Praseodymium-neodymium alloy low-temperature electrolysis cell voltage optimization method and system

Abstract

The invention relates to the technical field of rare earth electrolytic metallurgy, and discloses a praseodymium-neodymium alloy low-temperature electrolysis cell voltage optimization method and system.The method comprises the steps that multi-parameter coupling analysis is conducted on an electrolytic cell low-temperature electrolysis initial parameter set, and key influence factors are obtained; carrying out correlation analysis on the key influence factors by combining fluctuation characteristics of the cell voltage to obtain an influence weight sequence; determining a collaborative adjustment scheme of the polar distance and the current density according to the weight sequence; the cell voltage is synchronously adjusted based on the collaborative adjustment scheme, and accurate control over the two is achieved; the operation parameters of the electrolytic cell are collected and integrated to obtain a feedback parameter set, the feedback parameter set is fed back to a correlation analysis link to optimize the key influence factor identification precision, and finally a cell voltage optimization scheme is obtained. The technology can accurately identify the key influence factors, dynamically adapt to the operation change of the electrolytic cell, improve the stability of the cell voltage, reduce energy consumption and cost, and improve the efficiency of cell voltage optimization. The production efficiency and the product quality are ensured.

Description

Technical Field

[0001] This invention relates to the field of rare earth electrolytic metallurgy technology, and in particular to a method and system for optimizing cell voltage in low-temperature electrolysis of praseodymium-neodymium alloys. Background Technology

[0002] In the low-temperature electrolytic production of praseodymium-neodymium alloys, the stability of the cell voltage directly affects the electrolysis efficiency and product quality. Existing technologies often use single-parameter adjustment or simple parameter combination analysis to optimize the cell voltage, failing to fully explore the comprehensive impact of the coupling effect between multiple parameters on cell voltage fluctuations. This results in the inability to accurately identify the key factors affecting the cell voltage, making it difficult to formulate targeted adjustment strategies. Consequently, the accuracy of cell voltage optimization is low, failing to meet the stringent requirements of low-temperature electrolysis processes for cell voltage stability.

[0003] Existing cell voltage optimization schemes lack dynamic feedback and iterative optimization mechanisms. After an adjustment is made based on the initial parameters, it is impossible to collect parameter changes during the subsequent operation of the electrolytic cell in a timely manner and feed them back into the optimization process. This makes it difficult for the optimization scheme to adapt to the dynamic changes in the operating status of the electrolytic cell. Not only is it easy for the cell voltage to fluctuate again, but the lag in the adjustment scheme also reduces the efficiency of cell voltage optimization, increases the energy consumption and cost of praseodymium-neodymium alloy low-temperature electrolytic production, and makes it impossible to achieve continuous and stable production operation. Summary of the Invention

[0004] This invention provides a method and system for optimizing cell voltage in the low-temperature electrolysis of praseodymium-neodymium alloys, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for optimizing cell voltage in low-temperature electrolysis of praseodymium-neodymium alloys, comprising:

[0006] S1. Perform multi-parameter coupling analysis on the initial parameter set of the electrolytic cell during the low-temperature electrolysis process to obtain the key influencing factors of the electrolytic cell;

[0007] S2. Based on the fluctuation characteristics of the cell voltage in the electrolytic cell, a correlation analysis is performed on the key influencing factors to obtain the influence weight sequence of the electrolytic cell;

[0008] S3. Based on the influence weight sequence, determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density;

[0009] S4. Based on the aforementioned coordinated adjustment scheme, the slot voltage is synchronously adjusted to achieve precise control of the electrode spacing and current density.

[0010] S5. Based on the precise control of the electrode spacing and current density, the operating parameters of the electrolytic cell are collected and integrated to obtain the feedback parameter set of the electrolytic cell;

[0011] S6. Feedback the set of feedback parameters to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme of the slot voltage.

[0012] In a preferred embodiment, the multi-parameter coupled analysis of the initial parameter set of the electrolyzer during the low-temperature electrolysis process to obtain the key influencing factors of the electrolyzer includes:

[0013] Real-time parameters such as electrolyte temperature, electrolyte composition, current density, and electrode spacing in the electrolytic cell are collected to obtain the initial parameter set of the electrolytic cell;

[0014] Correlation analysis is performed on the parameters in the initial parameter set to obtain the parameter coupling relationship of the electrolytic cell;

[0015] Based on the influence strength of the parameter coupling relationship, the core screening of the cell voltage fluctuation parameters of the electrolytic cell is carried out to obtain the key influencing factors of the electrolytic cell.

[0016] In a preferred embodiment, the step of performing a correlation analysis on the key influencing factors based on the fluctuation characteristics of the cell voltage in the electrolytic cell to obtain the influence weight sequence of the electrolytic cell includes:

[0017] The voltage of the tank is monitored in real time to obtain the fluctuation amplitude and frequency of the voltage.

[0018] Based on the aforementioned key influencing factors, a correlation analysis is performed on the fluctuation amplitude and fluctuation frequency to obtain the degree of correlation between the key influencing factors and the fluctuation amplitude and fluctuation frequency.

[0019] Based on the degree of correlation, the key influencing factors are weighted to obtain the weight factors of the key influencing factors;

[0020] The weighting factors are normalized to obtain the influence weight sequence of the electrolytic cell.

[0021] In a preferred embodiment, the step of assigning weights to the key influencing factors based on the degree of correlation to obtain the weight factors of the key influencing factors includes:

[0022] Based on the degree of correlation, weight allocation calculations are performed on the key influencing factors to obtain the weight factors of the key influencing factors, wherein the weight allocation calculation formula is:

[0023] ;

[0024] in, Indicates the first The weighting factors of the aforementioned key influencing factors, Indicates the first The degree of correlation between the aforementioned key influencing factors and the fluctuation range. Indicates the first The degree of correlation between the aforementioned key influencing factors and the aforementioned fluctuation frequency. Indicates the first The duration coefficient of the effects of the aforementioned key influencing factors Indicates the first The frequency coefficients of the key influencing factors mentioned above This represents the correlation adjustment coefficient. This represents the intensity adjustment coefficient. This represents the sum of the weighted eigenvalues ​​of all the key influencing factors.

[0025] In a preferred embodiment, determining the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the influence weight sequence includes:

[0026] Based on the influence weight sequence, the weights of the electrode spacing and current density in the electrolytic cell are compared and arranged to obtain the priority relationship between the electrode spacing and current density weights.

[0027] The priority relationship is quantized and ordered to obtain the adjustment priority of the electrode spacing and current density;

[0028] Based on the adjustment priority, a decision is made on the operating state of the electrolytic cell to obtain a coordinated adjustment scheme for the electrode spacing and current density.

[0029] In a preferred embodiment, the synchronous adjustment of the slot voltage based on the coordinated adjustment scheme to achieve precise control of the electrode spacing and current density includes:

[0030] Based on the aforementioned coordinated adjustment scheme, the initial parameter set is tuned and optimized to obtain the control commands for the electrode spacing and current density.

[0031] Based on the control command, the slot voltage is controlled in a coordinated manner to obtain the response change of the slot voltage;

[0032] Based on the change in response, the electrode spacing and current density in the coordinated adjustment scheme are finely adjusted to achieve precise control of the electrode spacing and current density.

[0033] In a preferred embodiment, the precise control of the electrode spacing and current density, along with the collection and integration of operating parameters of the electrolytic cell to obtain a set of feedback parameters for the electrolytic cell, includes:

[0034] Based on the precise control of the electrode spacing and current density, noise filtering is performed on the operating parameters of the electrolytic cell to obtain the processed operating parameters.

[0035] The processed operating parameters are aligned and integrated in chronological order to obtain the feedback parameter set of the electrolytic cell.

[0036] In a preferred embodiment, feeding back the feedback parameter set to S2 to optimize the identification accuracy of the key influencing factors and obtain an optimized scheme for the tank voltage includes:

[0037] By analyzing and comparing the feedback parameter set with the initial parameter set, the changing trend of the operating parameters in the electrolytic cell can be obtained;

[0038] Based on the aforementioned trend, the weighting factors are adjusted over time to obtain an updated influence weight sequence.

[0039] Based on the updated influence weight sequence, multi-objective optimization is performed on the key influencing factors to obtain an optimized scheme for the slot voltage.

