Process parameter control method in preparation of ammonium tetramolybdate by sulfuric acid precipitation

Abstract

The application discloses a process parameter control method in the production of ammonium tetramolybdate prepared by sulfuric acid acid precipitation, and particularly relates to the technical field of high-purity molybdenum compound preparation, which is used for solving the problem of inconsistent crystallization environment and product purity fluctuation caused by the end point pH value control in the prior art; the method is realized by the following steps: real-time monitoring of the pH value, reaction temperature and magnesium ion concentration in the reaction process, synchronous monitoring of the conductivity, determination of the real-time supersaturation state of the solution according to the cooperative change relationship between the conductivity and the pH value, comparison of the real-time supersaturation state with the preset target to judge the deviation, evaluation of the occurrence state risk of the magnesium ion based on the reaction temperature and the pH value when the deviation exists, priority sorting of the process intensity control dimension and the material purity control dimension according to the risk evaluation result, and identification of the dominant factor of the primary nucleation rate of the ammonium tetramolybdate crystal based on the priority sorting.

Description

Technical Field

[0001] This invention relates to the field of high-purity molybdenum compound preparation technology, and more specifically, to a method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation. Background Technology

[0002] Ammonium tetramolybdate is a key precursor for the preparation of high-purity and ultra-high-purity molybdenum metal, molybdenum alloys, and other molybdenum-specific materials. Its purity and physical morphology directly determine the performance of the final electronic-grade materials. In hydrometallurgical industries, the sulfuric acid precipitation crystallization method is commonly used to prepare ammonium tetramolybdate from ammonium molybdate solutions. By adding sulfuric acid to the ammonium molybdate solution to adjust the pH of the system, molybdenum precipitates out as ammonium tetramolybdate crystals, thus achieving the purification and enrichment of molybdenum. This production process involves multiple unit operations, including neutralization reactions, crystal nucleation, and growth. Controlling the process parameters is crucial for achieving high-purity, high-recovery products.

[0003] In existing production technologies, monitoring and controlling the pH value at the reaction endpoint is generally relied upon as a key means to ensure the stability of the ammonium tetramolybdate precipitation process. However, as a macroscopic and outcome-based chemical parameter, pH value has an inherent lag in response to the critical nucleation and early growth stages of the crystallization process. At the same time, the supersaturation of the solution system, as the real driving force for crystal precipitation, exhibits a non-linear relationship with pH value due to the interaction of multiple factors such as the composition of the raw material solution, temperature, and impurity ion concentration. This makes it difficult to accurately reproduce a consistent crystallization environment in actual production, even if the endpoint pH value is controlled within a narrow target range. This leads to fluctuations in product purity and crystal morphology between batches, making it impossible to consistently meet the stringent requirements for precursor quality in the manufacture of high-purity molybdenum special materials. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Methods for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation include: S1. Real-time monitoring of pH value, reaction temperature and magnesium ion concentration during the sulfuric acid precipitation reaction; S2. Simultaneously monitor the conductivity during the reaction process and determine the real-time supersaturation state of the solution based on the synergistic relationship between conductivity and pH value. S3. Compare the real-time oversaturation state with the preset target oversaturation state to determine if there is a deviation. S4. When deviations exist, assess the risk of magnesium ions on the occurrence state of ammonium tetramolybdate crystals based on the current reaction temperature and pH value. The occurrence state risk includes surface adsorption risk and lattice embedding risk. S5. Based on the assessment results of the risk of the occurrence state, prioritize the process intensity control dimension to which the reaction temperature belongs and the material purity control dimension to which the magnesium ion concentration belongs. S6. Identify the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals based on priority ranking, and adjust the addition rate of concentrated sulfuric acid or the reaction temperature accordingly.

[0006] Furthermore, real-time monitoring of pH, reaction temperature, and magnesium ion concentration during the sulfuric acid precipitation reaction process includes: The pH value in the reaction solution inside the sulfuric acid precipitation reactor is continuously measured using an online pH meter. The reaction temperature is continuously collected by a temperature sensor installed inside the reactor. The concentration of magnesium ions in the reaction solution is detected in real time using an online ion-selective electrode. Furthermore, monitoring data on pH value, reaction temperature, and magnesium ion concentration were collected and recorded synchronously over time.

[0007] Furthermore, the conductivity of the reaction process is monitored simultaneously, and the real-time supersaturation state of the solution is determined based on the synergistic relationship between conductivity and pH value, including: An online conductivity meter was installed in the reaction solution inside the sulfuric acid precipitation reactor to continuously measure and obtain the conductivity. The conductivity obtained from continuous measurements is correlated with the pH value obtained from continuous measurements in real time. Based on the correlation between the trends of conductivity and pH, characteristic signals of the crystallization nucleation initiation point during the reaction process are identified. The real-time supersaturation state of the solution is determined by comparing the characteristic signal of the crystallization nucleation initiation point with the preset standard trajectory.

[0008] Furthermore, the characteristic signals for identifying the crystallization nucleation initiation point during the reaction process include: capturing the coordinated change characteristics of the continuously measured pH value still in the decreasing phase when the rate of increase in the conductivity obtained by continuous measurement changes, and identifying the corresponding coordinated change characteristics as the characteristic signals for the crystallization nucleation initiation point.

[0009] Furthermore, the real-time oversaturation state is compared with the preset target oversaturation state to determine whether a deviation has occurred, including: The determined real-time oversaturation state is compared with the preset target oversaturation state. When the trend of real-time oversaturation exceeds the preset range of change corresponding to the target oversaturation state, it is determined that a deviation has occurred. When the trend of real-time oversaturation is within the preset range corresponding to the target oversaturation, it is determined that no deviation has occurred.

[0010] Furthermore, when deviations exist, the risk of magnesium ions affecting the occurrence state of ammonium tetramolybdate crystals is assessed based on the current reaction temperature and pH value. This risk includes surface adsorption risk and lattice embedding risk, including: When the reaction temperature obtained by real-time measurement is higher than the preset temperature threshold and the pH value obtained by continuous measurement is in a rapid decline phase, the risk of magnesium ion occurrence state is mainly assessed based on surface adsorption risk. When the reaction temperature obtained by real-time measurement is lower than the preset temperature threshold and the pH value obtained by continuous measurement is in a slow decreasing phase, the risk of magnesium ion occurrence state is mainly assessed based on the risk of lattice embedding.

[0011] Furthermore, assessing the risk of magnesium ion occurrence state as primarily surface adsorption risk includes: when the reaction temperature obtained by real-time measurement is continuously higher than the preset temperature threshold, and the decrease in pH value obtained by continuous measurement exceeds the preset range per unit time, the current condition is determined to be primarily surface adsorption risk.

