Extraction, compounding and regeneration system for copper element in smelting ash

Abstract

The invention relates to the technical field of metal extraction and material regeneration, and discloses an extraction, compounding and regeneration system for copper element in smelting ash, comprising: a raw material pretreatment module for screening, crushing and microwave activating treatment of materials; the multi-mode occurrence state identification module is used for analyzing the copper form by fusing spectrum, valence state and image information; the dynamic leaching regulation and control module is used for constructing an optimization model based on the characteristic data and adjusting leaching parameters; the compound regenerated material preparation module is used for respectively converting the extracting solution and the residues into an MOF material and a geopolymer; and the closed-loop feedback optimization module is used for adjusting identification and leaching parameters in real time based on performance detection data, and systematic optimization and efficient resource recovery are realized. By adopting the technical scheme of combining screening, crushing and microwave activation in the raw material pretreatment module, particle refinement and internal occurrence structure release of the smelting ash material are realized, and the technical effect of remarkably improving the exposure degree of the copper element is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of metal extraction and material regeneration, and in particular to a system for extracting, compounding and regenerating copper elements in smelting ash. Background Art

[0002] In modern industry and environmental governance, smelter ash, a significant solid waste from nonferrous metal smelting, contains not only large amounts of unrecovered copper but also a complex matrix of silicates and aluminosilicates. Left untreated, it can lead to resource waste and heavy metal pollution, threatening ecological security. Copper has extremely high industrial value and is used in various fields, including conductivity, catalysis, and alloy manufacturing. Therefore, efficient copper extraction from smelter ash is not only a necessary step in waste utilization but also a key component in promoting resource recycling and green manufacturing.

[0003] In existing technologies, mechanical crushing combined with chemical additive treatment is commonly used to simply improve the leaching efficiency of materials. Some processes also use single spectroscopy or chemical analysis methods to analyze the metal occurrence form, providing basic data for subsequent process optimization. At the same time, many leaching processes operate based on fixed parameters and can achieve moderate copper extraction rates under specific conditions. In addition, some technologies introduce multiple offline detection and manual adjustment mechanisms to gradually improve process parameters to ensure a certain material recovery rate and process stability. These existing methods have played a positive role in specific application ranges and promoted the development of resource-based processing.

[0004] However, existing technologies rely on a single mechanical or chemical pretreatment method, which can only break up external particles but cannot deeply open the complex internal packaging structure, making it difficult to fully release the copper element. This directly leads to a lack of multi-dimensional information input in the occurrence identification module, and the system's grasp of space, valence, and structure is insufficient, resulting in one-sided judgments. Furthermore, the fixed-parameter leaching strategy solidifies the optimization space, making it difficult to balance extraction and impurity control, and often inefficient in production. Most importantly, the existing optimization process relies solely on offline detection and manual intervention adjustments, making it difficult to achieve real-time and dynamic optimization, making the entire process chain rigid and slow when the material changes. To this end, those skilled in the art have proposed a system for extracting, compounding, and regenerating copper elements from smelting ash to solve the above problems. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a system for extracting, compounding and regenerating copper elements in smelting ash, which solves the problems in the existing technology of lack of system integration in the smelting ash copper extraction process and difficulty in achieving intelligent optimization of the entire process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A system for extracting, compounding and regenerating copper from smelting ash, comprising:

[0007] The raw material pretreatment module is used to refine the particles and loosen the structure of the smelting ash raw materials through screening, crushing and microwave activation to obtain pretreated materials;

[0008] A multimodal copper occurrence state identification module, based on the pretreated material and integrating laser-induced breakdown spectroscopy information, X-ray photoelectron spectroscopy valence state information, and scanning electron microscope image information, identifies the spatial distribution, chemical valence state, and packaging structure of the copper element in the pretreated material to obtain copper morphological characteristic data;

[0009] A dynamic leaching control module, based on the copper morphological characteristic data, constructs a multi-objective optimization model including copper extraction efficiency and impurity concentration control objectives, and uses an evolutionary algorithm to globally optimize and dynamically adjust the pH value, temperature, and time parameters of the leaching reaction to obtain a copper ion leachate and leaching residue;

[0010] The composite recycled material preparation module synthesizes a metal-organic framework composite material based on the copper ion leachate and leach residue by electrodeposition and organic ligand reaction, and solidifies the leach residue into a geopolymer by alkali-induced polymerization reaction, thereby realizing the composite recycling of copper resources and solid waste;

[0011] The closed-loop feedback optimization module establishes a performance response model based on the performance test data of the metal-organic framework composite material and the geopolymer, and performs real-time feedback adjustment on the recognition weights and control parameters in the multimodal copper occurrence state recognition module and the dynamic leaching control module.

