Method for recovering precious metal from solution containing precious metal ions

Abstract

The invention relates to the field of precious metal recovery, and discloses a method for recovering precious metal from a solution containing precious metal ions, which comprises the following steps: establishing a mathematical physical model of an electrolysis process, the mathematical physical model comprising ion diffusion and migration, electrode reaction kinetics and current distribution simulation; according to the mathematical physical model, a multi-objective optimization problem is constructed, and by optimizing current density distribution, the geometric design of an electrolytic bath and electrode reaction conditions, the recovery efficiency of precious metal is maximized, and energy consumption in the electrolysis process is minimized; and a particle swarm optimization algorithm is adopted to optimize the current density distribution, and the current density in the electrolysis process is adjusted. By optimizing current density distribution, electrolytic bath design and electrode reaction conditions and combining a real-time monitoring and dynamic adjustment mechanism, the precious metal recovery efficiency is remarkably improved, energy consumption is reduced, and meanwhile the stability and adaptability of the electrolysis process are improved.

Description

Technical Field

[0001] The present invention relates to the field of precious metal recovery, in particular to a method for recovering precious metals from a solution containing precious metal ions. Background Art

[0002] In modern society, precious metals are widely used in industries such as electronics, automobiles, aerospace, and medicine. With the advancement of industrial production, the recovery of precious metals has become particularly important. Traditional methods for recovering precious metals mostly rely on electrolysis technology, which not only can efficiently extract precious metals from solutions but also can reduce the waste of precious metals. However, although electrolysis plays a key role in precious metal recovery, existing electrolysis technology still has many technical bottlenecks, which affect the recovery efficiency and energy consumption.

[0003] Currently, electrolysis is used to optimize the recovery process for precious metals by leveraging current density and electrolytic cell design. Conventional techniques influence electrode reactions by adjusting current density, thereby controlling the deposition of precious metals. In some approaches, the geometric design of the electrolytic cell and the arrangement of the electrodes can modestly improve recovery efficiency and reduce uneven metal deposition. However, current density control in existing techniques is relatively simple, and electrolytic cell design is based solely on conventional experience, failing to fully consider fluid flow optimization.

[0004] However, the existing technology also has some obvious shortcomings. First, the distribution of current density is often uneven, resulting in local current being too high or too low, which in turn triggers the electrode polarization effect, reduces the deposition efficiency of precious metals, and increases energy consumption. Secondly, the electrolytic cell design has not been fully optimized, and the dead zones and unevenness of fluid flow lead to low recovery efficiency. Furthermore, the existing technology lacks real-time monitoring and dynamic adjustment mechanisms, and cannot flexibly adjust the electrolysis process according to changes in production, resulting in an unstable production process and large fluctuations in recovery efficiency. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method for recovering precious metals from solutions containing precious metal ions, which solves the problems of uneven current density distribution in the electrolysis process, severe electrode polarization effect, uneven fluid flow, and lack of real-time monitoring and dynamic adjustment mechanism in the existing technology.

[0006] To achieve the above object, the present invention is implemented by the following technical solution: A method for recovering precious metals from a solution containing precious metal ions, comprising the following steps: Establishing a mathematical and physical model of the electrolysis process, the mathematical and physical model including simulation of ion diffusion and migration, electrode reaction kinetics and current distribution; Based on the mathematical and physical model, a multi-objective optimization problem is constructed to maximize the recovery efficiency of precious metals and minimize the energy consumption during the electrolysis process by optimizing the current density distribution, electrolytic cell geometry design, and electrode reaction conditions; The particle swarm optimization algorithm is used to optimize the current density distribution and adjust the current density during the electrolysis process; Optimize electrolyzer geometry design through fluid dynamics simulation; According to the electrode reaction optimization plan, control the current density and electrolysis time to reduce the electrode polarization effect; Verify the recovery efficiency and energy consumption performance of the optimized electrolysis process through small-scale experiments; Based on the experimental verification results, the optimization parameters are adjusted, and the optimization scheme is applied to industrial production to monitor and further optimize the electrolysis process.

[0007] Preferably, the mathematical and physical model includes: The Nernst-Planck equation is used to describe the diffusion and migration of metal ions in solution, which takes into account the movement and diffusion rate of ions under the action of an electric field. Use the Tafel equation to describe the relationship between the electrode overpotential and current density, which helps analyze the kinetics of the electrode reaction; The Laplace equation is used to describe the distribution of the electric field in the electrolytic cell and calculate the spatial distribution of the current density.

[0008] Preferably, the multi-objective optimization problem includes: The first term is the square integral of the current density, which is mainly used to describe the energy consumption in the electrolysis process. By optimizing the current density distribution, the energy consumption can be minimized. The second item is the recycling efficiency item, which aims to maximize the recycling efficiency of precious metals.

[0009] Preferably, the particle swarm optimization algorithm includes: By updating the particle's velocity and position through multiple iterations, the optimal solution is gradually approached; According to the optimization objective function, the particle finds the current density distribution in the search space that minimizes energy consumption and maximizes recovery efficiency.

[0010] Preferably, the electrolytic cell geometric design includes: Fluid dynamics simulation is used to describe the flow state of the solution using the Navier-Stokes equation to ensure that the solution flows evenly in the electrolytic cell; By optimizing the tank shape and electrode arrangement, the flow dead zone is reduced and the flow uniformity of the solution is improved.

[0011] Preferably, the electrode reaction optimization scheme includes: Control the current density during the electrolysis process to ensure uniform current density distribution; Adjust the electrolysis time to adapt to the best recovery effect under different operating conditions.

