A method and system for controlling process parameters for copper tailings purification

Abstract

The present application relates to copper tailings purification technical field, disclose a kind of copper tailings purification process parameter control method and system, the method is by obtaining multi-source process information, and it is associated analysis, can intelligently identify the sustained deviation of slurry concentration measurement.When identifying this deviation, the system can accurately determine that the first process information exists measurement deviation, and according to the deviation degree of other process information, the compensation amount for correcting the first process information is calculated. By applying the compensation amount to the original measurement value, the corrected slurry concentration information closer to the true value can be obtained, so as to generate more accurate mill water regulation control signal.

Description

Technical Field

[0001] This invention relates to the field of copper tailings purification technology, and more specifically, to a method and system for controlling process parameters in copper tailings purification. Background Technology

[0002] In the industrial production environment of copper tailings resource utilization, the purification process of copper tailings slurry is the core of the entire production line, directly determining the final quality of subsequent products such as foundry sand, high-strength functional aggregates, artificial stone, or cat litter. In this crucial stage, slurry concentration control is paramount. To monitor and adjust the slurry concentration in real time, online slurry concentration meters are typically installed in key pipelines on the production line. These measuring probes need to be immersed in highly abrasive copper tailings slurry for extended periods. This slurry contains not only numerous fine mineral particles but also components with high hardness, such as quartz. As the slurry flows at high speed through the pipelines, these hard mineral particles continuously impact and rub against the probe surface. Summary of the Invention

[0003] This invention provides a method and system for controlling parameters in the copper tailings purification process. It aims to solve a series of problems in the copper tailings purification process, such as measurement deviation caused by wear of the online slurry concentration meter, which in turn leads to misjudgment of the digital filtering program, error in feedforward adjustment, poor grinding effect, decreased flotation efficiency, and reagent adjustment errors in the upper-level expert control system. Ultimately, these problems cause the production process to fall into a vicious cycle, resulting in substandard product quality that is difficult to diagnose and correct.

[0004] The technical solution of this application is as follows: In a first aspect, this application discloses a method for controlling parameters in a copper tailings purification process, including: The process involves acquiring information on the first process, which reflects the slurry concentration; the second process, which reflects the mill's operating load; the third process, which reflects the grinding effect; and the fourth process, which reflects the effect of mill water volume regulation. A correlation analysis is performed on the first process information, the second process information, the third process information, and the fourth process information to identify whether there is a persistent mismatch pattern between the first process information and the second process information, the third process information, or the fourth process information; When this persistent mismatch pattern is detected, it is determined that there is a measurement deviation in the first process information; Based on the degree of deviation of the second process information, the third process information, and the fourth process information, calculate the compensation amount used to correct the first process information; The compensation amount is applied to the first process information to obtain the corrected slurry concentration information; a control signal for adjusting the mill water volume is generated based on the corrected slurry concentration information.

[0005] This technical solution enables timely detection and correction of deviations in slurry concentration measurements through correlation analysis of multi-process information, thereby avoiding chain reactions in subsequent control links caused by measurement errors. It effectively improves the stability and control accuracy of the copper tailings purification process and solves the problem of difficulty in diagnosing and correcting measurement deviations caused by sensor wear in existing technologies.

[0006] Furthermore, the method for controlling parameters in the copper tailings refining process includes identifying whether a persistent mismatch pattern exists between the first process information and the second, third, or fourth process information, comprising: Based on the accurate pre-judgment of the first process information, the grinding characteristics of the material are inferred according to the first process information, and the expected values ​​of the second process information, the third process information, and the fourth process information are calculated based on the inferred grinding characteristics of the material. Based on the pre-judgment that the material grinding characteristics remain unchanged, the actual concentration is inferred from the information of the second process, the third process, and the fourth process, and the expected values ​​of the information of the second process, the third process, and the fourth process are recalculated based on the inferred actual concentration. By comparing the degree of deviation between the actual and expected values ​​of each process information under two pre-judgment conditions, the main sources of the mismatch pattern were determined to be deviations in concentration measurement or changes in material properties.

[0007] This technical solution allows for a more accurate diagnosis of the root cause of the mismatch by comparing the degree of deviation under two different pre-judgment conditions. It determines whether the mismatch is due to deviation in concentration measurement or changes in material properties, providing a basis for subsequent precise correction and avoiding misjudgment.

[0008] Based on the above, this application further proposes that when the degree of deviation under two pre-judgments is similar or both exceed a preset range, the step of determining the source of the mismatch pattern includes: At the same time, adjust the grinding characteristics parameters of the material and the concentration measurement deviation; By repeatedly trying different calculations, we sought the combination of material grinding characteristic parameters and concentration measurement deviations that minimized the overall deviation between the actual and expected values ​​of the second, third, and fourth process information. The deviation in concentration measurement found is used as the compensation amount.

[0009] This technical solution enables the system to simultaneously adjust material grinding characteristic parameters and concentration measurement deviation under complex conditions through iterative optimization, thereby finding the optimal compensation amount and more accurately correcting concentration measurement deviation. This improves the system's adaptability to complex working conditions and the accuracy of correction.

[0010] Furthermore, the method for controlling parameters in the copper tailings refining process includes finding the combination of material grinding characteristic parameters and concentration measurement deviations that minimizes the overall deviation between the actual and expected values ​​of the second, third, and fourth process information, including: Real-time data on mill feed rate, mill speed, steel ball filling rate, and mill liner wear are used as mill operating condition information. Based on the mill's operating condition information, the weights of the second process information, the third process information, and the fourth process information in the comprehensive deviation calculation are dynamically adjusted. Calculate the weighted overall deviation based on the adjusted proportions; With the goal of minimizing the weighted overall deviation, the parameters of the material's grinding characteristics and the concentration measurement deviation are adjusted through repeated trial calculations.

[0011] This technical solution enables the dynamic adjustment of the weight of each process information in the comprehensive deviation calculation based on the mill's operating conditions, making the compensation calculation more consistent with the actual operating conditions and improving the accuracy of compensation and the robustness of the system.

[0012] In some preferred embodiments, the method for controlling parameters in the copper tailings refining process, wherein adjusting the parameters of the material grinding characteristics and the concentration measurement deviation through repeated trial calculations includes: Calculate the expected values ​​of each process information based on the parameters of the material's grinding characteristics and the concentration measurement deviation; Calculate the difference between the actual and expected values ​​of each process information; Based on this difference, determine the parameters of the material's grinding characteristics and the adjustment direction and magnitude of the concentration measurement deviation; The overall deviation is recalculated based on the adjusted parameters of the material grinding characteristics and the concentration measurement deviation, and this process is repeated until the overall deviation converges to the preset range or reaches the maximum number of adjustments.

[0013] This technical solution enables the iterative adjustment of material grinding characteristic parameters and concentration measurement deviation to gradually optimize the overall deviation, ensuring that the calculation process of the compensation amount can converge to the optimal solution, thereby improving the efficiency and accuracy of the correction.

[0014] As a technological improvement, the parameter control method for the copper tailings purification process also includes: Continuously acquire information on mill feed rate, mill speed, slurry viscosity inside the mill, and mill liner wear as information on changes in mill operating conditions; Evaluate the convergence speed and the degree of fluctuation in the overall deviation during the iterative trial calculation process; Based on the information on the changes in the mill's operating conditions and the convergence speed, the adjustment range is limited.

[0015] This technical solution can dynamically limit the adjustment range based on changes in mill operating conditions and trial convergence, effectively preventing over-adjustment or oscillation, and improving the stability and safety of the system under complex and variable operating conditions.

