Online intelligent monitoring method and system for voltage of lead electrolytic cell

Abstract

The invention relates to the technical field of electrolytic cell monitoring, in particular to an online intelligent monitoring method and system for the voltage of a lead electrolytic cell, and the method comprises the steps: collecting the cell voltage, electrolyte voltage and electrolyte temperature of the lead electrolytic cell at each moment in a monitoring interval; determining an inductive common-mode interference condition of the monitoring interval, and obtaining an electrolytic high-temperature interference condition of the monitoring interval; and in combination with the induction common-mode interference condition and the electrolysis high-temperature interference condition, voltage interference factors of a monitoring interval are obtained, and the anode voltage drop, the cathode voltage drop and the electrolysis voltage drop of the lead electrolysis cell are corrected so as to evaluate the voltage monitoring condition of the lead electrolysis cell. Therefore, the precision and reliability of voltage monitoring of the lead electrolytic cell are improved.

Description

Technical Field

[0001] This application relates to the field of electrolytic cell monitoring technology, specifically to a method and system for online intelligent monitoring of lead electrolytic cell voltage. Background Technology

[0002] The voltage of the lead electrolytic cell is a key control technical indicator in the lead hydrometallurgical industry. By collecting, transmitting and analyzing the voltage data of the electrolytic cell in real time, intelligent monitoring and optimization of the hydrometallurgical process can be achieved. If the voltage of the electrolytic cell is too high, impurity metals with an electrochemical series lower than lead will dissolve and precipitate at the cathode, affecting the chemical quality of the cathode lead and increasing the consumption of electrical energy. If the voltage of the electrolytic cell is too low, the cathode lead deposition will be rough or even stop. At too low a voltage, the reaction kinetics may also be limited, causing other metal ions to compete for precipitation at the cathode, affecting the purity of the cathode lead.

[0003] In the lead hydrometallurgical process, the electrolysis workshop is located in an environment of strong current, high magnetic field, and complex temperature fluctuations. Traditional online voltage monitoring technology for lead electrolytic cells is affected by the strong electromagnetic field environment of the electrolysis workshop, which can induce electromagnetic induction. The alternating magnetic field will generate eddy currents in the conductor. The conductor eddy currents generated by the strong electromagnetic field and the Lorentz force will cause serious drift in the voltage monitoring data of the lead electrolytic cell. At the same time, the electrolysis reaction in the electrolytic cell and the operation of the smelting workshop will generate high temperatures, which will cause changes in the conductivity of the electrolyte. This will amplify the risk of temperature drift and gain error in the voltage monitoring data of the lead electrolytic cell, resulting in significant lag and distortion in the voltage monitoring data of the lead electrolytic cell. This will make it impossible to detect voltage anomalies in the lead electrolytic cell in time, which may lead to local overheating of the electrolytic cell equipment, electrolyte splashing, or even electrode plate ablation, causing safety accidents. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for online intelligent monitoring of lead electrolytic cell voltage. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a method for online intelligent monitoring of voltage in a lead electrolytic cell, the method comprising the following steps: The cell voltage, electrolyte voltage, and electrolyte temperature of the lead electrolytic cell were collected at various times within the monitoring interval. Based on the peak distribution of the tank voltage within the monitoring interval, the monitoring interval is divided into various local time periods. The periodic similarity of the tank voltage between any two local time periods within the monitoring interval, as well as the trend distribution of the peaks, are analyzed. Combined with the degree of disorder in the distribution of the low-frequency component and the degree of energy attenuation of the high-frequency component of the electrolyte voltage in the frequency domain within the monitoring interval, the induced common-mode interference status of the monitoring interval is determined. The abrupt change points of electrolyte voltage and electrolyte temperature within the monitoring interval were obtained respectively. The time lag of the abrupt change point of electrolyte voltage relative to the abrupt change point of electrolyte temperature, as well as the changing trend of the abrupt change point of electrolyte temperature, were analyzed. Based on the trend correlation between the abrupt change points of electrolyte temperature and electrolyte voltage, the electrolytic high temperature interference status in the monitoring interval was determined. By combining the inductive common-mode interference and the electrolytic high-temperature interference, the voltage interference factor in the monitoring range is obtained, and the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell are corrected respectively to evaluate the voltage monitoring status of the lead electrolytic cell.

