Antimony salt purification process zinc powder automatic control method, device and system

Abstract

The application discloses a zinc powder automatic control method, device and system for antimony salt purification process, which comprises the following steps: constructing a copper and cobalt removal fitting model, calculating the optimal potential setting value by using a particle swarm algorithm and setting the potential setting value; updating the potential setting value by using a fuzzy algorithm and a case reasoning method; constructing a three-dimensional fuzzy rule according to the difference between the current potential measurement value and the potential setting value, generating a zinc powder adjustment amount, adding the current zinc powder addition amount to obtain a new zinc powder amount setting value, writing the new zinc powder amount setting value into a DCS system on line, and controlling the automatic feeding device to add zinc powder. The above steps are repeated to achieve stable potential control and realize the minimum zinc powder consumption. The zinc powder automatic control method for the antimony salt purification process, which automatically adjusts the zinc powder addition amount according to the potential fluctuation, realizes accurate control of the zinc powder addition amount, effectively reduces the zinc powder consumption in zinc hydrometallurgy, and reduces the production cost in the antimony salt purification process.

Description

Technical Field

[0001] This invention relates to the field of zinc smelting technology, specifically to an automatic control method, apparatus, system, and readable storage medium for zinc powder in an antimony salt purification process. Background Technology

[0002] The hydrometallurgical zinc smelting process mainly consists of four steps: roasting, leaching, purification, and electrolysis. The leaching step, through neutral and acidic leaching, dissolves zinc-containing materials (zinc concentrate, zinc leaching residue, etc.) to form an electrolyte solution primarily composed of zinc ions but containing impurity ions such as copper, cadmium, nickel, and cobalt. Without purification, these impurity ions will compete with zinc ions for electrons during subsequent electrolysis, depositing alongside them, reducing current efficiency, increasing power consumption, lowering zinc powder purity, corroding the cathode, and even affecting the safe and stable operation of the hydrometallurgical zinc smelting process. Therefore, a purification step is needed between leaching and electrolysis to reduce the concentration of impurity ions to the requirements of the electrolysis step.

[0003] Redox potential (ORP) is a comprehensive indicator used to detect the macroscopic redox properties of all substances in a solution. ORP reflects the macroscopic ability of a solution to donate or gain electrons. In the cobalt removal process, zinc, the sole reducing agent, provides all the electrons, while cobalt ions, copper ions, antimony ions, and hydrogen ions act as oxidants, taking electrons from the zinc powder. The lower the ORP value, the easier it is for cobalt ions to be reduced and deposited. Therefore, ORP is a means of measuring the real-time electrochemical reaction status in a solution. The greater the amount of zinc powder added during the cobalt removal process, the lower the ORP, the higher the cobalt removal rate, and the lower the outlet cobalt ion concentration. When the cobalt ion outlet concentration in the purification process is below a specified threshold, the amount of zinc powder added should be reduced, under certain conditions, to lower production costs.

[0004] Currently, the amount of zinc powder added during the antimony salt purification process is adjusted by on-site operators based on their personal experience and the test results of cobalt and copper ion concentrations in the solution at the purification tank outlet. However, due to frequent changes in the operating conditions of the cobalt removal process, the amount of zinc powder added is dynamic. On-site operators also manage multiple indicators simultaneously, which can lead to excessive zinc powder addition or untimely control of certain indicators during actual operation. This increases the cost of the cobalt removal process and creates safety hazards. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies and provide an automatic control method for zinc powder in an antimony salt purification process that automatically adjusts the amount of zinc powder added based on potential fluctuations. This method achieves precise control of the amount of zinc powder added, effectively reduces the consumption of zinc powder per unit, and lowers the production cost during the purification process.

[0006] The present invention also provides an automatic control device, system and readable storage medium for zinc powder in antimony salt purification process.

[0007] The technical solution adopted in this invention is as follows:

[0008] An automatic control method for zinc powder in an antimony salt purification process includes a set of purification tanks arranged sequentially and connected by pipelines, an automatic feeding device installed above the purification tanks, and a potentiometer installed in the purification tanks to acquire real-time potential values. The automatic feeding device is controlled by a DCS system, and the readings of the potentiometer are directly transmitted to the DCS system via sensors. The automatic control method for zinc powder includes:

[0009] Parameters of each purification tank are collected periodically to obtain an operation log; the parameters include the concentrations of cobalt ions and copper ions in the solution at the inlet of the purification tank and the outlet of the last purification tank, as well as the potential inside the purification tank.

