Process optimization method and system for extracting heavy metal elements using supercritical carbon dioxide

Abstract

The present invention provides a process optimization method and system for extracting heavy metal elements with supercritical carbon dioxide, which relates to the field of computer technology, including: obtaining the type, initial concentration, and pH-temperature parameters of the heavy metals in the wastewater to be treated; performing coordination competition modeling based on the pH-temperature parameters of the solution in combination with the complex stability constant to obtain an activation site distribution model; constructing an interphase migration model based on the activation site distribution model in combination with supercritical carbon dioxide phase change parameters; performing extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set; performing extraction efficiency surface fitting based on the stability criterion set and the heavy metal type to obtain a collaborative optimization response surface; and optimizing and generating an optimal process parameter domain based on the collaborative optimization response surface. The present invention improves the efficiency of heavy metal extraction and enhances the controllability of the process.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a process optimization method and system for extracting heavy metal elements using supercritical carbon dioxide. Background Art

[0002] Supercritical carbon dioxide (SC-CO2) extraction technology, as an environmentally friendly method, offers advantages for extracting heavy metals. However, current technologies still face numerous challenges in process optimization. Existing SC-CO2 extraction techniques typically rely on empirical formulas or simple experimental parameter settings for optimization. These methods primarily consider basic parameters such as solution temperature and pressure, but often overlook the solution's pH, temperature fluctuations, and the varying properties of different heavy metals.

[0003] While some existing technologies attempt to optimize extraction processes through experimental data and empirical models, the applicability and accuracy of these optimization schemes are limited due to a lack of dynamic coupling analysis of various factors involved in the extraction process, particularly the behavior of heavy metal ions in different solution environments. Existing technologies fail to fully consider the dynamic dissociation of heavy metal ions in solution, the distribution of activation sites, and the interaction between the fluid and the solution. This makes it difficult to improve the efficiency of heavy metal extraction processes and makes precise control of optimal process parameters difficult in practical applications.

[0004] Based on the above shortcomings of the prior art, there is an urgent need for a process optimization method and system for extracting heavy metal elements with supercritical carbon dioxide. Summary of the Invention

[0005] The purpose of the present invention is to provide a process optimization method and system for extracting heavy metal elements with supercritical carbon dioxide to improve the above-mentioned problems. To achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present application provides a process optimization method for extracting heavy metal elements using supercritical carbon dioxide, comprising:

[0007] Obtain the types of heavy metals in the wastewater to be treated, their initial concentrations, and the pH-temperature parameters of the solution;

[0008] Based on the pH-temperature parameters of the solution and the stability constant of the complex, coordination competition modeling is performed to obtain an activation site distribution model;

[0009] According to the activation site distribution model and in combination with supercritical carbon dioxide phase transition parameters, an interphase migration model is constructed;

[0010] performing an extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set;

[0011] Performing extraction efficiency surface fitting according to the stability criterion set and the heavy metal types to obtain a collaborative optimization response surface;

[0012] Optimization is performed based on the collaborative optimization response surface to generate an optimal process parameter domain.

[0013] In a second aspect, the present application also provides a process optimization system for extracting heavy metal elements using supercritical carbon dioxide, comprising:

[0014] An acquisition module is used to obtain the types of heavy metals in the wastewater to be treated, their initial concentrations, and the pH-temperature parameters of the solution;

[0015] a processing module, configured to perform coordination competition modeling based on the pH-temperature parameters of the solution and in combination with the stability constant of the complex to obtain an activation site distribution model;

[0016] A modeling module, for constructing an interphase migration model based on the activation site distribution model and supercritical carbon dioxide phase transition parameters;

[0017] an analysis module, configured to perform extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set;

[0018] A fitting module, configured to perform extraction efficiency surface fitting based on the stability criterion set and the heavy metal species to obtain a collaborative optimization response surface;

[0019] An output module is used to optimize and generate an optimal process parameter domain based on the collaborative optimization response surface.

[0020] The beneficial effects of the present invention are:

[0021] The present invention combines the solution pH-temperature parameters, complex stability constants and supercritical carbon dioxide phase transition parameters, and adopts coordination competition modeling, dynamic dissociation characteristic analysis, interphase migration model construction and extraction path evolution analysis to achieve accurate modeling of heavy metal ion behavior and efficient optimization of the extraction process, thereby generating an optimal process parameter domain, improving extraction efficiency and enhancing process controllability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1This is a flow chart of a process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of a process optimization system for extracting heavy metal elements with supercritical carbon dioxide according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a process optimization device for extracting heavy metal elements using supercritical carbon dioxide as described in an embodiment of the present invention.

