Copper flotation control methods, devices, equipment, and media based on the degree of froth mineralization.

Abstract

This invention provides a copper flotation control method, apparatus, equipment, and medium based on the degree of foam mineralization. The method includes: acquiring target foam characteristic information for copper flotation; constructing a foam mineralization degree model based on the target foam characteristic information; calculating the degree of foam mineralization using the target foam characteristic information through the foam mineralization degree model; establishing a parameter database for the copper flotation process; determining the target operating state of the copper flotation process using the parameter database and the degree of foam mineralization through a prior probability method; determining the target control function for the copper flotation process based on the target operating state; determining the control target based on the target control function; determining the prior control decision for the copper flotation process based on the control target and the prior probability; and adjusting the prior control decision through a preset state feedback model to obtain the next control decision. This invention can realize intelligent monitoring of the flotation foam mineralization state and optimized control of production operations, improving the stability of concentrate grade and the comprehensive metal recovery rate.

Description

Technical Field

[0001] This invention relates to the field of collaborative computing technology for process data, and in particular to a method, apparatus, equipment and medium for controlling copper flotation based on the degree of foam mineralization. Background Technology

[0002] Foam flotation is a process that uses the differences in hydrophilicity among different mineral particles to screen for useful minerals. The quality of the flotation process is crucial to the economic and technical indicators of the entire concentrator. However, copper ore flotation involves complex and variable raw material properties, a long process flow, and high coupling between operations. Abnormal conditions such as overflowing and settling tanks frequently occur during production, leading to problems such as unstable concentrate grades and high tailings runoff. In particular, the low level of automation in flotation production and the heavy reliance on manual experience severely restrict the stability of flotation production indicators and the improvement of economic benefits.

[0003] For a long time, flotation operations have relied primarily on operators observing visual characteristics such as the color and size of foam on the surface of the flotation cell to identify flotation production indicators, including grade. This information is then used to adjust flotation liquid level, aeration rate, and reagent dosage to ensure the grade of the flotation product is within acceptable limits. By combining real-time measured foam image data with information extraction technology and online grade prediction, the subjective arbitrariness of operator observation can be avoided, while also improving the real-time feedback of status parameters, thus contributing to a higher level of automated control in the flotation process. Current flotation process control is mainly based on single-machine yield control of flotation machines, which does not fully utilize the characteristic parameters inherent in the flotation foam. Furthermore, relying solely on single-machine yield control is too simplistic and cannot address abnormal operating conditions caused by the complex environment of the flotation process.

[0004] Patent [202111615444.2] proposes a method for characterizing the mineralization degree of flotation foam, which includes a Relative Mineralization Degree (RMD) model. This model is highly robust and can effectively characterize the mineralization degree of flotation foam. However, this patent cannot specify corresponding control strategies based on the mineralization degree of flotation foam to improve concentrate grade stability and overall metal recovery rate. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a copper flotation control method, apparatus, equipment and medium based on the degree of foam mineralization.

[0006] This invention provides the following technical solution:

[0007] In a first aspect, this application provides a copper flotation control method based on the degree of froth mineralization, including:

[0008] Obtain copper flotation foam information during the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information;

[0009] A foam mineralization degree model is constructed based on the target foam feature information, and the foam mineralization degree is calculated using the target foam feature information through the foam mineralization degree model.

[0010] A parameter database for the copper flotation process is established, and the target operating state of the copper flotation process is determined by using the parameter database and the degree of foam mineralization through a priori probability method.

[0011] Based on the target operating state, determine the target control function for the copper flotation process, and determine the control target based on the target control function;

[0012] The prior control decision for the copper flotation process is determined based on the control objective and the prior probability.

[0013] The prior control decisions are dynamically evaluated and adjusted in priority using a preset state feedback model to obtain the next control decision.

[0014] In one embodiment, selecting target foam feature information from the foam feature information includes:

[0015] Obtain production data for copper flotation, and determine the quality influencing factors of copper flotation based on the production data;

[0016] The foam feature information is obtained by flotation foam image analysis equipment, and the target foam feature information corresponding to the quality influencing factors is extracted from the foam feature information. The foam feature information includes image information.

