CN114653485B - Flotation process fuzzy control method based on foam flow velocity - Google Patents
Flotation process fuzzy control method based on foam flow velocity Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
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- B03D1/028—Control and monitoring of flotation processes; computer models therefor
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Abstract
The application relates to the field of froth flotation automation, and discloses a flotation process fuzzy control method based on froth flow velocity. Firstly, detecting the flow velocity of flotation foam by adopting an image processing method, combining a flow velocity set value, respectively calculating set values of aeration air pressure and foam layer thickness in the flotation process by adopting a fuzzy control method, and sending the set values to a PID controller. In order to enhance the self-adaptive capacity of the fuzzy controller and meet the control requirements under various flow speed set values, a multi-input multi-output fuzzy control method of self-adaptive parameters according to the flow speed set values is provided, so that the foam flow speed tracking set values are better realized. The application overcomes the subjective uncertainty of manually adjusting the air pressure and the thickness of the foam layer, realizes the purpose of tracking the actual flow speed of the foam to the set flow speed, provides conditions for the subsequent regulation and control of grade and recovery rate, and improves the economic and technical indexes of the flotation process.
Description
Technical Field
The application relates to the field of froth flotation automation, in particular to a flotation process fuzzy control method based on froth flow velocity.
Background
Mineral resources occupy important positions in the national economic development process, and more than 95% of energy sources and 85% of industrial raw materials in China are taken from the mineral resources. Most mineral resources in nature can be utilized only after mineral separation. As mineral resources are non-renewable natural resources, with the increasing consumption of high-grade mineral resources, people pay more and more attention to the sorting level of minerals in order to fully utilize limited mineral resources and also to protect the ecological environment on which humans depend.
Froth flotation is the only flotation method still in use at present, and is also the most important and widely used beneficiation method in mineral processing technology. The main problems faced at present are long technological process, complex process, many interference factors and the like in the froth flotation process, so that intelligent optimization control of flotation is difficult to realize. The failure to accurately acquire the state information of the flotation process is also an important factor for restricting the automatic development of the flotation process. So in the past, workers often use naked eyes to observe the color, the size, the texture and the flow rate of flotation foam, judge the current flotation working condition according to experience, and adjust an actuator so as to control the flotation process. The flotation state is judged according to manual experience, and the mode of controlling the flotation process has the problems of strong subjectivity, inconvenience in statistics and archiving, inaccuracy, unreliability and the like, and the precision and efficiency of the flotation process are easily reduced, so that the current industrial production requirement cannot be met. In the past decades, methods of computer vision, intelligent control, etc. have evolved rapidly. The application of the emerging technologies to flotation process monitoring and control is a foundation stone for building intelligent factories, and has very important significance for improving economic and technical indexes of flotation processes and reducing labor intensity of workers.
The flotation rate constant and the bubble surface area flow rate show a strong dependence, so that the froth flow rate can be used as an indication quantity of flotation rate, froth belt ore degree and the like, and the parameters have a relatively clear corresponding relation with flotation performance. In actual flotation production, the characteristics of the flow rate of the flotation froth are related to working condition parameters such as the pH value and the mineral content of ore pulp, the dosage and the like, and the different poles of the flow rate of the flotation froth greatly influence the time of collision and combination of mineral particles and the flotation froth in the froth layer, so that the influence on the yield and the concentrate grade of flotation is great. Therefore, controlling the froth flow rate plays a significant role in flotation process control. The fuzzy algorithm is indeed very suitable for use in time-lapse systems, since the difficult quantification and uncertainty of time-lapse systems determines that it requires uncertain processing means. However, since the flotation process is complex and variable, the control index also tends to change, so that calibration of the fuzzy controller is necessary. In view of the above, the application adjusts the parameters of the fuzzy controller through the flow rate set value, enhances the self-adaptive capacity of the fuzzy controller, calculates the increment value of the aeration air pressure and the foam layer thickness in the flotation process, and realizes the foam flow rate control.
Disclosure of Invention
The application aims to provide a flotation process fuzzy control method based on a foam flow rate. In order to enhance the self-adaptive capacity of the fuzzy controller and meet the control requirements under various flow speed set values, a multi-input multi-output fuzzy control method of self-adaptive parameters according to the flow speed set values is provided, so that the foam flow speed tracking set values are better realized.
