Organic fluorine wastewater treatment method and system based on multi-modal simulation

Abstract

The invention discloses an organic fluorine wastewater treatment method and system based on multi-modal simulation. The method comprises the following steps: analyzing a wastewater sample to obtain suspended particle density distribution and dissolved state concentration indexes; the natural aggregation tendency is evaluated, and if natural effective separation cannot be achieved, the dynamic process that particles aggregate to form a separable structure under external intervention is simulated; iteratively optimizing the simulation parameters to obtain an optimized aggregation condition parameter set; predicting the form and stability of a separable structure, matching and simulating a physical separation mode, and identifying a wastewater characteristic region with a poor expected separation effect; and finally, aiming at the region, adjusting aggregation parameters to generate an enhancement scheme, evaluating the conversion efficiency and outputting verified aggregation processing scheme parameters. According to the method, virtual simulation and intelligent optimization are utilized, the pollutant aggregation behavior is accurately regulated and controlled, the whole-process collaborative design from microcosmic aggregation to macroscopic separation is realized, and the process development efficiency and the treatment effect stability are improved.

Description

Technical Field

[0001] This invention relates to the field of organic fluorine wastewater treatment technology, and in particular to an organic fluorine wastewater treatment method and system based on multimodal simulation. Background Technology

[0002] In the field of industrial wastewater treatment, the treatment of wastewater containing organofluorine compounds has always been a challenging task. Due to their inherent chemical inertness and stability, these substances pose a strong resistance to traditional biodegradation and conventional chemical oxidation processes. Although the industry has developed a combination of technologies such as coagulation sedimentation, adsorption filtration and even advanced oxidation, a long-standing and difficult-to-cure problem has become increasingly prominent in practical engineering applications: a large number of extremely small organofluorine particles or molecular substances cannot be effectively removed by existing means.

[0003] Specifically, current engineering practices face a dilemma: on the one hand, removing organic fluoride often requires the use of large amounts of coagulants, flocculants, or adsorbents, along with sophisticated multi-stage filtration systems, resulting in significant manpower and material costs. On the other hand, effluent monitoring after treatment often shows that a considerable proportion of organic fluoride pollutants still penetrate the entire process. The fundamental reason is that organic fluoride in wastewater exists in extremely fine and dispersed forms. Some of it is suspended as submicron or even nanoscale colloidal particles, while others exist as completely dissolved molecules. These tiny pollutants are far smaller than the effective range of conventional gravity sedimentation and easily penetrate or even clog the pores of the filter media. More importantly, due to their surface charge, solvation layer, and other stabilizing factors, they lack the motivation to naturally aggregate and are unlikely to spontaneously collide and combine into flocs or aggregates large enough to be captured by conventional separation methods. Therefore, even with the investment of large amounts of reagents and equipment, it is like using a fishing net with too large a mesh to dredge fine sand—the effect is minimal and highly unstable. This results in a large number of tiny organic fluorine compounds that fail to grow and be effectively captured being discharged with the wastewater, posing a persistent environmental pollution hazard. Therefore, how to proactively and effectively intervene and induce these highly dispersed tiny pollutants to aggregate in a targeted manner, forming larger structures with dense structures and suitable sizes, is a core technical challenge that must be overcome to break through the performance ceiling of existing treatment technologies and achieve stable emissions compliance. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the technical bottleneck in the prior art, which is difficult to promote the effective aggregation and stable separation of highly dispersed organofluorine microparticles, resulting in low treatment efficiency, serious waste of reagents and substandard effluent in traditional processes. The present invention provides an organofluorine wastewater treatment method and system based on multimodal simulation, which can accurately predict and regulate the aggregation behavior of pollutants through virtual simulation and intelligent optimization, and realize the whole-process collaborative design of organofluorine substances from micro-aggregation to macro-separation.

[0005] To address the aforementioned technical problems, this invention provides a method for treating organic fluoride wastewater based on multimodal simulation, comprising the following steps: Concentration distribution data and particle size distribution data of organofluorine compounds were obtained from wastewater samples to determine the suspended particle density distribution and dissolved concentration index of organofluorine substances in wastewater. Based on the density distribution and dissolved concentration of suspended particles, the natural aggregation tendency of organic fluorine substances is evaluated. When the evaluation results show that organic fluorine substances cannot be effectively separated by natural static sedimentation, the dynamic process of suspended particles aggregating to form separable structures under external intervention is simulated. The morphological prediction data for the formation of separable structures is obtained from the simulated dynamic process, and the simulation parameters are iteratively optimized to obtain the optimal set of aggregation condition parameters that enable the formation of separable structures. Based on the optimized aggregation condition parameter set, the morphology and stability of separable structures are predicted; based on the analysis results of the predicted morphology and stability, the corresponding physical separation methods are matched and simulated, and the wastewater characteristic areas with poor expected separation effect under the physical separation method are identified. For characteristic regions where the expected separation effect is poor, the aggregation condition parameter set is adjusted and optimized to generate an aggregation enhancement scheme. The efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme is evaluated to output the parameters of the validated aggregation processing scheme.

[0006] In one embodiment of the present invention, determining the suspended particle density distribution and dissolved concentration of organic fluorine substances in wastewater includes: Wastewater samples were treated in steps. First, they were initially filtered through a microporous membrane. The filtrate was labeled as the dissolved phase sample. The retained material was redispersed in a pure medium and labeled as the suspended phase sample. Fluorine-specific detection was performed on the dissolved phase and suspended phase samples to obtain the total fluorine concentration in the dissolved phase and the total fluorine concentration in the suspended phase, respectively. The suspended phase sample was subjected to gradient centrifugation at multiple speeds. After each centrifugation, the supernatant and precipitate were separated, and the mass and fluorine content of the precipitate were determined. Based on the centrifugal force thresholds corresponding to each precipitate and their mass-fluorine content distribution relationship, a centrifugal sedimentation-mass distribution spectrum of suspended particles was established. The total fluorine concentration in the dissolved phase is mapped to a dissolved state concentration index. Simultaneously, based on the total fluorine concentration in the suspended phase and the centrifugal sedimentation-mass distribution spectrum, the total fluorine contribution of particles precipitated under different centrifugal force thresholds is calculated, generating a suspended particle sedimentation distribution with the centrifugal force threshold as the horizontal axis and the fluorine mass concentration of particles precipitated per unit volume under the corresponding centrifugal force threshold as the vertical axis.

[0007] In one embodiment of the present invention, if a non-monotonic jump occurs in the fluoride content measurement value of precipitates from adjacent centrifugation stages during gradient centrifugation analysis, a data correction and state supplementation determination is performed: Composite sampling was performed on the adjacent centrifuged precipitates and supernatant between which non-monotonic transitions occurred. An electroosmosis-assisted micro-area enrichment method was used to make the samples migrate directionally and accumulate in a specific collection unit under a DC electric field to obtain enriched interface material samples. Intermittent weak perturbation observations were performed on the interface material sample, and the change patterns of its transmittance or scattering intensity were recorded under alternating static and perturbation states. If the change pattern showed rapid reversibility with perturbation, the interface material was determined to be a stable colloidal dispersion. If the change pattern showed irreversible step change with perturbation, the interface material was determined to be in a metastable critical aggregation state. For the portion determined to be a stable colloidal dispersion, its fluorine content is recalculated by incorporating it into the distribution range of smaller particles; for the portion determined to be in a critical aggregation state, it is treated as an independent polymerizable component, marked with a specific identifier in the size-mass distribution spectrum, and used as the priority target for subsequent simulation of aggregation dynamics.