[0012] Furthermore, assessing the risk of magnesium ion occurrence state as primarily lattice embedding risk includes: when the reaction temperature obtained by real-time measurement is continuously lower than or equal to a preset temperature threshold, and the decrease in pH value obtained by continuous measurement per unit time is lower than or equal to a preset range, the current condition is determined to be primarily lattice embedding risk.

[0013] Furthermore, based on the risk assessment results of the occurrence state, the process intensity control dimension corresponding to the reaction temperature and the material purity control dimension corresponding to the magnesium ion concentration are prioritized, including: When assessing the risk of magnesium ion occurrence state as mainly the risk of surface adsorption, the process intensity control dimension of reaction temperature should be placed before the material purity control dimension of magnesium ion concentration. When assessing the risk of magnesium ion occurrence state primarily based on lattice embedding risk, the material purity control dimension, which corresponds to magnesium ion concentration, should be placed before the process intensity control dimension, which corresponds to reaction temperature.

[0014] Furthermore, based on priority ranking, the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals were identified, and the addition rate of concentrated sulfuric acid or the reaction temperature was adjusted accordingly, including: When the process intensity control dimension of reaction temperature is placed before the material purity control dimension of magnesium ion concentration, reaction temperature is identified as the dominant factor for the primary nucleation rate of ammonium tetramolybdate crystals, and the reaction temperature is controlled by adjusting the reactor cooling system. When the material purity control dimension, which corresponds to magnesium ion concentration, is placed before the process intensity control dimension, which corresponds to reaction temperature, the concentrated sulfuric acid addition rate is identified as the dominant factor for the primary nucleation rate of ammonium tetramolybdate crystals, and the concentrated sulfuric acid addition rate is controlled by adjusting the metering pump frequency.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By establishing a multi-parameter collaborative monitoring and dynamic control mechanism, precise control of the essential driving force of the crystallization process is achieved, breaking through the limitations of traditional single-point control based on the endpoint pH value. By synchronously tracking the synergistic changes in conductivity and pH value, the key signal of the crystallization nucleation initiation point can be keenly captured, thereby accurately reflecting the real-time supersaturation state of the solution system. This effectively overcomes the lag in pH value response, enabling operators to perceive the changing trend of the crystallization environment in the early stages of precursor formation. At the same time, by establishing a dynamic comparison mechanism between the supersaturation state and the target value, timely and accurate judgment basis is provided for process adjustment, significantly improving the controllability and stability of the production process.

[0016] 2. By constructing a risk assessment and multi-dimensional control priority decision-making system for the occurrence state, precise control over the influence of impurity ions is achieved. Based on the synergistic analysis of the characteristics of reaction temperature and pH value changes, it is possible to effectively distinguish between two different risk modes: magnesium ion adsorption on the crystal surface and lattice embedding. This allows for targeted adjustment of the intervention sequence of process intensity control and material purity control dimensions. The differentiated control strategy based on risk characteristics ensures that the dominant influencing factors on the primary nucleation rate can be quickly identified when process deviations occur. The most effective intervention is implemented by adjusting the addition rate of concentrated sulfuric acid or the reaction temperature, forming a complete closed-loop control from state perception and risk assessment to precise execution. This provides a reliable guarantee for the preparation of high-purity, highly uniform ammonium tetramolybdate crystals, and is particularly suitable for the stringent requirements of precursor quality in the manufacture of electronic-grade high-purity and ultra-high-purity molybdenum special materials. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the process parameter control method in the production of ammonium tetramolybdate by sulfuric acid precipitation according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: Figure 1The present invention provides a method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation, including: S1. Real-time monitoring of pH value, reaction temperature and magnesium ion concentration during the sulfuric acid precipitation reaction; S2. Simultaneously monitor the conductivity during the reaction process and determine the real-time supersaturation state of the solution based on the synergistic relationship between conductivity and pH value. S3. Compare the real-time oversaturation state with the preset target oversaturation state to determine if there is a deviation. S4. When deviations exist, assess the risk of magnesium ions on the occurrence state of ammonium tetramolybdate crystals based on the current reaction temperature and pH value. The occurrence state risk includes surface adsorption risk and lattice embedding risk. S5. Based on the assessment results of the risk of the occurrence state, prioritize the process intensity control dimension to which the reaction temperature belongs and the material purity control dimension to which the magnesium ion concentration belongs. S6. Identify the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals based on priority ranking, and adjust the addition rate of concentrated sulfuric acid or the reaction temperature accordingly.

[0020] When implementing real-time monitoring of pH, reaction temperature, and magnesium ion concentration during the sulfuric acid precipitation reaction, the specific procedures are as follows: An online pH meter is installed in the reaction solution within the sulfuric acid precipitation reactor. This online pH meter employs a composite electrode structure combining an acid-resistant glass electrode and a reference electrode, with a measurement range covering pH values ​​from 0 to 14 and a measurement accuracy of ±0.1 pH. The online pH meter is fixed to the side wall of the reactor at one-third of the liquid level from the bottom via a flange interface, ensuring that the electrode sensing part is completely immersed in the flowing reaction solution. The signal output of the online pH meter is connected to a distributed control system, continuously measuring and obtaining pH data at a sampling frequency of once per second. The measurement data is displayed in real time on the human-machine interface and stored in a real-time database.

[0021] The reaction temperature is continuously collected by a temperature sensor installed inside the reactor. This temperature sensor uses a platinum resistance temperature detector, with a measurement range covering 0°C to 150°C and an accuracy of ±0.5°C. The temperature sensor is installed near the agitator blades at the bottom of the reactor via a threaded interface, ensuring that the measurement point is located in the main flow area of ​​the reaction liquid. The temperature sensor signal is converted into a 4-20 mA standard signal by a temperature transmitter and transmitted to the distributed control system. The reaction temperature data is continuously collected at a sampling frequency of once per second. The collected data and pH value data are synchronously stored in a real-time database and share the same timestamp.

[0022] An online ion-selective electrode is used to detect the magnesium ion concentration in the reaction solution in real time. This online ion-selective electrode employs a detection unit combining a PVC membrane magnesium ion selective electrode and a dual salt bridge reference electrode. Its measurement range covers magnesium ion concentrations from 1 mg / L to 100 mg / L, with a measurement accuracy of ±2% of full scale. The online ion-selective electrode is fixed to the side wall of the reaction vessel 20 cm from the online pH meter installation location via a flange interface, and an anti-clogging filter is installed at the electrode tip. The online ion-selective electrode is connected to a dedicated ion meter, and the ion meter's output signal is connected to a distributed control system via an RS485 communication interface. The magnesium ion concentration in the reaction solution is detected in real time at a sampling frequency of once every 10 seconds. The detection data is converted to standard concentration units and stored in a real-time database.

