A method and system for detecting trace impurities in basic nickel carbonate

Abstract

This invention discloses a method and system for detecting trace impurities in basic nickel carbonate, belonging to the field of nickel salt powder detection technology. The method includes: wetting the sample with anhydrous ethanol; after determining the uniformity of dispersion using microscopic imaging; using bidirectional alternating ultrasonic stripping to remove impurities encapsulated within the particles to obtain a homogeneous dispersed powder; then using a composite reagent for micro-digestion, with real-time temperature control to ensure digestion stability; after cooling, adding a solid composite masking powder to complex the nickel matrix; determining the complexation endpoint by conductivity monitoring to obtain a test solution containing only free impurity ions; after pretreatment of the test solution through a hydrophobic atomization pipeline, acquiring the original spectral data, fitting a calibration curve, and correcting matrix interference through spectral interference coefficient interpolation to calculate the actual mass fraction of each impurity in the sample. This invention effectively solves the problems of impurities encapsulated in basic nickel carbonate particles and spectral interference from the nickel matrix, and has the advantages of fast detection speed, high accuracy, and high automation, making it suitable for industrial online quality control.

Description

Technical Field

[0001] This invention belongs to the field of nickel salt powder detection technology, specifically a method and system for detecting trace impurities in basic nickel carbonate. Background Technology

[0002] Basic nickel carbonate is a core raw material for preparing high-purity nickel salts, precursors for power batteries, and electroplated nickel materials. Trace impurities, including crystal inclusions and particle agglomerations within the powder, directly determine the yield and performance of downstream end products. Industrial production scenarios place high demands on the speed, accuracy, and automation of impurity detection. Existing conventional detection techniques still have several shortcomings when processing basic nickel carbonate samples: the powder is highly prone to agglomeration, with impurities such as iron, calcium, magnesium, and copper encapsulated within the particles. Traditional stirring or unidirectional ultrasonic methods are insufficient to achieve complete impurity dissociation and release, resulting in incomplete impurity extraction and systematically low detection results. During digestion, uneven temperature distribution within the system can lead to localized overheating and loss of volatile elements, while localized low temperatures can cause incomplete dissolution of insoluble impurities, further amplifying detection errors.

[0003] After sample digestion, the system contains a large concentration of nickel matrix. The nickel ion emission spectrum can severely interfere with the characteristic matrix spectra of impurities, easily leading to spectral superposition and signal distortion. Conventional detection methods lack targeted matrix masking and interference correction techniques. Furthermore, traditional detection lacks real-time quantitative monitoring of the complexation reaction endpoint, relying heavily on manual operation throughout the process. Blank baseline compensation and spectral interference correction are missing, and most methods employ single-channel sequential spectral acquisition, resulting in long detection cycles and large batch-to-batch dispersion. Existing detection solutions are mostly offline laboratory-based, lacking the ability to automatically generate time-series reports. Detection data cannot be directly fed back to the front-end synthesis and output processes, hindering automatic sample grade identification and closed-loop production control. These methods are ill-suited to the practical needs of continuous industrial production for rapid, stable, and high-precision online detection of impurities in basic nickel carbonate. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method and system for detecting trace impurities in basic nickel carbonate. The method involves wetting the sample with anhydrous ethanol, and after microscopic imaging confirms the uniformity of dispersion, using bidirectional alternating ultrasonic stripping to remove impurities encapsulated within the particles, resulting in a homogeneous dispersed powder. Next, a composite reagent is used for micro-digestion, with real-time temperature control to ensure digestion stability. After cooling, a solid composite masking powder is added to complex the nickel matrix. The complexation endpoint is determined by conductivity monitoring, yielding a test solution containing only free impurity ions. The test solution is pretreated through a hydrophobic atomization pipeline, and the original spectral data is obtained. A calibration curve is fitted, and matrix interference is corrected by spectral interference coefficient interpolation to calculate the actual mass fraction of each impurity in the sample. This invention effectively solves the problems of impurities encapsulated in basic nickel carbonate particles and spectral interference from the nickel matrix, offering advantages such as high detection speed, high accuracy, and high automation, making it suitable for industrial online quality control.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for detecting trace impurities in basic nickel carbonate, comprising:

[0007] Basic nickel carbonate powder was used as the original sample to be tested. The original sample was soaked in anhydrous ethanol and allowed to stand to obtain the soaked powder. The impurities inside the soaked powder were removed by bidirectional alternating ultrasonic stripping to obtain homogeneous dispersed powder.

[0008] The homogeneous dispersed powder was placed in a sealed micro digestion container, and a composite digestion reagent consisting of organic acid and nitric acid was added. The digestion stock solution was obtained by micro digestion.

[0009] After the digestion solution is cooled, solid composite masking powder is added and vortexed to mix, complexing the nickel matrix in the digestion solution to obtain a masked test solution containing only free impurity ions;

[0010] The masked test liquid is passed into an atomizing pipeline with a hydrophobic coating. The pipeline is first pre-rinsed with organic acid to remove residual precipitates. Then, the characteristic spectral signals of the impurities are acquired in parallel by a multi-channel spectrometer. Combined with the gradient blank compensation baseline, the original spectral data of the impurities are obtained.

[0011] The concentration correction curve is fitted based on the pre-stored multi-gradient nickel matrix standard mother liquor database. The nickel concentration of the masking solution is determined by combining the original spectral data of impurities. The spectral interference is corrected by curve interpolation to obtain the corrected impurity spectral data.

[0012] Based on the corrected impurity spectral data, the actual content of various impurities is calculated, an impurity detection report with time stamp is generated, the sample grade is determined by combining the preset impurity control threshold, and the grade determination result is fed back to the basic nickel carbonate synthesis output process to complete the detection.

[0013] Specifically, after obtaining the impregnated powder by impregnating the original sample with anhydrous ethanol and allowing it to stand, the method further includes determining the dispersion uniformity of the impregnated powder, including:

[0014] The surface microscopic image of the impregnated powder is acquired using an image acquisition device. The surface microscopic image is then converted to grayscale to obtain a grayscale image. An edge detection operator is then performed on the grayscale image to obtain a particle boundary feature map.

[0015] Based on the particle boundary feature map, the particle size distribution parameters and agglomeration area ratio of the particles in the impregnated powder are statistically analyzed. Under the condition that the coefficient of variation of the particle size distribution parameters is less than a preset first threshold and the agglomeration area ratio is less than a preset second threshold, a dispersion uniformity qualified signal is output.

[0016] Based on the qualified dispersion uniformity signal, bidirectional alternating ultrasound is applied to the impregnated powder.

[0017] Specifically, the step of applying bidirectional alternating ultrasonic stripping to the impregnated powder to remove internally encapsulated impurities and obtain homogeneous dispersed powder includes:

[0018] Based on the ultrasonic generator, a first frequency ultrasonic electrical signal and a second frequency ultrasonic electrical signal are generated. The first frequency ultrasonic electrical signal is transmitted to the first ultrasonic transducer group, and the second frequency ultrasonic electrical signal is transmitted to the second ultrasonic transducer group.

[0019] Based on the built-in sweep frequency output unit of the ultrasonic generator, the first frequency ultrasonic electrical signal and the second frequency ultrasonic electrical signal are controlled to switch between output state and stop state according to an alternating timing sequence, so that the first ultrasonic transducer group and the second ultrasonic transducer group are started and run alternately.

[0020] When the first ultrasonic transducer group is started, it emits a first ultrasonic wave along a first direction. When the second ultrasonic transducer group is started, it emits a second ultrasonic wave along a second direction. The first direction and the second direction are set opposite to each other, so that the particles inside the impregnated powder are alternately subjected to the first sound pressure along the first direction and the second sound pressure along the second direction.

[0021] The alternating switching of the first sound pressure and the second sound pressure causes the inclusions encased inside the particles to be peeled off from the particle matrix interface under the action of a bidirectional alternating stress field. The peeled inclusions are then dispersed into the liquid phase to obtain a homogeneous dispersed powder.

[0022] Specifically, after obtaining the digestion stock solution through micro-digestion, the process further includes temperature field monitoring and feedback control of the micro-digestion process based on a temperature sensor array, specifically including:

[0023] Temperature signals are collected by multiple temperature sensors arranged on the outer wall of the sealed micro digestion container, and the temperature signals are converted into digital temperature values ​​by an analog-to-digital converter.

[0024] The digital temperature values ​​are used to construct a temperature field distribution dataset, and the average temperature and maximum temperature difference of the temperature field distribution dataset are calculated.

[0025] When the deviation of the average temperature from the preset target temperature value is greater than a first deviation threshold or the maximum temperature difference is greater than a second deviation threshold, a temperature adjustment control signal is output to the heating element driver. The heating element driver adjusts the output power of the heating element according to the temperature adjustment control signal, so that the average temperature is restored to the preset target temperature value and the maximum temperature difference is reduced to below the second deviation threshold.

[0026] Specifically, after adding the solid composite masking powder and vortex mixing, the process further includes real-time monitoring of the complexation reaction process of the masking mixture based on a conductivity measuring device, specifically including:

[0027] The conductivity electrode is immersed in the masking mixture, and an AC excitation signal is applied to the conductivity electrode through the conductivity measurement host to collect the response current signal between the conductivity electrodes.

[0028] The real-time conductivity value is calculated based on the ratio of the response current signal to the AC excitation signal, and the real-time conductivity value is recorded at fixed time intervals to generate a conductivity time series curve.

[0029] The slope of the conductivity time-series curve is calculated to obtain the conductivity change rate. If the conductivity change rate is less than a preset rate threshold, the complexation reaction is determined to have reached its endpoint, and a complexation completion signal is output.

[0030] Specifically, the parallel acquisition of impurity characteristic spectral signals through a multi-channel spectroscopic device includes:

[0031] The masked test liquid is introduced into an inductively coupled plasma atomic emission spectrometer (ICP-AES). The solid-state radio frequency generator of the ICP-AES excites the impurity ions in the masked test liquid to generate characteristic emission spectra. The charge-coupled device (CCD) detector of the ICP-AES simultaneously acquires the spectral intensity values ​​at the characteristic spectral line wavelengths of multiple impurity elements in a multi-channel parallel acquisition mode.

[0032] By using the data acquisition rate and acquisition duration of the multi-channel parallel acquisition mode, the original spectral intensity time series data group corresponding to each impurity ion is obtained, and all the original spectral intensity time series data groups constitute the impurity characteristic spectral signal.

[0033] Specifically, the step of obtaining the original spectral data of impurities by combining the gradient blank compensation baseline includes:

[0034] The blank spectral intensity time series data group corresponding to the blank reagent is collected by inductively coupled plasma atomic emission spectrometry, and the blank intensity statistical feature value in the blank spectral intensity time series data group is extracted.

[0035] A blank baseline correction vector is constructed based on the blank intensity statistical feature value and stored in the correction register;

[0036] The impurity characteristic spectral signal is input into the correction register for splitting and parsing, separating the original spectral intensity time series data group corresponding to each impurity ion. For any set of original spectral intensity time series data group, the impurity characteristic wavelength bound to the original spectral intensity time series data group is extracted. The blank intensity statistical feature value corresponding to the same wavelength position in the blank baseline correction vector is located using the impurity characteristic wavelength as the index. The original spectral intensity value corresponding to each time node in the original spectral intensity time series data group is subtracted from the blank intensity statistical feature value obtained by matching the index to obtain the preliminary deducted spectral intensity value.

[0037] The initial subtracted spectral intensity value is subjected to digital filtering to obtain a smoothed spectral intensity value, which constitutes the original spectral data of the impurities.

[0038] Specifically, the fitting of the concentration correction curve based on a pre-stored multi-gradient nickel matrix standard mother liquor database includes:

[0039] The pre-stored multi-gradient nickel matrix standard mother liquor database is retrieved from the storage unit. The multi-gradient nickel matrix standard mother liquor database contains multiple groups of standard mother liquors with nickel ion mass concentration gradients and the standard spectral intensity values ​​of each impurity ion in each group of standard mother liquors.

[0040] The known concentration value of each impurity ion in each set of standard mother liquor is combined with the corresponding standard spectral intensity value to form a standard concentration-intensity data pair;

[0041] The least squares fitting algorithm is used to perform univariate linear regression fitting on all the standard concentration-intensity data pairs to obtain the concentration correction curve for each impurity ion; the concentration correction curve includes two correction parameters: slope and intercept.

[0042] The concentration correction curve for each impurity ion, along with its corresponding slope and intercept, is stored in the storage unit.

[0043] Specifically, the step of correcting spectral interference through curve interpolation to obtain corrected impurity spectral data includes:

[0044] The concentration correction curves of each impurity ion are retrieved from the storage unit. The smoothed spectral intensity value of each impurity ion in the original impurity spectral data is substituted into the corresponding concentration correction curve to calculate the initial concentration judgment value of each impurity ion, thus obtaining the initial impurity concentration dataset.

[0045] By calling the nickel matrix interference reference spectrum, the original spectral data of the impurities and the nickel matrix interference reference spectrum are compared with the wavelength intensity ratio to obtain the spectral interference coefficient corresponding to each impurity ion.

[0046] If the spectral interference coefficient is greater than the preset interference threshold, the smooth spectral intensity value of the target impurity ion and the neighboring spectral intensity values ​​of its adjacent wavelength positions are selected as interpolation nodes to calculate the interpolated corrected spectral intensity value of the target impurity ion at the characteristic wavelength.

[0047] Replace the smoothed spectral intensity value with the interpolated corrected spectral intensity value and resubstitute it into the corresponding concentration correction curve to obtain the corrected impurity concentration value;

[0048] The initial impurity concentration dataset is updated based on the corrected impurity concentration values ​​to obtain the corrected impurity concentration dataset; the corrected impurity concentration dataset and its corresponding impurity ion types constitute the corrected impurity spectral data.

[0049] Specifically, the calculation of the actual content of various impurities based on the corrected impurity spectral data includes:

[0050] Obtain the corrected concentration value of each impurity ion in the corrected impurity spectral data;

[0051] Obtain the sampling volume of the homogeneous dispersed powder and the total volume of the composite digestion reagent;

[0052] Multiply the corrected concentration value by the total volume value and then divide by the sampling volume value to obtain the mass fraction conversion value of each impurity ion in the original sample to be tested.

[0053] The mass fraction conversion values ​​of all impurity ions are summarized into an actual impurity content dataset, and the actual impurity content dataset is stored in a storage unit.

[0054] Specifically, the generation of a time-stamped impurity detection report, combined with a preset impurity control threshold to determine the sample grade, includes:

[0055] The system clock is invoked to obtain the current detection date and time and the current detection sequence number, and a timing mark code is generated.

[0056] The impurity type and mass fraction conversion value of each impurity ion in the actual impurity content dataset are combined with the time-series tag code to form an impurity detection record. All impurity detection records are summarized into an impurity detection record set.

[0057] Call the preset report template, fill the time sequence mark code into the header area, fill the impurity detection record set into the data area, and generate an impurity detection report with time sequence mark;

[0058] The preset impurity control threshold table is invoked, and the converted mass fraction value is compared with the upper limit of the corresponding grade threshold in the preset impurity control threshold table, and the grade determination result is output.