[0008] In one embodiment of the present invention, assessing the natural aggregation tendency of organofluorine substances includes: Based on the density distribution of suspended particles, the natural sedimentation process of particles of various sizes within a set time period is simulated in a virtual sedimentation environment. Based on the simulation results, the residual concentration ratio of suspended particles in the upper clear liquid zone after sedimentation is calculated, and the interface clarity between the clear liquid zone and the concentrated zone is quantified. The dissolved concentration index is converted into an inhibition coefficient for particle sedimentation, and the inhibition coefficient is introduced into sedimentation simulation to reflect the stabilizing effect of dissolved substances. Based on the residual concentration ratio, interface clarity, and inhibition coefficient, the results are compared with the preset process feasibility standards. If the simulation results show that effective solid-liquid separation cannot be achieved within the allowable time of the process, it is determined that effective separation cannot be achieved through natural settling.

[0009] In one embodiment of the present invention, simulating the dynamic process of suspended particles aggregating to form a separable structure under external intervention includes: Define a set of external intervention parameters, which should include at least the type and concentration of chemical reagents, pH adjustment range, stirring intensity and time; The determined suspended particle density distribution data is loaded as the initial particle swarm for simulation; Under the set external intervention parameters, the collision and adhesion process between particles driven by Brownian motion, fluid shear and chemical forces is simulated, and the evolution of particle size distribution over simulation time is dynamically tracked. Record and output the size distribution data, aggregate morphology data, and dynamic evolution trajectory data of the virtual particle swarm as a function of simulation time under the set intervention parameters.

[0010] In one embodiment of the present invention, morphological prediction data for the aggregation to form separable structures is obtained from a simulated dynamic process, and simulation parameters are iteratively optimized, including: From the dynamic evolution data output by the simulation process, the characteristic parameters of the aggregates formed at the preset time point or the final moment are extracted. The characteristic parameters include the average equivalent diameter of the aggregates, the structural looseness represented by the fractal dimension, and the number concentration of the aggregates. The characteristic parameters of the aggregates are mapped to predicted separation process performance indicators, including theoretical settling velocity calculated based on equivalent diameter and fluid viscosity, and filter media clogging tendency assessed based on structural porosity and quantity concentration. The predicted separation process performance indicators are compared with the preset process target requirements, which include the minimum settling velocity value and the maximum clogging tendency value. Based on the comparison results, if the performance indicators do not fully meet the process target requirements, the external intervention parameters are modified according to the predetermined adjustment strategy until one or more sets of external intervention parameters are obtained that can make the predicted performance indicators simultaneously meet all process target requirements. The parameter set is then output as the optimized aggregation condition parameter set.

[0011] In one embodiment of the present invention, predicting the morphology and stability of separable structures based on an optimized set of aggregation condition parameters includes: Based on the optimized set of aggregation condition parameters, dynamic simulation of the aggregation process is carried out under standard chemical and hydraulic conditions to generate a statistically representative virtual aggregate sample library. For each aggregate in the virtual aggregate sample library, calculate and assign it key properties for predicting separation behavior, including at least: equivalent hydraulic diameter, wet density, and structural toughness coefficient characterizing its resistance to breakage under shear. Based on the preset attribute range, the virtual aggregate sample library is divided into feature subsets with different attribute combinations; Output the virtual aggregate sample library and the attribute data and feature subset partitioning information of all its individuals as the morphological and stability prediction results of the separable structure.

[0012] In one embodiment of the present invention, based on the analysis results of the predicted morphology and stability, the corresponding physical separation method is matched and simulated, including: Establish one or more physical separation methods, including at least one core separation mechanism that can reflect gravity, centrifugal force, filtration interception or bubble adsorption; For each individual aggregate in the virtual aggregate sample library, based on its assigned equivalent hydraulic diameter, wet density, and structural toughness coefficient, one or more separation methods are determined to perform dynamic trajectory simulation to determine whether it has been effectively separated. Statistical analysis of the simulation results of all virtual aggregates was performed to generate a separation performance map with aggregate attributes as coordinates, showing the efficiency differences in the separation of aggregates in different attribute regions; From the separation performance map, attribute regions with low separation efficiency are identified, and these attribute regions are mapped back to the corresponding feature subsets. The particle types and states in the original wastewater represented by the feature subsets are identified as wastewater feature regions with poor expected separation effects.

[0013] In one embodiment of the present invention, the efficiency of the conversion from a suspended state to a separable structure under an aggregation enhancement scheme is evaluated to output validated aggregation processing scheme parameters, including: Based on the feature regions where the expected separation effect is poor, identify at least one core aggregate property that needs to be changed. Based on the optimized set of aggregation condition parameters, one or two key process parameters are adjusted according to the properties of the core aggregate to form a candidate set of enhancement parameters. A dynamic simulation of aggregation was performed using a candidate enhancement parameter set. The proportion of the total mass of aggregates belonging to the original characteristic region after the simulation to the total fluorine mass of the initial suspended phase was calculated to determine its conversion efficiency. The set of enhanced parameters that meet the conversion efficiency requirements will be used as the parameters for the final scheme.

[0014] To address the aforementioned technical problems, this invention also provides an organic fluoride wastewater treatment system based on multimodal simulation, capable of implementing the above method, comprising: The data acquisition and state analysis module is used to acquire concentration distribution data and particle size distribution data of organic fluorine compounds from wastewater samples, and to determine the suspended particle density distribution and dissolved concentration index of organic fluorine substances in wastewater based on the data. The natural aggregation assessment module is used to assess the natural aggregation tendency of organic fluorine substances based on the density distribution and dissolved concentration of the suspended particles, and to trigger the aggregation process simulation module when the assessment results show that the organic fluorine substances cannot be effectively separated by natural static sedimentation. The aggregation process simulation module is used to simulate the dynamic process of suspended particles aggregating to form separable structures under external intervention; The parameter optimization module is used to obtain morphological prediction data of the aggregation forming a separable structure from the dynamic process output by the aggregation process simulation module, and generate an optimized aggregation condition parameter set that enables the formation of the separable structure by iteratively optimizing the simulation parameters. The separation performance prediction module is used to predict the morphology and stability of the separable structure based on the optimized aggregation condition parameter set. The separation method matching and simulation module is used to match and simulate the corresponding physical separation method based on the morphology and stability analysis results output by the separation performance prediction module, and to identify the wastewater characteristic areas where the expected separation effect is poor under the physical separation method. The enhancement scheme generation and verification module is used to adjust the optimized aggregation condition parameter set to generate an aggregation enhancement scheme for the feature regions with poor expected separation effect, evaluate the efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme, and output the verified aggregation processing scheme parameters.