[0023] Monitoring data for pH value, reaction temperature, and magnesium ion concentration are collected and recorded synchronously over time. This synchronization is achieved through the clock synchronization module of the distributed control system. All monitoring data is marked with a unified timestamp at the time of collection, with timestamp accuracy down to the millisecond level. The distributed control system is configured with a real-time data platform. This platform uses a relational database as its core, establishing a data table containing four fields: timestamp, pH value, reaction temperature, and magnesium ion concentration. Each row of data represents complete monitoring data collected at the same time. The data table is partitioned and stored according to production batch number, with each batch forming an independent data file. The data retention period is no less than 3 years. The real-time data platform also provides a data query interface, supporting queries for historical monitoring data by time range or batch number. Query results are displayed in tabular form and can be exported to standard format files.

[0024] The online pH meter requires three-point calibration before use, using standard buffer solutions with pH values ​​of 4.01, 6.86, and 9.18 respectively. Calibration is performed before the start of each batch of production. The temperature sensor requires two-point calibration before use, focusing on its freezing point and boiling point to ensure accuracy across the entire measurement range. The online ion-selective electrode requires standard curve calibration before use, using standard solutions with concentrations of 1 mg / L, 10 mg / L, and 50 mg / L to establish the electrode response curve. Calibration is also performed before the start of each batch of production. All calibration processes are recorded electronically and stored in the distributed control system calibration database. Calibration data includes calibration time, calibrator personnel, calibration results, and calibration solution batch number information.

[0025] The data acquisition frequency was set according to process requirements. pH value and reaction temperature were acquired using a high-frequency acquisition mode with a 1-second interval; magnesium ion concentration was acquired using a medium-frequency acquisition mode with a 10-second interval. Data from different acquisition frequencies were fused on a real-time data platform using a timestamp alignment algorithm to form a unified time-series monitoring dataset. The timestamp alignment algorithm used the nearest neighbor interpolation method to match the low-frequency magnesium ion concentration data with the high-frequency pH value and reaction temperature data at the same timestamp, ensuring temporal consistency across all monitoring data.

[0026] A data validity check mechanism is implemented during real-time monitoring, triggering an alarm when monitored data continuously exceeds set ranges. The pH setting range is 1.5 to 3.5, the reaction temperature setting range is 40°C to 80°C, and the magnesium ion concentration setting range is 0 mg / L to 50 mg / L. Data validity checks include range checks, rate of change checks, and consistency checks. Range checks ensure data remains within a reasonable process range, rate of change checks prevent sudden data changes, and consistency checks verify the logical relationships between different monitoring parameters. Any data anomaly will trigger an audible and visual alarm and display specific alarm information on the human-machine interface, prompting operators to take timely action.

[0027] The setting ranges for monitoring parameters are determined through analysis of historical production data. For example, the pH setting range of 1.5 to 3.5 was determined by statistically analyzing the actual pH distribution ranges of multiple successful batches. The reaction temperature setting range of 40°C to 80°C was determined by examining the relationship between crystallization kinetics and product quality. The magnesium ion concentration setting range of 0 mg / L to 50 mg / L was determined based on the magnesium impurity content specifications in the raw materials and product purity requirements. All setting ranges are configured during system initialization and can be adjusted according to process optimization needs.

[0028] When implementing the step of simultaneously monitoring the conductivity during the reaction process and determining the real-time supersaturation state of the solution based on the synergistic relationship between conductivity and pH, the specific operation is as follows: An online conductivity meter is installed in the reaction liquid within the sulfuric acid precipitation reactor. This online conductivity meter employs a four-electrode measurement principle, using platinum electrodes. The measurement range covers 0 mSiemens per centimeter to 1000 mSiemens per centimeter, with a measurement accuracy of ±2% of full scale. The online conductivity meter is fixed to the side wall of the reactor at half the liquid level from the bottom via a flange interface, ensuring that the electrodes are completely submerged in the flowing reaction liquid and away from direct impact from the stirrer. The signal output of the online conductivity meter is connected to a distributed control system, continuously measuring conductivity data at a sampling frequency of, for example, twice per second. The measured data is converted from analog to digital and displayed in real-time on the human-machine interface and stored in a real-time database conductivity data table.

[0029] The conductivity and pH values ​​obtained from continuous measurements are mapped in real time through a data coordination engine of the distributed control system. The data coordination engine reads the continuously measured conductivity and pH data, both with millisecond-level timestamps, from the real-time database. Since there may be slight differences in the sampling time points of the conductivity and pH data, the data coordination engine uses a time-series alignment algorithm, such as nearest neighbor matching, to pair the conductivity and pH data according to their timestamps, ensuring that each conductivity data point has a corresponding pH data point at that time. The aligned data forms a conductivity-pH collaborative dataset, which includes a timestamp field, a conductivity value field, and a pH value field, and is updated and stored in the collaborative database at a frequency of, for example, two records per second.

[0030] Based on the correlation between conductivity and pH trends, characteristic signals indicating the initiation point of crystallization nucleation during the reaction process are identified. The conductivity trend is obtained by calculating the first derivative of the conductivity data, i.e., the conductivity change rate, calculated as the difference between two adjacent conductivity measurements divided by the time interval in seconds. The pH trend is obtained by calculating the first derivative of the pH data, i.e., the pH change rate, using the same method. When identifying characteristic signals, a threshold for the conductivity change rate is first set. This threshold is determined by analyzing the distribution of conductivity change rates near the crystallization nucleation initiation point in historical production data; for example, 80% of the average conductivity change rate of multiple successful batches can be used as the threshold. Simultaneously, conditions for determining the pH decrease phase are set, requiring the continuously measured pH change rate to remain negative and its absolute value to be greater than a preset decrease threshold. This decrease threshold is determined through process experiments; for example, after observing the effect of different pH decrease rates on crystallization nucleation under laboratory conditions, it is set to, for example, a decrease of 0.5 pH per minute. When the system detects that the rate of change of conductivity changes from a positive value to a negative value or decreases beyond the threshold of the rate of change of conductivity, and at the same time the rate of change of pH value obtained by continuous measurement meets the judgment condition of the decreasing stage, the system records the conductivity value and pH value at the current time point and identifies this coordinated change feature as the characteristic signal of the crystal nucleation initiation point.

