A nitric acid electrolytic copper growth process parameter analysis system

Abstract

This invention provides a parameter analysis system for the growth process of nitric acid electrolytic copper, relating to the field of electrolytic copper production technology. It includes a multi-source process data acquisition unit for collecting the operating status of the electrolytic cell during the nitric acid electrolytic copper production process to form a multi-dimensional process data stream; and an experimental analysis data fusion unit for accessing laboratory test data corresponding to the nitric acid electrolytic copper production batch, performing time alignment and data standardization on the laboratory test data to form a data set. This nitric acid electrolytic copper growth process parameter analysis system collects the operating status of the electrolytic cell through the multi-source process data acquisition unit, fuses it with laboratory test data, and analyzes the relationship between electrolytic process parameters and cathode copper growth behavior through a real-time correlation analysis model. This achieves real-time monitoring and optimization of the nitric acid electrolytic copper production process, improving the accuracy and efficiency of the production process.

Description

Technical Field

[0001] This invention relates to the field of electrolytic copper production technology, specifically to a parameter analysis system for the growth process of nitric acid electrolytic copper. Background Technology

[0002] Current nitric acid electrolytic copper production processes primarily rely on pre-set formulas and personnel experience. Basic instruments are installed on the production site to measure cell voltage, current density, and electrolyte temperature. Operators read and manually record the instrument readings. Some production lines are equipped with data acquisition systems to upload discrete measurements to a central control room computer for centralized display and historical curve generation. Analysis of electrolyte components, such as nitric acid concentration, copper ion concentration, and impurity content, is performed using a combination of timed manual sampling and offline laboratory analysis, resulting in delays of several hours or even longer. Stability control and process adjustments throughout the entire process heavily depend on engineers' experienced judgment of the trends of various independent parameters. The existing technical architecture lacks automated analysis tools that deeply integrate multi-source data, analyze the coupling relationships between parameters in real time, and dynamically diagnose deposition quality.

[0003] The core flaw in existing technologies lies in the lack of a unified time reference and data standard between the discretely collected operating parameters such as cell voltage, current density, and electrolyte temperature from the production site and the offline laboratory testing data. This makes it difficult to effectively integrate and synchronously correlate these two types of data within the same production batch and timeline. Due to the lack of a real-time correlation analysis mechanism for the production process, it is difficult to form real-time, quantitative evaluations of key growth indicators such as cathode copper deposition rate, and it is also difficult to identify their changing trends and deviation risks in a timely manner. Operators still mainly rely on isolated parameters and lagging information for experience-based adjustments and post-event handling, resulting in a lack of data support for process optimization. This makes it difficult to achieve fine-tuning of the process window, leading to fluctuations in product qualification rate and output stability, thus restricting production efficiency and quality improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a parameter analysis system for the growth process of nitric acid electrolytic copper. The technical problem this invention aims to solve is: how to construct a time-aligned fusion and real-time correlation analysis process for multi-source production data and experimental detection data to address the difficulties in unified fusion and analysis of production parameters and experimental data, the difficulty in real-time evaluation of cathode copper growth status, and the difficulty in predicting quality trends in the existing nitric acid electrolytic copper production process.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a parameter analysis system for the growth process of copper via nitric acid electrolysis, comprising: Multi-source process data acquisition unit: used to collect the operating status of the electrolytic cell and form a multi-dimensional process data stream during the production of copper electrolysis from nitric acid; Experimental analysis data fusion unit: used to access laboratory test data corresponding to the nitric acid electrolytic copper production batch, and to perform time alignment and data standardization on the laboratory test data to form a data set; Real-time correlation analysis unit: used to perform synchronous correlation analysis on the multidimensional process data stream and the data set to build a real-time analysis model; Growth state characterization unit: used to extract features from the growth state parameters of cathode copper based on the real-time analysis model to form a state characterization result, wherein the cathode copper is pure copper reduced during the production process of nitric acid electrolytic copper; Quality trend prediction unit: used to perform trend analysis on the growth state parameters based on the state characterization results to form quality risk trend prediction results.