[0017] In one embodiment, the step of constructing a foam mineralization degree model based on the target foam feature information, and calculating the foam mineralization degree using the target foam feature information through the foam mineralization degree model, includes:

[0018] The foam mineralization degree model was constructed using the following method:

[0019] RMD = R[(WS),(KA),(QF),(PCM)] T +X

[0020] Wherein, RMD represents the degree of foam mineralization, R represents the total coefficient matrix, W represents the foam size coefficient matrix, S represents the foam size matrix, K represents the foam area coefficient matrix, A represents the foam area matrix, Q represents the foam stability coefficient matrix, F represents the foam stability matrix, P represents the foam color coefficient matrix, CM represents the foam color feature matrix, and X represents a constant.

[0021] In one embodiment, determining the target operating state of the copper flotation process using the parameter database and the degree of foam mineralization via a priori probability method includes:

[0022] Define the state space of the copper flotation process, set multiple state limits, and divide the state space into multiple initial operating states through the multiple state limits;

[0023] According to the standard process for copper flotation, the evaluation criteria for the initial operating state during the copper flotation process are determined, and the initial operating state is determined based on the evaluation criteria, the parameter database, and the degree of foam mineralization.

[0024] The probability density of each initial running state is determined by comparing it with prior samples.

[0025] Based on the probability density, the posterior probability corresponding to each initial running state is determined, the error rate is calculated based on the posterior probability, and the result with the smallest error rate is taken as the target running state.

[0026] In one embodiment, determining the target control function for the copper flotation process based on the target operating state, and determining the control target based on the target control function, includes:

[0027] Based on the target operating state, determine the performance index of the objective function:

[0028] when hour,

[0029]

[0030] when hour,

[0031]

[0032] when hour

[0033]

[0034] Where J is the performance index of the objective function, θ is the total number of samples, μ is the number of samples, δ1 is the allowable deviation range of concentrate grade, and δ2 is the allowable deviation range of tailings grade. This is the concentrate grade adaptability coefficient. The yield adaptation factor, β1 is the tailings grade adaptability coefficient, CV is the froth velocity matrix, and β1 is the flotation concentrate grade. * For flotation concentrate grade, CV * This represents the target value for the foam flow rate.

[0035] In one embodiment, determining the prior control decision for the copper flotation process based on the control objective and prior probabilities includes:

[0036] Determine the control decision variables for copper flotation, and generate control variables based on the control decision variables. The control decision variables include flotation liquid level, aeration rate, collector, pH adjuster, frother, and inhibitor.

[0037] Define a one-dimensional control decision matrix for control decisions, and calculate the probability of occurrence of each control decision under each target operating state based on the control variables and the one-dimensional control decision matrix.

[0038] The prior control decision for the copper flotation process is determined based on the control objective and the prior probability.

[0039] In one implementation, the step of dynamically evaluating and adjusting the priority of the prior control decision through a state feedback model to obtain the next control decision includes:

[0040] Based on the control objective and prior probabilities, determine the priority of each control decision variable, adjust the high-priority control decision variables, and obtain state feedback;

[0041] The state deviation is calculated using a preset state feedback model and the state feedback.

[0042] The priority of the prior control decision is dynamically adjusted using the state deviation, and the next control decision is calculated.

[0043] Secondly, this application provides a copper flotation control device based on the degree of froth mineralization, comprising:

[0044] The extraction module is used to acquire copper flotation foam information during the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information.

[0045] A construction module is used to construct a foam mineralization degree model based on the target foam feature information, and to calculate the foam mineralization degree using the target foam feature information through the foam mineralization degree model;

[0046] A module is established to create a parameter database for the copper flotation process. The target operating state of the copper flotation process is determined by using the parameter database and the degree of foam mineralization through a priori probability method.

[0047] The determination module is used to determine the target control function of the copper flotation process based on the target operating state, and to determine the control target based on the target control function;

[0048] The acquisition module is used to determine the prior control decisions for the copper flotation process based on the control objective and prior probability.

[0049] The adjustment module is used to dynamically evaluate and adjust the priority of the prior control decision through a preset state feedback model to obtain the next control decision.