[0026] Markings in the figure: 800, a process optimization device for extracting heavy metal elements with supercritical carbon dioxide; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, processing module; 903, modeling module; 904, analysis module; 905, fitting module; 906, output module. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a process optimization method for extracting heavy metal elements using supercritical carbon dioxide.

[0031] See also Figure 1 , the figure shows that the method includes steps S100 to S600.

[0032] Step S100, obtaining the type, initial concentration and pH-temperature parameters of heavy metals in the wastewater to be treated;

[0033] As can be understood, the types and initial concentrations of heavy metals are qualitatively and quantitatively analyzed using methods such as atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) to determine the types and concentrations of various heavy metal elements in the water samples. The pH and temperature of the solution are measured using an on-site pH meter and temperature sensor. These parameters affect the solubility and complexation behavior of heavy metal ions, as well as the solubility of supercritical carbon dioxide. Accurately acquiring this data not only provides essential input for subsequent coordination competition modeling, interphase migration modeling, and process optimization, but also ensures that the extraction process is carried out under appropriate conditions, thereby improving heavy metal extraction efficiency and process controllability.

[0034] Step S200, performing coordination competition modeling based on the solution pH-temperature parameters and the complex stability constant to obtain an activation site distribution model;

[0035] It should be noted that the complex stability constant is an important indicator for describing the ability of metal ions to bind to ligands. It is analyzed by thermodynamic coupling method with the pH and temperature in the solution to determine the binding strength of heavy metal ions to ligands under different conditions. This step uses coordination competition modeling to analyze the competitive relationship between heavy metal ions and complexes under different pH and temperature conditions and identify the main activation sites. These activation sites refer to the preferential areas where heavy metal ions bind to organic complexes under given conditions. These sites are usually most active at specific pH values ​​and temperatures. The modeling of this process involves not only thermodynamic calculations of various chemical reactions in solution, but also takes into account the dissociation of the complex, the dynamic behavior of metal ions in solution, and the interaction between different ligands in solution.

[0036] Step S300: constructing an interphase migration model based on the activation site distribution model and the supercritical carbon dioxide phase transition parameters;

[0037] In this step, the interaction between the ion concentration gradient in the solution and the supercritical carbon dioxide fluid was first considered, and a dynamic diffusion coefficient matrix was used to describe the mass transfer between the solution and fluid interface. Then, by quantifying the probability of migration path selection and combining it with the phase transition behavior of supercritical carbon dioxide, a dynamic field reflecting the mass transfer between the solution and supercritical carbon dioxide was derived. This model not only accurately describes the mass transfer effect of supercritical carbon dioxide during the extraction process, but also takes into account the dynamic changes under different pH and temperature conditions, thus providing a theoretical basis for subsequent extraction path optimization.

[0038] Step S400: performing extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set;

[0039] Specifically, the interphase migration model has provided a detailed kinetic description of the material migration between the solution and supercritical carbon dioxide. On this basis, the evolution of the extraction path is analyzed in combination with the initial concentration data, and a time-varying concentration gradient field is constructed. Preferably, the migration rate is discretized and solved through an explicit time integration algorithm, which can obtain the concentration change trajectory of each path during the extraction process and further judge the stability of different paths. The stability criterion set evaluates the stability and feasibility of different paths during the extraction process by calculating the convergence characteristics of each path under critical perturbations, thereby identifying the optimal extraction path.

[0040] Step S500: performing extraction efficiency surface fitting based on the stability criterion set and the heavy metal types to obtain a collaborative optimization response surface;

[0041] It's understandable that changes in extraction efficiency are not only related to stability but also closely correlated with the physicochemical properties of heavy metals (such as ionic charge density and complex selectivity). Preferably, through multi-constraint modeling, the stability criterion is converted into boundary constraints for process parameters such as extraction pressure and temperature. A Kriging proxy model is then applied to interpolate the parameter space, resulting in a collaborative optimization response surface. This response surface accurately reflects changes in extraction efficiency under different process conditions, helping to optimize process design.

[0042] Step S600: Optimize and generate an optimal process parameter domain based on the collaborative optimization response surface.

[0043] It should be noted that this step precisely assigns weights to the optimization objectives by analyzing the interaction between extraction efficiency and energy consumption in the collaborative optimization response surface and combining the efficiency-energy consumption correlation mapping. Based on this, a multi-objective optimization decision function based on weighted fuzzy membership is used, using a non-dominated sorting optimization algorithm to traverse the entire parameter space to find the global optimal solution and ultimately generate the optimal process parameter domain.

[0044] Furthermore, step S200 includes steps S210 to S230.