[0006] Preferably, the operating status includes electrolysis voltage, electrolysis current, electrolyte temperature, copper nitrate concentration in the electrolyte, and corresponding time information. The operating status forms continuous time series data at fixed time intervals, and the fixed time intervals are set to the second, minute, or hour level according to the needs of the production process.

[0007] Preferably, the time alignment is based on the time identifier of the multidimensional process data stream, mapping the laboratory test data to the production time interval corresponding to the operating status of the electrolytic cell, and the data standardization includes unit unification and numerical range normalization.

[0008] Preferably, the steps of the synchronous correlation analysis are as follows: S1. The parameters in the multidimensional process data stream are aligned to the same time axis using a timestamp alignment method to form a synchronous process data sequence; S2. Align the dataset to the corresponding time points of the synchronization process data sequence using a time window mapping method to form a synchronization experiment data sequence; S3. Perform a collaborative change analysis on the synchronization process data sequence and the synchronization experiment data sequence to construct the real-time analysis model.

[0009] Preferably, the synergistic change analysis employs partial least squares regression, which analyzes the synchronous process data sequence and the synchronous experimental data sequence to obtain the statistical correlation between electrolysis process parameters and key indicators of cathode copper growth. The electrolysis process parameters are obtained from the multidimensional process data stream, and the key indicators of cathode copper growth are obtained from the dataset. Based on the statistical correlation, the real-time analysis model is established.

[0010] Preferably, the real-time analysis model uses the electrolysis voltage, electrolysis current, electrolyte temperature, and copper nitrate concentration in the electrolyte from the synchronous process data sequence as input features, and uses the cathode copper growth state parameters corresponding to the input features from the synchronous experimental data sequence as output targets, wherein the growth state parameters include the deposition rate.

[0011] Preferably, the growth state parameters of the cathode copper include deposition rate, crystal morphology, and internal stress.

[0012] Preferably, the trend analysis involves identifying the variation patterns of growth state parameters over different time periods, performing time series prediction based on the variation patterns to obtain predicted values, comparing the predicted values ​​with preset process quality and safety boundaries, and generating a quality risk warning signal as the quality risk trend prediction result when the predicted value exceeds the process quality and safety boundaries.

[0013] This invention provides a parameter analysis system for the nitric acid electrolytic copper growth process. It has the following beneficial effects: This nitric acid electrolytic copper growth process parameter analysis system collects the operating status of the electrolytic cell through a multi-source process data acquisition unit, integrates it with laboratory test data, and analyzes the relationship between electrolysis process parameters and cathode copper growth behavior through a real-time correlation analysis model. This enables real-time monitoring and optimization of the nitric acid electrolytic copper production process, improving the accuracy and efficiency of the production process.

[0014] Partial least squares regression was used to analyze the co-variation of synchronous process data sequences and experimental data sequences, constructing a real-time analysis model and extracting cathode copper growth state parameters. By analyzing the growth state trends, quality risks can be predicted, and early warning signals can be generated when process quality exceeds safety boundaries, thereby improving the quality control capability of the production process. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a system structure for implementing an invention; Figure 2 It is a data flow diagram for realizing an invention; Figure 3 This is a flowchart of a real-time correlation analysis unit for implementing an invention. Detailed Implementation

[0016] 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.

[0017] Example 1 like Figure 1-3 As shown, this embodiment of the invention provides a parameter analysis system for the growth process of copper via nitric acid electrolysis, comprising: Multi-source process data acquisition unit: Used to collect the operating status of the electrolytic cell and form a multi-dimensional process data stream during the nitric acid electrolytic copper production process. The operating status includes electrolysis voltage, electrolysis current, electrolyte temperature, copper nitrate concentration in the electrolyte, and corresponding time information. The operating status is generated as continuous time series data at fixed time intervals, which can be set to the second, minute, or hour level according to the needs of the production process.