[0010] A cobalt removal fitting model is constructed, and the operation log is fitted using an artificial neural network to generate a data model between potential and copper-cobalt ion concentration under different parameter conditions; based on the data model, the optimal potential setpoint is calculated and set using a particle swarm optimization algorithm.

[0011] If the reaction is normal, the difference between the cobalt ion outlet concentration of the last purification tank and the set threshold is calculated by collecting the concentration. The potential compensation value is generated by fuzzy algorithm and added to the original potential setting value to update the potential setting value.

[0012] If the reaction conditions change significantly, a case-based reasoning method is used. The amount of zinc powder added and the real-time potential value are used as the input features of the case, and the concentrations of cobalt ions and copper ions at the inlet are used as the solution features of the case. A case library for the cobalt removal process is constructed. The similarity function is used to calculate the similarity between the current case and each case in the library, and the solution features of the current case are inferred. A new potential setpoint is generated using the cobalt removal fitting model. The original potential setpoint is compensated based on the new potential setpoint, and the potential setpoint is updated.

[0013] Based on the difference between the current real-time potential value and the potential set value, a three-dimensional fuzzy rule is constructed to generate a new zinc powder amount set value by adding the zinc powder adjustment amount to the current zinc powder addition amount.

[0014] The new zinc powder quantity setting value is written into the DCS system online, and the automatic feeding device is controlled to add zinc powder according to the new zinc powder quantity.

[0015] This cycle is repeated to achieve stable potential control and minimize the amount of zinc powder used.

[0016] Furthermore, the aforementioned set of purification tanks consists of three purification tanks.

[0017] Furthermore, the steps for obtaining the potential setpoint are as follows:

[0018] S1. Collect parameters of each purification tank at regular intervals and obtain the operation log; the parameters include the concentration of cobalt ions and copper ions in the solution at the inlet of the purification tank and the outlet of the last purification tank, and the potential inside the purification tank;

[0019] S2. Using an artificial neural network, the correlation between the copper and cobalt ion concentrations at the inlet of the purification tank, the flow rate, the redox potential, and the copper and cobalt ion concentrations at the outlet of the purification tank was fitted to obtain the relationship between the potential and the copper and cobalt removal efficiency under different inlet conditions. The functional relationship is shown in Equation 1:

[0020]

[0021] in, and These represent the concentrations of cobalt and copper ions upon entering and leaving the purification tank, respectively. ORP represents the potential within the purification tank. P () indicates the established reaction model of the purification tank;

[0022] S3. Based on the data model, the optimal potential setpoint is calculated using the particle swarm optimization algorithm. The particle positions are combinations of the potential setpoints of the purification tank. The fitness function of the algorithm is designed as the product of the sum of the ORPs of the purification tank and the penalty coefficient. The calculation formula is shown below:

[0023] Pos=(ORP1,ORP2,ORP3)#(2)

[0024] Fit=fit(Pos)×Pun(Pos)#(3)

[0025] Where Pos is the particle's position, Fit is the fitness value calculated based on the particle's position, and fit(Pos) calculates the raw score of the particle's position, as shown in the following formula:

[0026] fit(Pos)=ORP1+ORP2+ORP3#(4)

[0027] Among them, ORP1, ORP2, and ORP3 are the potential settings for each purification tank;

[0028] S4. Introduce the penalty coefficient Pun(Pos), calculated as follows:

[0029]

[0030] in, It is the cobalt ion outlet concentration of the last purification tank, when If the threshold is exceeded, it indicates that this set of potential settings cannot meet the cobalt removal requirements. The calculations will utilize the established purification tank reaction model f P ();

[0031] S5. Using formula #(1), iterate repeatedly to obtain the inlet and outlet ion concentrations of each purification tank under the set potential values.

[0032] Furthermore, the steps for obtaining the potential compensation value are as follows:

[0033] S1. Generate the potential compensation value using fuzzy rules, as shown in the following formula:

[0034]

[0035] Where ΔORP1, ΔORP2, and ΔORP3 are the potential adjustment amounts generated in the purification tank. The new potential setting value is obtained by adding the adjustment amount to the current potential setting value. Fuz1 () represents a fuzzy function. This represents the difference between the actual cobalt ion outlet concentration and the set threshold.

[0036] S2. If the reaction is normal, the difference between the actual cobalt ion outlet concentration of the last purification tank and the set threshold is calculated by collecting the data. A fuzzy algorithm is then used to generate a potential compensation value, which is added to the original potential setting value to update the potential setting value.

