PLC (Programmable Logic Controller) control method and system for stabilizing ore dressing pulp

Abstract

The invention discloses a PLC control method and system for stable control of ore dressing pulp flow, and relates to the technical field of gravity ore dressing. The control system mainly comprises an upper computer DCS (Distributed Control System), a PLC (Programmable Logic Controller), a touch screen, a flow meter, a concentration meter and an execution assembly. The control method mainly comprises the following steps: acquiring ore pulp flow data of each pipeline through a detection element; a dynamic median-arithmetic average filtering fusion method is adopted to filter the ore pulp flow data, and a dynamic weight coefficient alpha is calculated according to a formula alpha = sigma / (sigma + delta x + epsilon) so as to eliminate the influence of pulse flow noise on collected data during ore feeding; and the filtered flow data serve as feedback signals to be input into a pulse suppression PID controller, and the pulse suppression PID controller adopts a control strategy combining incomplete differential, differential advance and integral separation to automatically adjust the opening degree of an execution assembly. According to the invention, the anti-interference capability of the PLC control system to noise is obviously improved, and the method has the advantages of high precision, fast response, strong stability and the like in flow control of ore pulp such as feeding of gravity beneficiation such as spiral beneficiation.

Description

Technical Field

[0001] This invention relates to the field of gravity mineral processing, and more specifically to a PLC control method and system for stabilizing the flow rate of gravity mineral processing slurry. Background Technology

[0002] In gravity mineral processing, whether using spiral concentrators or shaking tables, strict control of the feed slurry flow rate is crucial. Unstable feed, excessively high or low flow rates, significantly impact mineral separation efficiency. In actual production, a primary separation system typically corresponds to multiple secondary separation systems. The feed flow rate of the primary separation system is not fixed; it must be selectively opened or closed based on the primary system's feed flow rate and the optimal capacity of the separation equipment. Furthermore, the feed flow rate of the secondary separation system needs adjustment to achieve optimal separation results. Therefore, ensuring stable feed and dynamic slurry flow distribution between the primary and secondary separation systems is a critical problem that needs to be solved in gravity mineral processing.

[0003] Existing PLC control systems have the following technical problems in mineral processing slurry flow control: On the one hand, pulse feeding will generate pulse flow noise, and traditional filtering methods are difficult to effectively eliminate the influence of pulse noise on the flow detection signal; on the other hand, traditional PID controllers are prone to integral saturation and differential term oscillation when facing pulse noise interference, resulting in decreased control accuracy and slower system response. Summary of the Invention

[0004] The purpose of this invention is to provide a PLC control method and system for stable control of mineral slurry flow in mineral processing, so as to ensure stable feeding of the primary ore separation system to the secondary ore separation system and dynamic distribution of feed flow in gravity mineral processing.

[0005] In a first aspect, the present invention provides a PLC control method for stabilizing the flow rate of mineral processing slurry, employing the following technical solution:

[0006] A PLC control method for stabilizing mineral processing slurry, characterized by comprising the following steps:

[0007] S1. Obtain slurry flow data for each pipeline through detection elements;

[0008] S2. The slurry flow data is filtered using a dynamic median-arithmetic mean filtering fusion method to reduce the impact of pulse feeding and random noise. The dynamic median-arithmetic mean filtering can be described as follows:

[0009]

[0010] Where: α represents the dynamic weighting coefficient, which is used to control the mixing ratio of median filtering and mean filtering, α∈[0,1];

[0011] x n This represents the analog input value at the nth sampling time.

[0012] Median(x n () represents the median value of the analog input;

[0013] N represents N sampled values;

[0014] x n-i Represents the sampled values ​​at historical moments;

[0015] The dynamic adjustment of the dynamic weighting coefficient α can be described as follows:

[0016]

[0017] Where: σ represents the standard deviation;

[0018] |Δx| represents the difference between the two most recent samples;

[0019] ε is a constant to prevent the denominator from being zero;

[0020] S3. The filtered flow data is used as a feedback signal to input the pulse suppression PID controller. The pulse suppression PID controller adopts a control strategy that combines incomplete differentiation, derivative-first and integral separation, and outputs a control signal to adjust the opening degree of the execution component.