[0015] The technical solution of the present invention has the following advantages compared with the prior art: The present invention provides a multimodal simulation-based method for treating organic fluoride wastewater, offering a technical solution for the treatment of organic fluoride compounds and fine particulate matter suspended in wastewater. By analyzing the concentration distribution and particle size data of wastewater samples, the density distribution and dissolved concentration of suspended particles are obtained, thereby assessing the tendency for natural aggregation. For suspended particles that cannot be effectively separated by natural settling, agglomeration simulation technology is used to induce them to form separable structures under external intervention. Agglomeration optimization is achieved through iterative optimization of aggregation parameters to form an optimized aggregation scheme. Through microscopic simulation and system optimization, the traditional experience-based, trial-and-error approach to flocculant addition and process debugging is transformed into a predictable and designable precise process based on the characteristics of the pollutants themselves. This not only greatly improves the first-time success rate of treatment scheme development but also saves significant experimental costs and time.

[0016] More importantly, after determining the optimal set of aggregation condition parameters, the wastewater treatment method of this invention further considers how to separate flocs. It uses the optimized parameters to predict the morphology and stability of the formed flocs, and intelligently matches the most suitable physical separation method accordingly. It also simulates the separation process, which can provide early warning of potential bottlenecks in the separation process and identify which pollution characteristic areas may still fail to separate. Finally, the scheme re-adjusts and optimizes the aggregation scheme for these predicted difficult areas, forming the final enhanced treatment parameters and evaluating its overall conversion efficiency. In principle, it ensures that the formed aggregates are designed for separation, and ultimately outputs a virtual-verified overall process scheme that not only enables effective particle aggregation but also ensures efficient separation of aggregated products. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart of the steps of the organic fluoride wastewater treatment method based on multimodal simulation of the present invention; Figure 2 This is a flowchart of the steps in this invention to determine the suspended particle density distribution and dissolved concentration of organic fluorine substances in wastewater. Figure 3 This is a flowchart illustrating the steps of the present invention for assessing the natural aggregation tendency of organofluorine substances; Figure 4 This is a flowchart illustrating the steps of the present invention to simulate the dynamic process of suspended particles aggregating to form a separable structure under external intervention; Figure 5 This is a flowchart of the iterative optimization of simulation parameters according to the present invention; Figure 6 This is a flowchart of the steps in this invention to predict morphology and stability and match the corresponding physical separation method; Figure 7 This is a flowchart of the steps for evaluating the efficiency of the conversion of a suspended state to a separable structure under the aggregation enhancement scheme; Figure 8 This is a structural framework diagram of the organic fluorine wastewater treatment system based on multimodal simulation of the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] Reference Figure 1As shown, the multimodal simulation-based organic fluoride wastewater treatment method of the present invention aims to solve the problem of how to achieve effective aggregation and controllable separation of microparticles. Its core logic in solving this problem is not simply to improve reagents or equipment, but to introduce a virtual engineering system that first diagnoses, then designs, and finally verifies, thus reshaping the development path of the treatment process from a fundamental perspective. Specifically, it includes the following technical solutions: Concentration distribution data and particle size distribution data of organofluorine compounds were obtained from wastewater samples to determine the suspended particle density distribution and dissolved concentration index of organofluorine substances in wastewater. The solution uses in-depth characterization of wastewater samples to accurately obtain the concentration and particle size distribution of organic fluorine substances, thereby determining the suspension density and solubility of organic fluorine. This is like creating a quantitative profile of the stability of the current dispersed system, clarifying the specific state of the object that needs to be modified—how dispersed and how stable it is.

[0020] Based on the density distribution and dissolved concentration of suspended particles, the natural aggregation tendency of organic fluorine substances is evaluated. When the evaluation results show that organic fluorine substances cannot be effectively separated by natural static sedimentation, the dynamic process of suspended particles aggregating to form separable structures under external intervention is simulated. The plan further introduces a pre-judgment step: assessing the natural aggregation tendency of these pollutants. This is not a simple numerical comparison, but a judgment based on physicochemical principles: if the assessment finds that they cannot be separated by the simplest natural sedimentation, it clearly indicates that conventional physical methods have basically failed and external intervention (such as adding flocculants, adjusting pH, etc.) must be sought to promote particle aggregation.

[0021] The morphological prediction data for the formation of separable structures is obtained from the simulated dynamic process, and the simulation parameters are iteratively optimized to obtain the optimal set of aggregation condition parameters that enable the formation of separable structures. At this point, the core advantage of the solution becomes apparent—instead of immediately conducting time-consuming and material-intensive experiments in a real reaction tank, it first simulates the dynamic process of particle aggregation and growth under external intervention in a computer. Through this simulation, it is possible to proactively see whether the floc structure that may be formed under different intervention conditions is ideal (i.e., a separable structure), and through iterative calculations, quickly find the optimal set of intervention condition parameters. This is equivalent to conducting thousands of flocculation experiments in the digital world at extremely low cost and finding the best formula.

[0022] Based on the optimized aggregation condition parameter set, the morphology and stability of separable structures are predicted; based on the analysis results of the predicted morphology and stability, the corresponding physical separation methods are matched and simulated, and the wastewater characteristic areas with poor expected separation effect under the physical separation method are identified. Furthermore, the solution does not stop at forming flocs, but further considers how to separate the flocs. It further utilizes the optimized parameters to predict the morphology and stability of the formed flocs, and intelligently matches the most suitable physical separation method (such as selecting a filter with a specific pore size or a specific settling velocity) accordingly. It also simulates the separation process. This step can provide early warning of potential bottlenecks in the separation process and identify which pollution characteristic areas (such as particles within a certain size range) may still fail to separate.

[0023] For characteristic regions where the expected separation effect is poor, the aggregation condition parameter set is adjusted and optimized to generate an aggregation enhancement scheme. The efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme is evaluated to output the parameters of the validated aggregation processing scheme.

[0024] Finally, the solution addresses these predicted challenging areas by adjusting and optimizing the aggregation scheme to form the final enhanced processing parameters and evaluate its overall conversion efficiency, thereby outputting a set of highly customized processing technology guidance schemes that have undergone thorough virtual validation.

[0025] Therefore, the core beneficial effect of the technical solution of this invention is fundamental. Through microscopic simulation and system optimization, it transforms the traditional experience-based, trial-and-error process of flocculant addition and process debugging into a precise process that is predictable and designable based on the characteristics of the pollutants themselves. This not only greatly improves the success rate of treatment solution development and saves a lot of experimental costs and time, but more importantly, it ensures in principle that the aggregates formed are designed for separation, thereby systematically improving the removal efficiency and process stability of the most difficult-to-capture tiny and dissolved organic fluorine pollutants, providing an innovative technical path to solve the industry problem of fine particle escape.

[0026] Current testing methods typically only provide a general total fluoride concentration and cannot distinguish whether pollutants exist in dissolved or suspended particulate form. This leads to a lack of specificity in subsequent process selection. At the same time, even if suspended particles are identified, traditional particle size analysis can only measure the quantity distribution of physical size and cannot reveal the fluoride pollution load carried by particles of different sizes. This causes the treatment process design to lose its precise target and can only rely on experience for blind trial and error.

