CN108672025B - A kind of multi-state optimal decoupling control method of steel ball coal-grinding pulverized coal preparation system - Google Patents
A kind of multi-state optimal decoupling control method of steel ball coal-grinding pulverized coal preparation system Download PDFInfo
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- 239000003245 coal Substances 0.000 title claims abstract description 137
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 67
- 239000010959 steel Substances 0.000 title claims abstract description 67
- 238000000227 grinding Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 24
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- 238000013178 mathematical model Methods 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims abstract description 6
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 238000010298 pulverizing process Methods 0.000 claims description 49
- 239000000843 powder Substances 0.000 claims description 32
- 239000011159 matrix material Substances 0.000 claims description 30
- 238000003801 milling Methods 0.000 claims description 21
- 238000001035 drying Methods 0.000 claims description 15
- 230000004913 activation Effects 0.000 claims description 6
- 238000001704 evaporation Methods 0.000 claims description 6
- 230000008020 evaporation Effects 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000000498 ball milling Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000002817 coal dust Substances 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 208000011580 syndromic disease Diseases 0.000 claims description 3
- 238000009423 ventilation Methods 0.000 claims description 3
- 230000008878 coupling Effects 0.000 abstract description 3
- 238000010168 coupling process Methods 0.000 abstract description 3
- 238000005859 coupling reaction Methods 0.000 abstract description 3
- 230000006978 adaptation Effects 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 abstract 1
- 230000008859 change Effects 0.000 description 6
- 239000002274 desiccant Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000005303 weighing Methods 0.000 description 2
- 238000012369 In process control Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C17/00—Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls
- B02C17/10—Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls with one or a few disintegrating members arranged in the container
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C17/00—Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls
- B02C17/18—Details
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C25/00—Control arrangements specially adapted for crushing or disintegrating
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The present invention provides a kind of multiplexing mine optimal decoupling control method of steel ball coal-grinding pulverized coal preparation system, is related to automatic control technology field.This method initially sets up steel ball coal-grinding pulverized coal preparation system mathematical model, and the parameter of steel ball coal-grinding pulverized coal preparation system under different operating conditions is substituted into mathematical model, obtains controller design model；The nonlinear terms in model are estimated using neural network；Then optimal decoupling performance index function is selected, optimal decoupling control device is designed；It is finally the variation for adapting to possible operating point in steel ball coal-grinding pulverized coal preparation system operational process, designs switching mechanism, adaptation system dynamically changes.The multiplexing mine optimal decoupling control method of steel ball coal-grinding pulverized coal preparation system provided by the invention, adapt to the control of the steel ball coal-grinding pulverized coal preparation system under different operating conditions, and the coupling between variable can be eliminated realize decoupling control, guarantee to reduce production cost while yield and product quality.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a multi-working-condition optimal decoupling control method for a steel ball coal-grinding and pulverizing system.
Background
The powder-making system of the steel ball coal mill is generally adopted in domestic coal-fired thermal power plants. In a coal-fired thermal power plant, the power consumption of a powder making system of a steel ball coal mill is very large and accounts for about 50 percent of the whole power consumption of the plant. In addition, the pulverizing system is located at the front end of the thermodynamic system of the whole thermal power plant, and the running condition of the pulverizing system directly influences the safety and the economical efficiency of the whole generating set. Due to frequent change of coal types, coal supply of coal sources is unstable, and the dynamic characteristics of the coal sources change along with the change of moisture of raw coal, so that the process dynamic change of a system is large. And because the outlet temperature, the inlet negative pressure and the inlet-outlet differential pressure of the coal mill are mutually coupled, a control system consisting of a single-loop controller adopting a decentralized control design can not be put into automatic operation for a long time and only can be controlled manually, so that the accidents of overtemperature, grinding blockage and powder spraying are caused. The decoupling control of the complex industrial process with strong coupling, strong nonlinearity and dynamic characteristics changing along with the operation condition becomes a key problem to be solved urgently in process control.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-working-condition optimal decoupling control method of a steel ball coal-grinding pulverizing system, which realizes decoupling control on the steel ball coal-grinding pulverizing system under different working conditions.