[0031] The real-time supersaturation state of the solution is determined by comparing the characteristic signal of the crystallization nucleation initiation point with a preset standard trajectory. The preset standard trajectory is established by collecting historical data from multiple ideal production batches. Specifically, batches meeting the crystallization quality requirements are selected from the historical database, and the conductivity and pH values ​​corresponding to the crystallization nucleation initiation points in each batch are extracted. The average and standard deviation of these values ​​are calculated to form the center point and tolerance range of the standard trajectory. For example, the center point of the standard trajectory might correspond to a conductivity of 550 mSiemens per centimeter and a pH of 2.3, with a tolerance range set at ±50 mSiemens per centimeter and ±0.3 pH. In real-time monitoring, the characteristic signal of the identified crystallization nucleation initiation point is compared with the preset standard trajectory. The Euclidean distance between the characteristic signal point and the center point of the standard trajectory is calculated using the formula: the square of the difference between the characteristic signal conductivity value and the standard conductivity value, plus the square of the difference between the characteristic signal pH value and the standard pH value, and then the square root is taken. If the calculated distance is less than the preset tolerance distance threshold, such as 60 millisiemens per centimeter, the real-time oversaturation state is determined to meet the target oversaturation state; if the distance is greater than or equal to the tolerance distance threshold, the real-time oversaturation state is determined to deviate from the target state. The tolerance distance threshold is determined through classification analysis of qualified and unqualified batches in historical data, for example, by using cluster analysis to find the optimal separation distance.

[0032] The characteristic signals for identifying the crystallization nucleation initiation point during the reaction process specifically include capturing the coordinated change characteristic when the rate of increase in conductivity, obtained from continuous measurements, reverses, while the corresponding pH value, obtained from continuous measurements, is still in a decreasing phase. The rate of increase reversal refers to the moment when the conductivity value increases over time but the rate of increase slows significantly. This is identified by monitoring the decrease in the rate of change of conductivity; for example, a rate reversal is determined when the rate of change of conductivity decreases by more than 10% over three consecutive sampling periods. The decreasing phase refers to the phase where the pH value continuously decreases over time. This is confirmed by checking that the rate of change of pH value is continuously negative and the duration exceeds a preset time threshold, for example, exceeding 30 seconds. The coordinated change characteristic refers to the temporal overlap between the conductivity rate reversal event and the pH value decreasing event. The system determines the overlap by comparing the time difference between the conductivity rate reversal time point and the pH value decreasing time point. When the time difference is less than a preset time tolerance, such as 5 seconds, the coordinated change characteristic is considered valid, and the corresponding conductivity value and pH value at that moment are taken as the characteristic signals for the crystallization nucleation initiation point.

[0033] All threshold and parameter settings are based on process data and experimental verification. For example, the conductivity change rate threshold is set by statistically analyzing the conductivity change rate data distribution near the crystallization nucleation point in multiple production batches. The 25th percentile of the distribution is taken as the initial value of the conductivity change rate threshold, and then fine-tuned according to actual production results. The pH decrease threshold is determined by conducting crystallization experiments at different pH decrease rates, and optimized based on the product crystal morphology and purity results. The preset standard trajectory is updated every six months, and the center point and tolerance range of the standard trajectory are recalculated by collecting data from all qualified batches during this period. The real-time supersaturation state determination process is performed, for example, every 10 seconds, to ensure timely reflection of changes in the reaction state. Intermediate results of data processing, such as conductivity change rate and pH change rate, are temporarily stored in a cache and automatically cleared after processing to ensure system operating efficiency.

[0034] When comparing the real-time oversaturation state with the preset target oversaturation state and determining whether a deviation has occurred, the specific operation is as follows: The determined real-time supersaturation state is compared with the preset target supersaturation state. The real-time supersaturation state is obtained from the comparison between the characteristic signal based on the crystallization nucleation initiation point and the preset standard trajectory in the previous steps. This state is represented in numerical form, for example, by calculating the Euclidean distance between the characteristic signal point and the center point of the standard trajectory. The Euclidean distance is calculated by adding the square of the difference between the characteristic signal conductivity value and the standard conductivity value, plus the square of the difference between the characteristic signal pH value and the standard pH value, and then taking the square root. The preset target supersaturation state is established by analyzing the distribution of supersaturation states of multiple ideal batches in historical production data. Specifically, this involves collecting supersaturation state data corresponding to the crystallization nucleation initiation point from, for example, more than 50 successful production batches. This data comes from a supersaturation state record table in a historical database, which includes batch number, supersaturation state value, and timestamp fields. The average and standard deviation of these data are calculated to form the target supersaturation state center value and its corresponding variation range. The variation range is set to be within ±2 times the standard deviation of the target supersaturation state center value. For example, if the target supersaturation state center value is 60 millisiemens per centimeter and the standard deviation is 5 millisiemens per centimeter, then the variation range is 50 millisiemens per centimeter to 70 millisiemens per centimeter. During comparison, the system reads the real-time supersaturation state value and compares it with the preset target supersaturation state center value, calculating the absolute difference between the two. The absolute difference is calculated by subtracting the absolute value of the target supersaturation state center value from the real-time supersaturation state value, and then comparing this difference with the preset variation range boundary.

[0035] When the trend of real-time oversaturation exceeds the preset target oversaturation range, a deviation is identified. The trend of real-time oversaturation is calculated by taking the first derivative of the real-time oversaturation data, i.e., the rate of change of oversaturation. The formula is the difference between two adjacent real-time oversaturation measurements divided by the time interval in seconds. The time interval is set according to the data acquisition frequency; for example, if the acquisition frequency is once per second, the time interval is one second. The condition for the trend exceeding the range is that the absolute value of the real-time oversaturation rate of change is greater than a preset rate of change threshold. This threshold is determined by analyzing the distribution of oversaturation rate of change in historical data for qualified and unqualified batches. For example, 1.5 times the average oversaturation rate of change of multiple successful batches can be used as the threshold. The unit of the threshold is consistent with the unit of the oversaturation rate of change, e.g., millisiemens per centimeter per second. Simultaneously, the system monitors whether the real-time oversaturation value remains outside the range. For example, if the real-time oversaturation value is below the lower limit or above the upper limit of the range for three consecutive sampling periods, the trend is identified as exceeding the range. The lower and upper limits of the variation range are set using statistical methods. The lower limit is the target supersaturation state center value minus three standard deviations, and the upper limit is the target supersaturation state center value plus three standard deviations, to ensure coverage of normal process fluctuations. Deviation determination results trigger an alarm signal, which is displayed as a red warning icon on the human-machine interface and recorded in the alarm log database.

