Intelligent monitoring management system in NiB binary alloy preparation process

Abstract

The invention relates to the technical field of alloy preparation, and discloses an intelligent monitoring management system in a NiB binary alloy preparation process. The method comprises the following steps: acquiring full-band spectral data through a laser-induced breakdown spectroscopy technology, identifying characteristic spectral lines of impurity elements, calculating the accurate concentration of each impurity element by adopting a spectral line intensity ratio method, calculating an optimal formula of a selective remover based on a chemical activity database, and generating a sequential control scheme by utilizing a precise feeding control algorithm. And dynamic adjustment is realized through closed-loop optimization control. According to the invention, the technical problems of insufficient detection precision and poor removal selectivity of trace impurity elements are solved, and high-precision detection and selective removal of ppm-level impurities are realized.

Description

Technical Field

[0001] This invention relates to the field of alloy preparation technology, and more specifically, to an intelligent monitoring and management system for the preparation process of NiB binary alloys. Background Technology

[0002] NiB binary alloys are important functional materials with wide applications in electronic devices, magnetic materials, and precision instruments. With the increasing demands for material purity and performance in modern industry, precise control of impurity elements during the preparation process has become a key technical challenge.

[0003] Currently, impurity detection in the NiB alloy preparation process mainly employs traditional chemical analysis methods, such as atomic absorption spectrometry and inductively coupled plasma atomic emission spectrometry. These methods suffer from insufficient sensitivity when detecting trace impurities, making it difficult to accurately identify and quantify impurity content at the ppm level. Regarding impurity removal, existing technologies primarily rely on traditional purification methods such as chemical precipitation and extraction. These methods lack selectivity for specific impurity elements and often employ a crude purification approach.

[0004] The aforementioned deficiencies in the existing technology have led to a major technical problem: the inability to accurately detect and selectively remove trace impurity elements, resulting in the loss of effective components, low purification efficiency, and seriously affecting the final quality and application performance of NiB binary alloys. Summary of the Invention

[0005] This invention provides an intelligent monitoring and management method for the preparation process of NiB binary alloys, which solves the technical problems of insufficient detection accuracy and poor removal selectivity of trace impurity elements in related technologies.

[0006] This invention discloses an intelligent monitoring and management method for the preparation process of NiB binary alloys. The method includes: acquiring full-band spectral data of the NiB binary alloy sample using laser-induced breakdown spectroscopy; identifying characteristic spectral lines of impurity elements based on a spectral database; calculating the precise concentration of each impurity element using the spectral line intensity ratio method; eliminating matrix effects by using the ratio of the characteristic spectral line intensity of the impurity element to the reference spectral line intensity of the matrix element; converting the intensity ratio into impurity element concentration based on a pre-established standard curve to generate an impurity distribution map; obtaining the standard electrode potential and complexation stability constant of each impurity element based on a chemical activity database; calculating the optimal formulation of the selective remover using the potential difference criterion and complexation selectivity coefficient; inputting the formulation parameters into a precision feeding control algorithm; generating a time-series control scheme including addition time, addition rate, and addition location by comprehensively considering reaction kinetic parameters and mass transfer diffusion coefficients; and monitoring the dynamic changes in the impurity element concentration in the alloy in real time, adjusting the remover formulation and addition parameters when the impurity concentration does not reach the expected target to achieve closed-loop optimization control.

[0007] This invention discloses a system for executing the above-mentioned intelligent monitoring and management method, including a laser-induced breakdown spectrometer, a chemical activity database server, and a precision feeding control device.

[0008] Furthermore, the identification of the characteristic spectral lines of the impurity elements includes: segmenting the spectral data according to wavelength intervals to generate a spectral intensity sequence; using a peak detection algorithm to identify the peak position in the spectral intensity sequence, identifying the peak by calculating the intensity gradient change and setting threshold conditions; matching the identified peak wavelength with a standard spectral line library of impurity elements, and determining the type of impurity element by wavelength difference comparison and similarity calculation.