A multi-working-condition optimal decoupling control method for a steel ball coal-milling and pulverizing system comprises the following steps:
step 1, establishing a mathematical model of a steel ball coal-grinding and pulverizing system;
the established mathematical model of the steel ball coal-milling and pulverizing system is as follows:
wherein, wm(t) coal mill load, Gh(t) is the mass flow of hot air in the blower system, Gw(t) is the mass flow of the medium-temperature air in the blast system, and mu (t) is the concentration of the coal powder in the coal mill; csb、Cm、Ch、Cw、Cl、CvRespectively the heat of steel balls, the heat of coal powder, the heat of hot air, the heat of warm air, the heat of cold air and the heat of ventilation in the coal mill; t isr、TlRespectively measuring the temperature of raw coal and the temperature of cold air in a coal mill; t ishIs the temperature of the hot blast in the blast system, TwIs the temperature of warm air in the blower system, TdFor drying the air temperature G in the pipelined、GlAir flow in the drying pipeline and leakage air flow are respectively; wsbFor the mass of the steel ball in the coal mill, R1Is the sum of the lifting resistance of the coarse powder separator and the coal powder, VbFor the volume of the coal mill, R is an ideal gas constant, and T is the joint of the blast system and the drying pipelineThermodynamic temperature of inlet pipe, V1For the quantity of gas, omega, in the millthrFor mill wind speed in coal mills, PoAt an initial value of the outlet pressure of the coarse separator and the fine separator, psb，ρmThe densities of the steel ball and the coal powder in the coal mill are respectively; cr(Wy) Is the specific heat of the raw coal, Br max(Wy) For maximum production of coal mills, Bm(t，Wy) For coal mill production, Q0(Wy) Heat consumed for water evaporation in coal mills, Qc(Wy) For heat generated during the crushing process of coal mills, Wδ(Wy) For the evaporation of water per unit in coal mills, Gi(t) is the air mass flow at the mill inlet, Go(t) is the air mass flow at the outlet of the coal mill;
coal mill load wm(t) mass flow rate G of hot air in the blower systemh(t) mass flow rate G of warm air in blower systemw(t) and the coal dust concentration μ (t) inside the coal mill are calculated as follows:
wherein f ish，fwRespectively the drag coefficients of the hot blast valve and the warm blast valve in the blast system, Pδh，PδwThe pressure difference between the inlet and the outlet of the hot blast valve and the pressure difference between the inlet and the outlet of the hot blast valve are respectively;
substituting parameters of the steel ball coal-milling and pulverizing system under different working conditions into the models (1) - (7), and linearizing near a working point to obtain the following controller design model:
y＝x(t)
wherein x (t) is a three-dimensional state variable corresponding to the outlet temperature, inlet negative pressure and inlet-outlet differential pressure of the coal mill, ui(t) three-dimensional control variables respectively corresponding to the actual coal feeding speed, hot air quantity and warm air quantity at the ith working point of the coal mill, Ai∈R3×3And Bi∈R3×3Are all constant value matrices, vi(. is) a bounded nonlinear term with a maximum upper limit of VmThe i is 1, 2, the m is the ith working point of the steel ball coal-grinding and powder-making system, and the m is the total number of the working points of the steel ball coal-grinding and powder-making system;
step 2, estimating a nonlinear term v in the steel ball coal-milling and coal-pulverizing system model by adopting a BP neural networki(·)；
Using BP neural network to pair non-linear terms vi(. o) estimate vi(. cndot.) is expressed in the following form:
vi(·)＝Wσ(Vφ(x，ui))+ε(t) (9)
wherein W ∈ R3×kAnd V ∈ Rk×3Are respectively ideal weight matrixes of an output layer and a hidden layer of the neural network and satisfy | | W | | luminanceF≤WM，V||F≤VM，WM、VMRespectively representing the upper bound of the weight W, V, wherein k is the number of neurons in a hidden layer; phi (x, u)i) Is an excitation function vector; ε (t) is the neural network approximation error and is bounded, i.e. | | ε | | calculation of luminanceF≤εM，εMRepresenting an upper bound of a neural network approximation error epsilon; the activation function sigma (-) is a hyperbolic tangent function, and satisfies | | | sigma (-) to count luminanceF≤σM，σMRepresents the upper bound of the activation function σ (·);
non-linear term viEstimation ofExpressed by the following formula:
wherein,andrespectively estimating ideal weight matrixes W and V;
and step 3: designing an optimal decoupling controller aiming at the steel ball coal-grinding and pulverizing system models under different working conditions;