[0016] As a further improvement, the parameter control method for the copper tailings purification process includes limiting the adjustment range, including: Assess the mill's operating condition changes to determine the level of change. The convergence status of the iterative trial-and-error process is evaluated to determine the convergence level; Based on the level of change in the operating condition and the level of convergence, determine the first limiting coefficient of the current adjustment trend and the second limiting coefficient of the historical adjustment record; Analyze the changing trends of the mill's operating parameters, and adjust the first and second limiting coefficients based on these trends; The adjustment range is limited based on the adjusted limit coefficient.

[0017] This technical solution enables the dynamic adjustment of the limiting coefficients based on the level of change in operating conditions and the level of convergence, combined with the changing trends of mill operating parameters. This makes the adjustment range more precise and intelligent, further enhancing the system's adaptability and control accuracy.

[0018] As a system extension, this method for controlling parameters in the copper tailings refining process includes determining a first limiting coefficient for the current adjustment trend and a second limiting coefficient for historical adjustment records, including: When the combination of the operating condition change level and the convergence state level does not have a corresponding record in the preset limit coefficient rules, select multiple limit coefficient configurations that are closest to the combination from the limit coefficient rules; Based on the actual deviations of each process information under the multiple constraint coefficient configurations, calculate the difference between the actual deviation and the expected deviation of each process information; The first and second restriction coefficients are adjusted based on this difference. When the repeated trial calculations converge to the preset stable state, the determined constraint coefficient configuration is associated with the combination of the current operating condition change level and the convergence state level, and added to the constraint coefficient rule.

[0019] This technical solution enables the system to learn and accumulate historical data, dynamically update and improve the constraint coefficient rules, and make intelligent decisions based on historical experience when facing unknown or complex working conditions, thereby improving the system's learning ability and long-term operational stability.

[0020] To improve the scheme, the parameter control method for copper tailings refining process also includes the step of maintaining the limit coefficient rule: Regularly evaluate the historical performance of each limit coefficient configuration in this limit coefficient rule under different mill operating conditions; Based on the historical performance, identify the limiting coefficient configuration in the limiting coefficient rule where the performance is consistently below the preset standard; Update or replace the limit coefficient configuration for identified performance that is consistently below the preset standard.

[0021] This technical solution enables regular maintenance and optimization of the constraint coefficient rules, ensuring the effectiveness and adaptability of the rules, avoiding a decline in control effectiveness due to rule aging or inapplicability, and guaranteeing the long-term stable operation and continuous optimization of the system.

[0022] Secondly, this application also discloses a parameter control system for copper tailings purification process, used to implement the above-mentioned copper tailings purification process parameter control method, the system comprising: The information acquisition module is used to acquire first-process information reflecting slurry concentration, second-process information reflecting mill operating load, third-process information reflecting grinding effect, and fourth-process information reflecting mill water volume adjustment effect. The correlation analysis module is used to perform correlation analysis on the first process information, the second process information, the third process information, and the fourth process information to identify whether there is a persistent mismatch pattern between the first process information and the second process information, the third process information, or the fourth process information. The deviation determination module is used to determine that there is a measurement deviation in the first process information when the continuous mismatch pattern is detected; The compensation calculation module is used to calculate the compensation amount for correcting the first process information based on the degree of deviation of the second process information, the third process information, and the fourth process information. The correction module is used to apply the compensation amount to the first process information to obtain the corrected slurry concentration information; The signal generation module is used to generate a control signal for adjusting the mill water volume based on the corrected slurry concentration information.

[0023] This technical solution provides a system for implementing the aforementioned control methods. Through modular design, the various functional units are clearly defined, making the entire control process clearer and more efficient, thus providing the hardware and software foundation for the automated control of copper tailings refining processes. Beneficial Effects The copper tailings refining process parameter control method and system disclosed in this application acquires multi-dimensional process information such as slurry concentration, mill operating load, grinding effect, and mill water flow regulation effect, and performs correlation analysis on this information to effectively identify whether there is a continuous mismatch pattern in the slurry concentration measurement. When such a pattern is identified, the system can accurately determine that there is a deviation in the slurry concentration measurement, and calculate a compensation amount for correcting the slurry concentration information based on the degree of deviation of other process information. After applying this compensation amount to the original slurry concentration information, the corrected slurry concentration information is obtained, and a control signal for mill water flow regulation is generated accordingly.

[0024] This technical solution fundamentally solves the problem in existing technologies where online slurry concentration meters deviate from their measurement accuracy due to wear, and digital filtering programs are unable to effectively identify and correct this deviation. It avoids misjudgments and incorrect adjustments by feedforward regulation based on erroneous concentration information, thus effectively preventing a series of chain reactions such as excessively concentrated mill slurry, decreased grinding efficiency, reduced flotation recovery, and deterioration of concentrate quality. Furthermore, this method also avoids invalid or harmful reagent adjustment commands from the upper-level expert control system due to erroneous information from the lower-level system, thereby significantly reducing reagent consumption and saving production costs.

[0025] In summary, this application, through the correlation analysis of multi-process information and intelligent compensation mechanism, realizes real-time diagnosis and precise correction of slurry concentration measurement deviation during copper tailings purification, effectively improving the stability and control accuracy of the entire production process. It overcomes the difficulty in diagnosing and correcting measurement deviations caused by sensor wear in existing technologies, achieving significant technological progress and economic benefits. Attached Figure Description

[0026] Figure 1 This is a schematic flowchart of a method for controlling parameters in a copper tailings purification process provided in an embodiment of the present invention; Figure 2 This is a flowchart of a method for identifying the existence of a persistent mismatch pattern provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a parameter control system for copper tailings purification process provided in an embodiment of the present invention. Detailed Implementation

[0027] 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.

[0028] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for controlling parameters in a copper tailings purification process according to an embodiment of the present invention, comprising: S11, acquire the first process information reflecting the slurry concentration, the second process information reflecting the mill operating load, the third process information reflecting the grinding effect, and the fourth process information reflecting the mill water volume adjustment effect; S12, perform correlation analysis on the first process information, the second process information, the third process information and the fourth process information to identify whether there is a continuous mismatch pattern between the first process information and the second process information, the third process information or the fourth process information; S13, when the continuous mismatch pattern is detected, it is determined that there is a measurement deviation in the first process information; S14, calculate the compensation amount for correcting the first process information based on the degree of deviation of the second process information, the third process information and the fourth process information; S15, apply the compensation amount to the first process information to obtain the corrected slurry concentration information; generate a control signal for adjusting the mill water volume based on the corrected slurry concentration information.

[0029] This application aims to intelligently identify and correct deviations in slurry concentration measurement through multi-source information correlation analysis, thereby providing accurate slurry concentration information to guide mill water volume adjustment, breaking the vicious cycle caused by measurement deviations in existing technologies, and improving the stability and efficiency of copper tailings purification process.

[0030] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0031] First-stage information: This typically refers to the slurry concentration data acquired in real time by sensors such as online slurry concentration meters. This information is crucial for adjusting the mill's water flow.

[0032] Second-stage information: Data reflecting the mill's operating load, such as mill motor current and mill cylinder vibration frequency. Mill load is closely related to slurry concentration and material grinding characteristics.

[0033] Third-stage information: Data reflecting the grinding effect, such as mill discharge particle size and grinding product fineness. The grinding effect directly affects the efficiency of subsequent flotation.