[0009] Furthermore, the standard curve is established using a polynomial regression method. After logarithmically transforming the intensity ratio, a nonlinear relationship between concentration and ratio is established through a polynomial function. The polynomial includes constant terms, linear terms, quadratic terms, and cubic terms, and the coefficients of each term are determined through standard sample calibration experiments.

[0010] Furthermore, the calculation of the optimal formulation of the selective remover includes: determining the reduction priority of impurity elements relative to matrix elements based on the potential difference criterion; selecting a remover with higher complexing ability for the target impurity element based on the complexation selectivity coefficient; and calculating the theoretical dosage of the remover based on the principle of mass conservation, taking into account impurity concentration, alloy melt density and volume, molecular weight of the remover and impurity elements, and reaction efficiency coefficient.

[0011] Furthermore, the precision feeding control algorithm includes: standardizing input parameters with different physical dimensions and numerical ranges, scaling each parameter to a preset range to eliminate dimensional differences; establishing a reaction-mass transfer coupling model, and performing multi-objective optimization with the objective functions of maximizing impurity removal efficiency and minimizing matrix component loss; the output timing control parameters include addition time, addition rate, and addition position.

[0012] Furthermore, the objective function balances removal efficiency and matrix loss through weighting coefficients, which are determined based on process requirements and cost-benefit analysis.

[0013] Furthermore, the execution of the timing control scheme includes: converting the addition time into the start time and duration setpoint of the feeding pump; converting the addition rate into the flow control signal of the feeding pump; and converting the addition position into coordinate commands for the agitator speed and the feeding port position.

[0014] Furthermore, the closed-loop optimization control includes: real-time monitoring of the changing trend of impurity element concentration in the alloy and evaluation of removal effect; generating compensation effect evaluation and iterative optimization suggestions based on deviation analysis results; converting the effect evaluation results into suggested values ​​for increasing or decreasing the amount of remover, and converting the optimization suggestions into adjustment parameters for feeding sequence and switching instructions for remover type.

[0015] Furthermore, the method is applicable to the detection and removal of trace impurity elements at the ppm level, including at least one of Fe, Cr, and Cu.

[0016] This invention achieves high-precision detection of trace impurity elements at the ppm level by employing laser-induced breakdown spectroscopy combined with the spectral line intensity ratio method. It achieves selective removal of specific impurity elements through selective removal agent formulation calculation based on a chemical activity database and a precision feeding control algorithm. Furthermore, it realizes dynamic adjustment and iterative optimization through closed-loop optimization control, solving the technical problems of insufficient detection accuracy and poor removal selectivity of trace impurity elements in existing technologies. This invention achieves significantly improved impurity detection sensitivity, significantly increased removal efficiency, and minimized matrix component loss, providing an effective technical solution for the high-quality preparation of NiB binary alloys. Attached Figure Description

[0017] Figure 1 This is a flowchart of the intelligent monitoring and management method for the NiB binary alloy preparation process of the present invention. Figure 2 This is a flowchart of the laser-induced breakdown spectral data processing and impurity element characteristic spectral line identification of the present invention; Figure 3 This is a flowchart of the method for calculating the precise concentration of impurity elements using the spectral line intensity ratio method of the present invention; Figure 4 This is a flowchart of the optimal formulation of selective removal agents calculated based on a chemical activity database, according to the present invention. Detailed Implementation

[0018] In the preparation of NiB binary alloys, the presence of impurity elements can severely affect the alloy's performance and quality. Conventional compositional analysis methods lack sufficient sensitivity for detecting trace elements, making it difficult to accurately identify and quantify impurity content at the ppm level. Furthermore, traditional purification methods lack selectivity, failing to precisely remove specific impurity elements and often employing extensive purification processes, resulting in the loss of effective components and low purification efficiency.

[0019] Therefore, there is a need for an intelligent monitoring and management method that can accurately detect trace impurity elements and achieve selective and precise removal.