the controller design model (8) is rewritten into the following equivalent form:
wherein A isi 1＝diagAiIs a diagonal matrix whose diagonal elements are the elements of the main diagonal of matrix A, Ai 2＝Ai-A1A matrix with a main diagonal element of zero; b isi 1＝diagBiIs a diagonal matrix, the elements on the diagonal are matrix BiElement on the main diagonal, Bi 2＝Bi-Bi 1A matrix with a main diagonal element of zero;
introducing an auxiliary input vector zi(t)∈R3The following decoupling controllers were designed:
Si 1ui(t)+Si 2ui(t)＝-Kix(t)+Lizi(t) (12)
wherein L isiIs a diagonal matrix, Si 1，Si 2And KiIs a coefficient matrix and satisfies the following relation:
Si 1Ai 2＝Bi 1Ki (13)
Si 1Bi 2＝Bi 1Si 2 (14)
substituting equation (12) into equation (11) and using equations (13) and (14), the following expression is obtained:
due to the matrix Ai 1Andall the steel ball coal-milling and coal-pulverizing system models are diagonal matrixes, so that the steel ball coal-milling and coal-pulverizing system model is composed of 3 independent univariate systems, and complete decoupling between input and states is realized;
order toWriting equation (8) as follows:
for equation (16), the auxiliary input value z is designedi(t) minimizes the following performance index J:
wherein e (t) xd(t) -x (t) is the tracking error, xd(t) is the expected output of the steel ball coal-milling powder-making system model, Q ═ QT≥0，R＝RTMore than 0 is a weighting matrix;
if it isThe device is completely controllable, and the device is capable of controlling,fully observable, optimal tracking control with interference zi(t) is
Wherein P satisfies the Riccati equation shown in the formula (19),is an approximation of the syndrome vector g (t), as shown in equation (20):
therefore, the equation (16) and the auxiliary input amount zi(t), obtaining an optimal decoupling controller u (t) as shown in the following formula:
and 4, step 4: the design switching mechanism is used for selecting the optimal decoupling controller corresponding to the optimal model, and the specific method comprises the following steps:
(1) forming a multi-model set by a plurality of steel ball coal-grinding and coal-pulverizing system models so as to cover the variation range of the steel ball coal-grinding and coal-pulverizing system parameters;
(2) the following performance indicator functions are defined:
wherein D isi(t) is a performance index function for model i, er iOutput and actual for ith modelThe error between the outputs of the ball-milling coal pulverizing system is that lambda is a forgetting factor and the value is between 0.9 and 1, alpha and beta are the weights of the performance index function for adjusting the errors in the prior art and the prior art respectively and the value is between 0 and 1; selecting a model which enables the performance index to be minimum as an optimal model;
(3) and selecting an optimal decoupling controller corresponding to the optimal model to control the steel ball coal-grinding and pulverizing system.
According to the technical scheme, the invention has the beneficial effects that: the optimal decoupling control method for the multiple working conditions of the steel ball coal milling and pulverizing system provided by the invention can realize the optimal decoupling of the ball mill system, can adapt to the dynamic change of the system caused by the change of the working point, and avoids the overlarge influence of coupling factors and nonlinear terms in the conventional single-loop control.
Drawings
Fig. 1 is a block diagram of a system for pulverizing ball-milled coal according to an embodiment of the present invention;
FIG. 2 is a flowchart of a multi-condition optimal decoupling control method for a steel ball coal milling and pulverizing system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of estimating a nonlinear term in a model of a coal pulverizing system using a BP neural network according to an embodiment of the present invention;
fig. 4 is a schematic diagram of optimally decoupling a steel ball coal-milling and pulverizing system by using an optimal decoupling controller and a switching mechanism according to an embodiment of the present invention.
In the figure, 1, a raw coal hopper; 2. a coal feeder; 3. a belt conveyor; 4. a drying pipeline; 5. a coal mill; 6. a coarse powder separator; 7. a fine powder separator; 8. a pulverized coal bunker.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, taking a certain existing steel ball coal mill pulverizing system as an example, the optimal decoupling control method for the multi-working-condition optimal decoupling control method of the steel ball coal mill pulverizing system is used for performing optimal decoupling control on the pulverizing system.
