Intelligent control method and system for flotation column process based on online feedback of tailings ash content

Abstract

The present invention relates to an intelligent control method and system for a flotation column process based on online feedback of tailings ash content, which belongs to the field of flotation technology and solves the problem of poor stability of the flotation column process in the prior art. The specific steps include: real-time collection of working condition data related to tailings ash content during the flotation column production process; based on the state variable data that meets the working condition requirements in the collected working condition data, using the trained predictive control model to obtain predicted control variable data; in response to the tailings ash content value read in real time being qualified, using an actuator to perform regulation according to the predicted control variable data; in response to the tailings ash content value being unqualified, using the predicted control variable data and the tailings ash content value, based on the quadratic programming optimization algorithm, perform control variable tuning to obtain the optimal regulation sequence; using the actuator to perform regulation according to the optimal regulation sequence; in the production process, using the predictive control model and the optimization algorithm to perform cyclic tuning to achieve control strategy stability.

Description

Technical Field

[0001] The present invention relates to the field of flotation technology, and in particular to an intelligent control method and system for a flotation column process based on online feedback of tailings ash content. Background Art

[0002] Flotation is a primary technology for sorting fine-particle minerals and is currently widely used in mineral processing plants. In coal sorting, it is primarily used to improve the quality and reduce ash content of fine coal slimes. With the adoption of mechanized and intelligent mining technologies, the proportion of fine coal slimes in raw coal washing is increasing. This results in large volumes of coal slime in coal processing plants, reduced floatability, and reduced economic efficiency. Flotation processes can be categorized into flotation machines and flotation columns based on equipment properties. Flotation columns are more suitable for sorting fine coal slimes. However, these processes have high requirements for feed concentration and particle size, resulting in large system throughput. Controlling individual process parameters can significantly impact sorting performance, making precise control crucial for ensuring effective flotation column separation. Furthermore, rapidly evolving policies and technologies are placing increasing demands on intelligent control of flotation column processes. Currently, domestic coal slime flotation column processes generally lack intelligent control technology. Key challenges include insufficient process variable detection accuracy, poor control strategy stability, and the lack of system-wide control of product ash content.

[0003] With the increasing prevalence of artificial intelligence technology in industrial applications, new technologies are supporting the intelligent control of flotation column processes. Taking coal slime flotation as an example, process variables primarily include flotation feed properties, flotation column equipment, and product variables. These parameters exhibit strong nonlinear relationships. Current traditional control methods, often supplemented by manual experience, are inaccurate and frequently result in loss of clean coal products and reagents during production. Furthermore, online tailings ash content detection technology has matured. Incorporating flotation tailings ash content as a feedback control factor can effectively prevent the entrainment of flotation clean coal in coal slime.

[0004] In summary, among the current related technologies, the production method of the flotation column process is backward, and the existing manually-dominated control method has poor stability due to the influence of subjective factors, resulting in frequent occurrence of low flotation clean coal recovery rate and high reagent loss during the production process. Summary of the Invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide a flotation column process intelligent control method and system based on online feedback of tailings ash content, so as to solve the problem of poor stability of the flotation column process in the prior art.

[0006] The purpose of the present invention is mainly achieved through the following technical solutions:

[0007] On the one hand, an embodiment of the present invention provides an intelligent control method for a flotation column process based on online feedback of tailings ash content, comprising the following steps:

[0008] Real-time collection of working condition data related to tailings ash content during flotation column production;

[0009] Based on the state variable data that meets the working condition requirements in the collected working condition data, the trained predictive control model is used to obtain the predicted control variable data;

[0010] In response to the tailings ash value read in real time being qualified, utilizing an actuator to perform regulation according to the predicted control variable data;

[0011] In response to the tailings ash value being unqualified, using the predicted control variable data and the tailings ash value, based on a quadratic programming optimization algorithm, to tune the control variables to obtain an optimal control sequence; and using an actuator to perform control according to the optimal control sequence;

[0012] During the production process, the predictive control model and optimization algorithm are used to perform cycle tuning.