In order to achieve the above object, the present application provides a flotation process fuzzy control method based on froth flow rate, comprising:
setting a flow rate set value according to a flotation working condition;
detecting the foam flow velocity to obtain a flow velocity detection value, and calculating a flow velocity change trend;
judging whether the current working condition is normal or not, if the current working condition is abnormal, not performing flow rate control, and if the current working condition is normal, performing flow rate control;
the flow rate control includes: and adjusting parameters of the fuzzy controller by using the flow rate set value, taking the flow rate detection value and the flow rate change trend as inputs of the fuzzy controller, calculating the air pressure set value and the foam layer thickness set value by using a fuzzy control method, and adjusting the air pressure and the foam layer thickness according to the calculation result so as to control the foam flow rate.
Preferably, the setting of the flow rate set point according to the flotation working condition specifically includes:
(1) If the flotation foam is unstable (the flotation foam is frequently broken and cannot overflow in blocks after lasting for 1-3 min), the foam color is light (the image gray value is 180-255), the mineral content is small (0-15%), and the flow speed set value is reduced by 1-5 mm/s;
(2) If the flotation foam is stable, the foam color is dark (the gray value of the image is 0-120), the mineral content is large (45% -100%), and the set value of the flow rate is increased by 1-5 mm/s. And ensures that the flow rate set point is within a certain range (10 mm/s-35 mm/s).
Preferably, the foam flow rate is detected, specifically comprising the following steps:
(1) Forming an original foam image set G1 according to continuous frame RGB foam image data acquired by a foam flotation site;
(2) And detecting the flow velocity of the flotation foam by adopting an image processing technology to obtain a flow velocity value V reflected by the current foam image.
Preferably, the calculated flow rate variation trend is specifically calculated from the variation amplitude of the current flow rate V in a fixed time.
Preferably, the determination of whether the current working condition is normal or not is specifically that if V is in the proper range [ V max ～V min ]The current flow rate is in a normal operating condition.
Preferably, the adjusting the parameters of the fuzzy controller with the flow rate set value specifically includes the following steps:
(1) Selecting the 'slow flow velocity SV', 'normal flow velocity NV', 'fast flow velocity FV' to describe the current flow velocity V
(2) Selecting a deceleration DV, a stable ZV and a fast flow UV to describe a flow speed change trend EV;
(3) The robustness of the control system is enhanced by adapting the parameters of the fuzzy controller to the flow rate set values; correcting parameters of the fuzzy controller according to the flow rate set value before entering the fuzzy controller each time:
according to the flow velocity set value V set The theory domain of the flow velocity V and the flow velocity change trend EV is adjusted, and V epsilon [0,2V ] set ]，EV∈[-V set ，V set ]The method comprises the steps of carrying out a first treatment on the surface of the Through the flow velocity set point V set Adjusting membership function parameters of the fuzzy controller;
f(V，k c V set ，k σ V set )
wherein f represents a Gaussian membership function, k c V set Represents the central value proportion parameter, k σ V set Representing the standard deviation ratio parameter. Preferably, the air pressure set value and the foam layer thickness set value are calculated by a fuzzy control method, and specifically comprise the following steps:
(1) Selecting a large-scale air pressure reducing BDA, a small-scale air pressure reducing SDA, an air pressure unchanged ZA, a small-scale air pressure increasing SIA and a large-scale air pressure increasing BIA to describe an output air pressure increasing value UA;
(2) Selecting a foam layer thickness increment value UT which is output by the description of ' greatly reducing the foam layer thickness BDT ', ' slightly reducing the foam layer thickness SDT ', ' not changing ZT ' the foam layer thickness ', ' slightly increasing the foam layer thickness SIT ', ' greatly increasing the foam layer thickness BIT ';
(3) Designing a fuzzy control rule of the air pressure increment value;
(4) Calculating the membership degree of each semantic meaning of the output variable by using a product method according to the fuzzy control rule, and taking the maximum value as a calculation result of the semantic meaning membership degree;
(5) Performing anti-blurring by using a gravity center method, and calculating an output variable air pressure increment value UA and a foam layer thickness increment value UT;
(6) Calculating air pressure set value and foam layer thickness set value and limiting amplitude
In SA k 、SA k-1 Respectively representing the set values of the air pressure at the moment k and the moment k-1; UA (UA) k The air pressure increment value calculated at the moment k is represented; SA (SA) min 、SA max The maximum value and the minimum value of the air pressure amplitude limit are adopted; ST (ST) k 、ST k-1 Respectively representing the set values of the thickness of the foam layer at the time k and the time k-1; UT (UT) k Foam representing the calculation of the moment kLayer thickness increment value; ST (ST) min 、ST max Limiting the maximum value and the minimum value of the foam layer thickness;
(7) Will calculate the result SA k As the current air pressure PID controller setting value, ST k As the current foam layer thickness PID controller setpoint.