The steel ball coal mill pulverizing system is shown in figure 1, and comprises a raw coal hopper 1, a coal feeder 2, a belt conveyor 3, a drying pipeline 4, a coal mill 5, a coarse powder separator 6, a fine powder separator 7 and a hot air and warm air blast system, wherein the process flow is as follows: the crushed raw coal leaks from a raw coal hopper 1, enters a coal feeder 2, is sent into a drying pipeline 4 through a belt conveyor 3, and is mixed with a drying agent in the drying pipeline. The quantity of the drying agent in the drying pipeline and the heat carried by the drying agent are changed by adjusting the hot air flow and the warm air flow of the air blowing system. The mixture of raw coal and drying agent enters a coal mill 5 through a drying pipeline 4, is ground into coal powder with qualified fineness and temperature through the extrusion effect of steel balls in the coal mill, and then enters a coarse powder separator 6 along with air flow for separation under the suction effect of a powder exhauster. The coal powder with unqualified fineness returns to the coal mill 5 through a powder return pipe of the coarse powder separator to be continuously ground, the coal powder with qualified fineness and temperature enters the fine powder separator 7 along with the air flow, and the separated coal powder is stored in a coal powder bin 8 and is conveyed to a boiler for combustion through a powder exhauster.
The control of the coal mill 5 mainly includes the control of coal feeding speed, the control of hot air feeding amount and the control of warm air feeding amount. The coal feeding speed is mainly controlled by a weighing system consisting of a belt scale, a frequency converter, a feeding motor, a weighing sensor and the like. The control of the hot air supply quantity and the control of the warm air supply quantity are mainly related to the opening degree of an air supply valve and can be realized through PID control.
A multi-working-condition optimal decoupling control method of a steel ball coal-milling and pulverizing system is shown in figure 2 and comprises the following steps:
step 1, establishing a mathematical model of a steel ball coal-grinding and pulverizing system;
the established mathematical model of the steel ball coal-milling and pulverizing system is as follows:
wherein, wm(t) coal mill load, Gh(t) is the mass flow of hot air in the blower system, Gw(t) is the mass flow of the medium-temperature air in the blast system, and mu (t) is the concentration of the coal powder in the coal mill; csb、Cm、Ch、Cw、Cl、CvRespectively the heat of steel balls, the heat of coal powder, the heat of hot air, the heat of warm air, the heat of cold air and the heat of ventilation in the coal mill; t isr、TlRespectively measuring the temperature of raw coal and the temperature of cold air in a coal mill; t ishIs the temperature of the hot blast in the blast system, TwIs the temperature of warm air in the blower system, TdFor drying the air temperature G in the pipelined、GlAir flow in the drying pipeline and leakage air flow are respectively; wsbFor the mass of the steel ball in the coal mill, R1Is the sum of the lifting resistance of the coarse powder separator and the coal powder, VbFor the volume of the coal mill, R is an ideal gas constant, T is the thermodynamic temperature of an inlet pipe at the connection of the blast system and the drying pipeline, V1For the quantity of gas, omega, in the millthrFor mill wind speed in coal mills, PoAt an initial value of the outlet pressure of the coarse separator and the fine separator, psb，ρmThe densities of the steel ball and the coal powder in the coal mill are respectively; cr(Wy) Is the specific heat of the raw coal, Br max(Wy) For maximum production of coal mills, Bm(t，Wy) For coal mill production, Q0(Wy) Heat consumed for water evaporation in coal mills, Qc(Wy) For heat generated during the crushing process of coal mills, Wδ(Wy) For the evaporation of water per unit in coal mills, Gi(t) is the air mass flow at the mill inlet, Go(t) is the air mass flow at the outlet of the coal mill;
coal mill load wm(t) mass flow rate G of hot air in the blower systemh(t) mass flow rate G of warm air in blower systemw(t) and the coal dust concentration μ (t) inside the coal mill are calculated as follows:
wherein f ish，fwRespectively the drag coefficients of the hot blast valve and the warm blast valve in the blast system, Pδh，PδwThe pressure difference between the inlet and the outlet of the hot blast valve and the pressure difference between the inlet and the outlet of the hot blast valve are respectively;
substituting parameters of the steel ball coal-milling and pulverizing system under different working conditions into the models (1) - (7), and linearizing near a working point to obtain the following controller design model:
y＝x(t)