[0045] Step S210, performing thermodynamic coupling based on the pH-temperature parameters of the solution and the stability constant of the complex, and calculating the dissociation coefficient matrix under each temperature gradient to obtain a dynamic dissociation feature set of the heavy metal-organic complex;

[0046] It is understandable that the pH and temperature of the solution significantly affect the binding stability of metal ions and ligands, so it is necessary to model them through thermodynamic coupling methods. The complex stability constant describes the binding ability of metal ions and ligands. Combined with the pH and temperature conditions of the solution, the dissociation coefficient matrix at different temperatures is calculated using a thermodynamic model. The dissociation coefficient matrix provides the dynamic dissociation behavior of heavy metal-organic complexes under different temperature gradients, that is, the binding and dissociation of metal ions and ligands in the solution. This process not only takes into account the effect of temperature changes on the stability of the complex, but also reveals the effect of temperature gradients on the dissociation of heavy metal ions and complex formation, providing a theoretical basis for subsequent ion activity analysis and extraction process optimization. Specifically, the chemical reaction formula corresponding to thermodynamic coupling is:

[0047] ;

[0048] in, It is a heavy metal-organic complex, indicating metal ions With organic ligand complexes formed by binding; is the charge number of the metal ion; is the charge number of the organic ligand; is the temperature-dependent dissociation coefficient, which indicates the dissociation coefficient of the complex at temperature Dissociative tendencies under is the enthalpy change of the complexation reaction; is a natural constant; is the gas constant; is the solution temperature; It is the reference temperature, used to calibrate the benchmark value of the dissociation coefficient.

[0049] Step S220: performing ion activity distribution analysis based on the dynamic dissociation feature set, and obtaining a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions;

[0050] It should be noted that the dynamic dissociation feature set provides the dissociation behavior of heavy metal ions and complexes, but to further analyze the activity distribution of heavy metal ions in solution, ion activity analysis is required. The activity of free heavy metal ions refers to the effective concentration of these ions in solution, which directly affects their ability to participate in complexation and migration. By accurately calculating the three-dimensional activity distribution of free heavy metal ions, the concentration changes of ions at different locations and under different time conditions can be reflected. The ion activity distribution map provides detailed ion behavior information for the subsequent extraction process, revealing which areas in the solution have higher ion activity. These high-activity areas are the key areas for heavy metal ion migration and extraction. Specifically, the chemical expression corresponding to the ion activity distribution analysis is:

[0051] ;

[0052] in, It is the activity of free heavy metal ions, reflecting their effective chemical activity; is the average activity coefficient; is the absolute value of the charge of the metal ion; is the solution ionic strength; is the concentration of free metal ions.

[0053] Step S230 : identifying activation sites based on the dynamic activity distribution map, and generating an activation site distribution model by clustering high activity regions.

[0054] It can be understood that activation sites refer to the preferential areas where heavy metal ions bind to ligands in solution. The high ion activity in these areas means that the ion binding ability is strongest in these areas and the extraction efficiency is also optimal. These activation sites can be identified by clustering analysis of high-activity areas in the dynamic activity distribution map. Cluster analysis groups the activity distribution data and classifies areas with similar activity characteristics into one category, thereby identifying the most active areas. The activation sites corresponding to these areas play an important role in the actual extraction process. They are the areas where heavy metal ions preferentially migrate and complex. The generated activation site distribution model can provide important spatial information for subsequent interphase migration modeling and extraction process, ensuring that the optimized process can perform efficient extraction at the correct location. The surface adsorption reaction formula corresponding to the activation site identification is:

[0055] ;

[0056] in, is the surface binding site density of the activation site; is the adsorption equilibrium constant, which indicates the binding strength between metal ions and activation sites; is the concentration of occupied activation sites (number / mm³), indicating the degree of enrichment of heavy metal ions in key areas; For metal ions; For adsorption complex.

[0057] Furthermore, step S300 includes steps S310 to S330.

[0058] Step S310: performing supercritical fluid state analysis based on the activation site distribution model and supercritical carbon dioxide phase transition parameters, and obtaining a dynamic transmission parameter set by calculating a diffusion coefficient dynamic matrix;

[0059] In this process, the fluid state is first analyzed by combining the activation site distribution model and the phase change characteristics of supercritical carbon dioxide. The phase change behavior of supercritical carbon dioxide at different temperatures and pressures will affect its ability to dissolve and transport heavy metal ions. Therefore, by analyzing the fluid state of supercritical carbon dioxide under different conditions and calculating the dynamic matrix of the diffusion coefficient, the transport characteristics under different conditions can be obtained. The diffusion coefficient matrix reflects the diffusion ability of ions in supercritical carbon dioxide and determines the rate at which solute molecules migrate from the solution phase to the supercritical carbon dioxide phase. The calculation of this matrix takes into account factors such as temperature, pressure, and ion concentration to generate a set of dynamic transport parameters, which provides key data support for subsequent mass transfer coupling and migration rate analysis.