[0005] In one embodiment, dividing the monitoring interval into various local time periods includes: The peak detection algorithm is used to obtain the peaks of the slot voltage at all times within the monitoring interval. The time corresponding to the peak is used as the dividing point to obtain the local time periods of the monitoring interval.

[0006] In one embodiment, determining the induced common-mode interference status of the monitoring interval includes: Calculate the Hurst exponent of the sequence of all peak data of the tank voltage within the monitoring interval, and calculate the cumulative sum of the phase lock values ​​of the tank voltage between any two local time periods within the monitoring interval; combine the Hurst exponent and the cumulative sum to obtain the periodic fluctuation of the tank voltage within the monitoring interval. The electrolyte voltage in the monitoring interval is divided into high-frequency sub-signals and low-frequency sub-signals using a signal decomposition algorithm; the information entropy of all components of all low-frequency sub-signals is calculated; the slope of the fitted line of each high-frequency sub-signal is obtained; and the reciprocal of the slope is taken as the energy attenuation rate of each high-frequency sub-signal. By combining the information entropy with the energy attenuation rate of all high-frequency sub-signals, the coupling dielectric interference of the monitoring interval is obtained; The induced common-mode interference status in the monitoring interval is a fusion result of the periodic fluctuation of the slot voltage and the coupling dielectric interference.

[0007] In one embodiment, the periodic fluctuation of the slot voltage is positively correlated with both the Hurst exponent and the summation.

[0008] In one embodiment, the sum of the energy attenuation rates of all high-frequency sub-signals is calculated and multiplied by the information entropy to obtain the coupling dielectric interference of the monitoring interval.

[0009] In one embodiment, determining the electrolytic high-temperature interference status within the monitoring interval includes: The abrupt changes in electrolyte voltage and electrolyte temperature within the monitoring interval are respectively composed into time series. The sum of the differences between the time of the electrolyte temperature abrupt change point and the time of the electrolyte voltage abrupt change point in all the same order is calculated and multiplied by the Hurst exponent of the time series of electrolyte temperature abrupt changes to obtain the lag of changes in electrolyte temperature and voltage within the monitoring interval. The trend terms of electrolyte temperature change points and electrolyte voltage change points are obtained by using the trend decomposition algorithm, and the correlation coefficient between the trend terms of electrolyte temperature change points and electrolyte voltage change points is calculated. By combining the negative correlation index mapping result of the correlation coefficient with the change lag, the electrolysis high-temperature interference status is obtained.

[0010] In one embodiment, the voltage interference factor is positively correlated with the induced common-mode interference condition and negatively correlated with the electrolytic high-temperature interference condition.

[0011] In one embodiment, the correction of the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell includes: Calculate the average anode voltage drop, average cathode voltage drop, and average electrolytic voltage drop of the lead electrolytic cell within the monitoring interval, and record them as the average voltage drop. Calculate the absolute value of the difference between the voltage interference factor and the preset threshold, and calculate the product of the absolute value of the difference and the average value of each voltage drop; If the voltage interference factor is greater than a preset threshold, calculate the difference between the average voltage drop and the product, and use it as the corrected average voltage drop; otherwise, calculate the sum of the average voltage drop and the product, and use it as the corrected average voltage drop.

[0012] In one embodiment, the assessment of the lead electrolytic cell voltage monitoring status includes: If the corrected average voltage drop is less than or equal to the corresponding rated voltage drop, it is determined that there is no abnormality in the monitoring voltage during the operation of the lead electrolytic cell; otherwise, it is determined that there is an abnormality in the monitoring voltage during the operation of the lead electrolytic cell.