[0034] Fourth process information: Data reflecting the effectiveness of mill water volume regulation, such as mill feed water volume and mill discharge water volume. Water volume regulation is the main means of controlling slurry concentration.

[0035] Persistent mismatch pattern: This refers to a deviation or trend that does not conform to normal process logic between the information from the first process and the information from the second, third, or fourth process over a period of time. This mismatch usually indicates a problem with the measurement of a certain parameter or an anomaly in the process.

[0036] Measurement deviation: refers to the systematic error between the sensor measurement value and the actual value. In this application, it mainly refers to the deviation of the measured value of the first process information (slurry concentration) from the true value.

[0037] Compensation amount: A numerical value used to correct for measurement deviations in the initial process information. By applying the compensation amount to the original measurement value, corrected slurry concentration information closer to the true value can be obtained.

[0038] The core of the copper tailings purification process parameter control method proposed in this application lies in intelligently identifying and correcting deviations in slurry concentration measurement through multi-source information correlation analysis, thereby providing accurate slurry concentration information to guide mill water volume adjustment.

[0039] Specifically, this method first requires acquiring information on the following processes: first, the slurry concentration; second, the mill's operating load; third, the grinding effect; and fourth, the mill's water flow regulation effect. This information can be acquired in various ways. For example, the first-process information can be obtained from an online slurry concentration meter installed on the slurry pipeline, which can be a sensor based on conductivity, density, or ultrasonic principles. The second-process information can be obtained from a mill motor current sensor, a mill vibration sensor, or a mill power meter. The third-process information can be obtained from an online particle size analyzer or through periodic sampling and laboratory analysis. The fourth-process information can be obtained from a flow meter or a level sensor. In practical applications, these sensors typically collect data in real time and transmit the data to a central control system for processing.

[0040] After obtaining this process information, correlation analysis needs to be performed on the first, second, third, and fourth process information to identify any persistent mismatch patterns between them. Correlation analysis can be implemented using various methods. For example, a statistical model based on historical data can be established to analyze the normal correlations between the process information. When real-time data deviates from these normal correlations, a mismatch may exist. Another approach is to establish a predictive model based on a physical model. For instance, the mill load and grinding effect can be predicted based on the mill feed rate and water volume, and then the predicted values ​​can be compared with the actual measured values. If the deviation between the first process information and the remaining process information consistently exceeds a preset threshold, a persistent mismatch pattern can be identified. For example, when the slurry concentration meter shows a normal concentration, but the mill motor current remains consistently high (indicating excessive load) and the grinding product particle size is coarse (indicating poor grinding effect), a persistent mismatch pattern may exist.

[0041] When a persistent mismatch pattern is detected, the system determines that there is a measurement deviation in the first process information. This determination is based on a deep understanding of the copper tailings refining process. Under normal operating conditions, there are inherent physical and technological logical connections between the various process information. If this connection is continuously broken, while other process information (such as mill load, grinding efficiency, and water flow regulation) remains relatively stable or shows a reasonable trend, then deviation in the slurry concentration measurement becomes the most likely explanation. For example, if the mill feed rate and water flow regulation are normal, but the concentration meter reading remains consistently low, and both the mill load and grinding efficiency indicate that the slurry is too thin, then it can be determined that there is a measurement deviation in the concentration meter.

[0042] Once a measurement deviation is determined in the first process information, a compensation amount is needed to correct it based on the degree of deviation of the second, third, and fourth process information. The compensation amount can be calculated using various methods. For example, a multivariate regression model can be established, taking the actual values ​​of the second, third, and fourth process information as input and outputting a compensation amount to correct the first process information. Another method is based on expert experience or a fuzzy logic system, dynamically adjusting the compensation amount according to the degree and direction of deviation of each process information. For example, if the mill load is too high, the grinding effect is poor, and the water flow regulation shows insufficient water flow, it can be inferred that the actual concentration is higher than the measured concentration, thus calculating a positive compensation amount.

[0043] Finally, the calculated compensation is applied to the first process information to obtain the corrected slurry concentration information. For example, if the original first process information is X and the calculated compensation is C, then the corrected slurry concentration information is X+C. This corrected slurry concentration information will more accurately reflect the actual slurry concentration. Based on the corrected slurry concentration information, the system generates control signals for adjusting the mill water flow. For example, if the corrected slurry concentration information indicates that the slurry is too thick, the system will generate a control signal to increase the mill water flow; if it indicates that the slurry is too thin, it will generate a control signal to decrease the mill water flow. These control signals are sent to actuators, such as regulating valves or water pumps, to achieve precise adjustment of the mill water flow.

[0044] The copper tailings refining process parameter control method proposed in this application can intelligently identify continuous deviations in slurry concentration measurements by acquiring multi-source process information and performing correlation analysis. When such deviations are identified, the system can accurately determine that there is a measurement deviation in the first process information and calculate a compensation amount to correct the first process information based on the degree of deviation of other process information. By applying the compensation amount to the original measurement value, a corrected slurry concentration information closer to the true value can be obtained, thereby generating a more accurate mill water flow regulation control signal.

[0045] Compared with existing technologies, the core innovation of this application lies in its ability to proactively identify and correct measurement deviations in online slurry concentration meters. Traditional methods often rely on digital filtering programs to process sensor signals, but these programs struggle to identify and correct slow and persistent systematic errors. When wear and tear on the concentration meter leads to measurement deviations, existing systems adjust based on erroneous concentration information, resulting in deteriorated grinding performance and even erroneous decisions by the upper-level expert control system.

[0046] This application constructs an intelligent diagnosis and correction mechanism by introducing correlation analysis of multi-source process information. For example, when a persistent mismatch occurs between the slurry concentration meter reading and the mill load, grinding effect, and water flow regulation effect, this application can identify this abnormal pattern and attribute it to a deviation in concentration measurement. This diagnostic capability is not possessed by existing technologies. Furthermore, this application can quantify and calculate the compensation amount required to correct the concentration measurement deviation based on the degree of deviation of other process information, thereby correcting the original, erroneous concentration information into more accurate slurry concentration information.

[0047] For example, in existing technologies, if the concentration meter continuously underreports the actual concentration due to wear, the system may mistakenly interpret the slurry as too thin, thus reducing the water intake. This results in a higher actual slurry concentration inside the mill, further reducing grinding efficiency. This application, however, can identify the underreporting deviation of the concentration meter, calculate a positive compensation amount, and feed the corrected concentration information back to the control system. At this point, the control system will increase the water intake based on the corrected and accurate concentration information, restoring the mill slurry concentration to the target value, thereby ensuring grinding performance and flotation efficiency.

[0048] Therefore, this application not only solves the problem of measurement deviation caused by wear during long-term operation of online slurry concentration meters, but more importantly, it breaks the vicious cycle caused by the transmission of measurement errors from the lower level to the upper level control system. By providing accurate slurry concentration information, this application can significantly improve the stability and efficiency of the copper tailings purification process, reduce production costs, and improve product quality, demonstrating significant technological advancement and practical value.

[0049] In some embodiments described above, the method of identifying a persistent mismatch pattern between the first process information and the second, third, or fourth process information is proposed. However, in its implementation, if only the mismatch pattern is identified without further distinguishing whether the mismatch is due to a deviation in slurry concentration measurement or an actual change in the material grinding characteristics, subsequent compensation and control strategies may be inaccurate. For example, if the system misjudges a concentration measurement deviation and corrects it when the material characteristics actually change, it may lead to improper mill water flow adjustment, affecting the purification effect.