[0037] If the reaction conditions undergo significant changes, the potential setpoint should be reset using a case-based reasoning method. The specific steps are as follows:

[0038] The zinc powder addition amount and real-time potential values ​​of all purification tanks are used as input features for the cases, and the inlet cobalt ion and copper ion concentrations are used as solution features. A case library for the cobalt removal process is constructed using laboratory data. In the case library, each case is characterized by conditional features F. A ={Zn1, Zn2, Zn3, ORP1, ORP2, ORP3} and solution characteristics F S ={C Co …C Cu The composition is as follows: Zn1, Zn2, and Zn3 represent the zinc powder addition amounts in the three purification tanks; ORP1, ORP2, and ORP3 represent the potentials of the three purification tanks; C Co C Cu The concentrations of cobalt and copper ions at the inlet of the second-stage purification system are represented by Case C in the case library. k The working condition description for (k = 1, 2, ..., m) is as follows: Solution characteristics are described as follows Define the current case condition C * The characteristics are: Define the characteristics of the current case Features in the case library The similarity function is:

[0039]

[0040] in, express The i-th element in w i Let w represent the weighting coefficients for different elements, where all w are weighted coefficients. i An equal value of 1 is equivalent to calculating the average value for each case C in the case library. k Compared with current case C * The similarity between cases is used to select the r cases with the highest similarity as similar cases, and the solution features of the current case are inferred accordingly.

[0041]

[0042] Among them, Sim i This represents the similarity between the i-th similar case and the current case. Given the solution features of the i-th similar case, infer the solution features of the current case. Then, recalculate the potential setpoint using formula #(1);

[0043]

[0044] The original setting value is compensated based on the new setting value to obtain the new potential setting value:

[0045] ORP new =ORP * ×γ+ORP old ×(1-γ)#(10)

[0046] The coefficient γ determines the degree of updating of the potential setting value. Here, we set the coefficient γ to 1 and use the newly generated setting value.

[0047] Furthermore, the steps of the potential stabilization control method are as follows:

[0048] A three-dimensional fuzzy rule is constructed, and a fuzzy control method is used to stabilize the potential. The three dimensions of the fuzzy rule are the difference between the actual value and the set value, the first derivative of the difference, and the second derivative of the difference. The calculation formula is as follows:

[0049]

[0050] e′ k =e k -e k-1 #(12)

[0051] e″ k =e′ k -e′ k-1 #(13)

[0052] in, and These represent the measured potential value and the set value at the current moment, respectively, e k e′ represents the difference between the measured value and the set value at the current moment. k It is the first derivative of the difference at the current time, e″ k It is the second derivative, e k Its function is to reduce the difference between the measured value and the actual value, e k Add zinc powder if the result is positive, subtract zinc powder if the result is negative. k The larger the absolute value of e′, the greater the adjustment range of zinc powder. k Its function is to suppress the changing trend of ORP, making the change process smooth and preventing large fluctuations. k The function and e′ k Similarly, by predicting future trends in ORP and making compensation in advance, predictions often have some deviation. To maintain potential stability, based on e″ k The resulting adjustment amount will not be too large, similar to e k and e′ k In comparison, only a slight adjustment was made to the original zinc powder addition amount. The updated formula for the zinc powder addition amount is as follows:

[0053] ΔZn=f FUZ2 (e k ,e′ k ,e″ k )#(14)

[0054] Among them, f FUZ2 () represents the three-dimensional fuzzy rule function of the design, and ΔZn is the zinc powder adjustment amount generated based on the potential difference value. It is added to the current zinc powder addition amount to obtain the new zinc powder setting value.

[0055] An automatic control device for zinc powder in an antimony salt purification process, the device comprising:

[0056] The potential optimization setting module is used to assign reasonable potential setting values ​​to different purification tanks based on the cobalt ion and copper ion concentrations at the inlet of each purification tank.

[0057] The potential compensation adjustment module is used to adjust the potential setpoint according to the actual situation during normal operation to adapt to changes in the reaction environment;

[0058] The potential stabilization control module is used to adjust the amount of zinc powder added to the purification tank to ensure that the actual potential value is consistent with the set value.

[0059] Furthermore, the device also includes a potential change feedback adjustment module, which generates a new set value to update the original set value when a significant change occurs in the operating conditions, thereby obtaining a new potential set value.

[0060] An automatic control system for zinc powder in an antimony salt purification process includes a set of purification tanks arranged sequentially and connected by pipelines, a DCS system, and an automatic control device for zinc powder in the antimony salt purification process. The DCS system is communicatively connected to the automatic control device for zinc powder in the antimony salt purification process. An automatic feeding device is installed above the purification tanks, and a potentiometer is installed in the purification tanks. The automatic feeding device is controlled by the DCS system, and the readings of the potentiometers are directly transmitted to the DCS system.