[0012] Preferably, the raw material pretreatment module includes:

[0013] The particle screening unit is used to remove particles with a diameter greater than 1 mm from the smelting ash using a multi-stage vibrating screen;

[0014] A mechanical crushing unit for crushing the smelting ash particles to no larger than 75 microns using a planetary ball mill;

[0015] The microwave activation unit is used to heat the smelting ash in a microwave field to induce selective cracking of the silicate-copper nested structure inside the smelting ash.

[0016] Preferably, the multimodal copper occurrence state identification module includes:

[0017] A spectral information acquisition unit, used to obtain a two-dimensional copper element intensity distribution map through laser-induced breakdown spectroscopy;

[0018] Valence state analysis unit, used to analyze the copper element in Cu using X-ray photoelectron spectroscopy 0 、Cu + and Cu 2+ The relative proportions of the three valence states;

[0019] An image recognition unit, which is used to collect scanning electron microscope images of smelting ash and perform semantic segmentation and recognition of the copper element's packaging structure using a convolutional neural network;

[0020] The feature fusion unit is used to fuse the above data dimensions based on the tensor decomposition algorithm and output the copper morphological feature data set for leaching optimization.

[0021] Preferably, the copper morphological feature data of the feature fusion unit is based on tensor The CP decomposition is expressed as:

[0022]

[0023] in: is a three-dimensional tensor, where the dimensions correspond to position, valence, and structure information; R is the tensor rank, indicating the number of feature dimensions; u r , v r , w r represent position eigenvector, valence eigenvector and structure eigenvector respectively; ° represents vector outer product operation.

[0024] Preferably, the dynamic leaching control module includes:

[0025] Optimization modeling unit, used to construct a dual-objective optimization function to maximize copper extraction efficiency and minimize impurity content;

[0026] Evolutionary algorithm parameter adjustment unit, used to optimize pH value, reaction temperature, and leaching time variables based on genetic algorithm;

[0027] The leaching implementation unit is used to set the reactor conditions according to the optimal solution and add the leaching agent to carry out constant temperature and constant time stirring leaching to obtain the copper ion leachate and residue.

[0028] Preferably, the objective function of the optimization modeling unit is set as:

[0029] Maximize copper extraction efficiency objective function:

[0030] F1=α1·C free +α2·C encap ;

[0031] Minimize the impurity content objective function:

[0032] F2=β1·C Fe +β2·C Si +β3·C Zn ;

[0033] Where: C free is the free copper concentration; C encapis the concentration of copper released after breaking the package; C Fe , C Si , C Zn is the concentration of impurity elements; α1, α2, β1, β2, β3 are empirical weight coefficients.

[0034] Preferably, the composite recycled material preparation module includes:

[0035] A metal-organic framework synthesis unit is used to react a copper ion solution with 2-methylimidazole under stirring to form a Cu-MOF complex, which is then freeze-dried;

[0036] The geopolymer preparation unit is used to mix the leaching residue with an alkali activator and cure it in a hot and humid environment for 48 hours to form a stable aluminum-silicon cross-linked structure.

[0037] Preferably, the synthesis reaction of the metal organic framework synthesis unit satisfies the following stoichiometric relationship:

[0038] Cu 2+ +2C4H6N2→Cu(C4H5N2)2+2H + ;

[0039] Among them: Cu 2+ is copper ion; C4H6N2 is 2-methylimidazole ligand; Cu(C4H5N2)2 is MOF framework product; 2H + As a by-product.

[0040] Preferably, the closed-loop feedback optimization module includes:

[0041] A performance testing unit, used to test the specific surface area, porosity, specific capacitance of the metal-organic framework composite material, as well as the compressive strength and heavy metal leaching rate of the geopolymer;

[0042] Response modeling unit, used to construct a mathematical model of the association between material properties and leaching parameters based on multi-factor regression;

[0043] The parameter control unit is used to adjust the image recognition weight, valence state recognition dimension weight and parameter setting value of the dynamic leaching module in the copper occurrence state recognition module according to the model output.

[0044] Preferably, the material property function of the response modeling unit is modeled as a response surface model:

[0045] Y=γ0+γ1x1+γ2x2+γ3x3+γ 12 x1x2+γ 13 x1x3+γ 23 x2x3;

[0046] Where: Y represents the comprehensive performance score; x1 is the pH value; x2 is the temperature; x3 is the leaching time; γ0, γ1,…, γ 23 is the regression coefficient.

[0047] The present invention provides a system for extracting, compounding and regenerating copper from smelting ash. It has the following beneficial effects:

[0048] 1. The present invention adopts a combined technical solution of screening, crushing and microwave activation in the raw material pretreatment module to achieve particle refinement of the smelting ash material and loosening of the internal structure, achieving the technical effect of significantly improving the exposure of the copper element. Compared with the single mechanical crushing or chemical auxiliary agent activation solution in the existing technology, it avoids the problems of excessive damage to the material surface and incomplete cracking of the packaging structure, and effectively solves the technical shortcoming of insufficient reaction interface in the subsequent extraction process.