[0060] Step S320: performing interphase mass transfer coupling processing according to the dynamic transfer parameter set, solving the surface ion concentration gradient of the activation site and the fluid turbulence effect simultaneously to obtain the interphase migration rate dynamic field;

[0061] In this step, combined with the dynamic transfer parameter set obtained in step S310, interphase mass transfer coupling analysis is further performed. Interphase mass transfer refers to the material migration process between the solution phase and the supercritical carbon dioxide phase. The rate at which ions migrate from the solution phase to the supercritical carbon dioxide phase is affected by multiple factors, including the ion concentration gradient on the surface of the activation site and the fluid turbulence effect. By solving these factors together, a dynamic field of migration rate can be obtained, which describes the migration rate distribution in different regions. This process accurately quantifies the migration process between the solution and supercritical carbon dioxide through interphase mass transfer coupling processing, thereby improving the accuracy and efficiency of the extraction process.

[0062] Step S330 : Modeling the migration path based on the dynamic field of interphase migration rate, and generating an interphase migration model by quantifying the path selection probability of ions migrating across the interface.

[0063] It should be noted that this step further performs migration path modeling based on the dynamic field of interphase migration rate. The purpose of migration path modeling is to determine the specific migration path of ions from the solution phase to the supercritical carbon dioxide phase and quantify the migration probability of each path. By calculating the selection probability of ions on different paths, the path of ion migration can be accurately predicted, thereby optimizing the extraction process. In practical applications, different migration paths have different migration rates and stabilities. Quantifying the path selection probability helps to optimize the extraction path and improve the extraction efficiency. The generated interphase migration model can reflect the dynamic changes in the ion migration process and ensure the efficiency and stability of the extraction process.

[0064] Furthermore, step S400 includes steps S410 to S430.

[0065] Step S410: performing extraction kinetic modeling based on the interphase migration model and the initial concentration data, and obtaining a dynamic evolution basic model by establishing a time-varying concentration gradient field;

[0066] It can be understood that the time-varying concentration gradient field describes the temporal and spatial variations in heavy metal ion concentration in the solution during the extraction process. The dynamic evolution model can reveal the dynamic changes in heavy metal concentration at different time points and spatial locations, providing basic data for subsequent path-time simulations and stability analysis.

[0067] Step S420: performing a path time-history simulation based on the dynamic evolution basic model, using an explicit time integration algorithm to discretize and solve the migration rate field, iteratively calculating the spatiotemporal evolution trajectory of the concentration field, and obtaining a dynamic evolution map of the extraction path;

[0068] In this process, the explicit time integration algorithm calculates the changes in heavy metal concentration over time and space by discretizing time and space. In each time step, by discretizing and solving the migration rate field, the concentration changes of heavy metals at different spatial nodes can be obtained. Through iterative calculation, the concentration field evolution trajectory of the entire extraction process is finally obtained, and then the dynamic evolution map of the extraction path is drawn. This map can intuitively display the distribution of heavy metal ions in the solution at different time nodes, revealing the trend of concentration changes and path evolution during the extraction process. This simulation process can dynamically capture the concentration changes during the extraction process, thereby providing a basis for process adjustment and optimization, and improving the controllability and efficiency of the extraction process. The discretization solution formula is:

[0069] ;

[0070] in, For the Time step, Heavy metal concentrations at spatial nodes; For the Time step, Heavy metal concentrations at spatial nodes; For the Time step, Heavy metal concentrations at spatial nodes; is the time step; is the supercritical CO2 diffusion coefficient; is the migration rate; is the spatial step length.

[0071] Step S430: Perform stability threshold determination based on the dynamic evolution graph, and generate a set of extraction process stability criteria by calculating the convergence characteristics of each migration path under critical disturbance.

[0072] In this step, the concentration evolution information obtained from the dynamic evolution map is used to analyze the stability of each migration path. By calculating the Lyapunov index, the stability of each path under critical perturbations can be determined. The Lyapunov index measures the stability of the system. If the value is less than 0, it indicates that the path is stable, that is, the path will not change drastically under disturbance, indicating that the path is reliable and stable. Through this calculation, stable paths in the extraction process can be identified, thus providing a basis for optimizing the extraction strategy. The calculation formula for the dynamic stability criterion of multiple paths in the extraction process is:

[0073] ;

[0074] in, is the maximum Lyapunov exponent, which is used to indicate the stability of the system. The path is determined to be stable; For the Migration paths in time The concentration disturbance value of is the normalized perturbation applied at the initial moment; represents the total number of migration pathways analyzed; Indicates the sequence number of the migration path.

