CN111624887A - Electric dust removal control method and related device - Google Patents
Electric dust removal control method and related device Download PDFInfo
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- CN111624887A CN111624887A CN202010512450.4A CN202010512450A CN111624887A CN 111624887 A CN111624887 A CN 111624887A CN 202010512450 A CN202010512450 A CN 202010512450A CN 111624887 A CN111624887 A CN 111624887A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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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
- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C3/00—Separating dispersed particles from gases or vapour, e.g. air, by electrostatic effect
- B03C3/017—Combinations of electrostatic separation with other processes, not otherwise provided for
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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
- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C3/00—Separating dispersed particles from gases or vapour, e.g. air, by electrostatic effect
- B03C3/34—Constructional details or accessories or operation thereof
- B03C3/66—Applications of electricity supply techniques
- B03C3/68—Control systems therefor
Abstract
The invention provides an electric precipitation control method, an electric precipitation control device, a readable storage medium and a controller, wherein the electric precipitation control method comprises the steps of obtaining a net flue gas and dust emission prediction model through pre-training, wherein the input of the model comprises boiler working condition parameters and operation parameters of electric precipitation equipment, namely, the influence of the boiler working condition parameters on the net flue gas and dust emission is considered; when the actual boiler working condition parameters change or the actual clean flue gas and dust emission value is larger than a preset clean flue gas and dust emission threshold value, acquiring corresponding clean flue gas and dust emission predicted values of a plurality of operation parameter combinations under the current actual boiler working condition parameters by using a clean flue gas and dust emission prediction model; calculating energy consumption values corresponding to the plurality of operation parameter combinations; and finally, the operation parameter combination which meets the emission requirement and has the minimum energy consumption value is used as a target operation parameter combination to control the operation of the electric dust removal equipment. The purposes of reducing energy consumption and improving control accuracy are achieved, manual operation is not needed in the whole process, and the control response speed is improved.
Description
Technical Field
The invention relates to the technical field of electric precipitation equipment, in particular to an electric precipitation control method, an electric precipitation control device, a readable storage medium and a controller.
Background
The electric dust removal equipment of the coal-fired power plant mainly has the function of purifying flue gas generated by coal combustion of the coal-fired power plant. The working principle is that positive and negative ions and electrons between electrodes collide to generate charge by manufacturing a high-voltage electrostatic field, dust particles with the electrons and the ions move to the opposite electrode under the pushing of the action of the electric field force and are loaded on the opposite electrode, and dust on the electrodes falls into a dust collecting hopper in modes of rapping and the like, so that the pollution of the dust to the atmosphere is reduced.
At present, the adjustment method of electric dust removal equipment comprises a manual operation method and an intelligent control method combining clean flue gas dust emission according to the load change of a boiler. The manual operation method depends on manual experience to carry out operation regulation and control, and the clean flue gas and dust emission exceeding standard possibly caused by the change of boiler working condition parameters such as coal type, temperature, flue gas amount and the like is responded by large active power output; however, the effect of the method completely depends on the experience of technicians, the technical level and the like, the requirement on the professional degree of the technicians is high, the control has poor hysteresis and controllability, and the electric dust remover is required to be kept at high active power, so that the energy consumption is high. According to the intelligent control method combining the clean smoke dust emission with the change of the boiler load, the influence of the change of boiler working condition parameters such as coal type, temperature, smoke amount and the like is not considered, so that the control accuracy is not high.
Disclosure of Invention
In view of the above, the present invention provides an electric dust removal control method, an electric dust removal control device, a readable storage medium, and a controller, which are intended to achieve the purposes of reducing energy consumption, improving control accuracy, and improving control response speed.