[0018] Experimental Analysis Data Fusion Unit: This unit is used to integrate laboratory testing data corresponding to the production batches of nitric acid electrolytic copper, and to perform time alignment and data standardization on the laboratory testing data to form a dataset. Time alignment uses the time identifier of the multidimensional process data stream as a benchmark to map the laboratory testing data to the production time interval corresponding to the operating status of the electrolytic cell. Data standardization includes dimensional unification and numerical range normalization.

[0019] Real-time correlation analysis unit: Used to perform synchronous correlation analysis on multidimensional process data streams and datasets to build real-time analysis models. The steps of synchronous correlation analysis are as follows: S1. The parameters in the multidimensional process data stream are aligned to the same time axis using timestamp alignment to form a synchronous process data sequence.

[0020] S2. Align the dataset to the corresponding time points of the synchronization process data sequence using a time window mapping method to form a synchronization experiment data sequence.

[0021] S3. A real-time analysis model was constructed by conducting a co-variation analysis of the synchronous process data sequence and the synchronous experimental data sequence. The co-variation analysis employed partial least squares regression (PLR) to analyze the synchronous process data sequence and the synchronous experimental data sequence, obtaining the statistical correlation between electrolysis process parameters and key indicators of cathode copper growth. The electrolysis process parameters were obtained from the multidimensional process data stream, while the key indicators of cathode copper growth were obtained from the dataset. Based on these statistical correlations, a real-time analysis model was established. The real-time analysis model used the electrolysis voltage, electrolysis current, electrolyte temperature, and copper nitrate concentration in the electrolyte from the synchronous process data sequence as input features, and the cathode copper growth state parameters corresponding to the input features from the synchronous experimental data sequence as output targets. These growth state parameters included the deposition rate.

[0022] Growth State Characterization Unit: This unit is used to extract features from the growth state parameters of cathode copper based on a real-time analysis model, forming a state characterization result. Cathode copper is pure copper reduced during the production of copper via nitric acid electrolysis. The growth state parameters of cathode copper include deposition rate, crystal morphology, and internal stress.

[0023] Quality trend prediction unit: Based on the state characterization results, it performs trend analysis on growth state parameters to generate quality risk trend prediction results. Trend analysis identifies the changing patterns of growth state parameters over different time periods, performs time series prediction based on these patterns to obtain predicted values, compares these predicted values ​​with preset process quality safety boundaries, and generates a quality risk early warning signal as the quality risk trend prediction result when the predicted value exceeds the process quality safety boundaries.

[0024] This invention utilizes real-time correlation analysis and prediction models to precisely control key parameters during the electrolytic copper growth process, optimizing production processes, reducing energy consumption, and improving production efficiency. Real-time monitoring and adjustment of parameters during electrolysis ensures stable cathode copper growth, avoids product quality fluctuations, and guarantees consistent copper quality. A quality trend prediction unit analyzes growth state parameter trends to identify potential quality risks in advance, issuing timely warnings to prevent quality problems. The system acquires key state parameters in real time, reducing the frequency of laboratory testing and saving testing costs and time.

[0025] Real-time data feedback mechanisms adjust process parameters, enhancing the adaptability of the production process and maintaining production stability under different operating conditions. The fusion and analysis of multi-source data provides a scientific basis for decision-making, reducing reliance on experience-based judgments and improving the accuracy and reliability of production decisions. These functions collectively support efficient control and quality assurance of the production process.

[0026] Example 2 This embodiment is based on a parameter analysis system for the growth process of copper electrolysis using nitric acid. By performing real-time correlation analysis on multi-source process data and experimental detection data during the copper electrolysis process using nitric acid, a real-time analysis model is established for quantitatively characterizing and predicting the growth state of cathode copper. The specific implementation method is as follows: 1. Process Data The data comes from a copper electrolysis production line using nitric acid, and includes parameters such as electrolysis voltage, electrolysis current, and copper nitrate concentration in the electrolytic cells. During a particular copper electrolysis production process, the system continuously collected process data and generated long-term data series. To illustrate the data synchronization and modeling process, only data from five consecutive time points are listed below: Electrolysis voltage, sampled at a frequency of once per second: 0.10V, 0.11V, 0.12V, 0.13V, 0.14V.