[0061] A computer-readable storage medium storing a program for an automatic control method of zinc powder in an antimony salt purification process, wherein the program of the automatic control method is executed by a processor to implement the steps of the automatic control method.

[0062] The beneficial effects of this invention are:

[0063] (1) This invention establishes the correlation between oxidation-reduction potential and reaction state, and indirectly reflects the cobalt removal effect inside the reactor through the change of potential inside the purification tank. This overcomes the problems of complex and variable working conditions and low frequency of key parameter testing in actual production, and reduces the influence of human factors.

[0064] (2) The potential change range is significantly different under different inlet conditions. Therefore, the potential setting value is obtained according to the different inlet cobalt ion and copper ion concentrations. In addition, since the inlet conditions change in real time, but the frequency of ion concentration testing is extremely low, it is not possible to obtain accurate inlet condition information in a timely manner. This invention continuously updates the inlet conditions through modeling and reasoning, and adjusts and precisely controls the amount of zinc powder added by timely optimizing the setting potential value.

[0065] (3) The final cobalt removal process uses the outlet cobalt ion concentration to provide feedback compensation for the potential. A concentration threshold is set, and a rule table is established based on the actual concentration to obtain the potential compensation value, ensuring that the outlet cobalt ion concentration meets the standard and reducing the zinc powder consumption in zinc hydrometallurgy.

[0066] (4) After obtaining the optimized potential value, a three-dimensional fuzzy rule is established based on the difference between the actual potential and the potential set value, the trend of potential change, and the second-order trend of potential change. The final zinc powder adjustment amount is added to the original zinc powder addition amount, and the new zinc powder amount set value is written online into the DCS system. The automatic feeding device is controlled to add zinc powder according to the new zinc powder amount, ensuring that the minimum amount of zinc powder is added under the premise that the cobalt ion concentration at the outlet meets the standard, thus reducing the purification cost. Attached Figure Description

[0067] Figure 1 This is a structural block diagram of the automatic control method of the present invention;

[0068] Figure 2 This is a schematic diagram of the automatic control device of the present invention;

[0069] Figure 3 This is a process flow diagram of the present invention;

[0070] Figure 4 This is a diagram showing the results of manual control of zinc powder during the antimony salt purification process in an embodiment of the present invention.

[0071] Figure 5 This is a diagram showing the automatic control results of zinc powder during the antimony salt purification process in an embodiment of the present invention. Detailed Implementation

[0072] Taking the cobalt removal process of a large zinc smelter as an example, the process flow of the present invention is as follows: Figure 3 As shown. The smelter's purification process is divided into three stages: stage one for copper and cadmium removal, stage two for cobalt and germanium removal, and stage three for residue removal. The copper and cadmium-removed liquid flowing out of stage one serves as the inlet for stage two. Stage two purification has four purification tanks. The copper-removed liquid from stage one flows sequentially through stage two starting from tank #4. In actual production, usually only three of the four purification tanks are used, typically in a 4-2-1 or 3-2-1 combination. The smelter's cobalt removal stage has an automatic feeding device B1, which can be programmed online via the DCS system. Tanks #4 and #3 share one device, while tank #2 uses a separate automatic feeding device B2. Tank #1 is not fed. The antimony salt and copper sulfate mixed solution, used as a catalyst, is added to either tank #4 or tank #3.

[0073] In actual industrial production, the testing and frequency of the second-stage purification process are shown in Table 1. It can be seen that there is no on-site testing data reflecting the reaction status of the second-stage purification process, and the detection frequency of cobalt ion concentration at the inlet and outlet is also low, limiting its guiding significance for continuous production processes.

[0074] Table 1. Test data for the second-stage purification process.

[0075]

[0076]

[0077] Figure 4 The data shows the changes in the total amount of zinc powder added, the redox potential of tank #1, and the outlet cobalt ion concentration during the manual control period. Figure 4 From the manually controlled data, we can see that on-site workers typically only significantly change the zinc powder setting after each 2-hour automated verification cycle. Data on cobalt ion outlet concentration shows that manual control has a certain lag, requiring a response only after the laboratory values ​​are available, which can easily lead to cobalt ion concentrations exceeding the threshold. Figure 3 Problems such as (the location indicated by the dotted line) or excessive zinc powder addition.

[0078] To address this issue, potentiometers were installed in each of the four purification tanks. These potentiometers allow for the real-time measurement of the oxidation-reduction potential (ORP) in each tank, providing a real-time indication of the progress of the oxidation-reduction reaction.