[0012] Preferably, the small-scale experimental verification includes: Setting different operating parameters, including electrolysis time, current density, and solution concentration, and then conducting comparative experiments; Test the improvement effects of different optimization schemes on precious metal recovery efficiency and energy consumption, and verify the effectiveness of the optimization algorithm through experimental data.

[0013] Preferably, the experimental verification results include: The distribution of current density to achieve better recovery efficiency; Solution concentration and electrolysis time are adjusted to optimize the overall electrolysis process, ensuring minimal energy consumption and high recovery rate.

[0014] Preferably, the industrial production includes: Use monitoring technology to monitor key parameters such as current density, temperature and solution concentration during the electrolysis process; Based on the monitoring data, the electrolyzer design and operating parameters are further adjusted through feedback control.

[0015] Preferably, the multi-objective optimization problem further includes: According to the concentration and type of precious metals in the solution, the current density distribution and the electrolytic cell geometry design during the electrolysis process are dynamically adjusted during the optimization process; Through dynamic adjustment, it can adapt to the recovery needs of various precious metal solutions.

[0016] The present invention provides a method for recovering precious metals from a solution containing precious metal ions. The method has the following beneficial effects: 1. This invention utilizes a multi-objective optimization technique based on mathematical physics models. By precisely optimizing current density distribution, electrolytic cell design, and electrode reaction conditions, it maximizes precious metal recovery efficiency while minimizing energy consumption during the electrolysis process. Compared to existing solutions that rely solely on single current density regulation, this invention avoids the polarization effects of localized excessive and insufficient current through global optimization, significantly improving recovery efficiency and reducing energy waste.

[0017] 2. By incorporating a particle swarm optimization algorithm, the present invention dynamically optimizes the current density distribution, achieving current uniformity during the electrolysis process. Unlike conventional fixed current density schemes in the prior art, the present invention allows for flexible adjustment of current density, thereby avoiding electrode polarization problems caused by excessive current density, improving the quality of precious metal deposition, and effectively extending the service life of the electrolysis equipment.

[0018] 3. The present invention utilizes a real-time monitoring and feedback adjustment mechanism for industrial applications, ensuring that all parameters during the electrolysis process remain optimal. Compared to the manual adjustment and static setting methods used in existing technologies, the present invention's real-time monitoring system automatically senses changes and makes adjustments, maximizing process stability and recovery efficiency while reducing labor costs and the risk of operational errors.

[0019] 4. This invention optimizes the electrolytic cell geometry through fluid dynamics simulation, fundamentally improving solution flow uniformity. Compared to conventional technologies with fixed cell designs and uneven flow, this optimized design ensures uniform current density distribution, avoids localized dead zones and overpotentials, significantly improving the uniformity and efficiency of precious metal recovery and further optimizing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] Please see the attached Figure 1 The present invention provides a method for recovering precious metals from a solution containing precious metal ions, comprising the following steps: S1. Establishing a mathematical and physical model of the electrolysis process, wherein the mathematical and physical model includes simulations of ion diffusion and migration, electrode reaction kinetics, and current distribution; Establishing a mathematical and physical model of the electrolysis process is fundamental to optimizing precious metal recovery. This model involves multiple key physical processes, including ion diffusion and migration, electrode reaction kinetics, and current distribution simulation. By comprehensively considering these processes, it is possible to effectively predict and adjust the changes in various parameters during the electrolysis process, thereby optimizing precious metal recovery efficiency, reducing energy consumption, and minimizing electrode polarization effects in practical applications.

[0023] In this example, a mathematical model was first used to comprehensively simulate the ion diffusion and migration, electrode reaction kinetics, and current distribution during the electrolysis process. This model provided a theoretical basis for subsequent optimization and computational support for particle swarm optimization (PSO) and fluid dynamics optimization. The precise description of these models enables a better understanding of the complex physical phenomena in the electrolysis process and enables effective control during electrolytic cell design and operation.

[0024] During electrolysis, metal ions migrate toward the electrode surface under the influence of an electric field, and their diffusion is influenced by the concentration gradient in the solution. To accurately describe this process, the Nernst-Planck equation is used to describe the diffusion and migration of ions. This equation accurately characterizes the migration and diffusion rates of ions under the influence of an electric field.

[0025] In general, the expression of the Nernst-Planck equation is: ; in, For ions The flux (mol / m²·s) represents the number of ions passing through a unit area per unit time; For ions The diffusion coefficient (m² / s) reflects the diffusion rate of ions; For ions Concentration (mol / m³); is the ion concentration gradient (mol / m³·m); For ions The charge number (dimensionless); is the Faraday constant (C / mol), which is 96485C / mol; is the electric field strength (V / m).

[0026] In this equation, ion migration includes the driving force of the electric field on the ions (the second term) and the response of ion diffusion to the concentration gradient (the first term). The Nernst-Planck equation can be used to simulate the distribution of ion concentrations in various regions of the solution and predict the migration and diffusion behavior of precious metal ions under different electrolytic operating conditions.

[0027] In some embodiments, the model also calculates the diffusion coefficient based on experimental data, taking into account factors such as solution viscosity and temperature. and electric field strength Make adjustments to improve the accuracy of the simulation.

[0028] The electrode reaction rate during electrolysis plays a decisive role in the deposition of precious metals. The kinetic behavior of the electrode reaction is described by the Tafel equation. This equation reveals the relationship between current density and overpotential, reflecting the nonlinear characteristics of the electrode surface reaction.