[0021] Furthermore, the pulse suppression PID controller employs incomplete differentiation, introducing a low-pass filter into the derivative term to reduce the impact of high-frequency noise; it uses derivative-first differentiation, only differentiating the measured value to avoid oscillations in the derivative term caused by sudden changes in the setpoint; and it employs integral separation, stopping integration when the error is too large to prevent integral saturation. The pulse suppression PID expression can be described as follows:

[0022]

[0023] Where: K p K i K d Represents proportional, integral, and differential gain;

[0024] e k This represents the error of the Kth sampling.

[0025] T s Indicates the sampling period;

[0026] PV k This represents the k-th measurement value;

[0027] T f The filtering time constant represents the incomplete derivative.

[0028] Furthermore, the sampling error and integral separation logic can be described as follows:

[0029] Sampling error:

[0030] e k =r k -PV k

[0031] Integral separation logic:

[0032]

[0033] Where: r k ε represents the set value, and ε represents the integral separation threshold.

[0034] Furthermore, the differential term is based solely on the measured rate of change PV. k -PV k-1 T f +T s Used to implement first-order low-pass filtering and suppress noise.

[0035] Furthermore, the method also includes a manual control mode, in which the opening degree of the actuator is directly controlled by inputting a control quantity corresponding to a 4-20mA current signal through a touch screen or a host computer DCS.

[0036] Furthermore, the detection element includes a slurry electromagnetic flow meter and a slurry concentration meter, and the actuation component includes a linear motion cylinder, a positioner, a valve stem, and a valve core.

[0037] Furthermore, the PLC controller communicates with the host DCS using the Modbus RTU protocol, and the PLC controller communicates with the touch screen using the Ethernet / IP protocol.

[0038] Furthermore, the constant ε is set to 0.001.

[0039] A PLC control system for stabilizing mineral processing slurry, characterized in that the system is used to implement the mineral processing slurry stabilization control method according to any one of claims 1-8, comprising:

[0040] The system includes a host computer, a PLC controller, detection elements, a human-machine interface, and execution components; the detection elements are connected to the input terminal of the PLC controller, the execution components are connected to the output terminal of the PLC controller, and the PLC controller has a bidirectional communication connection with the host computer.

[0041] Furthermore, the detection element includes a slurry electromagnetic flow meter for real-time detection of slurry flow in each pipeline, and a slurry concentration meter for real-time detection of slurry concentration in the ore distribution tank; the actuation component includes a valve composed of a linear motion cylinder, a positioner, a valve stem, and a valve core.

[0042] The beneficial effects of this invention are as follows:

[0043] 1. This invention employs a dynamic median-arithmetic mean filtering fusion method, which adaptively fuses median filtering and mean filtering through a dynamic weighting coefficient α. When the sampled values ​​are highly discrete (large standard deviation σ) and adjacent samples exhibit drastic changes (large |Δx|), according to the formula α=σ / (σ+|Δx|+ε), the dynamic weighting coefficient α approaches 1, enhancing the median filtering's ability to suppress impulse noise. When the sampled values ​​are stably distributed, the dynamic weighting coefficient α approaches 0, enhancing the mean filtering's smoothing effect on random noise. This method can adaptively adjust the filtering strategy in real time based on the signal's statistical characteristics.

[0044] 2. This invention employs a pulse suppression PID controller, combining three improvement strategies: incomplete differentiation, derivative-first, and integral separation. Incomplete differentiation reduces the impact of high-frequency noise by introducing a low-pass filter into the derivative term; derivative-first differentiation only differentiates the measured value, avoiding oscillations in the derivative term caused by sudden changes in the setpoint; integral separation stops integration when the error is too large, preventing integral saturation. The three strategies work synergistically to effectively eliminate the interference of pulse noise on the control system.