[0079] This embodiment uses the automatic control device of the present invention, such as... Figure 2 , adopt as Figure 1 The automatic control method enables precise control of the amount of zinc powder added, reducing the unit consumption of zinc powder during the purification process.

[0080] like Figure 1 , Figure 3 As shown, an automatic control method for zinc powder in an antimony salt purification process includes four purification tanks arranged sequentially and connected by pipelines, an automatic feeding device installed above the purification tanks, and a potentiometer installed in the purification tanks to obtain real-time potential values; the automatic feeding device is controlled by a DCS system, and the readings of the potentiometers are directly transmitted to the DCS system.

[0081] The automatic control method and automatic control device of the present invention include:

[0082] Potential optimization setting module 1

[0083] Used to assign appropriate oxidation-reduction potential settings to different purification tanks based on inlet conditions;

[0084] At the inlet of the second-stage purification tank (outlet of the first-stage purification tank), the outlet of purification tank #4 (#3), the outlet of purification tank #2, and the outlet of purification tank #1, solution samples were collected and manually analyzed at regular intervals to detect changes in the concentrations of cobalt and copper ions. After data collection, a series of correlation data between ORP and cobalt removal rate under different impurity ion concentrations and flow rates were obtained. An artificial neural network (ANN) was used to fit the correlation between the concentrations of copper and cobalt ions at the inlet of the purification tank, the flow rate, the redox potential, and the concentrations of copper and cobalt ions at the outlet of the purification tank. The relationship between the redox potential and the copper and cobalt removal effect under different inlet conditions was obtained, and the functional relationship is shown in Equation 1.

[0085]

[0086] in, and These represent the concentrations of cobalt and copper ions upon entering and leaving the purification tank, respectively. ORP represents the potential within the purification tank. P() represents the established reaction model of the cleanroom. Based on the data model, the optimal redox potential setpoint is calculated using the particle swarm optimization algorithm. The particle position is a combination of the cleanroom's ORP setpoints. The fitness function of the algorithm is designed as the product of the sum of the cleanroom's ORPs and the penalty coefficient, as shown in the following formula:

[0087] Pos = (ORP1, ORP2, ORP3)#(2

[0088] Fit=fit(Pos)×Pun(Pos)×(3

[0089] Where Pos is the particle's position, and Fit is the fitness value calculated based on the particle's position. fit(Pos) calculates the raw score of the particle's position, using the following formula:

[0090] fit(Pos) = ORP1 + ORP2 + ORP3 #(4

[0091] Where ORP1, ORP2, and ORP3 are the potential setpoints for the three purification tanks. Our goal is to minimize the amount of zinc powder added, as the amount of zinc powder added is negatively correlated with ORP; therefore, a more positive fit(Pos) is better. However, too little zinc powder will lead to excessive cobalt ion concentration, which needs to be limited. Therefore, a penalty coefficient Pun(Pos) is introduced, calculated as follows:

[0092]

[0093] in, It is the cobalt ion outlet concentration of the last purification tank, when If the threshold is exceeded, it means that this set of potential settings cannot meet the cobalt removal requirements. The calculations will utilize the established purification tank reaction model f P By iteratively applying formula (3-1), the inlet and outlet ion concentrations of the three purification tanks under the set potential values ​​can be obtained.

[0094] Potential compensation adjustment module 2

[0095] Potential optimization requires the cobalt and copper ion concentrations at the inlet of the second-stage process. In actual production, these two values ​​are tested every 6 hours, a relatively long interval. During this interval, the actual cobalt removal reaction state is highly likely to change. Failure to adjust the potential setting in a timely manner can result in wasted zinc powder or even excessive cobalt ion concentration at the outlet. Therefore, the potential setting needs to be adjusted based on the status feedback values ​​during operation. There are two status feedback values ​​in actual production: a real-time potential feedback value and an outlet cobalt ion test value every 2 hours. The outlet cobalt ion concentration feedback compensation module has a low feedback frequency, and the on-site testing equipment only takes 5ml of solution for testing each time, making it highly susceptible to random factors and unable to represent the overall situation. Therefore, the outlet cobalt ion concentration feedback module is not used as the primary compensation module, but only for minor adjustments. Here, fuzzy rules are used to generate the potential compensation value, as shown in the following formula:

[0096]

[0097] Where ΔORP1, ΔORP2, and ΔORP3 are the ORP adjustment values ​​for the three generated purification tanks. Adding the adjustment value to the current ORP setting yields the new setting value. Fuz1 () represents a fuzzy function. This represents the difference between the actual cobalt ion outlet concentration and the set threshold.

