Production control method and system for silver powder and storage medium

Abstract

The invention relates to the technical field of silver powder production control, and discloses a production control method and system for silver powder and a storage medium. The method comprises the following steps: mounting a sensor array on a chemical reduction reaction kettle to collect data to form a process parameter set; filtering the parameter set to obtain a quality prediction factor; performing sectional temperature regulation and control based on the predictive factor, and dynamically controlling the reaction to obtain silver powder suspension; and inputting the suspension into a self-adaptive centrifugal system, and adjusting process parameters according to the sedimentation efficiency. The problem of multi-parameter collaborative optimization in a traditional silver powder production control method is solved, the problems that in the chemical reduction method preparation process, the concentration of silver ions fluctuates, the adding rate of a reducing agent is unstable, and the reaction temperature is difficult to accurately control are solved, and self-adaptive centrifugal separation and intelligent washing control in the silver powder post-treatment process are achieved.

Description

Technical Field

[0001] This application relates to the field of silver powder production control technology, and in particular to a method, system and storage medium for silver powder production control. Background Technology

[0002] In traditional silver powder production technology, chemical reduction is the most commonly used method. This method controls the morphology and particle size distribution of silver powder by adjusting parameters such as temperature, pH value, and stirring speed within the reactor. Current technology primarily employs single-parameter PID control, where a separate control loop is set up for each process parameter, and each parameter is independently adjusted through proportional, integral, and derivative operations. This method has been used in industrial silver powder production for decades, and the produced silver powder is mainly used in conductive pastes, multilayer ceramic capacitors, and electronic packaging.

[0003] However, traditional single-parameter PID control methods have significant shortcomings and are ill-suited to addressing the multi-parameter coupling issues in silver powder production. First, parameters such as temperature, pH, and stirring speed exhibit complex interrelationships during the reaction process; controlling one parameter in isolation often leads to fluctuations in others, resulting in system instability. Second, traditional methods cannot automatically adjust process parameters at different stages based on the kinetics of silver powder formation, leading to a wide particle size distribution, irregular morphology, and poor batch-to-batch consistency. Furthermore, the centrifugation and washing steps in post-processing typically operate with fixed parameters, failing to dynamically adjust centrifugal force based on silver powder characteristics or automatically control the number of washes according to actual cleanliness, resulting in energy waste and product quality fluctuations.

[0004] With the increasing demands for silver powder quality in the electronics industry, especially in conductive paste applications where stringent requirements exist for uniform particle size, regular morphology, and consistent purity, there is an urgent need to develop a silver powder production method capable of multi-parameter synergistic control. Existing technologies struggle to achieve real-time monitoring and precise control of reaction process parameters, lacking data-driven quality prediction and adaptive parameter adjustment mechanisms. Furthermore, in the post-processing stage, how to adaptively adjust centrifugal force based on silver powder characteristics and determine the optimal washing strategy based on real-time cleanliness data have also become critical technical challenges that urgently need to be addressed. Summary of the Invention

[0005] This application provides a production control method, system, and storage medium for silver powder, which solves the problem of multi-parameter synergistic optimization in traditional silver powder production control methods, overcomes problems such as fluctuations in silver ion concentration, unstable addition rate of reducing agent, and difficulty in accurately controlling reaction temperature during the preparation process of chemical reduction method, and realizes adaptive centrifugal separation and intelligent washing control in the silver powder post-processing process.

[0006] In a first aspect, this application provides a production control method for silver powder, the method comprising: installing a temperature sensor array, a pH sensor, and a stirring speed sensor on a chemical reduction reactor to collect silver nitrate solution state data and obtain a set of reaction process parameters; filtering the set of reaction process parameters to obtain a quality prediction factor; implementing segmented temperature control based on the quality prediction factor, and dynamically controlling the reaction process through a reactor heating and cooling system, an intelligent pH adjusting pump, and a variable frequency stirrer to obtain a silver powder suspension; inputting the silver powder suspension into an adaptive centrifugal separation system, and adjusting the centrifugal force and washing frequency according to the real-time sedimentation efficiency.

[0007] Optionally, the chemical reduction reactor is equipped with a temperature sensor array, a pH sensor, and a stirring speed sensor to collect silver nitrate solution state data and obtain a set of reaction process parameters, including: Temperature sensors are installed at the top, upper middle, middle, lower middle, and bottom of the reactor to collect temperature data inside the reactor and obtain temperature distribution curves. The temperature distribution curve is periodically sampled, and temperature data is collected at a preset frequency to obtain a real-time temperature monitoring sequence. A pH sensor is installed in the reactor to collect pH change data during the reaction process. Data is collected at a rate higher than the temperature sampling frequency to obtain a pH change curve. A speed sensor is installed on the stirring device of the reactor to collect stirring speed data. Data is collected at a rate higher than the pH value sampling frequency. The temperature monitoring sequence, pH value change curve and stirring speed data are integrated to obtain the reaction process parameter set.

[0008] Optionally, the step of filtering the reaction process parameter set to obtain the quality prediction factor includes: Outlier screening was performed on the temperature data in the set of reaction process parameters to obtain valid temperature data; Interference is eliminated from the pH data in the set of reaction process parameters to obtain a smoothed pH curve; Fluctuation suppression is performed on the stirring speed data in the set of reaction process parameters to obtain a stirring uniformity index; Multi-window moving averages were performed on effective temperature data, pH smoothing curves, and stirring uniformity indices to obtain process stability indices. Based on the analysis of the process stability index, the influence of temperature gradient on crystal nucleus formation, pH value change on crystal growth, and stirring intensity on particle dispersion during the reaction process, a particle formation prediction model is obtained. The particle formation prediction model is compared with the particle size distribution data in historical production data. The quality prediction factor is obtained by calculating the correlation between the current process parameters and the target product quality.

[0009] Optionally, comparing the particle formation prediction model with particle size distribution data in historical production data, and obtaining the quality prediction factor by calculating the correlation between current process parameters and target product quality, includes: Historical batch data of conductive paste-grade silver powder were extracted from the silver powder production database, including reaction temperature curves, pH value change records, stirring speed logs, and corresponding scanning electron microscopy measurement results of silver powder particle size distribution, to obtain a silver powder quality standard dataset. Based on the particle formation prediction model, feature extraction is performed on the silver powder quality standard dataset to determine the numerical correspondence between temperature uniformity index and standard deviation of silver powder particle size, the numerical mapping relationship between pH stability time and sphericity of silver powder morphology, and the numerical correspondence between stirring Reynolds number and degree of silver powder agglomeration, thus obtaining the process-quality correlation matrix. Based on the process-quality correlation matrix, the deviation values ​​of the temperature gradient index, pH value fluctuation, and stirring uniformity of the current reaction process from the standard value are calculated to obtain the deviation value of the silver powder nucleus formation conditions. Based on the deviation values ​​of the silver powder nucleus formation conditions and the process deviation values ​​of the silver powder crystal growth stage, the range of silver powder particle size and morphological characteristics under the current process conditions are determined, and the silver powder quality prediction index is obtained. Based on the silver powder quality prediction index, the temperature control correction, pH value adjustment correction, and stirring speed correction are calculated to construct process parameter adjustment values ​​for the production of ultrafine spherical silver powder, thus obtaining the quality prediction factor.