[0050] In this regard, refer to Figure 2 , Figure 2 This is a flowchart of a method for identifying the existence of a persistent mismatch pattern according to an embodiment of the present invention. S12 includes: S121, based on the accurate pre-judgment of the first process information, the material grinding characteristics are deduced according to the first process information, and the expected values ​​of the second process information, the third process information and the fourth process information are calculated based on the deduced material grinding characteristics; S122, based on the pre-judgment that the material grinding characteristics remain unchanged, the actual concentration is deduced from the second process information, the third process information and the fourth process information, and the expected values ​​of the second process information, the third process information and the fourth process information are recalculated based on the deduced actual concentration; S123, compare the degree of deviation between the actual and expected values ​​of each process information under two pre-judgment conditions, and determine that the main source of the mismatch mode is the deviation of concentration measurement or the change of material properties.

[0051] Specifically, "accurate pre-judgment based on the first process information" means assuming that the measurement of slurry concentration is accurate and reliable. Under this assumption, by using the first process information (slurry concentration) in combination with known grinding models or empirical formulas, the grinding characteristics of the current material, such as the material's hardness and particle size distribution, can be calculated. Subsequently, based on the calculated material grinding characteristics, the theoretical or expected values ​​of the second process information (mill operating load), the third process information (grinding effect), and the fourth process information (mill water flow adjustment effect) under the current operating conditions can be predicted.

[0052] The phrase "preliminary judgment based on unchanged material grinding characteristics" refers to the assumption that the grinding characteristics of the material are stable and have not changed significantly within the current time period. Under this assumption, the actual slurry concentration can be deduced by using the actual measured values ​​of the second, third, and fourth process information through reverse derivation or inversion models. Then, based on the deduced actual concentration, the expected values ​​of the second, third, and fourth process information are recalculated.

[0053] In practical applications, "comparing the degree of deviation between the actual and expected values ​​of each process information under two pre-judgment conditions" refers to comparing the expected values ​​calculated under two assumptions (accurate concentration measurement or unchanged material properties) with the actual measured values ​​to quantify the deviation. For example, mean square error, absolute error, and other indices can be calculated. By comparing the magnitude of the deviation under the two assumptions, it can be determined which assumption is closer to the reality. If, under the assumption of "accurate first process information," the deviation between the actual and expected values ​​of each process information is large, while the deviation is small under the assumption of "unchanged material grinding characteristics," then it is likely that there is a deviation in concentration measurement. Conversely, if the deviation is large under the assumption of "unchanged material grinding characteristics," it is likely that the material properties have changed. Therefore, it can be determined that "the main source of the mismatch pattern is a deviation in concentration measurement or a change in material properties."

[0054] This application's solution constructs a dual verification mechanism by introducing two different pre-judgments (i.e., assuming accurate concentration measurement and assuming unchanged material grinding characteristics) and extrapolating the expected values ​​of other process information based on these assumptions. When the system detects a mismatch pattern, it no longer simply attributes it to measurement deviation, but rather accurately diagnoses the root cause of the mismatch pattern by comparing the degree of deviation between the actual and expected values ​​of each process information under the two assumptions. This method utilizes the inherent physical correlation between different process information, enabling the system to cross-verify from multiple dimensions, thereby improving the accuracy of identifying the root cause of the problem.

[0055] In some embodiments of this application, by comparing the degree of deviation between the actual and expected values ​​of each process information under two pre-judgment conditions, the main source of the mismatch pattern can be determined to be a deviation in concentration measurement or a change in material properties. However, in its implementation, when the degree of deviation under the two pre-judgment conditions is similar or both exceed the preset range, it is difficult to accurately distinguish whether the mismatch is caused by a deviation in concentration measurement or a change in material properties by simple comparison alone. This may lead to a misjudgment of the root cause of the problem, thereby affecting the subsequent compensation calculation and control effect.

[0056] In response, this application further proposes that when the deviations under the two pre-judgments are similar or both exceed a preset range, the steps for determining the source of the mismatch pattern include: Simultaneously adjust the parameters of the material grinding characteristics and the concentration measurement deviation; By repeatedly performing trial and error, we sought the combination of material grinding characteristic parameters and concentration measurement deviations that minimized the overall deviation between the actual and expected values ​​of the second, third, and fourth process information. The deviation in concentration measurement found is used as the compensation amount.

[0057] Specifically, simultaneously adjusting the parameters of material grinding characteristics and the deviation of concentration measurement means that when the main source of the mismatch pattern is uncertain, instead of assuming that one factor remains unchanged, both the parameters of material grinding characteristics and the deviation of concentration measurement are treated as variable factors and adjusted and optimized synchronously.

[0058] Among them, material grinding characteristic parameters can be understood as the inherent properties of the material that affect process information such as mill operating load and grinding effect, such as material hardness, particle size distribution, and grindability. Concentration measurement deviation refers to the difference between the slurry concentration sensor reading and the actual concentration. Through repeated trial and error, the system seeks the combination of material grinding characteristic parameters and concentration measurement deviation that minimizes the overall deviation between the actual and expected values ​​of the second, third, and fourth process information. This can be understood as an iterative optimization process. During this process, the system continuously tries different combinations of material grinding characteristic parameters and concentration measurement deviation, and calculates the expected values ​​of the second process information (reflecting mill operating load), the third process information (reflecting grinding effect), and the fourth process information (reflecting mill water flow regulation effect) based on these combinations.

[0059] Subsequently, these expected values ​​are compared with the actual values ​​to quantify the overall deviation. Minimizing the overall deviation is the optimization objective, aiming to find the parameter combination that best explains the current system state. Ultimately, the found concentration measurement deviation is used as a compensation amount. This means that once the parameter combination that minimizes the overall deviation is found through repeated trials, the concentration measurement deviation in this combination is considered the true deviation value of the current slurry concentration measuring instrument and is used as a compensation amount to correct the first process information (slurry concentration).

[0060] The proposed solution, when the deviations under two pre-judgment conditions are similar or both exceed preset ranges, no longer simply attributes it to a single factor, but adopts a more comprehensive strategy. By simultaneously adjusting the material grinding characteristic parameters and the concentration measurement deviation, and performing repeated trials, the system can search for the optimal solution within a broader parameter space. This method avoids making erroneous single assumptions under uncertain conditions, thus enabling a more accurate separation of the contribution of the concentration measurement deviation to the overall system deviation. By minimizing the combined deviation between the actual and expected values ​​of the second, third, and fourth process information, it can be ensured that the determined combination of material grinding characteristic parameters and concentration measurement deviation best fits the current grinding process data, thereby improving the accuracy of determining the slurry concentration measurement deviation.

[0061] This application further proposes a method for finding the combination of material grinding characteristic parameters and concentration measurement deviations that minimizes the overall deviation between the actual and expected values ​​of the second process information, the third process information, and the fourth process information, specifically including: Real-time data on mill feed rate, mill speed, steel ball filling rate, and mill liner wear are used as mill operating condition information. Based on the mill operating condition information, the weights of the second process information, the third process information, and the fourth process information in the comprehensive deviation calculation are dynamically adjusted; Calculate the weighted overall deviation based on the adjusted proportions; With the goal of minimizing the weighted overall deviation, the parameters of the material grinding characteristics and the concentration measurement deviation are adjusted through repeated trial calculations.

