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

Abstract

The invention provides a process optimization method and system for extracting heavy metal elements through supercritical carbon dioxide, and relates to the technical field of computers.The process optimization method comprises the steps that the type, initial concentration and solution pH-temperature parameters of heavy metal in to-be-treated sewage are obtained; according to the pH value-temperature parameters of the solution, coordination competition modeling is carried out by combining a complex stability constant, and an activation site distribution model is obtained; according to the activation site distribution model, combining supercritical carbon dioxide phase change parameters to construct an interphase migration model; carrying out extraction path evolution analysis according to the interphase migration model and the initial concentration to obtain a stability criterion set; according to the stability criterion set and the heavy metal type, extraction efficiency curved surface fitting is carried out, and a collaborative optimization response curved surface is obtained; and performing optimization according to the collaborative optimization response curved surface to generate an optimal process parameter domain. According to the method, the heavy metal extraction efficiency is improved, and the controllability of the process is enhanced.

Description

Technical Field

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

[0002] As an environmentally friendly extraction method, the supercritical carbon dioxide (SC-CO2) extraction technology has certain advantages in the extraction of heavy metal elements. However, there are still many challenges in the optimization of the current process. Existing supercritical carbon dioxide extraction technologies usually rely on empirical formulas or simple experimental parameter settings for optimization. These methods mainly consider basic parameters such as the temperature and pressure of the solution, but often ignore the acidity and alkalinity of the solution, temperature changes, and the characteristic differences of different heavy metal elements.

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

[0004] Based on the above disadvantages of the existing technologies, there is an urgent need for a method and system for optimizing the process of extracting heavy metal elements using supercritical carbon dioxide. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for optimizing the process of extracting heavy metal elements using supercritical carbon dioxide to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows: In the first aspect, the present application provides a method for optimizing the process of extracting heavy metal elements using supercritical carbon dioxide, including: Obtaining the types, initial concentrations, and solution acidity-temperature parameters of heavy metals in the sewage to be treated; According to the solution acidity-temperature parameters, combining with the complex stability constant to conduct coordination competition modeling to obtain an activation site distribution model; According to the activation site distribution model, combining with the supercritical carbon dioxide phase change parameters, constructing an interphase migration model; According to the interphase migration model and the initial concentration, conducting extraction path evolution analysis to obtain a set of stability criteria; According to the set of stability criteria and the types of heavy metals, conducting extraction efficiency surface fitting to obtain a synergistic optimization response surface; Optimize according to the collaborative optimization response surface to generate an optimal process parameter domain.

[0006] In a second aspect, the present application also provides a process optimization system for extracting heavy metal elements by supercritical carbon dioxide, including: An acquisition module for acquiring the types of heavy metals, the initial concentration, and the solution pH-temperature parameters in the sewage to be treated; A processing module for performing coordination competition modeling based on the solution pH-temperature parameters and combining the complex stability constant to obtain an activation site distribution model; A modeling module for constructing an interphase migration model based on the activation site distribution model and combining the supercritical carbon dioxide phase change parameters; An analysis module for performing extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a set of stability criteria; A fitting module for performing extraction efficiency surface fitting based on the set of stability criteria and the types of heavy metals to obtain a collaborative optimization response surface; An output module for optimizing according to the collaborative optimization response surface to generate an optimal process parameter domain.

[0007] The beneficial effects of the present invention are as follows: By combining the solution pH-temperature parameters, the complex stability constant, and the supercritical carbon dioxide phase change parameters, and adopting means such as coordination competition modeling, dynamic dissociation characteristic analysis, interphase migration model construction, and extraction path evolution analysis, the present invention realizes the accurate modeling of the behavior of heavy metal ions and the efficient optimization of the extraction process, thereby generating an optimal process parameter domain, improving the extraction efficiency, and enhancing the controllability of the process. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic flow chart of a process optimization method for extracting heavy metal elements by supercritical carbon dioxide described in the embodiments of the present invention; Figure 2 It is a schematic structural diagram of a process optimization system for extracting heavy metal elements by supercritical carbon dioxide described in the embodiments of the present invention; Figure 3 It is a schematic structural diagram of a process optimization device for extracting heavy metal elements by supercritical carbon dioxide described in the embodiments of the present invention.