[0010] Optionally, the step of implementing segmented temperature control based on the quality prediction factor, and dynamically controlling the reaction process through a reactor heating and cooling system, an intelligent pH adjusting pump, and a variable frequency stirrer to obtain a silver powder suspension, includes: Based on the quality prediction factors, the production process is divided into a preheating stage, a nucleation stage, a growth stage, and a stabilization stage. Temperature control target values ​​are set for each stage to obtain a temperature control scheme. According to the temperature control scheme, the heating and cooling system is controlled to heat the solution in the preheating stage, maintain a constant temperature in the nucleation stage, maintain a high temperature in the growth stage, and cool down in the stabilization stage, thus obtaining a temperature execution curve. Based on the quality prediction factor, the pH adjustment pump is controlled to add regulators to maintain an alkaline environment during the nucleation stage, and supplement and adjust according to pH changes during the growth stage to obtain a pH stable reaction system. The silver powder suspension is obtained by controlling the stirrer to perform high-speed stirring during the nucleation stage, medium-speed stirring during the growth stage, and intermittent stirring during the stabilization stage using the quality prediction factor.

[0011] Optionally, the step of inputting the silver powder suspension into an adaptive centrifugal separation system and adjusting the centrifugal force and washing frequency according to the real-time sedimentation efficiency includes: The silver powder suspension is fed into a centrifuge, an initial speed is set for pre-separation, the turbidity value of the supernatant is detected by a turbidity sensor, the initial sedimentation efficiency is calculated, and sedimentation state data is obtained. The centrifuge speed is dynamically adjusted based on the sedimentation state data. When the sedimentation efficiency is lower than the target value, the speed is increased. When the sedimentation efficiency has reached the target value, the speed is maintained to obtain the optimal centrifugation separation parameters. The separated silver powder was washed with conductivity detection, and the concentration of residual ions in the washing solution was measured. Washing continued when the conductivity was higher than the threshold and stopped when the conductivity was lower than the threshold, thus obtaining the control value for the number of washes. A multi-stage washing process is executed according to the washing number control value, and the washed silver powder is dried, while controlling the drying temperature and humidity parameters.

[0012] Optionally, the step of performing a multi-stage washing process according to the washing number control value, and drying the washed silver powder, while controlling the drying temperature and humidity parameters, includes: The silver powder precipitate after centrifugation is washed with deionized water for the first time, and the conductivity of the washing solution is measured. If the conductivity is greater than the first threshold, a second washing with deionized water is performed. If the conductivity is less than the first threshold, the ethanol washing stage is entered to obtain the primary washed silver powder. The primary washed silver powder is washed with ethanol solution, and the content of organic impurities in the washed silver powder is measured. If the content of organic impurities is greater than the second threshold, the ethanol washing is continued. If the content of organic impurities is less than the second threshold, the acidic solution washing stage is entered to obtain intermediate washed silver powder. According to the purity requirements of silver powder, the intermediate-grade washed silver powder is washed with dilute acid solution, and the content of metal impurities is measured. If the content of metal impurities is greater than the third threshold, the dilute acid washing continues. If the content of metal impurities is less than the third threshold, the washing process ends and the final washed silver powder is obtained. The final washed silver powder is fed into the drying system, and the drying temperature and drying time are set according to the silver powder particle size and application requirements, while controlling the humidity of the drying environment within the target range.

[0013] Secondly, this application provides a production control system for silver powder, the production control system for silver powder comprising: The data acquisition module is used to install a temperature sensor array, pH sensor and stirring speed sensor on the chemical reduction reactor to collect the state data of silver nitrate solution and obtain a set of reaction process parameters. The filtering module is used to perform filtering processing based on the set of reaction process parameters to obtain the quality prediction factor; The control module is used to implement segmented temperature regulation based on the quality prediction factor, and dynamically control the reaction process through the reactor heating and cooling system, intelligent pH adjustment pump and frequency converter to obtain silver powder suspension; The input module is used to input the silver powder suspension into the adaptive centrifugal separation system and adjust the centrifugal force and washing frequency according to the real-time sedimentation efficiency.

[0014] Thirdly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the above-described production control method for silver powder.

[0015] The technical solution provided in this application achieves comprehensive acquisition of silver nitrate solution state data by installing a temperature sensor array, pH sensor, and stirring speed sensor in the chemical reduction reactor, forming a complete set of reaction process parameters. This solves the problem of process fluctuations caused by incomplete parameter monitoring in traditional control methods. The acquired reaction process parameter set is filtered to obtain a quality prediction factor, significantly improving data quality and reliability and eliminating the impact of sensor noise and interference on control accuracy. This data processing method is particularly adaptable to the nonlinear dynamic characteristics of the silver powder production process, effectively smoothing fluctuations in process parameters and laying a data foundation for subsequent precise control. Based on the quality prediction factor, segmented temperature control is implemented. The reaction process is dynamically controlled through the reactor heating and cooling system, intelligent pH adjustment pump, and variable frequency stirrer, solving the problem of rigid parameter settings at each stage in traditional methods and achieving precise guidance throughout the silver powder growth process. This segmented control strategy is particularly suitable for the nucleation and growth kinetics of silver powder, automatically adjusting process parameters according to the needs of different stages, significantly improving the regularity of silver powder morphology and particle size uniformity. By feeding the silver powder suspension into an adaptive centrifugal separation system, the centrifugal force and washing frequency are adjusted according to the real-time sedimentation efficiency, solving the problems of energy waste and insufficient washing caused by traditional fixed-parameter centrifugation and washing methods. This adaptive control method specifically considers the sedimentation dynamics of silver powder particles and the impurity removal law, achieving precise control of the centrifugal separation and washing process, and significantly improving the purity and batch consistency of silver powder products.

[0016] The process parameter filtering algorithm, quality prediction model, and adaptive control strategy applied in this invention fully consider the special characteristics of the silver powder production process. By combining artificial intelligence algorithms with silver powder growth kinetics theory, it achieves intelligent control throughout the entire process, from data acquisition and processing to parameter control. In particular, the introduction of the quality prediction factor, by analyzing the correlation between process parameters and product quality, establishes a process-quality mapping model, enabling the control system to anticipate the impact of parameter adjustments on the final product quality. This achieves feedforward control, significantly improving control accuracy and response speed. Simultaneously, the dynamic parameter adjustment algorithm in the adaptive centrifugal separation system automatically optimizes centrifugal force based on real-time sedimentation efficiency data, ensuring separation effectiveness while minimizing energy consumption, demonstrating the significant contribution of algorithmic features to the solution. This method of applying artificial intelligence algorithms to a specific field of silver powder production effectively solves the problem of multi-parameter collaborative optimization, enabling precise control of the particle size, morphology, and purity of silver powder. It is particularly suitable for applications with extremely high silver powder quality requirements, such as high-end electronic pastes and multilayer ceramic capacitors. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of one embodiment of the silver powder production control method in this application. Figure 2 This is a schematic diagram of one embodiment of the production control system for silver powder in this application. Detailed Implementation

[0019] This application provides a method, system, and storage medium for controlling the production of silver powder. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the silver powder production control method in this application includes: Step S101: Install a temperature sensor array, pH sensor and stirring speed sensor on the chemical reduction reactor to collect silver nitrate solution state data and obtain a set of reaction process parameters; Step S102: Filter the reaction process parameter set to obtain the quality prediction factor; Step S103: Implement segmented temperature control based on quality prediction factors, and dynamically control the reaction process through the reactor heating and cooling system, intelligent pH adjustment pump and frequency converter to obtain silver powder suspension; Step S104: Input the silver powder suspension into the adaptive centrifugal separation system, and adjust the centrifugal force and washing times according to the real-time sedimentation efficiency.