[0029] Specifically, the Tafel equation is expressed as: ; in, is the overpotential (V), which is the difference between the potential on the electrode surface and the theoretical potential; is the current density (A / m²), which indicates the current flowing per unit area; is the Tafel slope (V), which measures the relationship between overpotential and current density; is the number of electrons transferred in the reaction (dimensionless); is the Faraday constant (C / mol), is the exchange current density (A / m²), which is the rate at which the reduction reaction occurs on the electrode surface.

[0030] In certain embodiments, the Tafel equation is used to calculate the rate of metal ion reduction on the electrode surface, which is closely related to the current density, overpotential, and the properties of the electrode material. By adjusting the current density and electrolysis time, the electrode overpotential can be controlled to optimize the metal deposition rate.

[0031] The current distribution in the electrolytic cell is an important factor in the electrolysis process, directly affecting the uniformity of precious metal deposition. In order to simulate the distribution of current density in the electrolytic cell, the Laplace equation is used to describe the distribution of the electric field.

[0032] The expression of the Laplace equation is: ; in, is the Laplace operator; is the electric potential (V).

[0033] This equation describes the distribution of electric potential within the electrolytic cell, which in turn affects the spatial distribution of current density. By numerically solving the Laplace equation, the electric field strength and current density in different regions of the electrolytic cell can be calculated, thereby optimizing the electrolytic cell design, ensuring uniform current distribution throughout the cell, and avoiding electrode polarization caused by excessively high current density in certain areas.

[0034] In practice, the Laplace equation is usually solved using numerical calculation methods such as finite element analysis (FEM), combining the actual electrolytic cell geometry and the physical properties of the solution. This provides accurate data support for the subsequent particle swarm optimization algorithm regarding current distribution.

[0035] The comprehensive mathematical and physical model of the electrolysis process in this example encompasses ion diffusion and migration, electrode reaction kinetics, and current distribution simulation, providing a theoretical foundation for subsequent multi-objective optimization algorithms. This model can be used to predict the effectiveness of precious metal recovery under different operating conditions, thereby optimizing the electrolysis process.

[0036] For example, optimizing current density distribution can be applied within the particle swarm optimization algorithm. By calculating the relationship between current density distribution and energy consumption, the current density solution with the highest energy efficiency can be found. Simultaneously, controlling the electrode reaction rate can further regulate the metal deposition rate during electrolysis, preventing electrode damage caused by excessive overpotential.

[0037] By accurately modeling and optimizing the physical phenomena in the electrolysis process, we can ensure that precious metal recovery efficiency is improved while energy consumption is reduced. Furthermore, the optimization results of these models provide a scientific basis for electrolytic cell design and process parameter adjustments in practical applications.

[0038] S2. Constructing a multi-objective optimization problem based on the mathematical and physical model to maximize the recovery efficiency of precious metals and minimize the energy consumption during the electrolysis process by optimizing the current density distribution, electrolytic cell geometry design, and electrode reaction conditions; After establishing the mathematical and physical model of the electrolysis process, we further constructed a multi-objective optimization problem, comprehensively considering the current density distribution, electrolytic cell geometry, and electrode reaction conditions to maximize precious metal recovery efficiency and minimize energy consumption during the electrolysis process. Multi-objective optimization aims to reconcile conflicts between different objectives and ensure the optimal performance balance when optimizing key parameters in the electrolysis process.

[0039] In this example, based on the established mathematical and physical model, a multi-objective optimization problem was constructed by precisely calculating the effects and influences of various physical steps in the electrolysis process. This optimization problem addresses two key objectives: first, optimizing the current density distribution and electrode reaction conditions to improve precious metal recovery efficiency; second, reducing energy consumption and waste during the electrolysis process through design optimization.

[0040] According to the aforementioned mathematical and physical model, the objective function of the multi-objective optimization problem consists of two components. The first component is the square integral of the current density, which is intended to describe the energy consumption during the electrolysis process. The square integral of the current density reflects the uneven current distribution and energy consumption during the electrolysis process. The goal is to minimize energy loss during the electrolysis process.

[0041] Specifically, the first term in the objective function can be expressed as: ; in, is the square integral of the current density, which represents the energy consumption during the electrolysis process is the current density (A / m²); is the volume of the electrolytic cell (m³); is the weight coefficient, which is used to balance the recovery efficiency and energy consumption.

[0042] By optimizing the current density distribution, the current distribution in the electrolytic cell can be uniformed, the excessive polarization in the local high current density area can be reduced, and thus the energy consumption can be reduced.

[0043] The second component is the recovery efficiency component, which aims to maximize the recovery efficiency of precious metals. During the electrolysis process, improving recovery efficiency relies on optimizing electrode reaction conditions and current density distribution. Optimizing current density distribution ensures uniform reaction across the electrode surface, thereby increasing the deposition rate of precious metals and minimizing metal loss.

[0044] The optimization of recovery efficiency can be expressed as follows: ; in, The recycling efficiency item aims to maximize the recovery efficiency of precious metals; is the weight coefficient used to balance energy consumption and recovery efficiency In the optimization process of this invention, a particle swarm optimization (PSO) algorithm is used to optimize the current density distribution. The core concept of PSO is to find the global optimal solution by simulating the movement and collaboration of particles. Each "particle" represents a possible current density distribution scheme, and the energy consumption and recovery efficiency are evaluated based on the current density distribution.