[0040] In a preferred embodiment, the step of performing multi-objective optimization on the key influencing factors based on the updated influence weight sequence to obtain an optimized scheme for the tank voltage includes:

[0041] Based on the updated influence weight sequence, multi-objective optimization calculations are performed on the factors among the key influencing factors to obtain the optimized adjustment values ​​of the key influencing factors. The formula for the multi-objective optimization calculation is as follows:

[0042] ;

[0043] in, Indicates the first The optimized adjustment values ​​for the aforementioned key influencing factors, Indicates the first The weight values ​​of the key influencing factors mentioned above. Indicates the first The optimization priority of the aforementioned key influencing factors, Indicates the coordination coefficient. This represents the sum of the products of the weights of all the aforementioned key influencing factors and their optimization priorities;

[0044] Based on the optimized adjustment values, the parameters among the key influencing factors are comprehensively planned to obtain the optimized scheme for the tank voltage.

[0045] To address the aforementioned problems, the present invention also provides a cell voltage optimization system for low-temperature electrolysis of praseodymium-neodymium alloys, the system comprising:

[0046] The parameter coupling analysis module is used to perform multi-parameter coupling analysis on the initial parameter set of the electrolyzer during the low-temperature electrolysis process to obtain the key influencing factors of the electrolyzer.

[0047] The influence weight ranking module is used to perform correlation analysis on the key influencing factors based on the fluctuation characteristics of the cell voltage in the electrolytic cell, and obtain the influence weight sequence of the electrolytic cell.

[0048] The scheme adjustment determination module is used to determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the influence weight sequence.

[0049] A voltage regulation and control module is used to synchronously regulate the slot voltage based on the aforementioned coordinated adjustment scheme, thereby achieving precise control of the electrode spacing and current density.

[0050] The parameter integration and feedback module is used to collect and integrate the operating parameters of the electrolytic cell based on the precise control of the electrode spacing and current density, so as to obtain the feedback parameter set of the electrolytic cell.

[0051] The voltage feedback optimization module is used to feed back the set of feedback parameters to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme of the slot voltage.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. This technology conducts multi-parameter coupling analysis on the initial parameter set during the low-temperature electrolysis process of the electrolytic cell, which can accurately uncover the coupling effect between parameters and effectively identify key factors affecting cell voltage. Then, by combining the cell voltage fluctuation characteristics, it performs correlation analysis on key influencing factors and generates an influence weight sequence. Based on this, it determines a coordinated adjustment scheme for electrode spacing and current density, which can achieve precise synchronous adjustment of cell voltage, greatly improve the accuracy of electrode spacing and current density control, and thus ensure the stability of cell voltage. This lays the foundation for improving the efficiency and product quality of low-temperature electrolysis of praseodymium-neodymium alloys.

[0054] 2. This technology, through the construction of a dynamic feedback and iterative optimization mechanism, after achieving precise control of electrode spacing and current density, collects and integrates the operating parameters of the electrolytic cell to form a feedback parameter set. This set is then fed back to the correlation analysis of key influencing factors, continuously optimizing the identification accuracy of key influencing factors and constantly improving the cell voltage optimization scheme. This iterative optimization mode allows the optimization scheme to adapt to the dynamic changes in the operating state of the electrolytic cell in real time, significantly improving the efficiency of cell voltage optimization, reducing energy consumption and costs in the low-temperature electrolytic production of praseodymium-neodymium alloys, and contributing to the achievement of continuous and stable production operation. Attached Figure Description

[0055] Figure 1This is a schematic flowchart of a cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloys provided in an embodiment of the present invention.

[0056] Figure 2 A functional block diagram of a cell voltage optimization system for low-temperature electrolysis of praseodymium-neodymium alloy provided in an embodiment of the present invention;

[0057] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0059] This application provides a method for optimizing the cell voltage in the low-temperature electrolysis of praseodymium-neodymium alloys. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for optimizing the cell voltage in the low-temperature electrolysis of praseodymium-neodymium alloys can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0060] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing the cell voltage in the low-temperature electrolysis of praseodymium-neodymium alloy according to an embodiment of the present invention. In this embodiment, the method for optimizing the cell voltage in the low-temperature electrolysis of praseodymium-neodymium alloy includes:

[0061] S1. Perform multi-parameter coupling analysis on the initial parameter set of the electrolytic cell during the low-temperature electrolysis process to obtain the key influencing factors of the electrolytic cell.

[0062] In this embodiment of the invention, the multi-parameter coupling analysis of the initial parameter set of the electrolyzer during the low-temperature electrolysis process to obtain the key influencing factors of the electrolyzer includes:

[0063] Real-time parameters such as electrolyte temperature, electrolyte composition, current density, and electrode spacing in the electrolytic cell are collected to obtain the initial parameter set of the electrolytic cell;

[0064] Correlation analysis is performed on the parameters in the initial parameter set to obtain the parameter coupling relationship of the electrolytic cell;

[0065] Based on the influence strength of the parameter coupling relationship, the core screening of the cell voltage fluctuation parameters of the electrolytic cell is carried out to obtain the key influencing factors of the electrolytic cell.

[0066] Specifically, real-time parameters are collected by installing temperature sensors, a component analyzer, a current sensor, and an electrode distance measuring instrument in the low-temperature electrolysis zone of the electrolytic cell. The temperature sensor needs to be in direct contact with the electrolyte to obtain accurate electrolyte temperature data. The component analyzer extracts a small amount of electrolyte sample to detect the composition of the electrolyte. The current sensor is connected in series in the electrolysis circuit to monitor the current in real time and calculate the current density. The electrode distance measuring instrument measures the distance between the electrode and the electrolyte liquid surface through laser ranging to obtain electrode distance data. The collected data of electrolyte temperature, electrolyte composition, current density, and electrode distance are integrated to form the initial parameter set of the electrolytic cell.

[0067] Furthermore, pairwise combinations of the four parameters in the initial parameter set—electrolyte temperature, electrolyte composition, current density, and electrode spacing—were analyzed. First, electrolyte temperature and electrolyte composition were selected, and the changes in electrolyte composition under different electrolyte temperatures and the corresponding fluctuations in electrolyte temperature were recorded to determine the trend of their mutual influence. Then, the interaction relationships between electrolyte temperature and current density, electrolyte temperature and electrode spacing, electrolyte composition and current density, electrolyte composition and electrode spacing, and current density and electrode spacing were analyzed sequentially. For example, the rise and fall of electrolyte temperature when current density changes, and the characteristics of current density change when electrode spacing is adjusted, were observed. By summarizing the variation patterns of all pairwise combinations of parameters, the specific correlation patterns of mutual influence between each parameter were clarified, thereby obtaining the parameter coupling relationship of the electrolyzer.

[0068] Furthermore, based on the established parameter coupling relationships, the influence of each coupling relationship on cell voltage fluctuations is first analyzed. For example, when electrolyte temperature and current density form a specific coupling relationship, the amplitude and frequency of cell voltage fluctuations under this condition are recorded and compared with cell voltage fluctuations under other coupling relationships to determine the strength level of the influence of different coupling relationships on cell voltage fluctuations. Then, based on the strength of the influence, the parameters involved in the coupling relationships that have the most significant impact on cell voltage fluctuations are selected. These parameters can directly and critically cause cell voltage fluctuations, and these selected parameters are identified as the key influencing factors of the electrolytic cell.

[0069] In summary, in the voltage optimization of praseodymium-neodymium alloy low-temperature electrolytic cells, conducting multi-parameter coupling analysis on the initial parameter set of the low-temperature electrolysis process to obtain key influencing factors has significant practical value. By collecting real-time parameters such as electrolyte temperature, electrolyte composition, current density, and electrode spacing in the electrolytic cell to construct the initial parameter set, it can comprehensively cover the core parameter dimensions related to cell voltage in low-temperature electrolysis, avoiding information gaps in subsequent analysis due to missing parameters, and providing a complete and reliable data foundation for subsequent accurate analysis of cell voltage influencing factors.

[0070] In summary, correlation analysis of parameters in the initial parameter set can clearly identify the interaction relationships between different parameters, allowing staff to fully grasp the dynamic influence patterns between parameters. This breaks through the limitations of traditional single-parameter analysis that ignores parameter correlations, provides a clear direction for a deeper understanding of the causes of tank voltage fluctuations, and assists in subsequent targeted analysis work.

[0071] In summary, core screening of cell voltage fluctuation parameters based on the influence intensity of parameter coupling relationships can focus on the parameters that have the most significant impact on cell voltage fluctuations, eliminate interference from secondary factors, and accurately identify key influencing factors. This process can avoid ineffective adjustments to irrelevant parameters in subsequent optimization work, making the optimization direction clearer, providing precise targets for formulating cell voltage optimization strategies, effectively improving the efficiency and accuracy of subsequent optimization work, and better adapting to the requirements of low-temperature electrolysis process for cell voltage stability.