[0062] Specifically, the mill operating condition information refers to key parameters that reflect the current operating status of the mill in real time, including the mill feed rate, mill speed, steel ball filling rate, and mill liner wear degree. Among these, the mill feed rate directly affects the mill's load and processing capacity; the mill speed determines the motion state of the grinding media and the grinding intensity; the steel ball filling rate affects the effective working area and energy transfer efficiency of the grinding media; and the mill liner wear degree reflects changes in the mill's internal structure, thus affecting grinding efficiency and product particle size. The real-time acquisition of these parameters aims to provide an accurate basis for subsequent deviation weight adjustments.

[0063] Furthermore, dynamically adjusting the weights of the second, third, and fourth process information in the comprehensive deviation calculation based on the mill's operating condition information means intelligently allocating the weights of different process information deviations in the comprehensive deviation calculation according to the specific operating conditions of the mill. For example, when the mill feed rate is large or the mill speed is high, the deviation of the second process information regarding the mill's operating load may be more critical in indicating the grinding state, and its weight in the comprehensive deviation calculation will be increased accordingly. Conversely, when the mill liner wear is high, the deviation of the third process information regarding the grinding effect may be more indicative, and its weight will be increased. This dynamic adjustment can be achieved through preset operating condition-weight mapping rules, machine learning models based on historical data, or expert systems. Its purpose is to enable the comprehensive deviation calculation to more accurately reflect the true impact of each process information on the grinding state under the current operating conditions.

[0064] Therefore, the weighted comprehensive deviation is calculated based on the adjusted proportions. Specifically, the deviation between the actual and expected values ​​of each process information is multiplied by its corresponding dynamically adjusted proportion, and then the weighted deviations are summed or combined through other mathematical methods to obtain a weighted comprehensive deviation that can more accurately reflect the current grinding state.

[0065] The solution proposed in this application addresses the aforementioned problems by fully considering the impact of the dynamic nature of mill operating conditions on the importance of information from each process. By acquiring mill operating condition information in real time, such as mill feed rate, mill speed, steel ball filling rate, and mill liner wear, the system can accurately determine the current operating state of the mill. Based on this, according to different operating conditions, the system dynamically adjusts the weighting of the second process information (reflecting mill operating load), the third process information (reflecting grinding effect), and the fourth process information (reflecting mill water flow regulation effect) in the comprehensive deviation calculation. This dynamic weighting mechanism ensures that when searching for the combination of material grinding characteristic parameters and concentration measurement deviations that minimizes the weighted comprehensive deviation, priority is given to the process information that has the greatest impact on the grinding state under the current operating conditions. This avoids judgment errors caused by simple averaging or fixed-weight calculations of the comprehensive deviation under complex and variable operating conditions.

[0066] This application further proposes adjusting the parameters of the material grinding characteristics and the concentration measurement deviation through repeated trial calculations, including: The expected values ​​of each process information are calculated based on the parameters of the material grinding characteristics and the concentration measurement deviation. Calculate the difference between the actual and expected values ​​of each process information; Based on the difference, determine the parameters of the material grinding characteristics and the adjustment direction and adjustment range of the concentration measurement deviation; The overall deviation is recalculated based on the adjusted material grinding characteristics parameters and the concentration measurement deviation, and this process is repeated until the overall deviation converges to the preset range or reaches the maximum number of adjustments.

[0067] Specifically, during repeated trial calculations, firstly, based on the currently set parameters of material grinding characteristics and concentration measurement deviations, the expected values ​​of the second, third, and fourth process information are calculated using a preset model or empirical formula. These expected values ​​represent the theoretical performance of the mill operating load, grinding effect, and mill water flow regulation effect under the current assumptions.

[0068] Secondly, these calculated expected values ​​are compared with the actual values ​​of the second, third, and fourth process information obtained, thereby calculating the difference between the actual and expected values ​​of each process information. This difference reflects the degree of inconsistency between the current parameter settings and the actual operating conditions.

[0069] Furthermore, based on the magnitude and direction of these differences, the adjustment direction and magnitude of the parameters of the material grinding characteristics and the concentration measurement deviation can be systematically determined. For example, if the actual value is higher than the expected value, it may be necessary to increase a certain parameter; if the actual value is lower than the expected value, it may be necessary to decrease that parameter. The adjustment magnitude can be determined proportionally based on the magnitude of the difference, or a step adjustment strategy can be adopted. Finally, according to the adjusted parameters of the material grinding characteristics and the concentration measurement deviation, the overall deviation is recalculated, and the above steps are repeated. This iterative process will continue until the overall deviation converges to a preset stable range, indicating that a relatively matching parameter combination has been found, or the preset maximum number of adjustments has been reached to prevent infinite looping.

[0070] This application's solution effectively addresses the inefficiencies and poor convergence inherent in traditional iterative calculations by introducing a structured iterative adjustment mechanism. Through this technical solution, the application provides a more efficient, stable, and intelligent iterative calculation method. This method, with its explicit iterative steps and feedback mechanism, significantly improves the accuracy and convergence speed of adjusting material grinding characteristic parameters and concentration measurement deviations, effectively avoiding the blindness and instability that may occur in traditional calculations. Consequently, it can more accurately identify slurry concentration measurement deviations and calculate more accurate compensation amounts, thereby improving the overall accuracy and reliability of parameter control in the copper tailings purification process and providing a more reliable basis for mill water volume adjustment.

[0071] In some embodiments described above, the parameters of material grinding characteristics and concentration measurement deviations are adjusted through repeated trial calculations, and the adjustment direction and magnitude are determined based on the differences in information from each process. However, in actual copper tailings purification processes, the mill operating conditions may continuously change, and the convergence of the repeated trial calculation process may be affected by various factors. If the adjustment magnitude is not appropriately limited, the system may experience overshoot, oscillation, or slow convergence under dynamic operating conditions, affecting the stability and efficiency of control.

[0072] In response, the aforementioned methods for controlling parameters in the copper tailings refining process also include: Continuously acquire information on mill feed rate, mill speed, slurry viscosity inside the mill, and mill liner wear as information on changes in mill operating conditions; Evaluate the convergence speed and the degree of fluctuation in the overall deviation during the iterative trial calculation process; The adjustment range is limited based on the mill operating condition change information and the convergence speed.

[0073] Specifically, mill operating condition change information refers to key parameters reflecting the current operating status of the mill, including mill feed rate, mill speed, internal slurry viscosity, and mill liner wear. Among these, the mill feed rate directly affects the mill load and the concentration of the internal material; the mill speed determines the motion state of the grinding media and the impact frequency on the material; the internal slurry viscosity reflects the slurry's fluidity and the characteristics of the grinding environment; and the mill liner wear affects the mill's grinding efficiency and energy consumption. Continuously acquiring this information aims to provide a comprehensive external environmental basis for subsequent adjustment limits.

[0074] Furthermore, evaluating the convergence speed and the degree of fluctuation of the overall deviation in the iterative trial-and-error process refers to a quantitative analysis of the speed at which the deviations of the material grinding characteristic parameters and concentration measurements approach the optimal value during the aforementioned iterative trial-and-error process, as well as the stability of the overall deviation during the convergence process. The convergence speed reflects the efficiency of the algorithm in finding the optimal solution, while the degree of fluctuation of the overall deviation indicates whether the algorithm oscillates near the optimal solution or gets trapped in a local optimum. By evaluating these internal states, the effectiveness and stability of the current adjustment strategy can be determined.