[0027] Electrolysis current, sampled at a frequency of once per second: 0.09A, 0.10A, 0.11A, 0.12A, 0.13A.

[0028] Copper nitrate concentrations, sampled at a frequency of once per minute: 0.4 mol / L, 0.5 mol / L, 0.6 mol / L, 0.7 mol / L, 0.8 mol / L.

[0029] 2. Construction of data sequences for synchronization process All process data needs to be synchronized via timestamp alignment. Because the sampling frequencies of process data and experimental data differ, a time alignment algorithm is used for data processing. Based on the voltage and current sequences after timestamp alignment, the temperature estimate corresponding to each second time point is calculated according to the temperature response model.

[0030] For the experimental data, the nearest neighbor method is used for mapping to align the experimental data with the process data collected every second.

[0031] Calculation of deposition rate and electrolyte temperature: The deposition rate and electrolyte temperature are calculated based on Faraday's law and the electrolysis reaction model. The deposition rate formula is:

[0032] in, ρ is the deposition rate, in mm / h; I is the electrolysis current, in A (1A = 1C / s); M is the molar mass of copper, in g / mol (the molar mass of copper is 63.55 g / mol); n is the number of electrons in copper (copper is a divalent ion, therefore n = 2); F is the Faraday constant, in C / mol (approximately 96500 C / mol); ρ is the density of copper, in g / cm³. 3 Approximately 8.96 g / cm³ 3 .

[0033] The deposition rate is closely related to the electrolysis current and the copper ion concentration. Under Faraday's law, the deposition rate is directly related to the current, while the copper nitrate concentration indirectly affects the deposition rate by influencing factors such as polarization and current efficiency η. Therefore, the deposition rate calculated using the current is: When the current is 0.09A:

[0034] Conversion:

[0035] Similarly, the deposition rate gradually increases with changes in electrolytic current and copper nitrate concentration.

[0036] Table 1: Deposition rate data.

[0037]

[0038] The formula for calculating electrolyte temperature is:

[0039] in, This is the initial temperature, in °C. and It is the temperature coefficient that determines the effect of voltage and current on temperature. and It is the baseline value.

[0040] The initial temperature is the temperature of the electrolyte at the beginning. Based on data from the actual production environment, the initial temperature is measured to be 45°C by a temperature sensor.

[0041] Temperature coefficients usually need to be determined experimentally by measuring temperature changes under different electrolysis currents and voltages.

[0042] When the electrolysis voltage increases by 1V, the temperature increases by 0.5℃, then α = 0.5℃ / V.

[0043] When the temperature increases by 0.3℃ for every 1A increase in electrolysis current, then β = 0.3℃ / A.

[0044] Table 2: Electrolyte temperature data table.

[0045]

[0046] 3. Experimental data time window mapping Experimental data is collected every 10 minutes, and needs to be aligned with the process data collected every second. Laboratory testing involves periodically weighing and measuring the thickness of the cathode copper sample to calculate the deposition rate per unit time. The first data point, 0.119 mm / h, was aligned with the synchronization process data at the 10-minute mark; the second data point, 0.132 mm / h, was aligned with the synchronization process data at the 20-minute mark; the third data point, 0.146 mm / h, was aligned with the synchronization process data at the 30-minute mark; the fourth data point, 0.159 mm / h, was aligned with the synchronization process data at the 40-minute mark; and the fifth data point, 0.172 mm / h, was aligned with the synchronization process data at the 50-minute mark.

[0047] When the time points of experimental data and process data are not perfectly aligned, the nearest neighbor method is used for mapping. Specifically, the process data point that is closest to the experimental data is selected for association.

[0048] 4. Synergistic Change Analysis Partial least squares regression was used to analyze the co-variation of synchronous process data and experimental data. The aim of the analysis was to establish the statistical correlation between electrolysis process parameters and key indicators of cathode copper growth.