[0050] Thirdly, this application provides an electronic device including a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to implement the copper flotation control method based on the degree of foam mineralization as described in the first aspect.

[0051] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed, implements the copper flotation control method based on the degree of foam mineralization as described in the first aspect.

[0052] The embodiments of the present invention have the following beneficial effects:

[0053] The copper flotation control method based on the degree of foam mineralization provided by this invention improves the accuracy of the degree of foam mineralization by constructing a foam mineralization model based on copper flotation foam information. Then, it identifies the operating state using prior probability and posterior probability methods, and makes control decisions and adjustments based on the operating state. This realizes intelligent monitoring of flotation foam mineralization state and optimized control of production operation, thereby improving the stability of concentrate grade and the comprehensive metal recovery rate.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A schematic diagram of a copper flotation control method based on the degree of foam mineralization is shown.

[0057] Figure 2 A schematic diagram of a method for obtaining target foam feature information is shown.

[0058] Figure 3 A schematic diagram of a method for determining a target operating state is shown;

[0059] Figure 4 A schematic diagram of a priori control decision acquisition method is shown;

[0060] Figure 5 A schematic diagram of a method for obtaining final control decisions is shown.

[0061] Figure 6 A schematic diagram of the framework structure of a copper flotation control device based on the degree of foam mineralization is shown.

[0062] Explanation of key component symbols:

[0063] 600. Copper flotation control device based on the degree of foam mineralization; 601. Extraction module; 602. Construction module; 603. Establishment module; 604. Determination module; 605. Acquisition module; 606. Adjustment module. Detailed Implementation

[0064] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0065] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] Example 1

[0068] See Figure 1 , Figure 1 This embodiment provides a schematic flowchart of a copper flotation control method based on the degree of froth mineralization. This method can be used for grade analysis of various raw ores to improve the stability of concentrate grade and the comprehensive metal recovery rate. The method includes:

[0069] S101. Obtain copper flotation foam information during the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information.

[0070] See Figure 2 Step S101 includes:

[0071] S1011. Obtain the production data of copper flotation, and determine the quality influencing factors of copper flotation based on the production data.

[0072] Specifically, on-site monitoring of flotation foam phenomena can be conducted, process flow investigations can be carried out, production data can be collected and relevant databases can be established; the main factors that may affect the grade can be analyzed, including the properties of the raw ore, the quality of the grinding products, and foam characteristic parameters.

[0073] S1012. Obtain the foam feature information through a flotation foam image analysis device, and extract the target foam feature information corresponding to the quality influencing factors from the foam feature information. The foam feature information includes image information.

[0074] Specifically, the installation location of the flotation foam image analyzer can be determined by observing the mineralization phenomenon of flotation foam. Then, the flotation foam image analyzer is installed to acquire foam characteristic information, and target foam characteristic information corresponding to the quality influencing factors is extracted from this information.

[0075] This embodiment obtains target foam feature information through flotation foam image analysis equipment and uses technologies such as machine vision to replace traditional manual inspection, thereby improving the accuracy and scientific validity of target foam feature information.

[0076] S102. Construct a foam mineralization degree model based on the target foam feature information, and calculate the foam mineralization degree using the target foam feature information through the foam mineralization degree model.

[0077] Specifically, the foam mineralization degree model is constructed in the following manner:

[0078] RMD = R[(WS),(KA),(QF),(PCM)] T +X

[0079] Wherein, RMD represents the degree of foam mineralization, R represents the total coefficient matrix, W represents the foam size coefficient matrix, S represents the foam size matrix, K represents the foam area coefficient matrix, A represents the foam area matrix, Q represents the foam stability coefficient matrix, F represents the foam stability matrix, P represents the foam color coefficient matrix, CM represents the foam color feature matrix, and X represents a constant.

[0080] Then, based on the foam relative mineralization degree model structure, corresponding foam characteristic parameters are selected to determine the mathematical model of the mineralization degree model. The minimum mineralization degree value ε is then determined. min The highest degree of mineralization ε max State control limit δ min and δ max .