[0049] 2. In the present invention, the multimodal copper occurrence state identification module adopts a fusion algorithm scheme of three-source information: spectrum, valence state, and image, achieving the technical effect of comprehensively analyzing the spatial distribution and chemical environment of copper elements. Traditional schemes usually rely only on a single spectrum or chemical analysis method, resulting in insufficient recognition dimensions and one-sided occurrence state judgment. The present invention breaks through this limitation, allowing the system to more accurately locate the complex state of copper at the microscopic level, greatly improving the starting point accuracy of leaching optimization.

[0050] 3. The present invention introduces a dual-objective optimization model and evolutionary algorithm through a dynamic leaching control module, finds the optimal leaching conditions under multiple parameters and multiple constraints, and achieves a dynamic balance between copper extraction rate and impurity suppression. Compared with traditional fixed processes or empirical parameter adjustment methods, this data-driven optimization solution significantly reduces manual trial and error, improves the efficiency of the production process, and successfully solves the problem of difficulty in synchronously optimizing parameters under complex occurrence conditions.

[0051] 4. The closed-loop feedback optimization module introduced in the present invention enables the system to have real-time adjustment capabilities based on material properties, which allows the entire process chain to achieve adaptive closed-loop optimization for the first time. Unlike the existing technology that relies on offline detection and manual analysis, this solution forms a multi-module data-driven dynamic self-adjustment through response modeling and parameter callback, effectively overcoming the problem of insufficient long-term operational stability caused by material fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the system architecture of the present invention;

[0053] Figure 2 This is a schematic diagram of the raw material pretreatment module framework of the present invention;

[0054] Figure 3 This is a schematic diagram of the architecture of the multimodal copper occurrence state recognition module of the present invention;

[0055] Figure 4 This is a schematic diagram of the dynamic leaching control module framework of the present invention;

[0056] Figure 5 This is a schematic diagram of the modular framework for preparing the composite recycled material of the present invention;

[0057] Figure 6 This is a schematic diagram of the closed-loop feedback optimization module architecture of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Please see the attached Figure 1 -Attached Figure 6 The embodiment of the present invention provides a system for extracting, compounding and regenerating copper from smelting ash, comprising:

[0060] The raw material pretreatment module is used to refine the particles and loosen the structure of the smelting ash raw materials through screening, crushing and microwave activation to obtain pretreated materials;

[0061] Specifically, in the copper extraction, compounding, and regeneration system for smelting ash of the present invention, the raw material pretreatment module serves as the initial step in the entire process. Its performance and operation significantly impact subsequent copper occurrence state identification, dynamic leaching control, and material regeneration steps. To ensure accurate identification and sufficient leaching, targeted pretreatment of the smelting ash's physical structure and microscopic distribution is required to improve the exposure of the occurrence structure and the uniformity of the composition.

[0062] Generally speaking, smelting ash raw materials have dense structures, coarse particles, and are highly packed with metal elements. Direct leaching will result in low copper extraction rates and significant impurity disturbance. Therefore, the introduction of a pretreatment module with screening, crushing, and microwave selective excitation capabilities at the initial stage of the system is the foundation for subsequent efficient identification and precise control.

[0063] In this embodiment, the raw material pretreatment module includes a particle screening unit, a mechanical crushing unit and a microwave activation unit.

[0064] As a possible implementation method, the particle screening unit is set as a double-layer vibrating screen, the aperture of the first screen layer is set to 2 mm to remove larger unreacted particles; the aperture of the second screen layer is set to 1 mm to refine the material distribution and ensure uniform particle size in downstream processing.

[0065] In some embodiments, the smelting ash sample, after screening, entering the mechanical pulverization unit is first processed in a high-energy planetary ball mill. Specifically, the milling conditions are set as follows: a rotation speed of 500 rpm, a processing time of 15 minutes, and a ball-to-material ratio of 10:1. This combination of parameters can reduce the smelting ash particle size to less than 75 microns without causing excessive thermal degradation, thereby increasing the specific surface area and exposing the boundary regions of the copper-bearing structure.

[0066] To further break the physical encapsulation of copper within silicate, aluminosilicate, or glassy structures, this example incorporates a microwave activation unit for selective structural excitation. This unit utilizes a 2.45 GHz, 600 W microwave source. The smelted ash sample is placed in a rotating ceramic crucible and irradiated with microwaves continuously for 3 to 5 minutes in an air environment.

[0067] The mechanism of microwave activation is based on the localized differences in microwave absorption by heterogeneous media. In a representative model, this process can be simplified as the excitation of a thermal tension response model of the encapsulated structure:

[0068] σ=E·α·ΔT;

[0069] Where: σ is the thermal stress (Pa); E is the Young's modulus of the host phase material (Pa); α is the linear thermal expansion coefficient of the material (1 / K); ΔT is the local temperature rise formed during the microwave action (K).

[0070] In some embodiments, for the occurrence mode of the Cu-Si-O structure, simulation results show that when ΔT exceeds 120K, a thermal stress exceeding 150 MPa can be generated at the occurrence interface, which is sufficient to cause the amorphous silicate phase structure to crack and form microcrack channels.