[0013] Furthermore, the predictive control model is trained based on a multi-input multi-output long short-term memory model, including:

[0014] Collect historical control data during the flotation column production process to obtain a flotation column process production data set; wherein the state variable data in the production data set is used as the input data set, and the corresponding control variable data is used as the output data set;

[0015] Based on a multi-input and multi-output long short-term memory model, training is performed using a loss function calculated using the mean square error of the control variables to obtain a trained predictive control model.

[0016] Further, obtaining the optimal regulatory sequence includes:

[0017] Obtaining a cost function based on the tailings ash value and the control variable data;

[0018] Optimize with the goal of minimizing the cost function to obtain the optimal control variable output;

[0019] The input features are extracted based on the state variables inputted by the predictive control model, the contribution values ​​of the input features are calculated, and the corresponding optimal control variables are sorted according to the contribution values ​​to obtain the optimal regulation sequence.

[0020] Furthermore, the cost function is expressed as:

[0021]

[0022] Among them, Q and F are both adjustment matrices; Y is the sample matrix to be optimized, including the tailings ash value and the control variables; k is the time or serial number of the current optimized sample.

[0023] Furthermore, according to the local interpretable model-independent interpretation technology, the contribution value is calculated based on the following formula:

[0024] F(x i )=argminL(x i )+Ω(g),

[0025] Among them, x i is the input feature; L is the loss function; Ω is the complexity of the model.

[0026] Furthermore, the loss function is:

[0027]

[0028] L=Loss(t)+Loss(t+1),

[0029] Among them, L is the loss function; t is the current data moment; n is the total number of control variables; y is the true value of the control variable; y p is the predicted value of the control variable; w is the weight coefficient.

[0030] Furthermore, the state variables include feed flow, feed concentration, dilution water flow, circulation flow, circulation pressure, foam layer thickness and tailings ash content; the control variables include foaming agent addition amount, collector addition amount, feed valve opening, circulation valve opening, water supply valve opening and tailings gate motor operation.

[0031] Furthermore, when the collected working condition data does not meet the working condition requirements, the control variables output by the flotation column production process control feedback are sent to the corresponding actuator to complete the control until the working condition requirements are met.

[0032] On the other hand, an embodiment of the present invention provides an intelligent control system for a flotation column process based on online feedback of tailings ash content, comprising:

[0033] Production parameter detection module, used to collect working condition data of flotation column production process through detection devices;

[0034] The predictive control module is used to train the predictive control model; based on the real-time data of the state variables that meet the working condition requirements in the collected working condition data, the trained predictive control model is used to obtain predicted control variable data; in response to the qualified tailings ash value read in real time, the actuator is used to perform regulation according to the predicted control variable data; in response to the unqualified tailings ash value, the predicted control variable data and the tailings ash value are used to perform control variable tuning based on a quadratic programming optimization algorithm to obtain an optimal control sequence; the actuator is used to perform regulation according to the optimal control sequence; and during the production process, the predictive control model and the optimization algorithm are used to perform cyclic tuning;

[0035] A control module, configured to send the optimal control sequence to a corresponding actuator in the form of a control instruction via a PLC controller;

[0036] The executive mechanism is used to execute the control instructions to complete intelligent regulation.

[0037] Further, obtaining the optimal regulatory sequence includes:

[0038] calculating a cost function based on the tailings ash value and the predicted output of the control variable;

[0039] Optimize with the goal of minimizing the cost function to obtain the optimal control variable output;

[0040] The input features are extracted based on the state variables input into the predictive control model, the contribution values ​​of the input features are calculated, and the corresponding optimal control variables are sorted according to the contribution values ​​to obtain the optimal regulation sequence.