[0020] The method of this embodiment includes the following steps; According to an embodiment of this invention, an intelligent monitoring and management method for the preparation process of NiB binary alloys is provided. This method is executed in a hardware environment consisting of a laser-induced breakdown spectrometer, a chemical activity database server, and a precision feeding control device, and includes the following steps: Step 100: Obtain laser-induced breakdown spectral data of the NiB binary alloy sample and identify characteristic spectral lines of impurity elements. A NiB binary alloy sample was placed in a laser-induced breakdown spectrometer, and plasma was generated on the sample surface by laser pulse excitation to acquire full-band spectral data. Based on the preset characteristic wavelength ranges of impurity elements in the spectral database, the characteristic spectral lines corresponding to each impurity element were identified, and the position information of the impurity spectral lines and their corresponding spectral intensity values ​​were output.

[0021] It should be noted that the identification of characteristic spectral lines of impurity elements mentioned above refers to matching the acquired full-band spectral data with the standard spectral lines of impurity elements stored in the spectral database, and determining the type of impurity element through a peak position matching algorithm. Specifically, step 101: segment the spectral data according to wavelength intervals to generate a spectral intensity sequence; step 102: use a peak detection algorithm to identify the peak positions in the spectral intensity sequence, wherein the input of the peak detection algorithm is the spectral intensity sequence. ,in This indicates the first wavelength point. This indicates the second wavelength point. This represents the total number of wavelength points in the spectral data, and the output is a set of peak wavelength locations. ,in Indicates the position of the first peak wavelength. Indicates the position of the second peak wavelength. The total number of detected peaks is represented by the peaks identified by calculating the intensity gradient change and setting threshold conditions; Step 103: The identified peak wavelengths are matched with the standard spectral library of impurity elements to determine the impurity element type and corresponding characteristic spectral lines. The input of the peak position matching algorithm is the set of peak wavelength positions and the standard spectral library data, and the output is the impurity element type identifier and matching confidence. Element identification is achieved by wavelength difference comparison and similarity calculation.

[0022] Step 200: Calculate the precise concentration of each impurity element using the spectral line intensity ratio method to generate an impurity distribution map. Based on the characteristic spectral lines of the impurity elements identified in step 100, the precise concentration of each impurity element is calculated using the spectral line intensity ratio method. For each impurity element, its main characteristic spectral line and reference spectral line are selected, the intensity ratio is calculated, and the ratio is converted into a concentration value through a standard curve, generating an impurity distribution map containing information on the type, concentration, and spatial distribution of the impurity element.

[0023] It should be noted that the above-mentioned spectral line intensity ratio method refers to using the ratio of the characteristic spectral line intensity of the impurity element to the reference spectral line intensity of the matrix element or internal standard element to eliminate matrix effects and spectral interference. Specifically, step 201: For impurity elements... The selected wavelength is The main characteristic spectral lines, and their spectral intensity are measured. Step 202: Select a reference spectral line for the matrix element Ni or B, with a wavelength of [wavelength value missing]. Measure its spectral intensity Step 203: Calculate the strength ratio Step 204: Based on the pre-established standard curve Calculate impurity elements concentration ,in Indicates impurity elements The mass concentration is expressed in ppm.

[0024] The aforementioned function The specific implementation is as follows: First, the intensity ratio A logarithmic transformation is performed to improve linearity, and then a concentration-ratio relationship is established through polynomial regression, specifically in the form of... ,in Represents the natural logarithm function. The constant term regression coefficient, The regression coefficients are linear terms. The regression coefficients are quadratic terms. The coefficients are cubic regression coefficients, all of which are regression parameters determined through standard sample calibration experiments. This polynomial form can reflect the nonlinear relationship between spectral intensity and elemental concentration, and effectively compensate for the impact of matrix effects and spectral line interference on the accuracy of quantitative analysis.

[0025] Step 300: Calculate the optimal formulation of the selective removal agent based on the chemical activity database of impurity elements. Based on the impurity distribution map obtained in step 200, the chemical activity parameters of each impurity element are retrieved from the chemical activity database, including redox potential, complexation capacity, and activation energy. Based on these parameters, the optimal formulation of the selective removal agent for each impurity element is calculated, and the type, dosage, and order of addition of the removal agent are output.

