concentration data of porous carriers in the fluidized bed reactor and determine the difference information between the concentration and the preset target concentration; Based on the difference information, the reflux ratio of the fine crystals and part of the carrier is calculated; If the reflux ratio exceeds the preset range, the mixing ratio data will be adjusted. Based on the reflux ratio, the conveying rate of the fine crystals and part of the carrier is adjusted, and the fine crystals and part of the carrier are conveyed to the inlet of the fluidized bed reactor.