[0041] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0042] 1. The present invention proposes to select real-time tailings ash content as an online feedback condition, utilize the acquisition of flotation column process variables, establish a flotation column process intelligent control system based on prediction and optimization control, realize the intelligent start and stop and operation of the flotation column process, and achieve the stability of the control strategy;

[0043] 2. Based on the multi-input multi-output (MIMO) model, a predictive control technology analysis and optimization control module for predicting the control sequence and action amplitude of target variables were established, and an intelligent control system for the flotation column process was established, which improved the separation efficiency and intelligence level of the flotation column process system.

[0044] 3. The system is applied to the coal slime flotation process system. Through online feedback of tailings ash content, it can effectively prevent the entrainment of flotation clean coal in the coal slime, improve the clean coal recovery rate of the coal slime flotation process system, and increase economic benefits for the target enterprise.

[0045] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0047] Figure 1 This is a flow chart of an intelligent control method for a flotation column process based on online feedback of tailings ash content according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic structural diagram of an intelligent control system for a flotation column process with online feedback of tailings ash content according to an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of feed flow fluctuation after exponential smoothing of feed flow data according to an embodiment of the present invention;

[0050] Figure 4 This is a control flow chart of a flotation column process system based on MIMO model prediction according to an embodiment of the present invention;

[0051] Figure 5 This is a schematic structural diagram of an intelligent control system for a flotation column process based on online feedback of tailings ash content according to an embodiment of the present invention.

[0052] Reference numerals:

[0053] 1- Collector metering pump, 2- Frother metering pump, 3- Slurry pre-treater, 4- Feed flowmeter, 5- Feed concentration meter, 6- Dilution water valve, 7- Dilution water flowmeter, 8- Feed valve, 9- Pressure gauge, 10- Foam layer thickness meter, 11- Bubble generator, 12- Flotation concentrate discharge port, 13- Tailings gate motor, 14- Tailings ash meter, 15- Tailings discharge port, 16- Flotation column, 17- Circulation pipeline, 18- Circulation flowmeter, 19- Circulation valve. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0055] Example 1

[0056] A specific embodiment of the present invention discloses an intelligent control method for flotation column process based on online feedback of tailings ash content, such as Figure 1 As shown, the following steps are included:

[0057] Step S1: real-time collection of operating data related to tailings ash content during flotation column production;

[0058] Step S2: Based on the state variable data that meets the working condition requirements in the collected working condition data, the trained predictive control model is used to obtain predicted control variable data;

[0059] Step S3: in response to the tailings ash value read in real time being qualified, using the actuator to perform regulation according to the obtained predicted control variable data;

[0060] Step S4: In response to the tailings ash value being unqualified, using the predicted control variable data and the tailings ash value, and based on a quadratic programming optimization algorithm, performing control variable tuning to obtain an optimal control sequence; and using an actuator to perform control according to the optimal control sequence;

[0061] Step S5: During the production process, the predictive control model and optimization algorithm are used to perform cycle tuning.

[0062] Through the above method, on the basis of the existing coal slime flotation column process, by combining the control variables and tailings ash index in the flotation column process production process, and using the predictive control model and optimization algorithm, the flotation column intelligent control with online feedback of tailings ash content is realized, the sorting efficiency of the flotation column process system is improved, and the stability of the control strategy is achieved.

[0063] Specifically, in step S1, the coal slime flotation column production process includes the flotation feed stage, the flotation column equipment stage, the flotation reagent stage, and the flotation product stage. Operating data during the flotation column production process includes feed flow rate, feed concentration, dilution water flow rate, circulation pressure, foam layer thickness, collector flow rate, frother flow rate, tailings ash content, and circulation flow rate.

[0064] Specifically, in step S2, state variables closely related to the tailings ash index are extracted from the operating condition data, including feed flow rate, feed concentration, circulation volume (including circulation flow rate and circulation pressure), foam layer thickness and tailings ash content as variables to be monitored for intelligent control.