[0021] It is understood that the executing entity of this application can be a production control system for silver powder, or it can be a terminal or a server; no specific limitation is made here. This application's embodiment uses a server as the executing entity for illustration.

[0022] Specifically, a temperature sensor array, a pH sensor, and a stirring speed sensor are installed in the chemical reduction reactor to collect state data of the silver nitrate solution. In practice, five temperature sensors are installed at the top, upper-middle, middle, lower-middle, and bottom of the reactor, forming a temperature monitoring array to acquire the internal temperature distribution. The temperature data collected by these sensors forms a temperature distribution curve, through which the temperature gradient changes within the reactor can be observed. Then, the temperature distribution curve is periodically sampled, for example, every 10 seconds, forming a real-time temperature monitoring sequence. Simultaneously, a pH sensor is installed in the reactor to monitor pH changes during the reaction process, with a sampling frequency higher than the temperature sensors, for example, every 5 seconds, to obtain a pH change curve. A stirring speed sensor is installed on the stirring device, with a sampling frequency even higher, for example, every 2 seconds, to acquire stirring speed data. These three types of data are integrated to form a complete set of reaction process parameters.

[0023] The reaction process parameter set was filtered to obtain quality prediction factors. First, outlier screening was performed on the temperature data, identifying and replacing data points deviating from the normal range to obtain valid temperature data. Interference elimination processing was performed on the pH data to remove abrupt changes caused by reagent addition, resulting in a smooth pH curve. Fluctuation suppression was applied to the stirring speed data to eliminate speed fluctuations during motor start-up and shutdown, obtaining a stable stirring uniformity index. Then, a multi-window moving average was performed on the three types of processed data: averaging 5 points for temperature data, 3 points for pH data, and 10 points for stirring speed data, to obtain a process stability index. Based on this index, the effects of temperature gradient on crystal nucleation, pH changes on crystal growth, and stirring intensity on particle dispersion were analyzed to generate a particle formation prediction model. Finally, this model was compared with historical production data to calculate the correlation between the current process parameters and the target product quality, thus obtaining the quality prediction factor.

[0024] Segmented temperature control is implemented based on quality predictive factors. The production process is divided into four stages: preheating, nucleation, growth, and stabilization. Different temperature control target values ​​are set for each stage, forming a temperature control scheme. According to this scheme, the heating and cooling system is controlled to heat the silver nitrate solution to the required nucleation temperature during the preheating stage, maintain a constant temperature during the nucleation stage to promote crystal nucleation, maintain a higher temperature during the growth stage to promote crystal growth, and gradually lower the temperature during the stabilization stage to prevent secondary nucleation. Simultaneously, based on quality predictive factors, the pH adjustment pump is controlled to add alkaline regulators during the nucleation stage to maintain a suitable pH environment, and to supplement and adjust the pH according to real-time changes during the growth stage to maintain pH stability. The quality predictive factors also control the stirrer to use high-speed stirring during the nucleation stage to promote uniform nucleation, medium-speed stirring during the growth stage to avoid damaging the crystals, and intermittent stirring during the stabilization stage to prevent silver powder agglomeration, ultimately obtaining a silver powder suspension.

[0025] The silver powder suspension is fed into an adaptive centrifugal separation system, and the centrifugal force and number of washes are adjusted based on the real-time sedimentation efficiency. First, the suspension is pre-separated by initial centrifugation at an initial speed. The turbidity of the supernatant is detected by a turbidity sensor to calculate the initial sedimentation efficiency. The centrifuge speed is dynamically adjusted based on the sedimentation efficiency; the speed is increased when the sedimentation efficiency is insufficient, and maintained when the target speed is reached, thus obtaining the optimal centrifugation parameters. The separated silver powder is then washed, and the concentration of residual ions in the washing solution is monitored by conductivity. Washing continues when the conductivity exceeds a threshold and stops when it falls below the threshold, determining the number of washes. A multi-stage washing process is performed, including washing with deionized water to remove water-soluble impurities, washing with ethanol to remove organic impurities, and, if necessary, washing with an acidic solution to remove metallic impurities. Finally, the product silver powder is dried to obtain the final product.

[0026] In one specific embodiment, the process of performing step S101 may specifically include the following steps: Temperature sensors are installed at the top, upper middle, middle, lower middle, and bottom of the reactor to collect temperature data inside the reactor and obtain temperature distribution curves. Periodically sample the temperature distribution curve and collect temperature data at a preset frequency to obtain a real-time temperature monitoring sequence; A pH sensor is installed in the reactor to collect pH change data during the reaction process. Data is collected at a rate higher than the temperature sampling frequency to obtain a pH change curve. A speed sensor is installed on the stirring device of the reactor to collect stirring speed data. Data is collected at a rate higher than the pH value sampling frequency. The temperature monitoring sequence, pH value change curve and stirring speed data are integrated to obtain the reaction process parameter set.

[0027] Specifically, temperature sensors are installed at the top, upper-middle, middle, lower-middle, and bottom of the reactor, forming a temperature sensor array. Each temperature sensor is a platinum resistance thermometer (Pt100) with a measurement accuracy of ±0.1℃ and a measurement range of 0-100℃. These sensors are evenly distributed along the height of the reactor, located at the top (10cm from the top), one-quarter of the way from the top, middle, one-quarter of the way from the bottom, and the bottom (10cm from the bottom). This distribution allows for comprehensive monitoring of temperature changes at different heights within the reactor, generating temperature gradient data. When the silver nitrate solution undergoes a chemical reduction reaction in the reactor, these five temperature sensors simultaneously collect data, recording the real-time temperature values ​​at each location and generating a temperature data matrix that changes over time. After time stamping, this data forms a temperature distribution curve, showing the temperature change trend at each location over time during the reaction. The acquired temperature distribution curve is periodically sampled according to a preset frequency. The temperature sampling frequency is set to once every 10 seconds, meaning a set of temperature data (containing temperature values ​​at 5 locations) is read from the temperature sensor array every 10 seconds. The sampling clock is controlled by the timer module of the control system to ensure the accuracy of the sampling interval. The sampled data are arranged in chronological order to form a temperature monitoring time series, which includes the temperature changes throughout the entire reaction process. Each data point in the series includes a timestamp, the temperature values ​​at the 5 locations, and the calculated temperature gradient (the difference between the top and bottom temperatures). A pH sensor is installed in the reactor to collect pH change data during the reaction process. The pH sensor is a glass electrode pH meter with a measurement range of 0-14 and an accuracy of ±0.05 pH units. The sensor is installed in the middle of the reactor, immersed in the reaction solution, ensuring full contact between the electrode head and the reaction solution. The pH data acquisition frequency is set to once every 5 seconds, higher than the temperature data acquisition frequency, because pH changes have a more direct impact on the formation and growth rate of silver powder nuclei, requiring more precise monitoring. The pH data is also timestamped to form a pH change curve, recording the changes in the acidity and alkalinity of the solution during the reaction process.