[0072] S2. Based on the fluctuation characteristics of the cell voltage in the electrolytic cell, a correlation analysis is performed on the key influencing factors to obtain the influence weight sequence of the electrolytic cell.

[0073] In this embodiment of the invention, the step of performing correlation analysis on the key influencing factors based on the fluctuation characteristics of the cell voltage in the electrolytic cell to obtain the influence weight sequence of the electrolytic cell includes:

[0074] The voltage of the tank is monitored in real time to obtain the fluctuation amplitude and frequency of the voltage.

[0075] Based on the aforementioned key influencing factors, a correlation analysis is performed on the fluctuation amplitude and fluctuation frequency to obtain the degree of correlation between the key influencing factors and the fluctuation amplitude and fluctuation frequency.

[0076] Based on the degree of correlation, the key influencing factors are weighted to obtain the weight factors of the key influencing factors;

[0077] The weighting factors are normalized to obtain the influence weight sequence of the electrolytic cell.

[0078] In this embodiment of the invention, the step of assigning weights to the key influencing factors based on the degree of correlation to obtain the weight factors of the key influencing factors includes:

[0079] Based on the degree of correlation, weight allocation calculations are performed on the key influencing factors to obtain the weight factors of the key influencing factors, wherein the weight allocation calculation formula is:

[0080] ;

[0081] in, Indicates the first The weighting factors of the aforementioned key influencing factors, Indicates the first The degree of correlation between the aforementioned key influencing factors and the fluctuation range. Indicates the first The degree of correlation between the aforementioned key influencing factors and the aforementioned fluctuation frequency. Indicates the first The duration coefficient of the effects of the aforementioned key influencing factors Indicates the first The frequency coefficients of the key influencing factors mentioned above This represents the correlation adjustment coefficient. This represents the intensity adjustment coefficient. This represents the sum of the weighted eigenvalues ​​of all the key influencing factors.

[0082] Specifically, during the low-temperature electrolysis process in the electrolytic cell, a high-precision voltage sensor is used to continuously monitor the cell voltage in real time. The sensor's sampling frequency is set to collect data 10 times per second to ensure that subtle changes in the cell voltage can be captured. Each collected cell voltage data is compared with a set standard voltage value, and the difference between each voltage data and the standard value is calculated. The maximum difference is the fluctuation amplitude of the cell voltage. At the same time, the number of times the cell voltage difference exceeds the allowable fluctuation range per unit time is counted, and this number is the fluctuation frequency of the cell voltage. The fluctuation amplitude and fluctuation frequency of the cell voltage are recorded and organized in the above way.

[0083] Furthermore, for each identified key influencing factor, the corresponding changes in the amplitude and frequency of the cell voltage fluctuation are observed. For example, taking electrolyte temperature as a key influencing factor, as the electrolyte temperature gradually increases within a certain range, the increase or decrease in the amplitude of the cell voltage fluctuation and the number of changes in the fluctuation frequency are recorded after each temperature change. The average change in amplitude and the average number of changes in frequency for every 1°C change in temperature are calculated to determine the degree of correlation between electrolyte temperature and fluctuation amplitude and frequency. In the same way, the specific change patterns of the amplitude and frequency of the cell voltage fluctuation are analyzed when other key influencing factors change. By comparing the significance of the changes in amplitude and frequency caused by different key influencing factors, the degree of correlation between each key influencing factor and fluctuation amplitude and frequency is determined.

[0084] Furthermore, the formula for calculating the degree of correlation is: ;in, Indicates the first The degree of correlation between key influencing factors and the magnitude of fluctuations. Indicates the first Key influencing factors in time The value, Indicates time The fluctuation range value, Indicates the first The average value of key influencing factors over time series. This represents the average value of the fluctuation amplitude over the time series. This represents the number of data points in the time series; the final correlation value is the weighting factor of the key influencing factors.

[0085] Furthermore, the weighting factors of all key influencing factors are summed to obtain the total weighting factors. Then, the weighting factor of each key influencing factor is divided by this total to calculate the proportion of each weighting factor in the total. This proportion is the normalized weight value. All key influencing factors are arranged in descending order of their normalized weight values ​​to form an ordered sequence, which is the influence weight sequence of the electrolyzer.

[0086] Specifically, no. To obtain the weighting factors for the key influencing factors, it is necessary to first determine the values ​​of each parameter in the formula. These parameters are all derived from the real-time monitoring and analysis results of the low-temperature electrolysis process in the electrolytic cell. The correlation between the key influencing factors and the fluctuation range is determined by first monitoring the tank voltage in real time to obtain fluctuation range data, then matching the real-time change data of the key influencing factor with the fluctuation range data at the same time point, and calculating the degree of fit between the two trends through data comparison and matching. The correlation between a key influencing factor and its fluctuation frequency is determined by real-time monitoring of the tank voltage fluctuation frequency data. The real-time change data of this key influencing factor is correlated with the fluctuation frequency data in chronological order, and the correlation ratio of the number of changes per unit time between the two is calculated. The duration coefficient of each key influencing factor is calculated by statistically analyzing the duration of its continuous impact on the cell voltage and then comparing it to the total operating time of the electrolytic cell. The frequency coefficient of each key influencing factor is obtained by statistically analyzing the number of times it affects cell voltage fluctuations per unit time. The correlation adjustment coefficient is determined by collecting correlation data between key influencing factors and cell voltage fluctuations from multiple historical electrolysis studies, combined with the current requirements for cell voltage stability in low-temperature electrolysis processes, and using historical data fitting analysis to determine a fixed value. This value is used to balance the influence of correlation degree in the weighting calculation. The formula for the correlation adjustment coefficient is: ,in, Indicates the correlation adjustment coefficient. The average value representing the degree of historical correlation. The standard deviation represents the degree of historical correlation; the effect intensity adjustment coefficient analyzes the impact of the duration and frequency of key influencing factors in historical data on the optimization effect of tank voltage. Combined with the current process control standards for effect intensity, a fixed value is determined through data statistical analysis to adjust the proportion of the product of duration and frequency in the weight calculation. The formula for the effect intensity adjustment coefficient is: ,in, This represents the intensity adjustment coefficient. This represents the average historical intensity of the action. The standard deviation represents the historical effect strength; the sum of the weighted eigenvalues ​​of all key influencing factors is obtained by calculating the individual weighted eigenvalue of each key influencing factor by multiplying the correlation adjustment coefficient by the square of its correlation with the fluctuation amplitude plus the square of its correlation with the fluctuation frequency, plus the effect strength adjustment coefficient multiplied by its effect duration coefficient multiplied by the effect frequency coefficient, and then summing all the individual weighted eigenvalues.

[0087] Furthermore, the significance of this formula lies in integrating the correlation information between key influencing factors and tank voltage fluctuation characteristics, as well as the information on the duration and frequency of action of key influencing factors, to calculate the first... The relative importance of the first key influencing factor to the tank voltage among all key influencing factors is specifically reflected in the number of key influencing factors. The weighting factors for each key influencing factor are calculated by using a correlation adjustment coefficient to balance the influence of the correlation between the key influencing factor and the fluctuation amplitude and frequency, and an effect intensity adjustment coefficient to balance the influence of the effect duration and frequency of the key influencing factor. Finally, the ratio of the comprehensive influence value of each key influencing factor to the sum of the comprehensive influence values ​​of all key influencing factors is calculated to obtain the final weighting factor. The weighting factors of key influencing factors can clarify the degree to which these key influencing factors need to be focused on in slot voltage optimization, providing a basis for subsequently determining the coordinated adjustment scheme of pole spacing and current density.

[0088] Furthermore, the trend of the formula is reflected in: the first As the correlation between a key influencing factor and the volatility increases, its square also increases, leading to an increase in the value of "the correlation adjustment coefficient multiplied by the square of the correlation plus the square of the correlation with the volatility frequency." With the sum of the weighted eigenvalues ​​of all key influencing factors remaining constant, the weight factor of that key influencing factor increases. Similarly, as the correlation with the volatility frequency increases, its square also increases, similarly increasing the corresponding values ​​mentioned above, thus increasing the weight factor. When the duration coefficient of a key influencing factor increases, its product with the frequency coefficient increases, leading to an increase in the value of the "intensity adjustment coefficient multiplied by this product." With the total sum remaining constant, this weighting factor increases. Similarly, when the frequency coefficient increases, its product with the duration coefficient increases, also increasing the corresponding value, thus increasing this weighting factor. An increase in the correlation adjustment coefficient, if the first... If the correlation between the key influencing factors and the fluctuation amplitude and frequency remains unchanged, the value of "correlation adjustment coefficient multiplied by the sum of squares of the two correlation degrees" increases, and its proportion in the total increases, thus increasing the weighting factor. If the effect intensity adjustment coefficient increases, and if the effect duration coefficient and effect frequency coefficient of the key influencing factor remain unchanged, the value of "effect intensity adjustment coefficient multiplied by the product of the two" increases, and its proportion in the total increases, thus increasing the weighting factor. The weighted eigenvalues ​​of other key influencing factors increase, and the sum of the weighted eigenvalues ​​of all key influencing factors increases. The weighted eigenvalues ​​of the key influencing factors remain unchanged, while their weighting factors decrease.