[0045] 3. This invention combines dynamic filtering with a pulse suppression PID controller, forming a complete noise suppression solution for the specific application scenario of stable control of mineral processing slurry flow. It significantly improves the anti-interference ability of the PLC control system against pulse feeding noise and has the advantages of high precision, fast response, and strong stability. Attached Figure Description

[0046] Figure 1 This is a flowchart of the manual value input control method of the present invention;

[0047] Figure 2 This is a flowchart of the automatic PID control method of the present invention;

[0048] Figure 3 This is a control system diagram of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0050] Example 1

[0051] Please see Figure 1 , Figure 3 As shown, this embodiment provides a manual PLC control method for stabilizing mineral processing slurry, implemented through a host computer DCS system, a PLC controller, a touch screen, a flow meter, a concentration meter, and the actuator. The DCS system and the PLC controller communicate via Modbus RTU, while the touch screen and the PLC controller communicate via Ethernet / IP protocol.

[0052] The manual control method in this embodiment includes the following steps:

[0053] S1. Acquire slurry flow data from each pipeline through detection elements. The PLC controller obtains analog signals from the electromagnetic flowmeters and concentration meters of each branch through the AI ​​input module and converts them into digital signals.

[0054] S2. The slurry flow data is filtered using a dynamic median-arithmetic mean filtering fusion method to eliminate the effects of pulse feeding and random noise.

[0055] The dynamic median-arithmetic mean filtering fusion method is as follows:

[0056]

[0057] Where: α represents the dynamic weighting coefficient, used to control the mixing ratio of median filtering and mean filtering, α∈[0,1]; x n Median(x) represents the analog input value at the nth sampling time. n ) represents the median of the analog input value; N represents the N sampled values; x n-i This represents the sampled value at a historical moment.

[0058] The dynamic weighting coefficient α is dynamically adjusted according to the following formula:

[0059]

[0060] Where: σ represents the standard deviation; |Δx| represents the difference between the two most recent samples; ε is a constant, set to 0.001, to prevent the denominator from being zero.

[0061] The filtered traffic data is transmitted to the host computer and displayed on the touch screen in real time.

[0062] S3. In manual control mode, the opening degree of the actuator can be directly controlled by inputting the control quantity corresponding to the 4-20mA current signal through the touch screen or the host computer DCS.

[0063] Specifically, the feed flow rate of the ore pump is acquired by the DCS and transmitted to the PLC controller. The touchscreen human-machine interface reads the feed flow rate from the PLC's internal data display. The human-machine interface allows selection between remote DCS control and local human-machine interface control; the default is remote DCS control. After selecting a control mode, a proportional digital signal for valve opening is input to the PLC controller. The PLC controller converts the digital signal into an analog current signal, which is then output to the valve positioner via the AQ module. The valve positioner controls the valve cylinder to move to the target position based on the input current signal.

[0064] In this embodiment, the detection elements include a slurry electromagnetic flow meter and a slurry concentration meter. The slurry electromagnetic flow meter is used to detect the slurry flow rate in each pipeline in real time, and the slurry concentration meter is used to detect the slurry concentration in the ore distribution tank in real time. The actuation components include a linear motion cylinder, a positioner, a valve stem, and a valve core.

[0065] In this embodiment, the touch screen includes a PLC operation interface, which includes real-time display of slurry flow rate and concentration, automatic and manual PID control of the execution component, automatic control parameter adjustment, communication status monitoring and display with the host computer, PLC module alarm, and switching between remote and local control.

[0066] Example 2

[0067] The difference between this embodiment and Embodiment 1 is that, please refer to... Figure 2 , Figure 3 As shown, this embodiment provides an automatic PLC control method for the actuator of mineral processing slurry stabilization, which is implemented through a PLC controller, a touch screen, a flow meter, a concentration meter, and the actuator.

[0068] The automatic control method in this embodiment includes the following steps:

[0069] S1. Obtain slurry flow data from each pipeline through detection elements. Analog signals of flow values ​​are obtained from the flow meters of each branch, and then converted into digital signals by the PLC controller.

[0070] S2. The slurry flow data is filtered using a dynamic median-arithmetic mean filtering fusion method.

[0071] The experiment adjusted parameters such as the sampling period, dynamic weight, and number of sampled values ​​of the dynamic median-arithmetic mean filtering algorithm. The dynamic median-arithmetic mean filtering algorithm was used to filter the digital signal of the flow rate, eliminating the interference of impulse noise on the flow rate signal. The filtered flow rate was displayed through a human-computer interaction interface.