[0028] A speed sensor was installed on the agitator of the reactor to collect agitation speed data. The speed sensor was a Hall effect speed sensor with a measurement range of 0-1000 rpm and an accuracy of ±1 rpm. The sensor was fixed on the shaft of the agitator motor, and the agitation speed was measured by detecting the rotation frequency of the motor shaft. The agitation speed data was collected every 2 seconds, higher than the pH data collection frequency, because fluctuations in agitation speed directly affect the mixing uniformity and mass transfer efficiency of the reaction solution, requiring more timely monitoring and adjustment. The agitation speed data was also timestamped to form an agitation speed data curve. The collected temperature monitoring sequence, pH value change curve, and agitation speed data were time-aligned and integrated to form a complete set of reaction process parameters. During data integration, the three types of data were first sorted according to timestamps, then time point alignment was performed. For asynchronous data points, linear interpolation was used to calculate the estimated value of the corresponding time point. The final reaction process parameter set included multiple parameters such as time, five temperature points, temperature gradient, pH value, and agitation speed, providing complete raw data for subsequent data processing and quality control. In practical applications, such as in a silver powder production process, the temperature in the middle of the reactor gradually increased from the initial 25℃ to 45℃ and then remained stable. The temperature difference between the top and bottom was always controlled within 2℃. The pH value gradually decreased from 10.5 after the initial addition of alkaline reagent to 9.8 and remained stable. The stirring speed was maintained at 500 rpm during the crystal nucleation stage and adjusted to 300 rpm during the growth stage. Through the coordinated monitoring and recording of these parameters, a complete set of process parameters was formed.

[0029] In one specific embodiment, the process of performing step S102 may specifically include the following steps: Outlier screening was performed on the temperature data in the set of reaction process parameters to obtain valid temperature data; Interference was eliminated from the pH data in the set of reaction process parameters to obtain a smoothed pH curve; Fluctuations in the stirring speed data within the set of reaction process parameters are suppressed to obtain a stirring uniformity index. Multi-window moving averages were performed on effective temperature data, pH smoothing curves, and stirring uniformity indices to obtain process stability indices. Based on the analysis of process stability indicators, the effects of temperature gradient on crystal nucleus formation, pH value change on crystal growth, and stirring intensity on particle dispersion, a particle formation prediction model was obtained. By comparing the particle formation prediction model with particle size distribution data in historical production data, and calculating the correlation between current process parameters and target product quality, a quality prediction factor is obtained.

[0030] Specifically, the 3σ principle is used to identify outliers. This involves calculating the average and standard deviation of temperature data, and identifying data deviating from the average by more than three times the standard deviation as outliers. For identified outliers, the average of the preceding and following data is used to replace them, ensuring data continuity. For example, in silver powder production, if a temperature sensor experiences a sudden change in a short period (e.g., a sudden jump from 45℃ to 65℃ and then quickly back to 46℃), this data, which violates thermodynamic laws, is identified as an outlier and replaced, thus obtaining valid temperature data. Interference elimination processing is performed on pH data, primarily targeting pH fluctuations caused by reagent addition. A median filtering method is used, taking data points within a certain window, sorting them by size, and selecting the value at the median as the new value for that point. This method effectively eliminates short-term spikes. In silver powder production, when pH adjusters are added to the reactor, rapid pH fluctuations often occur within a short time. After median filtering, these fluctuations, which do not represent the actual reaction state, are smoothed, resulting in a smoothed pH curve reflecting the true reaction environment.

[0031] Fluctuation suppression processing is applied to the stirring speed data using a low-pass filtering algorithm to filter out high-frequency fluctuations during the stirring process, while retaining low-frequency trends. Specifically, this is achieved by setting a cutoff frequency, performing a Fourier transform on the stirring speed data, retaining frequency components below the cutoff frequency, and then performing an inverse transform to obtain smoothed data. The root mean square error (RMSE) of the processed data is calculated as an indicator of stirring uniformity; a smaller MSE indicates more uniform stirring. In silver powder production, the start-up and shutdown of the stirring motor and power grid fluctuations can cause irregular fluctuations in the stirring speed. Fluctuation suppression processing yields a uniformity indicator that accurately reflects the stirring state.

[0032] Multi-window moving average processing was applied to the effective temperature data, pH smoothing curves, and mixing uniformity index. This involved averaging the data using time windows of varying sizes: a longer time window (e.g., 5 data points) for temperature data, a medium window (e.g., 3 data points) for pH data, and a shorter window (e.g., 2 data points) for mixing uniformity index. The resulting time series data obtained through multi-window processing reflects the variation characteristics of different parameters at different time scales, thus forming a comprehensive process stability index.

[0033] Based on the analysis of process stability indicators, the influence of various parameters on silver powder formation is determined. First, a model is established to correlate temperature gradient with the number of crystal nuclei; a smaller temperature gradient results in more uniform crystal nuclei formation. Next, a model is established to correlate pH stability with crystal growth rate; smaller pH fluctuations lead to more regular crystal growth. Finally, a model is established to correlate mixing uniformity with silver powder particle dispersion; more uniform mixing results in better particle dispersion. These three models are then combined to form a complete particle formation prediction model, capable of predicting the particle size and morphology of silver powder formed under specific process parameters. The particle formation prediction model is compared with historical production data. First, data from historically high-quality production batches, including process parameters and corresponding silver powder particle size distribution data, are extracted from the database. By calculating the similarity between current process parameters and historical high-quality batch parameters, as well as the matching degree between the prediction model output and the target product quality characteristics, a quality prediction factor is derived. This factor reflects both the stability of the current process state and predicts the quality level of the final product, providing a basis for subsequent process parameter adjustments. For example, in a silver powder production process, after processing the collected temperature, pH value and stirring speed data as described above, it was found that the temperature gradient was too large (the temperature difference between the top and bottom reached 5°C), which was predicted to lead to uneven crystal nucleus formation and a low quality prediction factor. Subsequently, the heating system was adjusted to make the temperature distribution more uniform, resulting in a high-quality silver powder product with a narrower particle size distribution.

[0034] In one specific embodiment, the process of comparing the particle formation prediction model with particle size distribution data in historical production data may specifically include the following steps: Historical batch data of conductive paste-grade silver powder were extracted from the silver powder production database, including reaction temperature curves, pH value change records, stirring speed logs, and corresponding scanning electron microscopy measurement results of silver powder particle size distribution, to obtain a silver powder quality standard dataset. Based on the particle formation prediction model, feature extraction was performed on the silver powder quality standard dataset to determine the numerical correspondence between temperature uniformity index and standard deviation of silver powder particle size, the numerical mapping relationship between pH stabilization time and sphericity of silver powder morphology, and the numerical correspondence between stirring Reynolds number and degree of silver powder agglomeration, thus obtaining the process-quality correlation matrix. Based on the process-quality correlation matrix, the deviation values ​​of the temperature gradient index, pH value fluctuation, and stirring uniformity of the current reaction process from the standard value are calculated to obtain the deviation value of the silver powder nucleus formation conditions. Based on the deviation values ​​of silver powder nucleus formation conditions and the process deviation values ​​of silver powder crystal growth stages, the particle size range and morphological characteristics of silver powder under the current process conditions are determined, and the silver powder quality prediction index is obtained. Based on the silver powder quality prediction index, the correction amounts for temperature control, pH adjustment, and stirring speed are calculated to construct process parameter adjustment values ​​for the production of ultrafine spherical silver powder, thus obtaining the quality prediction factor.