[0010] Markings in the figure: 800, a process optimization device for extracting heavy metal elements by supercritical carbon dioxide; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component; 901, an acquisition module; 902, a processing module; 903, a modeling module; 904, an analysis module; 905, a fitting module; 906, an output module. Specific embodiments

[0011] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein generally can be arranged and designed in a variety of 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0012] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0013] Embodiment 1: This embodiment provides a process optimization method for extracting heavy metal elements by supercritical carbon dioxide.

[0014] See Figure 1 , which shows that this method includes steps S100 to S600.

[0015] Step S100, obtain the types of heavy metals, initial concentrations, and solution pH-temperature parameters in the sewage to be treated; It is understandable that the types and initial concentrations of heavy metals are qualitatively and quantitatively analyzed by methods such as atomic absorption spectrometry (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) to determine the types and concentrations of various heavy metal elements in the water sample. The acidity (pH) and temperature of the solution are obtained through on-site monitoring pH meters and temperature sensors, and these parameters affect the solubility of heavy metal ions, complexation behavior, and the dissolution ability of supercritical carbon dioxide. Accurately obtaining these data not only provides the necessary inputs for subsequent coordination competition modeling, interphase migration modeling, and process optimization, but also ensures that the extraction process can be carried out under appropriate conditions, thereby improving the heavy metal extraction efficiency and process controllability.

[0016] Step S200: Based on the solution acidity-temperature parameters, combined with the complex stability constant, conduct coordination competition modeling to obtain the activated site distribution model; It should be noted that the complex stability constant is an important indicator to describe the binding ability of metal ions with ligands. It is analyzed with the acidity and temperature in the solution through thermodynamic coupling methods to determine the binding strength of heavy metal ions with ligands under different conditions. Through coordination competition modeling in this step, the competition relationship between heavy metal ions and complexes under different acidity and temperature conditions can be analyzed, and the main activated sites can be identified. These activated sites refer to the preferential regions where heavy metal ions bind to organic complexes under given conditions. Usually, at specific pH values and temperatures, the activity of these sites is the strongest. The modeling of this process not only involves thermodynamic calculations of various chemical reactions in the solution, but also considers the dissociation of complexes, the dynamic behavior of metal ions in the solution, and the interaction of different ligands in the solution.

[0017] Step S300: Based on the activated site distribution model, combined with the supercritical carbon dioxide phase change parameters, construct an interphase migration model; In this step, first, the interaction between the ion concentration gradient in the solution and the supercritical carbon dioxide fluid is considered, and the diffusion coefficient dynamic matrix is used to describe the mass transfer between the solution and the fluid interface. Then, by quantifying the selection probability of the migration path and combining the phase change behavior of supercritical carbon dioxide, a dynamic field reflecting the mass transfer between the solution and supercritical carbon dioxide is obtained. This model can not only accurately describe the mass transfer effect of supercritical carbon dioxide in the extraction process, but also consider the dynamic changes under different acidity and temperature conditions, thereby providing a theoretical basis for subsequent extraction path optimization.

[0018] Step S400: Based on the interphase migration model and the initial concentration, conduct extraction path evolution analysis to obtain a set of stability criteria; Specifically, the interphase transfer model has provided a detailed kinetic description of the mass transfer between the solution and supercritical carbon dioxide. On this basis, by combining the initial concentration data for the evolution analysis of the extraction path, a time-varying concentration gradient field is constructed. Preferably, through an explicit time integration algorithm for discretely solving the transfer rate, the concentration change trajectories of each path during the extraction process can be obtained, and the stability of different paths can be further judged. The set of stability criteria 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.

[0019] Step S500: According to the set of stability criteria and the types of heavy metals, perform extraction efficiency surface fitting to obtain a co-optimization response surface. It can be understood that the change in extraction efficiency is not only related to stability but also closely related to the physical and chemical properties of heavy metals (such as ionic charge density, complexation selectivity, etc.). Preferably, through multi-constraint modeling, the stability criteria are transformed into boundary constraint conditions for process parameters such as extraction pressure and temperature, and the Kriging surrogate model is applied to interpolate the parameter space, thereby obtaining a co-optimization response surface. This response surface can accurately reflect the change in extraction efficiency under different process conditions and help optimize the process design.