[0075] Furthermore, step S500 includes steps S510 to S530.

[0076] Step S510: performing metal characteristic parameterization processing based on the heavy metal type data, and obtaining a heavy metal characteristic parameterization mapping table by establishing a correlation equation between heavy metal ion charge density and extraction selectivity;

[0077] During this process, heavy metal species data was first used to analyze the charge density, ionic radius, and other physicochemical properties of different heavy metal ions, as these parameters directly influence their behavior during the extraction process. By establishing a correlation equation between charge density and extraction selectivity, the extraction tendency of different heavy metals in a supercritical carbon dioxide environment could be quantified. By characterizing the relationship between the charge density of heavy metal ions and their extraction selectivity, a parameterized mapping table for heavy metal properties was established. This table establishes clear connections between different heavy metals, enhances the adaptability of the extraction process to differences in heavy metal species, and improves extraction selectivity and process accuracy.

[0078] Step S520: Perform multi-constraint modeling based on the stability criterion set, and generate a set of constraint equations by converting the path stability threshold into boundary constraints in the extraction pressure-temperature parameter space;

[0079] It's understandable that selecting a stable path can effectively improve the reliability of the extraction process. Therefore, it's necessary to translate the path stability threshold into constraints on specific process parameters during the extraction process. Through multi-constraint modeling, the stability threshold is associated with parameters such as pressure and temperature, resulting in a set of boundary constraints. These constraints not only reflect the stability of the extraction path but also ensure that the extraction process can proceed within a controllable range under specific process conditions.

[0080] Step S530: Generate a collaborative surface based on the set of constraint equations, apply the Kriging proxy model to interpolate and calculate the parameter space response value, and generate a collaborative optimization response surface.

[0081] It should be noted that collaborative surface generation is the core of the entire optimization process. It integrates the multiple constraints obtained in the previous steps with the relationships between various process parameters. The Kriging proxy model uses interpolation to calculate response values ​​within the parameter space, generating a response surface that reflects extraction efficiency and stability under different process conditions. This response surface describes the complex relationships between various extraction process performance indicators (such as efficiency and energy consumption) and process parameters (such as pressure and temperature), providing an intuitive visualization tool for optimization decision-making.

[0082] Furthermore, step S600 includes steps S610 to S630.

[0083] Step S610: performing multi-objective parameter correlation analysis based on the collaborative optimization response surface, and generating an efficiency-energy consumption correlation mapping relationship by extracting the interaction characteristics between extraction efficiency and energy consumption in the response surface;

[0084] It's clear that by analyzing the correlation between these two parameters in the response surface and establishing an efficiency-energy consumption mapping, we can clearly identify the balance between efficiency and energy consumption under different process parameters. This analysis helps reveal the combined impact of process parameter adjustments on extraction efficiency and energy consumption, providing a theoretical basis for subsequent optimization.

[0085] Step S620: configuring optimization target weights according to the efficiency-energy consumption correlation mapping relationship, and obtaining an optimization target function set by constructing a multi-objective optimization decision function based on weighted fuzzy membership;

[0086] It should be noted that due to the trade-off between extraction efficiency and energy consumption, appropriate weights must be assigned to each objective to achieve optimal process optimization. The weighted fuzzy membership method can flexibly adjust the weights of each objective based on different process requirements and actual operating conditions, thereby constructing a multi-objective optimization decision function. This function integrates the relationship between extraction efficiency and energy consumption to form a comprehensive evaluation standard to guide process optimization. The technical effect is that the weighted fuzzy membership method can more accurately find the optimal balance between multiple optimization objectives, ensuring that the final optimization goal meets the needs of actual applications.

[0087] Step S630 : searching for a global optimal solution according to the optimization objective function set, traversing the parameter space by applying a non-dominated sorting optimization algorithm, and generating an optimal process parameter domain.

[0088] Specifically, the set of optimization objective functions integrates multiple key factors, including heavy metal extraction efficiency, energy consumption, and stability. A non-dominated sorting optimization algorithm is used to balance these objectives, traversing the entire parameter space and searching for the optimal solution that satisfies all objectives simultaneously. This algorithm identifies a Pareto-optimal set of process parameters—one that cannot further improve any one objective without sacrificing the others.