wherein x (t) is a three-dimensional state variable corresponding to the outlet temperature, inlet negative pressure and inlet-outlet differential pressure of the coal mill, ui(t) three-dimensional control variables respectively corresponding to the actual coal feeding speed, hot air quantity and warm air quantity at the ith working point of the coal mill, Ai∈R3×3And Bi∈R3×3Are all constant value matrices, vi(. is) a bounded nonlinear term with a maximum upper limit of VmThe i is 1, 2, the m is the ith working point of the steel ball coal-grinding and powder-making system, and the m is the total number of the working points of the steel ball coal-grinding and powder-making system;
step 2, estimating a nonlinear term v in the steel ball coal-milling and coal-pulverizing system model by adopting a BP neural networki(. The), as shown in FIG. 3, the specific method is:
using BP neural network to pair non-linear terms vi(. o) estimate vi(. cndot.) is expressed in the following form:
vi(·)＝Wσ(Vφ(x，ui))+ε(t) (9)
wherein W ∈ R3×kAnd V ∈ Rk×3Are respectively ideal weight matrixes of an output layer and a hidden layer of the neural network and satisfy | | W | | luminanceF≤WM，||V||F≤VM，WM、VMRespectively representing the upper bound of the weight W, V, wherein k is the number of neurons in a hidden layer; phi (x, u)i) Is an excitation function vector; ε (t) is the neural network approximation error and is bounded, i.e. | | ε | | calculation of luminanceF≤εM，εMRepresenting an upper bound of a neural network approximation error epsilon; the activation function sigma (-) is a hyperbolic tangent function, and satisfies | | | sigma (-) to count luminanceF≤σM，σMRepresents the upper bound of the activation function σ (·);
non-linear term viEstimation ofExpressed by the following formula:
wherein,andrespectively estimating ideal weight matrixes W and V;
and step 3: designing an optimal decoupling controller aiming at the steel ball coal-grinding and pulverizing system models under different working conditions;
the controller design model (8) is rewritten into the following equivalent form:
wherein A isi 1＝diagAiIs a diagonal matrix whose diagonal elements are the elements of the main diagonal of matrix A, Ai 2＝Ai-Ai 1A matrix with a main diagonal element of zero; b isi 1＝diagBiIs a diagonal matrix, the elements on the diagonal are matrix BiElement on the main diagonal, Bi 2＝Bi-Bi 1A matrix with a main diagonal element of zero;
introducing an auxiliary input vector zi(t)∈R3The following decoupling controllers were designed:
Si 1ui(t)+Si 2ui(t)＝-Kix(t)+Lizi(t) (12)
wherein L isiIs a diagonal matrix, Si 1，Si 2And KiIs a coefficient matrix and satisfies the following relation:
Si 1Ai 2＝Bi 1Ki (13)
Si 1Bi 2＝Bi 1Si 2 (14)
substituting equation (12) into equation (11) and using equations (13) and (14), the following expression is obtained:
due to the matrix Ai 1Andare diagonal matrixes, therefore, the formula (15) is composed of 3 independent univariate systems, and the completeness between the input and the state is realizedDecoupling;
order toWriting equation (8) as follows:
for equation (16), the auxiliary input value z is designedi(t) minimizes the following performance index J:
wherein e (t) xd(t) -x (t) is the tracking error, xd(t) is the expected output of the steel ball coal-milling powder-making system model, Q ═ QT≥0，R＝RTMore than 0 is a weighting matrix;
if it isThe device is completely controllable, and the device is capable of controlling,fully observable, optimal tracking control with interference zi(t) is
Wherein P satisfies the Riccati equation shown in the formula (19),is an approximation of the syndrome vector g (t), as shown in equation (20):
therefore, the equation (16) and the auxiliary input amount zi(t), obtaining an optimal decoupling controller u (t) as shown in the following formula:
and 4, step 4: the design switching mechanism is used for selecting the optimal decoupling controller corresponding to the optimal model, and the specific method comprises the following steps:
(1) forming a multi-model set by a plurality of steel ball coal-grinding and coal-pulverizing system models so as to cover the variation range of the steel ball coal-grinding and coal-pulverizing system parameters;
(2) the following performance indicator functions are defined:
wherein D isi(t) is a performance index function for model i, er iThe error between the ith model output and the actual ball-milling coal pulverizing system output is represented by lambda which is a forgetting factor and takes a value between 0.9 and 1, and alpha and beta which are respectively weights of the performance index function for adjusting the current error and the past error take a value between 0 and 1; selecting a model which enables the performance index to be minimum as an optimal model;
(3) and selecting an optimal decoupling controller corresponding to the optimal model to control the steel ball coal-grinding and pulverizing system.