[0075] Therefore, limiting the adjustment range based on the mill operating condition change information and the convergence speed means dynamically correcting or constraining the adjustment range calculated during repeated trials, taking into account both the mill's current external operating environment (operating condition change information) and the convergence performance of the internal algorithm (convergence speed and fluctuation level). For example, when the mill operating conditions change drastically or the convergence speed is slow, the adjustment range can be appropriately reduced to avoid system oscillation; when the mill operating conditions are stable and the convergence speed is fast, the adjustment range can be appropriately widened to accelerate convergence. The purpose is to ensure the stability and efficiency of the adjustment process under different operating conditions and convergence states.

[0076] The proposed solution effectively addresses the aforementioned issues because, when determining the adjustment range, it no longer relies solely on the deviations of information from each process, but instead incorporates a comprehensive consideration of both the external operating conditions of the mill and the convergence state of the internal algorithm. Through this technical solution, the application significantly improves the stability and adaptability of the parameter control method for copper tailings purification. By monitoring mill operating condition changes in real time, the system can predict potential disturbances and adjust the adjustment range accordingly, effectively avoiding system oscillations or instability caused by improper adjustments under complex and variable conditions. Simultaneously, the evaluation of the convergence speed and overall deviation fluctuations during repeated trial calculations allows the algorithm to self-aware of its convergence state, thereby optimizing the adjustment strategy while ensuring convergence and avoiding overshoot or slow convergence. Therefore, the proposed solution achieves intelligent dynamic limitation of the adjustment range, making the entire parameter control process more stable and efficient, and better adaptable to the dynamic changes in mill operating conditions in actual industrial production, thus improving the overall efficiency and product quality of copper tailings purification.

[0077] In some embodiments described above, a limitation on the adjustment range is proposed to avoid system instability or slow convergence due to excessively large adjustment ranges during repeated trial calculations. However, in practical applications, mill operating conditions are complex and varied, and simple fixed limitations may not be suitable for all conditions, potentially leading to overly conservative limitations that affect convergence efficiency, or insufficient limitations that cause oscillations. Therefore, this application further proposes a specific method for limiting the adjustment range, aiming to dynamically adjust the limitation strategy based on changes in mill operating conditions and the convergence status of the trial calculation process, thereby improving the robustness and efficiency of control.

[0078] Specifically, the above-mentioned restrictions on the adjustment range include: Assess the mill operating condition changes to determine the level of change. The convergence status of the iterative trial-and-error process is evaluated to determine the convergence level; Based on the operating condition change level and the convergence state level, determine the first limiting coefficient of the current adjustment trend and the second limiting coefficient of the historical adjustment record; Analyze the changing trends of the mill's operating parameters, and adjust the first and second limiting coefficients according to the changing trends; The adjustment range is limited based on the adjusted limiting coefficient.

[0079] Assessing changes in mill operating conditions to determine the level of change involves comprehensively judging the stability or drastic fluctuation of the current mill operating status based on continuously acquired information such as mill feed rate, mill speed, internal slurry viscosity, and mill liner wear. For example, significant fluctuations in feed rate or speed can be classified as a high level of change, while stable parameters indicate a low level of change. The aim is to quantify the dynamic nature of the current mill operating environment.

[0080] Furthermore, assessing the convergence state of the iterative trial-and-error process to determine the convergence level involves judging the stability of the current adjustment process based on indicators such as the convergence speed, fluctuation amplitude, and whether divergence trends appear during the iterative trial-and-error process. For example, a fast convergence speed and small fluctuations can be judged as a high convergence level; slow convergence or oscillations indicate a low convergence level. The purpose is to reflect the effectiveness and stability of the current parameter adjustment.

[0081] Based on this, according to the level of operating condition change and the level of convergence, a first constraint coefficient for the current adjustment trend and a second constraint coefficient for historical adjustment records are determined. The first constraint coefficient is used to constrain the maximum step size of parameter adjustment in the current iteration, preventing excessively large single adjustments; the second constraint coefficient is used to refer to historical adjustment records to macroscopically control the overall adjustment magnitude, for example, a more conservative constraint may be adopted under operating conditions with poor historical performance. These constraint coefficients can be pre-stored in a rule base and retrieved by looking up tables according to different level combinations.

[0082] Furthermore, the changing trends of the mill's operating parameters are analyzed, and the first and second constraint coefficients are adjusted accordingly. This means that the constraint coefficients are not fixed but can be fine-tuned based on the real-time changing trends of the mill's operating conditions. For example, when it is predicted that the operating conditions will further deteriorate, the constraints can be tightened appropriately; when the operating conditions tend to stabilize, the constraints can be relaxed appropriately to accelerate convergence.

[0083] Finally, the adjustment range is limited based on the adjusted constraint coefficients. Specifically, the adjustment range will be limited to a range jointly determined by the first and second constraint coefficients, ensuring that parameter adjustments can respond quickly to changes while maintaining system stability.

[0084] This application's solution achieves dynamic and adaptive limitation of the adjustment range by introducing information on changes in mill operating conditions and evaluating the convergence status of repeated trial calculations. Through this technical solution, this application enables refined and adaptive control of the parameter adjustment range in the copper tailings purification process. Compared to simple fixed limitations, this solution dynamically adjusts the limitation coefficient based on real-time changes in mill operating conditions and the convergence status of the parameter adjustment process, effectively avoiding problems such as system oscillation, slow convergence, or divergence caused by improper adjustment ranges. This significantly improves the stability and accuracy of mill water volume regulation, making slurry concentration correction more reliable, thereby optimizing the overall efficiency and product quality of copper tailings purification. Furthermore, through dynamic adjustment of the limitation coefficient, this application's solution also enhances the system's adaptability to complex and variable operating conditions, reduces the need for manual intervention, and improves the level of automation.

[0085] This application further proposes steps for determining a first constraint factor for the current adjustment trend and a second constraint factor for historical adjustment records, including: When the combination of the operating condition change level and the convergence state level does not have a corresponding record in the preset constraint coefficient rules, select multiple constraint coefficient configurations that are closest to the combination from the constraint coefficient rules; Based on the actual deviation of each process information under the configuration of the multiple constraint coefficients, calculate the difference between the actual deviation and the expected deviation of each process information; Adjust the first restriction coefficient and the second restriction coefficient based on the difference; When the repeated trial calculations converge to the preset stable state, the determined constraint coefficient configuration is associated with the combination of the current operating condition change level and the convergence state level, and added to the constraint coefficient rule.

[0086] Specifically, the constraint coefficient rules can be understood as a knowledge base or database storing combinations of different operating condition change levels and convergence state levels with corresponding constraint coefficient configurations. When the system identifies that the current combination of operating condition change level and convergence state level does not have a direct match in the rule, an intelligent matching mechanism is needed to find alternative solutions. Selecting multiple constraint coefficient configurations that are closest to the combination means calculating the similarity between the current combination and existing combinations in the rule base, for example, using algorithms such as weighted distance or fuzzy matching, to identify several historical configurations that are most similar in terms of operating condition characteristics and convergence behavior.

[0087] This process involves calculating the difference between the actual deviation and the expected deviation of each process information under the multiple constraint coefficient configurations. The purpose is to evaluate the actual performance of these similar configurations under current or similar operating conditions. The actual deviation refers to the difference between the actual values ​​of process information such as mill operating load, grinding effect, and mill water flow regulation effect after applying a certain constraint coefficient configuration and the expected values ​​predicted by the model. By comparing these actual deviations with the system's expected deviation, the applicability of the selected constraint coefficient configuration can be quantified. Adjusting the first and second constraint coefficients based on the difference means using this deviation information as feedback to fine-tune the initially selected constraint coefficients to better adapt them to the current specific situation. For example, gradient descent, heuristic search, or rule-based adaptive adjustment strategies can be used.