[0035] Specifically, data from high-quality batches meeting the requirements for conductive paste applications were selected based on product quality grades. The extracted data included process parameter records (reaction temperature curves, pH value change records, and stirring speed logs) and corresponding quality inspection results (particle size distribution and morphological characteristics). The temperature curves contained temperature data from five locations within the reactor, the pH value records included pH changes throughout the reaction process, and the stirring speed logs recorded the stirring speed changes at different stages. Particle size distribution data came from laser particle size analyzer measurements, and morphological characteristics were derived from scanning electron microscopy image analysis. These data were linked and exported by batch number using database queries to form a silver powder quality standard dataset. Feature extraction was performed on the silver powder quality standard dataset based on a particle formation prediction model. First, the temperature uniformity index was calculated, i.e., the root mean square value of the temperature difference between the top and bottom of the reactor; a smaller value indicates a more uniform temperature distribution. Then, the pH stabilization time was calculated, i.e., the proportion of the total reaction time during which the pH value remained within the target range. Next, the stirring Reynolds number was calculated based on the stirring speed, impeller diameter, and solution viscosity, reflecting the liquid flow state. By statistically analyzing the correspondence between these characteristic values ​​and the final silver powder quality parameters (particle size standard deviation, sphericity, and agglomeration), a mapping relationship between process parameters and product quality is established, forming a process-quality correlation matrix.

[0036] The deviations of various parameters in the current reaction process from standard values ​​are calculated based on the process-quality correlation matrix. First, the current temperature gradient is compared with the standard values ​​in the correlation matrix to calculate the temperature gradient deviation. Similarly, the deviations of pH fluctuation and stirring uniformity from the standard values ​​are calculated. These deviations collectively reflect the difference between the current silver powder nucleation conditions and ideal conditions, forming the silver powder nucleation condition deviation value. Based on the silver powder nucleation condition deviation value and the process deviation value at the growth stage, the quality characteristics of the silver powder under the current process conditions are predicted. Using the mapping relationship in the correlation matrix, the possible silver powder particle size range and morphological characteristics are calculated based on the current deviation values, forming the silver powder quality prediction index. This index comprehensively reflects the expected particle size distribution, morphological regularity, and agglomeration tendency of the product.

[0037] The correction amount of process parameters is calculated based on the silver powder quality prediction index. When the quality prediction index indicates a wide particle size distribution, the required increase in stirring speed is calculated; when the predicted morphology is not regular enough, the required pH value adjustment is calculated; when the predicted agglomeration trend is obvious, the required temperature gradient change is calculated. These correction amounts are combined into a complete process parameter adjustment scheme, forming a quality prediction factor to guide the parameter control of subsequent production processes. For example, in the production of silver powder for conductive silver paste, analysis of historical data revealed a negative correlation between temperature uniformity index and particle size standard deviation, a positive correlation between pH stabilization time and silver powder sphericity, and a U-shaped relationship between stirring Reynolds number and agglomeration degree. A large temperature gradient was detected in the current production process. The process-quality correlation matrix was used to deduce that this might lead to a wider particle size distribution. The required temperature gradient value was immediately calculated, and the heating system was adjusted to make the temperature distribution more uniform. Ultimately, the particle size distribution of the silver powder was successfully controlled, resulting in high-quality silver powder that meets the requirements of conductive paste.

[0038] In one specific embodiment, the process of executing step S103 may specifically include the following steps: Based on the quality prediction factors, the production process is divided into a preheating stage, a nucleation stage, a growth stage, and a stabilization stage. Temperature control target values ​​are set for each stage to obtain a temperature control scheme. According to the temperature control scheme, the heating and cooling system heats the solution in the preheating stage, maintains a constant temperature in the nucleation stage, maintains a high temperature in the growth stage, and cools down in the stabilization stage, thus obtaining the temperature execution curve. Based on the quality prediction factor, the pH adjustment pump adds regulators to maintain an alkaline environment during the nucleation stage and supplements and adjusts the pH according to the pH changes during the growth stage to obtain a pH stable reaction system. By controlling the stirrer to operate at high speed during the nucleation stage, medium speed during the growth stage, and intermittent stirring during the stabilization stage using a quality prediction factor, a silver powder suspension was obtained.

[0039] Specifically, the silver powder production process is divided into four stages based on quality predictive factors. First, by analyzing the temperature control correction, pH adjustment correction, and stirring speed correction within the quality predictive factors, the time nodes and target values ​​for process parameters for each stage are determined. The preheating stage refers to the process from the addition of raw materials to the reaction temperature reaching the temperature required for crystal nucleation, typically accounting for 15-20% of the entire production process. The nucleation stage refers to the process of a large number of crystal nuclei forming, accounting for approximately 20-25% of the time. The growth stage is the process of crystals gradually growing, accounting for approximately 40-50% of the time. The stabilization stage is the process of crystal morphology and size stabilizing, accounting for approximately the remaining 10-15% of the time. Based on the target product characteristics indicated by the quality predictive factors, specific temperature control target values ​​are set for each stage, such as 25-30℃ for the preheating stage, 35-40℃ for the nucleation stage, 45-50℃ for the growth stage, and 30-35℃ for the stabilization stage, forming a complete temperature control scheme.

[0040] The heating and cooling system is operated according to the temperature control scheme. During the preheating stage, the reactor jacket heater is activated to heat the silver nitrate solution from room temperature to the required nucleation temperature. The heating rate is determined by the quality prediction factor and is typically controlled at 1-2℃ / min to prevent localized overheating and uneven nucleation. Once the temperature reaches the target value for the nucleation stage, the control system automatically switches to isothermal mode, precisely controlling the heating power to maintain a constant temperature. Temperature fluctuations are controlled within ±0.5℃ to maintain a stable environment suitable for crystal nucleus formation. Upon entering the growth stage, the temperature is again increased to the target value to promote the migration and deposition of silver ions to the crystal nucleus surface, accelerating crystal growth. When growth reaches the expected level, the system enters the stabilization stage, where the control system lowers the temperature to slow down the diffusion and deposition rate of silver ions, preventing secondary nucleation and irregular growth. The entire process forms a temperature execution curve that meets the requirements of silver powder generation kinetics.

[0041] The pH adjustment pump is controlled based on a quality predictor factor to maintain a suitable pH environment at different stages. During the nucleation stage, an alkaline regulator (such as sodium hydroxide solution) is added according to the pH correction amount in the quality predictor factor to adjust the pH of the reaction system to the range of 9.5-10.5, promoting silver ion reduction and crystal nucleation. The addition rate of the pH adjustment pump is regulated by a PID control algorithm, calculating the output value based on the deviation between the real-time pH value and the target value, the integral value of the deviation, and the rate of change of the deviation, achieving precise control. During the growth stage, as the reaction proceeds, the system pH gradually decreases. Supplemental adjustments are made based on the pH trend to maintain the pH within the range of 8.5-9.5, ensuring a stable growth environment. The stability of the pH directly affects the regularity of the silver powder morphology; by precisely controlling the fluctuation range of the pH, uniform silver powder morphology is ensured.