The application has the following beneficial effects:
the application provides a flotation process fuzzy control method based on a foam flow rate. In order to enhance the self-adaptive capacity of the fuzzy controller, a multi-input multi-output fuzzy control method for self-adaptive parameters according to a flow rate set value is provided, the unstable output of the fuzzy controller caused by the overlarge range of the flow rate set value is overcome, the requirements of different flow rate set values under complex working conditions are met, the robustness of a control system is enhanced, and therefore the foam flow rate tracking set value is realized. The application overcomes the subjective uncertainty of manually adjusting the air pressure and the thickness of the foam layer, so as to provide conditions for the subsequent regulation and control of grade and recovery rate, thereby improving the economic and technical indexes of the flotation process.
The application will be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow rate control cycle chart of a preferred embodiment of the present application;
FIG. 2 is a flow rate control block diagram of a preferred embodiment of the present application;
fig. 3 is a graph of flow rate control results according to a preferred embodiment of the present application.
Detailed Description
Embodiments of the application are described in detail below with reference to the attached drawings, but the application can be implemented in a number of different ways, which are defined and covered by the claims.
Example 1
Referring to fig. 1 and 2, the embodiment discloses a flotation process fuzzy control method based on a froth flow rate, which includes:
setting a flow rate set value according to a flotation working condition;
detecting the foam flow velocity to obtain a flow velocity detection value, and calculating a flow velocity change trend;
judging whether the current working condition is normal or not, if the current working condition is abnormal, not performing flow rate control, and if the current working condition is normal, performing flow rate control;
the flow rate control includes: and adjusting parameters of the fuzzy controller by using the flow rate set value, taking the flow rate detection value and the flow rate change trend as inputs of the fuzzy controller, calculating the air pressure set value and the foam layer thickness set value by using a fuzzy control method, and adjusting the air pressure and the foam layer thickness according to the calculation result so as to control the foam flow rate.
Specifically, a certain tin ore dressing plant flotation column was used as a case for analysis. And installing a foam flow rate control cabinet on an industrial site of a certain tin ore dressing plant.
Firstly, forming an original foam image set G1 according to continuous frame RGB foam image data acquired by a foam flotation site; and detecting the flow velocity of the flotation foam by adopting an image processing technology to obtain a flow velocity value V reflected by the current foam image.
Judging whether the current working condition is normal or not according to the detected foam flow velocity value V, if V is in a proper range [5 mm/s-40 mm/s ], the current flow velocity is in a normal working condition state, continuing to perform flow velocity control, and otherwise, not performing flow velocity control.
And calculating the flow rate change trend according to the current flow rate V change amplitude in a fixed time (1-3 minutes).
The flow rate setting value is set by an operator according to the flotation working condition, if flotation foam is unstable (the flotation foam is frequently broken for 1-3 min and cannot overflow in a block), the foam color is light (the image gray value is 180-255), the mineral content is small (0-15%), and the flow rate setting value is reduced by 1-5 mm/s; if the flotation foam is stable, the foam color is dark (the gray value of the image is 0-120), the mineral content is large (45% -100%), and the set value of the flow rate is increased by 1-5 mm/s. And ensures that the flow rate set point is within a certain range (10 mm/s-35 mm/s).
Because the flotation working condition is complex and changeable, the flow rate set value is increased or decreased according to the specific working condition, the condition that the value range is overlarge is easy to occur, and the robustness of the control system is enhanced by adopting the mode of self-adapting the parameters of the fuzzy controller to the flow rate set value.