In this embodiment, the actual coal feeding speed is measured by the belt weigher, the actual desiccant gas flow rate is measured by the gas flow meter, and the actual coal feeding speed and gas feeding amount are controlled. And obtaining the size of the coal powder particles through a particle size analyzer, and separating coarse coal from fine coal. And obtaining the humidity information of the raw coal through a humidity analyzer for determining the working point of the ball-milling coal pulverizing system. The collected data are finally transmitted to an industrial control computer through an industrial network through a PLC, control calculation is completed in the industrial control computer, the optimal coal feeder rotating speed, the hot air opening and the warm air opening are calculated, the industrial control computer sends the values to the PLC through the network, and the PLC controls the actual coal feeding speed and the actual coal feeding amount to be consistent with the given coal feeding speed and the given coal feeding amount. In this embodiment, a process of performing decoupling control on the ball-milling coal pulverizing system by using the optimal decoupling control and indirect switching controller is shown in fig. 4.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (1)
1. A multi-working-condition optimal decoupling control method of a steel ball coal milling and pulverizing system is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a mathematical model of a steel ball coal-grinding and pulverizing system;
the established mathematical model of the steel ball coal-milling and pulverizing system is as follows:
wherein, wm(t) coal mill load, Gh(t) is the mass flow of hot air in the blower system, Gw(t) is the mass flow of the medium temperature air in the blast system, and [ mu ] (t) is the coal in the coal millPowder concentration; csb、Cm、Ch、Cw、Cl、CvRespectively the heat of steel balls, the heat of coal powder, the heat of hot air, the heat of warm air, the heat of cold air and the heat of ventilation in the coal mill; t isr、TlRespectively measuring the temperature of raw coal and the temperature of cold air in a coal mill; t ishIs the temperature of the hot blast in the blast system, TwIs the temperature of warm air in the blower system, TdFor drying the air temperature G in the pipelined、GlAir flow in the drying pipeline and leakage air flow are respectively; wsbFor the mass of the steel ball in the coal mill, R1Is the sum of the lifting resistance of the coarse powder separator and the coal powder, VbFor the volume of the coal mill, R is an ideal gas constant, T is the thermodynamic temperature of an inlet pipe at the connection of the blast system and the drying pipeline, V1For the quantity of gas, omega, in the millthrFor mill wind speed in coal mills, PoAt an initial value of the outlet pressure of the coarse separator and the fine separator, psb，ρmThe densities of the steel ball and the coal powder in the coal mill are respectively; cr(Wy) Is the specific heat of the raw coal, Brmax(Wy) For maximum production of coal mills, Bm(t，Wy) For coal mill production, Q0(Wy) Heat consumed for water evaporation in coal mills, Qc(Wy) For heat generated during the crushing process of coal mills, Wδ(Wy) For the evaporation of water per unit in coal mills, Gi(t) is the air mass flow at the mill inlet, Go(t) is the air mass flow at the outlet of the coal mill;
coal mill load wm(t) mass flow rate G of hot air in the blower systemh(t) mass flow rate G of warm air in blower systemw(t) and the coal dust concentration μ (t) inside the coal mill are calculated as follows:
wherein f ish，fwRespectively the drag coefficients of the hot blast valve and the warm blast valve in the blast system, Pδh，PδwThe pressure difference between the inlet and the outlet of the hot blast valve and the pressure difference between the inlet and the outlet of the hot blast valve are respectively;
substituting parameters of the steel ball coal-milling and pulverizing system under different working conditions into the models (1) - (7), and linearizing near a working point to obtain the following controller design model:
y＝x(t)
wherein x (t) is a three-dimensional state variable corresponding to the outlet temperature, inlet negative pressure and inlet-outlet differential pressure of the coal mill, ui(t) three-dimensional control variables respectively corresponding to the actual coal feeding speed, hot air quantity and warm air quantity at the ith working point of the coal mill, Ai∈R3 ×3And Bi∈R3×3Are all constant value matrices, vi(. is) a bounded nonlinear term with a maximum upper limit of VmThe i is 1, 2, the m is the ith working point of the steel ball coal-grinding and powder-making system, and the m is the total number of the working points of the steel ball coal-grinding and powder-making system;