[0081] Then, standard samples are selected, mineralization degree model coefficients are calculated, and the mathematical model formula for mineralization degree is determined. The mathematical model for mineralization degree is then encapsulated and calculated online to obtain real-time mineralization degree values, and a historical database of parameters is established.

[0082] This embodiment improves the calculation efficiency and accuracy of foam mineralization degree by constructing a foam mineralization degree model.

[0083] S103. Establish a parameter database for the copper flotation process, and determine the target operating state of the copper flotation process using the parameter database and the degree of foam mineralization through a priori probability method.

[0084] Specifically, the parameter database includes the raw ore grade β0, flotation concentrate grade β1, flotation tailings grade β2, grinding product particle size M, pulp pH value, and flotation liquid level setpoint FL. sp Aeration volume FT, flotation liquid level measurement FL PV The matrix includes: chemical dosage matrix FA, foam size matrix CD, foam area matrix CS, foam stability matrix CF, foam color feature matrix CM, and foam flow rate matrix CV.

[0085] See Figure 3 Step S103 includes:

[0086] S1031. Define the state space of the copper flotation process, set multiple state limits, and divide the state space into multiple initial operating states through the multiple state limits.

[0087] The copper flotation process is defined to have n states.

[0088] Define the state space as Ω j ,exist:

[0089] Ω j =(ω1,ω2,ω3,…ω n )

[0090] Where ω i (i = 1, 2, 3…n) represent the various running states in the state space.

[0091] Flotation process parameter state definition and calculation. The parameters involved in the subsequent calculations of the flotation process operation state are all states of individual variables. The state calculation of each variable parameter is achieved by designing state limit values ​​for that variable.

[0092] The specific state division is as follows: Based on the distribution of each variable according to the statistical histogram, k state limits are set, and the variable is divided into k+1 states through the k state limits.

[0093] S1032. Based on the standard copper flotation process, determine the evaluation criteria for the initial operating state during the copper flotation process, and determine the initial operating state based on the evaluation criteria, the parameter database, and the degree of foam mineralization.

[0094] Based on the theoretical knowledge of mineralization degree in flotation process, we find representative flotation operating states as evaluation standard sample libraries and record the corresponding mineralization degree RMD, raw ore grade β0, concentrate grade β1, tailings grade β2, froth velocity matrix CV, pulp pH value and other parameters.

[0095] For consistency in subsequent calculations, the following definition is provided:

[0096] X = (x1, x2, x3, x4, x5, x6)

[0097] Where: X is a one-dimensional parameter state matrix, x1 represents the mineralization degree RMD state, x2 represents the raw ore grade β0 state, x3 represents the concentrate grade β1 state, x4 represents the tailings grade β2 state, x5 represents the foam flow rate CV state, and x6 represents the slurry pH value state.

[0098] S1033. By comparing prior samples for each initial operating state, the probability density of each initial operating state is determined.

[0099] To improve the adaptability and accuracy of the samples, N sets of production data samples were selectively chosen from different raw ore grade distribution ranges, thus obtaining the state space samples under the full production mode. See Tables 1 and 2.

[0100] The prior probability density of flotation operating states is calculated. Experienced flotation operators can estimate the current flotation operating state based on prior experience. Based on the above idea, the probability density of each flotation state ω is calculated. i The conditional probability density P(X|ω) i )calculate.

[0101] Where X contains (+1) 6 The middle state combination is achieved by combining each float state ω. i By comparing prior samples, the flotation state ω is determined. i Under these conditions, the probability P(X) of each parameter state combination occurring is...j | i ), (j=1,2,…(k+1) 6 Finally, the probability density histogram of all states is obtained, i.e., the probability density P(X|ω) i ).

[0102] Table 1. Distribution of Sample Data

[0103]

[0104] Table 2 State Distribution

[0105]

[0106] S1034. Based on the probability density, determine the posterior probability corresponding to each initial running state, calculate the error rate based on the posterior probability, and take the result with the smallest error rate as the target running state.