[0013] Secondly, embodiments of this application also provide an online intelligent monitoring system for lead electrolytic cell voltage, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application analyzes the peak trend of the electrolytic cell voltage and combines it with the low-frequency and high-frequency components in the frequency domain analysis of the electrolyte voltage to determine the induced common-mode interference in the monitoring range. This reflects the periodic drift of the cell voltage caused by the induced potential and common-mode interference formed by the strong electromagnetic environment during the operation of the lead electrolytic cell, as well as the degree of coupling dielectric interference of the electrolyte voltage. It quantifies the possibility of falsely high voltage readings in the lead electrolytic cell monitoring, improving the accuracy and reliability of the voltage monitoring. Furthermore, it determines the high-temperature interference in the monitoring range, reflecting the degree of lag between electrolyte temperature and voltage caused by the heat of electrolytic reaction and the high temperature in the smelting workshop during the operation of the lead electrolytic cell, as well as the degree of electrolyte voltage- The negative correlation between temperatures, by identifying and quantifying the impact of high-temperature interference, reflects the possibility of falsely low voltage monitoring in lead electrolytic cells, enhancing the early warning capability of voltage fluctuation trends. Furthermore, by combining the induced common-mode interference and the electrolytic high-temperature interference, the voltage interference factor in the monitoring range is obtained, accurately reflecting the induced potential and common-mode interference formed by the strong external electromagnetic environment during the operation of the lead electrolytic cell, as well as the lag and distortion of the electrolytic cell voltage monitoring data caused by high temperature. This enables precise correction of the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell, improving the accuracy and stability of voltage monitoring and ensuring the safe operation of the lead electrolytic cell. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of an online intelligent monitoring method for lead electrolytic cell voltage according to one embodiment of this application; Figure 2 A flowchart for determining the induced common-mode interference status in the monitoring interval; Figure 3 A flowchart was created to determine the high-temperature interference conditions of the electrolysis in the monitoring area. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a lead electrolytic cell voltage online intelligent monitoring method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the online intelligent monitoring method and system for lead electrolytic cell voltage provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of an online intelligent monitoring method for lead electrolytic cell voltage according to an embodiment of this application. The method includes the following steps: S1: Collect the cell voltage, electrolyte voltage, and electrolyte temperature of the lead electrolytic cell at various times within the monitoring interval, and perform preprocessing.

[0021] This application takes the voltage monitoring of a single lead electrolytic cell as an example for subsequent processing. It uses multi-core signal lines and copper busbars for connection and employs voltage sensors to acquire the cell voltage and electrolyte voltage applied between the anode and cathode of the lead electrolytic cell, respectively. It also acquires the electrolyte temperature data of the lead electrolytic cell in real time by deploying temperature sensors. The cell voltage, electrolyte voltage data and electrolyte temperature data are all acquired synchronously, and the acquisition frequency is set to 10Hz. The implementer can set it according to the actual situation. This embodiment does not impose any restrictions on this.

[0022] To prevent data loss due to external environmental interference during data acquisition and to address the impact of different dimensions on subsequent analysis, the acquired lead electrolytic cell voltage, electrolyte voltage, and electrolyte temperature data were filled with missing values ​​using cubic spline interpolation. Furthermore, Z-score standardization was applied to unify the dimensions of the cell voltage, electrolyte voltage, and electrolyte temperature data. Both cubic spline interpolation and Z-score standardization are well-known techniques, and their specific processes are not detailed here.

[0023] S2. Based on the peak distribution of the tank voltage within the monitoring interval, the monitoring interval is divided into various local time periods. The periodic similarity of the tank voltage between any two local time periods within the monitoring interval, as well as the trend distribution of the peaks, are analyzed. Combined with the degree of disorder in the distribution of the low-frequency component of the electrolyte voltage in the frequency domain and the degree of energy attenuation of the high-frequency component, the induced common-mode interference status of the monitoring interval is determined.

[0024] During the operation of a lead electrolytic cell, the alternating magnetic field generated by the 10,000-ampere DC bus and the high-frequency rectifier, according to Faraday's law of electromagnetic induction, will generate a millivolt-level induced potential in the sampling loop, which will be connected in series with the actual electrolytic cell voltage and electrolyte voltage. Furthermore, the high-frequency component will couple to the input of the signal amplifier through the distributed capacitance, forming common-mode interference. This will cause the lead electrolytic cell voltage monitoring data to drift and become distorted, exacerbating the misjudgment of abnormal voltage monitoring data. Consequently, it will be impossible to grasp the true voltage status of the lead electrolytic cell in a timely manner, affecting the quality of cathode lead products and the reliable operation of the lead electrolytic cell equipment.