[0020] Step S600: Optimize according to the co-optimization response surface to generate an optimal process parameter domain.

[0021] It should be noted that in this step, by analyzing the interaction between extraction efficiency and energy consumption in the co-optimization response surface and combining the efficiency-energy consumption correlation mapping relationship, the weight allocation of the optimization objective can be accurately configured. On this basis, based on a multi-objective optimization decision function of weighted fuzzy membership, the non-dominated sorting optimization algorithm traverses the entire parameter space to find the global optimal solution, and finally generates an optimal process parameter domain.

[0022] Furthermore, step S200 includes steps S210 to S230.

[0023] Step S210: Perform thermodynamic coupling based on the solution pH-temperature parameter and the complex stability constant, and obtain the dynamic dissociation characteristic set of the heavy metal-organic complex by calculating the dissociation coefficient matrix at each temperature gradient. It is understandable that the acidity, alkalinity, and temperature of the solution significantly affect the binding stability between metal ions and ligands. Therefore, it is necessary to model them through thermodynamic coupling methods. The complex stability constant describes the binding ability between metal ions and ligands. Combining the acidity, alkalinity, 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 considers the influence of temperature changes on the stability of the complex but also reveals the influence of temperature gradients on the dissociation of heavy metal ions and the formation of complexes, providing a theoretical basis for subsequent ion activity analysis and extraction process optimization. Specifically, the chemical reaction formula corresponding to thermodynamic coupling is: ; where, is the heavy metal-organic complex, representing the complex formed by the binding of metal ions and the organic ligand ; is the charge number of the metal ion; is the charge number of the organic ligand; is the temperature-dependent dissociation coefficient, representing the dissociation tendency of the complex at temperature ; is the enthalpy change of the complexation reaction; is the natural constant; is the gas constant; is the solution temperature; is the reference temperature, used to calibrate the reference value of the dissociation coefficient.

[0024] Step S220: Perform ion activity distribution analysis based on the dynamic dissociation feature set. By calculating the three-dimensional activity distribution of free heavy metal ions, a dynamic activity distribution map is obtained; It should be noted that the dynamic dissociation feature set provides the dissociation behavior of heavy metal ions and complexes. However, to further analyze the activity distribution of heavy metal ions in the solution, ion activity analysis is required. The activity of free heavy metal ions refers to the effective concentration of these ions in the 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 under different positions and time conditions can be reflected. The ion activity distribution map provides detailed ion behavior information for the subsequent extraction process, revealing which regions in the solution have high ion activity, and these high-activity regions are the key regions for heavy metal ion migration and extraction. Specifically, the chemical expression corresponding to ion activity distribution analysis is: ; where, is the activity of free heavy metal ions, reflecting their effective chemical activity; is the mean activity coefficient; is the absolute value of the charge number of metal ions; is the ionic strength of the solution; is the concentration of free metal ions.

[0025] Step S230: Identify activation sites according to the dynamic activity distribution map, and generate an activation site distribution model by performing cluster analysis on the high-activity regions.

[0026] It can be understood that the activation site refers to the preferential region where heavy metal ions bind to ligands in the solution. The high ionic activity in these regions means that the ion binding ability is the strongest and the extraction efficiency is also the best in these regions. By performing cluster analysis on the high-activity regions in the dynamic activity distribution map, these activation sites can be identified. Cluster analysis groups the activity distribution data and divides the regions with similar activity characteristics into one category, thereby identifying the most active regions. The activation sites corresponding to these regions play an important role in the actual extraction process. They are the regions 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 position. The surface adsorption reaction formula corresponding to the activation site identification is: ; Among them, is the surface binding site density of the activation site; is the adsorption equilibrium constant, indicating the binding strength between metal ions and activation sites; is the concentration of occupied activation sites (per mm³), indicating the enrichment degree of heavy metal ions in the key region; is the metal ion; is the adsorption complex.