[0065] At the same time, based on expert knowledge, the early warning boundary of historical control data is determined, and the steady-state interval of the corresponding state variable is set. In the non-early warning range, the coal slime flotation column process system is intelligently controlled by monitoring the state variables. This includes using a feedback mechanism to control state variables that do not meet the operating conditions to a stable range; for state variables that meet the operating conditions, cyclic tuning is performed in combination with the tailings ash value. The specific steps for setting the steady-state interval of the state variable are as follows:

[0066] S211, collect historical control data of flotation column production process in sections, including detection variables and control variables;

[0067] S212. Incorporate expert knowledge to eliminate abnormal data, organize historical control data according to time series, and normalize the existing data based on the following formula:

[0068]

[0069] S213. For each single historical control data, perform exponential smoothing to obtain a smooth parameter change trajectory and remove errors caused by noise and instrument fluctuations. The exponential smoothing calculation formula for historical control data is as follows:

[0070] p n =α*x n +(1-α)*(α*x n-1 +(1-α)*p n-2 ),

[0071] Among them, p is the predicted value after exponential smoothing; α is the attenuation weight; x is the original historical control data; and n is the number of exponential times.

[0072] S214. Arrange the pre-processed historical control data of the flotation column process production, and determine the warning boundary of the historical control detection variable data and the steady-state interval of the corresponding state variable based on expert knowledge.

[0073] For example, in combination with the equipment performance of the flotation column, the parameter fluctuation range of the flotation clean coal ash content is set to be less than 0.5%, as shown in Table 1, which is the set steady-state operating range (normal range):

[0074] Table 1

[0075] variable Stable operating range Feed flow 250-350m3 / h Feed concentration 50-80g / L Foam layer thickness 35-60cm Circulation flow 250-350m3 / h Circulating pressure 0.10-0.20MPa

[0076] Within the above range, if the corresponding state variable is outside the stable operating range, the intelligent control feedback mechanism of the flotation column production process is utilized to send the control variable output by the control feedback to the corresponding actuator through a control module such as a PLC controller to control the above flotation column state variable within the steady-state operating range. If it cannot be controlled within the steady-state operating range, a system alarm is triggered.

[0077] It should be noted that among the state variables, feed concentration is primarily determined by the dilution water valve opening, circulation flow is controlled by the circulation valve opening, and foam layer thickness and slurry level are controlled by the tailings gate motor. Therefore, control variables that influence the state variable values ​​are identified within the control data as control objects. These control variables include the collector metering pump, frother metering pump, dilution water valve opening, feed valve opening, tailings gate motor, and circulation valve opening.

[0078] Specifically, when the state variables are within the stable operating range, the trained predictive control model is used to obtain the corresponding predicted output of the control variable data based on the real-time collected state variable data;

[0079] Exemplarily, a predictive control model is trained based on a multi-input multi-output long short-term memory model (MIMO), including:

[0080] Step S221: The historical control data of the flotation column production process collected in segments are preprocessed to obtain a flotation column process production data set, and the flotation column process production data set is randomly divided into training data and test data according to the one-to-one correspondence between input variables and output variables; wherein the state variable data in the production data set is used as the input data set, and the corresponding control variable data is used as the output data set;

[0081] For example, all detection instruments and actuators are connected to the PLC controller, and all flotation column process production control data is stored on the local server with the help of SQL database. It took one month to collect 35,000 data from the flotation column control system of the coal preparation plant. Industry flotation experts and professionals were invited to eliminate historical data, and then normalize the existing data. After data normalization, exponential smoothing operation was performed on the historical data of each detection variable to eliminate errors caused by large fluctuations in the dependent variable. The smoothing index was selected as 2, the attenuation weight α was set to 0.9, and the smoothing calculation process formula was as follows:

[0082] p2=α*x2+(1-α)*(α*x1+(1-α)*p0),

[0083] Among them, such as Figure 3 As shown, the vertical axis is the flow value, the unit is Kg / m3; at the initial moment, p0=x0, the effect diagram of the exponential smoothing of the feed flow data.