[0089] In summary, in the optimization of voltage in low-temperature electrolytic cells for praseodymium-neodymium alloys, conducting correlation analysis on key influencing factors based on the characteristics of cell voltage fluctuations to obtain the influence weight sequence has significant practical value. Real-time monitoring of cell voltage and acquisition of fluctuation amplitude and frequency can accurately capture the dynamic changes in cell voltage and grasp its specific instability. This provides an intuitive and accurate reference for subsequent analysis of the correlation between key influencing factors and cell voltage fluctuations, avoiding analytical biases caused by incomplete understanding of cell voltage fluctuations.

[0090] In summary, by combining key influencing factors with correlation analysis of fluctuation amplitude and fluctuation frequency, we can clearly identify the degree of influence of different key influencing factors on tank voltage fluctuation, understand which factors have a greater impact on the fluctuation amplitude of tank voltage and which factors are more likely to cause high-frequency fluctuations in tank voltage, break through the previous limitations of vague understanding of the role of key influencing factors, and provide a clear basis for subsequent weight allocation.

[0091] In summary, by assigning weights to key influencing factors based on their correlation and obtaining weight factors, and then normalizing these weight factors to form an influence weight sequence, the relative importance of each key influencing factor to the cell voltage can be intuitively presented. Through this sequence, staff can quickly identify the key areas of optimization work, avoiding excessive effort on secondary factors in subsequent adjustments. This makes the optimization strategy more targeted, lays the foundation for developing a scientific and reasonable cell voltage optimization scheme, helps improve the efficiency and accuracy of cell voltage optimization, and better meets the requirements of low-temperature electrolysis processes for cell voltage stability.

[0092] In summary, in the voltage optimization of praseodymium-neodymium alloy low-temperature electrolytic cells, assigning weights to key influencing factors based on their correlation to obtain weight factors can provide accurate priority criteria for subsequent optimization work and has significant practical value.

[0093] In summary, this process integrates multi-dimensional information on key influencing factors in a specific way. It considers the correlation between key influencing factors and the amplitude and frequency of tank voltage fluctuations, as well as the duration and frequency of the effects of key influencing factors. This allows the weight allocation to be no longer limited to a single dimension and to more comprehensively reflect the actual impact of each key influencing factor on the tank voltage. Among these factors, the correlation degree adjustment coefficient can balance the influence of correlation degree in the weight calculation, and the effect intensity adjustment coefficient can adjust the degree of influence of the duration and frequency of the effects on the weight results, avoiding excessive interference of information from a certain dimension on the weight allocation.

[0094] In summary, the final weighting factors clearly reflect the relative importance of each key influencing factor on the cell voltage, preventing staff from blindly investing in secondary factors in subsequent optimizations. This makes the optimization direction clearer, provides a reliable basis for determining the coordinated adjustment scheme of electrode spacing and current density, helps improve the targeting and accuracy of cell voltage optimization, and better adapts to the requirements of low-temperature electrolysis process for cell voltage stability.

[0095] S3. Based on the influence weight sequence, determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density.

[0096] In this embodiment of the invention, determining the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the influence weight sequence includes:

[0097] Based on the influence weight sequence, the weights of the electrode spacing and current density in the electrolytic cell are compared and arranged to obtain the priority relationship between the electrode spacing and current density weights.

[0098] The priority relationship is quantized and ordered to obtain the adjustment priority of the electrode spacing and current density;

[0099] Based on the adjustment priority, a decision is made on the operating state of the electrolytic cell to obtain a coordinated adjustment scheme for the electrode spacing and current density.

[0100] Specifically, the weight values ​​corresponding to the electrolytic cell electrode spacing and the current density are extracted from the influence weight sequence. These two weight values ​​are then directly compared. If the weight value of the electrode spacing is greater than the weight value of the current density, the electrode spacing has a higher priority than the current density among the factors affecting the cell voltage. If the weight value of the current density is greater than the weight value of the electrode spacing, the current density has a higher priority than the electrode spacing. If the two weight values ​​are equal, they are determined to have the same priority. Through this comparison and arrangement, the priority relationship between the electrode spacing and the current density weights is clarified.

[0101] Furthermore, based on the established priority relationship between the electrode spacing and current density weights, a quantization and ranking process is performed. If the electrode spacing priority is higher than the current density priority, the adjustment priority of the electrode spacing is quantized and assigned a value of 2, and the adjustment priority of the current density is quantized and assigned a value of 1. If the current density priority is higher than the electrode spacing priority, the adjustment priority of the current density is quantized and assigned a value of 2, and the adjustment priority of the electrode spacing is quantized and assigned a value of 1. If the two priorities are the same, they are both quantized and assigned a value of 1.5. Through this quantization and ranking process, the adjustment priorities of the electrode spacing and current density are obtained.

[0102] Furthermore, combining the obtained adjustment priorities of electrode spacing and current density, and simultaneously collecting the current operating status data of the electrolytic cell, including current electrode spacing values, current density values, electrolyte temperature, electrolyte composition, and other operating parameters, if the electrode spacing adjustment has a higher priority, the adjustment range and direction of the electrode spacing are determined first based on the cell voltage optimization requirements. For example, if the current cell voltage is higher than the standard value and the electrode spacing is too large, the reduction range of the electrode spacing is determined, and then the adjustment range of the current density is matched according to the expected change after the electrode spacing adjustment to ensure that the two adjustments can work together to stabilize the cell voltage. If the current density adjustment has a higher priority, the current density adjustment scheme is determined first, and then the electrode spacing adjustment scheme is matched. If the two priorities are the same, the adjustment range and direction of the electrode spacing and current density are planned simultaneously so that the two adjustments cooperate with each other. Through such operating status decision-making, a coordinated adjustment scheme for electrode spacing and current density is formed.

[0103] In summary, in the voltage optimization of praseodymium-neodymium alloy low-temperature electrolytic cells, determining the coordinated adjustment scheme of electrode spacing and current density based on the influence weight sequence can make the adjustment strategy more targeted and scientific, effectively improving the cell voltage optimization effect. By comparing and arranging the weights of electrode spacing and current density in the influence weight sequence, the difference in the importance of the two on the cell voltage can be clearly identified, and it can be known which factor plays a more critical role in the stability of cell voltage under the current operating state. This avoids ambiguity in the judgment of the importance of the two factors during subsequent adjustments and provides a basis for the adjustment direction.

[0104] In summary, quantifying and ranking the priority relationships transforms the difference in importance between electrode spacing and current density into an intuitive ranking result, allowing staff to quickly grasp the order of adjustments without having to make complex judgments about their priorities. This simplifies the decision-making process and improves the efficiency of adjustment preparation.

[0105] In summary, making decisions by combining adjustment priorities with the real-time operating status of the electrolyzer ensures that the adjustment plan aligns with actual production conditions. By clarifying the order of adjustments and combining the actual values ​​of the current electrode spacing and current density with the overall operating parameters of the electrolyzer, an adjustment plan that coordinates the two can be developed. This avoids new fluctuations in cell voltage caused by adjusting only one factor, achieves synergistic optimization of electrode spacing and current density, better promotes the stabilization of cell voltage, and meets the stringent requirements of low-temperature electrolysis processes for cell voltage.

[0106] S4. Based on the aforementioned coordinated adjustment scheme, the slot voltage is synchronously adjusted to achieve precise control of the electrode spacing and current density.

[0107] In this embodiment of the invention, the step of synchronously adjusting the slot voltage based on the coordinated adjustment scheme to achieve precise control of the electrode spacing and current density includes:

[0108] Based on the aforementioned coordinated adjustment scheme, the initial parameter set is tuned and optimized to obtain the control commands for the electrode spacing and current density.