[0027] Furthermore, step S300 includes steps S310 to S330.

[0028] Step S310: Analyze the supercritical fluid state according to the activation site distribution model and the supercritical carbon dioxide phase change parameters, and obtain a set of dynamic transport parameters by calculating the diffusion coefficient dynamic matrix; 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.

[0029] 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; 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.

[0030] 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.

[0031] 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.

[0032] Furthermore, step S400 includes steps S410 to S430.

[0033] Step S410: Perform extraction kinetics modeling based on the interphase migration model and initial concentration data. By establishing a time-varying concentration gradient field, obtain the basic dynamic evolution model; It can be understood that the time-varying concentration gradient field describes the change of heavy metal ion concentration in the solution over time and space during the extraction process. The basic dynamic evolution model can reveal the dynamic changes of heavy metal concentration at different time points and different spatial positions, thus providing basic data for subsequent path time-course simulation and stability analysis.

[0034] Step S420: Conduct path time-course simulation according to the basic dynamic evolution model. Use the explicit time integration algorithm to discretize and solve the migration rate field, and iteratively calculate the spatio-temporal evolution trajectory of the concentration field to obtain the dynamic evolution map of the extraction path; In this process, the explicit time integration algorithm calculates the change of heavy metal concentration over time and space through discretization of time and space. Within each time step, by discretely solving the migration rate field, the concentration change of heavy metals at different spatial nodes can be obtained. Through iterative calculation, the evolution trajectory of the concentration field during 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 and path evolution of concentration changes during the extraction process. This simulation process can dynamically capture the concentration changes during the extraction process, thus providing a basis for process adjustment and optimization, and improving the controllability and efficiency of the extraction process. The discretization solution formula is: ; where, is the heavy metal concentration at the time step and spatial node; is the heavy metal concentration at the time step and spatial node; is the heavy metal concentration at the time step and spatial node; is the time step size; is the supercritical CO2 diffusion coefficient; is the migration rate; is the spatial step size.

[0035] Step S430: Determine the stability threshold according to the dynamic evolution map. By calculating the convergence characteristics of each migration path under critical perturbations, generate a set of stability criteria for the extraction process.

[0036] In this step, by using the concentration evolution information obtained from the dynamic evolution map, the stability analysis of each migration path is carried out. By calculating the Lyapunov exponent, the stability of each path under critical perturbations can be judged. The Lyapunov exponent 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 violently under perturbations, indicating that the path is reliable and stable. Through this calculation, the stable paths in the extraction process can be identified, providing a basis for optimizing the extraction strategy. The calculation formula for the multi-path dynamic stability criterion in the extraction process is: ; where, is the maximum Lyapunov exponent, used to represent the stability of the system. If then the path is judged to be stable; is the concentration perturbation value of the th migration path at time ; is the normalized perturbation applied at the initial time; represents the total number of migration paths analyzed; represents the serial number of the migration path.

[0037] Furthermore, step S500 includes steps S510 to S530.

[0038] Step S510, perform metal characteristic parameterization according to the heavy metal type data. By establishing the correlation equation between the charge density of heavy metal ions and extraction selectivity, a heavy metal characteristic parameterization mapping table is obtained; In this process, first, the charge density, ionic radius, and other physical and chemical characteristics of different heavy metal ions are analyzed using the heavy metal type data. These parameters directly affect their behavior in the extraction process. By establishing the correlation equation between the charge density and extraction selectivity, the extraction tendency of different heavy metals in the supercritical carbon dioxide environment can be quantified. By characterizing the relationship between the charge density of heavy metal ions and their extraction selectivity, a heavy metal characteristic parameterization mapping table is established, which can establish a clear connection between different heavy metals, enhance the adaptability of the extraction process to heavy metal type differences, and improve the extraction selectivity and process accuracy.