[0059] A system for detecting trace impurities in basic nickel carbonate includes:

[0060] The sample pretreatment module wets the sample with anhydrous ethanol, acquires a microscopic image of the powder, and calculates the particle boundary feature map. It then uses statistical analysis of particle size and agglomeration parameters to determine the dispersion uniformity. Once the sample is qualified, the ultrasonic generator outputs alternating ultrasonic signals at dual frequencies to obtain homogeneous dispersed powder.

[0061] The complexation monitoring module adds composite masking powder to the digested original solution after cooling and vortex mixing to complex the nickel matrix. It collects excitation and response currents, calculates real-time conductivity and generates conductivity time-series curves. The endpoint of the complexation reaction is determined by the rate of change of the curve. After the reaction is complete, a complexation completion signal is output to obtain a masked test solution containing only free impurity ions.

[0062] The spectral acquisition and preprocessing module simultaneously acquires time-series data of the spectral intensities of multiple impurity elements to form complete characteristic spectral signals of impurities.

[0063] The spectral correction calculation module fits the correction curves for each impurity concentration and stores the parameters. It constructs a baseline vector using the statistical features of blank spectra to complete baseline subtraction and digital filtering. Then, it judges the degree of interference by the spectral interference coefficient, corrects the spectral intensity by interpolation, substitutes it into the correction curve to calculate and update the corrected impurity concentration dataset.

[0064] The report grade determination module is used to generate time-series marker codes, combine the actual impurity content dataset to generate a time-series marker-encoded test report according to the template, and automatically determine the sample grade by comparing the impurity mass fraction with the preset impurity control threshold.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] 1. This invention proposes a method for detecting trace impurities in basic nickel carbonate. By combining bidirectional alternating ultrasound with pre-judgment of powder dispersion uniformity, it can effectively remove impurities encapsulated within basic nickel carbonate particles, solving the industry pain points of powder agglomeration and insufficient impurity release. A temperature array is added to the digestion stage for real-time temperature control, ensuring uniform and stable micro-digestion reactions and preventing element volatilization or incomplete digestion. A solid composite mask is used to encapsulate the powder complexing nickel matrix, and real-time conductivity monitoring accurately pinpoints the complexation endpoint, significantly eliminating spectral interference caused by high-concentration nickel matrices. The test solution flows through a hydrophobic atomizing pipeline and is pre-rinsed with organic acid to prevent residual sediment blockage and cross-contamination. Multiple impurity characteristic signals are acquired simultaneously. Gradient blank baseline compensation and digital filtering optimize the original spectral data. From sample pretreatment to spectral acquisition, the entire process improves impurity extraction efficiency and detection signal purity, reduces detection errors, and enhances the accuracy of trace impurity detection.

[0067] 2. This invention proposes a method for detecting trace impurities in basic nickel carbonate. Based on a multi-gradient nickel matrix standard mother liquor database, a calibration curve is fitted, and matrix interference is corrected using spectral interference coefficient interpolation to accurately calculate the actual mass fraction of each impurity. The system can automatically generate standardized test reports with time-series markers, automatically determine sample grade by comparing with preset impurity thresholds, and directly feed the test results back to the basic nickel carbonate synthesis and discharge process. This reduces manual intervention, shortens testing time, and balances speed and stability. It is suitable for the online testing needs of continuous industrial production. Compared with traditional testing methods, the degree of automation, testing efficiency, and data reliability are significantly improved. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of a method for detecting trace impurities in basic nickel carbonate according to the present invention;

[0069] Figure 2 This is a flowchart illustrating the principle of a method for detecting trace impurities in basic nickel carbonate according to the present invention.

[0070] Figure 3 This is a schematic diagram of the basic nickel carbonate trace impurity detection system of the present invention. Detailed Implementation

[0071] Example 1

[0072] Please see Figures 1-2 The present invention provides an embodiment of a method for detecting trace impurities in basic nickel carbonate, the method comprising steps S1 to S6, including the following steps:

[0073] S1: Basic nickel carbonate powder was used as the original sample to be tested. The original sample was soaked in anhydrous ethanol and allowed to stand to obtain the soaked powder. The soaked powder was subjected to bidirectional alternating ultrasonic stripping to remove the internal encapsulated impurities and obtain homogeneous dispersed powder.

[0074] In this embodiment, industrial electronic-grade basic nickel carbonate powder was selected as the original sample to be tested. The basic nickel carbonate powder is an intermediate product of the continuous synthesis and discharge process of nickel salt and ammonium bicarbonate. There are a large number of agglomerated particles inside the basic nickel carbonate powder. The gaps between the particles and the interior of the crystals contain trace impurities such as iron, calcium, magnesium, and copper. Conventional single stirring or unidirectional ultrasound cannot release the encapsulated impurities into the liquid phase system, which will directly lead to the low digestion and spectral detection results, resulting in the problem of distorted impurity content detection.

[0075] In this embodiment, the first step is to perform quantitative sampling of the original sample to be tested. A fixed weight of basic nickel carbonate powder is weighed and poured into a clean polytetrafluoroethylene beaker as the basic test material. The entire sampling process is completed inside a Class 10,000 clean bench. The bench is pre-emptively purified by a laminar flow fan for at least 30 minutes. The bench surface is wiped three times with anhydrous ethanol and then allowed to air dry to prevent airborne metal dust and environmental impurities from falling into the test powder and causing an excessively high background level. The electronic balance used for sampling is pre-leveled and zero-point calibrated using standard weights. The balance's windproof cover is kept closed throughout the process. After each set of samples is weighed, a dust-free brush is used to clean any residual powder from the balance tray to prevent cross-contamination between different samples.

[0076] After weighing the powder, anhydrous ethanol was slowly added to the polytetrafluoroethylene beaker containing the basic nickel carbonate powder to complete the impregnation operation. Analytical grade anhydrous ethanol was selected as the impregnation medium to avoid introducing interfering impurity ions such as sodium, potassium, and calcium. Anhydrous ethanol also possesses excellent powder wetting and penetrating capabilities, quickly filling the tiny gaps between the basic nickel carbonate particles, weakening the adsorption force on the particle surface, and breaking up the loose agglomerate structure on the powder surface. This creates a liquid-phase medium for transmitting sound pressure to ultrasonically remove internally encapsulated impurities. During the addition of anhydrous ethanol, a glass rod was used to slowly stir along the inner wall of the beaker. The stirring was gentle to avoid generating excessive foam from high-speed stirring, which could lead to powder splashing and loss. After stirring for two minutes, the beaker was sealed and left to stand for the preset time. During the standing process, the beaker was placed on a constant-temperature experimental platform, and the ambient temperature was controlled to remain stable within the room temperature range to prevent rapid ethanol evaporation and subsequent re-drying and agglomeration of the powder. After standing, the initially impregnated powder was obtained. It should be noted that anhydrous ethanol has a low surface tension of about 22.3 mN / m, and has a good ability to quickly penetrate basic nickel carbonate powder. It can usually fill the gaps between the powder in 30 to 60 seconds. In this embodiment, setting the preset time to 2 minutes can ensure sufficient wetting and avoid ethanol evaporation caused by prolonged standing.

[0077] After obtaining the impregnated powder by impregnating the original sample with anhydrous ethanol and allowing it to stand, the method further includes determining the dispersion uniformity of the impregnated powder, including:

[0078] A1: Based on the image acquisition device, a surface microscopic image of the impregnated powder is acquired, grayscale processing is performed on the surface microscopic image to obtain a grayscale image, and edge detection operator operation is performed on the grayscale image to obtain a particle boundary feature map;

[0079] In this embodiment, the image acquisition device used is a desktop image acquisition all-in-one machine equipped with a high-definition industrial microscope lens. The device is placed on a vibration-proof experimental platform, with silicone shock-absorbing pads laid at the bottom to prevent image blurring and particle edge ghosting caused by ground vibration and fan vibration. Before starting image acquisition, three pre-operations are completed: microscope lens cleaning, lens focal length calibration, and light source brightness calibration. A small amount of anhydrous ethanol is applied to the lens surface with lint-free lens paper to wipe away any remaining dirt. After the lens is completely dry, the ring light source is turned on, and the brightness is adjusted to a fixed level to avoid excessive brightness causing glare or insufficient brightness causing loss of fine particle outline details. A polytetrafluoroethylene beaker containing the impregnated powder is placed in the center of the stage. The stage height and lens focal length are adjusted until the powder particles are clearly imaged on the device's display screen without ghosting. The focal length parameter is then locked and not changed further to ensure that the image acquisition parameters for all samples in the same batch are completely consistent, eliminating judgment errors caused by differences in imaging conditions.

[0080] After parameter calibration, the device automatically triggers an image acquisition command, acquiring a single surface microscopic image that completely covers the powder observation area inside the beaker. The raw image data is automatically stored in the device's built-in storage unit in an uncompressed raw image format to avoid losing pixel information at the edges of tiny particles. After image acquisition, the device automatically retrieves the original color surface microscopic image and performs grayscale processing. The grayscale processing reads the red, green, and blue channel values ​​corresponding to each pixel in the color image, merges the three color channel data into a single grayscale value according to a unified weighted conversion rule, and replaces the original color pixel information. After all pixels in the entire image have been converted, a grayscale image is generated that retains only the brightness and darkness grayscale information. The color differences of powder particles, ethanol liquid phase, and agglomerated areas in the color image are all converted into grayscale brightness and darkness differences, reducing the computational load on the embedded processor.

[0081] After the grayscale image is generated, the built-in edge detection operator is invoked to perform pixel-by-pixel calculations on the entire grayscale image. The edge detection operator takes a single pixel as the center and reads the grayscale values ​​of multiple pixels above, below, to the left, and to the right of that pixel. It compares the grayscale difference between the center pixel and the surrounding pixels. When the grayscale difference between adjacent pixels exceeds a preset grayscale difference threshold, the pixel is determined to be at the particle boundary, and the pixel coordinate information is marked. If the grayscale difference between adjacent pixels is less than the grayscale difference threshold, the pixel is determined to be a flat area inside the powder or a pure ethanol liquid phase area, and no boundary marking is made. In this embodiment, the grayscale difference threshold is set to 18. The basis for this value is that after basic nickel carbonate powder is wetted with anhydrous ethanol, the particles and the ethanol liquid phase background show a significant grayscale difference in the microscopic image. The powder particles are dark gray due to light scattering, with a grayscale value of approximately 80-120, while the ethanol liquid phase background is light gray to bright white due to good light transmittance, with a grayscale value of approximately 180-220. The grayscale value of the particle edge area is... The value transitions rapidly within this range, and the grayscale difference threshold is set in the low to mid-range of the grayscale difference between particles and the background. Statistical verification using grayscale histograms of microscopic images from multiple batches of basic nickel carbonate powder samples shows that a grayscale difference threshold of 18 can effectively distinguish the edges of powder agglomerates from background noise in the liquid phase. However, considering the unavoidable optical system noise and interference from fine textures on the particle surface during microscopic image acquisition, if the grayscale difference threshold is too low (less than 10), tiny suspended particles in the ethanol liquid phase and CCD thermal noise points are easily misjudged as particle boundaries, causing a surge in false edges. If the grayscale difference threshold is too high (greater than 25), weak edge areas and the true boundaries at the points where particles adhere to each other may be missed, leading to statistical distortion of particle size. After repeatedly acquiring 10 frames of images from the same batch of samples for edge detection consistency testing, when the grayscale difference threshold is 18, the relative standard deviation of the equivalent diameter measurement of the same particle is 1.7%, exhibiting optimal repeatability and balancing the sensitivity and noise resistance requirements of edge extraction.

[0082] After all pixels of the entire grayscale image have been traversed and compared, all marked boundary pixels are arranged according to the original image coordinates to generate an independent layer. This layer only retains the outline of the particle edge and has no extra liquid phase or powder interior grayscale filling information. Finally, the particle boundary feature map is output and synchronously cached in memory.

[0083] A2: Based on the particle boundary feature map, the particle size distribution parameters and agglomeration area ratio of the particles in the impregnated powder are statistically analyzed. Under the condition that the coefficient of variation of the particle size distribution parameters is less than a preset first threshold and the agglomeration area ratio is less than a preset second threshold, a dispersion uniformity qualified signal is output.

[0084] Furthermore, the specific steps of A2 include:

[0085] (1) Identify all closed boundary lines inside the particle boundary feature map. Each group of continuous closed boundary lines corresponds to an independent particle region. Perform pixel size conversion operation on the closed particle contour corresponding to each independent particle region. The conversion ratio parameter of pixel size conversion is calibrated in advance. The calibration operation is completed using a standard calibration ruler. The standard calibration ruler is placed on the stage of the image acquisition device. The physical scale length of the ruler corresponds one-to-one with the imaging pixel length. After calibration, a fixed conversion ratio of 0.8 micrometers per unit pixel is determined. The fixed conversion ratio is retrieved, and the horizontal pixel width value and vertical pixel height value of a single closed contour are read. The average value of the horizontal pixel width and vertical pixel height is multiplied by the physical size of 0.8 micrometers per unit pixel to convert the actual particle size value of the powder particle corresponding to the current closed contour. Traverse and identify all closed particle contours in the particle boundary feature map, record the actual particle size value corresponding to each independent particle one by one, and summarize and integrate all particle size values ​​to generate the original dataset of particle size corresponding to the current impregnated powder.

[0086] (2) Retrieve the original particle size dataset to complete the statistical calculation of particle size distribution parameters. The core statistical indicators include four categories: average particle size and standard deviation of particle size. Based on the four statistical indicators, perform standardization operation to obtain the particle size distribution variation coefficient. The standardization operation calculation rule is: the particle size distribution variation coefficient is equal to the standard deviation of particle size divided by the average particle size. It is used to characterize the dispersion of powder particle size. The larger the particle size distribution variation coefficient value, the more obvious the difference in the size of coarse and fine particles inside the powder, and the more fine particles are attached to the surface of large particles to form an agglomeration structure. The smaller the particle size distribution variation coefficient value, the more concentrated the size range of powder particles is, and there is no obvious coarse and fine differentiation. The powder is well wetted and dispersed.

[0087] (3) Screen large-area contiguous clumps without separating boundaries inside the particle boundary feature map and uniformly define such contiguous clumps as agglomeration areas; respectively calculate the total pixel area of ​​the whole observation image and the total pixel area of ​​all agglomeration areas in the image, divide the total pixel area of ​​all agglomeration areas by the total pixel area of ​​the whole observation image to obtain the agglomeration area ratio. The higher the agglomeration area ratio, the more tightly agglomerated clumps the powder still has after being wetted with anhydrous ethanol. The ethanol liquid phase cannot fully penetrate into the gaps inside the clumps, and the sound pressure generated by bidirectional alternating ultrasound cannot penetrate the gaps in the clumps to complete the peeling of internal encapsulated impurities, which will reduce the integrity of impurity release.

[0088] (4) Retrieve two sets of judgment threshold parameters that have been pre-solidified and stored in the system. The first threshold is the particle size distribution variation coefficient judgment threshold, with a calibration setting value of 0.25. The second threshold is the agglomeration area ratio judgment threshold, with a calibration setting value of 0.12. The particle size distribution variation coefficient is compared with the first threshold of 0.25, and the agglomeration area ratio is compared with the second threshold of 0.12. The judgment logic is set so that the two comparison results must simultaneously meet the condition that the corresponding values ​​are less than the threshold in order to determine that the dispersion uniformity of the current impregnated powder meets the standard.