[0107] Therefore, it exists:

[0108]

[0109] P(X j |1)+(X j |2)+(X j |3)+…+P(X j | n ) = 1

[0110] The posterior probability calculation of the flotation operating state, that is, when a certain type of condition occurs, it belongs to state ω. i The probability of.

[0111]

[0112] Where P(ω) i | j ) indicates that under specific working condition X j Under the given conditions, the flotation state is ω i This probability is the posterior probability. P(ω) i Let be the prior probability of state i. For the random process of flotation, the prior probabilities of all flotation states are equal and their sum is 1.

[0113] Based on the Bayesian decision rule with minimum error rate, the current operating condition X is calculated. j Belongs to the flotation process operating state ω i After calculating the posterior probability, a Bayesian discriminant method is used for the final state determination. Since the error rate is a measure of classification performance, the final state can be determined by calculating the error rate.

[0114]

[0115] In the formula, P(e|X) j ) indicates operating condition X j Under the condition, belonging to ω i The error rate in each state. The state ω with the lowest error rate is chosen as the final decision state.

[0116] This embodiment uses prior probability and posterior probability methods, as well as Bayesian decision rules, to identify the flotation operation status, thereby improving the differentiation effect and accuracy of the target operation status.

[0117] S104. Determine the target control function for the copper flotation process based on the target operating state, and determine the control target based on the target control function.

[0118] The control objectives of the flotation process are determined. The multi-modal nature of the flotation process is reflected in the fact that different control objectives need to be considered under different flotation conditions. It is a typical dynamic multi-objective optimization problem. However, in practice, one control objective is taken as the main objective and the other control objectives are taken as constraints to solve the problem by prioritizing the control objectives.

[0119] The main control objectives of the flotation process are as follows:

[0120] ① The grade of the flotation concentrate meets the standard and is as close as possible to the process target value;

[0121] ②Under the premise that the concentrate grade meets the standard, increase the foam flow rate to increase the concentrate yield;

[0122] ③ The grade of flotation tailings should meet the standards and be as low as possible.

[0123] Therefore, the performance index of the objective function can be determined based on the target operating state:

[0124] when hour,

[0125]

[0126] when hour,

[0127]

[0128] when hour,

[0129]

[0130] Where J is the performance index of the objective function, θ is the total number of samples, μ is the number of samples, δ1 is the allowable deviation range of concentrate grade, and δ2 is the allowable deviation range of tailings grade. This is the concentrate grade adaptability coefficient. The yield adaptation factor, β1 is the tailings grade adaptability coefficient, CV is the froth velocity matrix, and β1 is the flotation concentrate grade. * For, CV * for.

[0131] The above formula shows that changes in the properties of the raw ore lead to changes in the overall control target. When the properties of the raw ore remain unchanged, the control target will also switch according to changes in the working conditions.

[0132] S105. Determine the prior control decision for the copper flotation process based on the control objective and prior probability.

[0133] See Figure 4 Step S105 includes:

[0134] S1051. Determine the control decision variables for copper flotation, and generate control variables based on the control decision variables. The control decision variables include flotation liquid level, aeration rate, collector, pH adjuster, frother, and inhibitor.

[0135] The definition of control variables and control step size in the flotation process: Since the flotation process is a complex industrial engineering process, and control decisions have significant uncertainties, the adjustment of many control variables is fuzzy and fraught with uncertainty. Therefore, based on prior experience, the control decision variables for copper flotation can be determined as flotation liquid level y1, aeration rate y2, collector y3, pH adjuster y4, frother y5, and depressant y6.

[0136] but:

[0137] Y=(y1, y2, y3, y4, y5, y6),

[0138] Y is a control variable.

[0139] S1052. Define a one-dimensional control decision matrix for control decisions. Based on the control variables and the one-dimensional control decision matrix, calculate the prior probability of each control decision under each target operating state.

[0140] Define D as a one-dimensional control decision matrix, with an adjustment step size of d for each control variable, and define that the control variable has l adjustment steps, i.e.:

[0141] D = (d1, d2, ..., d6)

[0142] It can be seen that each control decision uses l 6 A combination of control step sizes.

[0143] S1053. Determine the prior control decision for the copper flotation process based on the control objective and the prior probability.