[0025] Specifically, during the operation of a lead electrolytic cell, the more severe the induced potential or common-mode interference caused by the strong external electromagnetic field, the more pronounced the periodic disturbances in the cell voltage data become due to the influence of the strong electromagnetic field coupling effect on the current distribution. Furthermore, the strong electromagnetic field generates a common-mode voltage in the grounding circuit, making the fluctuation peak of the cell voltage data more trend-like. At the same time, the high impedance of the electrolyte circuit, affected by common-mode current coupling, leads to unstable ground potential and generates significant low-frequency noise. The high-frequency selective interference generated by the dielectric properties of the electrolyte causes the high-frequency component energy of the electrolyte voltage data to decay faster.

[0026] Based on the above analysis, this embodiment sets up monitoring intervals, with the length of each interval set to 10 minutes. Implementers can set this length according to their actual needs; this embodiment does not impose any restrictions. Taking any one monitoring interval as an example, the preprocessed cell voltage data within that interval are arranged into a cell voltage data sequence according to time sequence. Using the cell voltage data sequence of the lead electrolytic cell within the monitoring interval as input, the AMPD (Automatic Multiscale-based Peak Detection) algorithm is used to obtain all peak data in the cell voltage data sequence. Using the time corresponding to each peak data point as a dividing point, the monitoring interval is divided into local time periods, and the cell voltage data within each local time period are combined into a cell voltage subsequence.

[0027] Furthermore, the PLV phase lock value between any two slot voltage subsequences within the monitoring interval is obtained using the PLV algorithm. A larger PLV phase lock value indicates a more similar period between the slot voltage subsequences. Next, all peak data points in the slot voltage data sequence within the monitoring interval are combined to form a peak sequence. The Hurst exponent of the peak sequence is calculated, and combined with the cumulative sum of the phase lock values ​​of the slot voltages between any two local time periods within the interval, the periodic volatility of the slot voltage within the monitoring interval is obtained. The periodic volatility of the slot voltage is positively correlated with both the Hurst exponent and the cumulative sum.

[0028] In this embodiment, the product of the Hurst exponent and the sum is used as the periodic fluctuation of the cell voltage within the monitoring interval. The periodic fluctuation of the cell voltage reflects the degree of fluctuation in the peak cell voltage data caused by strong electromagnetic field coupling effects and the common-mode voltage generated by the grounding loop, as well as the periodic similarity of the cell voltage data. During the operation of the lead electrolytic cell, the more severe the induced potential and common-mode current interference caused by the strong external electromagnetic environment, the more long-term trend the peak data in the cell voltage data sequence exhibits, the higher the periodic similarity between the cell voltage sub-sequences within the monitoring interval, and the greater the periodic fluctuation of the cell voltage within the monitoring interval.

[0029] Furthermore, the time-series sequence composed of all electrolyte voltages within the monitoring interval is divided into various frequency bands using a wavelet packet decomposition algorithm. In this embodiment, the Daubechies 4 (db4) wavelet is used, and a three-level decomposition is performed to obtain the decomposed high-frequency and low-frequency sub-signals. The information entropy of all components of all low-frequency sub-signals is calculated. A linear fit is performed on each high-frequency sub-signal using the least squares method to obtain the slope of the fitted line for each high-frequency sub-signal. The reciprocal of the slope of the fitted line for each high-frequency sub-signal is used as the energy attenuation rate of each high-frequency sub-signal. The wavelet packet decomposition algorithm is a well-known existing technology.

[0030] In this embodiment, the sum of the energy attenuation rates of all high-frequency sub-signals within the monitoring interval is calculated and multiplied by the information entropy to obtain the coupling dielectric interference of the monitoring interval. The coupling dielectric interference characterizes the differences in low-frequency sub-signals caused by interference from common-mode current in a strong electromagnetic environment and the dielectric properties of the electrolyte during the operation of the lead electrolytic cell, as well as the degree of rapid energy attenuation of high-frequency sub-signals. The more significant the energy difference of the frequency components in the low-frequency band of the electrolyte voltage data in the frequency domain, the greater the degree of disorder, and the greater the energy attenuation rate of the frequency components in the high-frequency band, the stronger the coupling dielectric interference of the monitoring interval.