In order to achieve the above object, the following solutions are proposed:
in a first aspect, the invention provides an electric precipitation control method, which comprises the following steps:
when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and dust is larger than the preset emission threshold value of the clean flue gas and dust, adjusting the operation parameters of the electric dust removal equipment for N times to obtain N operation parameter combinations, wherein N is an integer not less than 2;
for each operation parameter combination, inputting a net flue gas and dust emission prediction model obtained by pre-training by taking all operation parameters in the operation parameter combination and actual boiler working condition parameters as input parameters to obtain a net flue gas and dust emission prediction value output by the net flue gas and dust emission prediction model;
calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and taking the running parameter combination with the minimum energy consumption value as a target running parameter combination to control the electric dust removal equipment to run, wherein the predicted value of the emission of the clean flue gas dust is not more than the emission threshold of the clean flue gas dust.
Optionally, the operating parameter combination includes: secondary voltage, secondary current and rapping frequency;
the actual boiler working condition parameters comprise: flue gas amount, boiler load, inlet dust concentration, coal as fired sulfur content, coal as fired moisture content and air preheater outlet temperature.
Optionally, the initial weight of the flue gas amount is 0.1, the initial weight of the boiler load is 0.2, the initial weight of the inlet dust concentration is 0.2, the initial weight of the coal as fired sulfur content is 0.05, the initial weight of the coal as fired moisture is 0.05, the initial weight of the air preheater outlet temperature is 0.05, the initial weight of the secondary voltage is 0.2, the initial weight of the secondary current is 0.1, and the initial weight of the rapping frequency is 0.05.
Optionally, after the step of controlling the operation of the electric dust removal equipment by using the operation parameter combination with the minimum energy consumption value, in which the predicted value of the emission of the clean flue gas dust is not greater than the threshold value of the emission of the clean flue gas dust, as a target operation parameter combination, the method further includes:
and optimizing the parameters of the prediction model of the emission of the clean flue gas and the dust by using the combination of the current actual emission value of the clean flue gas and the dust, the actual working condition parameters of the boiler and the operation parameters.
Optionally, the training process of the prediction model for emission of clean flue gas and dust includes:
screening out key parameters from working condition parameters of a boiler and operation parameters of electric precipitation equipment according to an electric precipitation mechanism;
acquiring historical data of the key parameters and the clean flue gas dust emission values in one-to-one correspondence;
and taking the historical data as a sample, and training the clean flue gas dust emission prediction model.
Optionally, the prediction model for clean flue gas dust emission specifically comprises:
and the multilayer neural network model is trained by applying a forward propagation algorithm and a direction propagation algorithm.
In a second aspect, an electric dust removal control device is provided, which includes:
the operation parameter combination generating unit is used for adjusting the operation parameters of the electric dust removal equipment for N times when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas dust is larger than the preset emission threshold value of the clean flue gas dust to obtain N operation parameter combinations, wherein N is an integer not less than 2;
the clean flue gas and dust emission prediction unit is used for inputting all the operation parameters in the operation parameter combinations and actual boiler working condition parameters as input parameters for each operation parameter combination, inputting a clean flue gas and dust emission prediction model obtained by pre-training, and obtaining a clean flue gas and dust emission prediction value output by the clean flue gas and dust emission prediction model;
the energy consumption calculation unit is used for calculating an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and the parameter selection unit is used for taking the running parameter combination with the minimum energy consumption value, with the predicted value of the emission of the clean flue gas dust being not more than the emission threshold value of the clean flue gas dust, as a target running parameter combination to control the electric dust removal equipment to run.
Optionally, the operating parameter combination includes: secondary voltage, secondary current and rapping frequency;
the actual boiler working condition parameters comprise: flue gas amount, boiler load, inlet dust concentration, coal as fired sulfur content, coal as fired moisture content and air preheater outlet temperature.
Optionally, the initial weight of the flue gas amount is 0.1, the initial weight of the boiler load is 0.2, the initial weight of the inlet dust concentration is 0.2, the initial weight of the coal as fired sulfur content is 0.05, the initial weight of the coal as fired moisture is 0.05, the initial weight of the air preheater outlet temperature is 0.05, the initial weight of the secondary voltage is 0.2, the initial weight of the secondary current is 0.1, and the initial weight of the rapping frequency is 0.05.
Optionally, the electric dust removal control device further comprises: and the online optimization unit is used for optimizing the parameters of the clean flue gas and dust emission prediction model by using the current actual clean flue gas and dust emission value, the actual boiler working condition parameters and the operation parameter combination after the parameter selection unit is executed.