[0144] Based on the prior experience method in step S103, it is known that the prior probability density calculation for flotation process control decisions allows experienced flotation operators to estimate the current flotation operating state and provide corresponding control decisions or control decision directions based on prior experience. Based on this idea, the various states ω are then calculated. i Next decision probability density P(D|ω) i )calculate.

[0145] Where D contains l 6 The middle state combination is achieved by combining each float state ω. i By comparing prior samples, the flotation state ω is determined. i Below, the probability P(D) of each decision combination occurring. m |ω i ), (m=1,2,…l 6 Finally, the probability density histogram of all decisions is obtained, i.e., the probability density P(D|ω) i ).

[0146] Therefore, it exists:

[0147]

[0148] P(D m |ω1)+P(D m |ω2)+P(D m |ω3)+…+P(D m |ω n ) = 1.

[0149] To obtain ω in any flotation state i The probability P(D|ω) of each control decision combination occurring is given below. i However, the flotation process is highly uncertain, and the prior nature of the control decision is constantly changing. Therefore, a method for determining directional consistency can be designed to obtain the final control decision.

[0150] Specifically, first select the float state ω i The H decision combinations with the highest probability of making a decision are as follows:

[0151] D = (D1, D2, ..., D) H )

[0152] Define the adjustment step direction for each variable as negative (-1), unchanged (0), and increased (1).

[0153] Calculate the probability of adjustment direction for each control variable in group H.

[0154]

[0155]

[0156]

[0157] Where θ = (1, 2, ... 6).

[0158] Select each variable y θ The adjustment direction with the highest probability is used as the reference vector τ.

[0159]

[0160] Calculate the decision combinations for H, D = (D1, D2, ... D). H In the given set of decision vectors, the decision with the smallest cosine value of α between the angle α between each set of decision vectors and the reference vector is the final prior control decision D. m .

[0161]

[0162] S106. The prior control decision is dynamically evaluated and adjusted in priority using a preset state feedback model to obtain the next control decision.

[0163] See Figure 5 Step S106 includes:

[0164] S1061. Based on the control objective and prior probability, determine the priority of each control decision variable, adjust the high-priority control decision variables, and obtain state feedback.

[0165] Since flotation process control is a multi-input, multi-output process, the simultaneous adjustment of multiple control variables in prior decisions may increase system disturbances, hindering control stability and performance analysis. Therefore, an expert system and feedback linearization are designed to decouple the control decisions, thus decoupling the control of multiple variables.

[0166] Specifically, the aforementioned prior control decision D m It is a combination of multiple inputs adjusted together, based on expert experience and the principle of uncertainty, for each control variable y. t (t = 1, 2, ... 6) are marked with priority.

[0167] Table 3 Initial Priority

[0168]

[0169] S1062. Calculate the state deviation using the preset state feedback model and the state feedback.

[0170] After adjusting the action based on expert rules, it is assumed that the change of the state parameter X within this time period is linearly related to the action. Calculate the state X and the state changes ΔX of each component parameter of the parameter state matrix within a certain flotation time:

[0171] ΔX = X(t) - X(t-1);

[0172]

[0173] X(t) is the parameter state matrix at time t, X(t-1) is the parameter state matrix at time t-1, and E is the feedback deviation integral.

[0174] The feedback linear deviation E is used to evaluate whether the current operating condition has changed or is moving in the direction of error reduction, and to determine whether the action adjustment based on expert rules is effective.

[0175] S1063. The priority of the prior control decision is dynamically adjusted using the state deviation, and the next control decision is calculated.

[0176] Then, based on the validity assessment, each control variable y... t Priority is dynamically set using (=1,2…6). The specific method is as follows:

[0177] When E→′-′ or E→′0′, it indicates that the current operating condition is moving in the direction of increasing error or remaining unchanged, and the effectiveness of the control action is poor. Adjust the action priority p of each control variable, reduce the priority of the action just executed, and adjust the reduction standard according to the degree of feedback linear deviation E.

[0178] When E→′+′, it indicates that the current operating condition is moving in the direction of reducing error, the control action is effective, and the action priority p of each control variable is maintained.