[0036] When the trend of real-time supersaturation state changes within the preset target supersaturation state's corresponding change range, it is determined that no deviation has occurred. The criteria for determining that the trend is within the change range are that the absolute value of the real-time supersaturation state's change rate is less than or equal to a preset change rate threshold, and the real-time supersaturation state value fluctuates within the change range. The system monitors the relationship between the real-time supersaturation state value and the boundary of the change range in real time. If the value is within the change range and the trend is stable, for example, the real-time supersaturation state's change rate is less than 50% of the change rate threshold for five consecutive sampling periods, it is determined that no deviation has occurred. The width of the change range is adjusted through process optimization experiments. For example, under laboratory conditions, the impact of different change range widths on the product crystal quality is tested, and the width value that maximizes the product qualification rate is selected as the final setting. The product qualification rate is determined through crystal morphology analysis and purity testing. Crystal morphology analysis uses a microscope to observe crystal size and shape, and purity testing uses chemical analysis methods to measure impurity content. When no deviation occurs, the system maintains the current process parameters and records the status information in the production log database.

[0037] All threshold and parameter settings are based on data analysis and experimental verification. The preset target oversaturation state center value is updated periodically using a moving average method, with an update cycle of every three months. The new center value is recalculated based on the production data of the most recent six months. The moving average method uses a simple average calculation, which is to sum the oversaturation state values ​​of all qualified batches in the most recent six months and then divide by the batch number. The rate of change threshold is determined through regression analysis. For example, a correlation model between the rate of change of oversaturation state and product purity is established. The rate of change value that optimizes product purity is selected as the initial threshold value, and then fine-tuned according to real-time production results. The fine-tuning process is iteratively optimized by comparing the difference between the actual product quality and the expected target. The boundary values ​​of the change range are monitored using control chart methods. For example, the mean and range of oversaturation state data are analyzed using Xbar-R control charts. The boundary values ​​of the change range are set according to the control limits, which are taken as the average value plus or minus three standard deviations. The real-time comparison process is executed, for example, every 5 seconds, to ensure timely reflection of state changes. Intermediate results from data comparisons, such as absolute differences and rates of change, are temporarily stored in a cache. This cache is the memory storage area of ​​the distributed control system and is automatically cleared after processing to ensure system efficiency. Deviation determination results are recorded in the production log database in real time and trigger corresponding alarms or control commands to ensure process stability. The production log database includes timestamps, real-time oversaturation status values, target oversaturation status center values, absolute differences, trend determination results, and deviation flag fields. The data retention period is no less than three years.

[0038] When implementing the step of assessing the risk of magnesium ions affecting the occurrence state of ammonium tetramolybdate crystals based on the current reaction temperature and pH value in the event of deviation, the specific operation is as follows: This step is executed through a risk assessment logic unit in the distributed control system. This unit is a software functional component integrated into the application layer of the control system. When a deviation is determined from comparing the real-time supersaturation state with the preset target supersaturation state, the system immediately initiates the occurrence state risk assessment process. The real-time measured reaction temperature is obtained from data continuously collected by a temperature sensor installed inside the reactor. This data is updated once per second and stored in a real-time database. The continuously measured pH value is obtained from data continuously measured by an online pH meter, which is also updated once per second. The system reads this real-time data and performs a risk assessment based on preset temperature and pH decrease thresholds. Occurrence state risks include surface adsorption risk and lattice embedding risk. Surface adsorption risk refers to magnesium ions attaching to the surface of ammonium tetramolybdate crystals through physical processes, while lattice embedding risk refers to magnesium ions entering the internal lattice structure of ammonium tetramolybdate crystals through chemical substitution.

[0039] When the reaction temperature obtained from real-time measurement is higher than the preset temperature threshold and the pH value obtained from continuous measurement is in a rapid decline phase, the risk assessment of magnesium ion occurrence state is mainly based on surface adsorption risk. The preset temperature threshold is determined by analyzing the reaction temperature distribution of the batch with the best crystal quality in historical production data. Specifically, the reaction temperature data of multiple successful batches are collected, the average value and standard deviation of these data are calculated, and the average value plus one standard deviation is taken as the initial value of the preset temperature threshold. Then, it is fine-tuned through process experiments. For example, the effect of different temperatures on crystal surface adsorption is tested under laboratory conditions to determine the final threshold. The degree of adsorption is verified by measuring the magnesium ion content on the crystal surface in laboratory tests. The determination of the rapid decline phase of the continuously measured pH value is based on the pH value change rate calculation. The pH value change rate is obtained by calculating the amount of pH value change per unit time, with the unit time set to, for example, 1 minute. The change amount is calculated by subtracting the pH value at the start time from the pH value at the end time. If the absolute value of the pH value change rate is greater than the preset rapid decline amplitude threshold, it is determined to be in a rapid decline phase. The rapid decline amplitude threshold is set by analyzing the pH value change rate distribution corresponding to surface adsorption risk events in historical data. For example, 80% of the average pH value change rate of multiple risk events is taken as the threshold. The system monitors whether the reaction temperature obtained by real-time measurement is continuously higher than the preset temperature threshold. For example, if the temperature is higher than the threshold for five consecutive sampling cycles, the system also checks whether the pH value change rate obtained by continuous measurement meets the condition of rapid decline. When both conditions are met, the system assesses the risk of magnesium ion occurrence state as mainly surface adsorption risk.

[0040] The specific implementation of assessing the risk of magnesium ion occurrence primarily based on surface adsorption risk involves determining that the current condition is dominated by surface adsorption risk when the real-time measured reaction temperature is consistently higher than a preset temperature threshold, and the decrease in pH value per unit time exceeds a preset range. The decrease range per unit time is quantified by calculating the change in pH value over a fixed time interval, such as 5 minutes. The formula for calculating the change is the initial pH value minus the final pH value. The preset range is determined through process optimization experiments. For example, while keeping other parameters constant, the rate of pH decrease is adjusted, and the amount of magnesium ion adsorbed on the crystal surface is observed. The rate of decrease when the adsorption amount significantly increases is selected as the initial value of the preset range, which is then calibrated based on production data. The system calculates the pH decrease range in real time and compares it with the preset range. If the decrease range exceeds the preset range and the reaction temperature remains above the preset temperature threshold, a surface adsorption risk flag is triggered, and this flag is recorded in the risk assessment database.