[0109] Based on the control command, the slot voltage is controlled in a coordinated manner to obtain the response change of the slot voltage;

[0110] Based on the change in response, the electrode spacing and current density in the coordinated adjustment scheme are finely adjusted to achieve precise control of the electrode spacing and current density.

[0111] Specifically, the target adjustment range, adjustment direction, and corresponding parameter constraints for electrode spacing and current density are extracted from the coordinated adjustment scheme. This information is compared with the initial values ​​of electrode spacing and current density in the initial parameter set to clarify the differences between the initial parameters and the target adjustment requirements. Tuning optimization rules are formulated according to the degree of difference. For example, if the initial electrode spacing value is higher than the target adjustment upper limit, the basic range for downward adjustment of the electrode spacing is determined; if the initial current density value is lower than the target adjustment lower limit, the basic range for upward adjustment of the current density is determined. Simultaneously, considering the limitations on the parameter change rate imposed by the low-temperature electrolysis process of the electrolytic cell, the basic range is decomposed into several consecutive small-amplitude adjustment steps, each corresponding to a specific numerical change. These instructions, including adjustment steps and numerical changes, are integrated to form control instructions for electrode spacing and current density.

[0112] Furthermore, based on the control commands for electrode spacing and current density, the electrode spacing adjustment mechanism and current control device of the electrolytic cell perform adjustment actions. The electrode spacing adjustment mechanism gradually changes the distance between the electrodes and the electrolyte surface according to the steps in the command, while the current control device gradually adjusts the current magnitude in the electrolytic circuit to change the current density. During the adjustment process, the cell voltage data is continuously collected by the voltage monitoring equipment, recording the specific numerical change of the cell voltage after each execution of the control command. For example, the cell voltage decreases after executing an electrode spacing reduction command, and the cell voltage increases after executing a current density increase command. These numerical changes are organized in chronological order to obtain the response change of the cell voltage.

[0113] Furthermore, the matching degree between the response change of the tank voltage and the execution effect of the control command is analyzed. If the response change shows that the tank voltage approaches the standard value by less than expected, for example, the tank voltage drops less than expected after executing the pitch adjustment command, it is determined that the pitch adjustment is insufficient, and the pitch reduction needs to be appropriately increased based on the collaborative adjustment scheme. If the response change shows that the tank voltage exceeds the standard value range, for example, the tank voltage rises above the standard value after executing the current density adjustment command, it is determined that the current density adjustment is too large, and the current density increase needs to be appropriately reduced. Based on these analysis results, the adjustment parameters of pitch and current density in the collaborative adjustment scheme are corrected. After correction, the control command is executed again and the tank voltage response change is monitored. This process is repeated until the tank voltage stabilizes within the standard range. At this point, the values ​​of pitch and current density meet the requirements for precise control, achieving precise control of pitch and current density.

[0114] In summary, in the optimization of the voltage of the praseodymium-neodymium alloy low-temperature electrolytic cell, the synchronous adjustment of the cell voltage based on the collaborative adjustment scheme to achieve precise control of the electrode spacing and current density can effectively ensure the stability of the cell voltage and meet the requirements of the low-temperature electrolysis process.

[0115] In summary, by combining the coordinated adjustment scheme with the initial parameter set for tuning and optimization and generating control commands, the adjustment of electrode spacing and current density can have a clear operational basis. The initial parameter set contains the basic data of the electrolytic cell operation. Through tuning and optimization, the requirements of the coordinated adjustment scheme can be transformed into specific and executable control commands, avoiding the blindness of adjustment operations and ensuring that the adjustment direction and magnitude of electrode spacing and current density meet the optimization target, thus laying the foundation for subsequent synchronous adjustment.

[0116] In summary, by coordinating the control of the tank voltage according to the control commands and observing the response changes, the actual impact of the adjustment operation on the tank voltage can be grasped in real time. By monitoring the response changes of the tank voltage, the fluctuation of the tank voltage after the adjustment of the electrode spacing and current density can be intuitively understood, it can be judged whether the current adjustment is moving towards stabilizing the tank voltage, and deviations between the adjustment effect and the expectation can be detected in a timely manner, providing real-time feedback for further optimization.

[0117] In summary, fine-tuning the electrode spacing and current density based on the response changes of the cell voltage can gradually narrow the gap between actual and ideal parameters. For deviations that occur during response changes, the parameters in the coordinated adjustment scheme are fine-tuned to make the coordination between electrode spacing and current density more precise. This avoids cell voltage instability caused by excessive adjustment amplitude or parameter mismatch, and ultimately achieves precise control of both, so that the cell voltage remains stable within the range that meets the process requirements, ensuring the efficiency and product quality of praseodymium-neodymium alloy low-temperature electrolysis.

[0118] S5. Based on the precise control of the electrode spacing and current density, the operating parameters of the electrolytic cell are collected and integrated to obtain the feedback parameter set of the electrolytic cell.

[0119] In this embodiment of the invention, the precise control of the electrode spacing and current density, and the collection and integration of the operating parameters of the electrolytic cell to obtain the feedback parameter set of the electrolytic cell, include:

[0120] Based on the precise control of the electrode spacing and current density, noise filtering is performed on the operating parameters of the electrolytic cell to obtain the processed operating parameters.

[0121] The processed operating parameters are aligned and integrated in chronological order to obtain the feedback parameter set of the electrolytic cell.

[0122] Specifically, based on the precise control of electrode spacing and current density, the range of electrolytic cell operating parameters to be collected is determined. These include five core parameters: electrolyte temperature, electrolyte composition, real-time electrode spacing, real-time current density, and cell voltage. This ensures that the collected parameters remain consistent with the initial parameter set to meet subsequent comparative analysis needs. For each type of operating parameter, corresponding data acquisition equipment is used. Electrolyte temperature is collected using a contact temperature sensor, electrolyte composition using an online component analyzer, real-time electrode spacing using a laser rangefinder, real-time current density calculated from a current sensor combined with the electrolytic cell electrode area, and cell voltage using a high-precision voltage sensor. The system collects data, and during the acquisition process, the timestamp of each type of parameter is recorded synchronously to avoid data time misalignment. Then, noise filtering is performed on each type of operating parameter. Taking electrolyte temperature as an example, the system first counts 10 consecutive temperature data acquisitions and identifies data points that significantly deviate from the normal fluctuation range. These abnormal data points are judged as noise data. Then, the average value of two adjacent normal data points is used to replace the noise data points to ensure the continuity and accuracy of the temperature data. Following the same noise identification and replacement logic, the electrolyte composition, real-time electrode distance, real-time current density, and tank voltage data are processed in sequence to finally obtain the processed operating parameters for all categories.

[0123] Furthermore, after acquiring the processed operating parameters, a unified timeline is established based on the acquisition timestamp. The time interval of the timeline is set to match the parameter acquisition frequency. Each type of processed operating parameter is mapped to a node on the timeline according to the timestamp. For example, the electrolyte temperature, electrolyte composition, real-time electrode distance, real-time current density, and cell voltage data acquired and processed at a certain moment are uniformly associated with the corresponding time node. If a certain type of parameter is missing at a certain time node, the parameter acquisition records before and after that time node need to be retrieved again to confirm whether it is caused by a temporary failure of the acquisition equipment. If it is a temporary failure, the average value of the data of the two adjacent time nodes before and after that parameter is used to supplement the node, ensuring that the five types of operating parameters under each time node are complete and without missing data. After all the processed operating parameters are integrated according to the time nodes, a dataset containing complete operating parameters in chronological order is formed. This dataset is the feedback parameter set of the electrolyzer.

[0124] In summary, in the voltage optimization of praseodymium-neodymium alloy low-temperature electrolytic cells, the precise control of electrode spacing and current density to collect and integrate electrolytic cell operating parameters to obtain a feedback parameter set can provide high-quality data support for subsequent optimization iterations and ensure the continuous effectiveness of optimization work.

[0125] In summary, by relying on the precise control of electrode spacing and current density to perform noise filtering on operating parameters, interference information during parameter acquisition can be removed. When acquiring operating parameters of an electrolytic cell, invalid or biased data may be generated due to slight equipment vibration, environmental signal interference, etc. Noise filtering can screen out parameters that truly reflect the operating status of the electrolytic cell, ensuring that the processed operating parameters are accurate and reliable, avoiding false data from misleading subsequent analysis, and laying the foundation for the quality of the feedback parameter set.