[0042] The quality predictive factor controls the agitator to employ different stirring strategies at different stages. During the nucleation stage, the agitator is set to high speed (typically 400-600 rpm), with vigorous stirring ensuring thorough mixing of the reducing agent and silver ions, creating a uniform nucleation environment and avoiding uneven nucleation caused by localized supersaturation. During the growth stage, the stirring speed is reduced to a medium level (typically 200-300 rpm) to ensure adequate mixing of the reactants while avoiding excessive shear force that could damage the formed crystal structure. During the stabilization stage, an intermittent stirring mode is used, i.e., periodically turning the agitator on and off (e.g., 30 seconds on, 10 seconds off) to prevent silver powder particles from agglomerating or breaking due to prolonged stirring. The precise execution of the stirring strategy has a decisive impact on the particle size distribution and morphological uniformity of the silver powder. For example, in the production of silver powder for conductive paste, quality predictive factor analysis revealed the need to produce spherical silver powder with a particle size of 1.5±0.2μm. Based on this, a four-stage temperature curve was set: preheating stage 28℃, nucleation stage 38℃, growth stage 48℃, and stabilization stage 33℃. Combined with a pH of 10.2 and high-speed stirring at 500rpm during the nucleation stage, a pH of 9.0 and medium-speed stirring at 250rpm during the growth stage, and intermittent stirring mode (40 seconds on and 15 seconds off) during the stabilization stage, a high-quality silver powder suspension with narrow particle size distribution and regular morphology was finally obtained.

[0043] In one specific embodiment, the process of executing step S104 may specifically include the following steps: The silver powder suspension is fed into a centrifuge, an initial speed is set for pre-separation, the turbidity value of the supernatant is detected by a turbidity sensor, the initial sedimentation efficiency is calculated, and sedimentation state data is obtained. The centrifuge speed is dynamically adjusted based on sedimentation state data. When the sedimentation efficiency is lower than the target value, the speed is increased. When the sedimentation efficiency has reached the target value, the speed is maintained to obtain the optimal centrifugation separation parameters. The separated silver powder was washed with conductivity detection, and the concentration of residual ions in the washing solution was measured. Washing continued when the conductivity was higher than the threshold and stopped when the conductivity was lower than the threshold, thus obtaining the control value for the number of washes. A multi-stage washing process is executed based on the control value of the number of washes, and the washed silver powder is dried by controlling the drying temperature and humidity parameters.

[0044] Specifically, after the silver powder suspension is fed into the centrifuge, an initial rotation speed is set for pre-separation. The initial rotation speed is set to 800 rpm, a value pre-calculated based on the average particle size and density of the silver powder. Once the pre-separation process begins, a turbidity sensor installed at the centrifuge supernatant outlet monitors the turbidity of the supernatant in real time. The turbidity sensor uses the principle of light scattering to measure the intensity of light scattering by suspended particles in the liquid; a higher turbidity value indicates a greater amount of residual silver powder particles in the supernatant. The initial sedimentation efficiency is calculated based on the turbidity value using the formula: Sedimentation efficiency (%) = 100 - (Supernatant turbidity value / Original suspension turbidity value) × 100. This data is recorded to form sedimentation status data, including four parameters: time, rotation speed, turbidity value, and sedimentation efficiency.

[0045] The centrifuge speed is dynamically adjusted based on sedimentation data using an adaptive control algorithm. The current sedimentation efficiency is compared to the target sedimentation efficiency (usually set at 99%). When the calculated sedimentation efficiency is lower than the target value, the control system automatically increases the centrifuge speed. The increase is proportional to the efficiency difference; the larger the difference, the greater the speed increase. The adjustment formula is: New speed = Current speed × (1 + 0.1 × (Target efficiency - Current efficiency) / Target efficiency). For example, if the initial speed is 800 rpm and the sedimentation efficiency is 95%, and the target efficiency is 99%, the new speed is adjusted to 800 × (1 + 0.1 × (99% - 95%) / 99%) = 832 rpm. The system checks and adjusts the sedimentation efficiency every 30 seconds until the target value is reached. When the sedimentation efficiency reaches or exceeds the target value, the current speed is maintained, and the optimal centrifugation parameters are recorded, including the final speed value, the time required to reach the target efficiency, and energy consumption data.

[0046] The separated silver powder is washed using conductivity testing. First, a certain amount of deionized water is added to the precipitated silver powder for an initial wash. After washing, a portion of the washing solution is extracted, and its conductivity value is measured using a conductivity meter. The conductivity value reflects the concentration of residual ions in the washing solution. The measured conductivity value is compared with a preset threshold (usually 10 μS / cm). When the conductivity is higher than the threshold, it indicates that the washing solution still contains a large number of impurity ions, and washing needs to continue. The system automatically records the current number of washes and starts the next washing cycle. The conductivity measurement and comparison process is repeated after each wash until the conductivity is lower than the threshold after a certain wash, indicating that the impurity ions have been sufficiently removed. At this point, the washing process is stopped, and the total number of washes is recorded as the control value for the number of washes.

[0047] A multi-stage washing process is executed according to the controlled number of washes, specifically by performing different types of washes sequentially according to different washing purposes. First, deionized water washing is performed to remove water-soluble impurities such as nitrate ions and unreacted silver ions. The number of washes is determined according to the controlled number of washes, typically 2-4 times. Next, ethanol washing is performed to remove surface-adsorbed organic matter and residual moisture, 1-2 times. In special cases, such as for high-purity silver powder, a dilute acid washing step is added, using a dilute nitric acid solution (approximately 0.01 mol / L) once to remove possible metallic impurities. After washing, the silver powder is dried. The wet silver powder is transferred to a drying device, with the drying temperature set at 60-80℃ and the relative humidity controlled within the range of 5-15%. The drying time is determined according to the amount of silver powder and its moisture content, typically 2-4 hours. For example, in the production of silver powder for a conductive paste, the concentration of the silver powder suspension obtained from the reaction was 10 g / L. The initial centrifugation speed was set at 800 rpm, and the turbidity of the supernatant was measured to be 8% of the original suspension, resulting in an initial sedimentation efficiency of 92%. Since the target efficiency was set at 99%, the system automatically increased the speed to 880 rpm, achieving a sedimentation efficiency of 99.2%, which was recorded as the optimal centrifugation parameter. Subsequently, washing was performed. After the first wash, the conductivity was measured at 85 μS / cm, higher than the threshold of 10 μS / cm, so washing continued. After the third wash, the conductivity dropped to 8 μS / cm, lower than the threshold, and the number of washes was determined to be 3. A multi-stage washing process was performed, including three washes with deionized water and one wash with ethanol. Finally, the product was dried at 70°C and 10% relative humidity for 3 hours to obtain a high-purity, low-agglomeration silver powder product.