[0049] Data preprocessing: All process data were standardized using Z-score before analysis, which involves subtracting the mean from the data and then dividing by the standard deviation to ensure consistency in the units of measurement for different data.

[0050] Missing data is filled using linear interpolation to ensure data integrity.

[0051] Outlier handling: Any data exceeding the 3σ range will be removed or repaired using interpolation to maintain data stability.

[0052] Synergistic Change Analysis: Partial least squares regression analysis revealed a significant linear correlation between electrolysis voltage and deposition rate, which can be approximated as follows:

[0053] The deposition rate is linearly related to the electrolysis voltage. Specifically, for every 1V increase in electrolysis voltage, the deposition rate increases by 0.05mm / h.

[0054] 5. Real-time analysis model construction Based on the results of the synergistic change analysis, a real-time analysis model is constructed. The model input includes process data such as electrolysis voltage, electrolysis current, and electrolyte temperature. The model output includes at least the predicted value of deposition rate, and further outputs the quality risk assessment results corresponding to the preset process quality and safety boundary. The quality risk assessment results are early warning signs and risk indices.

[0055] A real-time analytics model is trained using machine learning methods to perform real-time predictions. Input features: Electrolysis voltage: 0.10V, 0.11V, 0.12V, 0.13V, 0.14V; Electrolysis current: 0.09A, 0.10A, 0.11A, 0.12A, 0.13A; Electrolyte temperature: 45.0℃, 45.008℃, 45.016℃, 45.024℃, 45.032℃.

[0056] Output targets: deposition rates of 0.119 mm / h, 0.132 mm / h, 0.146 mm / h, 0.159 mm / h, and 0.172 mm / h.

[0057] Model training: The training dataset will be based on data collected during the first 6 hours of continuous production line operation. The dataset will be divided into a training set (80% of the data) and a test set (20% of the data).

[0058] When partitioning the data, a rolling window method is used to ensure that there is no time overlap between the training set and the test set.

[0059] During training, a support vector machine regression model was used, and K-fold cross-validation was used to optimize the model's hyperparameters.

[0060] Real-time prediction results: When the current electrolysis voltage is 0.12V, the real-time analysis model predicts: .

[0061] Therefore, the real-time predicted deposition rate is 0.106 mm / h.

[0062] 6. Quality monitoring and early warning Threshold setting basis: The quality warning threshold is determined based on statistical analysis of historical data. Specifically, a 95% confidence interval for the deposition rate is calculated using historical data, and an upper limit threshold of 0.22 mm / h is set, which is suitable for the stability requirements under current production conditions.

[0063] The threshold can be configured based on the statistical results of historical stable period data and can be updated periodically as the operating conditions change.

[0064] If the predicted deposition rate exceeds the process safety threshold of 0.22 mm / h based on the real-time prediction, the system will issue a quality risk warning.

[0065] In this embodiment, the predicted value is 0.106 mm / h, which does not exceed the preset threshold, so production continues. If the predicted value exceeds 0.22 mm / h, the system will issue a warning signal, prompting the operator to adjust production parameters such as electrolysis voltage and electrolysis current.

[0066] In summary, the implementation results demonstrate that the real-time analysis model can continuously output predicted results of the cathode copper growth state under actual production conditions and compare them with the preset process quality and safety boundaries, thus achieving effective discrimination of the growth state change trend. This embodiment proves the applicability and effectiveness of the real-time correlation analysis unit in the nitric acid electrolytic copper production process, providing repeatable and quantifiable data for quality control and process stability in the production process.

[0067] Example 3 This embodiment is based on a parameter analysis system for the nitric acid electrolytic copper growth process. By performing trend analysis and extrapolation calculations on the time series of cathode copper deposition rates during the nitric acid electrolytic copper production process, it enables early prediction of changes in quality risk trends. The specific implementation method is as follows: 1. Source and Production Conditions of the Examples Based on an actual operation of a nitric acid electrolytic copper production line under normal production conditions: The production line includes an electrolytic cell group numbered 03, which operates continuously during the time period corresponding to the example, without any process parameter switching, equipment shutdown, or manual intervention during operation.