The parameters of the fuzzy controller are corrected according to the flow rate set values before entering the fuzzy controller each time, and the specific steps are as follows:
(1) Selecting the 'slow flow velocity SV', 'normal flow velocity NV', 'fast flow velocity FV' to describe the current flow velocity V
(2) Selecting a deceleration DV, a stable ZV and a fast flow UV to describe a flow speed change trend EV;
(3) According to the flow velocity set value V set The theory domain of the flow velocity V and the flow velocity change trend EV is adjusted, and V epsilon [0,2V ] set ]，EV∈[-V set ，V set ]；
(4) Through the flow velocity set point V set Adjusting membership function parameters of the fuzzy controller;
f(V，k c V set ，k σ V set )
wherein f represents a Gaussian membership function, k c V set Represents the central value proportion parameter, k σ V set Represents the standard deviation proportional parameter, k c V set ∈{0，1，2}，k σ V set ∈[0.1，0.4]。
Finally, calculating an air pressure set value and a foam layer thickness set value through a fuzzy control method, and sending the air pressure set value and the foam layer thickness set value to an air pressure and foam layer thickness PID controller, wherein the method specifically comprises the following steps of:
(1) Selecting a large-scale air pressure reducing BDA, a small-scale air pressure reducing SDA, an air pressure unchanged ZA, a small-scale air pressure increasing SIA and a large-scale air pressure increasing BIA to describe an output air pressure increasing value UA;
(2) Selecting a foam layer thickness increment value UT which is output by the description of ' greatly reducing the foam layer thickness BDT ', ' slightly reducing the foam layer thickness SDT ', ' not changing ZT ' the foam layer thickness ', ' slightly increasing the foam layer thickness SIT ', ' greatly increasing the foam layer thickness BIT ';
(3) Designing a fuzzy control rule of the air pressure increment value;
(4) Calculating the membership degree of each semantic meaning of the output variable by using a product method according to the fuzzy control rule, and taking the maximum value as a calculation result of the semantic meaning membership degree;
(5) Performing anti-blurring by using a gravity center method, and calculating an output variable air pressure increment value UA and a foam layer thickness increment value UT;
(6) Calculating an air pressure set value and a foam layer thickness set value and limiting amplitude;
in SA k 、SA k-1 Respectively representing the set values of the air pressure at the moment k and the moment k-1; UA (UA) k The air pressure increment value calculated at the moment k is represented; SA (SA) min 、SA max The maximum value of the air pressure limit is 650kpa, and the minimum value is 400kpa; ST (ST) k 、ST k-1 Respectively representing the set values of the thickness of the foam layer at the time k and the time k-1; UT (UT) k Representing the foam layer thickness increment value calculated at the time k; ST (ST) min 、ST max Limiting the foam layer thickness to a maximum value of 1000mm and a minimum value of 600mm;
(7) Will calculate the result SA k As the current air pressure PID controller setting value, ST k As the current foam layer thickness PID controller setpoint.
The control result of the control period of 2min and 1400 times is selected, and referring to fig. 3, the actual flow speed fluctuates up and down at the set value, so that the purpose of tracking the set value of the actual flow speed is realized.
In summary, the application provides a flotation process fuzzy control method based on a froth flow rate, which comprises the steps of firstly setting a flow rate control period and a flow rate set value through a human-computer interaction interface, detecting the froth flow rate of flotation by using an image processing method, combining the flow rate set value, respectively calculating the set values of the aeration air pressure and the froth layer thickness in the flotation process by using a fuzzy control method, and finally sending the set values to a PID controller. In order to enhance the self-adaptive capacity of the fuzzy controller and meet the control requirements under various flow speed set values, a multi-input multi-output fuzzy control method of self-adaptive parameters according to the flow speed set values is provided, so that the foam flow speed tracking set values are better realized.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (6)
1. A flotation process fuzzy control method based on foam flow rate is characterized by comprising the following steps:
setting a flow rate set value according to a flotation working condition;
detecting the foam flow velocity to obtain a flow velocity detection value, and calculating a flow velocity change trend;
judging whether the current working condition is normal or not, if the current working condition is abnormal, not performing flow rate control, and if the current working condition is normal, performing flow rate control: if the flotation foam is unstable, namely the flotation foam is frequently broken for 1-3 min and cannot overflow in a lump, the gray value of a foam image is 180-255, and the mineral content is 0-15%, the set value of the flow rate is reduced by 1-5 mm/s; if the flotation foam is stable, the gray value of the foam image is 0-120, the mineral content is 45% -100%, the flow rate set value is increased by 1-5 mm/s, and the flow rate set value is ensured to be 10-35 mm/s;
the flow rate control includes: and adjusting parameters of the fuzzy controller by using the flow rate set value, taking the flow rate detection value and the flow rate change trend as inputs of the fuzzy controller, calculating the air pressure set value and the foam layer thickness set value by using a fuzzy control method, and adjusting the air pressure and the foam layer thickness according to the calculation result so as to control the foam flow rate.
2. The froth flow rate-based flotation process fuzzy control method of claim 1, wherein the detecting the froth flow rate comprises the steps of:
(1) Forming an original foam image set G1 according to continuous frame RGB foam image data acquired by a foam flotation site;
(2) And detecting the flow velocity of the flotation foam by adopting an image processing technology to obtain a flow velocity value V reflected by the current foam image.