[0031] By integrating the periodic fluctuations of the cell voltage with the coupled dielectric interference, the induced common-mode interference status within the monitoring range is determined. This is used to characterize the periodic drift of the cell voltage caused by the induced potential formed by the strong electromagnetic environment and common-mode interference during the operation of the lead electrolytic cell, as well as the degree of coupled dielectric interference of the electrolyte voltage. The more significant the induced common-mode interference, the more likely it is to cause falsely high voltage readings in the lead electrolytic cell monitoring, resulting in larger errors.

[0032] It should be noted that fusion means combining multiple variables, which can be done by addition, multiplication, addition-multiplication fusion, or averaging. This embodiment does not limit this.

[0033] In this embodiment, the expression for the induced common-mode interference status of the monitoring interval is: In the formula, This represents the induced common-mode interference status in the i-th monitoring interval during the operation of the lead electrolytic cell. This refers to the periodic fluctuation of the cell voltage in the i-th monitoring interval during the operation of the lead electrolytic cell. Let norm() be the coupling dielectric interference in the i-th monitoring interval during the operation of the lead electrolytic cell, and let norm() be the normalization function, such that... The value range is within [0,1]. The flowchart for determining the induced common-mode interference status within the monitoring interval is as follows: Figure 2 As shown.

[0034] S3. Obtain the abrupt change points of electrolyte voltage and electrolyte temperature within the monitoring interval, analyze the time lag of the electrolyte voltage abrupt change point relative to the electrolyte temperature abrupt change point, and the changing trend of the electrolyte temperature abrupt change point. Combine the trend correlation between the electrolyte temperature abrupt change point and the electrolyte voltage abrupt change point to determine the electrolytic high temperature interference status within the monitoring interval.

[0035] During the operation of lead electrolytic cells, the electrolytic reaction in the electrolytic cells and the operation of the smelting workshop will generate high temperatures. The temperature drift and gain error caused by the high temperature environment to the voltage monitoring data of lead electrolytic cells make it difficult to accurately grasp the degree of distortion of the voltage monitoring data of lead electrolytic cells, which in turn masks the true voltage status of the electrolytic cells and increases the risk of safety accidents.

[0036] Specifically, during the operation of lead electrolytic cells, the more severe the interference from the external environment due to the electrolysis reaction and the high temperature generated in the smelting workshop on the monitoring of electrolytic cell voltage data, the more obvious the increase in electrolyte temperature gradient becomes. Moreover, due to the time required for temperature transfer, the correlation lag of electrolyte voltage data affected by temperature is more significant. At the same time, the increase in electrolyte temperature will cause ohmic voltage drop and polarization voltage drop in the electrolyte. The more severe the interference from high temperature in the external environment, the higher the negative correlation between the trend changes of electrolyte voltage data and electrolyte temperature data.

[0037] Based on the above analysis, this embodiment uses the electrolyte temperature data at all times within the monitoring interval to form an electrolyte temperature data sequence. The electrolyte voltage data sequence and electrolyte temperature data sequence within the monitoring interval are used as inputs, respectively. The Pettitt mutation point detection algorithm is employed to obtain each mutation point in the electrolyte voltage and electrolyte temperature data sequences within the monitoring interval. The mutation points in the electrolyte voltage data sequence are then arranged in the order of appearance to form an electrolyte voltage mutation point sequence, and the mutation points in the electrolyte temperature data sequence are arranged in the same order of appearance to form an electrolyte temperature mutation point sequence. Using the electrolyte voltage and electrolyte temperature mutation point sequences as inputs, the STL (Seasonal-Trend Decomposition using Loess) sequence decomposition algorithm is employed to obtain the trend terms of each mutation point in the electrolyte voltage and electrolyte temperature mutation point sequences. Both the Pettitt mutation point detection algorithm and the STL sequence decomposition algorithm are well-known technologies, and the specific acquisition process will not be elaborated further.