[0041] When the real-time measured reaction temperature is below a preset temperature threshold and the continuously measured pH value is in a slow decreasing phase, the risk assessment of the magnesium ion occurrence state is primarily based on lattice embedding risk. The preset temperature threshold is set using the same historical data analysis method, but the average value minus one standard deviation is used as the initial value, which is then verified experimentally using X-ray diffraction analysis of the magnesium ion content in the crystal lattice. The determination that the continuously measured pH value is in a slow decreasing phase is based on the pH change rate. If the absolute value of the pH change rate is less than or equal to a preset slow decreasing amplitude threshold, it is determined to be in a slow decreasing phase. The slow decreasing amplitude threshold is set by analyzing the distribution of pH change rates corresponding to lattice embedding risk events in historical data; for example, 50% of the average pH change rate across multiple risk events is used as the threshold. The system monitors whether the real-time measured reaction temperature remains below the preset temperature threshold, for example, if the temperature is below the threshold for five consecutive sampling periods. Simultaneously, it checks whether the continuously measured pH change rate meets the slow decreasing condition. When both conditions are met, the system assesses the magnesium ion occurrence state risk primarily as lattice embedding risk.

[0042] The specific implementation of assessing the risk of magnesium ion occurrence state as primarily lattice intercalation risk involves determining that the current condition is predominantly lattice intercalation risk when the real-time measured reaction temperature remains below or equal to a preset temperature threshold, and the decrease in pH value per unit time, as measured continuously, is below or equal to a preset range. The calculation method for the decrease range per unit time is the same as for surface adsorption risk assessment, with a fixed time interval set, for example, 10 minutes, to capture slower trends. The preset range is determined through experiments specifically targeting lattice intercalation risk, such as analyzing the magnesium ion content in the crystal lattice at different pH decrease rates, selecting the decrease rate value when the content significantly increases as the initial value of the preset range. The system calculates the pH decrease range in real time and compares it with the preset range. If the decrease range is below or equal to the preset range and the reaction temperature remains below or equal to the preset temperature threshold, a lattice intercalation risk flag is triggered, and this flag is recorded in the risk assessment database.

[0043] All threshold and parameter settings are based on data analysis and experimental verification. The preset temperature threshold is updated every six months, with new thresholds recalculated based on the most recent year's production data, adjusted using a moving average method (summing the most recent data and dividing by the number of data points). The pH decrease threshold is optimized through regression models, for example, establishing a correlation between pH change rate and magnesium ion presence, selecting the change rate value with the highest risk probability as a threshold reference. The unit time interval is set according to process characteristics, for example, selecting the interval value that best distinguishes risk types through sensitivity analysis. The risk assessment process is executed, for example, every 10 seconds, to ensure timely response to deviations. Intermediate results from data processing, such as pH change rate and decrease magnitude, are temporarily stored in a cache and automatically cleared after processing. Risk assessment results are transmitted in real-time to the priority ranking step for subsequent control decisions. The system also includes an anomaly handling mechanism; when data is lost or sensors malfunction, risk assessment is paused and a maintenance alarm is triggered to ensure process safety.

[0044] When prioritizing the process intensity control dimension (to which reaction temperature belongs) and the material purity control dimension (to which magnesium ion concentration belongs) based on the risk assessment results of the occurrence state, the specific operation is as follows: This step is executed in the distributed control system through a priority sorting logic unit, a software functional component integrated into the decision-making layer of the control system. The process intensity control dimension refers to the control direction of altering crystallization kinetics by adjusting the reaction temperature, while the material purity control dimension refers to the control direction of altering raw material impurity levels by adjusting magnesium ion concentration. The system obtains the assessment results from the occurrence state risk assessment step, which are stored in the risk assessment database as flags, including surface adsorption risk flags and lattice embedding risk flags. The risk assessment database establishes data communication with the priority sorting logic unit through a database connection interface to ensure real-time transmission of assessment results.

[0045] When assessing the risk of magnesium ion presence primarily as surface adsorption risk, the process intensity control dimension (responsible for reaction temperature) is prioritized over the material purity control dimension (responsible for magnesium ion concentration). The system triggers this sorting rule by querying the surface adsorption risk flag status in the risk assessment database. When the surface adsorption risk flag is active, the priority sorting logic unit immediately executes the sorting operation. The sorting operation is implemented by setting priority flags. The system has two priority flags: a process intensity control priority flag and a material purity control priority flag. When it is necessary to prioritize the process intensity control dimension (responsible for reaction temperature) over the material purity control dimension (responsible for magnesium ion concentration), the system sets the process intensity control priority flag to high priority and the material purity control priority flag to low priority. Priority status is stored using binary encoding; for example, high priority corresponds to a value of 1, and low priority corresponds to a value of 0. The sorting results are updated in real-time to the control priority database, which includes a timestamp field, a process intensity control priority status field, and a material purity control priority status field.

[0046] When assessing the risk of magnesium ion occurrence primarily as lattice embedding risk, the material purity control dimension, which pertains to magnesium ion concentration, is prioritized over the process intensity control dimension, which pertains to reaction temperature. The system triggers this sorting rule by querying the lattice embedding risk flag status in the risk assessment database. When the lattice embedding risk flag is active, the priority sorting logic unit executes the corresponding sorting operation. At this time, the system sets the material purity control priority flag to high priority and the process intensity control priority flag to low priority. The sorting logic is based on the analysis of the dominant factors for different risk types. Surface adsorption risk is mainly affected by the crystallization rate, which is more sensitive to changes in reaction temperature; therefore, the process intensity control dimension takes priority. Lattice embedding risk is mainly affected by crystal growth quality, which is more sensitive to impurity concentration; therefore, the material purity control dimension takes priority. The dominant factor analysis is completed through historical data mining, such as analyzing the impact of adjustments to different control dimensions on the final product quality across multiple production batches.

[0047] The priority ranking process comprises three sub-steps: risk type identification, priority decision-making, and status update. Risk type identification is accomplished by reading risk flags from the risk assessment database; the system queries the flag status every 10 seconds. Priority decision-making is performed based on a preset mapping relationship stored in a priority configuration table, which includes a risk type field and its corresponding priority field. Status update involves writing the decision result to the priority control database and simultaneously sending an update command to the human-computer interface. The entire ranking process employs a transaction processing mechanism to ensure data consistency. If data anomalies occur during ranking, the system rolls back to the previous stable state. The transaction processing mechanism is implemented through database transactions. After a transaction begins, a series of operations are executed; the transaction is committed only if all operations succeed, otherwise it is rolled back.

[0048] The control priority database is designed to support historical query functionality, retaining the most recent 1000 sorted records, each containing complete priority status information. The system also includes a priority conflict detection mechanism; when two control dimensions are simultaneously set to high priority, the system automatically detects and triggers an exception handler. The exception handler reassesses the risk status and re-sorts the data based on the latest assessment. The sorting results are updated every 10 seconds, synchronized with the risk assessment cycle to ensure the timeliness of control strategies. The exception handler includes conflict identification logic and recovery logic. Conflict identification logic checks the combined state of priority flags, while recovery logic reinitializes the priority flags and re-executes the sorting process.