[0126] In summary, aligning and integrating the processed operating parameters in chronological order creates a clear and sequential set of feedback parameters. Since the electrolytic cell's operating parameters change dynamically over time, this chronological integration fully presents the changes at different times. It clearly reflects the response patterns of other operating parameters after precise control of the electrode spacing and current density. This facilitates subsequent comparison and analysis of the feedback parameter set with the initial parameter set, accurately capturing parameter change trends and providing intuitive and systematic data for optimizing the identification accuracy of key influencing factors and improving the cell voltage optimization scheme.

[0127] S6. Feedback the set of feedback parameters to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme of the slot voltage.

[0128] In this embodiment of the invention, feeding back the feedback parameter set to S2 to optimize the identification accuracy of the key influencing factors and obtain an optimized scheme for the slot voltage includes:

[0129] By analyzing and comparing the feedback parameter set with the initial parameter set, the changing trend of the operating parameters in the electrolytic cell can be obtained;

[0130] Based on the aforementioned trend, the weighting factors are adjusted over time to obtain an updated influence weight sequence.

[0131] Based on the updated influence weight sequence, multi-objective optimization is performed on the key influencing factors to obtain an optimized scheme for the slot voltage.

[0132] In this embodiment of the invention, the step of performing multi-objective optimization on the key influencing factors based on the updated influence weight sequence to obtain an optimized scheme for the tank voltage includes:

[0133] Based on the updated influence weight sequence, multi-objective optimization calculations are performed on the factors among the key influencing factors to obtain the optimized adjustment values ​​of the key influencing factors. The formula for the multi-objective optimization calculation is as follows:

[0134] ;

[0135] in, Indicates the first The optimized adjustment values ​​for the aforementioned key influencing factors, Indicates the first The weight values ​​of the key influencing factors mentioned above. Indicates the first The optimization priority of the aforementioned key influencing factors, Indicates the coordination coefficient. This represents the sum of the products of the weights of all the aforementioned key influencing factors and their optimization priorities;

[0136] Based on the optimized adjustment values, the parameters among the key influencing factors are comprehensively planned to obtain the optimized scheme for the tank voltage.

[0137] Specifically, five types of parameter data—electrolyte temperature, electrolyte composition, current density, electrode spacing, and cell voltage—are extracted from the feedback parameter set and the initial parameter set, respectively. This ensures that the parameter types and acquisition time periods in the two parameter sets match for accurate comparison. Parameters of the same type in both sets are matched one-to-one according to time sequence. For example, the electrolyte temperature at a certain moment in the initial parameter set is compared with the electrolyte temperature at the same time point in the feedback parameter set, and the difference is calculated. Similarly, pairwise comparisons of electrolyte composition, current density, electrode spacing, and cell voltage are performed sequentially to obtain the change in the difference of each parameter at different time points. By continuously tracking these changes in difference, the direction and magnitude of the increase or decrease of the difference for each parameter over time are observed. For example, if the difference in electrolyte temperature gradually decreases over time, it indicates that the electrolyte temperature is changing towards stability; if the difference in current density first increases and then decreases, it indicates that the current density has fluctuated and then returned to stability. By combining the change patterns of the differences of all parameters, the changing trends of the operating parameters in the electrolytic cell are finally obtained.

[0138] Furthermore, based on the obtained operating parameter change trends, the dynamic changes in the influence of various parameters on the tank voltage are analyzed. For example, if the change trend shows that the fluctuation range of the difference in the electrode spacing parameter gradually increases, and the corresponding fluctuation of the tank voltage also intensifies, it indicates that the influence of the electrode spacing on the tank voltage is increasing, and the weight factor of the key influencing factor corresponding to the electrode spacing needs to be increased. If the difference in the electrolyte component parameters gradually stabilizes, and the fluctuation range of the tank voltage affected by it decreases, it indicates that the influence of the electrolyte component on the tank voltage is decreasing, and the weight factor of the key influencing factor corresponding to the electrolyte component needs to be decreased. Following this analytical logic, and combining the correlation between the parameter change trend and the tank voltage fluctuation, the original weight factor of each key influencing factor is adjusted accordingly. The adjustment range is determined based on the closeness of the correlation between the parameter difference change range and the tank voltage fluctuation. For example, the weight factor with a significant increase in influence is increased by 0.2, and the weight factor with a significant decrease in influence is decreased by 0.1. After the weight factors of all key influencing factors are adjusted, the adjusted weight factors are normalized to obtain the updated influence weight sequence.

[0139] Furthermore, based on the updated impact weight sequence, the weight priority of each key influencing factor is clarified. Key influencing factors with higher weight values ​​have higher priority in multi-objective optimization. The core objectives of multi-objective optimization are determined to be stabilizing the cell voltage to the set standard range, reducing electrolysis energy consumption, and improving product purity. Each key influencing factor is associated with these three core objectives, and the contribution of each key influencing factor to different objectives is analyzed. For example, adjusting the electrode spacing has the highest contribution to stabilizing the cell voltage, while adjusting the current density has the highest contribution to reducing electrolysis energy consumption. Based on the weight priority and objective contribution, specific optimizations are formulated for each key influencing factor. The direction and magnitude of adjustments are determined. For example, the pole pitch with the highest weight and the greatest contribution to stabilizing the tank voltage is adjusted downwards if the current pole pitch value is higher than the standard range. The adjustment magnitude is determined based on the updated weight value and the difference between the current tank voltage and the standard value. For the current density with the second highest weight and a significant contribution to reducing energy consumption, if the current current density leads to high energy consumption, the adjustment direction is moderately reduced. The adjustment magnitude is based on the premise of not affecting the stability of the tank voltage. The adjustment directions, magnitudes, and corresponding constraints of all key influencing factors are integrated to form a complete and executable operation plan. This plan is the optimization plan for the tank voltage.

[0140] Specifically, based on the updated influence weight sequence, the weight value of each key influence factor is first determined. Simultaneously, considering the requirements of the praseodymium-neodymium alloy low-temperature electrolysis process, the optimization priority of each key influence factor is determined. Key influence factors with higher weight values ​​have a more significant impact on cell voltage stability, and therefore have a higher optimization priority. For example, if the electrode spacing has the highest weight value in the updated influence weight sequence, its optimization priority is set to the highest level. Then, the coordination coefficient is determined, where the formula for calculating the coordination coefficient is: ,in, Indicates the coordination coefficient. This represents the standard deviation of the set of historical optimized adjustment values. This indicates the current slot voltage value. This represents the standard cell voltage value. This coefficient needs to be set with reference to historical optimization data and the current operating status of the electrolytic cell. It is used to balance the interaction between different key influencing factors during optimization and adjustment, and to avoid the negative impact of excessive adjustment of a single factor on other parameters. Next, multi-objective optimization calculations are carried out for each key influencing factor. In the calculation, the weight value of the key influencing factor is first multiplied by the corresponding optimization priority to obtain the weighted priority value of the factor. Then, the weighted priority value is multiplied by the coordination coefficient to obtain the numerator. At the same time, the product of the weight value of all key influencing factors and their respective optimization priorities is calculated, and these products are added together to obtain the denominator. Finally, the numerator is divided by the denominator, and the result is the optimization adjustment value of the key influencing factor. The optimization adjustment values ​​of all key influencing factors are calculated in this way.

[0141] Furthermore, after obtaining the optimized adjustment values ​​of all key influencing factors, the direction and magnitude of parameter adjustment corresponding to each optimized adjustment value are analyzed. For example, if the optimized adjustment value of a key influencing factor is positive and large, it indicates that the parameter corresponding to that factor needs to be adjusted significantly in the direction of increasing. If the optimized adjustment value is negative and small, it indicates that the parameter needs to be adjusted slightly in the direction of decreasing. Then, based on the actual operating parameters of the electrolyzer, it is determined whether there is a conflict between the parameter adjustments of each key influencing factor. For example, the electrode spacing needs to be significantly increased to stabilize the cell voltage, while the current density needs to be slightly decreased to reduce energy consumption. It is necessary to confirm whether the simultaneous increase in electrode spacing and decrease in current density will have an impact on other parameters such as electrolyte temperature that exceeds the allowable range of the process. If there is a conflict, the adjustment magnitude of the conflicting parameters is adjusted according to the magnitude of the optimized adjustment value and the priority of the parameter's impact on the cell voltage to ensure that the adjustments of each parameter are coordinated. Finally, the parameter adjustment direction, adjustment magnitude, and adjusted parameter control range of all key influencing factors are integrated to form a scheme that covers all key parameters, meets the requirements of multi-objective optimization, and can be directly used for electrolyzer operation. This scheme is the cell voltage optimization scheme.