step 2, estimating a nonlinear term in the steel ball coal-milling and coal-pulverizing system model by adopting a BP neural network;
using BP neural network to pair non-linear terms vi(. o) estimate vi(. cndot.) is expressed in the following form:
vi(·)＝Wσ(Vφ(x，ui))+ε(t) (9)
wherein W ∈ R3×kAnd V ∈ Rk×3Are respectively ideal weight matrixes of an output layer and a hidden layer of the neural network and satisfy | | W | | luminanceF≤WM，||V||F≤VM，WM、VMRespectively representing the upper bound of the weight W, V, wherein k is the number of neurons in a hidden layer; phi (x, u)i) Is an excitation function vector; ε (t) is the neural network approximation error and is bounded, i.e. | | ε | | calculation of luminanceF≤εM，εMRepresenting an upper bound of a neural network approximation error epsilon; the activation function sigma (-) is a hyperbolic tangent function, and satisfies | | | sigma (-) to count luminanceF≤σM，σMRepresents the upper bound of the activation function σ (·);
non-linear term viEstimation ofExpressed by the following formula:
wherein,andrespectively estimating ideal weight matrixes W and V;
step 3, designing an optimal decoupling controller for the steel ball coal-milling and coal-pulverizing system models under different working conditions;
the controller design model (8) is rewritten into the following equivalent form:
wherein A isi 1＝diagAiIs a diagonal matrix, the elements on the diagonal are matrix AiElement on the main diagonal, Ai 2＝Ai-Ai 1A matrix with a main diagonal element of zero; b isi 1＝diagBiIs a diagonal matrix, the elements on the diagonal are matrix BiElement on the main diagonal, Bi 2＝Bi-Bi 1A matrix with a main diagonal element of zero;
introducing an auxiliary input vector zi(t)∈R3The following decoupling controllers were designed:
Si 1ui(t)+Si 2ui(t)＝-Kix(t)+Lizi(t) (12)
wherein L isiIs a diagonal matrix, Si 1，Si 2And KiIs a coefficient matrix and satisfies the following relation:
Si 1Ai 2＝Bi 1Ki (13)
Si 1Bi 2＝Bi 1Si 2 (14)
substituting equation (12) into equation (11) and using equations (13) and (14), the following expression is obtained:
due to the matrix Ai 1Andall are diagonal matrixes, so that the formula (15) is composed of 3 independent single variable systems, and complete decoupling between input and states is realized;
order toWriting equation (8) as follows:
for equation (16), the auxiliary input value z is designedi(t) minimizing the following performance index j:
wherein e (t) xd(t) -x (t) is the tracking error, xd(t) is the expected output of the steel ball coal-milling powder-making system model, Q ═ QT≥0，R＝RTMore than 0 is a weighting matrix;
if it isThe device is completely controllable, and the device is capable of controlling,fully observable, optimal tracking control with interference zi(t) is
Wherein P satisfies the Riccati equation shown in the formula (19),is an approximation of the syndrome vector g (t), as shown in equation (20):
therefore, the equation (16) and the auxiliary input amount zi(t), obtaining an optimal decoupling controller u (t) as shown in the following formula:
step 4, designing a switching mechanism for selecting an optimal decoupling controller corresponding to the optimal model;
(1) forming a multi-model set by a plurality of steel ball coal-grinding and coal-pulverizing system models so as to cover the variation range of the steel ball coal-grinding and coal-pulverizing system parameters;
(2) the following performance indicator functions are defined:
wherein D isi(t) is a performance index function for model i, er iThe error between the ith model output and the actual ball-milling coal pulverizing system output is represented by lambda which is a forgetting factor and takes a value between 0.9 and 1, and alpha and beta which are respectively weights of the performance index function for adjusting the current error and the past error take a value between 0 and 1; selecting a model which enables the performance index to be minimum as an optimal model;
(3) and selecting an optimal decoupling controller corresponding to the optimal model to control the steel ball coal-grinding and pulverizing system.
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