[0084] From the data collected above, 24,826 valid data were extracted and randomly divided into training data and test data in a ratio of 8:2; among them, 19,860 training data were used as the training set and 4,966 test data were used as the test set.

[0085] Step S222: Based on the output variables of the MIMO training flotation column production process, i.e., the control variables, after the flotation column process production data set is input into the model, the calculation formula of the first input gate is as follows:

[0086] C=tanh(W c k (t-1) +U c x t +b),

[0087] Among them, W and U are linear relationship coefficients respectively; b is bias; tanh is activation function; k is hidden state variable; t is time series; x is flotation production feature vector.

[0088] Step S223: After obtaining the output of the input gate, the backbone parameters of the MIMO model are updated and passed to an output gate. The output calculation formula of the output gate O is as follows:

[0089] O=δ(W*h (t-1) +U*x (t) +b)⊙tanh(C (t) ),

[0090] Among them, δ is the sigmoid activation function; W and U are the updated linear relationship coefficients; b is the updated bias; C (t) is the hidden state output at time t; x is the output data of the previous stage.

[0091] Step S224: Continuously execute step S222 and step S223 to perform forward propagation on the flotation column process status data input by the model to calculate the model output. The model forward propagation prediction output calculation formula is as follows:

[0092] P (t) =δ(V*O (t) +c),

[0093] Among them, O (t) is the output gate output at time t; V is a fixed coefficient; c is the output unit state at time t.

[0094] Step S225: Based on step S224, the model parameters are back-propagated and the loss value of the model prediction result is calculated based on the mean square error. The loss function of back-propagation is divided into two parts: one is the loss at the calculation time, and the other is the loss after the calculation time; the loss function calculation formula is as follows:

[0095]

[0096] L=Loss(t)+Loss(t+1),

[0097] Where L is the loss function; t is the current data moment; t+1 is the next data moment; n is the total number of samples of the control variable; y is the true value of the control variable; y p is the predicted value of the control variable; w is the weight coefficient.

[0098] Step S226: Train and optimize the model parameters until the model loss value stabilizes and reaches the optimal value, export and save the flotation column process history control model, and save the data for later model update and analysis.

[0099] For example, a multi-input multi-output (MIMO) temporal memory network model is established based on the Pytorch structure, as shown in Table 2, which is the main structure design of the model.

[0100] Table 2

[0101] Layer Type (Name) Output dimension Parameter scale First layer LS_1 (None, 9, 9) 8848 Dropout (None, 9, 9) 0 Second layer LS_1 (None, 15, 256) 78842 Dropout (None, 15, 256) 0 The third layer LS_1 (None, 15, 256) 1357624 Dropout (None, 15, 256) 0 Fourth layer LS_1 (None, 15, 256) 1357642 Dropout (None, 15, 256) 0 Fifth layer Timedistri (None, 15, 6) 512

[0102] The hyperparameters used in model training were: TIME_STEPS = 15, BATCH_SIZE = 64, INPUT_SIZE = 32, OUTPUT_SIZE = 6, PRED_SIZE = 32, CELL_SIZE = 256, and LR = 0.0001. With this hyperparameter combination, the model underwent multiple forward and backward passes. Finally, after 800 epochs of training, the model achieved an MSE of 0.12 on the test set.

[0103] Specifically, in step S3, the tailings ash content y Ad This is one of the primary target variables and reference variables for flotation column control systems. If the tailings ash content is acceptable, it is assumed that there is no flotation clean coal mixed in the tailings. If the tailings ash content is too low, it is assumed that the flotation column separation is not performing well. By reading the tailings ash meter, the real-time tailings ash content is obtained to verify whether the tailings ash content is acceptable. If it is, the actuator performs control according to the obtained predicted control variable data.