[0038] Furthermore, this embodiment determines the electrolytic high-temperature interference status within the monitoring range to characterize the degree of lag in the change between electrolyte temperature and voltage caused by the heat of electrolytic reaction and the high temperature in the smelting workshop during the operation of the lead electrolytic cell, as well as the negative correlation trend between electrolyte voltage and temperature. Specifically: For the electrolyte temperature and voltage abrupt change sequence, the difference between the times of the electrolyte temperature and voltage abrupt change points with the same position is calculated. It should be noted that the "same position" refers to their position within the electrolyte temperature and voltage abrupt change sequence. The cumulative result of all the differences within the monitoring interval is calculated and multiplied by the Hurst exponent of the electrolyte temperature abrupt change sequence to obtain the lag of electrolyte temperature and voltage changes within the monitoring interval. The lag reflects the positive correlation trend of electrolyte temperature during lead electrolysis and the degree of lag between electrolyte temperature and voltage data. During lead electrolysis, the more severe the influence of external environmental electrolysis reactions or high temperatures in the smelting workshop on the lead electrolysis voltage monitoring data, the more obvious the increase in electrolyte temperature gradient, and the more significant the lag between electrolyte temperature and voltage abrupt change data, i.e., the greater the obtained lag.

[0039] The Pearson correlation coefficients of all trend terms in the electrolyte temperature abrupt change sequence and all trend terms in the electrolyte voltage abrupt change sequence are calculated. The negative correlation index mapping result of the Pearson correlation coefficients is then fused with the change lag to obtain the electrolysis high-temperature interference status. The implementer can choose other existing feasible correlation calculation methods, such as cosine similarity.

[0040] In this embodiment, the expression for the electrolytic high-temperature interference status in the monitoring interval is: In the formula, This refers to the high-temperature interference status of the electrolysis in the i-th monitoring interval during the operation of the lead electrolytic cell; The lag in the changes of electrolyte temperature and voltage within the i-th monitoring interval during the operation of the lead electrolytic cell; Let be the Pearson correlation coefficient within the i-th monitoring interval during the operation of the lead electrolytic cell, where e is the natural constant and norm() is the normalization function, such that... The value range is within the range [0,1]. Among them, The exponential mapping result of the Pearson correlation coefficient is intended to avoid a denominator of 0. The negative correlation index mapping result of the Pearson correlation coefficient indicates a negative correlation between the electrolytic high-temperature interference and the Pearson correlation coefficient. The flowchart for determining the electrolytic high-temperature interference within the monitoring interval is as follows. Figure 3 As shown.

[0041] The high-temperature interference in electrolysis reflects the lag in changes in electrolyte temperature and voltage caused by the heat of electrolysis reaction and the high temperature in the smelting workshop during the operation of the lead electrolytic cell, as well as the strength of the negative correlation between electrolyte voltage and temperature. The higher the negative correlation between the trend changes of electrolyte voltage data and electrolyte temperature data, the smaller the Pearson correlation coefficient, indicating that the negative correlation trend between electrolyte voltage and temperature data caused by high temperature during the operation of the lead electrolytic cell is more obvious, and ultimately the high-temperature interference is greater. In this case, it is more likely to cause falsely low voltage monitoring in the lead electrolytic cell, resulting in a large error.

[0042] S4. Combining the inductive common-mode interference and the electrolytic high-temperature interference, the voltage interference factor in the monitoring range is obtained, and the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell are corrected respectively to evaluate the voltage monitoring status of the lead electrolytic cell.

[0043] During the operation of lead electrolytic cells, the more severe the induced potential and common-mode interference caused by the strong external electromagnetic environment, the more likely it is to cause a falsely high voltage in the lead electrolytic cell monitoring, that is, the measured value will be abnormally high. Conversely, the more severe the impact of the electrolytic reaction and the high temperature generated in the smelting workshop, the more likely it is to cause a falsely low voltage in the lead electrolytic cell monitoring, that is, the measured value will be abnormally low. The greater the induced common-mode interference and the high temperature interference of electrolysis in the monitoring range, the higher the degree of influence of environmental interference on the lead electrolytic cell voltage.