[0068] The data used by the quality trend prediction unit is acquired and calculated in real time during the production process by the on-site data acquisition system and the real-time correlation analysis unit. Before entering the quality trend prediction unit, the above data has undergone time synchronization, outlier removal, and consistency verification.

[0069] In this embodiment, data from the 03 electrolytic cell during a 60-minute stable operating period are selected as the analysis object, and the time analysis step is set to 10 minutes.

[0070] 2. Input data for the quality trend prediction unit Within the aforementioned 60-minute operating interval, the real-time correlation analysis unit calculated the cathode copper deposition rate based on process parameters such as electrolysis voltage, electrolysis current, electrolyte temperature, and copper nitrate concentration, as follows: Table 3: Results of copper deposition rate at cathode.

[0071]

[0072] The above deposition rate data have been verified for consistency with laboratory test results for the corresponding time period, and the deviation between the calculated results and the experimental test values ​​is controlled within ±0.005 mm·h. -1 Within the specified range, the accuracy requirements of the input data for the quality trend prediction unit are met.

[0073] 3. Trend Feature Calculation Process The quality trend prediction unit performs first-order difference calculations on the deposition rate time series, obtaining a change of 0.01 mm·h between adjacent time windows. -1 .

[0074] Based on this, statistical calculations were performed on the above changes to obtain the average change in deposition rate within the 60-minute analysis window: .

[0075] The deposition rate maintained a continuous monotonically increasing characteristic throughout the entire analysis window, without any reverse change or significant fluctuations.

[0076] 4. Quality Trend Judgment Rules and Results The trend prediction unit's built-in trend judgment rules are based on statistical results of historical production data from 420 stable operating time windows over the past 12 months. Specific judgment criteria include: The deposition rate exhibits a monotonically increasing trend over at least three consecutive time windows, with an average change of at least 0.008 mm·h. -1 .

[0077] In this embodiment, the deposition rate increased monotonically over five consecutive time windows, with an average change of 0.01 mm·h. -1 The above trend determination conditions are met. The quality trend prediction unit outputs a determination result indicating that the deposition rate has a continuous upward trend based on the average change.

[0078] 5. Trend extrapolation calculation results Assuming the trend determination is valid, the quality trend prediction unit performs time series extrapolation calculations on the subsequent operating status based on the actual changes within the current 60-minute analysis window, with an extrapolation slope of 0.01 mm·h. -1 .

[0079] The extrapolation calculation results are as follows: The calculated deposition rate at 70 minutes was 0.24 mm·h. -1 The calculated deposition rate at 80 minutes was 0.25 mm·h. -1 .

[0080] The above calculation results are based on the current continuous production conditions and no additional correction parameters have been introduced.

[0081] 6. Process quality and safety boundaries and risk assessment Based on the statistical results of deposition rates from 186 qualified production batches over the past year, the preset deposition rate process quality safety boundary in the quality trend prediction unit is 0.24 mm·h. -1 And the 95th percentile value is used as the upper limit for quality and safety.

[0082] Compare the trend extrapolation results with the quality and safety boundary: The calculated deposition rate at 70 minutes equals the quality safety boundary, and the calculated deposition rate at 80 minutes exceeds the quality safety boundary by 0.01 mm·h. -1 .

[0083] Based on the above analysis, the quality trend prediction unit determined that there was a trend of the deposition rate exceeding the process quality control range during subsequent operation.

[0084] 7. Risk Level Determination and Output Results The preset risk level classification rule in the quality trend prediction unit is: when the predicted value exceeds the quality safety boundary but the excess does not exceed 0.02 mm·h -1 At that time, it was determined to be a Level 1 quality risk trend.