[0039] Step S520, perform multi-constraint condition modeling according to the stability criterion set. By converting the path stability threshold into the boundary constraint conditions in the extraction pressure-temperature parameter space, a set of constraint equations is generated; It is understandable that the selection of a stable path can effectively improve the reliability of the extraction process. Therefore, it is necessary to convert the path stability threshold into the constraint conditions of specific process parameters in the extraction process. Through multi-constraint modeling, the stability threshold is associated with parameters such as pressure and temperature to obtain a set of boundary constraint conditions. These conditions not only reflect the stability of the extraction path but also ensure that the extraction process can be carried out within a controllable range under specific process conditions.

[0040] Step S530: Generate a collaborative surface according to the set of constraint equations, and apply the Kriging surrogate model to interpolate and calculate the response values in the parameter space to generate a collaborative optimization response surface.

[0041] It should be noted that the generation of the collaborative surface is the core of the entire optimization process, which integrates the mutual relationships between the multi-constraint conditions obtained in the previous steps and each process parameter. The Kriging surrogate model calculates the response values in the parameter space through interpolation to generate a response surface that can reflect the extraction efficiency and stability under different process conditions. This response surface can describe the complex relationships between various performances (such as efficiency, energy consumption, etc.) in the extraction process and process parameters (such as pressure, temperature, etc.), providing an intuitive visualization tool for optimization decisions.

[0042] Furthermore, step S600 includes steps S610 to S630.

[0043] Step S610: Conduct multi-objective parameter correlation analysis according to the collaborative optimization response surface. By extracting the interaction characteristics of extraction efficiency and energy consumption in the response surface, an efficiency-energy consumption correlation mapping relationship is generated. It is understandable that by analyzing the correlation between these two in the response surface and establishing an efficiency-energy consumption correlation mapping relationship, the balance point between efficiency and energy consumption under different process parameters can be determined. This analysis helps to reveal the comprehensive impact of process parameter adjustment on extraction efficiency and energy consumption, providing a theoretical basis for subsequent optimization.

[0044] Step S620: Configure the weights of the optimization objectives according to the efficiency-energy consumption correlation mapping relationship. By constructing a multi-objective optimization decision function based on weighted fuzzy membership, an optimization objective function set is obtained. It should be noted that due to the trade-off relationship 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 according to different process requirements and actual operating conditions, thereby constructing a multi-objective optimization decision function. This function forms a comprehensive evaluation criterion by integrating the relationship between extraction efficiency and energy consumption to guide process optimization. The technical effect is that through the weighted fuzzy membership method, the best balance point can be found more accurately among multiple optimization objectives, ensuring that the final optimization objective meets the requirements of practical applications.

[0045] Step S630: Search for the global optimal solution according to the set of optimization objective functions. By applying the non-dominated sorting optimization algorithm to traverse the parameter space, an optimal process parameter domain is generated.

[0046] Specifically, first, the set of optimization objective functions has comprehensively considered multiple key factors such as heavy metal extraction efficiency, energy consumption, and stability. The non-dominated sorting optimization algorithm is used to balance these objectives, traverse the entire parameter space, and find the optimal solution that can satisfy all objectives simultaneously. Through the non-dominated sorting optimization algorithm, a set of Pareto optimal solutions can be screened out, that is, a set of process parameters that cannot further improve any one objective without sacrificing other objectives.

[0047] The generated optimal process parameter domain specifically includes process parameters that can provide the best heavy metal removal effect in the actual sewage treatment process, such as temperature, pressure, fluid flow rate, supercritical carbon dioxide concentration, etc. The significance of the optimal process parameter domain is that it provides a systematic parameter range for sewage treatment plants, and these parameters can achieve the optimal heavy metal extraction efficiency under different operating conditions while taking into account energy consumption and process stability.

[0048] The following table is a comprehensive comparison table of the process optimization method for extracting heavy metal elements using supercritical carbon dioxide in this application and the traditional process without using this method in terms of multi-category heavy metal extraction efficiency, residual concentration, and process economy indicators.

[0049] Table 1 Comparison table of heavy metal extraction effects (unit: mg / L)

[0050] In Table 1, is copper ion; is lead ion; is cadmium ion; is zinc ion; is nickel ion. It can be seen from Table 1 that the technical advantages of this method include: Efficient extraction: The identification of activation sites improves the accuracy of the mass transfer target area and reduces ineffective adsorption; Deep removal: The activity distribution correction solves the problem of underestimated activity under high ionic strength and reduces the residue to 0.5 mg / L; Stable recovery: The dynamic stability control reduces the impact of process fluctuations on dissociation to ±1.5%.