[0027] To address the aforementioned problems, this invention incorporates corresponding improvements during the data acquisition phase, referring to... Figure 2As shown, this embodiment constructs a logically rigorous physical separation and correlation detection scheme. First, a microporous membrane with a specific pore size is used to filter the wastewater sample. For example, 1 liter of wastewater from a chemical industrial park is vacuum filtered using a 0.45-micron polyethersulfone membrane. This physical separation operation forcibly separates the sample into a dissolved phase and a suspended phase, thereby achieving quantitative separation of the two core states at the source. After filtration, all filtrate is collected as the dissolved phase sample, and the residue on the filter membrane is carefully rinsed with deionized water and ultrasonically dispersed. After re-volume adjustment, it is used as the concentrated suspended phase sample.

[0028] Based on this, fluorine-specific detection was performed on the two phase samples to accurately obtain the total fluorine concentration in the dissolved phase and the total fluorine concentration in the suspended phase, thus completing the precise qualitative and quantitative determination of the pollutant state. For example, the fluoride content of the dissolved phase filtrate was determined by combustion hydrolysis-ion chromatography to obtain the total fluorine concentration in the dissolved phase, which can be directly used as an indicator of dissolved concentration. At the same time, the total fluorine content of the concentrated suspended phase sample was determined to obtain the total fluorine concentration in the suspended phase.

[0029] For the separated suspended phase, a gradient centrifugation process with multiple speeds was employed. After each centrifugation stage, the precipitate was collected and its mass and fluoride content were determined. For example, a 50 mL suspended sample was centrifuged sequentially in a high-speed centrifuge at equivalent centrifugal forces of 500 g, 2000 g, 8000 g, and 15000 g. After each centrifugation stage, the supernatant was carefully separated before proceeding to the next stage, and the precipitate was collected. The precipitate obtained from each stage was weighed, and its fluoride content was determined using bomb ion chromatography. Assuming the experimentally measured fluoride mass of the precipitated particles at 500 g centrifugation force is F1, at 2000 g it is F2, and so on, F3, F4, etc., a centrifugal sedimentation-mass distribution spectrum of suspended particles can be established. This spectrum directly reflects the distribution of fluoride mass in the precipitated particles separated from wastewater at different centrifugal force thresholds, thus directly linking the particle sedimentation behavior to its actual pollution contribution (fluoride mass).

[0030] Finally, through data integration, the total fluoride concentration in the dissolved phase was directly mapped to a dissolved concentration index. Simultaneously, based on the total fluoride concentration in the suspended phase and the aforementioned centrifugal sedimentation-mass distribution spectrum, the fluoride concentration contributed by particles precipitated at different centrifugal force thresholds per unit volume of the original wastewater was calculated. The total fluoride concentration in the suspended phase was proportionally allocated to the intervals corresponding to each centrifugal force threshold, generating a suspended particle sedimentation distribution with the centrifugal force threshold as the horizontal axis and the fluoride mass concentration of particles precipitated per unit volume at the corresponding centrifugal force threshold as the vertical axis. This distribution clearly defines the concentration of fluoride pollutants that can be removed under different separation intensities, providing initial field data reflecting the separability of pollutants based on actual separation experiments for subsequent simulations.

[0031] This embodiment not only achieves precise separation and quantification of pollutant states, but also pioneers a characterization method that correlates particle size with pollution load. The two standardized indicators generated—dissolved concentration and suspended particle density distribution—have clear physical meaning and engineering guidance value, becoming a reliable foundation for subsequent accurate simulation and process optimization. This fundamentally improves the scientific nature and effectiveness of treatment solution development and avoids overall process failure caused by fuzzy input data.

[0032] In actual data collection, the non-ideal behavior of suspended particles in complex organic fluorine wastewater interferes with the accuracy of characterization. Traditional centrifugation analysis is based on the ideal assumption that particles are independent and stable individuals. However, real wastewater often contains metastable aggregates loosely bound by weak forces or stable ultrafine colloids that precipitate abnormally due to environmental changes. These two types of substances can cause anomalies in centrifugation data. For example, the fluorine content precipitated at lower speeds may be higher than that precipitated at slightly higher speeds, i.e., a non-monotonic jump occurs. If this distorted data is directly used to construct distribution spectra and guide subsequent simulations, it will lead to fundamental errors in the entire optimization process.

[0033] To address this issue, a judgment and correction process was further introduced during the detection of fluoride wastewater in the above embodiments. When a non-monotonic jump in data from adjacent centrifugation stages was detected, the abnormal interface substances were first precisely captured. Specifically, the precipitates from the two adjacent stages where the anomaly occurred, along with the supernatant between them, were mixed to form a composite sample. This sample was injected into a microchannel equipped with electrodes, and a suitable DC electric field was applied. Under the influence of the electric field, the charged colloids and aggregates in the sample migrated directionally towards a specific electrode and eventually accumulated in a porous hydrogel collection unit near the electrode, thereby obtaining a concentrated and undisturbed interface substance sample.

[0034] Subsequently, the physical identification stage of the material aggregation state is entered. The enriched sample is transferred to an optical sample cell, irradiated with a laser beam and its transmittance or scattered light intensity is monitored. Periodic weak ultrasonic perturbations are applied to the sample cell through a piezoelectric ceramic actuator, and the observation system records the optical signal response simultaneously: if the sample is a stable colloidal dispersion with a robust structure, the signal will only show rapid, small-amplitude oscillations after the perturbation and immediately return to its original state, exhibiting a rapid reversible mode; if the sample is a metastable critical aggregate, weak perturbations are enough to cause irreversible collapse or fusion of its structure, and the optical signal will undergo a significant and permanent step change.

[0035] Finally, based on the diagnostic results, the original distribution spectrum is intelligently corrected and enhanced. For portions identified as stable colloidal dispersions, their fluorine content is deducted from the current level of abnormal precipitation and recalculated within a smaller, theoretically defined centrifugation interval, thus correcting misjudgments caused by changes in centrifugation conditions. For portions identified as being in a critical aggregation state, they are defined as special polymerizable components. In the final size-mass distribution spectrum, their fluorine content is recorded not only at their actual precipitation locations but also highlighted with prominent dashed boxes or stars. This highlighting has significant downstream guiding implications: in subsequent aggregation process simulations, the simulation algorithm will prioritize reading and processing these polymerizable data, setting them as key targets requiring external intervention (such as pH adjustment or the addition of specific flocculants) for transformation, thereby making the entire simulation optimization more focused and efficient.

[0036] In assessing the natural aggregation tendency of organic fluorine compounds, technicians typically rely on a single parameter such as median particle size or total suspended solids concentration for rough estimations. This simplistic approach completely ignores two key real-world factors: first, suspended particle groups have a continuous size distribution, with significant differences in settling velocities between particles of different sizes, meaning a few fine particles can severely hinder overall separation efficiency; second, the presence of dissolved organic matter significantly alters the physicochemical properties of particle surfaces, creating steric hindrance or electrostatic repulsion, inhibiting particle aggregation and settling. Traditional assessment methods, unable to quantify both of these effects simultaneously, often lead to misjudgments: either overestimating natural settling efficiency, resulting in a large amount of fine particulate pollutants failing to be effectively removed and being directly emitted; or being overly conservative, prematurely abandoning natural settling as a low-cost option and instead adopting more complex and energy-intensive enhanced treatment processes, causing unnecessary resource waste.