[0098] The potential change feedback adjustment module has a high feedback frequency and good real-time performance, and will serve as the main compensation module. In actual control, the potential is stabilized near the set value by adjusting the amount of zinc powder added. When the reaction is normal, stable potential control can usually be achieved.

[0099] Potential change feedback adjustment module 3

[0100] When the reaction conditions change significantly, the potential setpoint needs to be adjusted. A new potential setpoint is generated using the case reasoning method.

[0101] The zinc powder addition amount and potential setpoint of three purification tanks were used as input features for the cases, and the inlet cobalt ion and copper ion concentrations were used as solution features. A case library for cobalt removal processes was constructed using laboratory data. In the case library, each case consists of conditional features FA = {Zn1, Zn2, Zn3, ORP1, ORP2, ORP3} and solution features F. S ={C Co C CuThe composition is as follows: Zn1, Zn2, and Zn3 represent the zinc powder addition amounts in the three purification tanks (in actual production, Zn1 is always 0), ORP1, ORP2, and ORP3 represent the redox potentials of the three purification tanks, and C... Co C Cu Represents the cobalt and copper ion concentrations at the inlet of the second-stage purification system. Define Case C in the case library. k The working condition description for (k = 1, 2, ..., m) is as follows: Solution characteristics are described as follows Define the current case condition C * The characteristics are: Define the characteristics of the current case Features in the case library The similarity function is:

[0102]

[0103] in, express The i-th element in w i Let w represent the weighting coefficients for different elements, where all w are weighted coefficients. i An equal value of 1 is equivalent to calculating the average. This involves calculating the average value for each case C in the case library. k Compared with current case C * The similarity between cases is used to select the r cases with the highest similarity as similar cases, and the solution features of the current case are inferred accordingly.

[0104]

[0105] Among them, Sim i This represents the similarity between the i-th similar case and the current case. This is the solution feature of the i-th similar case. Infer the solution feature of the current case. Then, the potential optimization setting module used in Section 3.1 is used to generate new potential setting values.

[0106]

[0107] The original setting value is compensated based on the new setting value to obtain the new potential setting value:

[0108] ORP new =ORP * ×γ+ORP old ×(1-γ)#(10)

[0109] The coefficient γ determines the degree of updating of the potential setting value. Here, we set the coefficient γ to 1, which means that the newly generated setting value is used completely.

[0110] Potential Stabilization Control Module 4

[0111] The function of the potential stabilization control module is to stabilize the actual redox potential within the purification tank around a set value. A three-dimensional fuzzy rule is constructed here, and a fuzzy control method is used to stabilize the potential. The three dimensions of the fuzzy rule are the difference between the actual value and the set value, the first derivative of the difference, and the second derivative of the difference, and their calculation formulas are as follows:

[0112]

[0113] e′ k =e k -e k-1 #(12)

[0114] e″ k =e′ k -e′ k-1 #(13)

[0115] in, and These represent the measured potential value and the set value at the current moment, respectively, e k e′ represents the difference between the measured value and the set value at the current moment. k It is the first derivative of the difference at the current time, e″ k It is the second derivative. k Its function is to reduce the difference between the measured value and the actual value, e k Add zinc powder if the result is positive, subtract zinc powder if the result is negative. k The larger the absolute value of e′, the greater the adjustment range for zinc powder. k Its function is to suppress the changing trend of ORP, making the change process smooth and preventing large fluctuations. k The function and e′ k Similarly, by predicting future trends in ORP, compensation can be made in advance. It should be noted that predictions usually have some degree of bias; to maintain potential stability, compensation should be made based on e″. k The resulting adjustment amount will not be too large, similar to e k and e′ k In comparison, only a slight adjustment has been made to the original zinc powder addition amount. The updated formula for the zinc powder addition amount is as follows:

[0116] ΔZn=f FUZ2 (e k ,e′ k ,e″ k )#(14)

[0117] Among them, f FUZ2 () represents the three-dimensional fuzzy rule function of the design, and ΔZn is the zinc powder adjustment amount generated based on the potential deviation. Adding it to the current zinc powder addition amount will give the new zinc powder setting value.

[0118] Figure 5 The data displayed for the second day of automatic control shows that the automatic control system adjusts the zinc powder addition at a higher frequency based on potential changes, effectively suppressing potential fluctuations. The potential fluctuation amplitude during automatic control is smaller than that during manual control. Furthermore, the cobalt ion outlet concentration remained below the set threshold while being close to it, indicating that this invention can effectively reduce zinc powder consumption in hydrometallurgical zinc production and lower production costs during zinc purification while ensuring qualified cobalt ion concentration.