[0089] The generated optimal process parameter domain specifically includes process parameters that can provide the best heavy metal removal results in actual wastewater treatment processes, such as temperature, pressure, fluid flow rate, and supercritical carbon dioxide concentration. The significance of the optimal process parameter domain is that it provides a systematic parameter range for wastewater treatment plants to achieve the optimal heavy metal extraction efficiency under different operating conditions, while taking into account energy consumption and process stability.

[0090] The following table is a comprehensive comparison table of the extraction efficiency, residual concentration and process economic indicators of multiple categories of heavy metals using the process optimization method of supercritical carbon dioxide extraction of heavy metal elements applied in this application and the traditional process that does not adopt this method.

[0091] Table 1 Comparison of heavy metal extraction effects (unit: mg / L)

[0092]

[0093] In Table 1, is copper ion; For lead ions; It is cadmium ion; is zinc ion; As shown in Table 1, the technical advantages of this method include:

[0094] Efficient extraction: Activation site identification improves the accuracy of mass transfer target areas and reduces ineffective adsorption;

[0095] Deep removal: Activity distribution correction solves the problem of high ionic strength Activity was underestimated, and the residual level was reduced to 0.5 mg / L;

[0096] Stable recovery: Dynamic stability control reduces process fluctuations to The effect of dissociation was reduced to ±1.5%.

[0097] Furthermore, step S620 includes steps S621 to S623.

[0098] Step S621: Prioritize based on the efficiency-energy consumption correlation mapping relationship. By classifying the efficiency-energy consumption interaction areas in the parameter space, and combining the membership threshold, determine the target priority levels of different process intervals, and generate classification boundaries of efficiency-dominant areas, energy-sensitive areas, and balanced areas.

[0099] This process first analyzes the interaction between efficiency and energy consumption in the parameter space based on the efficiency-energy consumption correlation mapping relationship and divides it into different regions. By applying thresholds to the membership of these regions, it is possible to identify which process intervals are primarily dominated by efficiency, which are more sensitive to energy consumption, or where there is a balance between efficiency and energy consumption. The classified regions can help determine the priority of each target in different regions. Through this division, different optimization strategies can be implemented for different regions based on the actual application needs.

[0100] Step S622: construct a dynamic weight function based on the classification boundary, and generate a dynamic weight factor set by establishing a weight distribution model based on regional membership.

[0101] In this step, combined with the classification boundaries generated in step S621, each region is assigned a different weight based on its regional membership. These weights reflect the importance of efficiency and energy consumption targets within each region. For example, in efficiency-dominant regions, extraction efficiency is given a higher weight, while in energy-sensitive regions, energy consumption is favored to be reduced. Therefore, through the membership-based weight assignment model, different weight factors can be flexibly assigned to each region to better reflect the trade-offs between objectives. This model dynamically adjusts the weight factors based on the characteristics of different regions, ensuring that the importance of each objective is appropriately reflected during the optimization process.

[0102] Step S623: Perform function synthesis based on the dynamic weight factor set to generate an optimization target function set.

[0103] It should be noted that this set of optimization objective functions combines each objective according to the set dynamic weights to ensure the balance of each objective during the optimization process. By synthesizing the optimization objective functions, a unified, multi-objective optimization decision function can be generated, providing a basis for the subsequent search for the global optimal solution.

[0104] Example 2:

[0105] like Figure 2 As shown, this embodiment provides a process optimization system for extracting heavy metal elements with supercritical carbon dioxide, the system comprising:

[0106] The acquisition module 901 is used to obtain the types of heavy metals in the wastewater to be treated, the initial concentration and the pH-temperature parameters of the solution;

[0107] Processing module 902 is used to perform coordination competition modeling based on the solution pH-temperature parameters and the complex stability constant to obtain an activation site distribution model;

[0108] Modeling module 903, for constructing an interphase migration model based on the activation site distribution model and supercritical carbon dioxide phase transition parameters;

[0109] Analysis module 904, for performing extraction path evolution analysis based on the interphase migration model and initial concentration to obtain a stability criterion set;

[0110] The fitting module 905 is used to perform extraction efficiency surface fitting based on the stability criterion set and the heavy metal types to obtain a collaborative optimization response surface;

[0111] The output module 906 is used to generate an optimal process parameter domain by performing optimization according to the collaborative optimization response surface.

[0112] In a specific embodiment of the present invention, the processing module 902 includes:

[0113] The first processing unit is used to perform thermodynamic coupling based on the pH-temperature parameter of the solution and the stability constant of the complex, and obtain a dynamic dissociation feature set of the heavy metal-organic complex by calculating the dissociation coefficient matrix under each temperature gradient;

[0114] The second processing unit is used to perform ion activity distribution analysis based on the dynamic dissociation feature set, and obtain a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions;

[0115] The third processing unit is used to identify activation sites according to the dynamic activity distribution map and generate an activation site distribution model by performing cluster analysis on high activity areas.