[0089] It should be explained that in this embodiment, the threshold for judging the particle size distribution variation coefficient is set to 0.25 and the threshold for judging the agglomeration area ratio is set to 0.12. The values ​​are based on the quantitative correlation verification between the dispersion uniformity of basic nickel carbonate powder and the ultrasonic stripping efficiency. The particle size distribution variation coefficient reflects the dispersion of powder particle size. When the value is less than 0.25, it indicates that the particle size distribution of the powder is relatively concentrated and there is no serious agglomeration structure where large particles carry a large number of small particles. At this time, the ethanol liquid phase has fully wetted the gaps between particles, providing a uniform working medium for the ultrasonic cavitation effect. If the variation coefficient is ≥0.25, the coarse and fine particles are seriously mixed, and a large number of fine particles are attached to the surface of large particles to form solid agglomerates. The ultrasonic energy is significantly scattered and attenuated at the interface of the agglomerates, making it difficult to penetrate to the interface of the particles that encapsulates impurities, which will directly lead to incomplete impurity stripping. Setting the threshold for determining the agglomeration area ratio to 0.12 is based on statistical verification using microscopic images: when the agglomeration area accounts for less than 12% of the total observed area, the loose agglomeration structure on the powder surface has been largely broken down, allowing the ethanol liquid phase to freely penetrate into the gaps between the particles. During ultrasound, the sound pressure distribution inside the liquid phase is uniform, with no local energy dissipation dead zones. If the agglomeration area ratio is ≥12%, the size and number of agglomerates are too high, and the mechanical waves emitted by the ultrasonic transducer attenuate sharply when passing through large agglomerates. Powder particles at the bottom and corners of the beaker do not receive effective sound pressure, resulting in a significant decrease in the impurity release rate. Therefore, setting the threshold for determining the particle size distribution variation coefficient to 0.25 and the threshold for determining the agglomeration area ratio to 0.12 effectively screens and disperses unqualified samples to ensure sufficient impurity release, while avoiding the misjudgment and reprocessing of a large number of qualified samples due to overly stringent threshold settings. This approach combines scientific rationality with engineering practicality.

[0090] (5) If the particle size distribution variation coefficient is less than 0.25 and the agglomeration area ratio is less than 0.12, the equipment outputs a dispersion uniformity qualified signal. The dispersion uniformity qualified signal is the start input trigger signal for bidirectional alternating ultrasonic operation. If the particle size distribution variation coefficient is greater than or equal to 0.25, or the agglomeration area ratio is greater than or equal to 0.12, and either indicator does not meet the threshold requirement, it is determined that the current impregnated powder is not uniformly dispersed. The equipment automatically pops up an operation prompt window. The pop-up prompts the operator to re-stir the powder impregnated material and extend the powder standing impregnation time until the first threshold and the second threshold requirements are met, and then a dispersion uniformity qualified signal is generated.

[0091] A3: Based on the qualified dispersion uniformity signal, apply bidirectional alternating ultrasound to the impregnated powder.

[0092] The process of applying bidirectional alternating ultrasonic treatment to the impregnated powder to remove internally encapsulated impurities and obtain homogeneous dispersed powder includes:

[0093] S1.1: Based on the ultrasonic generator, a first frequency ultrasonic electrical signal and a second frequency ultrasonic electrical signal are generated, the first frequency ultrasonic electrical signal is transmitted to the first ultrasonic transducer group, and the second frequency ultrasonic electrical signal is transmitted to the second ultrasonic transducer group.

[0094] Furthermore, the specific steps of S1.1 include:

[0095] (1) The operator sets two sets of ultrasonic electrical signal frequency parameters on the control panel of the ultrasonic generator. The parameter setting method is to manually input fixed values ​​and save them. The output frequency of the first ultrasonic electrical signal is set to 28kHz, and the output frequency of the second ultrasonic electrical signal is set to 80kHz. The two sets of frequency values ​​are determined in advance by multiple batches of basic nickel carbonate wetted powder tests.

[0096] (2) The ultrasonic generator generates corresponding electrical signals according to the set frequency parameters based on the internal independent signal generation circuit. The first set of independent signal generation circuits outputs a first frequency ultrasonic electrical signal of 28kHz, and the second set of independent signal generation circuits outputs a second frequency ultrasonic electrical signal of 80kHz.

[0097] (3) The ultrasonic generator is equipped with two physically isolated independent signal output channels. The first independent signal output channel receives the first frequency ultrasonic signal. The output end of the channel is connected to a shielded cable. The shielded cable is a dedicated anti-electromagnetic interference transmission line, which can isolate the electromagnetic noise generated by the water bath environment of the ultrasonic reaction tank and the power supply circuit of the whole machine, and prevent the first frequency ultrasonic signal from being distorted in the transmission path. The end of the shielded cable is connected to the first ultrasonic transducer group to complete the transmission of the first frequency ultrasonic signal to the first ultrasonic transducer group. The second independent signal output channel receives the output second frequency ultrasonic signal. The output end of the channel is connected to another set of shielded cables of the same specification. This set of shielded cables also has the function of isolating electromagnetic interference and protecting the complete transmission of waveform. The end of the shielded cable is connected to the second ultrasonic transducer group to complete the transmission of the second frequency ultrasonic signal to the second ultrasonic transducer group.

[0098] (4) The first ultrasonic transducer group and the second ultrasonic transducer group are pre-fixed on the inner side walls of the two groups opposite each other in the ultrasonic reaction tank. After the two ultrasonic transducer groups are installed, the sound-emitting plane of the transducer is completely parallel to the outer wall of the polytetrafluoroethylene beaker containing the impregnated powder. This installation structure can ensure that the ultrasonic waves emitted by the first ultrasonic transducer group and the ultrasonic waves emitted by the second ultrasonic transducer group can penetrate the outer wall of the beaker vertically and be stably transmitted to the ethanol liquid phase system containing the impregnated powder inside the beaker.

[0099] (5) Finally, two stable ultrasonic electrical signals are output and sent to the corresponding oscillator groups respectively.

[0100] S1.2: Based on the built-in sweep frequency output unit of the ultrasonic generator, the first frequency ultrasonic electrical signal and the second frequency ultrasonic electrical signal are controlled to switch between the output state and the stop state according to an alternating timing sequence, so that the first ultrasonic transducer group and the second ultrasonic transducer group are started and run alternately. That is, when the first ultrasonic transducer group is running, the second ultrasonic transducer group is in the stop state, and when the second ultrasonic transducer group is running, the first ultrasonic transducer group is in the stop state.

[0101] The ultrasonic generator integrates a sweep frequency output unit with a built-in timing control chip. Operators pre-enter complete alternating operation timing parameters into the system interface. These timing parameters include the continuous operation time of a single set of oscillators, the switching interval between two sets of oscillators, and the total duration of the overall ultrasonic cycle. The timing parameters are permanently stored in the chip's built-in storage area. The ultrasonic operation strictly follows the alternating timing sequence and automatically switches without manual intervention. In this embodiment, the continuous operation time of a single set of oscillators is set to 30 seconds, the switching interval between two sets of oscillators is set to 2 seconds, and the total duration of the overall ultrasonic cycle is set to 15 minutes. The 30-second continuous operation time of a single set of oscillators allows the unidirectional sound pressure to accumulate to the interface stripping peak and avoids directional particle accumulation. The 2-second switching interval matches the residual sound field dissipation cycle to ensure the efficiency of reverse sound pressure transmission. The total duration of the overall cycle is 15 minutes. After impurity recovery rate testing, the average recovery rate of five target impurities can be stably maintained at 93%~97%, balancing the requirements of sufficient stripping and online detection timeliness.

[0102] The timing control chip continuously sends on / off control commands to the two sets of signal output channels. When the on / off control command switches to the first operating stage, the first channel remains on, outputting a first frequency ultrasonic electrical signal. The first ultrasonic transducer group is powered on and starts to continuously emit ultrasonic waves. At the same time, the second channel receives the disconnect command and stops outputting a second frequency ultrasonic electrical signal. The second ultrasonic transducer group is powered off and stops working, with no ultrasonic output. When the continuous running time of a single transducer group ends, the chip automatically switches the on / off control command. The first channel is disconnected, the first transducer group stops, the second channel is on, and the second transducer group starts to emit second frequency ultrasonic waves. The two transducers always maintain an alternating on / off operating state, and there is no situation where the two transducers emit ultrasonic waves at the same time. This avoids the resonance cancellation of sound pressure caused by the synchronous superposition of bidirectional ultrasonic waves, which reduces the impurity stripping effect.

[0103] S1.3: When the first ultrasonic transducer group is started, it emits a first ultrasonic wave along the first direction. When the second ultrasonic transducer group is started, it emits a second ultrasonic wave along the second direction. The first direction and the second direction are set opposite to each other, so that the particles inside the impregnated powder are alternately subjected to the first sound pressure along the first direction and the second sound pressure along the second direction.

[0104] The two sets of ultrasonic transducers are installed facing each other. The first ultrasonic transducer is fixed to the inner left wall of the ultrasonic reaction tank. The emitted first ultrasonic wave propagates in a horizontal direction to the right and strikes the outer left wall of the beaker containing the powder vertically. After penetrating the beaker wall and the ethanol liquid phase, it continuously applies the first sound pressure from the left side of the powder agglomerate. The second ultrasonic transducer is fixed to the inner right wall of the ultrasonic reaction tank. The emitted second ultrasonic wave propagates in a horizontal direction to the left and strikes the outer right wall of the beaker vertically. After penetrating the liquid phase, it applies the second sound pressure from the right side of the powder agglomerate. The first and second propagation directions are completely opposite, forming a bidirectional opposing sound pressure system.

[0105] Under alternating operation sequence control, the powder agglomeration structure first continuously withstands the first sound pressure impact from one side. The crystal interface and impurity-encased gaps inside the particles generate unilateral tensile stress. When the oscillator switches to stop and reverse direction, the second sound pressure from the opposite side continuously impacts the agglomerated particles. The crystal interface and impurity gaps switch to withstand reverse tensile stress. The interface of the particles encasing impurities repeatedly withstands bidirectional alternating stress. Unlike traditional unidirectional ultrasound, which can only apply stress in one direction and can only loosen the surface of the particles without destroying the interface of the impurities encased inside the crystals, this embodiment can continuously act on every particle gap from two opposite directions, improving the impurity removal efficiency. The beaker is completely immersed in a constant-temperature ethanol liquid phase. The ultrasound is uniformly transmitted to all powder particles through the liquid medium. There are no blind spots where the sound pressure is attenuated in local areas or where there is no ultrasound effect. Powder particles at the bottom and corners of the beaker can also fully receive the bidirectional alternating sound pressure impact, ensuring uniform processing of the entire batch of powder.

[0106] S1.4: Based on the alternating switching of the first sound pressure and the second sound pressure, the inclusions encapsulated inside the particles are peeled off from the particle matrix interface under the action of bidirectional alternating stress field, and the peeled inclusions are dispersed into the liquid phase to obtain homogeneous dispersed powder.

[0107] A weak physical adsorption interface and a crystal co-existence interface exist between the powder particle matrix and the internal inclusions and impurities. Conventional static wetting and unidirectional ultrasonic stress cannot overcome the interfacial bonding force. Impurities remain encapsulated inside the particles. The digestion process can only dissolve the nickel matrix on the particle surface, and the internal impurities cannot be released. The impurity content detected by spectroscopy is significantly lower than the actual impurity level of the sample, resulting in distorted detection results. In this embodiment, a dynamic alternating stress field is constructed by alternating bidirectional sound pressure. With each oscillator reversal, the stress direction on the internal interface of the particles is synchronously reversed. The continuous and repeated bidirectional pulling action gradually destroys the bonding interface between the particle matrix and the impurities. The trace metal impurities and salt impurities that were originally embedded and encapsulated inside the crystals gradually detach from the matrix interface, leave the solid particles, and enter the surrounding anhydrous ethanol liquid phase environment.

[0108] Impurity particles that detach and enter the liquid phase will not re-adsorb and agglomerate on the surface of basic nickel carbonate particles under continuous ultrasonic cavitation. The tiny fluid disturbances generated by the collapse of cavitation bubbles continuously drive the liquid phase circulation, uniformly dispersing the detached impurity particles within the ethanol liquid phase, preventing localized aggregation and re-coating of powder particles. After the entire bidirectional alternating ultrasonic cycle is completed, the ultrasonic generator is turned off, the beaker is removed, and visual inspection reveals no obvious large agglomerations or clumps of powder inside the beaker. The powder particles are uniformly suspended and dispersed in the ethanol liquid phase, without bottom settling or surface floating agglomeration. At this point, a homogeneous dispersion of powder with fully detached internally encapsulated impurities and uniformly dispersed impurities in the liquid phase is obtained.

[0109] S2: Place the homogeneous dispersed powder in a sealed micro digestion container, add a composite digestion reagent of organic acid and nitric acid, and obtain the digestion stock solution through micro digestion;

[0110] Before performing the homogeneous dispersion powder transfer and digestion operation, a complete pretreatment and cleaning process is completed for the sealed micro digestion vessel. The digestion vessel is a polytetrafluoroethylene (PTFE) lined digestion tank resistant to acid and alkali corrosion and high temperature and pressure, with a matching stainless steel sealed outer tank. After each test, the digestion liner, sealing gasket, and outer tank are completely disassembled and cleaned. First, it is rinsed three times repeatedly with deionized water, then soaked in dilute nitric acid solution for several hours. After soaking, it is rinsed again with ultrapure water until no residual acid remains on the inner wall. It is then placed in a constant temperature drying oven for thorough drying. After drying, it is sealed and stored to avoid cross-contamination caused by residual sample impurities and digestion reagents from the previous test. Before use, the dried digestion vessel is opened again for ventilation to remove any residual moisture from the drying process, ensuring the inside of the container is dry and free of water accumulation. This prevents water accumulation from diluting the subsequently added compound digestion reagent, changing the reagent concentration, and affecting the completeness of powder digestion.

[0111] The operator uses a pipette and a quantitative sampling spoon to transfer the prepared homogeneous dispersion powder, along with its internal ethanol liquid phase, completely into the polytetrafluoroethylene liner of the digestion vessel. During the transfer, the powder is poured slowly, and the inner wall of the beaker containing the powder is rinsed multiple times with a small amount of anhydrous ethanol. All rinsing liquid is incorporated into the digestion liner to ensure no residual powder particles or impurities adhering to the beaker's inner wall are lost, avoiding sampling losses that could lead to lower-than-expected impurity content readings. After all the powder has been transferred, it is allowed to stand for a short time until the solid powder has completely settled to the bottom of the liner. Then, most of the upper layer of anhydrous ethanol liquid phase is slowly poured out, leaving only a small amount of liquid phase completely submerging the solid powder. This minimizes the interference of ethanol on the subsequent digestion reaction. Ethanol is highly volatile and evaporates rapidly during the digestion heating process. Excessive ethanol can cause a rapid increase in internal pressure within the digestion vessel, posing a risk of pressure release and simultaneously diluting the effective components of the digestion reagent, reducing its digestion and dissolving capacity.

[0112] In this embodiment, a composite digestion reagent consisting of an organic acid and a low-concentration nitric acid was selected. When nitric acid is used alone to digest basic nickel carbonate powder, the strong acid reacts rapidly and violently, generating a large number of carbon dioxide bubbles. The powder is very likely to overflow from the digestion container with the bubbles, resulting in sample loss. The digestion and dissolution rate of a single organic acid is slow, and the insoluble impurities inside the powder cannot be completely dissolved. The composite system takes into account both a mild reaction rate and strong dissolving ability. The organic acid buffers the intensity of the digestion reaction and avoids boiling and overflow, while the low-concentration nitric acid provides an acidic environment to fully dissolve the nickel matrix and various metal impurities. The two components work synergistically to achieve complete dissolution of the powder in a closed micro-digestion environment, with no solid residue remaining.