[0048] In one specific embodiment, the process of executing a multi-stage washing process based on the washing number control value may specifically include the following steps: The silver powder precipitate after centrifugation is washed with deionized water for the first time, and the conductivity of the washing solution is measured. If the conductivity is greater than the first threshold, a second washing with deionized water is performed. If the conductivity is less than the first threshold, the ethanol washing stage is entered to obtain the primary washed silver powder. The primary washed silver powder is washed with ethanol solution, and the content of organic impurities in the washed silver powder is measured. If the content of organic impurities is greater than the second threshold, the ethanol washing is continued. If the content of organic impurities is less than the second threshold, the acidic solution washing stage is entered to obtain the intermediate washed silver powder. According to the purity requirements of silver powder, the intermediate-grade washed silver powder is washed with dilute acid solution and the content of metal impurities is measured. If the content of metal impurities is greater than the third threshold, the dilute acid washing continues. If the content of metal impurities is less than the third threshold, the washing process ends and the final washed silver powder is obtained. The final washed silver powder is fed into the drying system. The drying temperature and drying time are set according to the silver powder particle size and application requirements, and the humidity of the drying environment is controlled within the target range.

[0049] Specifically, the silver powder precipitate was mixed with deionized water at a volume ratio of 1:5 and stirred for 5 minutes to ensure sufficient contact. Then, it was centrifuged, and the supernatant was collected to measure its conductivity. Conductivity was measured using a conductivity meter; the electrode was inserted into the washing solution, and the value was read. The first threshold was set at 50 μS / cm. This value was determined based on the typical residual concentration of common ionic impurities in silver powder (such as nitrate and sodium ions), corresponding to approximately 10 ppm of ionic impurities. If the measured conductivity was greater than 50 μS / cm, it indicated that the content of water-soluble impurities was still high, requiring a second deionized water wash. If the conductivity was less than 50 μS / cm, the water-soluble impurities had been reduced to an acceptable level, and the washing procedure proceeded to the next stage. This judgment logic controlled the number of washes, avoiding unnecessary over-washing or under-washing, resulting in primary washed silver powder. The primary washed silver powder was then washed with an ethanol solution. Analytical grade ethanol (purity ≥99.7%) was mixed with silver powder at a volume ratio of 5:1, stirred for 10 minutes, and then centrifuged. The organic impurity content in the washed silver powder was measured using thermogravimetric analysis (TGA). A small sample of silver powder was heated from room temperature to 500°C under a nitrogen atmosphere, and the percentage of mass loss was measured; this value represents the organic impurity content. A second threshold was set at 0.5%, based on the maximum tolerance of silver powder for organic impurities in electronic paste applications. If the organic impurity content was greater than 0.5%, ethanol washing continued; if the organic impurity content was less than 0.5%, the organic impurities had been removed to a satisfactory level, and the washing process entered the acidic solution washing stage, yielding intermediate-grade washed silver powder.

[0050] Intermediate-grade washed silver powder is washed with dilute acid solution according to the purity requirements of the silver powder, especially for silver powder requiring high purity (such as for the preparation of high-end electronic pastes or multilayer ceramic capacitors). A 0.01 mol / L nitric acid solution is mixed with the silver powder at a volume ratio of 3:1, gently stirred for 5 minutes, and then immediately centrifuged to avoid prolonged contact that could cause the silver powder to dissolve. Inductively coupled plasma mass spectrometry (ICP-MS) is used to measure the content of metal impurities in the silver powder, mainly detecting the total content of heavy metals such as copper, iron, and lead. The third threshold is set at 100 ppm, a value determined based on the application requirements of high-purity silver powder. If the metal impurity content is greater than 100 ppm, dilute acid washing continues; if the metal impurity content is less than 100 ppm, the washing process ends, yielding the final washed silver powder that meets the purity requirements.

[0051] The final washed silver powder is fed into the drying system, and drying parameters are set according to the particle size characteristics of the silver powder and application requirements. For ultrafine silver powder with an average particle size of less than 2 μm, the drying temperature is set in the range of 60-70℃ to avoid agglomeration of silver powder particles due to high temperature; for medium-sized silver powder with a particle size of 2-5 μm, the drying temperature can be set in the range of 70-80℃. The drying time is determined according to the moisture content of the silver powder, usually between 2-4 hours. The moisture content is calculated by the mass difference between wet and dry silver powder. The humidity of the drying environment is controlled within the range of 10-15%. Too high humidity will prolong the drying time, while too low humidity may cause static charge accumulation and silver powder agglomeration. For example, in the production process of silver powder for conductive paste, after the first washing of the silver powder precipitate after centrifugation with deionized water, the conductivity of the washing liquid was measured to be 120 μS / cm, which is higher than the first threshold of 50 μS / cm. After a second washing, the conductivity dropped to 35 μS / cm, which is lower than the threshold, and the process entered the ethanol washing stage. After the initial washing of the silver powder with ethanol, thermogravimetric analysis showed that the organic impurity content was 0.8%, which was higher than the second threshold of 0.5%. A second ethanol wash was then performed, reducing the organic impurity content to 0.3%, at which point the acid washing stage began. Based on the requirements for using silver powder in highly conductive pastes, dilute acid washing was performed. ICP-MS showed that the metal impurity content was 82 ppm, which was lower than the third threshold of 100 ppm, thus ending the washing process. The final washed silver powder had an average particle size of 1.5 μm. A drying temperature of 65℃, a drying time of 3 hours, and an ambient humidity of 12% yielded a high-purity, low-agglomeration, and highly flowable silver powder product.

[0052] The production control method for silver powder in the embodiments of this application has been described above. The production control system for silver powder in the embodiments of this application is described below. Please refer to [link / reference]. Figure 2 One embodiment of the production control system for silver powder in this application includes: The data acquisition module 201 is used to install a temperature sensor array, a pH sensor and a stirring speed sensor on the chemical reduction reactor to acquire silver nitrate solution state data and obtain a set of reaction process parameters; Filtering module 202 is used to perform filtering processing based on the reaction process parameter set to obtain quality prediction factors; Control module 203 is used to implement segmented temperature control based on the quality prediction factor, and dynamically control the reaction process through the reactor heating and cooling system, intelligent pH adjustment pump and frequency converter to obtain silver powder suspension; The input module 204 is used to input the silver powder suspension into the adaptive centrifugal separation system and adjust the centrifugal force and washing times according to the real-time sedimentation efficiency.

[0053] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the method for controlling the production of silver powder.

[0054] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0055] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a silver powder production control device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production control method for silver powder, characterized in that, The method includes: A temperature sensor array, a pH sensor, and a stirring speed sensor were installed in the chemical reduction reactor to collect the state data of the silver nitrate solution and obtain a set of reaction process parameters. The quality prediction factor is obtained by filtering the set of reaction process parameters. Based on the quality prediction factor, segmented temperature control is implemented, and the reaction process is dynamically controlled by the reactor heating and cooling system, intelligent pH adjustment pump and frequency converter to obtain silver powder suspension. The silver powder suspension is fed into an adaptive centrifugal separation system, and the centrifugal force and number of washes are adjusted according to the real-time sedimentation efficiency.