[0116] In a specific embodiment of the present invention, the modeling module 903 includes:

[0117] The first modeling unit is used to analyze the supercritical fluid state based on the activation site distribution model and the supercritical carbon dioxide phase transition parameters, and obtain a dynamic transmission parameter set by calculating the diffusion coefficient dynamic matrix;

[0118] The second modeling unit is used to perform interphase mass transfer coupling processing based on the dynamic transfer parameter set, and jointly solve the surface ion concentration gradient of the activation site and the fluid turbulence effect to obtain the dynamic field of the interphase migration rate;

[0119] The third modeling unit is used to model the migration path according to the dynamic field of the interphase migration rate, and to generate an interphase migration model by quantifying the path selection probability of ions migrating across the interface.

[0120] Example 3:

[0121] Corresponding to the above method embodiment, this embodiment also provides a process optimization device for extracting heavy metal elements with supercritical carbon dioxide. The process optimization device for extracting heavy metal elements with supercritical carbon dioxide described below and the process optimization method for extracting heavy metal elements with supercritical carbon dioxide described above can be referenced to each other.

[0122] Figure 3 FIG. 8 is a block diagram of a process optimization device 800 for extracting heavy metal elements using supercritical carbon dioxide according to an exemplary embodiment. Figure 3 As shown, the process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide may include: a processor 801 and a memory 802. The process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0123] Among them, the processor 801 is used to control the overall operation of the process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide to complete all or part of the steps in the process optimization method for extracting heavy metal elements with supercritical carbon dioxide. The memory 802 is used to store various types of data to support the operation of the process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide. These data may, for example, include instructions for any application or method operating on the process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the process optimization device 800 for extracting heavy metal elements from supercritical carbon dioxide and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0124] In an exemplary embodiment, a process optimization device 800 for extracting heavy metal elements with supercritical carbon dioxide can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned process optimization method for extracting heavy metal elements with supercritical carbon dioxide.

[0125] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the aforementioned process optimization method for extracting heavy metal elements using supercritical carbon dioxide. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the process optimization device 800 for extracting heavy metal elements using supercritical carbon dioxide to implement the aforementioned process optimization method for extracting heavy metal elements using supercritical carbon dioxide.

[0126] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A process optimization method for extracting heavy metal elements with supercritical carbon dioxide, characterized in that: include: Obtain the types of heavy metals in the wastewater to be treated, their initial concentrations, and the pH-temperature parameters of the solution; Based on the pH-temperature parameters of the solution and the stability constant of the complex, coordination competition modeling is performed to obtain an activation site distribution model; According to the activation site distribution model and in combination with supercritical carbon dioxide phase transition parameters, an interphase migration model is constructed; performing an extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set; Performing extraction efficiency surface fitting according to the stability criterion set and the heavy metal types to obtain a collaborative optimization response surface; Optimization is performed based on the collaborative optimization response surface to generate an optimal process parameter domain.

2. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 1, characterized in that: Based on the pH-temperature parameters of the solution and the stability constant of the complex, coordination competition modeling is performed, including: According to the solution pH-temperature parameters and the complex stability constant, thermodynamic coupling is performed, and the dissociation coefficient matrix under each temperature gradient is calculated to obtain a dynamic dissociation characteristic set of the heavy metal-organic complex; Performing ion activity distribution analysis based on the dynamic dissociation feature set, and obtaining a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions; Activation sites are identified based on the dynamic activity distribution map, and an activation site distribution model is generated by clustering high activity regions.

3. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 1, characterized in that: According to the activation site distribution model and combined with the supercritical carbon dioxide phase transition parameters, an interphase migration model is constructed, including: Performing supercritical fluid state analysis based on the activation site distribution model and supercritical carbon dioxide phase transition parameters, and obtaining a dynamic transmission parameter set by calculating a diffusion coefficient dynamic matrix; Performing interphase mass transfer coupling processing based on the dynamic transfer parameter set, solving the surface ion concentration gradient of the activation site and the fluid turbulence effect simultaneously to obtain the interphase migration rate dynamic field; The migration path is modeled according to the interphase migration rate dynamic field, and the interphase migration model is generated by quantifying the path selection probability of ions migrating across the interface.

4. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 1, characterized in that: An extraction path evolution analysis is performed according to the interphase migration model and the initial concentration, including: Extraction kinetics modeling is performed based on the interphase migration model and initial concentration data, and a dynamic evolution basic model is obtained by establishing a time-varying concentration gradient field; The path time history simulation is performed according to the dynamic evolution basic model, the migration rate field is discretized and solved using an explicit time integration algorithm, and the spatiotemporal evolution trajectory of the concentration field is iteratively calculated to obtain a dynamic evolution map of the extraction path; A stability threshold is determined based on the dynamic evolution graph, and a set of extraction process stability criteria is generated by calculating the convergence characteristics of each migration path under critical disturbance.

5. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 1, characterized in that: Performing extraction efficiency surface fitting according to the stability criterion set and the heavy metal types includes: Performing metal property parameterization processing according to the heavy metal type data, and obtaining a heavy metal property parameterization mapping table by establishing a correlation equation between heavy metal ion charge density and extraction selectivity; Perform multi-constraint modeling based on the stability criterion set, and generate a set of constraint equations by converting the path stability threshold into a boundary constraint condition of the extraction pressure-temperature parameter space; A collaborative surface is generated according to the set of constraint equations, and a Kriging proxy model is applied to interpolate and calculate parameter space response values ​​to generate a collaborative optimization response surface.

6. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 1, characterized in that: Optimizing and generating an optimal process parameter domain based on the collaborative optimization response surface includes: Performing multi-objective parameter correlation analysis based on the collaborative optimization response surface, and generating an efficiency-energy consumption correlation mapping relationship by extracting the interaction characteristics between extraction efficiency and energy consumption in the response surface; Optimizing target weights according to the efficiency-energy consumption correlation mapping relationship, and obtaining an optimization target function set by constructing a multi-objective optimization decision function based on weighted fuzzy membership; A global optimal solution search is performed according to the optimization objective function set, and an optimal process parameter domain is generated by traversing the parameter space by applying a non-dominated sorting optimization algorithm.

7. The process optimization method for extracting heavy metal elements with supercritical carbon dioxide according to claim 6, characterized in that: Optimizing target weight configuration according to the efficiency-energy consumption correlation mapping relationship includes: Prioritization is performed based on the efficiency-energy consumption correlation mapping relationship, and by classifying the efficiency-energy consumption interaction areas in the parameter space, the target priority levels of different process intervals are determined in combination with the membership threshold, thereby generating classification boundaries of the efficiency-dominant area, the energy consumption-sensitive area, and the balance area; Performing a dynamic weight function construction process according to the classification boundary, and generating a dynamic weight factor set by establishing a weight distribution model based on regional membership; Function synthesis is performed according to the dynamic weight factor set to generate an optimization objective function set.

8. A process optimization system for extracting heavy metal elements using supercritical carbon dioxide, characterized in that: include: An acquisition module is used to obtain the types of heavy metals in the wastewater to be treated, their initial concentrations, and the pH-temperature parameters of the solution; a processing module, configured to perform coordination competition modeling based on the pH-temperature parameters of the solution and in combination with the stability constant of the complex to obtain an activation site distribution model; A modeling module, for constructing an interphase migration model based on the activation site distribution model and supercritical carbon dioxide phase transition parameters; an analysis module, configured to perform extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a stability criterion set; A fitting module, configured to perform extraction efficiency surface fitting based on the stability criterion set and the heavy metal species to obtain a collaborative optimization response surface; An output module is used to optimize and generate an optimal process parameter domain based on the collaborative optimization response surface.

9. The process optimization system for extracting heavy metal elements with supercritical carbon dioxide according to claim 8, characterized in that: The processing module includes: A first processing unit is configured to perform thermodynamic coupling based on the pH-temperature parameter of the solution and the stability constant of the complex, and obtain a dynamic dissociation feature set of the heavy metal-organic complex by calculating a dissociation coefficient matrix under each temperature gradient; a second processing unit, configured to perform ion activity distribution analysis based on the dynamic dissociation feature set, and obtain a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions; The third processing unit is used to identify activation sites according to the dynamic activity distribution map, and generate an activation site distribution model by performing cluster analysis on high activity areas.

10. The process optimization system for extracting heavy metal elements with supercritical carbon dioxide according to claim 8, characterized in that: The modeling module includes: A first modeling unit is configured to analyze the supercritical fluid state according to the activation site distribution model and the supercritical carbon dioxide phase transition parameters, and obtain a dynamic transmission parameter set by calculating a diffusion coefficient dynamic matrix; The second modeling unit is used to perform interphase mass transfer coupling processing according to the dynamic transfer parameter set, and solve the surface ion concentration gradient of the activation site and the fluid turbulence effect together to obtain the interphase migration rate dynamic field; The third modeling unit is used to perform migration path modeling according to the interphase migration rate dynamic field, and generate an interphase migration model by quantifying the path selection probability of ion migration across the interface.

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