[0179] Then the final decoupling control output D is obtained, and the final control output is:

[0180] Y(t)=(t-1)+.

[0181] This embodiment improves the accuracy of flotation foam mineralization by constructing a foam mineralization degree model based on copper flotation foam information. Then, it identifies the operating status using prior and posterior probability methods, and makes and adjusts control decisions based on the operating status. This realizes intelligent monitoring of flotation foam mineralization status and optimized control of production operation, thereby improving the stability of concentrate grade and the comprehensive metal recovery rate.

[0182] Example 2

[0183] See Figure 6 This application also provides a copper flotation control device 600 based on the degree of froth mineralization, comprising:

[0184] Extraction module 601 is used to acquire copper flotation foam information in the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information;

[0185] Construction module 602 is used to construct a foam mineralization degree model based on the target foam feature information, and to calculate the foam mineralization degree using the target foam feature information through the foam mineralization degree model;

[0186] Module 603 is used to establish a parameter database for the copper flotation process. The target operating state of the copper flotation process is determined by using the parameter database and the degree of foam mineralization through a priori probability method.

[0187] The determining module 604 is used to determine the target control function of the copper flotation process based on the target operating state, and to determine the control target based on the target control function;

[0188] The acquisition module 605 is used to determine the prior control decision for the copper flotation process based on the control objective and prior probability.

[0189] The adjustment module 606 is used to dynamically evaluate and adjust the prior control decision through a preset state feedback model to obtain the next control decision.

[0190] It is understood that the implementation method of the copper flotation control method based on the degree of foam mineralization described in Example 1 above is also applicable to this example, so it will not be described again here.

[0191] Example 3

[0192] This application also provides a computer device, which may be, but is not limited to, a desktop computer, a laptop, etc. Its form is not limited, mainly depending on whether it needs to support the interface display function of a web browser. Exemplarily, the computer device includes a memory and at least one processor. The memory stores a computer program, and the processor executes the computer program to implement the copper flotation control method based on the degree of foam mineralization described in Embodiment 1 above.

[0193] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0194] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). The memory stores computer programs, and the processor, upon receiving execution instructions, can execute the computer programs accordingly.

[0195] Furthermore, the memory may include a stored program area and a stored data area, wherein the stored program area may store the operating system and application programs required for at least one function; the stored data area may store data created based on the use of the computer device (such as iterative data, version data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0196] Example 4

[0197] This application also provides a computer-readable storage medium storing computer-executable instructions. When called and executed by a processor, the computer-executable instructions cause the processor to execute the copper flotation control method based on the degree of foam mineralization described in Embodiment 1 above.

[0198] It is understood that the implementation method of the copper flotation control method based on the degree of foam mineralization described in Example 1 above is also applicable to this example, so it will not be described again here.

[0199] The computer-readable storage medium can be either a non-volatile storage medium or a volatile storage medium. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0201] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0202] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0203] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0204] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0205] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0206] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A copper flotation control method based on the degree of froth mineralization, characterized in that, include: Obtain copper flotation foam information during the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information; A foam mineralization degree model is constructed based on the target foam feature information, and the foam mineralization degree is calculated using the target foam feature information through the foam mineralization degree model. A parameter database for the copper flotation process is established, and the target operating state of the copper flotation process is determined by using the parameter database and the degree of foam mineralization through a priori probability method. Based on the target operating state, determine the target control function for the copper flotation process, and determine the control target based on the target control function; The prior control decision for the copper flotation process is determined based on the control objective and the prior probability. The prior control decision is dynamically evaluated and adjusted in priority using a preset state feedback model to obtain the next control decision. The step of constructing a foam mineralization degree model based on the target foam feature information, and calculating the foam mineralization degree using the target foam feature information through the foam mineralization degree model, includes: The foam mineralization degree model was constructed using the following method: Where RMD represents the degree of foam mineralization, R represents the total coefficient matrix, W represents the foam size coefficient matrix, S represents the foam size matrix, K represents the foam area coefficient matrix, A represents the foam area matrix, Q represents the foam stability coefficient matrix, F represents the foam stability matrix, P represents the foam color coefficient matrix, CM represents the foam color feature matrix, and X represents a constant. The step of determining the target operating state of the copper flotation process using the parameter database and the degree of foam mineralization through a priori probability method includes: Define the state space of the copper flotation process, set multiple state limits, and divide the state space into multiple initial operating states through the multiple state limits; According to the standard process for copper flotation, the evaluation criteria for the initial operating state during the copper flotation process are determined, and the initial operating state is determined based on the evaluation criteria, the parameter database, and the degree of foam mineralization. The probability density of each initial running state is determined by comparing it with prior samples. Based on the probability density, the posterior probability corresponding to each initial running state is determined, the error rate is calculated based on the posterior probability, and the result with the smallest error rate is taken as the target running state.