[0119] The automated control methods, devices, and systems of this invention can be used to intelligently transform and upgrade antimony salt purification processes and devices.

[0120] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An automatic control method for zinc powder in an antimony salt purification process, characterized in that: It includes a set of purification tanks arranged in sequence and connected by pipes, an automatic feeding device set above the purification tanks, and a potentiometer set in the purification tanks to obtain real-time potential values; the automatic feeding device is controlled by the DCS system, and the readings of the potentiometer are directly transmitted to the DCS system. The automatic control method for zinc powder includes: The parameters of the purification tank are collected periodically to obtain the operation log; the parameters include the concentrations of cobalt ions and copper ions in the solution at the inlet and outlet of the last purification tank, as well as the potential inside the purification tank. A cobalt removal fitting model is constructed, and the operation log is fitted using an artificial neural network to generate a data model between potential and copper-cobalt ion concentration under different parameter conditions; based on the data model, the optimal potential setpoint is calculated and set using a particle swarm optimization algorithm. If the reaction is normal, the difference between the cobalt ion outlet concentration of the last purification tank and the set threshold is calculated by collecting the concentration. The potential compensation value is generated by fuzzy algorithm and added to the original potential setting value to update the potential setting value. If the reaction conditions change significantly, a case-based reasoning method is used. The amount of zinc powder added and the real-time potential value are used as the input features of the case, and the concentrations of cobalt ions and copper ions at the inlet are used as the solution features of the case. A case library for the cobalt removal process is constructed. The similarity function is used to calculate the similarity between the current case and each case in the library, and the solution features of the current case are inferred. A new potential setpoint is generated using the cobalt removal fitting model. The original potential setpoint is compensated based on the new potential setpoint, and the potential setpoint is updated. Based on the difference between the current real-time potential value and the potential set value, a three-dimensional fuzzy rule is constructed to generate a new zinc powder amount set value by adding the zinc powder adjustment amount to the current zinc powder addition amount. The new zinc powder quantity setting value is written into the DCS system online, and the automatic feeding device is controlled to add zinc powder according to the new zinc powder quantity. This cycle is repeated to achieve stable potential control and minimize the amount of zinc powder used.

2. The automatic control method for zinc powder in an antimony salt purification process according to claim 1, characterized in that, The aforementioned set of purification tanks consists of 3 purification tanks.

3. The automatic control method for zinc powder in an antimony salt purification process according to claim 2, characterized in that, The steps to obtain the potential setpoint are as follows: S1. Collect parameters of each purification tank at regular intervals and obtain the operation log; the parameters include the concentration of cobalt ions and copper ions in the solution at the inlet of the purification tank and the outlet of the last purification tank, and the potential inside the purification tank; S2. Using an artificial neural network, the correlation between the copper and cobalt ion concentrations at the inlet of the purification tank, the flow rate, the redox potential, and the copper and cobalt ion concentrations at the outlet of the purification tank was fitted to obtain the relationship between the potential and the copper and cobalt removal efficiency under different inlet conditions. The functional relationship is shown in Equation 1: in, , and , These represent the concentrations of cobalt and copper ions upon entering and leaving the purification tank, respectively. ORP represents the potential within the purification tank. This represents the established reaction model of the purification tank; S3. Based on the data model, the optimal potential setpoint is calculated using the particle swarm optimization algorithm. The particle positions are combinations of the potential setpoints of the purification tank. The fitness function of the algorithm is designed as the product of the sum of the ORPs of the purification tank and the penalty coefficient. The calculation formula is shown below: in, It refers to the position of the particle. It is the fitness value calculated based on the particle's position. The calculation is based on the raw score of the particle position, and the formula is as follows: in, This is the potential setting value for each purification tank; S4. Introducing a penalty coefficient The calculation formula is as follows: in, It is the cobalt ion outlet concentration of the last purification tank, when If the threshold is exceeded, it indicates that this set of potential settings cannot meet the cobalt removal requirements. The calculations will utilize the established reaction model of the purification tank. ; S5. Using the formula By iterating repeatedly, the inlet and outlet ion concentrations of each purification tank under the set potential values ​​are obtained.