[0071] Specifically, an intermittent heating strategy is adopted during the microwave activation process, controlling each heating cycle to 30 seconds and each cooling cycle to 15 seconds to prevent local sintering or overheating from causing secondary wrapping.

[0072] In one possible technical extension, the system can further introduce an infrared thermal imaging feedback mechanism to monitor the surface and internal temperature distribution of the sample, thereby dynamically controlling the microwave input power and achieving directional excitation and selective rupture of the encapsulated structure.

[0073] As an option, the pretreatment module can be configured as a continuous processing path, that is, each unit is connected by a screw feeder to improve processing efficiency and prevent oxidation or agglomeration of samples during transmission.

[0074] Through the structural design and operating parameter setting of the above-mentioned pretreatment module, the resolution accuracy of subsequent copper occurrence identification can be significantly improved, while providing a homogeneous and controllable reaction interface for the dynamic leaching reaction.

[0075] The multimodal copper occurrence state identification module, based on pre-treated materials, integrates laser-induced breakdown spectroscopy information, X-ray photoelectron spectroscopy valence state information, and scanning electron microscope image information to identify the spatial distribution, chemical valence state, and packaging structure of copper elements in pre-treated materials to obtain copper morphological characteristic data;

[0076] Specifically, in the copper extraction, compounding, and regeneration system for smelting ash of the present invention, the raw material undergoes screening, pulverization, and microwave activation in the pretreatment module, resulting in a pretreated material with refined particle size and loosened structure. To accurately identify the copper occurrence characteristics, a multimodal copper occurrence state identification module is employed in subsequent steps for in-depth analysis. This module not only provides key parameter inputs for the dynamic leaching control module but also directly determines the optimized starting point for the entire extraction and compounding process.

[0077] Typically, copper in smelting ash occurs in a variety of forms, including free particles, embedded states, and encapsulated states, with varying chemical valence states and structural environments. Therefore, a single detection method cannot fully analyze its spatial distribution and chemical state. This invention combines multi-source data acquisition and multimodal fusion to comprehensively and meticulously analyze the multidimensional characteristics of copper.

[0078] In this embodiment, the multimodal copper occurrence state identification module includes a spectral information acquisition unit, a valence state analysis unit, an image recognition unit and a feature fusion unit.

[0079] Specifically, the spectral information acquisition unit uses a laser-induced breakdown spectroscopy (LIBS) device to scan the pretreated material in a micro-area by focusing a high-power laser beam to obtain a two-dimensional copper element intensity distribution matrix.

[0080] In some embodiments, the scanning resolution is set to 50 μm, the laser pulse energy is 60 mJ, and the pulse width is 10 nanoseconds to ensure spatial accuracy of local analysis.

[0081] As an option, the valence state analysis unit uses X-ray photoelectron spectroscopy (XPS) to analyze the copper valence composition of the sample surface. This unit obtains the spectrum peak signal of the Cu2p energy level, combines the baseline subtraction and peak fitting algorithm to analyze the Cu 0 (elemental state), Cu +(cuprous state) and Cu 2+ (copper ion state) relative content ratio.

[0082] In one possible implementation, to avoid the influence of surface contamination, the sample needs to be treated with argon ion sputtering before XPS analysis. The sputtering time is set to 2 minutes and the current density is 2 mA / cm 2 .

[0083] In some embodiments, the image recognition unit uses a scanning electron microscope (SEM) combined with backscattered electron imaging to perform structural analysis of the encapsulation or embedding state of copper particles in the matrix. Combined with a convolutional neural network (CNN) architecture, the system can perform semantic segmentation on SEM images to distinguish between regions such as copper, silicate matrix, and voids. The CNN input image resolution is set to 1024×1024 pixels, and a ResNet-50 backbone network is used for feature extraction. A Softmax classifier is used to output pixel-level mask results.

[0084] In this embodiment, the feature fusion unit integrates the above multimodal data based on the tensor decomposition algorithm. Specifically, the CP (CANDECOMP / PARAFAC) decomposition method is used to model the spatial distribution, chemical valence and packaging state of copper elements into three-dimensional tensors. And decomposed into the sum of vector outer products of rank R:

[0085]

[0086] in: It is a three-dimensional feature tensor, and the dimensions correspond to the scanning position (H×W), valence category (such as Cu 0 、Cu + 、Cu 2+ ) and structural state (wrapped state, embedded state, free state); R is the tensor rank, which represents the number of characteristic factors of decomposition; u r is the factor vector in the position dimension, with dimension H×W; v r is the factor vector on the valence dimension, with a length of 3; w r is the factor vector on the structural dimension with a length of 3; ° is the outer product symbol, which represents the product operation in tensor decomposition.

[0087] In one possible implementation, the system normalizes each factor vector obtained by decomposition to ensure dimensional consistency between multimodal features and avoid bias during the optimization process.