[0049] All sorting rules and configuration parameters have been validated through process experiments, such as simulating different risk scenarios in the laboratory to test the control effect of the corresponding sorting strategies. Validation methods include comparing the crystal quality and impurity content of products under different sorting strategies and selecting the optimal strategy as the default configuration. The system also supports manual adjustment of sorting rules; operators can temporarily modify the priority order through the human-machine interface, and modification records are kept in detail in the operation log. Intermediate state data during the sorting process is temporarily stored in the system cache and automatically cleared after processing to ensure system efficiency. Process experiment validation uses a comparative experimental method, applying different sorting strategies under the same process conditions, and evaluating the effectiveness of the strategies by detecting the crystal morphology and chemical purity of the final product. The manual adjustment function is implemented through access control; different levels of operators have different modification permissions to ensure system security.

[0050] When implementing the step of prioritizing and identifying the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals and adjusting the addition rate of concentrated sulfuric acid or the reaction temperature accordingly, the specific operation is as follows: This step is executed in the distributed control system through the dominant factor identification and control adjustment unit, a software functional component integrated into the execution layer of the control system. The system obtains priority states from the control priority database from the priority ranking step, including process intensity control priority states and material purity control priority states. These states are stored as flag bits and updated every 10 seconds. Dominant factor identification is based on the comparison results of the priority states. The primary nucleation rate of ammonium tetramolybdate crystals is affected by both reaction temperature and the rate of concentrated sulfuric acid addition, but the priority ranking determines which factor plays a dominant role under the current conditions.

[0051] When the process intensity control dimension, which corresponds to the reaction temperature, is prioritized over the material purity control dimension, which corresponds to the magnesium ion concentration, the reaction temperature is identified as the dominant factor in the primary nucleation rate of ammonium tetramolybdate crystals. The system triggers this identification logic by querying the process intensity control priority status and material purity control priority status in the control priority database. When the process intensity control priority status is high and the material purity control priority status is low, the system confirms the reaction temperature as the dominant factor. The identification process includes reading the priority flag, comparing the priority order, and setting the dominant factor flag, which is stored in the control decision database. Subsequently, the system controls the reaction temperature by adjusting the reactor cooling system. The reactor cooling system is an external circulating cooling device, and the reaction temperature is changed by adjusting the cooling water flow rate. The control command is output as an analog signal from the distributed control system to the cooling system regulating valve. The analog signal ranges from 4 mA to 20 mA, corresponding to a cooling water flow rate of 0 L / min to 100 L / min. The temperature setpoint is determined through optimization using historical production data, such as the average reaction temperature of multiple ideal batches. The setpoint adjustment range is calculated based on the real-time supersaturation state deviation; the adjustment range increases when the deviation is large. The cooling system is adjusted using a proportional-integral-derivative (PID) control algorithm. The proportional coefficient, integral time, and derivative time are tuned through process experiments. For example, parameters are adjusted in a step response test to minimize the temperature stabilization time.

[0052] When the material purity control dimension, which corresponds to magnesium ion concentration, is prioritized over the process intensity control dimension, which corresponds to reaction temperature, the concentrated sulfuric acid addition rate is identified as the dominant factor in the primary nucleation rate of ammonium tetramolybdate crystals. The system triggers this identification logic by querying the material purity control priority status and process intensity control priority status in the control priority database. When the material purity control priority status is high and the process intensity control priority status is low, the system confirms the concentrated sulfuric acid addition rate as the dominant factor. The identification process is similar to the above, including status comparison and flag setting. Subsequently, the system controls the concentrated sulfuric acid addition rate by adjusting the frequency of the metering pump. The metering pump is a diaphragm metering pump, and the concentrated sulfuric acid flow rate is adjusted by changing the motor frequency. The control command is output from the distributed control system to the metering pump inverter, with a frequency range of 0 Hz to 50 Hz, corresponding to a concentrated sulfuric acid flow rate of 0 L / h to 10 L / h. The addition rate setpoint is determined through material balance calculations, for example, calculating the required amount of sulfuric acid based on the molybdate ion concentration and target pH value in the reaction solution, and then converting it into a flow rate setpoint. Frequency adjustment is based on real-time pH value changes; if the pH value drops too quickly, the frequency is reduced; if it drops too slowly, the frequency is increased. The metering pump control employs an incremental adjustment strategy, with the adjustment step size dynamically set according to priority status and risk type. For example, when surface adsorption risk is primary, the step size is smaller to avoid excessive disturbance.

[0053] The implementation of dominant factor identification comprises three sub-steps: priority status reading, dominant factor determination, and control command generation. Priority status reading is performed through a database query interface, with the system reading the latest status of the control priority database every 5 seconds. Dominant factor determination is executed through a logic comparison unit, which uses hardware logic circuits or software conditional judgment to map the priority status to the dominant factor type. Control command generation calls the corresponding control module based on the dominant factor type, generating analog or digital output signals. The entire identification and control process employs a closed-loop control principle, monitoring the adjustment effect in real time and optimizing subsequent operations through feedback signals. For example, after adjusting the reaction temperature, the system monitors the real-time supersaturation state change; if the deviation does not decrease, the dominant factor is reassessed. After adjusting the concentrated sulfuric acid addition rate, the system monitors the pH trend; if the expected value is not reached, the frequency setpoint is corrected.

[0054] The setting and optimization of control parameters are based on historical data and real-time process conditions. The reaction temperature setpoint is calculated using a regression model, with inputs including real-time supersaturation state, magnesium ion concentration, and historical ideal temperature data, and the output being the optimal temperature value. The concentrated sulfuric acid addition rate setpoint is determined using a material flow rate model, which considers reactor volume, initial concentration, and target crystallization rate, for example, using the mass conservation equation to calculate instantaneous sulfuric acid demand. The parameters of the proportional-integral-derivative (PID) control algorithm are tuned using the Ziegler-Nichols method, first determining the critical gain and period in open-loop testing, and then calculating the proportional gain, integral time, and derivative time. All parameters are stored in the system configuration database, supporting online adjustment and offline optimization.

[0055] An anomaly handling mechanism ensures the reliability of the control process. When sensor data is lost or actuators fail, the system switches to a backup control mode, such as using the last valid value or the default setpoint. Priority conflict detection is performed each time a dominant factor is identified; if two dimensions are simultaneously identified as high priority, the system pauses control and re-prioritizes the data. Control command outputs employ redundant verification, such as comparing dual-channel outputs to ensure signal accuracy. Historical control records are stored in the operation log, including timestamps, dominant factor types, control parameters, and adjustment results, for subsequent analysis and optimization.