[0026] It should be noted that the calculation of the optimal formulation of the selective removal agent mentioned above refers to selecting a removal agent with specific selectivity based on the differences in the chemical activity of the impurity elements, so as to achieve preferential removal of the target impurities without affecting the matrix composition. Specifically, step 301: Obtain impurity elements from the chemical activity database. Standard electrode potential and complexation stability constant Step 302: Based on the potential difference criterion Determine impurity elements Reduction priority relative to the matrix element Ni; Step 303: Based on the complexation selectivity coefficient Selecting impurity elements A removal agent with higher complexing ability; Step 304: Calculate the theoretical dosage of the removal agent based on the principle of mass conservation. ,in Impurity concentration (ppm) The density of the alloy melt (kg / m³) The volume of the alloy melt (m³) and The molecular weights (g / mol) of the remover and the impurity element are respectively. The reaction efficiency coefficient is in the denominator. Used for ppm unit conversion to ensure dimensional consistency.

[0027] In this embodiment, to improve the accuracy and controllability of the removal effect, the following steps are included in addition to step 300: Step 400: Input the formula parameters into the precision feeding control algorithm to generate a micro-addition timing scheme. Based on the remover formula calculated in step 300, considering reaction kinetics and mass transfer processes, a precision timing control scheme including addition time, addition rate, and addition location is generated to ensure uniform dispersion and sufficient reaction of the remover. The timing control parameters need to be further decoded into specific equipment control instructions: addition time. Convert the start time and duration settings of the feed pump to the addition rate. Convert the flow control signal to the feed pump, and add the position. Convert the coordinate commands to the mixer speed and the position of the feed inlet.

[0028] The aforementioned precision feeding control algorithm is a multi-objective optimization algorithm that comprehensively considers reaction kinetic parameters, mass transfer and diffusion coefficients, and mixing effects. The input to this algorithm is the removal agent formulation parameters. ,in Indicates the quality of the removal agent. Indicates the target impurity concentration; physical parameters of the alloy melt. ,in Indicates the melt temperature. Indicates dynamic viscosity. Density of the alloy melt; reaction kinetic constant ,in Represents the reaction rate constant. Indicates the activation energy of the reaction; the output is a timing control parameter. ,in Indicates the time to be added. Indicates the rate of addition. This indicates the insertion point. Since the input parameters have different physical dimensions and numerical ranges, the algorithm first standardizes all input parameters, scaling them to the [0,1] interval to eliminate the impact of dimensional differences on the optimization calculation. The algorithm establishes a reaction-mass transfer coupling model, with the objective function being to maximize removal efficiency and minimize matrix loss. Perform multi-objective optimization, where and These are the weighting coefficients. To improve impurity removal efficiency, This represents the matrix component loss rate.

[0029] In this embodiment of the application, to achieve closed-loop optimized control, the following steps are also included: Step 500: Analyze the dynamic changes in impurity concentration and output compensation effect evaluation and iterative optimization suggestions. The changing trend of impurity element concentration in the alloy is monitored in real time to evaluate the removal effect. When the impurity concentration does not reach the expected target, the removal agent formulation and addition parameters are adjusted based on the deviation analysis results to achieve dynamic compensation and iterative optimization. The compensation effect evaluation and iterative optimization suggestions need to be converted into specific control instructions: the effect evaluation results are converted into suggested values ​​for increasing or decreasing the amount of removal agent, and the optimization suggestions are converted into adjustment parameters for the feeding sequence and switching instructions for the type of removal agent.

[0030] This embodiment uses laser-induced breakdown spectroscopy to replace traditional chemical analysis methods. Through the physical principles of plasma excitation and spectral detection, it achieves high-sensitivity detection of trace impurity elements at the ppm level, overcoming the technical defects of insufficient sensitivity of conventional component analysis methods for trace elements.

[0031] The quantitative calculation mechanism based on the spectral line intensity ratio method eliminates matrix effects and spectral interference by using the intensity ratio of characteristic spectral lines of impurity elements to reference spectral lines of matrix elements, thus achieving accurate quantitative analysis of impurity element concentration and avoiding the inaccuracies of traditional qualitative analysis methods.