[0142] Specifically, no. To obtain the optimized adjustment values ​​of the key influencing factors, it is necessary to first determine the specific values ​​of each parameter in the formula. These parameters all come from the analysis and decision-making results of the low-temperature electrolysis process in the electrolytic cell; The weight values ​​of the key influencing factors are obtained after multi-parameter coupling analysis of the initial parameter set of the electrolyzer to identify the key influencing factors. Correlation analysis of these factors is then performed based on the characteristics of cell voltage fluctuations. The corresponding values ​​in the influence weight sequence are calculated through weight allocation and normalization. Specifically, the correlation between the key influencing factors and the amplitude and frequency of cell voltage fluctuations is analyzed first. Weight factors are then calculated by combining the duration coefficient, frequency coefficient, and adjustment coefficient. Finally, all weight factors are normalized to obtain the final weight value. The optimization priority of key influencing factors is determined based on the requirements of the low-temperature electrolysis process for cell voltage stability, combined with the degree of influence of key influencing factors on cell voltage fluctuations. First, the importance of each key influencing factor on the cell voltage optimization effect is ranked, and then the ranking results are quantified and assigned. For example, the key influencing factor with the highest degree of influence is assigned the highest quantified value, and so on, to obtain the optimization priority of each key influencing factor. The coordination coefficient is determined based on the production goals and process constraints of praseodymium-neodymium alloy low-temperature electrolysis. First, the specific requirements for electrolysis efficiency and product quality during production, as well as constraints such as the safety and energy consumption of the electrolytic cell operation, are clarified. By analyzing the implementation effects of the tank voltage optimization scheme under different coordination coefficients in historical production data, a fixed coefficient value is determined to balance the calculation results of the optimization adjustment values ​​of each key influencing factor, ensuring that the optimization scheme meets the actual production needs. The sum of the products of the weight values ​​and optimization priorities of all key influencing factors is obtained by multiplying the weight value of each key influencing factor with its corresponding optimization priority, and then accumulating the product results of all key influencing factors. The sum is the sum of the products of the weight values ​​and optimization priorities of all key influencing factors.

[0143] Furthermore, the significance of this formula lies in integrating the first... The weights, optimization priorities, and coordination coefficients of each key influencing factor are combined with the sum of the products of the weights and optimization priorities of all key influencing factors to calculate the [number of]th [factor]. The specific values ​​that need to be adjusted during the optimization of the tank voltage for the key influencing factor are the first... The optimized adjustment values ​​for key influencing factors are determined. During the calculation, weight values ​​reflect the relative importance of each key influencing factor to the tank voltage; optimization priorities clarify the order of adjustment for key influencing factors; and coordination coefficients balance the overall optimization objective with individual adjustment needs. Finally, the optimized adjustment values ​​for the first key influencing factor are determined. The ratio of the comprehensive adjustment basis for each key influencing factor to the sum of the comprehensive adjustment basis for all key influencing factors is calculated to obtain the final result. The optimized adjustment values ​​of key influencing factors can provide a clear basis for accurately adjusting the parameters of key influencing factors, ensuring the scientific nature and operability of the tank voltage optimization scheme.

[0144] Furthermore, the formula trend is reflected in the current... When the weight of a key influencing factor increases, assuming the optimization priority and coordination coefficient remain unchanged, and the sum of the products of the weights of all key influencing factors and the optimization priority remains unchanged, the product of the weight of that key influencing factor and the optimization priority and coordination coefficient will increase, thereby making the product of the weight of the first key influencing factor, the optimization priority, and the coordination coefficient increase. The optimized adjustment value of the first key influencing factor increases; when the first When the optimization priority of a key influencing factor increases, assuming the weight value and coordination coefficient remain unchanged, and the total sum remains constant, the product of its weight value, optimization priority, and coordination coefficient will increase, leading to the... The optimized adjustment value of the first key influencing factor increases; when the coordination coefficient increases, the value of the second key influencing factor increases. With the weights and optimization priorities of the key influencing factors remaining constant, and their total sum remaining unchanged, the product of their weights, optimization priorities, and coordination coefficients will increase, causing the... The optimization adjustment value of the first key influencing factor increases; when the weight value or optimization priority of other key influencing factors increases, the sum of the products of the weight values ​​and optimization priorities of all key influencing factors will increase. If the first... The weights, optimization priorities, and coordination coefficients of the first key influencing factor remain unchanged, and the product of its weight, optimization priority, and coordination coefficient remains unchanged. At this point, the first... The optimized adjustment values ​​of key influencing factors will decrease.

[0145] In summary, in the voltage optimization of praseodymium-neodymium alloy low-temperature electrolytic cells, feeding back the feedback parameter set to S2 to improve the accuracy of key influencing factor identification and obtain cell voltage optimization schemes can enable the optimization strategy to continuously adapt to the dynamic operating state of the electrolytic cell, significantly improving the optimization effect and stability.

[0146] In summary, by comparing the feedback parameter set with the initial parameter set, the changing trends of the electrolyzer's operating parameters can be clearly captured. The initial parameter set is the basic state data of the electrolyzer before optimization, while the feedback parameter set is the operating data after precise control of the electrode spacing and current density. The comparison between the two can intuitively show the direction and magnitude of the increase or decrease of parameters such as electrolyte temperature and composition after optimization, clarify the dynamic change law of parameters with the optimization process, provide a real state basis for subsequent adjustments, and avoid optimization from deviating from the actual operating conditions.

[0147] In summary, adjusting the weighting factors according to the trend of parameter changes over time can update the influence weight sequence, making the importance ranking of key influencing factors more consistent with the current operating status. As the electrolytic cell operates, the degree of influence of each factor on the cell voltage may change. For example, a certain factor may have a small influence in the early stage, but its influence may increase after optimization due to the linkage effect of parameters. By adjusting the weighting factors over time, this change can be accurately matched, ensuring that the influence weight sequence always reflects the true influence priority and improving the accuracy of identifying key influencing factors.

[0148] In summary, multi-objective optimization of key influencing factors based on the updated influence weight sequence can form a more scientific cell voltage optimization scheme. This process revolves around multiple objectives such as cell voltage stability and energy consumption control. It combines the priority of current key influencing factors and plans the direction and magnitude of parameter adjustments in a targeted manner, avoiding the loss of one aspect due to single-objective optimization. This allows the optimization scheme to focus on the core influencing factors while taking into account the comprehensive needs of electrolysis production, ultimately achieving continuous stability of cell voltage and ensuring the efficiency and product quality of praseodymium-neodymium alloy low-temperature electrolysis.

[0149] In summary, in the optimization of the voltage of the praseodymium-neodymium alloy low-temperature electrolytic cell, multi-objective optimization of key influencing factors based on the updated influence weight sequence to obtain the cell voltage optimization scheme can make the optimization strategy more in line with actual production needs, take into account multiple optimization objectives, and improve the cell voltage control effect.

[0150] In summary, by calculating the optimized adjustment values ​​of key influencing factors based on the updated influence weight sequence, the adjustment direction and magnitude of each factor can be accurately quantified. This process fully combines the weight values ​​and optimization priorities of key influencing factors. The weight values ​​reflect the importance of the factors to the tank voltage, and the optimization priorities clarify the order of emphasis of factors in the adjustment. Then, the interaction between the adjustments of different factors is balanced by the coordination coefficient to avoid excessive adjustment of a single factor from interfering with other parameters. The final optimized adjustment values ​​provide a clear and reliable quantitative standard for the specific adjustment of each key factor, avoiding subjectivity and blindness in the adjustment operation.

[0151] In summary, by comprehensively planning the parameters of key influencing factors based on optimized adjustment values, a systematic and comprehensive cell voltage optimization scheme can be formed. During the comprehensive planning process, attention is not only paid to adjusting the parameters of individual key factors, but also to the synergistic cooperation between various factors. This ensures that after adjustment according to the optimized values, each factor can support each other and work together to achieve the cell voltage stability target. Simultaneously, it adapts to the requirements of the low-temperature electrolysis process in terms of energy consumption, product quality, and other aspects, avoiding cell voltage fluctuations or other production indicator imbalances caused by adjusting a single parameter in isolation. The resulting optimization scheme has strong operability and practicality, effectively promoting the continuous stability of the cell voltage within the process requirements, and ensuring the high efficiency and high quality of praseodymium-neodymium alloy low-temperature electrolysis production.

[0152] like Figure 2 The diagram shown is a functional block diagram of a cell voltage optimization system for low-temperature electrolysis of praseodymium-neodymium alloys provided in an embodiment of the present invention.