Optionally, the electric dust removal control device further comprises: the model training unit comprises a parameter screening subunit, a parameter acquisition subunit and a training subunit;
the parameter screening subunit is used for screening out key parameters from the working condition parameters of the boiler and the operation parameters of the electric precipitation equipment according to an electric precipitation mechanism;
the parameter acquisition subunit is used for acquiring historical data corresponding to the key parameters and the emission values of the clean flue gas dust one by one;
and the training subunit is used for training the clean smoke dust emission prediction model by taking the historical data as a sample.
Optionally, the prediction model for clean flue gas dust emission specifically comprises:
and the multilayer neural network model is trained by applying a forward propagation algorithm and a direction propagation algorithm.
In a third aspect, the present invention provides a readable storage medium, on which a program is stored, which when executed by a processor, implements the steps of any one of the electric dust removal control methods in the first aspect.
In a fourth aspect, the present invention provides a controller comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program, and implement each step of the electric dust removal control method in the first aspect.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the technical scheme, the electric precipitation control method, the electric precipitation control device, the readable storage medium and the controller are provided, the method comprises the steps of obtaining a net flue gas and dust emission prediction model through pre-training, wherein the input of the model comprises boiler working condition parameters and operation parameters of electric precipitation equipment, namely, the influence of the boiler working condition parameters on the net flue gas and dust emission is considered; when the actual boiler working condition parameters change or the actual clean flue gas and dust emission value is larger than a preset clean flue gas and dust emission threshold value, acquiring corresponding clean flue gas and dust emission predicted values of a plurality of operation parameter combinations under the current actual boiler working condition parameters by using a clean flue gas and dust emission prediction model; calculating energy consumption values corresponding to the plurality of operation parameter combinations; and finally, selecting an operation parameter combination with the minimum energy consumption value and the net smoke dust emission prediction value not greater than a preset net smoke dust emission threshold value as a target operation parameter combination to control the operation of the electric dust removal equipment. And then the purposes of reducing energy consumption and improving control accuracy are achieved, manual operation is not needed in the whole process, and the control response speed is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a boiler coal combustion emissions treatment scheme;
FIG. 2 is a flow chart of a training process of a flue gas dust emission prediction model provided by an embodiment of the present invention;
fig. 3 is a flowchart of an electric precipitation control method according to an embodiment of the present invention;
FIG. 4 is a flow chart of another electric dust removal control method provided by the embodiment of the invention;
fig. 5 is a schematic diagram of an electric dust removal control device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a controller according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to FIG. 1, a schematic diagram of a boiler coal combustion emission treatment scheme is shown. The coal is combusted in a boiler, dust after combustion passes through an SCR (Selective Catalytic Reduction) denitration and air preheater, and is discharged through a chimney after electric precipitation and desulfurization. The core idea of the invention is to predict the corresponding emission value of the clean flue gas and the dust when the electric dust removal equipment is in different operation parameters by utilizing a pre-trained prediction model of the emission of the clean flue gas and the dust, and further select the operation parameters which have lower energy consumption and meet the emission requirements to control the electric dust removal equipment.
The working condition parameters of the boiler comprise coal medium parameters, boiler load, flue gas amount, coal burning amount, outlet oxygen amount of the air preheater, outlet temperature of the air preheater, escape value of denitrated ammonia, inlet dust concentration and the like. The medium parameters of the furnace coal specifically comprise carbon content, hydrogen content, nitrogen content, sulfur content (namely, sulfur content of the furnace coal), ash content, moisture content (namely, moisture content of the furnace coal), fly ash specific resistance and the like. The operation parameters of the electric dust removing equipment comprise primary current, primary voltage, secondary current, secondary voltage, a control mode, a pulse duty ratio, vibration frequency, vibration time, vibration stop time, temperature of an insulation box, ash hopper heating temperature and the like. Because the total number of the parameters contained in the boiler working condition parameters and the operation parameters of the electric precipitation equipment is large, all the parameters are used as the input of the model, the calculated amount is large, and the calculation efficiency is influenced. Aiming at the problem, the invention screens out key parameters in a certain mode before the model training as the input of the model. Referring to fig. 2, the training process of the prediction model for net smoke dust emission specifically includes the following steps:
s21: and screening out key parameters from the working condition parameters of the boiler and the operation parameters of the electric precipitation equipment according to an electric precipitation mechanism.