[0045] In some embodiments, the particle update formula of the PSO algorithm is as follows: ; ; in, For particles Speed (m / s); For particles Position (m); Inertia weight, which controls the inertia of particles during the search process and is the learning factor, which controls the particle's ability to search between the individual optimal position and the global optimal position; and is a random number used to introduce randomness; For particles The individual optimal position of is the global optimal position; Indicates the current iteration step number.

[0046] Through the PSO algorithm, particles continuously iteratively search and eventually find a current density distribution solution that minimizes energy consumption and maximizes recovery efficiency.

[0047] Optimizing the geometric design of the electrolyzer is a key step in improving electrolysis efficiency. Based on the aforementioned mathematical and physical model, fluid dynamics simulation is used to optimize the shape of the electrolyzer and the electrode arrangement. The fluid dynamics simulation is based on the Navier-Stokes equations, which describe the flow state of the solution within the electrolyzer.

[0048] The Navier-Stokes equations are as follows: ; in, is the density of the solution (kg / m³) is the solution flow rate (m / s) is time (s); is the pressure of the solution (Pa); is the viscosity of the fluid (Pa·s); is the body force (N / m³); is the pressure gradient (unit: Pa / m), which represents the amount of pressure change per unit length; is the Laplace operator of flow velocity (unit: 1 / s²), which describes the spatial distribution of fluid velocity.

[0049] By numerically solving the Navier-Stokes equations, the flow of the solution within the electrolytic cell can be simulated. This simulation can provide information on flow inhomogeneities, dead zones, and key cell design parameters, enabling optimization of the cell geometry and electrode layout.

[0050] In practice, optimizing the design of the electrolytic cell can improve the uniformity of solution flow and reduce dead zones by changing the cell shape and adjusting the arrangement and spacing of the electrodes. This will directly promote a more uniform distribution of current density and improve the recovery efficiency of precious metals.

[0051] Optimizing electrode reaction conditions is achieved by adjusting current density and electrolysis time. Specifically, optimizing current density ensures a uniform reaction across the electrode surface, preventing polarization caused by excessive current density. Controlling electrolysis time allows for optimal recovery under varying operating conditions.

[0052] By controlling the current density and electrolysis time, the surface reaction of the electrode can be optimized, ensuring that the metal ions are uniformly reduced on the electrode surface and minimizing the generation of overpotential, thereby improving the deposition rate and the recovery efficiency of precious metals.

[0053] This multi-objective optimization process maximizes precious metal recovery efficiency under varying operating conditions while effectively reducing energy consumption. Optimizing current density, electrolytic cell geometry, and electrode reaction conditions all contribute to ensuring a highly efficient, stable, and energy-efficient recovery process.

[0054] S3. Using particle swarm optimization algorithm to optimize the current density distribution and adjust the current density during the electrolysis process; Optimizing the current density distribution during electrolysis is a key step in improving precious metal recovery efficiency and reducing energy consumption. Precisely adjusting the current density distribution not only improves the uniformity of the electrode reaction but also avoids excessive polarization caused by localized excessive current density. To achieve this goal, this example employs a particle swarm optimization (PSO) algorithm to optimize the current density distribution and adjust the current density during the electrolysis process.

[0055] This optimization process relies on information about the current distribution during the electrolysis process, provided by the aforementioned mathematical and physical models. By optimizing the current density distribution, a more uniform current density within the electrolytic cell is achieved, thereby improving electrolysis reaction efficiency and effectively reducing unnecessary energy consumption. Within a multi-objective optimization framework, a particle swarm optimization algorithm is used to simultaneously consider multiple factors to find the optimal current density distribution, ensuring an optimal balance between energy efficiency and recovery efficiency.

[0056] In this embodiment, the particle swarm optimization algorithm iteratively calculates the optimal current density distribution by simulating the search behavior of individual particles in the swarm. Each particle represents a current density distribution scheme, and the entire particle swarm explores the current density search space to find the optimal solution.

[0057] Specifically, the present invention uses a particle swarm optimization algorithm to globally optimize the current density distribution, taking into account the uneven current density within the electrolytic cell and ensuring that there are no areas of excessively high or low current density during the electrolysis process. This is because locally excessively high current density in the electrolytic cell can lead to electrode polarization, which reduces electrolysis efficiency and increases energy consumption.

[0058] In some embodiments, the optimization objective function of the current density distribution is constructed as follows: ; in, is the current density distribution (A / m²); is the volume of the electrolytic cell (m³); To optimize the objective function, the relationship between current density distribution and recovery efficiency is expressed; is the weight coefficient of recovery efficiency, which adjusts the balance between energy consumption and recovery efficiency; is a small volume element (unit: m³), which represents a small change in volume during the integration process. In practical applications, the particle swarm optimization algorithm iteratively updates the particle speed and position by adjusting the parameters of the current density distribution based on this objective function. Ultimately, it arrives at a current density distribution solution that minimizes energy consumption and maximizes recovery efficiency.

[0059] The optimization scheme in this embodiment can significantly improve the recovery efficiency during the electrolysis process and effectively reduce energy consumption. The optimized current density distribution can evenly distribute the current and avoid electrode polarization caused by excessive local current density. By precisely controlling the current density, the deposition rate of the precious metal is optimized, reducing precious metal loss. At the same time, energy loss during the electrolysis process is significantly reduced.

[0060] In some embodiments, current density optimization during the electrolysis process is not limited to a one-time adjustment, but can be dynamically adjusted based on experimental data and feedback. This means that in industrial applications, the current density distribution can be optimized in real time based on the actual electrolysis process, thereby ensuring optimal recovery efficiency and energy efficiency.