[0037] To solve the above problems, refer to Figure 3 As shown, this embodiment constructs a systematic evaluation method based on virtual simulation. First, based on the obtained accurate suspended particle density distribution data, a virtual sedimentation column model that matches the geometry of the actual sedimentation tank is constructed in the computer. Parameters such as fluid viscosity corresponding to the actual water temperature are set. By solving the sedimentation kinetic equation, the movement trajectory and spatial distribution of particles of different sizes, from 1 micrometer to tens of micrometers, are simulated within a set time (e.g., 2 hours). Finally, the particle concentration profile of the entire sedimentation column from top to bottom is obtained. This step eliminates the dependence on a single characteristic particle size and realistically reproduces the sedimentation process of a polydisperse particle system.

[0038] Next, two key engineering indicators are extracted from the simulation results: First, the ratio of the total fluoride concentration of suspended particles within a certain height range (e.g., the upper 1 / 3 region) of the virtual settling column to the initial concentration is calculated, which is the residual concentration ratio. This value directly quantifies the expected residual pollutant level in the effluent. Second, by analyzing the gradient change of the concentration profile, the ratio of the thickness of the transition section between the clear liquid zone and the sludge zone to the entire height of the settling column is calculated as a quantitative value of interface clarity. This value reflects the thoroughness of solid-liquid separation and sludge compression performance.

[0039] The impact of dissolved organic matter is incorporated into the evaluation system. In practice, based on the measured dissolved concentration, a pre-established experimental correlation curve is used to convert it into an inhibition coefficient between 0 and 1. This coefficient represents the reduction effect of dissolved organic matter on particle settling rate. In virtual settling simulations, the theoretical settling velocity of each particle size is multiplied by this inhibition coefficient to simulate the real physical scenario where dissolved substances encapsulate particles, increasing their stability. For example, a 10-micron particle that would normally settle within one hour may have its effective settling velocity reduced to 70% under the influence of the inhibition coefficient, requiring a longer settling time.

[0040] Finally, the residual concentration ratio, interface clarity, and inhibition coefficient obtained from the simulation calculation are compared with the process feasibility standards determined based on emission standards and pool volume to determine whether to trigger the subsequent aggregation process simulation. The standard is not a fixed theoretical value, but is dynamically determined through a systematic method that combines engineering constraints, treatment objectives, and safety margins. It may be set as follows: if the residual concentration ratio is lower than 0.1 (90% removal rate), the interface clarity is higher than 80%, and the inhibition coefficient is lower than 0.5 within 2 hours, then natural sedimentation is considered effective.

[0041] Reference Figure 4 As shown, the dynamic process of simulated suspended particles agglomerating to form separable structures under external intervention includes the following steps: A virtual experimental interface is constructed by defining a set of external intervention parameters, which directly correspond to controllable variables in actual engineering. For example, for a specific organic fluoride wastewater, the operator can set the following in the system: the chemical agent is polyaluminum chloride (PAC), the concentration gradient is set to 10, 20, and 30 mg / L; the pH adjustment range is set to 6.0, 7.0, and 8.0; the stirring intensity is set to rapid stirring at 200 rpm for 1 minute, followed by slow stirring at 50 rpm for 15 minutes. This step transforms the engineer's experience and knowledge into a set of instructions that can be recognized and executed by the computer.

[0042] Next, the precisely measured density distribution data of suspended particles is loaded as the initial particle group for simulation. For example, if the distribution data shows that the wastewater mainly contains particle groups with two characteristic peaks of 1-5 micrometers and 5-20 micrometers, a set of particles with corresponding quantity, size and spatial distribution will be generated in the virtual space as the initial reactant for all simulations, ensuring the consistency between the simulation starting point and the real wastewater.

[0043] Subsequently, under set external intervention parameters, the simulation simulates the collision and adhesion process between particles driven by Brownian motion, fluid shear, and chemical forces. Based on the principles of colloids and surface chemistry, each particle is endowed with the interaction potential energy generated by the electric double layer and adsorbed polymers. Taking the parameters "PAC concentration 20 mg / L, pH 7.0" as an example, after the simulation starts, the main forms of PAC hydrolysis products at the set pH and their adsorption on the particle surface are first calculated, and the charge and properties of the particle surface are dynamically updated. In the virtual stirred flow field, the particles move and approach each other due to fluid shear. When the distance between two particles reaches the nanoscale, the resultant force of van der Waals attraction and electrostatic repulsion between them is calculated in real time. If the added PAC effectively compresses the electric double layer or provides bridging, making the resultant force a net attraction, and the collision kinetic energy of the particles is sufficient to overcome the final energy barrier, the simulation engine determines that successful adhesion has occurred, and the two particles will merge into a larger aggregate. This process involves millions of calculations per second throughout the particle swarm, thereby dynamically tracking the evolution of particle size distribution over simulation time. The simulator can observe how the initial bimodal distribution gradually decays as the simulation progresses, while the smaller peaks gradually form and shift to the right, providing a visual overview of the dynamic panorama of floc growth.

[0044] Finally, the size distribution data, aggregate morphology data, and dynamic evolution trajectory data of the virtual particle swarm as a function of simulation time are recorded and output under the set intervention parameters. This is equivalent to a data log of a complete virtual experiment. For example, the output data package may include: at the 5th minute of the simulation, the median particle size of the particle swarm increases from the initial 5 micrometers to 15 micrometers; at the end of the slow stirring at the 15th minute, the median particle size reaches 45 micrometers, and the average fractal dimension of the flocs at this time is output (e.g., 1.8, reflecting a relatively loose structure); at the same time, the formation trajectory of key flocs is recorded, showing which initial particles were assembled from and how many collisions occurred.

[0045] Reference Figure 5As shown, based on the single virtual experiment implemented in the above embodiments, an automated evaluation-feedback-optimization intelligent decision-making system was established. This system transforms process debugging experience into a repeatable and scalable digital optimization algorithm, including the following technical solutions: First, it extracts the characteristic parameters of the aggregates formed at a preset time point or the final moment from the dynamic evolution data output from the simulation process. Continuing with the above implementation example, after the simulation is completed under certain parameters, it does not only record the final result, but also intelligently extracts key features from the massive amount of dynamic data output. For example, it extracts three core parameters at the end of slow stirring: the average equivalent diameter of the aggregates, the structural looseness characterized by fractal dimension, and the number concentration of aggregates per unit volume. These parameters completely define the physical morphology of the generated flocs from three dimensions: size, structural strength, and number density.

[0046] Subsequently, these morphological characteristics are transformed into performance indicators that can directly guide engineering design. That is, the characteristic parameters of the aggregates are mapped to predicted separation process performance indicators. Using the Stokes settling equation, based on the extracted equivalent diameter and the viscosity corresponding to the set water temperature, the theoretical average settling velocity of the floc group is automatically calculated. Simultaneously, based on fractal dimension and number concentration, a built-in empirical model assesses the tendency of this floc group to cause rapid pore blockage when passing through filter cloths or membranes of a specified pore size. Thus, the abstract floc morphology is transformed into quantifiable settling velocity and filtration risk—two engineering performance indicators crucial to subsequent separation unit operations.