[0088] As a technical extension, the feature fusion unit can also be combined with principal component analysis (PCA) or t-SNE dimensionality reduction methods to visualize high-dimensional feature data, so that operators can intuitively understand and make decisions about the copper occurrence state.

[0089] It's important to note that this module not only outputs copper morphological data but also provides parameter initialization values ​​based on spatial, chemical, and structural characteristics for the dynamic leaching control module, which is used to set input variables in multi-objective optimization modeling. Through the involvement of the multimodal recognition module, the system achieves efficient coupling from raw material characteristics to leaching strategies.

[0090] The dynamic leaching control module builds a multi-objective optimization model based on copper morphological characteristic data, including copper extraction efficiency and impurity concentration control objectives. It also uses an evolutionary algorithm to globally optimize and dynamically adjust the pH value, temperature, and time parameters of the leaching reaction to obtain copper ion leachate and leaching residue.

[0091] Specifically, in the copper extraction, compounding, and regeneration system for smelting ash described herein, copper morphological characteristics acquired by a multimodal copper occurrence state recognition module provide targeted parameter information for subsequent leaching reactions. To transform this multidimensional data into actionable process conditions, the system incorporates a dynamic leaching control module for global optimization and dynamic control of the leaching reaction. This module not only considers copper extraction efficiency but also impurity suppression and process stability, thereby providing high-quality copper ion leachate and residue products for subsequent compounding and regeneration material preparation.

[0092] Generally speaking, it is difficult to achieve the best leaching effect under complex occurrence conditions by adjusting a single parameter. Therefore, the present invention proposes a parameter control mechanism based on a multi-objective optimization model to achieve multi-factor, multi-objective system optimization.

[0093] In this embodiment, the dynamic leaching control module includes an optimization modeling unit, an evolutionary algorithm parameter adjustment unit, and a leaching implementation unit.

[0094] Specifically, the optimization modeling unit constructed a dual-objective optimization function that simultaneously maximized copper extraction efficiency and minimized impurity concentration.

[0095] In some embodiments, the copper extraction efficiency objective function F1 is defined as:

[0096] F1=α1·C free +α2·C encap ;

[0097] Where: C free is the concentration of free copper (mg / L); C encap is the concentration of copper released after breaking the package (mg / L); α1 and α2 are empirical weight coefficients, which are used to adjust the contribution ratio of free copper and packaged copper in the optimization target.

[0098] At the same time, the impurity concentration minimization objective function F2 is defined as:

[0099] F2=β1·C Fe +β2·C Si +β3·C Zn ;

[0100] Where: C Fe , C Si , C Zn is the concentration of iron, silicon and zinc in the leachate (mg / L); β1, β2 and β3 are optimization weight parameters that control the constraint strength of different impurity elements.

[0101] As a possible implementation, the evolutionary algorithm parameter tuning unit uses a genetic algorithm (GA) for parameter optimization. The optimized variables include pH, reaction temperature, and leaching time. In some embodiments, the GA has an initial population size of 50, a maximum number of iterations of 100, a crossover probability of 0.8, and a mutation probability of 0.1 to balance exploration and exploitation capabilities.

[0102] In this embodiment, the fitness function of the optimization model adopts a weighted comprehensive form:

[0103]

[0104] Where: w1 and w2 are the weight coefficients of extraction efficiency and impurity suppression, satisfying w1+w2=1; F 1,max 、F 2,max is the maximum theoretical value of the objective function, which is used for normalization.

[0105] As an option, the system can introduce constraints such as maximum allowable acid consumption, maximum energy consumption and residue moisture content to ensure that the optimized solution is not only superior in theory but also practically feasible.

[0106] Specifically, the leaching implementation unit sets the process conditions of the stirred leaching reactor according to the optimized parameter conditions.

[0107] In some embodiments, a dilute sulfuric acid solution (concentration 1-2 mol / L) is selected as the leaching agent, the stirring rate is set to 300 rpm, and the liquid-solid ratio is controlled at 10:1 to ensure sufficient dissolution of copper.

[0108] In a possible technical extension, the module can be equipped with online pH, conductivity and redox potential monitoring devices to achieve real-time closed-loop regulation of the reaction process.

[0109] For example, when the online monitoring signal shows that the pH drops beyond the set range, the system automatically adjusts the addition rate of the acid or alkali agent to keep the reaction system stable.

[0110] Notably, the module not only outputs copper ion leachate and residue products but also generates a process data log, providing the necessary input data for the subsequent closed-loop feedback optimization module. By coupling multi-objective optimization with an evolutionary algorithm, the system can dynamically adapt to varying smelting ash conditions, achieving an efficient and controllable copper extraction process.