3. The froth flow rate-based flotation process fuzzy control method of claim 1 wherein the calculated flow rate trend is calculated from the magnitude of the current flow rate V change over a fixed period of time.
4. The method according to claim 1, wherein the determining whether the current working condition is normal is specifically that if V is in a proper range [ V max ～V min ]The current flow rate is in a normal operating condition.
5. The froth flow rate-based flotation process fuzzy control method of claim 1, wherein the adjusting the parameters of the fuzzy controller with the flow rate set point comprises the steps of:
(1) Selecting a 'slow flow rate SV', 'normal flow rate NV', 'fast flow rate FV' to describe the current flow rate V;
(2) Selecting a deceleration DV, a stable ZV and a fast flow UV to describe a flow speed change trend EV;
(3) The robustness of the control system is enhanced by adapting the parameters of the fuzzy controller to the flow rate set values; correcting parameters of the fuzzy controller according to the flow rate set value before entering the fuzzy controller each time:
according to the flow velocity set value V set The theory domain of the flow velocity V and the flow velocity change trend EV is adjusted, and V epsilon [0,2V ] set ],EV∈[-V set ,V set ]；
Through the flow velocity set point V set Adjusting membership function parameters of the fuzzy controller;
f(V,k c V set ,k σ V set )
wherein f represents a Gaussian membership function, k c V set Represents the central value proportion parameter, k σ V set Representing the standard deviation ratio parameter.
6. The froth flow rate-based flotation process fuzzy control method of claim 1, wherein the calculating the air pressure set point and the froth layer thickness set point by the fuzzy control method comprises the steps of:
(1) Selecting a large-scale air pressure reducing BDA, a small-scale air pressure reducing SDA, an air pressure unchanged ZA, a small-scale air pressure increasing SIA and a large-scale air pressure increasing BIA to describe an output air pressure increasing value UA;
(2) Selecting a foam layer thickness increment value UT which is output by the description of ' greatly reducing the foam layer thickness BDT ', ' slightly reducing the foam layer thickness SDT ', ' not changing ZT ' the foam layer thickness ', ' slightly increasing the foam layer thickness SIT ', ' greatly increasing the foam layer thickness BIT ';
(3) Designing a fuzzy control rule of the air pressure increment value;
(4) Calculating the membership degree of each semantic meaning of the output variable by using a product method according to the fuzzy control rule, and taking the maximum value as a calculation result of the semantic meaning membership degree;
(5) Performing anti-blurring by using a gravity center method, and calculating an output variable air pressure increment value UA and a foam layer thickness increment value UT;
(6) Calculating an air pressure set value and a foam layer thickness set value and limiting amplitude;
in SA k 、SA k-1 Respectively representing the set values of the air pressure at the moment k and the moment k-1; UA (UA) k The air pressure increment value calculated at the moment k is represented; SA (SA) min 、SA max The maximum value and the minimum value of the air pressure amplitude limit are adopted; ST (ST) k 、ST k-1 Respectively representing the set values of the thickness of the foam layer at the time k and the time k-1; UT (UT) k Representing the foam layer thickness increment value calculated at the time k; ST (ST) min 、ST max Limiting the maximum value and the minimum value of the foam layer thickness;
(7) Will calculate the result SA k As the current air pressure PID controller setting value, ST k As the current foam layer thickness PID controller setpoint.
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| WO2014068478A2 (en) * | 2012-10-29 | 2014-05-08 | Francois Eberhardt Du Plessis | Provision of data on the froth in a froth flotation plant |
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| GB2491134A (en) * | 2011-05-23 | 2012-11-28 | Imp Innovations Ltd | Method and apparatus for froth flotation control for optimising gas recovery |
| PE20150608A1 (en) * | 2012-05-14 | 2015-05-11 | Tech Resources Pty Ltd | FOAM FLOTATION CONTROL |
| CN115586718A (en) * | 2022-10-14 | 2023-01-10 | 武汉理工大学 | Smith-fuzzy PID control method of B-type foam mixing system |
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| WO2014068478A2 (en) * | 2012-10-29 | 2014-05-08 | Francois Eberhardt Du Plessis | Provision of data on the froth in a froth flotation plant |
| CN108855631A (en) * | 2018-06-20 | 2018-11-23 | 北京矿冶科技集团有限公司 | A kind of flotation device yield control device based on froth images analyzer |
| CN110193428A (en) * | 2019-06-19 | 2019-09-03 | 北京矿冶科技集团有限公司 | A kind of flotation flowsheet yield optimal control method |
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