[0061] By optimizing the current density distribution using a particle swarm optimization algorithm, we can ensure the best balance between energy efficiency and recovery efficiency during the electrolysis process. This optimization process is highly flexible and can adapt to different types of precious metal ion solutions and automatically adjust the current density distribution and electrolytic cell design according to different recovery requirements. S4. Optimize electrolyzer geometry design through fluid dynamics simulation; Optimizing electrolytic cell geometry is a key step in improving electrolysis process efficiency. Through fluid dynamics simulation, we can effectively optimize the cell shape and electrode layout to ensure uniform solution flow within the cell, thereby avoiding dead zones and uneven current distribution during the electrolysis process, thereby improving precious metal recovery efficiency and reducing energy loss.

[0062] In this example, fluid dynamics simulation was used to analyze the flow of the solution within the electrolytic cell. The cell's geometric design was optimized to ensure uniform current density distribution. This process complements the aforementioned current density optimization step. Building on the particle swarm optimization algorithm's optimization of current density distribution, the overall electrolysis efficiency was further improved through optimization of the cell design.

[0063] To accurately describe the flow of solution within the electrolytic cell, we used the Navier-Stokes equations, which are highly effective in modeling the motion of liquids in various electrolytic cell configurations. The fluid's motion significantly influences the current distribution and uniformity of precious metal deposition during electrolysis. Therefore, fluid flow simulation provides a scientific basis for optimizing electrolytic cell design.

[0064] In this example, the results of fluid dynamics simulations were used to optimize the electrolytic cell's geometric design, including its shape, dimensions, and electrode layout. The core goal of this optimization step was to improve the flow uniformity of the solution within the cell, reduce dead zones, and avoid uneven current density distribution, thereby increasing the deposition efficiency of the precious metal.

[0065] Alternatively, the shape of the electrolyzer can be adjusted based on the fluid simulation results. For example, the inlet and outlet design can be optimized to ensure uniform liquid flow throughout the cell. Furthermore, the electrode arrangement can be adjusted based on the fluid flow to avoid areas with excessively high or low current density.

[0066] Specifically, the optimized electrolyzer design includes the following aspects: Flow path optimization: By simulating the solution flow path, we ensure that there are no local dead zones in the electrolytic cell. We also adjust the cell shape through fluid dynamics analysis to ensure that the solution flows as evenly as possible. Electrode layout optimization: Based on the results of fluid simulation, the position and shape of the electrodes are adjusted to avoid polarization on the electrode surface due to uneven current density, while ensuring more uniform deposition of precious metals.

[0067] In one possible implementation, numerical simulation and experimental verification are combined to gradually optimize the cell shape and electrode layout to achieve optimal fluid flow and ensure uniform current density distribution during the electrolysis process. This optimization process not only reduces energy waste caused by uneven current flow but also significantly improves precious metal recovery efficiency.

[0068] The optimal design of the electrolytic cell is closely related to the aforementioned current density optimization. By combining fluid dynamics simulation with particle swarm optimization, efficient electrolytic cell design and current density optimization are achieved. This combination ensures that every step of the electrolysis process is optimized, from uniform current density distribution to optimized fluid flow within the cell, ultimately achieving improved precious metal recovery efficiency and reduced energy consumption.

[0069] In some embodiments, the electrolyzer design is not limited to optimizing current density and fluid flow; it can also be dynamically adjusted based on feedback from actual applications. For example, if uneven current density occurs in certain areas during actual operation, the electrolyzer geometry or electrode placement can be adjusted to further optimize the solution flow path, ensuring a more uniform current density and thus improving overall recovery efficiency.

[0070] Optimizing the electrolytic cell geometry through fluid dynamics simulation can effectively improve the overall efficiency of the electrolysis process. The optimized electrolytic cell design not only reduces the unevenness of the solution flow, but also eliminates the polarization effect in areas with excessive current density, ensuring uniform deposition of precious metals.

[0071] In certain embodiments, optimizing the electrolytic cell geometry can reduce energy consumption by 30% to 50% while simultaneously improving precious metal recovery, particularly under conditions of low solution concentration or low recovery efficiency. This optimization approach makes the precious metal recovery process more stable and efficient, with significant potential for industrial application.

[0072] S5. According to the electrode reaction optimization plan, control the current density and electrolysis time to reduce the electrode polarization effect; Optimizing the electrode reaction during the electrolysis process is an important step in improving the efficiency of precious metal recovery and reducing energy consumption. The electrode polarization effect is a common problem in the electrolysis process. Especially at high current density, the electrode surface may experience an increase in overpotential, resulting in a decrease in electrolysis efficiency. In order to reduce the electrode polarization effect, the current density and electrolysis time must be precisely controlled. Combining the aforementioned current density distribution optimization and electrolytic cell geometry design optimization, this embodiment further proposes a solution to optimize the electrode reaction by adjusting the current density and electrolysis time, thereby effectively reducing the electrode polarization effect and increasing the deposition rate of precious metals.

[0073] In this embodiment, the optimization of the electrode reaction focuses on controlling the current density and electrolysis time by the following methods: Control current density: Optimize current density distribution through particle swarm optimization (PSO) to avoid excessive local current density and ensure uniformity of electrode surface reaction; Adjust the electrolysis time: According to the requirements of recovery efficiency, adjust the electrolysis time during the electrolysis process to avoid excessive deposition due to too long a time, thereby avoiding the occurrence of electrode polarization.

[0074] Controlling current density is key to mitigating electrode polarization during electrolysis. Excessive current density can lead to overpotential on the electrode surface, slowing the metal ion reduction reaction during electrolysis and even causing damage to the electrode surface or uneven metal deposition.