[0032] Based on the calculation of selective removal agent formulations using a database of chemical activity of impurity elements, selective removal of specific impurity elements is achieved through theoretical calculations of electrode potential difference and complexation selectivity coefficient. This overcomes the technical problems of traditional purification methods, such as lack of selectivity and easy loss of effective components.

[0033] Therefore, this embodiment solves the technical problem of accurately detecting and selectively removing trace impurity elements during the preparation of NiB binary alloys by utilizing the high sensitivity of spectroscopic detection technology, the high accuracy of ratio method calculation, and the high selectivity of chemical activity theory.

[0034] Laser-induced breakdown spectroscopy (LIBS) is a technique that uses a high-power pulsed laser to focus on the sample surface to generate a high-temperature plasma, and then performs elemental analysis by detecting the plasma emission spectrum.

[0035] Spectral line intensity ratio method: A spectroscopic analysis method that improves the accuracy of quantitative analysis by measuring and analyzing the intensity ratio of a spectral line to a reference spectral line to eliminate matrix effects and instrument fluctuations.

[0036] Chemical Activity Database: A database system that stores chemical thermodynamic and kinetic parameters such as standard electrode potentials, complexation stability constants, and reaction activation energies of various elements.

[0037] Selective removal agents: Based on the principle of differences in chemical activity, these are chemical reagents that preferentially react with specific impurity elements while having little impact on the matrix elements.

[0038] Impurity distribution map: A two-dimensional or three-dimensional data visualization graphic containing information on the types, concentration values, and spatial distribution of impurity elements.

[0039] Precision feeding control algorithm: A multi-objective optimization algorithm that comprehensively considers reaction kinetic parameters, mass transfer and diffusion coefficients and mixing effects. It calculates the optimal reactant addition timing, rate and position parameters through a reaction-mass transfer coupling model.

[0040] A precision electronic device manufacturer needs to produce high-purity NiB alloy targets, with a technical requirement that the total impurity element content be less than 50 ppm. Impurities such as Fe, Cr, and Cu were detected in the original alloy melt, requiring precise detection and selective removal using the method of this invention.

[0041] Step 100 Implementation Example: LIBS spectroscopy was performed on a 1.5 kg NiB alloy melt sample. The laser power was set to 10 MW / cm², and the pulse width was 5 ns. The key spectral data obtained are as follows: Table 1. Raw data from LIBS spectral detection;

[0042] Step 200 Implementation Example: Calculate the precise concentration of each impurity element using the spectral line intensity ratio method. Taking Fe as an example, Fe I 371.993 nm is selected as the analytical line, and Ni I 393.366 nm as the reference line. The intensity ratio... Using a pre-established standard curve The Fe concentration was calculated.

[0043] Table 2. Results of impurity element concentration analysis;

[0044] Step 300 Implementation Example: Calculating the selective removal agent formulation based on the chemical activity parameters of impurity elements. The Fe standard electrode potential is obtained by querying the database. V, Cr standard electrode potential V, Cu standard electrode potential V. Alloy melt density kg / m³, volume m³.

[0045] Table 3. Calculation results of selective removal agent formulations;

[0046] Step 400 Implementation Example: Input the formula parameters into the precision feeding control algorithm, melt temperature K, dynamic viscosity Pa·s. Optimization calculation yields the timing control scheme.

[0047] Table 4. Precision feeding control parameters;

[0048] Final result: After selective removal treatment, LIBS testing was performed again to verify the purification effect.

[0049] Table 5. Content of impurity elements after optimization treatment;

[0050] After treatment by the method of this invention, the total content of impurity elements decreased from 107.4 ppm to 24.3 ppm, meeting the technical requirements (<50 ppm), and the loss of matrix components was only 2.3%, achieving the effect of highly efficient and selective removal.