2. The copper flotation control method based on the degree of foam mineralization according to claim 1, characterized in that, The step of selecting target foam feature information from the foam feature information includes: Obtain production data for copper flotation, and determine the quality influencing factors of copper flotation based on the production data; The foam feature information is obtained by flotation foam image analysis equipment, and the target foam feature information corresponding to the quality influencing factors is extracted from the foam feature information. The foam feature information includes image information.

3. The copper flotation control method based on the degree of foam mineralization according to claim 1, characterized in that, The step of determining the target control function for the copper flotation process based on the target operating state, and determining the control target based on the target control function, includes: Based on the target operating state, determine the performance index of the objective function: when hour, ， when hour, ， when hour, ， in, The performance index of the objective function. The total number of samples, As a sample, This refers to the allowable deviation range for concentrate grade. This refers to the allowable deviation range for tailings grade. This is the concentrate grade adaptability coefficient. The yield adaptation factor, This is the tailings grade adaptability coefficient. For the foam velocity matrix, To determine the grade of the flotation concentrate, To determine the grade of the flotation concentrate, This represents the target value for the foam flow rate.

4. The copper flotation control method based on the degree of foam mineralization according to claim 1, characterized in that, The determination of the prior control decision for the copper flotation process based on the control objective and prior probabilities includes: Determine the control decision variables for copper flotation, and generate control variables based on the control decision variables. The control decision variables include flotation liquid level, aeration rate, collector, pH adjuster, frother, and inhibitor. Define a one-dimensional control decision matrix for control decisions, and calculate the prior probability of each control decision for each target operating state based on the control variables and the one-dimensional control decision matrix. The prior control decision for the copper flotation process is determined based on the control objective and the prior probability.

5. The copper flotation control method based on the degree of foam mineralization according to claim 4, characterized in that, The step of obtaining the next control decision by prioritizing and dynamically adjusting the prior control decision through a state feedback model includes: Based on the control objective and prior probabilities, determine the priority of each control decision variable, adjust the high-priority control decision variables, and obtain state feedback; The state deviation is calculated using a preset state feedback model and the state feedback. The priority of the prior control decision is dynamically adjusted using the state deviation, and the next control decision is calculated.

6. A copper flotation control device based on the degree of foam mineralization, characterized in that, For implementing the copper flotation control method based on the degree of foam mineralization as described in any one of claims 1-5, the apparatus comprises: The extraction module is used to acquire copper flotation foam information during the copper flotation process, extract foam feature information based on the copper flotation foam information, and select target foam feature information from the foam feature information. A construction module is used to construct a foam mineralization degree model based on the target foam feature information, and to calculate the foam mineralization degree using the target foam feature information through the foam mineralization degree model; A module is established to create a parameter database for the copper flotation process. The target operating state of the copper flotation process is determined by using the parameter database and the degree of foam mineralization through a priori probability method. The determination module is used to determine the target control function of the copper flotation process based on the target operating state, and to determine the control target based on the target control function; The acquisition module is used to determine the prior control decisions for the copper flotation process based on the control objective and prior probability. The adjustment module is used to dynamically evaluate and adjust the prior control decisions through a preset state feedback model to obtain the next control decision.

7. An electronic device, characterized in that, It includes a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to implement the copper flotation control method based on the degree of foam mineralization as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the copper flotation control method based on the degree of foam mineralization as described in any one of claims 1 to 5.

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