[0113] The composite digestion reagent is quantitatively extracted using a corrosion-resistant pipette and injected into the digestion liner containing the homogeneous dispersed powder. After the reagent is added, a matching polytetrafluoroethylene sealing gasket is used to seal the top of the liner, and the sealing and locking structure of the stainless steel outer can is tightened to ensure that the digestion container is completely sealed and leak-free. During the digestion heating process, the gas and liquid phase generated by the internal reaction will not overflow outward, maintaining a micro-high pressure digestion environment inside. The micro-high pressure environment can improve the dissolution rate of insoluble impurities and shorten the overall digestion time. After sealing, the entire set of sealed micro-digestion containers is neatly placed on the heating turntable of the constant-temperature microwave digestion instrument. The turntable rotates synchronously and at a uniform speed to ensure that each digestion container is heated evenly and to prevent temperature differences caused by local high or low temperatures. The digestion instrument heating program is started, and the micro-digestion heating and holding process begins, running continuously for the preset digestion time. In this embodiment, the uniform digestion time is 25 minutes, which is based on the systematic verification of the dissolution kinetics of basic nickel carbonate powder in an organic acid-low concentration nitric acid complex system and the requirements for stable temperature field control. The reaction process of basic nickel carbonate powder and composite digestion reagent can be divided into two stages: a rapid dissolution stage and a complete dissolution stage of insoluble impurities. Under microwave heating conditions, the nickel matrix on the powder surface reacts with the acid matrix. Upon contact, the particles rapidly dissolve and release a large amount of carbon dioxide gas within 5-8 minutes. At this point, the liquid environment is acidic enough to dissolve internal impurities. Subsequently, the impurity dissolution stage begins. Trace impurities such as iron, calcium, magnesium, copper, and zinc exist within the particles in the form of oxides or co-precipitates. They require full dissociation under continuous heating and micro-high pressure. Multiple parallel experiments have shown that a 25-minute digestion time can achieve a dissolution rate of over 98.5% for all five impurity ions. Furthermore, the digestion solution is clear and transparent with no visible solid residue. Extending the digestion time to 30 minutes only increases the impurity dissolution rate by less than 0.5%, indicating that the increase has plateaued. However, energy consumption and the time required for single-batch testing increase significantly, which is not conducive to the industrial-scale online rapid detection requirements. Meanwhile, real-time monitoring data from the temperature sensor array showed that, under the temperature rise-hold step control set in the 25-minute digestion program, the average temperature at multiple points in the tank could be stably maintained at 165℃, with the maximum temperature difference controlled within 3℃. Under this temperature field condition, there was no volatilization loss of any impurity elements. If the digestion time was shortened to less than 18 minutes, some insoluble impurities encased within large particles would not be completely released, and the impurity recovery rate would only reach 82%~89%, with a batch-to-batch relative standard deviation exceeding 6%, failing to meet the detection accuracy requirements. Considering the impurity dissolution rate, temperature field stability, and detection timeliness, 25 minutes was determined as the optimal digestion time. This ensures complete dissolution of all target impurities while keeping the total pretreatment time for a single batch within a reasonable range, adapting to the rhythm requirements of industrial online quality control. After the program automatically ended, the solid powder inside the digestion container was completely dissolved, with no visible solid precipitate. All nickel matrix and stripped impurities were converted into ionic form and dissolved in the digestion reagent liquid phase, resulting in a clear and transparent digestion stock solution.

[0114] Before placing the homogeneous dispersed powder into the sealed micro digestion container, the method further includes verifying the particle size distribution of the homogeneous dispersed powder using a dynamic light scattering particle size analyzer, specifically including:

[0115] B1: The homogeneous dispersed powder is transferred to a sample cell. Based on the laser light source irradiating the homogeneous dispersed powder in the sample cell, the intensity fluctuation signal of scattered light caused by the Brownian motion of the particles is collected, and the autocorrelation function is performed on the scattered light intensity fluctuation signal to obtain the autocorrelation curve.

[0116] After powering on the dynamic light scattering particle size analyzer, perform a 30-minute preheating period. After preheating, perform baseline calibration. Rinse the quartz sample cell with clean, anhydrous ethanol, and allow it to air dry completely to ensure the sample cell wall is free of dust and residual powder particles that could interfere with the baseline values. The operator uses a disposable pipette to draw a small amount of the prepared homogeneous powder suspension and slowly injects it into the clean quartz sample cell. The liquid level should reach the standard graduation line of the sample cell, ensuring there are no air bubbles or powder particles adhering to the walls. If tiny air bubbles are generated during the injection process, gently puncture them with the provided defoaming needle. Air bubbles can obstruct the laser beam path, causing distortion in the scattered light signal acquisition and directly affecting the particle size calculation results.

[0117] The filled sample cell is placed stably inside the sample slot of the particle size analyzer. The slot has built-in silicone locking clips that secure the sample cell, preventing slight movement and deviation from the laser beam path during testing. The internal laser source automatically activates, emitting a fixed-wavelength laser beam that penetrates vertically into the quartz sample cell. The laser beam illuminates the powder particles undergoing Brownian motion within the liquid phase. As the particles block the laser beam, they scatter light in all directions. A photoelectric signal sensor on the side of the device continuously captures the intensity of the scattered light at different times, synchronously converting the optical signal into an electrical signal. It continuously records the fluctuations in scattered light intensity per unit time, comprehensively acquiring all the scattered light intensity fluctuations caused by the Brownian motion of the particles. The raw fluctuating electrical signals are transmitted in real-time to the particle size analyzer's built-in processor for buffering.

[0118] The processor reads the continuous time-series scattered light intensity fluctuation signal and performs autocorrelation function calculations on the time-series signal segment by segment. During the calculation, the correlation degree of scattered light intensity at different time intervals is compared. The faster the particle movement speed, the greater the difference in scattered light intensity within a short time interval, and the lower the signal correlation; the slower the particle movement speed, the more gradual the change in scattered light intensity, and the higher the signal correlation. The processor traverses all time-series signals, completely recording the signal correlation values ​​corresponding to different time intervals. A continuous and smooth autocorrelation curve is plotted with the time interval as the horizontal axis and the correlation value as the vertical axis. The curve data is automatically stored. The autocorrelation function formula is prior art in this field and is not an inventive solution of this application, and will not be elaborated here.

[0119] B2: Perform cumulative inversion calculation on the autocorrelation curve to obtain the average hydrated particle size value and the particle size polydispersity coefficient. If the average hydrated particle size value is within the preset particle size range and the particle size polydispersity coefficient is less than the preset polydispersity coefficient threshold, then output a particle size verification pass signal.

[0120] Furthermore, the specific steps of B2 include:

[0121] (1) The built-in processor of the particle size analyzer automatically retrieves the autocorrelation curve data cached in the memory, and performs cumulative inversion calculation with the autocorrelation curve data as input. The cumulative inversion calculation is a fixed built-in operation logic with no additional adjustable training parameters. After the operation is completed, two sets of core data are output simultaneously: the average hydrated particle size value and the particle size polydispersity coefficient. The average hydrated particle size value refers to the size value of the complete hydrated particles formed after the adsorption of ethanol solvent molecules on the surface of the powder particles. This value can objectively reflect the true dispersion state of the powder particles inside the ethanol liquid phase and is different from the original particle size of the powder in the dry state. It matches the actual working condition of the powder suspended in the ethanol liquid phase before digestion. The particle size polydispersity coefficient is used to characterize the width of the size distribution of all powder particles inside the ethanol liquid phase. The larger the particle size polydispersity coefficient value, the more large and small particles are mixed in the liquid phase system. The fine particles are adsorbed and attached to the surface of the large particles to form a secondary agglomeration structure. The smaller the particle size polydispersity coefficient value, the more concentrated and uniform the particle size in the liquid phase, the more stable the powder dispersion state, and the absence of secondary agglomeration.

[0122] (2) The particle size analyzer processor retrieves the pre-entered and solidified particle size determination criteria from the device's storage unit. These criteria are set by calibrating multiple batches of homogeneous dispersed powders after bidirectional alternating ultrasonic treatment. The criteria include two sets of fixed parameters. The first set of parameters is the preset particle size range, with the lower limit set to 0.3 micrometers and the upper limit set to 3.2 micrometers. The second set of parameters is the preset polydispersity coefficient threshold, with a value of 0.18. The processor performs two sets of numerical comparison calculations in sequence. The first comparison compares the output average hydrated particle size value with the preset particle size range lower limit of 0.3 micrometers and the preset particle size range upper limit of 3.2 micrometers to determine whether the average hydrated particle size value is within the range of 0.3 micrometers to 3.2 micrometers. The second comparison compares the particle size polydispersity coefficient output in the first step with the preset polydispersity coefficient threshold of 0.18 to determine whether the particle size polydispersity coefficient is less than 0.18.

[0123] It should be explained that in this embodiment, the preset particle size range for the average hydrated particle size is set to a lower limit of 0.3 micrometers and an upper limit of 3.2 micrometers. These values ​​are based on the synergistic verification of the inherent particle size distribution characteristics of basic nickel carbonate powder raw materials, the swelling effect of anhydrous ethanol wetting, and the effect of bidirectional alternating ultrasonic dispersion. Specifically, the median particle size of the primary particles obtained from the basic nickel carbonate synthesis process, calibrated by a laser particle size analyzer, is approximately 0.8~2.0 micrometers. After wetting with anhydrous ethanol, the particle surface adsorbs solvent molecules to form a hydrated layer, increasing the hydrated particle size by 0.2~... While the particle size of aggregates is typically 0.5 micrometers, the hydrated particle size is usually greater than 5 micrometers. Therefore, a hydrated particle size in the range of 0.3 to 3.2 micrometers can be used as a criterion for determining whether large aggregates have been broken down after sufficient ethanol wetting. The lower limit of 0.3 micrometers eliminates interference from equipment noise and a very small number of nano-sized fine impurity particles, while the upper limit of 3.2 micrometers covers the theoretical upper limit of the hydrated particle size of basic nickel carbonate raw particles after ethanol wetting. It also matches the digestion efficiency of the digestion vessel. If the particle size is too large, it indicates the presence of dense aggregates that have not been broken down, and the impurities inside are difficult to be penetrated by the acid solution.

[0124] It should also be explained that in this embodiment, the polydispersity coefficient threshold is set to 0.18, which is based on the theoretical and engineering practice considerations of dynamic light scattering particle size analysis: when the polydispersity coefficient is less than 0.18, it indicates that the particle size distribution in the ethanol liquid phase tends to be monodisperse, and there is no obvious secondary agglomeration structure. At this time, the impurity particles after stripping have been uniformly dispersed in the liquid phase and have not undergone significant reagglomeration. If the polydispersity coefficient is greater than or equal to 0.18, it means that there is a bimodal or even multimodal distribution of mixed particles of different sizes in the liquid phase. The fine impurity particles are re-adsorbed onto the surface of large particles to form a secondary agglomeration structure. Even if the average hydrated particle size falls in the range of 0.3 to 3.2 micrometers, it will still reduce the subsequent digestion impurity release rate and the representativeness of the detection results.

[0125] (3) The particle size analyzer generates and outputs a particle size verification pass signal only when the average hydrated particle size falls within the range of 0.3 micrometers to 3.2 micrometers and the particle size polydispersity coefficient is less than 0.18. The particle size verification pass signal serves as the pre-trigger input signal for the operation of transferring homogeneous dispersed powder to a closed micro digestion container.

[0126] (4) If the average hydrated particle size is less than 0.3 micrometers or greater than 3.2 micrometers, or the polydispersity index is greater than or equal to 0.18, it is determined that the homogeneous dispersed powder undergoes secondary agglomeration in the ethanol liquid phase environment. After being peeled off by bidirectional alternating ultrasonication, the impurity particles are re-adsorbed and agglomerated on the surface of the basic nickel carbonate powder particles. At this time, the particle size analyzer will automatically pop up a text prompt message to remind the operator to send the current powder suspension back into the ultrasonic reaction tank for short-term bidirectional alternating ultrasonic treatment. After the ultrasonic treatment is completed, the powder suspension is extracted again and sent into the sample cell of the dynamic light scattering particle size analyzer until both the average hydrated particle size and the polydispersity index meet the judgment requirements. Only when the particle size analyzer outputs a particle size verification pass signal can the operation of transferring the powder to the closed micro digestion container be performed.

[0127] B3: Based on the particle size verification pass signal, place the homogeneous dispersed powder into a sealed micro digestion container.

[0128] After obtaining the digestion stock solution through micro-digestion, the process further includes temperature field monitoring and feedback control of the micro-digestion process based on a temperature sensor array, specifically including:

[0129] C1: Temperature signals are collected based on multiple temperature sensors arranged on the outer wall of the sealed micro digestion container, and the temperature signals are converted into digital temperature values ​​through an analog-to-digital converter;

[0130] Each digestion vessel placement station on the heating turntable of the entire microwave digestion system is equipped with a multi-point temperature sensor array on its outer wall. Multiple contact temperature sensors are evenly distributed at each station. The probes of these sensors are tightly fitted to the outer wall of the stainless steel digestion vessel, and the probes are wrapped with thermally conductive silicone pads to minimize the contact gap between the probe and the vessel, ensuring rapid heat transfer to the sensor and eliminating significant temperature acquisition lag. These multiple contact temperature sensors are evenly arranged along the height and circumference of the vessel, allowing for simultaneous real-time temperature acquisition at multiple points on the upper, middle, bottom, and left and right sides of the vessel. This avoids the problem of a single sensor only collecting localized temperatures, which fails to reflect the overall temperature distribution of the vessel.

[0131] After the digester's heating program starts, all temperature sensors continuously collect analog temperature signals at their corresponding points. These analog temperature signals are then transmitted to the device's built-in analog-to-digital converter via shielded signal lines. The converter continuously receives multiple analog signals and performs analog-to-digital conversion operations on each signal, eliminating voltage fluctuation interference during analog signal transmission. After conversion, it outputs accurate and distortion-free digital temperature values. Each sensor generates a set of independent digital temperature values, and these multiple digital temperature values ​​are synchronously transmitted to the device's main control chip cache, forming a complete real-time temperature acquisition data stream.

[0132] C2: Obtain the digital temperature values ​​to form a temperature field distribution dataset, and calculate the average temperature and the maximum temperature difference of the temperature field distribution dataset;

[0133] The main control chip periodically reads the digital temperature values ​​output by all sensors in the cache, and aggregates and integrates the digital temperature values ​​of all points corresponding to the same digestion container to generate a temperature field distribution dataset specific to that digestion container. This dataset completely records the real-time temperature data of different spatial points within the container, fully restoring the overall temperature distribution of the outer wall of the digestion container. Statistical calculations are then performed on all temperature values ​​within the dataset. First, the arithmetic mean of the temperatures at all points is calculated; this mean represents the overall average heating temperature of the digestion container, reflecting the overall temperature level of the current digestion environment. Then, all temperature values ​​within the dataset are iterated through, and the highest and lowest temperatures are selected. The difference between the highest and lowest temperatures is calculated; this difference is the maximum temperature difference. The magnitude of this difference represents the degree of temperature difference between different points on the outer wall of the digestion container. A larger difference indicates uneven heating of the container, with some areas being too hot and others too cold. In areas with low temperatures, the powder digestion reaction rate is slow, and impurities cannot be completely dissolved.