[0047] Next, the predicted separation process performance indicators are compared with the preset process target requirements. These target requirements are rigid standards pre-set based on the overall design constraints of the entire water treatment plant. For example, for the subsequent inclined plate sedimentation tank, the process target may require that the minimum settling velocity of flocs not be less than 3.0 m / h; for the subsequent deep bed filter, the clogging tendency should not be higher than medium risk. Comparing the predicted values ​​calculated in the previous step with these target values ​​will immediately determine that the simulation results under the current parameters do not fully meet the process target requirements.

[0048] Based on the comparison results, if the performance indicators do not fully meet the process target requirements, the external intervention parameters are modified according to the predetermined adjustment strategy. The built-in adjustment strategy is not random trial and error, but a heuristic rule based on causal relationships. For example, for insufficient settling velocity, the rule may prioritize increasing the PAC dosage to increase floc size or moderately lowering the pH to generate denser flocs; for excessive clogging tendency, the rule may suggest reducing the stirring intensity to reduce floc breakage and form a more robust structure or adjusting the type of reagent. Based on the performance indicator that is currently least satisfactory, the corresponding rule is selected to generate a new set of intervention parameters, and a new round of virtual dynamic simulation is initiated.

[0049] The above simulation-evaluation-adjustment cycle will be automatically repeated until one or more sets of parameters are found that can make all predicted performance indicators meet the requirements at the same time. At this point, it will be determined that a satisfactory solution has been found, and the parameter set will be output as an optimized aggregation condition parameter set. This parameter set is the optimal coagulation / flocculation process formula tailored for this specific wastewater to meet the requirements of subsequent separation processes.

[0050] After obtaining the optimized set of aggregation condition parameters through iteration, it is necessary to further transform the optimized process parameters into reliable predictions of the floc physical properties. This allows for a preliminary assessment, before implementation in actual engineering, of whether the formed aggregates can be separated during subsequent hydraulic shearing processes. Figure 6 As shown, the technical solution includes the following: First, based on the optimized aggregation condition parameter set, the aggregation process is dynamically simulated under standard chemical and hydraulic conditions. According to the input chemical agent type, concentration, pH value and stirring program parameters, combined with fluid force field calculation, the collision, attachment and recombination process between micro particles is simulated frame by frame. When the simulation reaches the preset termination condition (such as reaching a stable aggregation state or completing the set simulation time), the spatial configuration and connection relationship of all aggregates at the final moment are automatically frozen, thereby generating a virtual sample library containing all the final aggregates.

[0051] After the simulation was completed, the physical properties of each independent virtual aggregate in the sample library were analyzed and calculated. For each three-dimensional aggregate structure composed of numerous primary particles, its equivalent hydraulic diameter was calculated using a dedicated geometric analysis module. This diameter was derived by inversely based on the principle that aggregates have the same settling velocity as spherical particles in a static fluid. The wet density was calculated based on the solid mass of the aggregate, the volume of the liquid inside and its spatial distribution, and determined by the volume integral method. The structural toughness coefficient was evaluated through virtual mechanical testing: a standardized virtual shear force field was applied to the three-dimensional model of the aggregate, and its internal stress distribution and structural deformation were calculated using the finite element method. The coefficient was obtained by normalizing the coefficient based on the critical shear stress value at which irreversible structural failure occurs. These calculations endowed each aggregate with core physical parameters that determine its behavior in different separation force fields.

[0052] Subsequently, the sample library is automatically classified according to preset attribute value ranges. The classification rules are based on engineering experience, such as dividing the equivalent hydraulic diameter into several size ranges, wet density into different density levels, and structural toughness coefficient into high, medium, and low levels. All aggregates are traversed, and they are automatically assigned to the corresponding feature subsets based on their attribute values. The number of aggregates contained in each subset, the attribute statistical characteristics, and their proportion in the sample library are recorded.

[0053] Finally, the output includes a virtual aggregate sample library containing complete attribute data and feature subset partitioning information as the prediction result. The output data is in a structured format, containing a unique identifier for each aggregate, three-dimensional coordinate information, calculated attribute values, and the classification code of its feature subset. This output forms the complete input conditions for subsequent separation process simulation, enabling the simulation system to accurately predict the actual effectiveness of different physical separation methods based on differentiated floc groups that truly reflect the characteristics of optimal process products. This provides a reliable data foundation for identifying process bottlenecks and conducting targeted optimizations.

[0054] Furthermore, based on the above embodiments, the established digital floc sample library is used to accurately identify the pollutant characteristics that are still difficult to effectively separate under the current optimal coagulation conditions. This includes the following technical solutions: First, a corresponding parameterized physical model of the separation process is established based on the physical separation method to be evaluated. For example, for gravity sedimentation separation, a sedimentation velocity calculation model based on Stokes' law is established and corrected by combining the actual geometric dimensions and flow distribution of the sedimentation tank; for filtration interception, a filtration process simulator based on a pore network model or a standard clogging model is established; for air flotation separation, an efficiency model of bubble-flocculation adhesion and a rise rate calculation model need to be established. These models can calculate the theoretical behavior and final fate of a single particle or floc under this separation mechanism based on the input target physical parameters (such as particle size, density, and strength) and operating conditions (such as settling time, filtration velocity, and air-to-water ratio).

[0055] Subsequently, each individual aggregate in the output virtual aggregate sample library is input into the selected physical separation model for dynamic trajectory simulation and separation determination, based on its assigned equivalent hydraulic diameter, wet density, and structural toughness coefficient.

[0056] After simulating all individuals, the simulation results for all virtual aggregates are statistically analyzed to generate a separation performance map with the core attributes of the aggregates (e.g., equivalent diameter on the horizontal axis and wet density on the vertical axis) as coordinates. The entire attribute space is divided into a fine grid, with each grid containing several aggregates with similar attributes. The percentage of aggregates successfully separated within each grid is calculated, representing the separation efficiency for that attribute region. Visualization techniques are used to generate a color or contour map, clearly showing high-efficiency regions (e.g., separation efficiency > 90%) and low-efficiency regions (e.g., separation efficiency < 50%). This map visually reveals the effect of the separation process on flocs with different characteristics.

[0057] Finally, attribute regions with separation efficiency below a preset threshold (e.g., 60%) are automatically identified from the separation performance map, and these inefficient attribute regions are back-mapped back to the feature subset. Ultimately, the particle types and states in the original wastewater represented by the feature subset are formally identified as wastewater characteristic regions with poor expected separation performance. This identification not only points out the problem but also precisely pinpoints its essence—which physical properties led to separation failure. This result provides a direct and clear optimization target for subsequent steps: the upstream agglomeration process needs parameter adjustments, focusing on generating fewer flocs belonging to the feature subset, or changing their properties (such as increasing their size or density), thereby fundamentally eliminating the identified separation bottleneck.