[0072] The dynamic median-arithmetic mean filtering fusion method is as follows:

[0073]

[0074] Where: α represents the dynamic weighting coefficient, used to control the mixing ratio of median filtering and mean filtering, α∈[0,1]; x n Median(x) represents the analog input value at the nth sampling time. n ) represents the median of the analog input value; N represents the N sampled values; x n-i This represents the sampled value at a historical moment.

[0075] The dynamic weighting coefficient α is dynamically adjusted according to the following formula:

[0076]

[0077] Where: σ represents the standard deviation; |Δx| represents the difference between the two most recent samples; ε is a constant, set to 0.001, to prevent the denominator from being zero.

[0078] S3. The filtered flow data is used as a feedback signal to input the pulse suppression PID controller. The pulse suppression PID controller adopts a control strategy that combines incomplete differentiation, derivative-first and integral separation, and outputs a control signal to adjust the opening degree of the execution component.

[0079] Specifically, the DCS acquires the real-time feed flow rate of the feed pump and sends it to the PLC controller for display on the human-machine interface (HMI). PID automatic control is set through the HMI, and the flow rate to be allocated to each branch is set based on the real-time feed flow rate of the feed pump.

[0080] The pulse suppression PID controller employs incomplete differentiation, introducing a low-pass filter into the derivative term to reduce the impact of high-frequency noise; it uses derivative-first differentiation, only differentiating the measured value to avoid oscillations in the derivative term caused by sudden changes in the setpoint; and it employs integral separation, stopping integration when the error is too large to prevent integral saturation. The pulse suppression PID expression can be described as:

[0081]

[0082] Where: K p K i K d Represents proportional, integral, and differential gain; ek T represents the error of the Kth sampling; s Indicates the sampling period; PV k T represents the k-th measurement value; f The filtering time constant represents the incomplete derivative.

[0083] The formula for calculating the sampling error ek is:

[0084] e k =r k -PV k

[0085] Where: rk represents the set value.

[0086] The integral separation logic is expressed as follows:

[0087]

[0088] Where: ε represents the integral separation threshold, which is set to 1.5 to 2 times the maximum allowable steady-state error of the system.

[0089] The differential term is based solely on the measured rate of change PV k -PV k-1 T f +T s Used to implement first-order low-pass filtering and suppress noise.

[0090] In this embodiment, the method for adjusting the parameters of the pulse suppression PID controller is as follows:

[0091] Adjust the pulse suppression PID controller parameters through experience or experimental methods. For the proportional gain K... p Integral gain K i To adjust the differential gain Kd, first adjust the proportional gain K. p The gain is increased from a small value until the system response is fast but without overshoot; then the integral K is adjusted. i The value is gradually increased from a small value to eliminate steady-state error; finally, the differential Kd gain is adjusted to suppress overshoot.

[0092] Sampling period T s The sampling period T should be less than 1 / 10 to 1 / 20 of the system's dynamic response time. s The smaller the value, the higher the control precision; sampling period T s The larger the size, the stronger the noise resistance.

[0093] The filter time constant Tf is adjusted according to the system response. When noise is significant, the filter constant is increased; when overshoot increases or the system response slows down, the filter constant is decreased.

[0094] The integral separation threshold ε is slightly greater than the system's allowable steady-state error, but must be less than the large error range that triggers integral saturation.

[0095] The pulse suppression PID controller takes the flow digital signal as input and automatically adjusts the output of the control parameter digital signal. The output digital signal of the PID controller is converted into an analog signal by the AQ module and input to the valve positioner. The valve positioner controls the cylinder to move to the target position.

[0096] Example 3

[0097] The difference between this embodiment and Embodiments 1 and 2 is that, please refer to... Figure 3 As shown, this embodiment provides a PLC control system for stabilizing mineral processing slurry, including:

[0098] The system includes a host computer, a PLC controller, detection elements, a human-machine interface, and execution components. The detection elements are connected to the input terminals of the PLC controller, the execution components are connected to the output terminals of the PLC controller, and the PLC controller has a bidirectional communication connection with the host computer.

[0099] The detection element includes a slurry electromagnetic flow meter for real-time detection of slurry flow in each pipeline, and a slurry concentration meter for real-time detection of slurry concentration in the ore sorting tank.