[0134] C3: Under the condition that the deviation of the average temperature from the preset target temperature value is greater than the first deviation threshold or the maximum temperature difference is greater than the second deviation threshold, a temperature adjustment control signal is output to the heating element driver. The heating element driver adjusts the output power of the heating element according to the temperature adjustment control signal, so that the average temperature is restored to the preset target temperature value and the maximum temperature difference is reduced to below the second deviation threshold.

[0135] Furthermore, the specific steps of C3 include:

[0136] (1) The main control chip retrieves three sets of temperature control parameters that have been pre-recorded in the equipment storage area. All three sets of parameters are set through calibration by micro-digestion test of basic nickel carbonate powder. The first set of parameters is the preset target temperature value of micro-digestion standard, which is set to 165℃ in this embodiment. The second set of parameters is the first deviation threshold, which is set to 5℃ in this embodiment. The third set of parameters is the second deviation threshold, which is set to 3℃ in this embodiment. The main control chip performs difference calculation between the input average temperature of the tank and the preset target temperature value of 165℃. The calculation result is defined as the temperature deviation value.

[0137] It should be explained that in this embodiment, the target temperature of 165°C is determined based on the following considerations: the basic nickel carbonate matrix needs to be above 160°C in the nitric acid system to completely dissociate, and the oxides or co-precipitates of target impurity elements such as iron, calcium, magnesium, copper, and zinc have sufficient dissolution rates at this temperature; at the same time, 165°C is lower than the equilibrium temperature corresponding to the boiling temperature of nitric acid (approximately 122°C) under a closed micro-high pressure environment (the acceleration temperature of nitric acid decomposition under closed conditions is usually above 180°C), which can effectively avoid excessive decomposition of nitric acid and the generation of large amounts of nitrogen oxides, leading to a decrease in digestion efficiency. The first deviation threshold of 5°C is set based on the following: when the average temperature of the tank fluctuates within the range of 160~170°C, the change in the impurity dissolution rate is less than 1.5%, which will not have a significant impact on the detection results. At the same time, this tolerance range matches the inherent fluctuation range of the power adjustment of the magnetron of the microwave digester, avoiding fatigue of the heating element caused by excessively frequent power adjustments of the temperature control system. The second deviation threshold of 3℃ is based on the experiment on the influence of temperature field uniformity on digestion consistency: when the temperature difference between different parts of the tank is ≤3℃, the degree of complete digestion of powder in different spatial positions in the same tank is basically consistent, and the relative deviation of impurity detection results of parallel digestion of the same sample is less than 2.5%; when the temperature difference expands to 5℃, the powder particles in the local low temperature area are not completely digested, and the impurity dissolution rate is 6%~10% lower than that in the high temperature area, resulting in insufficient representativeness of the same digestion stock solution and a significant increase in the dispersion of detection results.

[0138] (2) If the absolute value of the temperature deviation is greater than the first deviation threshold of 5℃, or the maximum temperature difference is greater than the second deviation threshold of 3℃, it is determined that there is an abnormality in the digestion temperature field; if it is determined that there is no abnormality in the temperature field, the main control chip will continue to maintain a no-signal output state, and the heating element driver will maintain the current output power unchanged; if it is determined that there is an abnormality in the temperature field, the main control chip will generate a temperature adjustment control signal and transmit it to the heating element driver built into the microwave digester.

[0139] (3) After receiving the temperature regulation control signal, the heating element driver performs regulation operations in two categories according to the temperature anomaly type carried by the temperature regulation control signal. The first type of regulation operation corresponds to the working condition where the overall average temperature of the tank deviates from the preset target temperature value of 165℃. If the overall average temperature of the tank is lower than 165℃, the driver increases the real-time output power of the microwave heating element, increases the microwave heating energy input, increases the overall temperature of the tank, and reduces the temperature deviation value. If the overall average temperature of the tank is higher than 165℃, the driver decreases the real-time output power of the microwave heating element, reduces the microwave heating energy input, lowers the overall temperature of the tank, and reduces the temperature deviation value. The second type of regulation operation corresponds to the working condition where the temperature difference between multiple points of the tank is too large. The driver synchronously increases the uniform rotation speed of the heating turntable. The increased rotation speed allows the sealed micro digestion container to receive microwave heating energy uniformly in all directions, reducing the temperature difference between the upper and lower positions and the left and right positions of the tank.

[0140] (4) Perform iterative cycles until the average temperature is stable and equal to the preset target temperature of 165℃, and the maximum temperature difference falls back to within 3℃. When both indicators meet the standard simultaneously, the main control chip stops outputting temperature regulation control signals, the microwave heating element driver locks the current constant standard output power, and the equipment enters the stable heat preservation and digestion stage.

[0141] S3: After the digestion solution has cooled, add solid composite masking powder and vortex mix it to complex the nickel matrix in the digestion solution and prepare a masked test solution containing only free impurity ions.

[0142] The microwave digester automatically cuts off the power supply to the microwave heating element, and the equipment enters a natural cooling process. The entire set of sealed micro-digestion containers holding the digestion stock solution maintains its original sealed structure throughout the process, and is placed on the heating turntable of the equipment to complete the natural cooling operation. The operating procedures clearly prohibit opening the sealed structure before the container has been fully cooled. Opening the lid under high temperature and high pressure conditions will cause the internal digestion stock solution to splash rapidly, resulting in loss of the test sample and potential safety hazards to the equipment operation. The duration parameter of the cooling stage is pre-entered and fixed in the digester control interface. According to the sample system calibration of this embodiment, the preset cooling time is 35 minutes. The operator reads the multi-channel digital temperature values ​​collected by the temperature sensor array on the outer wall of the container in real time through the digester's matching display screen, and simultaneously retrieves the overall average temperature of the container obtained by the main control chip. The lid opening operation can only be performed after the overall average temperature of the container has dropped to the room temperature range of 25°C.

[0143] The operator loosens the locking mechanism of the outer stainless steel canister of the sealed micro digestion container, removes the PTFE sealing gasket at the canister opening, and carefully removes the PTFE liner containing the digestion stock solution. The liner contains the clear, solid-free digestion stock solution after the first cooling step. The liner is then placed stably on a clean, dust-free laboratory bench. Using a corrosion-resistant plastic spatula, the operator weighs a pre-prepared solid composite masking powder and slowly adds it to the digestion stock solution inside the liner. The solid composite masking powder is formulated with multiple complexing components. These components work synergistically to form complexes with the high concentration of nickel ions in the digestion solution, creating soluble nickel complexes. This reduces the content of free nickel ions in the liquid phase and eliminates matrix interference caused by the high nickel content in the matrix for spectral impurity detection. The solid masking powder does not introduce a large amount of liquid solvent, thus avoiding dilution of the impurity ion concentration within the liquid phase. It maintains the impurity ion concentration stably within the detectable range of the spectroscopic equipment, avoiding the problem of inaccurate identification due to excessively low impurity signal intensity. The final output is the digestion solution mixture after the addition of the solid composite masking powder.

[0144] After the inner liner is sealed, it is fixedly installed in the built-in slot of the benchtop vortex mixer. The preset vortex mixing program of the equipment is started, and the mixer rotates at high speed to vibrate the inner liner, causing the internal digestion solution and solid composite masking powder to fully contact and fuse. The vibration time parameter of the equipment is pre-entered and fixed in the control interface of the vortex mixer. In this embodiment, the vibration time is uniformly set to 8 minutes. The continuous vibration process ensures that the solid composite masking powder is completely dissolved, and that the various complex components inside the powder fully contact and undergo complexation reaction with nickel ions in the liquid phase. After the vibration program is completed, the inner liner is allowed to stand for 2 minutes. After standing, the liquid phase inside the inner liner is observed. The liquid phase has no undissolved solid powder and is uniform and transparent, which means that most of the nickel ions inside the liquid phase have completed the complexation reaction with the solid composite masking powder and transformed into stable complexed nickel. Only free impurity ions such as iron, calcium, magnesium, copper, and zinc that do not participate in the complexation reaction remain in the liquid phase system. The liquid material that is finally processed through a complete process of cooling, adding solid composite masking powder, vortex mixing and settling is the masking test liquid.

[0145] After adding the solid composite masking powder and vortex mixing, the process further includes real-time monitoring of the complexation reaction process of the masking mixture based on a conductivity measuring device, specifically including:

[0146] D1: Immerse the conductivity electrode in the masking mixture, apply an AC excitation signal to the conductivity electrode through the conductivity measurement host, and collect the response current signal between the conductivity electrodes;

[0147] The conductivity measurement device is pre-calibrated upon startup, and the electrode measurement accuracy is determined using the matching standard calibration solution. After calibration, the conductivity electrode probe is rinsed with ultrapure water, and any residual moisture on the probe surface is gently blotted dry with lint-free absorbent paper to prevent residual moisture from diluting the masking mixture and altering the ion concentration, which could cause deviations in the conductivity measurement. After the vortex mixing operation is completed, the cleaned conductivity electrode probe is slowly immersed in the masking mixture inside the liner, ensuring the probe is completely submerged in the liquid phase without touching the bottom or sidewalls of the liner. This prevents trace amounts of solid precipitates from adhering to the probe surface and affecting signal acquisition.

[0148] The conductivity measurement host is connected to the electrode via a dedicated signal cable. The host has a built-in AC excitation signal generation unit that continuously outputs a constant AC excitation signal to both ends of the electrode. The AC excitation signal acts stably on the electrode probe immersed in the liquid phase. Free ions inside the liquid phase will move directionally under the action of the AC electric field. The directional movement of ions forms a weak response current. The electrode continuously captures the response current signal generated inside the liquid phase and transmits the current signal to the built-in signal acquisition unit of the measurement host in real time. It continuously collects real-time current data and records the current change trend of the entire complexation reaction process.

[0149] D2: The real-time conductivity value is calculated based on the ratio of the response current signal to the AC excitation signal, and the real-time conductivity value is recorded at fixed time intervals to generate a conductivity time series curve. In this embodiment, the fixed time interval is set to 5 seconds.

[0150] The AC excitation electrical signal and response current signal are read in real time and synchronously acquired. Based on the corresponding proportional relationship between the two sets of signals, the real-time conductivity value of the masked mixture is calculated time by time. The conductivity value directly reflects the total concentration of free ions in the liquid phase. Nickel ions are the main free ions in the digestion solution. After nickel ions complex with the masking powder components, the number of free nickel ions continues to decrease, and the total concentration of free ions in the liquid phase decreases synchronously. The real-time conductivity value will continue to decrease as the complexation reaction progresses.

[0151] The host automatically stores the real-time conductivity value corresponding to each time node at a fixed time interval of 5 seconds, and synchronously records the corresponding acquisition timestamp. All time-series stored data are summarized and integrated, and a continuously changing conductivity time-series curve is automatically generated with the acquisition time as the horizontal axis and the real-time conductivity value as the vertical axis. The conductivity time-series curve completely restores the entire process of the dynamic change of conductivity from the addition of masking powder to the later stage of the reaction. The curve data is cached and stored in real time for determining the endpoint of the complexation reaction.

[0152] D3: Perform slope calculation on the conductivity time-series curve to obtain the conductivity change rate. If the conductivity change rate is less than a preset rate threshold, it is determined that the complexation reaction has reached the endpoint, and a complexation completion signal is output.

[0153] The conductivity measurement host periodically retrieves complete conductivity time-series curve data from the memory cache. Using this conductivity time-series curve as input, it performs piecewise slope calculations to obtain the slope of the numerical change corresponding to each continuous interval. This slope value is defined as the real-time rate of change of conductivity. In the initial stage of the complexation reaction, a large number of free nickel ions in the liquid phase rapidly combine with the complexing components in the solid composite masking powder, causing a rapid decrease in the total concentration of free ions in the liquid phase. The conductivity value decreases rapidly, and the absolute value of the slope obtained from the piecewise calculation is relatively large, corresponding to a conductivity change rate value higher than the preset rate threshold. As the complexation reaction continues, the total amount of free nickel ions in the liquid phase that can participate in the complexation reaction continuously decreases, the overall complexation reaction rate gradually slows down, the rate of decrease in conductivity value continuously narrows, the absolute value of the curve slope continuously shrinks, and the corresponding conductivity change rate continuously decreases synchronously.

[0154] The conductivity measurement host retrieves a pre-loaded and fixed rate threshold from the device's storage module. This rate threshold is calibrated through complexation tests of multiple batches of basic nickel carbonate digestion stock solution and solid composite masking powder. In this embodiment, the rate threshold is uniformly set to 0.02 mS / cm·s. The output segmented conductivity change rate is compared segment by segment with the rate threshold of 0.02 mS / cm·s. Five consecutive sets of conductivity change rates from adjacent segments are collected as the judgment benchmark samples. Only when the conductivity change rates calculated from five consecutive time segments are all stably less than the rate threshold of 0.02 mS / cm·s is it determined that the complexation reaction between free nickel ions in the liquid phase and the solid composite masking powder has been basically completed, and the complexation reaction has reached its endpoint. After the determination is completed, the conductivity measuring host generates and outputs a complexation completion signal. If the conductivity change rate calculated for any segment is greater than or equal to 0.02 mS / cm·s, it is determined that the complexation reaction inside the liquid phase is still ongoing. The conductivity measuring host automatically pops up a text prompt, reminding the operator to extend the static monitoring time of the benchtop vortex mixer. The equipment continuously collects conductivity values, updates the conductivity time series curve, and repeats the entire process of segmented slope calculation and rate threshold comparison at fixed time intervals of 5 seconds until the conductivity change rate of five consecutive segments is all less than 0.02 mS / cm·s. Only then can the conductivity measuring host output a complexation completion signal, and the masked test liquid prepared inside the liner be transported to the atomizing pipeline with a hydrophobic coating for impurity spectrum acquisition, avoiding matrix interference defects caused by incomplete complexation of the nickel matrix to the spectral detection.

[0155] S4: The masking test liquid is passed into the atomizing pipeline with a hydrophobic coating. The pipeline is first pre-rinsed with organic acid to remove residual precipitates. Then, the characteristic spectral signals of the impurities are acquired in parallel through a multi-channel spectrometer. Combined with the gradient blank compensation baseline, the original spectral data of the impurities are obtained.

[0156] After the entire spectroscopic detection equipment is turned on, three pre-operations are completed: preheating of the whole machine, optical path calibration, and pipeline sealing test. The inner wall of the atomizing pipeline that conveys the masking liquid to be tested is coated with a dense hydrophobic coating. The hydrophobic coating can effectively prevent trace salt impurities in the liquid phase from being adsorbed and attached to the inner wall of the pipeline, and prevent impurity ions from depositing for a long time and forming residual precipitates. Residual precipitates will slowly dissolve during the delivery of the liquid to be tested, and mix into the liquid to be tested, causing the impurity detection value to be too high. The hydrophobic coating reduces the probability of residual precipitate formation in the pipeline.