4. The automatic control method for zinc powder in the antimony salt purification process according to claim 2, characterized in that, The steps to obtain the potential compensation value are as follows: S1. Generate the potential compensation value using fuzzy rules, as shown in the following formula: in, This is the potential adjustment amount for the generated purification tank. The new potential setting is obtained by adding the adjustment amount to the current potential setting. Represents a fuzzy function. This represents the difference between the actual cobalt ion outlet concentration and the set threshold. S2. If the reaction is normal, the difference between the actual cobalt ion outlet concentration of the last purification tank and the set threshold is calculated by collecting the data. A fuzzy algorithm is then used to generate a potential compensation value, which is added to the original potential setting value to update the potential setting value. If the reaction conditions undergo significant changes, the potential setpoint should be reset using a case-based reasoning method. The specific steps are as follows: The zinc powder addition amount and real-time potential values ​​of all purification tanks are used as input features for the cases, and the inlet cobalt ion and copper ion concentrations are used as solution features. A case library for the cobalt removal process is constructed using laboratory data. In the case library, each case is characterized by conditional features. Reconciliation characteristics Composition, in which, This represents the amount of zinc powder added to the three purification tanks. The electrical potential represents the three purification tanks. The concentrations of cobalt and copper ions at the inlet of the second-stage purification system are defined in the case library. The working condition is described as follows The solution features are described as follows Define the current case working condition The characteristics are: Define the characteristics of the current case. Features in the case library The similarity function is: in, express The first in One element, Let the weighting coefficients of the different elements be denoted as , and let all ... An equal value of 1 is equivalent to calculating the average value for each case in the case library. Compared with the current case The highest similarity score is used. These cases are considered similar cases, and the solution characteristics of the current case are inferred accordingly: in, Indicates the first The similarity between the current case and a number of similar cases. It is the first Based on the solution characteristics of similar cases, the solution characteristics of the current case can be inferred. Then, use the formula Recalculate the potential setpoint; The original setting value is compensated based on the new setting value to obtain the new potential setting value: Among them, coefficient The degree of updating of the potential setpoint is determined here by the coefficient. Equals 1, use the newly generated setting.

5. The automatic control method for zinc powder in the antimony salt purification process according to claim 2, characterized in that, The steps of the potential stabilization control method are as follows: A three-dimensional fuzzy rule is constructed, and a fuzzy control method is used to stabilize the potential. The three dimensions of the fuzzy rule are the difference between the actual value and the set value, the first derivative of the difference, and the second derivative of the difference. The calculation formula is as follows: in, and These represent the measured potential value and the set potential value at the current moment, respectively. This represents the difference between the measured value and the set value at the current moment. It is the first derivative of the difference at the current time. It is the second derivative. Its function is to reduce the difference between the measured value and the actual value. Add zinc powder if the result is positive, and subtract zinc powder if the result is negative. The larger the absolute value, the greater the adjustment range for zinc powder. Its function is to suppress the changing trend of ORP, making the change process smooth and preventing large fluctuations. Function and Similarly, by predicting future ORP trends and making compensations in advance, it's important to understand that predictions often have some bias. To maintain potential stability, [further measures are needed]. The resulting adjustment amount will not be too large, compared to and In comparison, only a slight adjustment was made to the original zinc powder addition amount. The updated formula for the zinc powder addition amount is as follows: in, The three-dimensional fuzzy rule function representing the design. The zinc powder adjustment amount is generated based on the potential difference value, and is added to the current zinc powder addition amount to obtain the new zinc powder setting value.

6. The apparatus for the automatic control method of zinc powder in the antimony salt purification process according to any one of claims 1 to 5, characterized in that, The device includes, The potential optimization setting module is used to assign reasonable potential setting values ​​to different purification tanks based on the cobalt ion and copper ion concentrations at the inlet of each purification tank. The potential compensation adjustment module is used to adjust the potential setpoint according to the actual situation during normal operation to adapt to changes in the reaction environment; The potential stabilization control module is used to adjust the amount of zinc powder added to the purification tank to ensure that the actual potential value is consistent with the set value.

7. The apparatus according to claim 6, characterized in that, Also includes: The potential change feedback adjustment module is used to generate a new set value to update the original set value when a major change occurs in the operating conditions, thus obtaining a new potential set value.

8. The automatic control system of the zinc powder automatic control method for the antimony salt purification process according to any one of claims 1 to 5, characterized in that, The device includes a set of purification tanks arranged sequentially and connected by pipes, a DCS system, and the apparatus as described in claim 6 or 7. The DCS system is communicatively connected to the apparatus. An automatic feeding device is provided above the purification tanks, and a potentiometer is provided in the purification tanks. The automatic feeding device is controlled by the DCS system, and the reading of the potentiometer is directly transmitted to the DCS system.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for an automatic control method of zinc powder in an antimony salt purification process. When the program of the automatic control method is executed by a processor, it implements the steps of the automatic control method as described in any one of claims 1-5.

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