Claims

1. A method for intelligent monitoring and management in the preparation process of NiB binary alloys, characterized in that, Includes the following steps: Full-band spectral data of NiB binary alloy samples were obtained by laser-induced breakdown spectroscopy, and characteristic spectral lines of impurity elements were identified based on the spectral database. The precise concentration of each impurity element is calculated using the spectral line intensity ratio method. The matrix effect is eliminated by the ratio of the characteristic spectral line intensity of the impurity element to the reference spectral line intensity of the matrix element. Based on the pre-established standard curve, the intensity ratio is converted into the impurity element concentration to generate an impurity distribution map. The standard electrode potential and complexation stability constant of each impurity element are obtained from the chemical activity database of impurity elements. The optimal formulation of the selective removal agent is calculated by using the potential difference criterion and the complexation selectivity coefficient. The formula parameters are input into the precision feeding control algorithm, and a timing control scheme including addition time, addition rate and addition location is generated by comprehensively considering reaction kinetic parameters and mass transfer diffusion coefficient. Real-time monitoring of the dynamic changes in the concentration of impurity elements in the alloy; when the impurity concentration does not reach the expected target, the removal agent formula and addition parameters are adjusted to achieve closed-loop optimization control.

2. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The identification of the characteristic spectral lines of the impurity elements includes: The spectral data is segmented according to wavelength intervals to generate a spectral intensity sequence; Peak detection algorithms are used to identify peak positions in spectral intensity sequences, and peaks are identified by calculating intensity gradient changes and setting threshold conditions. The identified peak wavelengths are matched with a standard spectral library of impurity elements, and the types of impurity elements are determined by wavelength difference comparison and similarity calculation.

3. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The standard curve is established using a polynomial regression method. After logarithmically transforming the intensity ratio, a nonlinear relationship between concentration and ratio is established through a polynomial function. The polynomial includes constant terms, linear terms, quadratic terms, and cubic terms, and the coefficients of each term are determined through standard sample calibration experiments.

4. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The calculation of the optimal formulation of the selective removal agent includes: The reduction priority of impurity elements relative to matrix elements is determined based on the potential difference criterion. Select the removal agent with higher complexing ability for the target impurity element based on the complexation selectivity coefficient; The theoretical dosage of the remover is calculated based on the principle of mass conservation. The calculation takes into account the impurity concentration, the density and volume of the alloy melt, the molecular weight of the remover and impurity elements, and the reaction efficiency coefficient.

5. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The precision feeding control algorithm includes: Input parameters with different physical dimensions and numerical ranges are standardized to scale each parameter to a preset range to eliminate dimensional differences. A reaction-mass transfer coupling model was established, and multi-objective optimization was performed with the objective functions of maximizing impurity removal efficiency and minimizing matrix component loss. The output timing control parameters include addition time, addition rate, and addition position.

6. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 5, characterized in that, The objective function balances removal efficiency and matrix loss through weighting coefficients, which are determined based on process requirements and cost-benefit analysis.

7. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The execution of the timing control scheme includes: Convert the addition time into the start time and duration settings of the feed pump; The addition rate is converted into a flow control signal for the feed pump; Convert the addition location into coordinate commands for the mixer speed and the feed port position.

8. The intelligent monitoring and management method for the NiB binary alloy preparation process according to claim 1, characterized in that, The closed-loop optimization control includes: Real-time monitoring of the changing trends of impurity element concentrations in alloys and evaluation of removal effectiveness; Based on the deviation analysis results, generate compensation effect evaluation and iterative optimization suggestions; The effect evaluation results are converted into suggested values ​​for increasing or decreasing the dosage of the removal agent, and the optimization suggestions are converted into adjustment parameters for the feeding sequence and instructions for switching the type of removal agent.

9. The intelligent monitoring and management method for the NiB binary alloy preparation process according to any one of claims 1 to 8, characterized in that, The method is applicable to the detection and removal of trace impurity elements at the ppm level, including at least one of Fe, Cr, and Cu.

10. A system for executing the intelligent monitoring and management method for the NiB binary alloy preparation process according to any one of claims 1-9, characterized in that, It includes a laser-induced breakdown spectrometer, a chemical activity database server, and precision feeding control equipment.

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