[0075] Under normal circumstances, excessive current density during electrolysis can cause localized overpolarization. To address this, in this embodiment, the current density is optimized using the aforementioned particle swarm optimization algorithm to ensure uniform current distribution within the electrolytic cell and avoid excessive current density on the electrode surfaces. By optimizing the current density distribution, the generation of localized electrode overpotentials can be effectively reduced, promoting uniform deposition of precious metals.

[0076] Alternatively, current density optimization can also take into account the flow characteristics of different regions within the electrolytic cell and the properties of the electrode materials. In certain embodiments, by combining current density distribution simulation with fluid dynamics, the regional current density distribution within the electrolytic cell can be precisely adjusted to avoid excessive or insufficient current in certain regions, which could lead to electrode polarization or uneven deposition.

[0077] Specifically, the optimization of current density distribution can be adjusted by: On the electrode surface, avoid areas with excessively high current density that could cause local overpotential; During the electrolytic cell design process, ensure the uniformity of current density distribution to avoid polarization caused by local excessive current density.

[0078] The impact of electrolysis time on electrode reactions cannot be ignored. Generally speaking, excessive electrolysis time can lead to excessive deposition of precious metals, or even an uneven deposit layer; while too short an electrolysis time can result in reduced recovery. To ensure the efficiency of the electrode reaction, controlling the electrolysis time is crucial.

[0079] In this embodiment, the electrode reaction conditions are optimized by adjusting the electrolysis time. Specifically, the electrolysis time is flexibly adjusted according to the changes in the concentration of the precious metal ions, the reaction rate, and the current density. The purpose of adjusting the electrolysis time is to ensure that the precious metal is deposited evenly on the electrode surface and to avoid electrode polarization caused by excessive deposition. Controlling the electrolysis time ensures that the reaction proceeds at the optimal reaction rate and maximizes the recovery efficiency of the precious metal.

[0080] In one possible implementation, the electrolysis time may be controlled based on the following formula: ; in, is the electrolysis time (s); is the number of electrons transferred during electrolysis; is the Faraday constant (C / mol); is the electrode surface area (m²); is the noble metal ion concentration (mol / m³); is the current density (A / m²).

[0081] By adjusting the electrolysis time, the reaction efficiency can be met while avoiding the generation of overpotential caused by excessive electrolysis time, thereby reducing the polarization effect on the electrode surface.

[0082] Electrode polarization is one of the key issues affecting electrolysis efficiency. During electrolysis, when the current density is too high, the overpotential on the electrode surface increases, which reduces the reduction rate of metal ions and even causes corrosion of the electrode material.

[0083] In some embodiments, this polarization effect can be mitigated by controlling the current density and electrolysis time. Specifically, a uniform distribution of current density can avoid areas of excessively high current density on the electrode surface, while a reasonable electrolysis time can prevent polarization on the electrode surface due to excessive metal ion deposition.

[0084] In this way, the deposition of precious metals during the electrolysis process is more uniform, the electrolysis efficiency is improved, and the energy consumption is effectively reduced.

[0085] By controlling the current density and electrolysis time, and mitigating the electrode polarization effect, the recovery efficiency and energy efficiency of the electrolysis process can be significantly improved. The optimized electrolysis process not only ensures a uniform reaction on the electrode surface, but also avoids uneven deposition or electrode damage caused by overpotential.

[0086] In some embodiments, optimizing current density and electrolysis time can achieve efficient recovery of precious metals and reduce energy losses during the electrolysis process by 30% to 50%. This optimization scheme is applicable to a variety of precious metal ion solutions and has strong industrial applicability.

[0087] S6. Verify the recovery efficiency and energy consumption performance of the optimized electrolysis process through small-scale experiments; Optimizing the electrolysis process's recovery efficiency and energy consumption is achieved through multiple steps, including optimizing the current density distribution, electrolytic cell design, and electrode reaction conditions. To validate the effectiveness of these optimization measures, we conducted small-scale experiments to evaluate the performance of the optimized electrolysis process in real-world applications.

[0088] In this example, the optimized electrolysis process was validated through a series of small-scale experiments. The core objective of these experiments was to test the effectiveness of the optimized current density distribution, electrolytic cell geometry, and electrode reaction conditions in actual operation. Specifically, the experimental results were compared with conventional electrolysis methods in terms of recovery efficiency and energy consumption to confirm the advantages of the optimized solution.

[0089] In some cases, small-scale experiments were designed to control various operating parameters, such as current density, solution concentration, and electrolysis time. By varying these parameters within certain ranges, we were able to simulate the various conditions encountered during actual electrolysis and evaluate the performance of our optimized solutions.

[0090] Specifically, the experimental steps are as follows: Solution preparation: First, prepare solutions containing precious metal ions of different concentrations to simulate different solution conditions. Solution concentration is one of the important factors affecting recovery efficiency; Current density setting: Based on the results of the aforementioned particle swarm optimization algorithm, different current density distributions are set, and the actual current density distribution is monitored through the current measurement device in the electrolytic cell; Adjustment of electrolysis time: Based on the electrolysis time setting of the optimization scheme, the recovery effect at different times is evaluated to ensure that precious metals can be fully recovered without inducing overpotential.

[0091] Generally, by setting up multiple experimental groups, we can test the effects of current density, electrolysis time and solution concentration on recovery efficiency and energy consumption respectively.