[0058] In this embodiment, for characteristic regions where the expected separation effect is poor, a targeted adjustment strategy is further proposed: adjusting and optimizing the aggregation condition parameter set to generate an aggregation enhancement scheme, and evaluating the efficiency of the transformation from a suspended state to a separable structure under the aggregation enhancement scheme, so as to output validated aggregation processing scheme parameters, referring to... Figure 7 As shown, the process includes the following steps: First, for the identified feature regions where the expected separation effect is poor, analyze and determine at least one core aggregate property that needs to be changed. For example, if the feature region is mapped to an aggregate subset with an equivalent hydraulic diameter between 10 and 30 micrometers, then the determined core property is the aggregate size, and the optimization goal is to increase the proportion of aggregates in this size range or to transform them into larger sizes.

[0059] Subsequently, based on the original optimized aggregation condition parameter set, one or two key process parameters most directly related to the identified core attributes are adjusted. If the goal is to increase the size, the most direct adjustment based on the causal relationship in the knowledge base may be to increase the dosage of polymer flocculant or extend the slow stirring time, thus forming a candidate enhancement parameter set.

[0060] Next, using the formed candidate enhancement parameter set, a complete aggregation dynamic simulation is performed. Under exactly the same initial conditions (i.e. the same virtual wastewater particle samples), only the updated parameters are input, and the simulation program is run again to generate a new virtual aggregate sample library.

[0061] Subsequently, based on the newly generated sample library, the proportion of the total mass of aggregates belonging to the original characteristic region after simulation to the total fluorine mass of the initial suspended phase is calculated. This determines the conversion efficiency for this specific problem. All aggregates in the new sample library are re-evaluated, identifying individuals still belonging to the original 10-30 micrometer characteristic region. The total fluorine mass of these aggregates is then accumulated. Dividing this mass by the total fluorine mass of the suspended phase in the wastewater at the start of the simulation yields a specific percentage value. For example, if the original characteristic region accounts for 30% of the mass, and this proportion drops to 18% after simulation using the new parameters, then the conversion efficiency for this problem can be understood as 42% of the original problematic substance being effectively converted.

[0062] Finally, the conversion efficiency is compared with the preset process requirements. If the conversion efficiency meets the requirements (e.g., the requirement is to reduce the quality percentage of the original problem area to below 20%, while the simulation result is 18%), then the candidate enhancement parameter set is adopted as the final aggregation processing scheme parameters and output. If the conversion efficiency does not meet the requirements, the candidate scheme is discarded, and the process returns to trying to adjust other key parameters, or the original optimized aggregation condition parameter set is conservatively output, indicating that it is difficult to further improve this specific separation bottleneck more economically and effectively under the current technical path.

[0063] Reference Figure 8 As shown, in order to implement the above method, this embodiment also discloses an organic fluoride wastewater treatment system based on multimodal simulation, including: The data acquisition and state analysis module is used to acquire concentration distribution data and particle size distribution data of organic fluorine compounds from wastewater samples, and to determine the suspended particle density distribution and dissolved concentration index of organic fluorine substances in wastewater based on the data. The natural aggregation assessment module is used to assess the natural aggregation tendency of organic fluorine substances based on the density distribution and dissolved concentration of the suspended particles, and to trigger the aggregation process simulation module when the assessment results show that the organic fluorine substances cannot be effectively separated by natural static sedimentation. The aggregation process simulation module is used to simulate the dynamic process of suspended particles aggregating to form separable structures under external intervention; The parameter optimization module is used to obtain morphological prediction data of the aggregation forming a separable structure from the dynamic process output by the aggregation process simulation module, and generate an optimized aggregation condition parameter set that enables the formation of the separable structure by iteratively optimizing the simulation parameters. The separation performance prediction module is used to predict the morphology and stability of the separable structure based on the optimized aggregation condition parameter set. The separation method matching and simulation module is used to match and simulate the corresponding physical separation method based on the morphology and stability analysis results output by the separation performance prediction module, and to identify the wastewater characteristic areas where the expected separation effect is poor under the physical separation method. The aggregation scheme generation and verification module is used to adjust the optimized aggregation condition parameter set to generate an aggregation enhancement scheme for the feature regions where the expected separation effect is not good, evaluate the efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme, and output the verified aggregation processing scheme parameters.

[0064] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for treating organic fluoride wastewater based on multimodal simulation, characterized in that, Includes the following steps: Concentration distribution data and particle size distribution data of organofluorine compounds were obtained from wastewater samples to determine the suspended particle density distribution and dissolved concentration index of organofluorine substances in wastewater. Based on the density distribution and dissolved concentration of suspended particles, the natural aggregation tendency of organofluorine substances is evaluated. When the evaluation results show that organofluorine substances cannot be effectively separated by natural static sedimentation, the dynamic process of suspended particles aggregating to form separable structures under external intervention is simulated. The morphological prediction data for the formation of separable structures is obtained from the simulated dynamic process, and the simulation parameters are iteratively optimized to obtain the optimal set of aggregation condition parameters that enable the formation of separable structures. Based on the optimized aggregation condition parameter set, the morphology and stability of separable structures are predicted; based on the analysis results of the predicted morphology and stability, the corresponding physical separation methods are matched and simulated, and the wastewater characteristic areas with poor expected separation effect under the physical separation method are identified. For characteristic regions where the expected separation effect is poor, the aggregation condition parameter set is adjusted and optimized to generate an aggregation enhancement scheme, and the efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme is evaluated to output the parameters of the validated aggregation processing scheme.

2. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: Determine the suspended particulate density distribution and dissolved concentration of organic fluorine substances in wastewater, including: Wastewater samples were treated in steps. First, they were initially filtered through a microporous membrane. The filtrate was labeled as the dissolved phase sample. The retained material was redispersed in a pure medium and labeled as the suspended phase sample. Fluorine-specific detection was performed on the dissolved phase and suspended phase samples to obtain the total fluorine concentration in the dissolved phase and the total fluorine concentration in the suspended phase, respectively. The suspended phase sample was subjected to gradient centrifugation at multiple speeds. After each centrifugation, the supernatant and precipitate were separated, and the mass and fluorine content of the precipitate were determined. Based on the centrifugal force thresholds corresponding to each precipitate and their mass-fluorine content distribution relationship, a centrifugal sedimentation-mass distribution spectrum of suspended particles was established. The total fluorine concentration in the dissolved phase is mapped to a dissolved state concentration index. Simultaneously, based on the total fluorine concentration in the suspended phase and the centrifugal sedimentation-mass distribution spectrum, the total fluorine contribution of particles precipitated under different centrifugal force thresholds is calculated, generating a suspended particle sedimentation distribution with the centrifugal force threshold as the horizontal axis and the fluorine mass concentration of particles precipitated per unit volume under the corresponding centrifugal force threshold as the vertical axis.

3. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 2, characterized in that: During gradient centrifugation analysis, if a non-monotonic jump occurs in the fluoride content measurement values ​​of precipitates from adjacent centrifugation stages, a data correction and status supplementation determination is performed: Composite sampling was performed on the adjacent centrifuged precipitates and supernatant between which non-monotonic transitions occurred. An electroosmosis-assisted micro-area enrichment method was used to make the samples migrate directionally and accumulate in a specific collection unit under a DC electric field to obtain enriched interface material samples. Intermittent weak perturbation observations were performed on the interface material sample, and the change patterns of its transmittance or scattering intensity were recorded under alternating static and perturbation states. If the change pattern showed rapid reversibility with perturbation, the interface material was determined to be a stable colloidal dispersion. If the change pattern showed irreversible step change with perturbation, the interface material was determined to be in a metastable critical aggregation state. For the portion determined to be a stable colloidal dispersion, its fluorine content is recalculated by incorporating it into the distribution range of smaller particles; for the portion determined to be in a critical aggregation state, it is treated as an independent polymerizable component, marked with a specific identifier in the size-mass distribution spectrum, and used as the priority target for subsequent simulation of aggregation dynamics.

4. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: Assessing the natural accumulation tendency of organofluorine compounds, including: Based on the density distribution of suspended particles, the natural sedimentation process of particles of various sizes within a set time period is simulated in a virtual sedimentation environment. Based on the simulation results, the residual concentration ratio of suspended particles in the upper clear liquid zone after sedimentation is calculated, and the interface clarity between the clear liquid zone and the concentrated zone is quantified. The dissolved concentration index is converted into an inhibition coefficient for particle sedimentation, and the inhibition coefficient is introduced into sedimentation simulation to reflect the stabilizing effect of dissolved substances. Based on the residual concentration ratio, interface clarity, and inhibition coefficient, the results are compared with the preset process feasibility standards. If the simulation results show that effective solid-liquid separation cannot be achieved within the allowable time of the process, it is determined that effective separation cannot be achieved through natural settling.

5. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: Simulating the dynamic process of suspended particles aggregating to form separable structures under external intervention, including: Define a set of external intervention parameters, which should include at least the type and concentration of chemical reagents, pH adjustment range, stirring intensity and time; The determined suspended particle density distribution data is loaded as the initial particle swarm for simulation; Under the set external intervention parameters, the collision and adhesion process between particles driven by Brownian motion, fluid shear and chemical forces is simulated, and the evolution of particle size distribution over simulation time is dynamically tracked. Record and output the size distribution data, aggregate morphology data, and dynamic evolution trajectory data of the virtual particle swarm as a function of simulation time under the set intervention parameters.

6. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: Morphological prediction data for the formation of separable structures from the simulated dynamic process are obtained, and simulation parameters are iteratively optimized, including: From the dynamic evolution data output by the simulation process, the characteristic parameters of the aggregates formed at the preset time point or the final moment are extracted. The characteristic parameters include the average equivalent diameter of the aggregates, the structural looseness represented by the fractal dimension, and the number concentration of the aggregates. The characteristic parameters of the aggregates are mapped to predicted separation process performance indicators, including theoretical settling velocity calculated based on equivalent diameter and fluid viscosity, and filter media clogging tendency assessed based on structural porosity and quantity concentration. The predicted separation process performance indicators are compared with the preset process target requirements, which include the minimum settling velocity value and the maximum clogging tendency value. Based on the comparison results, if the performance indicators do not fully meet the process target requirements, the external intervention parameters are modified according to the predetermined adjustment strategy until one or more sets of external intervention parameters are obtained that can make the predicted performance indicators simultaneously meet all process target requirements. The parameter set is then output as the optimized aggregation condition parameter set.

7. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: Based on an optimized set of aggregation condition parameters, the morphology and stability of separable structures are predicted, including: Based on the optimized set of aggregation condition parameters, dynamic simulation of the aggregation process is carried out under standard chemical and hydraulic conditions to generate a statistically representative virtual aggregate sample library. For each aggregate in the virtual aggregate sample library, calculate and assign it key properties for predicting separation behavior, including at least: equivalent hydraulic diameter, wet density, and structural toughness coefficient characterizing its resistance to breakage under shear. Based on the preset attribute range, the virtual aggregate sample library is divided into feature subsets with different attribute combinations; Output the virtual aggregate sample library and the attribute data and feature subset partitioning information of all its individuals as the morphological and stability prediction results of the separable structure.

8. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 7, characterized in that: Based on the analysis results of the predicted morphology and stability, the corresponding physical separation methods are matched and simulated, including: Establish one or more physical separation methods, including at least one core separation mechanism that can reflect gravity, centrifugal force, filtration interception or bubble adsorption; For each individual aggregate in the virtual aggregate sample library, based on its assigned equivalent hydraulic diameter, wet density, and structural toughness coefficient, one or more separation methods are determined to perform dynamic trajectory simulation to determine whether it has been effectively separated. Statistical analysis of the simulation results of all virtual aggregates was performed to generate a separation performance map with aggregate attributes as coordinates, showing the efficiency differences in the separation of aggregates in different attribute regions; From the separation performance map, attribute regions with low separation efficiency are identified, and these attribute regions are mapped back to the corresponding feature subsets. The particle types and states in the original wastewater represented by the feature subsets are identified as wastewater feature regions with poor expected separation effects.

9. The method for treating organic fluoride wastewater based on multimodal simulation according to claim 1, characterized in that: The efficiency of the aggregation enhancement scheme in transforming the suspended state into a separable structure was evaluated to output validated aggregation treatment scheme parameters, including: Based on the feature regions where the expected separation effect is poor, identify at least one core aggregate property that needs to be changed. Based on the optimized set of aggregation condition parameters, one or two key process parameters are adjusted according to the properties of the core aggregate to form a set of candidate enhancement parameters. A dynamic simulation of aggregation was performed using a candidate enhancement parameter set. The proportion of the total mass of aggregates belonging to the original characteristic region after the simulation to the total fluorine mass of the initial suspended phase was calculated to determine its conversion efficiency. The set of enhancement parameters that meet the conversion efficiency requirements will be used as the parameters for the final scheme.

10. An organic fluoride wastewater treatment system based on multimodal simulation, capable of implementing the method described in any one of claims 1 to 9, characterized in that, include: The data acquisition and state analysis module is used to acquire concentration distribution data and particle size distribution data of organic fluorine compounds from wastewater samples, and to determine the suspended particle density distribution and dissolved concentration index of organic fluorine substances in wastewater based on the data. The natural aggregation assessment module is used to assess the natural aggregation tendency of organic fluorine substances based on the density distribution and dissolved concentration of the suspended particles, and to trigger the aggregation process simulation module when the assessment results show that the organic fluorine substances cannot be effectively separated by natural static sedimentation. The aggregation process simulation module is used to simulate the dynamic process of suspended particles aggregating to form separable structures under external intervention; The parameter optimization module is used to obtain morphological prediction data of the aggregation forming a separable structure from the dynamic process output by the aggregation process simulation module, and generate an optimized aggregation condition parameter set that enables the formation of the separable structure by iteratively optimizing the simulation parameters. The separation performance prediction module is used to predict the morphology and stability of the separable structure based on the optimized aggregation condition parameter set. The separation method matching and simulation module is used to match and simulate the corresponding physical separation method based on the morphology and stability analysis results output by the separation performance prediction module, and to identify the wastewater characteristic areas where the expected separation effect is poor under the physical separation method. The enhancement scheme generation and verification module is used to adjust the optimized aggregation condition parameter set to generate an aggregation enhancement scheme for the feature regions where the expected separation effect is poor, evaluate the efficiency of the conversion of the suspended state to a separable structure under the aggregation enhancement scheme, and output the verified aggregation processing scheme parameters.

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