[0056] The system also integrates human-machine interaction functions, allowing operators to manually override automatic controls through the interface and manually adjust settings, which are recorded in the audit log. The control cycle is synchronized with the priority ranking cycle to ensure overall coordination. All control logic and parameters are verified through simulation testing, such as reproducing different process scenarios in a simulation environment to test the effectiveness and robustness of the control strategy. Verification results are used to iteratively improve the control algorithm and parameter settings.

[0057] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0058] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0059] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0060] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0061] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0062] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0064] If a function is implemented as a software module 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 this application, in essence, or the part that contributes to the prior art, or a portion 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 computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. 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.

[0065] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0066] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation, characterized in that, include: S1. Real-time monitoring of pH value, reaction temperature and magnesium ion concentration during the sulfuric acid precipitation reaction; S2. Simultaneously monitor the conductivity during the reaction process and determine the real-time supersaturation state of the solution based on the synergistic relationship between conductivity and pH value. S3. Compare the real-time oversaturation state with the preset target oversaturation state to determine if there is a deviation. S4. When deviations exist, assess the risk of magnesium ions on the occurrence state of ammonium tetramolybdate crystals based on the current reaction temperature and pH value. The occurrence state risk includes surface adsorption risk and lattice embedding risk. S5. Based on the assessment results of the risk of the occurrence state, prioritize the process intensity control dimension to which the reaction temperature belongs and the material purity control dimension to which the magnesium ion concentration belongs. S6. Identify the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals based on priority ranking, and adjust the addition rate of concentrated sulfuric acid or the reaction temperature accordingly.

2. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, Real-time monitoring of pH, reaction temperature, and magnesium ion concentration during the sulfuric acid precipitation reaction, including: The pH value in the reaction solution inside the sulfuric acid precipitation reactor is continuously measured using an online pH meter. The reaction temperature is continuously collected by a temperature sensor installed inside the reactor. The concentration of magnesium ions in the reaction solution is detected in real time using an online ion-selective electrode. Furthermore, monitoring data on pH value, reaction temperature, and magnesium ion concentration were collected and recorded synchronously over time.

3. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, Simultaneous monitoring of the conductivity during the reaction process, and determination of the real-time supersaturation state of the solution based on the synergistic relationship between conductivity and pH, including: An online conductivity meter was installed in the reaction solution inside the sulfuric acid precipitation reactor to continuously measure and obtain the conductivity. The conductivity obtained from continuous measurements is correlated with the pH value obtained from continuous measurements in real time. Based on the correlation between the trends of conductivity and pH, characteristic signals of the crystallization nucleation initiation point during the reaction process are identified. The real-time supersaturation state of the solution is determined by comparing the characteristic signal of the crystallization nucleation initiation point with the preset standard trajectory.

4. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 3, characterized in that, The characteristic signals for identifying the crystallization nucleation initiation point during the reaction process include: capturing the synergistic change characteristics of the continuously measured pH value still in the decreasing phase when the rate of increase in conductivity changes, and identifying the corresponding synergistic change characteristics as the characteristic signals for the crystallization nucleation initiation point.

5. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, Compare the real-time oversaturation state with the preset target oversaturation state to determine if there is a deviation, including: The determined real-time oversaturation state is compared with the preset target oversaturation state. When the trend of real-time oversaturation exceeds the preset range of change corresponding to the target oversaturation state, it is determined that a deviation has occurred. When the trend of real-time oversaturation is within the preset range corresponding to the target oversaturation, it is determined that no deviation has occurred.

6. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, When deviations exist, the risk of magnesium ions affecting the occurrence state of ammonium tetramolybdate crystals is assessed based on the current reaction temperature and pH. This risk includes surface adsorption risk and lattice embedding risk, including: When the reaction temperature obtained by real-time measurement is higher than the preset temperature threshold and the pH value obtained by continuous measurement is in a rapid decline phase, the risk of magnesium ion occurrence state is mainly assessed based on surface adsorption risk. When the reaction temperature obtained by real-time measurement is lower than the preset temperature threshold and the pH value obtained by continuous measurement is in a slow decreasing phase, the risk of magnesium ion occurrence state is mainly assessed based on the risk of lattice embedding.

7. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 6, characterized in that, Assessing the risk of magnesium ion presence primarily based on surface adsorption risk includes: when the reaction temperature obtained from real-time measurements is consistently higher than a preset temperature threshold, and the decrease in pH value obtained from continuous measurements exceeds a preset range per unit time, the current condition is determined to be primarily based on surface adsorption risk.

8. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 6, characterized in that, Assessing the risk of magnesium ion occurrence state primarily based on lattice embedding risk includes: when the reaction temperature obtained from real-time measurement is consistently lower than or equal to a preset temperature threshold, and the decrease in pH value obtained from continuous measurement within a unit time is lower than or equal to a preset range, the current condition is determined to be primarily based on lattice embedding risk.

9. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, Based on the risk assessment results of the occurrence state, the process intensity control dimension (to which reaction temperature belongs) and the material purity control dimension (to which magnesium ion concentration belongs) are prioritized, including: When assessing the risk of magnesium ion occurrence state as mainly the risk of surface adsorption, the process intensity control dimension of reaction temperature should be placed before the material purity control dimension of magnesium ion concentration. When assessing the risk of magnesium ion occurrence state primarily based on lattice embedding risk, the material purity control dimension, which corresponds to magnesium ion concentration, should be placed before the process intensity control dimension, which corresponds to reaction temperature.

10. The method for controlling process parameters in the production of ammonium tetramolybdate by sulfuric acid precipitation according to claim 1, characterized in that, Based on priority ranking, the dominant factors affecting the primary nucleation rate of ammonium tetramolybdate crystals were identified, and the addition rate of concentrated sulfuric acid or the reaction temperature was adjusted accordingly, including: When the process intensity control dimension of reaction temperature is placed before the material purity control dimension of magnesium ion concentration, reaction temperature is identified as the dominant factor for the primary nucleation rate of ammonium tetramolybdate crystals, and the reaction temperature is controlled by adjusting the reactor cooling system. When the material purity control dimension, which corresponds to magnesium ion concentration, is placed before the process intensity control dimension, which corresponds to reaction temperature, the concentrated sulfuric acid addition rate is identified as the dominant factor for the primary nucleation rate of ammonium tetramolybdate crystals, and the concentrated sulfuric acid addition rate is controlled by adjusting the metering pump frequency.

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