[0104] Specifically, in step S4, if the tailings ash value is unqualified, the flotation column process is optimized and controlled using a predictive control model combined with an optimization mechanism. A cost function is defined based on the control variable data output from step S2 and the tailings ash value to characterize the control deviation between the predicted variable and the desired variable. Then, within the range given in Table 1, the optimal control sequence is found based on the quadratic programming (QP) algorithm to minimize the cost function. The specific steps include:

[0105] Step S41: Obtain a cost function based on the tailings ash value and the control variable data; the cost function is expressed as:

[0106]

[0107] Among them, Q and F are both adjustment matrices, such as positive definite / semi-positive definite diagonal matrices; k is the current optimization sample time or serial number; Y is the sample matrix to be optimized, including control variables and tailings ash values.

[0108] For example, the dimension of the control variable matrix is ​​set to 7, and in addition to the tailings ash value, it also includes 6 control variables: frother given, collector given, feed valve opening, circulation valve opening, water supply valve opening, tailings level motor opening. Set the cost function Y T The FY expansion formula is as follows:

[0109]

[0110] Step S42: Continuously optimize the cost function in step S41 until the minimum value of the cost function is found to obtain the optimal control variable;

[0111] Step S43: Extract input features based on the input state variables of the MIMO model, calculate the contribution values ​​of the input features, sort the corresponding optimal control variables according to the contribution values, and obtain the optimal control variable sequence of the control system, that is, the optimal regulation sequence.

[0112] For example, according to the Local Interpretable Model-agnostic Explanations (LIME) technique, the contribution value of the input feature in the MIMO model is calculated based on the following formula:

[0113] F(x i )=argminL(x i )+Ω(g),

[0114] Among them, x i is the corresponding input feature; L is the loss function; and Ω is the model complexity. Each input feature has its own control variable. The control variable tuning order is determined by the contribution value, and the system is controlled in this order.

[0115] Specifically, in step S5, the flotation column process system control process with online feedback of tailings ash content established based on the predictive control model and optimization algorithm is as follows: Figure 4 As shown, Fc is the output of the system setting (the output vector based on the control variable), fr is the flotation column control variable parameter within the allowable fluctuation range, y is the detection input variable, ys is the actual output value of the control variable, ya is the control variable output predicted by the MIMO model, and fa is the tailings ash content under system control. The cycle tuning strategy includes:

[0116] First, the current state variable is read. If the state variable is not within the normal range (as shown in Table 1), the control variable output by the control feedback of the flotation column production process is used to operate the corresponding valve using the corresponding actuator to adjust the state variable to the normal range.

[0117] Then, the current state variables that meet the conventional interval are input into the MIMO model to predict the control variables and obtain ya;

[0118] Finally, check whether the tailings ash content value read in real time by the tailings ash meter meets the requirements of the beneficiation plant / customer. If qualified, the actuator is used to perform regulation according to the obtained ya.

[0119] If it fails, the cost function J is calculated according to the QP optimization method to obtain the optimal control sequence, and the control is performed based on the optimal control sequence and the actuator; if the tailings ash content after control still does not meet the production setting range, repeat the above steps, read the real-time state variables again, and obtain the control variable prediction output based on the predictive control model, and re-judge whether the real-time value of the tailings ash content is qualified until the tailings ash content reaches the production setting range and stops updating this stage.

[0120] During the production process, the predictive control model and optimization algorithm are used for cyclic tuning to stabilize the state variables within the normal operating range while ensuring that the tailings ash content index is qualified, thereby ensuring the stability of the control strategy.