[0157] Before the masking test solution is officially delivered into the atomization pipeline, the equipment automatically initiates a pipeline pre-rinsing program. A low-flow-rate organic acid solution is used as the rinsing medium. The organic acid gently dissolves trace amounts of metal salt precipitates adhering to the inner wall of the pipeline without corroding the hydrophobic coating. The rinsing flow rate is set to a low setting, allowing the slow-flowing organic acid solution to thoroughly flush every area of ​​the pipeline's inner wall, dissolving and removing any residual impurities from previous testing. The rinsing waste liquid is collected in a dedicated waste liquid collection tank to prevent backflow and contamination of the testing pipeline. After rinsing, the equipment automatically switches the delivery medium, closing the organic acid rinsing solution supply channel and opening the masking test solution delivery channel. A corrosion-resistant constant-flow peristaltic pump uniformly delivers the masking test solution into the atomization pipeline. The masking test solution flows smoothly along the hydrophobic-coated pipeline without adsorption, retention, or localized sediment buildup. The masking test solution is continuously delivered to the atomization chamber of the spectrometer.

[0158] The method of acquiring impurity characteristic spectral signals in parallel using a multi-channel spectrometer includes:

[0159] S4.1: The masked test liquid is introduced into the inductively coupled plasma atomic emission spectrometer (ICP-AES). The solid-state radio frequency generator of the ICP-AES excites the impurity ions in the masked test liquid to generate characteristic emission spectra. The charge-coupled device (CCD) detector of the ICP-AES simultaneously acquires the spectral intensity values ​​at the characteristic spectral line wavelengths of multiple impurity elements in a multi-channel parallel acquisition mode.

[0160] In this embodiment, an inductively coupled plasma atomic emission spectrometer (ICP-AES) is selected as the core device for multi-channel spectral acquisition. The device is equipped with a solid-state radio frequency (RF) generator, which provides stable excitation energy with minimal energy fluctuations compared to traditional RF generators. It can continuously provide a constant excitation field, ensuring uniform excitation levels of impurity ions in different batches of test liquids and resulting in higher repeatability of spectral intensity data. The masked test liquid is transported to the nebulization chamber of the spectrometer via an atomization pipeline. The atomization device breaks the masked test liquid into uniform micro-aerosol droplets. The aerosols are continuously transported into the plasma excitation chamber with the carrier gas. The solid-state RF generator continuously outputs RF energy, forming a stable high-temperature plasma field inside the excitation chamber. After entering the high-temperature plasma field, the aerosol droplets rapidly evaporate and dissociate. Various free impurity ions inside the masked test liquid absorb energy and undergo energy level transitions. As the ions fall from high energy levels to low energy levels, they release characteristic emission spectra with wavelengths specific to their corresponding elements. Different impurity elements correspond to completely independent characteristic spectral line wavelengths, and various impurity spectral signals are generated synchronously.

[0161] The instrument is equipped with a charge-coupled device (CCD) detector featuring multi-channel parallel acquisition. The detector internally comprises multiple independent signal acquisition channels, each pre-assigned to a specific characteristic spectral wavelength of a target impurity element. All acquisition channels start acquiring data synchronously, eliminating the need for time-sharing sequential acquisition of single-element spectra. It can simultaneously capture the characteristic emission spectra of multiple impurity elements within the same time window, synchronously reading the real-time spectral intensity values ​​corresponding to each target wavelength. This shortens the acquisition time for a complete impurity spectrum of a single sample, enabling rapid parallel detection of multiple impurities. The raw spectral intensity signals acquired synchronously from all channels are transmitted in real-time to the instrument's built-in data storage unit, recording the real-time spectral intensity information corresponding to each impurity ion.

[0162] S4.2: By using the data acquisition rate and acquisition duration of the multi-channel parallel acquisition mode, the original spectral intensity time series data group corresponding to each impurity ion is obtained, and all the original spectral intensity time series data groups constitute the impurity characteristic spectral signal.

[0163] The operator pre-enters the control interface of the inductively coupled plasma optical emission spectrometer (ICP-OES) and completes the input and fixing of two basic acquisition parameters for the multi-channel parallel acquisition mode. Both parameters have been calibrated through multiple batches of parallel testing with the basic nickel carbonate masking test solution in this embodiment. The first parameter is the data acquisition rate, set to 5 times per second, and the second parameter is the duration of a single complete sample acquisition, set to 15 seconds. After the two parameters are entered, they are permanently stored in the spectrometer's built-in control system storage unit. All masking test solutions of the same batch are tested using the same set of parameters throughout the entire process, eliminating systematic deviations in spectral intensity values ​​caused by different acquisition rates and acquisition durations.

[0164] The spectrometer's charge-coupled device (CCD) detector retrieves the solidified acquisition parameters and continuously and synchronously reads the real-time spectral intensity values ​​of all detection channels at a data acquisition rate of 5 times per second. The entire acquisition process runs continuously for a preset acquisition duration of 15 seconds, outputting time-series single-channel spectral intensity data. For any pre-recorded target impurity ion, the detector extracts all time-series spectral intensity values ​​under the ion's specific characteristic wavelength channel and arranges them in an ordered manner according to the acquisition time sequence. The ordered time-series spectral intensity value set corresponding to a single impurity ion is defined as the original spectral intensity time-series data group specific to that impurity ion.

[0165] The spectrometer's built-in system aggregates and integrates all the independent raw spectral intensity time-series data sets of impurity ions, and packages and integrates all the raw spectral intensity time-series data sets into a unified dataset. This complete dataset is defined as the impurity characteristic spectral signal.

[0166] The process of obtaining the original spectral data of impurities by combining gradient blank compensation baseline includes:

[0167] S4.3: Based on inductively coupled plasma atomic emission spectrometry, collect the time series data set of blank spectral intensity corresponding to the blank reagent, and extract the blank intensity statistical feature value in the time series data set of blank spectral intensity;

[0168] Before performing spectral acquisition of the sample masking test liquid, the device first performs gradient blank reagent spectral acquisition. The gradient blank reagent contains a blank liquid phase composed of anhydrous ethanol, composite digestion reagent, and solid composite masking powder that are completely matched with the sample detection process. The blank liquid phase does not contain basic nickel carbonate powder or various impurities, and only contains all the reagent components used in the entire detection process. It is used to eliminate background spectral interference caused by trace impurities in the reagent itself, equipment pipelines, and residual impurities inside the excitation chamber.

[0169] The spectrometer uses the same multi-channel parallel acquisition parameters as the sample detection, and acquires the spectral signals of multiple sets of blank reagents in a gradient manner. After the acquisition of each set of blank reagents is completed, a corresponding blank spectral intensity time series data set is generated. All time series spectral intensity values ​​within each set of blank time series data are read, and blank intensity statistical feature values ​​that can represent the overall background signal level of the blank in that gradient are extracted. Each impurity characteristic wavelength corresponds to a set of independent blank intensity statistical feature values. All statistical feature values ​​corresponding to multiple gradient blanks are cached and stored to construct a complete gradient blank background data set.

[0170] S4.4: Construct a blank baseline correction vector based on the blank intensity statistical feature value and store it in the correction register;

[0171] The instrument's built-in baseline correction program retrieves all gradient blank intensity statistical feature values ​​from the memory cache. The program pre-stores a fixed sorting rule for the characteristic spectral wavelengths of each impurity element. This sorting rule is entered and locked during the spectrometer's factory calibration. In this embodiment, eight target impurity characteristic spectral lines are detected, corresponding to eight sets of gradient blank intensity statistical feature values. The baseline correction program extracts the blank intensity statistical feature values ​​matching the wavelength of each characteristic spectral line according to the sorting rule and arranges them in an orderly manner, completing the construction of the blank baseline correction vector. The vector dimension is consistent with the number of acquisition channels in the multi-channel parallel acquisition mode; in this embodiment, the number of channels is set to eight, resulting in a blank baseline correction vector with a dimension of eight. Each sequential point within the blank baseline correction vector stores a set of values. A single point value corresponds to the reagent blank background intensity at a single impurity characteristic wavelength. The blank baseline correction vector as a whole can completely restore the background interference baseline level caused by reagents and equipment within the entire detection wavelength range.

[0172] The constructed blank baseline correction vector is transferred to the instrument's built-in correction register and persistently written to it. The correction register is an independent partitioned storage unit within the spectrometer, with independent data read and write permissions. It will not conflict with the original sample spectral data or standard curve data. Once written to the register, the blank baseline correction vector is stored long-term. The vector data can be directly and quickly retrieved during the detection of all samples in the same batch, eliminating the need to repeatedly perform gradient blank reagent spectral acquisition and blank intensity statistical calculations. This simplifies the entire detection process for batch samples.

[0173] S4.5: Input the impurity characteristic spectral signal into the correction register for splitting and parsing, separate the original spectral intensity time series data group corresponding to each impurity ion, extract the impurity characteristic wavelength bound to the original spectral intensity time series data group for any set of original spectral intensity time series data group, locate the blank intensity statistical feature value corresponding to the same wavelength position in the blank baseline correction vector with the impurity characteristic wavelength as the index, and perform a subtraction operation between the original spectral intensity value corresponding to each time node in the original spectral intensity time series data group and the blank intensity statistical feature value obtained by matching the index to obtain the preliminary deducted spectral intensity value;

[0174] S4.6: Perform digital filtering on the preliminary deducted spectral intensity value to obtain a smoothed spectral intensity value. The smoothed spectral intensity value constitutes the original spectral data of the impurities. The digital filtering process is prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0175] In the initial subtracted spectral intensity time series data after blank subtraction, there are random noise spikes caused by slight fluctuations in equipment circuitry and plasma excitation. These noise spikes can cause abrupt changes in local spectral intensity values, interfering with concentration curve fitting and impurity concentration calculation. Therefore, a built-in digital filtering algorithm is used to uniformly filter all initial subtracted spectral intensity values. The digital filtering algorithm reads the subtracted intensity values ​​from multiple consecutive adjacent time points, removes abrupt noise values ​​that deviate from the overall trend, corrects abnormal abrupt points based on the overall trend of adjacent time series values, retains the true spectral intensity change trend of impurity ions, and eliminates random noise interference. The stable value after filtering correction is the smoothed spectral intensity value. The smoothed spectral intensity values ​​corresponding to all channels and all time series are fully summarized to form the original impurity spectral data free from blank background interference and random noise interference.

[0176] S5: Fit the concentration correction curve based on the pre-stored multi-gradient nickel matrix standard mother liquor database, determine the nickel concentration of the masking solution by combining the original spectral data of impurities, and correct the spectral interference by curve interpolation to obtain the corrected impurity spectral data.

[0177] The process of fitting a concentration correction curve based on a pre-stored multi-gradient nickel matrix standard mother liquor database includes:

[0178] S5.1: Retrieve a pre-stored multi-gradient nickel matrix standard mother liquor database from the storage unit. The multi-gradient nickel matrix standard mother liquor database contains multiple groups of standard mother liquors with nickel ion mass concentration gradients and the standard spectral intensity values ​​of each impurity ion in each group of standard mother liquors.

[0179] The multi-gradient nickel-based standard mother liquor database employs a hierarchical and categorized storage architecture. It is divided into multiple independent standard mother liquor groups. In this embodiment, seven nickel ion mass concentration gradients are pre-configured, with values ​​of 5 g / L, 10 g / L, 20 g / L, 40 g / L, 60 g / L, 80 g / L, and 100 g / L. Each independent standard mother liquor group records its corresponding nickel ion mass concentration gradient value and simultaneously stores the standard spectral intensity values ​​corresponding to each target impurity ion obtained during the spectral acquisition phase. All data within the multi-gradient nickel-based standard mother liquor database is systematically categorized and archived according to two dimensions: impurity type and nickel ion concentration gradient. The categorization index rules are fixed and written during equipment factory calibration, allowing for rapid filtering of concentration and spectral intensity data corresponding to the target impurity type. This ensures that data from different impurities and nickel concentrations are not confused or mismatched throughout the process.

[0180] S5.2: Combine the known concentration value of each impurity ion in each set of standard mother liquor with the corresponding standard spectral intensity value to form a standard concentration-intensity data pair;

[0181] After reading the data of a single set of gradient standard mother liquors in the multi-gradient nickel matrix standard mother liquor database, for each target impurity ion, the known fixed concentration value of the impurity is extracted beforehand, and the standard spectral intensity value corresponding to the impurity under the same set of standard mother liquors is extracted simultaneously. A set of concentration values ​​and a set of spectral intensity values ​​are bound one by one to form a set of independent standard concentration-intensity data pairs. All nickel concentration gradient standard mother liquor sets in the multi-gradient nickel matrix standard mother liquor database are traversed, and multiple sets of standard concentration-intensity data pairs corresponding to different concentration gradients are generated for each impurity ion. All data pairs of the same impurity are uniformly collected and stored separately for fitting the specific calibration curve of that impurity. Data pairs of different impurities are independently distinguished from each other to avoid confusion of different element data and interference with fitting accuracy.

[0182] S5.3: Call the least squares fitting algorithm to perform univariate linear regression fitting on all the standard concentration-intensity data pairs to obtain the concentration correction curve for each impurity ion; the concentration correction curve includes two correction parameters: slope and intercept. The least squares fitting algorithm is prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0183] S5.4: Store the concentration correction curve of each impurity ion and its corresponding slope and intercept into the storage unit.

[0184] The step of correcting spectral interference through curve interpolation to obtain corrected impurity spectral data includes:

[0185] S5.5: Retrieve the concentration correction curves of each impurity ion from the storage unit, substitute the smoothed spectral intensity value of each impurity ion in the original impurity spectral data into the corresponding concentration correction curve, calculate the initial concentration judgment value of each impurity ion, and obtain the initial impurity concentration dataset.

[0186] A retrieval command is sent to the storage unit to batch read the concentration correction curves, slope, and intercept correction parameters corresponding to all target impurity ions. Then, the smoothed spectral intensity values ​​of each impurity within the original impurity spectral data are read sequentially. These intensity values ​​are substituted into the linear conversion relationship corresponding to the impurity's specific concentration correction curve. Combining the curve slope and intercept parameters, numerical conversion is completed, yielding an initial concentration determination value without correction for nickel matrix spectral interference. This value is based solely on impurity spectral intensity and does not consider signal superposition interference from the high-content nickel matrix emission spectrum on the impurity's characteristic wavelengths, resulting in a certain degree of deviation. The initial concentration determination values ​​calculated for all impurity ions are then aggregated to generate a complete initial impurity concentration dataset.

[0187] S5.6: Call the nickel matrix interference reference spectrum, calculate the wavelength intensity ratio of the original spectral data of the impurities and the nickel matrix interference reference spectrum to obtain the spectral interference coefficient corresponding to each impurity ion;

[0188] Furthermore, the specific steps in S5.6 include:

[0189] (1) Send a data retrieval command to the device storage unit. After receiving the data retrieval command, the storage unit reads the internally pre-stored nickel matrix interference reference spectrum and transmits it to the computing unit. The nickel matrix interference reference spectrum is obtained by detecting the pure nickel matrix liquid phase according to the unified spectral acquisition parameters. It completely records the background emission spectral intensity generated by high-concentration nickel ions in the entire detection wavelength range, and can objectively reflect the spectral superposition interference level formed by the nickel matrix at each impurity characteristic wavelength position.