[0051] Furthermore, step S620 includes steps S621 to S623.

[0052] Step S621: Perform priority division according to the efficiency-energy consumption correlation mapping relationship. By classifying the interaction region of efficiency and energy consumption in the parameter space, combined with the membership threshold, determine the target priority levels of different process intervals, and generate the classification boundaries of the efficiency-dominated region, energy consumption-sensitive region, and balance region; In this process, first analyze the interaction between efficiency and energy consumption in the parameter space according to the efficiency-energy consumption correlation mapping relationship and divide it into different regions. By judging the membership threshold of these regions, it can be identified which process intervals are mainly dominated by efficiency, which intervals are more sensitive to energy consumption, or there is a balance region between efficiency and energy consumption. The classified regions can help determine the priorities of each target in different regions. Through this division, different optimization strategies can be applied to different regions according to the actual application requirements.

[0053] Step S622: Perform dynamic weight function construction processing according to the classification boundary. By establishing a weight assignment model based on regional membership, generate a set of dynamic weight factors; In this step, combined with the classification boundary generated in step S621, different weights are assigned to each region through regional membership. These weights reflect the importance of efficiency and energy consumption goals in different regions. For example, in the efficiency-dominated region, the extraction efficiency is given a higher weight, while in the energy consumption-sensitive region, energy consumption reduction is preferred. Therefore, through the membership-based weight assignment model, different weight factors can be flexibly assigned to each region to better reflect the trade-off between each target. This model dynamically adjusts the weight factors according to the characteristics of different regions, ensuring that the importance of each target in the optimization process is appropriately reflected.

[0054] Step S623: Perform function synthesis according to the set of dynamic weight factors to generate a set of optimized objective functions.

[0055] It should be noted that the set of optimized objective functions synthesizes each objective according to the set dynamic weights to ensure the balance of each objective during the optimization process. By synthesizing the optimized objective functions, a unified multi-objective optimization decision function can be generated, providing a basis for the subsequent search for the global optimal solution.

[0056] Embodiment 2: As Figure 2 shown, this embodiment provides a process optimization system for extracting heavy metal elements by supercritical carbon dioxide. The system includes: An acquisition module 901 for acquiring the types of heavy metals, initial concentrations, and solution pH-temperature parameters in the sewage to be treated; A processing module 902 for performing coordination competition modeling based on the solution pH-temperature parameters and combining the complex stability constants to obtain an activated site distribution model; A modeling module 903 for constructing an interphase migration model based on the activated site distribution model and combining the supercritical carbon dioxide phase change parameters; An analysis module 904 for performing extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a set of stability criteria; A fitting module 905 for performing extraction efficiency surface fitting based on the set of stability criteria and the types of heavy metals to obtain a collaborative optimization response surface; An output module 906 for generating an optimal process parameter domain according to the collaborative optimization response surface.

[0057] In a specific embodiment of the present invention, the processing module 902 includes: A first processing unit for performing thermodynamic coupling based on the solution pH-temperature parameters and the complex stability constants, and obtaining a dynamic dissociation characteristic set of heavy metal-organic complexes by calculating the dissociation coefficient matrix at each temperature gradient; A second processing unit for performing ion activity distribution analysis based on the dynamic dissociation characteristic set, and obtaining a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions; A third processing unit for performing activated site identification based on the dynamic activity distribution map, and generating an activated site distribution model by performing cluster analysis on the high-activity regions.

[0058] In a specific embodiment of the present invention, the modeling module 903 includes: A first modeling unit for performing supercritical fluid state analysis based on the activated site distribution model and the supercritical carbon dioxide phase change parameters, and obtaining a set of dynamic transport parameters by calculating the dynamic matrix of diffusion coefficients; 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; 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.

[0059] Example 3: 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.

[0060] 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.