[0121] For example, when the tailings ash content is less than 65%, it is determined that the flotation column separation process system does not meet the operating requirements. When the MIMO model is working, as shown in Table 3, an example of prediction on an industrial flotation column is:

[0122] Table 3

[0123] Input variables Output variables Feed flow rate 296.15m3 / h Collector flow rate 26.15L / h Feed concentration 55.26g / L Foaming agent flow rate 3.17L / h Dilution water flow rate 0m3 / h Feed valve opening 59.16% Foam layer thickness 48.17cm Circulation valve opening 78.38% Circulation flow rate 325.46m3 / h Dilution water valve opening 0% Circulating pressure 0.16MPa Tailings gate motor range 76.24% Tailings ash content 68.59% /

[0124] In this example, all data is stored by the local PLC controller on a local Windows server. A PyQt-based HMI host computer embeds the control model and sends action commands to the PLC controller, which then drives the control mechanisms. Furthermore, the local control system data is centrally connected to the coal preparation plant's industrial ring network, enabling remote data sharing. All data is stored for future model updates and analysis.

[0125] Compared to existing technologies, this embodiment provides an intelligent flotation column process control method based on online feedback of tailings ash content. By monitoring relevant state variables that influence tailings ash content, using a predictive control model to predict corresponding control variable data, and integrating this state variable data with an optimization mechanism, this method establishes intelligent flotation column process production control with online feedback of tailings ash content, ultimately achieving a stable control strategy. This online feedback of tailings ash content effectively prevents the entrainment of flotation clean coal in coal slime, thereby improving the clean coal recovery rate of the coal slime flotation process system.

[0126] Example 2

[0127] Another specific embodiment of the present invention discloses an intelligent control system for flotation column process based on online feedback of tailings ash content, such as Figure 5 Shown, including:

[0128] Production parameter detection module, used to collect working condition data of flotation column production process through detection devices;

[0129] A data set acquisition module is used to store the collected historical operating condition data, analyze the historical operating condition data based on expert knowledge after preprocessing, and obtain the operating condition range requirements that the state variables must meet;

[0130] The predictive control module is used to train the predictive control model; based on the real-time data of the state variables that meet the working condition requirements in the collected working condition data, the trained predictive control model is used to obtain predicted control variable data; in response to the qualified tailings ash value read in real time, the actuator is used to perform regulation according to the predicted control variable data; in response to the unqualified tailings ash value, the predicted control variable data and the tailings ash value are used to perform control variable tuning based on a quadratic programming optimization algorithm to obtain an optimal control sequence; the actuator is used to perform regulation according to the optimal control sequence; and during the production process, the predictive control model and the optimization algorithm are used to perform cyclic tuning;

[0131] A control module, configured to send the optimal control sequence to a corresponding actuator in the form of a control instruction via a PLC controller;

[0132] The executive mechanism is used to execute the control instructions to complete intelligent regulation.

[0133] Further, obtaining the optimal regulatory sequence includes:

[0134] calculating a cost function based on the tailings ash value and the predicted output of the control variable;

[0135] Optimize with the goal of minimizing the cost function to obtain the optimal control variable output;

[0136] The input features are extracted based on the state variables input into the predictive control model, the contribution values ​​of the input features are calculated, and the corresponding optimal control variables are sorted according to the contribution values ​​to obtain the optimal regulation sequence.

[0137] The system adds detection devices for feed flow, feed concentration and supplementary water flow in the flotation feed section of the flotation column process; Figure 2 As shown, the actuator adds feed valve opening and water supply valve opening control devices in the flotation feed section; adds circulation volume pressure, circulation volume flow, foam layer thickness, tailings liquid level and other detection devices in the flotation column equipment section, and adds circulation volume valve opening, tailings lifting motor and other control devices; adds collector flow, frother flow, collector liquid level, and frother liquid level detection devices in the flotation reagent section, and adds collector metering pump and frother metering pump control devices; adds tailings ash meter and setting time label in the flotation product section.

[0138] It should be noted that the system can be implemented according to the method described in any one of the solutions in Example 1. The relevant parts are referenced from each other and are not repeated in this embodiment.