[0111] The composite recycled material preparation module synthesizes metal-organic framework composite materials based on copper ion leachate and leach residue through electrodeposition and organic ligand reaction, and solidifies the leach residue into geopolymer through alkali-induced polymerization reaction, realizing the composite recycling of copper resources and solid waste;

[0112] Specifically, in a copper extraction and compounding regeneration system from smelting ash of the present invention, after optimization treatment by a dynamic leaching control module, the system obtains a leachate containing copper ions and an impurity-enriched residue. These intermediate products not only need to be properly handled to prevent secondary pollution, but should also be further utilized to maximize resource recovery and value-added. Therefore, the system is equipped with a compounding and regeneration material preparation module to convert the copper ion solution and the leaching residue into a metal-organic framework (MOF) composite material and a geopolymer, respectively, to achieve the coordinated utilization of copper resource recovery and solid waste.

[0113] Typically, copper ions in copper leachate serve as metal nodes in MOF materials, coordinating with organic ligands to form a highly ordered porous network. Leaching residue, on the other hand, is rich in silicon and aluminum oxides and possesses excellent geopolymerization potential. By systematically integrating these two products, diverse composite functional materials can be formed, opening up new avenues for the recycling of smelting ash.

[0114] In this embodiment, the composite regeneration material preparation module includes a metal organic framework synthesis unit and a geopolymer preparation unit.

[0115] Specifically, the metal-organic framework synthesis unit is based on the reaction of copper ion solution with 2-methylimidazole ligand.

[0116] In some embodiments, a copper ion solution (0.1 mol / L) and 2-methylimidazole (0.2 mol / L) were mixed in a 1:2 molar ratio at room temperature (25°C) and stirred for 12 hours. The resulting Cu-MOF composite was centrifuged, washed with ethanol, and freeze-dried to obtain a highly pure porous MOF material.

[0117] In one possible implementation, the synthesis reaction follows the following stoichiometric formula:

[0118] Cu 2+ +2C4H6N2→Cu(C4H5N2)2+2H + ;

[0119] Among them: Cu 2+ is copper ion, which comes from the leachate; C4H6N2 is 2-methylimidazole ligand; Cu(C4H5N2)2 is metal organic framework product; 2H + It is a by-product and needs to be neutralized by a buffer in the system to prevent pH from getting out of control.

[0120] As an option, the system can further regulate the type of organic ligands, such as introducing phthalic acid or polycarboxylic acid ligands, to adjust the pore size and surface chemical properties of the MOF material.

[0121] In some embodiments, the leached residue is solidified using a geopolymer preparation unit. Specifically, the residue is mixed with an alkaline activator (consisting of NaOH and Na2SiO3) in a mass ratio of 2.5:1, and deionized water is added and stirred to form a flowable slurry. This slurry is placed in a mold and cured under moist heat conditions (temperature 50-70°C, relative humidity ≥80%) for 48 hours. The resulting three-dimensional cross-linked aluminosilicate network structure exhibits excellent mechanical strength and chemical stability.

[0122] In order to quantify the material properties, this example introduces the following response parameters:

[0123] S BET :Specific surface area of ​​MOF material (m 2 / g);

[0124] P: average pore size of MOF material (nm);

[0125] R c : geopolymer compressive strength (MPa);

[0126] L HM : Leaching rate of heavy metals in solidified bodies (mg / L).

[0127] In a possible technical extension, the module can be configured with a performance control mechanism, that is, by adjusting the MOF precursor concentration, alkaline activator ratio or curing conditions, fine-tuning of the material's microstructure and macroscopic properties can be achieved.

[0128] For example, when the MOF surface area is less than 200 m 2 / g, the system automatically adjusts the organic ligand concentration to optimize the crystallinity.

[0129] It should be emphasized that the operation of this module is not only about product generation, but also includes waste liquid management and by-product treatment, such as neutralization of acidic by-products in the MOF reaction liquid and recovery and reuse of unreacted residual alkali produced by geopolymer reaction, so as to improve the overall green efficiency and economy of the system.

[0130] Through the intervention of the compound recycled material preparation module, the system realizes closed-loop utilization from copper ion recovery, impurity residue utilization to the final composite material output, reflecting a diversified and systematic resource utilization path.

[0131] The closed-loop feedback optimization module establishes a performance response model based on the performance test data of metal-organic framework composites and geopolymers, and performs real-time feedback adjustment on the identification weights and control parameters in the multimodal copper occurrence state identification module and the dynamic leaching control module.

[0132] Specifically, in a system for extracting, compounding and regenerating copper elements from smelting ash of the present invention, the system has successfully generated metal-organic framework composite materials and geopolymer products through the aforementioned compounding and regeneration material preparation module. However, to achieve efficient, stable and sustainable optimization of the overall process, relying solely on one-way operation is far from enough. To this end, the present invention specially provides a closed-loop feedback optimization module for adjusting the parameters of the front-end module in real time based on material performance data to ensure that the system is in the optimal working state for a long time. This module plays a connecting role in the entire system and is a key link in achieving dynamic coordination and performance improvement of multiple modules.