[0085] In this embodiment, the predicted deposition rate at the 80th minute is 0.25 mm·h. -1 Exceeding the quality and safety boundary by 0.01 mm·h -1 It meets the criteria for determining a Level 1 quality risk trend.

[0086] The quality trend prediction results output by the quality trend prediction unit include: The quality risk type is deposition rate exceeding limit trend, the risk trigger time is at 80 minutes, and the corresponding deposition rate value is 0.25 mm·h. -1 The risk level is identified as a Level 1 quality risk trend.

[0087] The above results are simultaneously output to the production monitoring system for reference in subsequent process adjustment decisions.

[0088] Through the above steps, the results show that the quality trend prediction unit can identify potential quality risk trends before the deposition rate reaches or exceeds the safety control limit, and output prediction results with clear time indication and risk level identification, providing reliable data support for quality control and stable operation of the nitrate electrolytic copper production process.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A parameter analysis system for the growth process of copper via nitric acid electrolysis, characterized in that, include: Multi-source process data acquisition unit: used to collect the operating status of the electrolytic cell and form a multi-dimensional process data stream during the production of copper electrolysis from nitric acid; Experimental analysis data fusion unit: used to access laboratory test data corresponding to the nitric acid electrolytic copper production batch, and to perform time alignment and data standardization on the laboratory test data to form a data set; Real-time correlation analysis unit: used to perform synchronous correlation analysis on the multidimensional process data stream and the data set to build a real-time analysis model; Growth state characterization unit: used to extract features from the growth state parameters of cathode copper based on the real-time analysis model to form a state characterization result, wherein the cathode copper is pure copper reduced during the production process of nitric acid electrolytic copper; Quality trend prediction unit: used to perform trend analysis on the growth state parameters based on the state characterization results to form quality risk trend prediction results.

2. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 1, characterized in that: The operating status includes electrolysis voltage, electrolysis current, electrolyte temperature, copper nitrate concentration in the electrolyte, and corresponding time information. The operating status forms a continuous time series data at fixed time intervals, which are set to the second, minute, or hour level according to the needs of the production process.

3. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 1, characterized in that: The time alignment is based on the time identifier of the multidimensional process data stream, mapping the laboratory test data to the production time interval corresponding to the operating status of the electrolytic cell. The data standardization includes dimensional unification and numerical range normalization.

4. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 1, characterized in that: The steps of the synchronous correlation analysis are as follows: S1. The parameters in the multidimensional process data stream are aligned to the same time axis using a timestamp alignment method to form a synchronous process data sequence; S2. Align the dataset to the corresponding time points of the synchronization process data sequence using a time window mapping method to form a synchronization experiment data sequence; S3. Perform a collaborative change analysis on the synchronization process data sequence and the synchronization experiment data sequence to construct the real-time analysis model.

5. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 4, characterized in that: The synergistic change analysis employs partial least squares regression, which analyzes the synchronous process data sequence and the synchronous experimental data sequence to obtain the statistical correlation between electrolysis process parameters and key indicators of cathode copper growth. The electrolysis process parameters are obtained from the multidimensional process data stream, and the key indicators of cathode copper growth are obtained from the dataset. Based on the statistical correlation, the real-time analysis model is established.

6. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 5, characterized in that: The real-time analysis model uses the electrolysis voltage, electrolysis current, electrolyte temperature, and copper nitrate concentration in the electrolyte from the synchronous process data sequence as input features, and uses the cathode copper growth state parameters corresponding to the input features from the synchronous experimental data sequence as output targets. The growth state parameters include the deposition rate.

7. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 1, characterized in that: The growth parameters of the cathode copper include deposition rate, crystal morphology, and internal stress.

8. The parameter analysis system for the nitric acid electrolytic copper growth process according to claim 1, characterized in that: The trend analysis involves identifying the variation patterns of growth state parameters over different time periods, performing time series prediction based on these patterns to obtain predicted values, comparing these predicted values ​​with preset process quality and safety boundaries, and generating a quality risk warning signal as the quality risk trend prediction result when the predicted value exceeds the process quality and safety boundaries.

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