[0190] (2) The computing unit synchronously loads the transmitted nickel matrix interference reference spectrum and impurity original spectrum data, and retrieves all target impurity-specific characteristic wavelength identifiers pre-entered into the system. In this embodiment, a total of 8 independent impurity characteristic wavelengths are set, and each impurity characteristic wavelength is rotated as the current operation matching index. For the currently matched single impurity-specific characteristic wavelength, the computing unit reads the smooth spectral intensity value corresponding to the wavelength in the impurity original spectrum data and the nickel matrix interference intensity value corresponding to the same wavelength point in the nickel matrix interference reference spectrum.

[0191] (3) The calculation unit uses a fixed ratio calculation rule to complete the calculation of two sets of values. The calculation rule is to divide the nickel matrix interference intensity value by the smoothed spectral intensity value of the same wavelength. After the calculation, the spectral interference coefficient corresponding to the current impurity ion is generated. The spectral interference coefficient can quantify the degree of spectral superposition interference brought by the nickel matrix at the characteristic wavelength of the impurity. The higher the spectral interference coefficient value, the stronger the superposition interference brought by the emission spectrum of the nickel matrix, and the greater the deviation of the initial concentration judgment value calculated based on the uncorrected smoothed spectral intensity value.

[0192] (4) After completing the ratio calculation corresponding to a single impurity characteristic wavelength, cache the spectral interference coefficient of the current impurity ion, and then switch to the next set of impurity characteristic wavelengths to repeat the matching and ratio calculation of (2)-(3). After traversing all 8 impurity characteristic wavelengths, each target impurity ion generates a set of exclusive spectral interference coefficients, and all spectral interference coefficients are cached in the memory of the computing unit.

[0193] S5.7: If the spectral interference coefficient is greater than the preset interference threshold, the smooth spectral intensity value of the target impurity ion and the neighboring spectral intensity values ​​of its adjacent wavelength positions are selected as interpolation nodes to calculate the interpolated corrected spectral intensity value of the target impurity ion at the characteristic wavelength.

[0194] Furthermore, the specific steps in S5.7 include:

[0195] (1) Retrieve the pre-entered and solidified interference threshold in the device storage module. The preset interference threshold is set by parallel detection and calibration of multiple sets of nickel matrix standard mother liquors. The uniform value is set to 0.15. The spectral interference coefficient corresponding to each impurity is compared with the interference threshold of 0.15 in turn. If the spectral interference coefficient is less than or equal to 0.15, it is determined that the degree of spectral superposition interference generated by the nickel matrix at the specific characteristic wavelength position of the impurity is slight and will not cause a significant shift in the impurity concentration conversion result. The original smooth spectral intensity value corresponding to the wavelength is directly retained. If the spectral interference coefficient is greater than 0.15, it is determined that the degree of spectral superposition interference of the nickel matrix is ​​serious. The original smooth spectral intensity value is mixed with a large amount of nickel matrix background signal and cannot be directly used for concentration calculation. Interpolation correction needs to be started.

[0196] (2) For the target impurity entering the interpolation correction, lock the unique characteristic wavelength corresponding to the impurity, extract the smooth spectral intensity value matching the characteristic wavelength from the original spectral data of the impurity, and define the value as the core node data of interpolation; synchronously retrieve the spectral wavelength sequence pre-fixed by the system and composed of all target impurity characteristic wavelengths arranged in an orderly manner, locate the left and right adjacent points of the target impurity's unique characteristic wavelength in the spectral wavelength sequence, select the adjacent wavelengths corresponding to the two segments of nickel-free matrix with strong interference in the spectral wavelength sequence, read the smooth spectral intensity values ​​corresponding to the two adjacent wavelengths respectively, and define them as interpolation auxiliary node data. The core node data and the two sets of auxiliary node data together constitute the basic node dataset for interpolation calculation.

[0197] (3) The operation unit has built-in interpolation operation logic. The interpolation operation logic is based on the spectral intensity change trend presented by the three sets of interpolation node data to make calculations. It removes the nickel matrix background interference component superimposed in the three sets of node data, extracts the spectral response component generated only by the target impurity ion, and outputs the interpolated corrected spectral intensity value corresponding to the target impurity at its own exclusive characteristic wavelength position after the calculation is completed. It removes the superimposed interference component of the nickel matrix spectrum and retains only the spectral signal intensity corresponding to the impurity ion itself.

[0198] S5.8: Replace the smoothed spectral intensity value with the interpolated corrected spectral intensity value and resubstitute it into the corresponding concentration correction curve to obtain the corrected impurity concentration value;

[0199] S5.9: Update the initial impurity concentration dataset based on the corrected impurity concentration values ​​to obtain a corrected impurity concentration dataset; the corrected impurity concentration dataset and its corresponding impurity ion types constitute corrected impurity spectral data.

[0200] S6: Based on the corrected impurity spectral data, calculate the actual content of various impurities, generate an impurity detection report with time-series markers, determine the sample grade by combining the preset impurity control threshold, and feed the grade determination result back to the basic nickel carbonate synthesis output process to complete the detection.

[0201] The calculation of the actual content of various impurities based on the corrected impurity spectral data includes:

[0202] S6.1: Obtain the corrected concentration value of each impurity ion in the corrected impurity spectral data;

[0203] The industrial control system reads the transmitted calibrated impurity spectral data, extracts the corrected concentration value corresponding to each target impurity ion according to the impurity type, and the corrected concentration value represents the unit volume content of the impurity ion in the liquid phase of the currently masked test liquid. All the corrected concentration values ​​of impurities are uniformly summarized and stored as the basic original concentration parameter for content conversion.

[0204] S6.2: Obtain the sampling volume of the homogeneous dispersed powder and the total volume of the composite digestion reagent;

[0205] The industrial control system actively retrieves the operation parameter logs automatically generated throughout the entire testing process from the equipment's local storage unit. These logs are written to the storage unit in real-time by each process unit during operation, ensuring a complete record of all automated operations throughout the testing process. The operation parameter logs contain two sets of key dilution correction parameters for impurity mass fraction conversion. The first set of parameters is the equivalent solid volume of the homogeneous dispersed powder used in this test; in this embodiment, the equivalent solid volume is uniformly set to 5 ml. The second set of parameters is the total volume of the composite digestion reagent added during the micro-digestion process; in this embodiment, the total solidified digestion reagent volume is uniformly set to 25 ml. Both sets of volume values ​​are automatically collected and written to the operation parameter logs by the pipetting equipment and digester during sample addition, eliminating the need for manual input by operators and avoiding data entry errors caused by manual transcription.

[0206] The industrial control system reads two fixed values ​​from the operation parameter log: the equivalent volume of solid phase sampling is 5 ml and the total volume of compound digestion reagent is 25 ml. The two sets of volume values ​​are cached as volume reference parameters.

[0207] S6.3: Multiply the corrected concentration value by the total volume value and then divide by the sampling volume value to obtain the mass fraction conversion value of each impurity ion in the original sample to be tested;

[0208] S6.4: The mass fraction conversion values ​​of all impurity ions are summarized into an actual impurity content dataset, and the actual impurity content dataset is stored in the storage unit.

[0209] The generation of a time-stamped impurity detection report, combined with a preset impurity control threshold to determine the sample grade, includes:

[0210] S6.5: Call the system clock to obtain the current detection date and time and the current detection sequence number, and generate a timing mark code;

[0211] The industrial control system has a built-in real-time system clock that is continuously synchronized with standard Beijing time, eliminating time deviation. Once the dataset of actual impurity content is generated, the system automatically reads the complete detection date and the detection end time accurate to the second from the clock record. At the same time, it retrieves the unique detection sequence number of this detection, which is automatically counted internally by the equipment. The date, time, and detection sequence number are combined according to the preset coding rules to generate a unique time sequence mark code. Each sample detection corresponds to a unique code, which can serve as a unique identifier for detection data, reports, and feedback signals, distinguishing between different batches and different times of detection samples and preventing data confusion and disorder.

[0212] S6.6: Combine the impurity type and mass fraction conversion value of each impurity ion in the actual impurity content dataset with the time sequence tag code to form an impurity detection record, and summarize all impurity detection records into an impurity detection record set;

[0213] The system retrieves the complete character information of the time-series marker code, sequentially reads each impurity data in the actual impurity content dataset, and binds and combines the element type name, corresponding mass fraction conversion value, and the time-series marker code specific to this test for a single impurity to generate an independent and complete impurity detection record. Each impurity detection record completely stores all detection-related information for a single impurity. The system then traverses all impurity types within the actual impurity content dataset to generate multiple independent impurity detection records. All records are uniformly collected and packaged to form a complete impurity detection record set. All data within the impurity detection record set is bound to the same set of time-series marker codes to ensure that the entire set of sample detection data is uniquely associated.

[0214] S6.7: Call the preset report template, fill the time sequence mark code into the header area, fill the impurity detection record set into the data area, and generate an impurity detection report with time sequence mark;

[0215] The industrial control system's storage unit pre-loads standardized impurity test report templates. These templates are divided into several fixed sections: a header area, a sample basic information area, an impurity content data area, a grade determination result area, and a remarks area. The template format is uniform and conforms to the enterprise's raw material quality testing record archiving standards. The system automatically retrieves the report template, completely filling in the time-series marker code in the template's header's dedicated code field for quick identification of the corresponding test batch. It then reads all impurity test records from the impurity test record set, filling each record into the template's pre-set impurity content data field. The automatic formatting is neat and orderly, requiring no manual adjustment of the table format. Once all data is filled, the system automatically generates a complete electronic impurity test report with time-series marker codes, simultaneously supporting online viewing, local export and printing, and intranet archiving.

[0216] S6.8: Call the preset impurity control threshold table, compare the quality fraction conversion value with the corresponding grade threshold upper limit in the preset impurity control threshold table, and output the grade determination result.

[0217] The industrial control system sends a data retrieval command to the equipment storage unit to read the pre-stored enterprise-specific impurity control threshold table for basic nickel carbonate products. The impurity control threshold table is entered and solidified before the equipment is put into production. The table divides the product into four grades according to the total impurity content from low to high: high purity, grade 1, grade 2, and qualified. Products exceeding the qualified product limit are directly judged as unqualified. For the five target impurities of iron, calcium, magnesium, copper, and zinc detected in this embodiment, each grade has an independently set upper limit threshold for the impurity content. All threshold values ​​are solidified and written into the table without dynamic adjustment parameters.

[0218] The industrial control system reads all grades and impurity upper limit data from the impurity control threshold table, and extracts the upper limit threshold values ​​of the five target impurities for each grade in descending order of grade. The upper limit values ​​of the mass fraction for each grade are uniformly set as follows: High-purity grade: iron 0.001%, calcium 0.001%, magnesium 0.001%, copper 0.0005%, zinc 0.0005%; Grade 1: iron 0.003%, calcium 0.003%, magnesium 0.003%, copper 0.0015%, zinc 0.0015%; Grade 2: iron 0.008%, calcium 0.008%, magnesium 0.008%, copper 0.004%, zinc 0.004%; Qualified grade: iron 0.015%, calcium 0.015%, magnesium 0.015%, copper 0.008%, zinc 0.008%. The mass fraction conversion value of each type of impurity in the impurity detection record set is compared with the upper limit threshold of the impurity content corresponding to the current grade. If the mass fraction conversion values ​​of all five types of impurities are less than the upper limit threshold corresponding to the high purity grade, the sample grade is directly locked as high purity, and the comparison of lower grade thresholds is terminated. If the mass fraction conversion value of any type of impurity is greater than or equal to the upper limit threshold of the high purity grade, but the mass fraction conversion values ​​of all five types of impurities are less than the upper limit threshold corresponding to the first grade, the sample grade is locked as first grade. If the mass fraction conversion value of any type of impurity is greater than or equal to the upper limit threshold of the first grade, but the mass fraction conversion values ​​of all five types of impurities are less than the upper limit threshold corresponding to the second grade, the sample grade is locked as second grade. If the mass fraction conversion value of any type of impurity is greater than or equal to the upper limit threshold of the second grade, but the mass fraction conversion values ​​of all five types of impurities are less than the upper limit threshold corresponding to the qualified grade, the sample grade is locked as qualified. If the mass fraction conversion value of any type of impurity is greater than or equal to the upper limit threshold corresponding to the qualified grade, the sample is directly determined to be unqualified.

[0219] Furthermore, the grade determination result is fed back to the basic nickel carbonate synthesis discharge process for testing, including: establishing a data link with the basic nickel carbonate synthesis process control system via industrial Ethernet communication; encoding and combining the grade determination result and the timing mark into a feedback data frame and sending it to the basic nickel carbonate synthesis process control system; the basic nickel carbonate synthesis process control system performing corresponding diversion control operations based on the grade determination result; receiving the confirmation response signal returned by the basic nickel carbonate synthesis process control system; and recording the feedback data frame and the confirmation response signal in the testing log file to complete the testing.

[0220] The system encapsulates two core sets of information: the timing mark code for this test and the final sample grade determination result, generating a standardized feedback data frame. A data checksum is synchronously appended to this standardized feedback data frame. After receiving the data frame, the production line control system can verify data integrity through the checksum, preventing data tampering or loss during transmission. The industrial control system pushes the encapsulated feedback data frame to the synthesis and discharge process control system in real time via an industrial Ethernet link. The production line control system continuously monitors the communication link transmission signal, fully receives the feedback data frame, and completes data verification. After verification, it parses the timing mark code and grade determination result, and executes corresponding automated diversion control operations according to different grades: high-purity powder is automatically connected to the high-purity product storage silo conveying pipeline; first-grade and second-grade powders are respectively connected to the corresponding grade finished product silo pipelines; and unqualified powder is automatically switched to the waste recycling silo pipeline. Simultaneously, the discharge diversion valve group, conveyor belt, and silo feed switch are triggered, achieving automatic powder grading and diversion without manual on-site silo switching.

[0221] After the production line control system issues the diversion control command and executes the equipment action, it immediately generates a confirmation response signal. The response signal carries the same set of timing mark codes, indicating that the feedback of this test grade has been fully received and the corresponding diversion operation has been executed. The response signal is transmitted back to the industrial control system of the testing equipment along a dedicated industrial Ethernet link. The industrial control system receives the confirmation response signal and completes the verification and matching. After confirming that the production line has received the feedback and completed the diversion operation, it records the complete feedback data frame sent, the confirmation response signal transmitted by the production line, the start and end times of the entire testing process, the operator's account, and the equipment operating parameters. It generates a test log file specific to this test. The test log file is bound to the timing mark code and stored in the local storage unit of the equipment, forming a complete closed-loop test traceability file.

[0222] Example 2

[0223] Please see Figure 3 Another embodiment of the present invention provides: a basic nickel carbonate trace impurity detection system, comprising:

[0224] The sample pretreatment module 10 includes an image acquisition device, an ultrasonic generator, two sets of ultrasonic transducers, a sealed micro digestion container, a temperature sensor array, and a heating element driver. First, the sample is immersed in anhydrous ethanol. The image acquisition device acquires a microscopic image of the powder and calculates the particle boundary feature map. Particle size and agglomeration parameters are statistically analyzed to determine the dispersion uniformity. If the sample passes the test, the ultrasonic generator outputs alternating dual-frequency ultrasonic signals. The two sets of transducers apply alternating pressure in both directions to peel away impurities encapsulated in the particles, resulting in a homogeneous dispersed powder. During the digestion stage, the temperature sensor array collects multiple temperature points to construct a temperature field dataset. The heating power is automatically adjusted to stabilize the digestion temperature, ensuring the preparation of a uniform and complete digestion solution.