[0133] Generally, relying solely on static parameter settings or empirical adjustments is insufficient to address the complex and changing properties of smelting ash. To overcome this limitation, the present invention employs a method based on performance response modeling and real-time feedback control. This method directly links the prepared material performance indicators to the control parameters of the front-end identification and leaching modules, achieving cross-module adaptive adjustment and optimization.

[0134] In this embodiment, the closed-loop feedback optimization module includes a performance detection unit, a response modeling unit, and a parameter control unit.

[0135] Specifically, the performance testing unit is used to obtain key performance indicators of metal-organic framework composites and geopolymers.

[0136] In some embodiments, for MOF materials, the detection content includes specific surface area S BET , average pore size P, specific capacitance C sp ;

[0137] For geopolymers, the test content includes compressive strength R c , volume density D v , heavy metal leaching rate L HM .

[0138] These data were obtained through BET surface area analyzer, mercury porosimeter, electrochemical workstation and static leaching experiment, ensuring the accuracy and comprehensiveness of the detection.

[0139] As a possible implementation method, the response modeling unit constructs a mathematical correlation model between performance and process parameters based on the multi-factor regression method or response surface analysis (RSM).

[0140] Specifically, the response model can be expressed as:

[0141] Y=γ0+γ1x1+γ2x2+γ3x3+γ 12 x1x2+γ 13 x1x3+γ 23 x2x3;

[0142] Where: Y is the comprehensive performance score (a dimensionless index calculated by weighting multiple performance factors); x1 is the pH value in the dynamic leaching control module (range 1.0-6.0); x2 is the leaching temperature (unit: K); x3 is the leaching time (unit: minute); γ0 is the regression constant; γ1, γ2, and γ3 are the first-order effect coefficients; γ 12 , γ 13 , γ 23 is the interaction coefficient.

[0143] In some embodiments, to improve the prediction accuracy of the model, the system introduces LASSO regularization technology to avoid multicollinearity problems.

[0144] In this embodiment, the parameter control unit performs parameter callback on the front-end module based on the output result of the response model.

[0145] Specifically, the system automatically adjusts the image recognition weights, valence feature dimension weights, etc. in the multimodal copper occurrence state recognition module, so as to focus on optimizing relevant dimensions in the next round of data collection.

[0146] At the same time, the optimal pH, temperature, and time parameters of the dynamic leaching control module are also updated in real time based on the feedback results.

[0147] For example, when the MOF specific surface area S is detected BET When descending, the system will give priority to adjusting the identification weight of the inclusion state in the copper occurrence state identification to guide the optimization model to focus more on the inclusion crushing process.

[0148] As an option, the module can be configured with a fuzzy controller or a reinforcement learning-based scheduling strategy to achieve multi-objective, multi-constraint closed-loop optimization in more complex scenarios.

[0149] For example, the system can switch optimization strategies between different smelting ash batches to achieve cross-batch adaptation.

[0150] It should be pointed out that the control signal generated by the closed-loop feedback optimization module is transmitted in the form of a digital signal through the system master control platform to ensure the accuracy and real-time communication between each module.

[0151] All historical data and feedback parameters are recorded in the database to provide data support for long-term system optimization and process upgrades.

[0152] By setting up a closed-loop feedback optimization module, the present invention not only achieves a high degree of coupling between multiple modules, but also provides adaptive and self-optimization capabilities for system operation, further enhancing the robustness and sustainability of the process.

[0153] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A system for extracting, compounding and regenerating copper from smelting ash, characterized in that: include: The raw material pretreatment module is used to refine the particles and loosen the structure of the smelting ash raw materials through screening, crushing and microwave activation to obtain pretreated materials; A multimodal copper occurrence state identification module, based on the pretreated material and integrating laser-induced breakdown spectroscopy information, X-ray photoelectron spectroscopy valence state information, and scanning electron microscope image information, identifies the spatial distribution, chemical valence state, and packaging structure of the copper element in the pretreated material to obtain copper morphological characteristic data; A dynamic leaching control module, based on the copper morphological characteristic data, constructs a multi-objective optimization model including copper extraction efficiency and impurity concentration control objectives, and uses an evolutionary algorithm to globally optimize and dynamically adjust the pH value, temperature, and time parameters of the leaching reaction to obtain a copper ion leachate and leaching residue; The composite recycled material preparation module synthesizes a metal-organic framework composite material based on the copper ion leachate and leach residue by electrodeposition and organic ligand reaction, and solidifies the leach residue into a geopolymer by alkali-induced polymerization reaction, thereby realizing the composite recycling of copper resources and solid waste; The closed-loop feedback optimization module establishes a performance response model based on the performance test data of the metal-organic framework composite material and the geopolymer, and performs real-time feedback adjustment on the recognition weights and control parameters in the multimodal copper occurrence state recognition module and the dynamic leaching control module.