[0100] The actuation component includes a valve consisting of a linear motion cylinder, a positioner, a valve stem, and a valve core.

[0101] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A PLC control method for stabilizing mineral processing slurry, characterized in that, Includes the following steps: S1. Obtain slurry flow data for each pipeline through detection elements; S2. The slurry flow data is filtered using a dynamic median-arithmetic mean filtering fusion method to reduce the impact of pulse feeding and random noise. The dynamic median-arithmetic mean filtering can be described as follows: Where: α represents the dynamic weighting coefficient, which is used to control the mixing ratio of median filtering and mean filtering, α∈[0,1]; x n This represents the analog input value at the nth sampling time. Median(x n () represents the median value of the analog input; N represents N sampled values; x n-i Represents the sampled values ​​at historical moments; The dynamic adjustment of the dynamic weighting coefficient α can be described as follows: Where: σ represents the standard deviation; |Δx| represents the difference between the two most recent samples; ε is a constant to prevent the denominator from being zero; S3. The filtered flow data is used as a feedback signal to input the pulse suppression PID controller. The pulse suppression PID controller adopts a control strategy that combines incomplete differentiation, derivative-first and integral separation, and outputs a control signal to adjust the opening degree of the execution component.

2. The PLC control method for stabilizing mineral processing slurry according to claim 1, characterized in that, The pulse suppression PID controller employs incomplete differentiation, introducing a low-pass filter into the derivative term to reduce the impact of high-frequency noise; it uses derivative-first differentiation, only differentiating the measured value to avoid oscillations in the derivative term caused by sudden changes in the setpoint; and it employs integral separation, stopping integration when the error is too large to prevent integral saturation. The pulse suppression PID expression can be described as follows: Where: K p K i K d Represents proportional, integral, and differential gain; e k This represents the error of the Kth sampling. T s Indicates the sampling period; PV k This represents the k-th measurement value; T f The filtering time constant represents the incomplete derivative.

3. The PLC control method for stabilizing mineral processing slurry according to claim 2, characterized in that, The sampling error and integral separation logic can be described as follows: Sampling error: and k =r k -PV k Integral separation logic: Where: r k ε represents the set value, and ε represents the integral separation threshold.

4. The PLC control method for stabilizing mineral processing slurry according to claim 2, characterized in that, The differential term is based solely on the measured rate of change PV k -PV k-1 T f +T s Used to implement first-order low-pass filtering and suppress noise.

5. The PLC control method for stabilizing mineral processing slurry according to claim 1, characterized in that, The method also includes a manual control mode, in which the control quantity corresponding to the 4-20mA current signal is input through the touch screen or the host computer DCS to directly control the opening degree of the actuator.

6. The PLC control method for stabilizing mineral processing slurry according to claim 1, characterized in that, The detection elements include a slurry electromagnetic flow meter and a slurry concentration meter, and the actuation components include a linear motion cylinder, a positioner, a valve stem, and a valve core.

7. The PLC control method for stabilizing mineral processing slurry according to claim 1, characterized in that, The PLC controller communicates with the host DCS via the Modbus RTU protocol, and the PLC controller communicates with the touch screen via the Ethernet / IP protocol.

8. The PLC control method for stabilizing mineral processing slurry according to claim 1, characterized in that, The constant ε is set to 0.

001.

9. A PLC control system for stabilizing mineral processing slurry, characterized in that, This system is used to implement the mineral processing slurry stabilization control method according to any one of claims 1-8, comprising: The system includes a host computer, a PLC controller, detection elements, a human-machine interface, and execution components; the detection elements are connected to the input terminal of the PLC controller, the execution components are connected to the output terminal of the PLC controller, and the PLC controller has a bidirectional communication connection with the host computer.

10. The PLC control system for stabilizing mineral processing slurry according to claim 9, characterized in that, The detection element includes a slurry electromagnetic flow meter for real-time detection of slurry flow in each pipeline, and a slurry concentration meter for real-time detection of slurry concentration in the ore sorting tank; the actuation component includes a valve consisting of a linear motion cylinder, a positioner, a valve stem, and a valve core.

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