[0153] The cell voltage optimization system 100 for low-temperature electrolysis of praseodymium-neodymium alloys described in this invention can be installed in electronic devices. Depending on the functions implemented, the cell voltage optimization system 100 may include a parameter coupling analysis module 101, an influence weight arrangement module 102, a scheme adjustment determination module 103, a voltage regulation control module 104, a parameter integration feedback module 105, and a voltage feedback optimization module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0154] In this embodiment, the functions of each module / unit are as follows:

[0155] The parameter coupling analysis module is used to perform multi-parameter coupling analysis on the initial parameter set of the electrolyzer during the low-temperature electrolysis process to obtain the key influencing factors of the electrolyzer.

[0156] The influence weight ranking module is used to perform correlation analysis on the key influencing factors based on the fluctuation characteristics of the cell voltage in the electrolytic cell, and obtain the influence weight sequence of the electrolytic cell.

[0157] The scheme adjustment determination module is used to determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the operating weight sequence.

[0158] A voltage regulation and control module is used to synchronously regulate the slot voltage based on the aforementioned coordinated adjustment scheme, thereby achieving precise control of the electrode spacing and current density.

[0159] The parameter integration and feedback module is used to collect and integrate the operating parameters of the electrolytic cell based on the precise control of the electrode spacing and current density, so as to obtain the feedback parameter set of the electrolytic cell.

[0160] The voltage feedback optimization module is used to feed back the feedback parameter set to S2 to optimize the identification accuracy of the key influencing factors and obtain an optimized scheme for the slot voltage. In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0161] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0163] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0164] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing cell voltage in low-temperature electrolysis of praseodymium-neodymium alloys, characterized in that, The method includes: S1. Perform multi-parameter coupling analysis on the initial parameter set of the electrolytic cell during the low-temperature electrolysis process to obtain the key influencing factors of the electrolytic cell; S2. Based on the fluctuation characteristics of the cell voltage in the electrolytic cell, a correlation analysis is performed on the key influencing factors to obtain the influence weight sequence of the electrolytic cell; S3. Based on the influence weight sequence, determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density; S4. Based on the aforementioned coordinated adjustment scheme, the slot voltage is synchronously adjusted to achieve precise control of the electrode spacing and current density. S5. Based on the precise control of the electrode spacing and current density, the operating parameters of the electrolytic cell are collected and integrated to obtain the feedback parameter set of the electrolytic cell; S6. Feedback the set of feedback parameters to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme of the slot voltage.

2. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, The initial parameter set of the electrolyzer during the low-temperature electrolysis process is subjected to multi-parameter coupled analysis to obtain the key influencing factors of the electrolyzer, including: Real-time parameters such as electrolyte temperature, electrolyte composition, current density, and electrode spacing in the electrolytic cell are collected to obtain the initial parameter set of the electrolytic cell; Correlation analysis is performed on the parameters in the initial parameter set to obtain the parameter coupling relationship of the electrolytic cell; Based on the influence strength of the parameter coupling relationship, the core screening of the cell voltage fluctuation parameters of the electrolytic cell is carried out to obtain the key influencing factors of the electrolytic cell.

3. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, Based on the fluctuation characteristics of the cell voltage in the electrolytic cell, a correlation analysis is performed on the key influencing factors to obtain the influence weight sequence of the electrolytic cell, including: The voltage of the tank is monitored in real time to obtain the fluctuation amplitude and frequency of the voltage. Based on the aforementioned key influencing factors, a correlation analysis is performed on the fluctuation amplitude and fluctuation frequency to obtain the degree of correlation between the key influencing factors and the fluctuation amplitude and fluctuation frequency. Based on the degree of correlation, the key influencing factors are weighted to obtain the weight factors of the key influencing factors; The weighting factors are normalized to obtain the influence weight sequence of the electrolytic cell.

4. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 3, characterized in that, The weighting of the key influencing factors based on the degree of correlation, to obtain the weighting factors of the key influencing factors, includes: Based on the degree of correlation, weight allocation calculations are performed on the key influencing factors to obtain the weight factors of the key influencing factors, wherein the weight allocation calculation formula is: ； in, Indicates the first The weighting factors of the aforementioned key influencing factors, Indicates the first The degree of correlation between the aforementioned key influencing factors and the fluctuation range. Indicates the first The degree of correlation between the aforementioned key influencing factors and the aforementioned fluctuation frequency. Indicates the first The duration coefficient of the effects of the aforementioned key influencing factors Indicates the first The frequency coefficients of the key influencing factors mentioned above This represents the correlation adjustment coefficient. This represents the intensity adjustment coefficient. This represents the sum of the weighted eigenvalues ​​of all the key influencing factors.

5. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, The step of determining the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the influence weight sequence includes: Based on the influence weight sequence, the weights of the electrode spacing and current density in the electrolytic cell are compared and arranged to obtain the priority relationship between the electrode spacing and current density weights. The priority relationship is quantized and ordered to obtain the adjustment priority of the electrode spacing and current density; Based on the adjustment priority, a decision is made on the operating state of the electrolytic cell to obtain a coordinated adjustment scheme for the electrode spacing and current density.

6. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, The method of synchronously adjusting the slot voltage based on the aforementioned coordinated adjustment scheme to achieve precise control of the electrode spacing and current density includes: Based on the aforementioned coordinated adjustment scheme, the initial parameter set is tuned and optimized to obtain the control commands for the electrode spacing and current density. Based on the control command, the slot voltage is controlled in a coordinated manner to obtain the response change of the slot voltage; Based on the change in response, the electrode spacing and current density in the coordinated adjustment scheme are finely adjusted to achieve precise control of the electrode spacing and current density.

7. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, Based on the precise control of the electrode spacing and current density, the operating parameters of the electrolytic cell are collected and integrated to obtain a set of feedback parameters for the electrolytic cell, including: Based on the precise control of the electrode spacing and current density, noise filtering is performed on the operating parameters of the electrolytic cell to obtain the processed operating parameters. The processed operating parameters are aligned and integrated in chronological order to obtain the feedback parameter set of the electrolytic cell.

8. The method for optimizing cell voltage in low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 1, characterized in that, The step of feeding back the feedback parameter set to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme for the slot voltage includes: By analyzing and comparing the feedback parameter set with the initial parameter set, the changing trend of the operating parameters in the electrolytic cell can be obtained; Based on the aforementioned trend, the weighting factors are adjusted over time to obtain an updated influence weight sequence. Based on the updated influence weight sequence, multi-objective optimization is performed on the key influencing factors to obtain an optimized scheme for the slot voltage.

9. The cell voltage optimization method for low-temperature electrolysis of praseodymium-neodymium alloy as described in claim 8, characterized in that, The step of performing multi-objective optimization on the key influencing factors based on the updated influence weight sequence to obtain an optimized scheme for the tank voltage includes: Based on the updated influence weight sequence, multi-objective optimization calculations are performed on the factors among the key influencing factors to obtain the optimized adjustment values ​​of the key influencing factors. The formula for the multi-objective optimization calculation is as follows: ； in, Indicates the first The optimized adjustment values ​​for the aforementioned key influencing factors, Indicates the first The weight values ​​of the key influencing factors mentioned above. Indicates the first The optimization priority of the aforementioned key influencing factors, Indicates the coordination coefficient. This represents the sum of the products of the weights of all the aforementioned key influencing factors and their optimization priorities; Based on the optimized adjustment values, the parameters among the key influencing factors are comprehensively planned to obtain the optimized scheme for the tank voltage.

10. A cell voltage optimization system for low-temperature electrolysis of praseodymium-neodymium alloy, characterized in that, The system includes: The parameter coupling analysis module is used to perform multi-parameter coupling analysis on the initial parameter set of the electrolyzer during the low-temperature electrolysis process to obtain the key influencing factors of the electrolyzer. The influence weight ranking module is used to perform correlation analysis on the key influencing factors based on the fluctuation characteristics of the cell voltage in the electrolytic cell, and obtain the influence weight sequence of the electrolytic cell. The scheme adjustment determination module is used to determine the coordinated adjustment scheme of the electrolytic cell electrode spacing and current density based on the influence weight sequence. A voltage regulation and control module is used to synchronously regulate the slot voltage based on the aforementioned coordinated adjustment scheme, thereby achieving precise control of the electrode spacing and current density. The parameter integration and feedback module is used to collect and integrate the operating parameters of the electrolytic cell based on the precise control of the electrode spacing and current density, so as to obtain the feedback parameter set of the electrolytic cell. The voltage feedback optimization module is used to feed back the set of feedback parameters to S2 to optimize the identification accuracy of the key influencing factors and obtain the optimization scheme of the slot voltage.

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