[0061] 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, so as to complete all or part of the steps in the above-mentioned 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 include, for example, 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, sent and received messages, pictures, audio, video, and so on. 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. Among them, the screen can 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, and the microphone is used to receive external audio signals. The received audio signals can be further stored in the memory 802 or sent through 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 with 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. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0062] In an exemplary embodiment, a process optimization device 800 for supercritical carbon dioxide extraction of heavy metal elements may 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, and is used to execute the above-mentioned process optimization method for supercritical carbon dioxide extraction of heavy metal elements.

[0063] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned process optimization method for supercritical carbon dioxide extraction of heavy metal elements are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of a process optimization device 800 for supercritical carbon dioxide extraction of heavy metal elements to complete the above-mentioned process optimization method for supercritical carbon dioxide extraction of heavy metal elements.

[0064] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope 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 by 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 by 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 by 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 by 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; Perform collaborative surface generation based on the set of constraint equations, apply Kriging surrogate model interpolation to calculate the response values in the parameter space, and generate a collaborative optimization response surface.

6. The process optimization method for extracting heavy metal elements by supercritical carbon dioxide according to claim 1, wherein Generate an optimal process parameter domain through optimization based on the collaborative optimization response surface, including: Conduct multi-objective parameter correlation analysis based on the collaborative optimization response surface, and generate an efficiency-energy consumption correlation mapping relationship by extracting the interaction characteristics of extraction efficiency and energy consumption in the response surface; Configure the weights of the optimization objectives based on the efficiency-energy consumption correlation mapping relationship, and obtain a set of optimization objective functions by constructing a multi-objective optimization decision function based on weighted fuzzy membership; Search for the global optimal solution based on the set of optimization objective functions, and generate an optimal process parameter domain by applying the non-dominated sorting optimization algorithm to traverse the parameter space.

7. The process optimization method for extracting heavy metal elements by supercritical carbon dioxide according to claim 6, characterized in that, Configure the weights of the optimization objectives based on the efficiency-energy consumption correlation mapping relationship, including: Conduct priority division based on the efficiency-energy consumption correlation mapping relationship, classify the interaction regions of efficiency-energy consumption in the parameter space, and combine the membership threshold to determine the target priority levels in different process intervals, generating the classification boundaries of the efficiency-dominated region, energy consumption-sensitive region, and balance region; Conduct processing for constructing a dynamic weight function based on the classification boundaries, and generate a set of dynamic weight factors by establishing a weight allocation model based on regional membership; Conduct function synthesis based on the set of dynamic weight factors to generate a set of optimization objective functions.

8. An optimization system for the process of extracting heavy metal elements by supercritical carbon dioxide, characterized in that, Including: An acquisition module for acquiring the types of heavy metals, initial concentrations, and solution pH-temperature parameters in the sewage to be treated; A processing module for performing coordination competition modeling based on the solution pH-temperature parameters and combined with the complex stability constant to obtain an activated site distribution model; A modeling module for constructing an interphase migration model based on the activated site distribution model and combined with the supercritical carbon dioxide phase change parameters; An analysis module for conducting extraction path evolution analysis based on the interphase migration model and the initial concentration to obtain a set of stability criteria; A fitting module for performing extraction efficiency surface fitting based on the set of stability criteria and the types of heavy metals to obtain a collaborative optimization response surface; An output module for generating an optimal process parameter domain through optimization based on the collaborative optimization response surface.

9. The process optimization system for extracting heavy metal elements by supercritical carbon dioxide according to claim 8, wherein, The processing module includes: A first processing unit for performing thermodynamic coupling based on the solution pH-temperature parameters and the complex stability constant, and obtaining a set of dynamic dissociation characteristics of heavy metal-organic complexes by calculating the dissociation coefficient matrix at each temperature gradient; A second processing unit for conducting ion activity distribution analysis based on the set of dynamic dissociation characteristics, and obtaining a dynamic activity distribution map by calculating the three-dimensional activity distribution of free heavy metal ions; A third processing unit for identifying activated sites based on the dynamic activity distribution map, and generating an activated site distribution model by performing clustering analysis on the high-activity regions.

10. The process optimization system for extracting heavy metal elements by 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.

Images