[0092] In the experiment, recovery efficiency and energy consumption are the two most important evaluation indicators. In order to scientifically evaluate the effect of the optimized electrolysis process, we analyzed the experimental data in the following ways: Recovery efficiency calculation: Recovery efficiency can be determined by measuring the change in the concentration of precious metal ions in the solution after electrolysis. The recovery efficiency is calculated by comparing the difference in precious metal concentrations before and after the experiment.

[0093] Specifically, the recycling efficiency It can be calculated by the following formula: ; in, is the concentration of noble metal ions in the solution before the experiment (mol / m³); is the concentration of noble metal ions in the solution after the experiment (mol / m³); is the recovery efficiency (percentage), which indicates the recovery effect of precious metals during the electrolysis process.

[0094] Through this formula, we can evaluate the recovery effect of the optimized solution under different experimental conditions.

[0095] Energy consumption measurement: Energy consumption is an important factor affecting the economic efficiency of the electrolysis process. In the experiment, we calculated the energy consumption by measuring the electrical energy consumed during the electrolysis process. It can be expressed by the following formula: ; in, is the voltage of the electrolytic cell (V); is the current (A); is the electrolysis time (seconds); Expressed as energy consumption.

[0096] By using this formula, we can compare the difference in energy consumption between the optimized electrolysis process and the traditional method.

[0097] In one possible implementation, we conducted a detailed comparison of experimental results. The experimental group used an optimized electrolysis process, including a particle swarm optimization-optimized current density distribution, an optimized electrolytic cell design, and electrode reaction conditions. The control group used a conventional electrolysis method, without optimizing the current density and electrolytic cell design.

[0098] According to experimental results, the optimized electrolysis process demonstrated significant advantages over traditional methods. The optimized group saw a 15% to 30% increase in recovery efficiency, while energy consumption decreased by 20% to 40%. These results demonstrate that by comprehensively optimizing current density, electrolytic cell design, and electrode reaction conditions, the present invention can significantly improve precious metal recovery efficiency and effectively reduce energy consumption during the electrolysis process.

[0099] Through small-scale experimental verification, we have obtained preliminary data on recovery efficiency and energy consumption. However, to further improve the effectiveness of the optimization scheme, further verification and optimization of various electrolysis process parameters can be carried out through larger-scale experiments and industrial applications. For example, in the recovery of different types of precious metals, it may be necessary to adjust the current density distribution, electrolysis time, and solution concentration to suit different solution conditions and metal types.

[0100] Furthermore, as experimental data accumulates, the current density and electrolysis time can be optimized with real-time feedback, thus achieving dynamic optimization. In industrial applications, this real-time adjustment can further improve recovery efficiency, reduce energy consumption, and better adapt to changing conditions in the production process.

[0101] S7. Adjust the optimization parameters based on the experimental verification results, and apply the optimization scheme to industrial production to monitor and further optimize the electrolysis process.

[0102] Following the aforementioned small-scale experimental validation and implementation of the optimization scheme, we further adjusted the optimization parameters based on the experimental results and applied this optimization scheme to an industrial production process. The core of this process lies in real-time monitoring of the electrolysis process, obtaining feedback data, and dynamically adjusting the optimization scheme based on this data to ensure continuous improvement in recovery efficiency and minimize energy consumption. In this way, we not only verified the effectiveness of the optimization scheme under laboratory conditions, but also ensured its efficient and stable application in industrial production.

[0103] The experimental results in this example provide valuable reference for parameter adjustments in subsequent industrial production. By monitoring key parameters such as current density, electrolysis time, and solution concentration during actual production, and combining them with real-time feedback data, adjustments can be made to maximize recovery efficiency and minimize energy consumption during the electrolysis process.

[0104] In small-scale experiments, we obtained preliminary data on the recovery efficiency and energy consumption of the optimized solution. Specifically, the experimental results show that after optimizing the current density distribution, electrolyzer geometry design, and electrode reaction conditions, the recovery efficiency is significantly improved and the energy consumption is significantly reduced.

[0105] Based on the experimental data, we further analyzed the key optimization parameters in the experiment and used them as a basis for adjustment. For example, during the experiment, we observed that the current density distribution and solution concentration had a significant impact on the recovery efficiency. By adjusting these parameters, we optimized the current distribution during the electrolysis process, making the current density more uniform and avoiding overpotential in areas with excessively high current density, thereby improving metal deposition uniformity and recovery efficiency.

[0106] Based on the experimental results, we further set control variables such as electrolysis time and solution concentration. Specifically, by adjusting the electrolysis time, we ensured that the precious metal could be fully deposited without excessive deposition under different solution conditions, thereby avoiding electrode polarization effects.

[0107] In industrial production, the optimization scheme of this invention not only applies to the initial optimization settings but also dynamically adjusts them through real-time monitoring technology. Generally, the electrolysis process in industrial production faces complex variations that differ from laboratory conditions, such as changes in solution concentration, temperature, and electrolytic cell design. To ensure the continued effectiveness of the optimization scheme, we introduced a real-time data acquisition and monitoring system, using sensors to monitor parameters such as current density, electrolysis time, and solution concentration.

[0108] Specifically, during industrial production, a real-time monitoring system continuously collects data such as current density, electrolysis time, and temperature, and transmits this data to a computing center for analysis. If the monitored data deviates from a preset range, the system automatically adjusts and optimizes the parameters to ensure optimal recovery efficiency and energy consumption during the electrolysis process. This feedback mechanism ensures the efficiency and stability of the electrolysis process in actual production.

[0109] For example, in some embodiments, if the current density in a certain area is detected to be too high, the real-time monitoring system will automatically adjust the current distribution in the area to make the current density uniform, avoiding the polarization effect caused by excessive current density, thereby ensuring uniform deposition of precious metals.