[0088] In practical applications, when the iterative calculation process converges to a preset stable state, the determined constraint coefficient configuration is associated with the combination of the current operating condition change level and the convergence state level, and added to the constraint coefficient rule. This aims to achieve self-learning and dynamic updating of the constraint coefficient rule. The preset stable state can refer to a situation where the overall deviation is consistently below a certain threshold, and the fluctuation range is within an acceptable range. In this way, the system can incorporate successfully verified constraint coefficient configurations as new knowledge points into the rule base, thereby continuously enriching and optimizing the constraint coefficient rule and improving the system's adaptability to unknown or complex operating conditions.

[0089] This application's solution addresses the problem of ineffective constraint coefficient determination when the combination of operating condition change level and convergence state level lacks a corresponding record in the preset rules by introducing a self-learning and dynamic update mechanism for constraint coefficient rules. Through this technical solution, the application significantly improves the adaptability and robustness of the parameter control method for copper tailings purification under complex and variable operating conditions. This solution allows the determination of constraint coefficients to move beyond static preset rules and instead dynamically adjust and learn based on actual operational feedback, thereby ensuring rapid convergence and high accuracy in the compensation calculation process. This effectively avoids over-adjustment or under-adjustment problems caused by inappropriate constraint coefficients, further improving the accuracy and stability of mill water flow regulation, and ultimately optimizing the overall efficiency and product quality of copper tailings purification.

[0090] In some embodiments described above in this application, to ensure the effectiveness of the adjustment range limitation, a limitation coefficient is determined based on the operating condition change level and the convergence state level. Under specific conditions, the determined limitation coefficient is configured and associated with the combination of the current operating condition change level and the convergence state level, and added to the limitation coefficient rule. However, in actual long-term operation, the mill's operating conditions may undergo continuous changes, such as equipment wear, material property fluctuations, or production process adjustments. These changes may cause the previously established limitation coefficient rules to gradually become inapplicable or degrade in performance. If these rules are not reviewed and optimized periodically, the effect of the adjustment range limitation may be weakened, thereby affecting the accuracy of the compensation calculation and the stability of the mill water flow regulation.

[0091] In response, this application further proposes steps for maintaining the aforementioned restriction coefficient rules: Periodically evaluate the historical performance of each constraint coefficient configuration in the aforementioned constraint coefficient rules under different mill operating conditions; Based on the historical performance, identify the limiting coefficient configurations in the limiting coefficient rules where the performance is consistently below the preset standard; Update or replace the limit coefficient configuration for identified performance that is consistently below the preset standard.

[0092] Specifically, the "periodic evaluation" can be understood as a systematic review of the constraint coefficient rules at preset time intervals (e.g., daily, weekly, or monthly) or when triggered by specific events (e.g., after a mill overhaul or when material properties change significantly). The purpose of the evaluation is to understand the performance of each constraint coefficient configuration in actual operation. The "historical performance" refers to the comprehensive data on the adjustment range limiting effect, the convergence speed of the trial calculation process, the fluctuation of the overall deviation, and the final control accuracy when a specific constraint coefficient configuration is applied under different mill operating conditions. This data can be recorded and stored in the system's historical database for subsequent analysis.

[0093] The phrase "identifying the limiting coefficient configurations whose performance consistently falls below the preset standard in the limiting coefficient rules" refers to analyzing historical performance data to identify limiting coefficient configurations that, over a period of time or under specific operating conditions, fail to meet the preset performance threshold (e.g., resulting in slow convergence speed, large fluctuations in overall deviation, or even divergence). The preset standard may include an upper limit for convergence time, a range for overall deviation fluctuations, and control accuracy requirements.

[0094] In practical applications, the step of "updating or replacing the identified constraint coefficient configurations whose performance consistently falls below the preset standard" can take several forms. For example, for poorly performing configurations, parameters can be fine-tuned (updated) based on new historical data or expert experience; or, if a configuration is determined to be completely ineffective or severely unsuitable, it can be removed from the rule base and a completely new, validated configuration can be introduced (replacement). The update or replacement process can be combined with machine learning algorithms to optimize the constraint coefficient generation strategy by retraining the model, or it can be adjusted through manual intervention.

[0095] The solution proposed in this application solves the problem of performance degradation of the constraint coefficient rules caused by factors such as changes in operating conditions, equipment aging, or imperfect initial rules during long-term operation by introducing a maintenance mechanism for the constraint coefficient rules.

[0096] Through the above technical solution, this application can effectively avoid the performance degradation of the constraint coefficient rules due to long-term use or changes in operating conditions, ensuring the long-term effectiveness and robustness of the adjustment range constraint strategy. This allows the control signal for mill water flow regulation to always be generated based on accurate slurry concentration information, thereby improving the overall control accuracy and stability of the copper tailings purification process. Furthermore, by continuously optimizing the constraint coefficient rules, the system can better adapt to various complex mill operating conditions, reducing the need for manual intervention and improving automation levels and production efficiency.

[0097] In some preferred embodiments, a specific example is given below. Assume that the system has been running for some time during the copper tailings refining process, accumulating a large amount of mill operation data and constraint coefficient application data. The system can be set to perform constraint coefficient rule maintenance weekly. During maintenance, the system analyzes the control effect of mill water flow regulation when specific constraint coefficient configurations were applied over the past week. For example, if a constraint coefficient configuration, after multiple applications, causes the convergence speed of repeated trial calculations of compensation to be significantly lower than the average level, or if the fluctuation range of the overall deviation remains large after convergence, or even shows a divergent trend under certain operating conditions, then this configuration will be marked as "poor performance".

[0098] Specifically, the system can set a threshold; for example, if a configuration causes the average convergence time of the trial calculation process to exceed 10 minutes, or the average volatility of the overall deviation to exceed 5%, its performance is considered to be below a preset standard. Once such a configuration is identified, the system can attempt to update it. For instance, based on recent mill operating data and historical success cases, a new set of constraint coefficient parameters can be generated through machine learning model retraining to replace the old parameters. Alternatively, if a configuration is found to consistently perform poorly under specific extreme conditions, while other configurations perform well under those conditions, the poorly performing configuration can be directly removed from the applicable rules for that extreme condition and replaced with a better-performing configuration. In this way, the constraint coefficient rule base is continuously optimized, ensuring that the system provides stable and efficient control under various operating conditions.

[0099] refer to Figure 3 , Figure 3 This is a schematic diagram of a parameter control system for a copper tailings purification process provided in an embodiment of the present invention, used to implement the above-mentioned copper tailings purification process parameter control method. The system includes: The information acquisition module is used to acquire first-process information reflecting slurry concentration, second-process information reflecting mill operating load, third-process information reflecting grinding effect, and fourth-process information reflecting mill water volume adjustment effect. The correlation analysis module is used to perform correlation analysis on the first process information, the second process information, the third process information, and the fourth process information to identify whether there is a continuous mismatch pattern between the first process information and the second process information, the third process information, or the fourth process information; The deviation determination module is used to determine that there is a measurement deviation in the first process information when the continuous mismatch pattern is detected; The compensation calculation module is used to calculate the compensation amount for correcting the first process information based on the degree of deviation of the second process information, the third process information, and the fourth process information. The correction module is used to apply the compensation amount to the first process information to obtain the corrected slurry concentration information; The signal generation module is used to generate a control signal for adjusting the mill water volume based on the corrected slurry concentration information.