2. The system for extracting, compounding and regenerating copper from smelting ash according to claim 1, characterized in that: The raw material pretreatment module includes: The particle screening unit is used to remove particles with a diameter greater than 1 mm from the smelting ash using a multi-stage vibrating screen; A mechanical crushing unit for crushing the smelting ash particles to no larger than 75 microns using a planetary ball mill; The microwave activation unit is used to heat the smelting ash in a microwave field to induce selective cracking of the silicate-copper nested structure inside the smelting ash.

3. The system for extracting, compounding and regenerating copper from smelting ash according to claim 1, characterized in that: The multimodal copper occurrence state recognition module includes: A spectral information acquisition unit, used to obtain a two-dimensional copper element intensity distribution map through laser-induced breakdown spectroscopy; Valence state analysis unit, used to analyze the copper element in Cu using X-ray photoelectron spectroscopy 0 、Cu + and Cu 2+ The relative proportions of the three valence states; An image recognition unit, which is used to collect scanning electron microscope images of smelting ash and perform semantic segmentation and recognition of the copper element's packaging structure using a convolutional neural network; The feature fusion unit is used to fuse the above data dimensions based on the tensor decomposition algorithm and output the copper morphological feature data set for leaching optimization.

4. The system for extracting, compounding and regenerating copper from smelting ash according to claim 3, characterized in that: The copper morphological feature data of the feature fusion unit is based on tensor The CP decomposition is expressed as: in: is a three-dimensional tensor, where the dimensions correspond to position, valence, and structure information; R is the tensor rank, indicating the number of feature dimensions; u r , v r , w r represent the positional eigenvector, valence eigenvector, and structural eigenvector, respectively; Represents a vector outer product operation.

5. The system for extracting, compounding and regenerating copper from smelting ash according to claim 1, characterized in that: The dynamic leaching control module includes: Optimization modeling unit, used to construct a dual-objective optimization function to maximize copper extraction efficiency and minimize impurity content; Evolutionary algorithm parameter adjustment unit, used to optimize pH value, reaction temperature, and leaching time variables based on genetic algorithm; The leaching implementation unit is used to set the reactor conditions according to the optimal solution and add the leaching agent to carry out constant temperature and constant time stirring leaching to obtain the copper ion leachate and residue.

6. The system for extracting, compounding and regenerating copper from smelting ash according to claim 5, characterized in that: The objective function of the optimization modeling unit is set as: Maximize copper extraction efficiency objective function: F1=α1·C free +α2·C encap ; Minimize the impurity content objective function: F2=β1·C Fe +β2·C Si +β3·C Zn ; Where: C free is the free copper concentration; C encap is the concentration of copper released after breaking the package; C Fe , C Si , C Zn is the concentration of impurity elements; α1, α2, β1, β2, β3 are empirical weight coefficients.

7. The system for extracting, compounding and regenerating copper from smelting ash according to claim 1, characterized in that: The composite regeneration material preparation module includes: A metal-organic framework synthesis unit is used to react a copper ion solution with 2-methylimidazole under stirring to form a Cu-MOF complex, which is then freeze-dried; The geopolymer preparation unit is used to mix the leaching residue with an alkali activator and cure it in a hot and humid environment for 48 hours to form a stable aluminum-silicon cross-linked structure.

8. The system for extracting, compounding and regenerating copper from smelting ash according to claim 7, characterized in that: The synthesis reaction of the metal organic framework synthesis unit satisfies the following stoichiometric relationship: <h2 style=";text-align:left;direction:ltr">Cu<h2 style=";text-align:left;direction:ltr"> 2+ <h2 style=";text-align:left;direction:ltr"> +2C4H6N2→Cu(C4H5N2)2+2H<h2 style=";text-align:left;direction:ltr"> + <h2 style=";text-align:left;direction:ltr"> ; Among them: Cu 2+ is copper ion; C4H6N2 is 2-methylimidazole ligand; Cu(C4H5N2)2 is MOF framework product; 2H + As a by-product.

9. The system for extracting, compounding and regenerating copper from smelting ash according to claim 1, characterized in that: The closed-loop feedback optimization module includes: A performance testing unit, used to test the specific surface area, porosity, specific capacitance of the metal-organic framework composite material, as well as the compressive strength and heavy metal leaching rate of the geopolymer; Response modeling unit, used to construct a mathematical model of the association between material properties and leaching parameters based on multi-factor regression; The parameter control unit is used to adjust the image recognition weight, valence state recognition dimension weight and parameter setting value of the dynamic leaching module in the copper occurrence state recognition module according to the model output.

10. The system for extracting, compounding and regenerating copper from smelting ash according to claim 9, characterized in that: The material performance function of the response modeling unit is modeled as a response surface model: Y=γ0+γ1x1+γ2x2+γ3x3+γ 12 x1x2+c 13 x1x3+c 23 x2x3; Where: Y represents the comprehensive performance score; x1 is the pH value; x2 is the temperature; x3 is the leaching time; γ0, γ1,…, γ 23 is the regression coefficient.

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