2. The production control method for silver powder according to claim 1, characterized in that, The chemical reduction reactor is equipped with a temperature sensor array, a pH sensor, and a stirring speed sensor to collect silver nitrate solution state data and obtain a set of reaction process parameters, including: Temperature sensors are installed at the top, upper middle, middle, lower middle, and bottom of the reactor to collect temperature data inside the reactor and obtain temperature distribution curves. The temperature distribution curve is periodically sampled, and temperature data is collected at a preset frequency to obtain a real-time temperature monitoring sequence. A pH sensor is installed in the reactor to collect pH change data during the reaction process. Data is collected at a rate higher than the temperature sampling frequency to obtain a pH change curve. A speed sensor is installed on the stirring device of the reactor to collect stirring speed data. Data is collected at a rate higher than the pH value sampling frequency. The temperature monitoring sequence, pH value change curve and stirring speed data are integrated to obtain the reaction process parameter set.

3. The production control method for silver powder according to claim 1, characterized in that, The step of filtering based on the reaction process parameter set to obtain the quality prediction factor includes: Outlier screening was performed on the temperature data in the set of reaction process parameters to obtain valid temperature data; Interference is eliminated from the pH data in the set of reaction process parameters to obtain a smoothed pH curve; Fluctuation suppression is performed on the stirring speed data in the set of reaction process parameters to obtain a stirring uniformity index; Multi-window moving averages were performed on effective temperature data, pH smoothing curves, and stirring uniformity indices to obtain process stability indices. Based on the analysis of the process stability index, the influence of temperature gradient on crystal nucleus formation, pH value change on crystal growth, and stirring intensity on particle dispersion during the reaction process, a particle formation prediction model is obtained. The particle formation prediction model is compared with the particle size distribution data in historical production data. The quality prediction factor is obtained by calculating the correlation between the current process parameters and the target product quality.

4. The production control method for silver powder according to claim 3, characterized in that, The process of comparing the particle formation prediction model with particle size distribution data from historical production data, and calculating the correlation between current process parameters and target product quality to obtain the quality prediction factor, includes: Historical batch data of conductive paste-grade silver powder were extracted from the silver powder production database, including reaction temperature curves, pH value change records, stirring speed logs, and corresponding scanning electron microscopy measurement results of silver powder particle size distribution, to obtain a silver powder quality standard dataset. Based on the particle formation prediction model, feature extraction is performed on the silver powder quality standard dataset to determine the numerical correspondence between temperature uniformity index and standard deviation of silver powder particle size, the numerical mapping relationship between pH stability time and sphericity of silver powder morphology, and the numerical correspondence between stirring Reynolds number and degree of silver powder agglomeration, thus obtaining the process-quality correlation matrix. Based on the process-quality correlation matrix, the deviation values ​​of the temperature gradient index, pH value fluctuation, and stirring uniformity of the current reaction process from the standard value are calculated to obtain the deviation value of the silver powder nucleus formation conditions. Based on the deviation values ​​of the silver powder nucleus formation conditions and the process deviation values ​​of the silver powder crystal growth stage, the range of silver powder particle size and morphological characteristics under the current process conditions are determined, and the silver powder quality prediction index is obtained. Based on the silver powder quality prediction index, the temperature control correction, pH value adjustment correction, and stirring speed correction are calculated to construct process parameter adjustment values ​​for the production of ultrafine spherical silver powder, thus obtaining the quality prediction factor.

5. The production control method for silver powder according to claim 1, characterized in that, The process involves segmented temperature control based on the quality prediction factor, dynamically controlling the reaction process through a reactor heating and cooling system, an intelligent pH adjusting pump, and a variable frequency stirrer to obtain a silver powder suspension, comprising: Based on the quality prediction factors, the production process is divided into a preheating stage, a nucleation stage, a growth stage, and a stabilization stage. Temperature control target values ​​are set for each stage to obtain a temperature control scheme. According to the temperature control scheme, the heating and cooling system is controlled to heat the solution in the preheating stage, maintain a constant temperature in the nucleation stage, maintain a high temperature in the growth stage, and cool down in the stabilization stage, thus obtaining a temperature execution curve. Based on the quality prediction factor, the pH adjustment pump is controlled to add regulators to maintain an alkaline environment during the nucleation stage, and supplement and adjust according to pH changes during the growth stage to obtain a pH stable reaction system. The silver powder suspension is obtained by controlling the stirrer to perform high-speed stirring during the nucleation stage, medium-speed stirring during the growth stage, and intermittent stirring during the stabilization stage using the quality prediction factor.

6. The production control method for silver powder according to claim 1, characterized in that, The step of inputting the silver powder suspension into an adaptive centrifugal separation system and adjusting the centrifugal force and washing frequency based on real-time sedimentation efficiency includes: The silver powder suspension is fed into a centrifuge, an initial speed is set for pre-separation, the turbidity value of the supernatant is detected by a turbidity sensor, the initial sedimentation efficiency is calculated, and sedimentation state data is obtained. The centrifuge speed is dynamically adjusted based on the sedimentation state data. When the sedimentation efficiency is lower than the target value, the speed is increased. When the sedimentation efficiency has reached the target value, the speed is maintained to obtain the optimal centrifugation separation parameters. The separated silver powder was washed with conductivity detection, and the concentration of residual ions in the washing solution was measured. Washing continued when the conductivity was higher than the threshold and stopped when the conductivity was lower than the threshold, thus obtaining the control value for the number of washes. A multi-stage washing process is executed according to the washing number control value, and the washed silver powder is dried, while controlling the drying temperature and humidity parameters.

7. The production control method for silver powder according to claim 6, characterized in that, The process of performing a multi-stage washing process according to the washing cycle control value, and drying the washed silver powder, including controlling the drying temperature and humidity parameters, includes: The silver powder precipitate after centrifugation is washed with deionized water for the first time, and the conductivity of the washing solution is measured. If the conductivity is greater than the first threshold, a second washing with deionized water is performed. If the conductivity is less than the first threshold, the ethanol washing stage is entered to obtain the primary washed silver powder. The primary washed silver powder is washed with ethanol solution, and the content of organic impurities in the washed silver powder is measured. If the content of organic impurities is greater than the second threshold, the ethanol washing is continued. If the content of organic impurities is less than the second threshold, the acidic solution washing stage is entered to obtain intermediate washed silver powder. According to the purity requirements of silver powder, the intermediate-grade washed silver powder is washed with dilute acid solution, and the content of metal impurities is measured. If the content of metal impurities is greater than the third threshold, the dilute acid washing continues. If the content of metal impurities is less than the third threshold, the washing process ends and the final washed silver powder is obtained. The final washed silver powder is fed into the drying system, and the drying temperature and drying time are set according to the silver powder particle size and application requirements, while controlling the humidity of the drying environment within the target range.

8. A production control system for silver powder, characterized in that, For implementing the production control method for silver powder as described in any one of claims 1-7, the production control system for silver powder comprises: The data acquisition module is used to install a temperature sensor array, pH sensor and stirring speed sensor on the chemical reduction reactor to collect the state data of silver nitrate solution and obtain a set of reaction process parameters. The filtering module is used to perform filtering processing based on the set of reaction process parameters to obtain the quality prediction factor; The control module is used to implement segmented temperature regulation based on the quality prediction factor, and dynamically control the reaction process through the reactor heating and cooling system, intelligent pH adjustment pump and frequency converter to obtain silver powder suspension; The input module is used to input the silver powder suspension into the adaptive centrifugal separation system and adjust the centrifugal force and washing frequency according to the real-time sedimentation efficiency.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to perform the production control method for silver powder as described in any one of claims 1 to 7.

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