[0100] This system, through a modular design, integrates functions such as acquiring, analyzing, determining deviations, calculating compensation, correcting information, and generating control signals for key parameters in the copper tailings purification process. Its core lies in constructing an intelligent control architecture capable of proactively monitoring and diagnosing potential deviations in online slurry concentration measurements, and ensuring the accuracy of mill water flow regulation through multi-source information collaborative correction. Therefore, this system effectively solves the measurement error propagation problem caused by sensor wear in traditional methods, avoiding a vicious cycle of decreased grinding efficiency, reduced flotation recovery, and excessive reagent consumption, thereby improving the stability and economic benefits of the entire purification process.

[0101] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling parameters in a copper tailings purification process, characterized in that, include: The process involves acquiring information on the first process, which reflects the slurry concentration; the second process, which reflects the mill's operating load; the third process, which reflects the grinding effect; and the fourth process, which reflects the effect of mill water volume regulation. A correlation analysis is performed on the first process information, the second process information, the third process information, and the fourth process information to identify whether there is a persistent mismatch pattern between the first process information and the second process information, the third process information, or the fourth process information; When the persistent mismatch pattern is detected, it is determined that there is a measurement deviation in the first process information; Based on the degree of deviation of the second process information, the third process information, and the fourth process information, calculate the compensation amount used to correct the first process information; The compensation amount is applied to the first process information to obtain the corrected slurry concentration information; A control signal for adjusting the mill water volume is generated based on the corrected slurry concentration information.

2. The method for controlling parameters in the copper tailings purification process according to claim 1, characterized in that, The step of identifying whether a persistent mismatch pattern exists between the first process information and the second process information, the third process information, or the fourth process information includes: Based on the accurate pre-judgment of the first process information, the material grinding characteristics are inferred according to the first process information, and the expected values ​​of the second process information, the third process information, and the fourth process information are calculated based on the inferred material grinding characteristics. Based on the pre-judgment that the material grinding characteristics remain unchanged, the actual concentration is inferred from the second process information, the third process information, and the fourth process information, and the expected values ​​of the second process information, the third process information, and the fourth process information are recalculated based on the inferred actual concentration. By comparing the degree of deviation between the actual and expected values ​​of each process information under two pre-judgment conditions, the main sources of the mismatch pattern were determined to be deviations in concentration measurement or changes in material properties.

3. The method for controlling parameters in the copper tailings purification process according to claim 2, characterized in that, When the degree of deviation under the two pre-judgments is similar or both exceed the preset range, the step of determining the source of the mismatch pattern includes: Simultaneously adjust the parameters of the material grinding characteristics and the concentration measurement deviation; By repeatedly performing trial and error, we sought the combination of material grinding characteristic parameters and concentration measurement deviations that minimized the overall deviation between the actual and expected values ​​of the second, third, and fourth process information. The deviation in concentration measurement found is used as the compensation amount.

4. The method for controlling parameters in the copper tailings purification process according to claim 3, characterized in that, The process of finding the combination of material grinding characteristic parameters and concentration measurement deviations that minimizes the overall deviation between the actual and expected values ​​of the second, third, and fourth process information includes: Real-time data on mill feed rate, mill speed, steel ball filling rate, and mill liner wear are used as mill operating condition information. Based on the mill operating condition information, the weights of the second process information, the third process information, and the fourth process information in the comprehensive deviation calculation are dynamically adjusted; Calculate the weighted overall deviation based on the adjusted proportions; With the goal of minimizing the weighted overall deviation, the parameters of the material grinding characteristics and the concentration measurement deviation are adjusted through repeated trial calculations.

5. The method for controlling parameters in the copper tailings purification process according to claim 4, characterized in that, The adjustment of the parameters of the material grinding characteristics and the concentration measurement deviation through repeated trial calculations includes: The expected values ​​of each process information are calculated based on the parameters of the material grinding characteristics and the concentration measurement deviation. Calculate the difference between the actual and expected values ​​of each process information; Based on the difference, determine the parameters of the material grinding characteristics and the adjustment direction and adjustment range of the concentration measurement deviation; The overall deviation is recalculated based on the adjusted parameters of the material grinding characteristics and the concentration measurement deviation, and this process is repeated until the overall deviation converges to the preset range or reaches the maximum number of adjustments.

6. The method for controlling parameters in the copper tailings purification process according to claim 5, characterized in that, The method further includes: Continuously acquire information on mill feed rate, mill speed, slurry viscosity inside the mill, and mill liner wear as information on changes in mill operating conditions; Evaluate the convergence speed and the degree of fluctuation in the overall deviation during the iterative trial calculation process; The adjustment range is limited based on the mill operating condition change information and the convergence speed.

7. The method for controlling parameters in the copper tailings purification process according to claim 6, characterized in that, The process of limiting the adjustment range includes: Assess the mill operating condition changes to determine the level of change. The convergence status of the iterative trial-and-error process is evaluated to determine the convergence level; Based on the operating condition change level and the convergence state level, determine the first limiting coefficient of the current adjustment trend and the second limiting coefficient of the historical adjustment record; Analyze the changing trends of the mill's operating parameters, and adjust the first and second limiting coefficients according to the changing trends; The adjustment range is limited based on the adjusted limiting coefficient.

8. The method for controlling parameters in the copper tailings purification process according to claim 7, characterized in that, The first limiting coefficient for determining the current adjustment trend and the second limiting coefficient for historical adjustment records include: When the combination of the operating condition change level and the convergence state level does not have a corresponding record in the preset constraint coefficient rules, select multiple constraint coefficient configurations that are closest to the combination from the constraint coefficient rules; Based on the actual deviation of each process information under the multiple constraint coefficient configurations, calculate the difference between the actual deviation and the expected deviation of each process information; Adjust the first restriction coefficient and the second restriction coefficient based on the difference; When the repeated trial calculations converge to the preset stable state, the determined constraint coefficient configuration is associated with the combination of the current operating condition change level and the convergence state level, and added to the constraint coefficient rule.

9. The method for controlling parameters in the copper tailings purification process according to claim 8, characterized in that, The method further includes the step of maintaining the constraint coefficient rule: Periodically evaluate the historical performance of each constraint coefficient configuration in the aforementioned constraint coefficient rules under different mill operating conditions; Based on the historical performance, identify the limiting coefficient configurations in the limiting coefficient rules where the performance is consistently below the preset standard; Update or replace the limit coefficient configuration for identified performance that consistently falls below the preset standard.

10. A parameter control system for copper tailings purification process, characterized in that, The system is used to implement the copper tailings purification process parameter control method according to any one of claims 1 to 9, the system comprising: The information acquisition module is used to acquire first-process information reflecting slurry concentration, second-process information reflecting mill operating load, third-process information reflecting grinding effect, and fourth-process information reflecting mill water volume adjustment effect. The correlation analysis module is used to perform correlation analysis on the first process information, the second process information, the third process information, and the fourth process information to identify whether there is a continuous mismatch pattern between the first process information and the second process information, the third process information, or the fourth process information; The deviation determination module is used to determine that there is a measurement deviation in the first process information when the continuous mismatch pattern is detected; The compensation calculation module is used to calculate the compensation amount for correcting the first process information based on the degree of deviation of the second process information, the third process information, and the fourth process information. The correction module is used to apply the compensation amount to the first process information to obtain the corrected slurry concentration information; The signal generation module is used to generate a control signal for adjusting the mill water flow based on the corrected slurry concentration information. Is there any abnormal risk in this passage?

Images