[0044] Therefore, in this embodiment, the induced common-mode interference and electrolytic high-temperature interference in the monitoring interval are used as inputs to the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) comprehensive evaluation method, and the output is a comprehensive evaluation value. The induced common-mode interference is positively correlated with the output comprehensive evaluation value, while the electrolytic high-temperature interference is negatively correlated. The weights in the TOPSIS comprehensive evaluation method are determined using the entropy weight method in this embodiment. Both the TOPSIS comprehensive evaluation method and the entropy weight method are existing known technologies, and their specific processes will not be elaborated upon.

[0045] The normalized value of the comprehensive evaluation is used as the voltage interference factor for the monitoring interval. The voltage interference factor is positively correlated with the induced common-mode interference and negatively correlated with the electrolytic high-temperature interference. A larger voltage interference factor indicates a higher likelihood of inflated voltage monitoring in the lead electrolytic cell, and vice versa. The normalization method for the comprehensive evaluation value uses the sigmoid function; implementers can choose other feasible normalization methods.

[0046] Within the monitoring range, this embodiment measures the anode voltage drop, cathode voltage drop, and electrolyte pressure drop of the lead electrolytic cell multiple times, calculates the average value of all measured anode voltage drops, the average value of all measured cathode voltage drops, and the average value of the electrolyte pressure drop, and records these three average values ​​as the average voltage drop value.

[0047] This embodiment presets a threshold T, which represents the critical judgment value for falsely high and falsely low voltage monitoring in the lead electrolytic cell. In this embodiment, T=0.5, but the implementer can set it according to the actual situation; this embodiment does not impose any restrictions on this. The absolute value of the difference between the voltage interference factor in the monitoring interval and the threshold T is calculated, and the product of the absolute value of the difference and the average voltage drop is calculated to represent the abnormal part of each average voltage drop. Further, if the voltage interference factor in the monitoring interval is greater than the threshold T, it indicates that the voltage monitoring of the lead electrolytic cell is falsely high. The difference between each average voltage drop and the product is calculated as the corrected average voltage drop. If the voltage interference factor in the monitoring interval is less than or equal to the threshold T, it indicates that the voltage monitoring of the lead electrolytic cell is falsely low. The sum of each average voltage drop and the product is calculated as the corrected average voltage drop.

[0048] The corrected average voltage drop values ​​eliminate the influence of strong external electromagnetic environment and high temperature during the operation of lead electrolytic cells, resulting in a more accurate voltage drop.

[0049] If the corrected average voltage drop is less than or equal to the corresponding rated voltage drop, it is determined that there is no abnormality in the monitored voltage during the operation of the lead electrolytic cell. Otherwise, it is determined that there is an abnormality in the monitored voltage during the operation of the lead electrolytic cell, and maintenance is required to prevent the lead electrolytic cell's malfunction from worsening over time and causing a safety accident. In this embodiment, the rated anode voltage drop is 0.34V, the electrolytic voltage drop is 1.57V, and the cathode voltage drop is 0.36V.

[0050] Based on the same inventive concept as the above method, this application embodiment also provides a lead electrolytic cell voltage online intelligent monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described lead electrolytic cell voltage online intelligent monitoring methods.

[0051] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0052] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0053] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for online intelligent monitoring of voltage in a lead electrolytic cell, characterized in that, The method includes the following steps: The cell voltage, electrolyte voltage, and electrolyte temperature of the lead electrolytic cell were collected at various times within the monitoring interval. Based on the peak distribution of the tank voltage within the monitoring interval, the monitoring interval is divided into various local time periods. The periodic similarity of the tank voltage between any two local time periods within the monitoring interval, as well as the trend distribution of the peaks, are analyzed. Combined with the degree of disorder in the distribution of the low-frequency component and the degree of energy attenuation of the high-frequency component of the electrolyte voltage in the frequency domain within the monitoring interval, the induced common-mode interference status of the monitoring interval is determined. The abrupt change points of electrolyte voltage and electrolyte temperature within the monitoring interval were obtained respectively. The time lag of the abrupt change point of electrolyte voltage relative to the abrupt change point of electrolyte temperature, as well as the changing trend of the abrupt change point of electrolyte temperature, were analyzed. Based on the trend correlation between the abrupt change points of electrolyte temperature and electrolyte voltage, the electrolytic high temperature interference status in the monitoring interval was determined. By combining the inductive common-mode interference and the electrolytic high-temperature interference, the voltage interference factor in the monitoring range is obtained, and the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell are corrected respectively to evaluate the voltage monitoring status of the lead electrolytic cell.

2. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 1, characterized in that, The division of the monitoring interval into various local time periods includes: The peak detection algorithm is used to obtain the peaks of the slot voltage at all times within the monitoring interval. The time corresponding to the peak is used as the dividing point to obtain the local time periods of the monitoring interval.

3. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 1, characterized in that, The determination of the induced common-mode interference status within the monitoring range includes: Calculate the Hurst exponent of the sequence of all peak data of the tank voltage within the monitoring interval, and calculate the cumulative sum of the phase lock values ​​of the tank voltage between any two local time periods within the monitoring interval; combine the Hurst exponent and the cumulative sum to obtain the periodic fluctuation of the tank voltage within the monitoring interval. The electrolyte voltage in the monitoring interval is divided into high-frequency sub-signals and low-frequency sub-signals using a signal decomposition algorithm; the information entropy of all components of all low-frequency sub-signals is calculated; the slope of the fitted line of each high-frequency sub-signal is obtained; and the reciprocal of the slope is taken as the energy attenuation rate of each high-frequency sub-signal. By combining the information entropy with the energy attenuation rate of all high-frequency sub-signals, the coupling dielectric interference of the monitoring interval is obtained; The induced common-mode interference status in the monitoring interval is a fusion result of the periodic fluctuation of the slot voltage and the coupling dielectric interference.

4. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 3, characterized in that, The periodic fluctuation of the slot voltage is positively correlated with both the Hurst exponent and the cumulative sum.

5. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 3, characterized in that, The sum of the energy attenuation rates of all high-frequency sub-signals is calculated and multiplied by the information entropy to obtain the coupling dielectric interference of the monitoring interval.

6. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 1, characterized in that, The determination of the electrolytic high-temperature interference status within the monitoring range includes: The abrupt changes in electrolyte voltage and electrolyte temperature within the monitoring interval are respectively composed into time series. The sum of the differences between the time of the electrolyte temperature abrupt change point and the time of the electrolyte voltage abrupt change point in all the same order is calculated and multiplied by the Hurst exponent of the time series of electrolyte temperature abrupt changes to obtain the lag of changes in electrolyte temperature and voltage within the monitoring interval. The trend terms of electrolyte temperature change points and electrolyte voltage change points are obtained by using the trend decomposition algorithm, and the correlation coefficient between the trend terms of electrolyte temperature change points and electrolyte voltage change points is calculated. By combining the negative correlation index mapping result of the correlation coefficient with the change lag, the electrolysis high-temperature interference status is obtained.

7. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 1, characterized in that, The voltage interference factor is positively correlated with the induced common-mode interference condition and negatively correlated with the electrolytic high-temperature interference condition.

8. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 1, characterized in that, The corrections to the anode voltage drop, cathode voltage drop, and electrolytic pressure drop of the lead electrolytic cell include: Calculate the average anode voltage drop, average cathode voltage drop, and average electrolytic voltage drop of the lead electrolytic cell within the monitoring interval, and record them as the average voltage drop. Calculate the absolute value of the difference between the voltage interference factor and the preset threshold, and calculate the product of the absolute value of the difference and the average value of each voltage drop; If the voltage interference factor is greater than a preset threshold, calculate the difference between the average voltage drop and the product, and use it as the corrected average voltage drop; otherwise, calculate the sum of the average voltage drop and the product, and use it as the corrected average voltage drop.

9. The online intelligent monitoring method for lead electrolytic cell voltage as described in claim 8, characterized in that, The assessment of the voltage monitoring status of the lead electrolytic cell includes: If the corrected average voltage drop is less than or equal to the corresponding rated voltage drop, it is determined that there is no abnormality in the monitoring voltage during the operation of the lead electrolytic cell; otherwise, it is determined that there is an abnormality in the monitoring voltage during the operation of the lead electrolytic cell.

10. An online intelligent monitoring system for lead electrolytic cell voltage, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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