[0139] Compared with the existing technology, this embodiment provides an intelligent control system for a flotation column process based on online feedback of tailings ash content. By adding corresponding detection and control devices to the flotation column process production process, and based on real-time detection and intelligent regulation of tailings ash content indicators and related variable parameters, the industrial flotation column process can be operated more efficiently, effectively improving the clean coal recovery rate and reducing the consumption of flotation agents, thereby achieving the goal of increasing the efficiency of mineral processing enterprises and improving the intelligence level of mineral processing plants.

[0140] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0141] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. An intelligent control method for flotation column process based on online feedback of tailings ash content, characterized in that: The steps include: Real-time collection of working condition data related to tailings ash content during flotation column production; Based on the state variable data that meets the operating condition requirements in the collected operating condition data, the trained predictive control model is used to obtain predicted control variable data; wherein the state variables include feed flow rate, feed concentration, dilution water flow rate, circulation flow rate, circulation pressure, foam layer thickness and tailings ash content; the control variables include foaming agent addition amount, collector addition amount, feed valve opening, circulation valve opening, water supply valve opening and tailings gate motor movement; In response to the tailings ash value read in real time being qualified, utilizing an actuator to perform regulation according to the predicted control variable data; In response to the tailings ash value being unqualified, using the predicted control variable data and the tailings ash value, based on a quadratic programming optimization algorithm, to tune the control variables to obtain an optimal control sequence; and using an actuator to perform control according to the optimal control sequence; During the production process, the predictive control model and optimization algorithm are used to perform cycle tuning; Obtaining the optimal regulatory sequence comprises: A cost function is obtained based on the tailings ash value and the control variable data; the cost function is expressed as: Wherein, Q and F are both adjustment matrices; Y is the sample matrix to be optimized, including the tailings ash value and the control variables; k is the time or serial number of the current optimization sample; Optimize with the goal of minimizing the cost function to obtain the optimal control variable output; Extract input features based on the state variables input to the predictive control model, calculate the contribution value of the input features, and sort the corresponding optimal control variables according to the contribution value to obtain the optimal regulation sequence; wherein, according to the local interpretable model-independent interpretation technology, the contribution value is calculated based on the following formula: F(x i )=argminL(x i )+Ω(g), Among them, x i is the input feature; L is the loss function; Ω is the complexity of the model.

2. The intelligent control method for flotation column process based on online feedback of tailings ash content according to claim 1, characterized in that: Training the predictive control model based on a multi-input multi-output long short-term memory model includes: Collect historical control data during the flotation column production process to obtain a flotation column process production data set; wherein the state variable data in the production data set is used as the input data set, and the corresponding control variable data is used as the output data set; Based on a multi-input and multi-output long short-term memory model, training is performed using a loss function calculated using the mean square error of the control variables to obtain a trained predictive control model.

3. The intelligent control method for flotation column process based on online feedback of tailings ash content according to claim 1 or 2, characterized in that: When the collected working condition data does not meet the working condition requirements, the control variables output by the flotation column production process control feedback are sent to the corresponding actuator to complete the control until the working condition requirements are met.

4. An intelligent control system for a flotation column process for operating the method according to any one of claims 1 to 3, characterized in that: include: Production parameter detection module, used to collect working condition data of flotation column production process through detection devices; Predictive control module, used for training predictive control models; It is also used to obtain predicted control variable data based on real-time data of state variables that meet the working condition requirements in the collected working condition data, using a trained predictive control model, and in response to the tailings ash value read in real time being qualified, using an actuator to perform regulation according to the predicted control variable data; in response to the tailings ash value being unqualified, using the predicted control variable data and the tailings ash value to tune the control variables based on a quadratic programming optimization algorithm to obtain an optimal regulation sequence; Utilizing an execution mechanism to perform regulation according to the optimal regulation sequence; During the production process, the predictive control model and optimization algorithm are used to perform cycle tuning; A control module, configured to send the optimal control sequence to a corresponding actuator in the form of a control instruction via a PLC controller; The executive mechanism is used to execute the control instructions to complete intelligent regulation.

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