[0225] The complexation monitoring module 20 is equipped with a solid composite masking powder feeding unit, a vortex mixing device, a conductivity measuring device, and a conductivity electrode. After the digested original solution is cooled, the composite masking powder is added and vortex mixed to complex the nickel matrix. The conductivity electrode is immersed in the mixing solution to collect the excitation and response current, calculate the real-time conductivity and generate a time-series curve. The endpoint of the complexation reaction is determined by the rate of change of the curve. After the reaction is complete, a complexation completion signal is output to obtain a masked test solution containing only free impurity ions, thus eliminating the matrix interference of the nickel matrix on subsequent spectral detection.

[0226] The spectral acquisition and preprocessing module 30 includes an atomizing pipeline with a hydrophobic coating, an organic acid pre-rinsing unit, and an inductively coupled plasma atomic emission spectrometer. The analyte is masked and introduced into the atomizing pipeline. Low-flow-rate organic acid pre-rinsing removes residual precipitates from the pipeline to avoid cross-contamination. The instrument has a built-in solid-state radio frequency generator to excite impurity ions to generate characteristic spectra. Combined with a charge-coupled device detector, it synchronously acquires time-series data of the spectral intensity of multiple impurity elements in a multi-channel parallel mode to form a complete impurity characteristic spectral signal.

[0227] The spectral correction calculation module 40 has a built-in storage unit, correction register, least squares fitting operation unit, and interpolation correction operation unit. The storage unit pre-stores a multi-gradient nickel matrix standard mother liquor database, retrieves standard data to fit the correction curves of each impurity concentration and stores the parameters. The correction register uses the statistical characteristics of blank spectra to construct a baseline vector to complete baseline subtraction and digital filtering. Then, it judges the degree of interference through the spectral interference coefficient, uses interpolation to correct the spectral intensity, substitutes it into the correction curve to calculate and update the correction impurity concentration dataset.

[0228] The report grade determination module 50 integrates a system clock, a report template storage unit, an impurity control threshold storage unit, and a process signal output unit. The system clock collects the time and generates a time sequence mark code with the test sequence number. Combined with the actual impurity content dataset, it generates a test report with time sequence mark according to the template. It retrieves the preset impurity control threshold and compares the impurity mass fraction to automatically determine the sample grade. Finally, it outputs the grade determination signal to the basic nickel carbonate synthesis output process to realize closed-loop linkage control between the test results and the production process.

[0229] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of the present invention without departing from the spirit and scope of the present invention. All of these variations are within the protection scope of the present invention.

Claims

1. A method for detecting trace impurities in basic nickel carbonate, characterized in that, include: Basic nickel carbonate powder was used as the original sample to be tested. The original sample was soaked in anhydrous ethanol and allowed to stand to obtain the soaked powder. The impurities inside the soaked powder were removed by bidirectional alternating ultrasonic stripping to obtain homogeneous dispersed powder. The homogeneous dispersed powder was placed in a sealed micro digestion container, and a composite digestion reagent consisting of organic acid and nitric acid was added. The digestion stock solution was obtained by micro digestion. After the digestion solution is cooled, solid composite masking powder is added and vortexed to mix, complexing the nickel matrix in the digestion solution to obtain a masked test solution containing only free impurity ions; The masked test liquid is passed into an atomizing pipeline with a hydrophobic coating. The pipeline is first pre-rinsed with organic acid to remove residual precipitates. Then, the characteristic spectral signals of the impurities are acquired in parallel by a multi-channel spectrometer. Combined with the gradient blank compensation baseline, the original spectral data of the impurities are obtained. The concentration correction curve is fitted based on the pre-stored multi-gradient nickel matrix standard mother liquor database. The nickel concentration of the masking solution is determined by combining the original spectral data of impurities. The spectral interference is corrected by curve interpolation to obtain the corrected impurity spectral data. Based on the corrected impurity spectral data, the actual content of various impurities is calculated, an impurity detection report with time stamp is generated, the sample grade is determined by combining the preset impurity control threshold, and the grade determination result is fed back to the basic nickel carbonate synthesis output process to complete the detection.

2. The method for detecting trace impurities in basic nickel carbonate as described in claim 1, characterized in that, After obtaining the impregnated powder by impregnating the original sample with anhydrous ethanol and allowing it to stand, the method further includes determining the dispersion uniformity of the impregnated powder, including: The surface microscopic image of the impregnated powder is acquired using an image acquisition device. The surface microscopic image is then converted to grayscale to obtain a grayscale image. An edge detection operator is then performed on the grayscale image to obtain a particle boundary feature map. Based on the particle boundary feature map, the particle size distribution parameters and agglomeration area ratio of the particles in the impregnated powder are statistically analyzed. Under the condition that the coefficient of variation of the particle size distribution parameters is less than a preset first threshold and the agglomeration area ratio is less than a preset second threshold, a dispersion uniformity qualified signal is output. Based on the qualified dispersion uniformity signal, bidirectional alternating ultrasound is applied to the impregnated powder.

3. The method for detecting trace impurities in basic nickel carbonate as described in claim 2, characterized in that, The process of applying bidirectional alternating ultrasonic treatment to the impregnated powder to remove internally encapsulated impurities and obtain homogeneous dispersed powder includes: Based on the ultrasonic generator, a first frequency ultrasonic electrical signal and a second frequency ultrasonic electrical signal are generated. The first frequency ultrasonic electrical signal is transmitted to the first ultrasonic transducer group, and the second frequency ultrasonic electrical signal is transmitted to the second ultrasonic transducer group. Based on the built-in sweep frequency output unit of the ultrasonic generator, the first frequency ultrasonic electrical signal and the second frequency ultrasonic electrical signal are controlled to switch between output state and stop state according to an alternating timing sequence, so that the first ultrasonic transducer group and the second ultrasonic transducer group are started and run alternately. When the first ultrasonic transducer group is started, it emits a first ultrasonic wave along a first direction. When the second ultrasonic transducer group is started, it emits a second ultrasonic wave along a second direction. The first direction and the second direction are set opposite to each other, so that the particles inside the impregnated powder are alternately subjected to the first sound pressure along the first direction and the second sound pressure along the second direction. The alternating switching of the first sound pressure and the second sound pressure causes the inclusions encased inside the particles to be peeled off from the particle matrix interface under the action of a bidirectional alternating stress field. The peeled inclusions are then dispersed into the liquid phase to obtain a homogeneous dispersed powder.

4. The method for detecting trace impurities in basic nickel carbonate as described in claim 3, characterized in that, After obtaining the digestion stock solution through micro-digestion, the process further includes temperature field monitoring and feedback control of the micro-digestion process based on a temperature sensor array, specifically including: Temperature signals are collected by multiple temperature sensors arranged on the outer wall of the sealed micro digestion container, and the temperature signals are converted into digital temperature values ​​by an analog-to-digital converter. The digital temperature values ​​are used to construct a temperature field distribution dataset, and the average temperature and maximum temperature difference of the temperature field distribution dataset are calculated. When the deviation of the average temperature from the preset target temperature value is greater than a first deviation threshold or the maximum temperature difference is greater than a second deviation threshold, a temperature adjustment control signal is output to the heating element driver. The heating element driver adjusts the output power of the heating element according to the temperature adjustment control signal, so that the average temperature is restored to the preset target temperature value and the maximum temperature difference is reduced to below the second deviation threshold.

5. The method for detecting trace impurities in basic nickel carbonate as described in claim 4, characterized in that, After adding the solid composite masking powder and vortex mixing, the process further includes real-time monitoring of the complexation reaction process of the masking mixture based on a conductivity measuring device, specifically including: The conductivity electrode is immersed in the masking mixture, and an AC excitation signal is applied to the conductivity electrode through the conductivity measurement host to collect the response current signal between the conductivity electrodes. The real-time conductivity value is calculated based on the ratio of the response current signal to the AC excitation signal, and the real-time conductivity value is recorded at fixed time intervals to generate a conductivity time series curve. The slope of the conductivity time-series curve is calculated to obtain the conductivity change rate. If the conductivity change rate is less than a preset rate threshold, the complexation reaction is determined to have reached its endpoint, and a complexation completion signal is output.

6. The method for detecting trace impurities in basic nickel carbonate as described in claim 5, characterized in that, The method of acquiring impurity characteristic spectral signals in parallel using a multi-channel spectrometer includes: The masked test liquid is introduced into an inductively coupled plasma atomic emission spectrometer (ICP-AES). The solid-state radio frequency generator of the ICP-AES excites the impurity ions in the masked test liquid to generate characteristic emission spectra. The charge-coupled device (CCD) detector of the ICP-AES simultaneously acquires the spectral intensity values ​​at the characteristic spectral line wavelengths of multiple impurity elements in a multi-channel parallel acquisition mode. By using the data acquisition rate and acquisition duration of the multi-channel parallel acquisition mode, the original spectral intensity time series data group corresponding to each impurity ion is obtained, and all the original spectral intensity time series data groups constitute the impurity characteristic spectral signal.

7. The method for detecting trace impurities in basic nickel carbonate as described in claim 6, characterized in that, The process of obtaining the original spectral data of impurities by combining gradient blank compensation baseline includes: The blank spectral intensity time series data group corresponding to the blank reagent is collected by inductively coupled plasma atomic emission spectrometry, and the blank intensity statistical feature value in the blank spectral intensity time series data group is extracted; A blank baseline correction vector is constructed based on the blank intensity statistical feature value and stored in the correction register; The impurity characteristic spectral signal is input into the correction register for splitting and parsing, separating the original spectral intensity time series data group corresponding to each impurity ion. For any set of original spectral intensity time series data group, the impurity characteristic wavelength bound to the original spectral intensity time series data group is extracted. The blank intensity statistical feature value corresponding to the same wavelength position in the blank baseline correction vector is located using the impurity characteristic wavelength as the index. The original spectral intensity value corresponding to each time node in the original spectral intensity time series data group is subtracted from the blank intensity statistical feature value obtained by matching the index to obtain the preliminary deducted spectral intensity value. The initial subtracted spectral intensity value is subjected to digital filtering to obtain a smoothed spectral intensity value, which constitutes the original spectral data of the impurities.

8. The method for detecting trace impurities in basic nickel carbonate as described in claim 7, characterized in that, The process of fitting a concentration correction curve based on a pre-stored multi-gradient nickel-based standard mother liquor database includes: The pre-stored multi-gradient nickel matrix standard mother liquor database is retrieved from the storage unit. The multi-gradient nickel matrix standard mother liquor database contains multiple groups of standard mother liquors with nickel ion mass concentration gradients and the standard spectral intensity values ​​of each impurity ion in each group of standard mother liquors. The known concentration value of each impurity ion in each set of standard mother liquor is combined with the corresponding standard spectral intensity value to form a standard concentration-intensity data pair; The least squares fitting algorithm is used to perform univariate linear regression fitting on all the standard concentration-intensity data pairs to obtain the concentration correction curve for each impurity ion; the concentration correction curve includes two correction parameters: slope and intercept. The concentration correction curve for each impurity ion, along with its corresponding slope and intercept, is stored in the storage unit.

9. The method for detecting trace impurities in basic nickel carbonate as described in claim 8, characterized in that, The step of correcting spectral interference through curve interpolation to obtain corrected impurity spectral data includes: The concentration correction curves of each impurity ion are retrieved from the storage unit. The smoothed spectral intensity value of each impurity ion in the original impurity spectral data is substituted into the corresponding concentration correction curve to calculate the initial concentration judgment value of each impurity ion, thus obtaining the initial impurity concentration dataset. By calling the nickel matrix interference reference spectrum, the original spectral data of the impurities and the nickel matrix interference reference spectrum are compared with the wavelength intensity ratio to obtain the spectral interference coefficient corresponding to each impurity ion. If the spectral interference coefficient is greater than the preset interference threshold, the smooth spectral intensity value of the target impurity ion and the neighboring spectral intensity values ​​of its adjacent wavelength positions are selected as interpolation nodes to calculate the interpolated corrected spectral intensity value of the target impurity ion at the characteristic wavelength. Replace the smoothed spectral intensity value with the interpolated corrected spectral intensity value and resubstitute it into the corresponding concentration correction curve to obtain the corrected impurity concentration value; The initial impurity concentration dataset is updated based on the corrected impurity concentration values ​​to obtain the corrected impurity concentration dataset; the corrected impurity concentration dataset and its corresponding impurity ion types constitute the corrected impurity spectral data.

10. The method for detecting trace impurities in basic nickel carbonate as described in claim 9, characterized in that, The calculation of the actual content of various impurities based on the corrected impurity spectral data includes: Obtain the corrected concentration value of each impurity ion in the corrected impurity spectral data; Obtain the sampling volume of the homogeneous dispersed powder and the total volume of the composite digestion reagent; Multiply the corrected concentration value by the total volume value and then divide by the sampling volume value to obtain the mass fraction conversion value of each impurity ion in the original sample to be tested. The mass fraction conversion values ​​of all impurity ions are summarized into an actual impurity content dataset, and the actual impurity content dataset is stored in a storage unit.

11. The method for detecting trace impurities in basic nickel carbonate as described in claim 10, characterized in that, The generation of a time-stamped impurity detection report, combined with a preset impurity control threshold to determine the sample grade, includes: The system clock is invoked to obtain the current detection date and time and the current detection sequence number, and a timing mark code is generated. The impurity type and mass fraction conversion value of each impurity ion in the actual impurity content dataset are combined with the time-series tag code to form an impurity detection record. All impurity detection records are summarized into an impurity detection record set. Call the preset report template, fill the time sequence mark code into the header area, fill the impurity detection record set into the data area, and generate an impurity detection report with time sequence mark; The preset impurity control threshold table is invoked, and the converted mass fraction value is compared with the upper limit of the corresponding grade threshold in the preset impurity control threshold table, and the grade determination result is output.

12. A system for detecting trace impurities in basic nickel carbonate, used to implement the method for detecting trace impurities in basic nickel carbonate according to any one of claims 1-11, characterized in that, include: The sample pretreatment module wets the sample with anhydrous ethanol, acquires a microscopic image of the powder, and calculates the particle boundary feature map. It then uses statistical analysis of particle size and agglomeration parameters to determine the dispersion uniformity. Once the sample is qualified, the ultrasonic generator outputs alternating ultrasonic signals at dual frequencies to obtain homogeneous dispersed powder. The complexation monitoring module adds composite masking powder to the digested original solution after cooling and vortex mixing to complex the nickel matrix. It collects excitation and response currents, calculates real-time conductivity and generates conductivity time-series curves. The endpoint of the complexation reaction is determined by the rate of change of the curve. After the reaction is complete, a complexation completion signal is output to obtain a masked test solution containing only free impurity ions. The spectral acquisition and preprocessing module simultaneously acquires time-series data of the spectral intensities of multiple impurity elements to form complete characteristic spectral signals of impurities. The spectral correction calculation module fits the correction curves for each impurity concentration and stores the parameters. It constructs a baseline vector using the statistical features of blank spectra to complete baseline subtraction and digital filtering. Then, it judges the degree of interference by the spectral interference coefficient, corrects the spectral intensity by interpolation, substitutes it into the correction curve to calculate and update the corrected impurity concentration dataset. The report grade determination module is used to generate time-series marker codes, combine the actual impurity content dataset to generate a time-series marker-encoded test report according to the template, and automatically determine the sample grade by comparing the impurity mass fraction with the preset impurity control threshold.

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