[0110] As industrial production continues, production conditions and solution states may change, so it is necessary to adjust the optimization parameters according to these changes. Specifically, the adjustment of optimization parameters includes the following aspects: Real-time adjustment of current density distribution: Based on real-time monitoring data, the current density distribution is dynamically adjusted to ensure uniform current density and avoid local current density being too high or too low; Flexible adjustment of electrolysis time: Dynamically adjust electrolysis time according to different solution concentrations and recovery requirements to ensure sufficient deposition of precious metals without generating overpotential; Automatic adjustment of solution concentration: By monitoring the concentration of metal ions in the solution, the concentration of the solution is automatically adjusted to ensure optimal recovery efficiency.

[0111] These adjustment mechanisms can optimize the electrolysis process in real time, improve production efficiency and stability, and minimize energy consumption.

[0112] In some embodiments, the real-time monitoring and feedback system includes multiple sensors, including temperature sensors, current sensors, and voltage monitors. By continuously collecting this data, key parameters of the electrolysis process can be comprehensively monitored. Analysis of this data allows for timely adjustments to the electrolytic cell design, current density, and electrolysis time, to maintain an efficient and stable production process.

[0113] For example, when it is monitored that the temperature of a certain electrolytic cell is rising or the current density is uneven, the control system will automatically adjust the output current of the current source or adjust the flow of solution inside the electrolytic cell to ensure that the current density in each area is evenly distributed and prevent efficiency loss or equipment damage caused by local overheating or overpotential.

[0114] By applying the optimization scheme to industrial production and incorporating real-time feedback for adjustments, precious metal recovery efficiency has been further improved while significantly reducing energy consumption during the electrolysis process. In practical applications, we have observed a 15% to 30% increase in recovery efficiency and a 20% to 40% reduction in energy consumption. These optimization results not only provide strong technical support for industrial production but also demonstrate good stability and adaptability in actual production processes.

[0115] 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 method for recovering precious metals from a solution containing precious metal ions, characterized in that: The following steps are involved: Establishing a mathematical and physical model of the electrolysis process, the mathematical and physical model including simulation of ion diffusion and migration, electrode reaction kinetics and current distribution; Based on the mathematical and physical model, a multi-objective optimization problem is constructed to maximize the recovery efficiency of precious metals and minimize the energy consumption during the electrolysis process by optimizing the current density distribution, electrolytic cell geometry design, and electrode reaction conditions; The particle swarm optimization algorithm is used to optimize the current density distribution and adjust the current density during the electrolysis process; Optimize electrolyzer geometry design through fluid dynamics simulation; According to the electrode reaction optimization plan, control the current density and electrolysis time to reduce the electrode polarization effect; Verify the recovery efficiency and energy consumption performance of the optimized electrolysis process through small-scale experiments; Based on the experimental verification results, the optimization parameters are adjusted, and the optimization scheme is applied to industrial production to monitor and further optimize the electrolysis process.

2. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The mathematical and physical model includes: The Nernst-Planck equation is used to describe the diffusion and migration of metal ions in solution, which takes into account the movement and diffusion rate of ions under the action of an electric field. Use the Tafel equation to describe the relationship between the electrode overpotential and current density, which helps analyze the kinetics of the electrode reaction; The Laplace equation is used to describe the distribution of the electric field in the electrolytic cell and calculate the spatial distribution of the current density.

3. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The multi-objective optimization problem includes: The first term is the square integral of the current density, which is mainly used to describe the energy consumption in the electrolysis process. By optimizing the current density distribution, the energy consumption can be minimized. The second item is the recycling efficiency item, which aims to maximize the recycling efficiency of precious metals.

4. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The particle swarm optimization algorithm includes: By updating the particle's velocity and position through multiple iterations, the optimal solution is gradually approached; According to the optimization objective function, the particle finds the current density distribution in the search space that minimizes energy consumption and maximizes recovery efficiency.

5. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The electrolytic cell geometry design includes: Fluid dynamics simulation is used to describe the flow state of the solution using the Navier-Stokes equation to ensure that the solution flows evenly in the electrolytic cell; By optimizing the tank shape and electrode arrangement, the flow dead zone is reduced.

6. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The electrode reaction optimization scheme includes: Control the current density during the electrolysis process to ensure uniform current density distribution; Adjust the electrolysis time to adapt to the best recovery effect under different operating conditions.

7. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The small-scale experimental verification includes: Setting different operating parameters, including electrolysis time, current density, and solution concentration, and then conducting comparative experiments; Test the improvement effects of different optimization schemes on precious metal recovery efficiency and energy consumption, and verify the effectiveness of the optimization algorithm through experimental data.

8. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The experimental verification results include: The distribution of current density to achieve better recovery efficiency; Solution concentration and electrolysis time are adjusted to optimize the overall electrolysis process, ensuring minimal energy consumption and high recovery rate.

9. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The industrialized production includes: Use monitoring technology to monitor key parameters such as current density, temperature and solution concentration during the electrolysis process; Based on the monitoring data, the electrolyzer design and operating parameters are further adjusted through feedback control.

10. The method for recovering precious metals from a solution containing precious metal ions according to claim 1, wherein: The multi-objective optimization problem further includes: According to the concentration and type of precious metals in the solution, the current density distribution and the electrolytic cell geometry design during the electrolysis process are dynamically adjusted during the optimization process; Through dynamic adjustment, it can adapt to the recovery needs of various precious metal solutions.

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