The formula of the dust removal efficiency is as follows:
simplification yields the equation:
wherein C1 is the inlet dust concentration, C2 is the outlet dust concentration, omega is the dust driving speed, A is the electric precipitation dust collection area, and Q is the flue gas volume. The outlet dust concentration of the electric dust removal equipment directly influences the emission value of the clean smoke dust. C1 is B D C/[1-E ], wherein B is the coal burning quantity, D is the ash content of the coal as fired, C is the fly ash content of the flue gas, and E is the fly ash combustible.
The main relevant parameters of the outlet dust concentration of the electric dust removal equipment are C1, omega and A, Q. ω is primarily related to the secondary voltage and current of the electric field of the electric precipitation apparatus, and the frequency of rapping.
Through the analysis, the rapping frequency of the electric dust removal equipment, the secondary voltage and the secondary current of an electric field, the flue gas volume, the boiler load, the inlet dust concentration, the sulfur content of the coal as fired, the moisture content of the coal as fired and the outlet temperature of an air preheater are determined and used as the input of a clean flue gas dust emission prediction model.
After the key parameters are screened out to be used as the input of the clean smoke dust emission prediction model, the initial weight of each input can be determined. The dust removal efficiency changes exponentially with the amount of flue gas, and therefore the initial weight of the amount of flue gas is set to 0.1. The boiler load is increased, the flue gas amount, the coal burning amount and the flue gas temperature are synchronously increased, and the flue gas amount is inversely proportional to the dust removal efficiency, so that the initial weight of the boiler load is set to be 0.2. The inlet dust concentration is the main parameter for reacting the amount of the coal ash and the coal-fired amount, and therefore the initial weight of the inlet dust concentration is set to 0.2. The sulfur content affects the specific resistance of dust, and is a key factor affecting the efficiency of electric precipitation, so the initial weight of the sulfur content in the coal as fired is set to be 0.05. Since the moisture content increases and the specific resistance of dust decreases, the initial weight of the moisture content of the coal as fired was set to 0.05. The outlet temperature of the air preheater is the inlet flue gas temperature of the electric dust removal equipment, the flue gas temperature is increased, the working condition flue gas quantity is increased, and the dust removal efficiency is reduced, so that the initial weight of the outlet temperature of the air preheater is 0.05. The value of ω is proportional to the square of the secondary voltage, thus setting the initial weight of the secondary voltage to 0.2. The value of ω is proportional to the magnitude of the secondary current, thus setting the initial weight of the secondary current to 0.1. An appropriate rapping frequency may lead to an improved efficiency of the electric dust removal, and therefore the initial weight of the rapping frequency is set to 0.05.
S22: and acquiring historical data of the key parameters and the clean smoke dust emission values in one-to-one correspondence.
The artificial intelligence server carries out data interaction with the decentralized centralized control system through a Mobus protocol or an OPC protocol to obtain working condition parameters of the boiler air smoke system; the artificial intelligence server carries out data interaction with an electric precipitation control system through a Modbus protocol or an OPC protocol, obtains the operation parameters of the electric precipitation equipment and the emission value of the clean smoke and dust, processes the data, aligns the data according to time, corresponds the data one by one according to the time sequence, and obtains the historical data of the key parameters and the emission value of the clean smoke and dust one by one.
S23: and taking the historical data as a sample, and training a net smoke dust emission prediction model.
And establishing a clean flue gas and dust emission prediction model which inputs the rapping frequency of the electric dust removal equipment, the secondary voltage and the secondary current of an electric field, the flue gas volume, the boiler load, the inlet dust concentration, the sulfur content of the coal as fired, the moisture content of the coal as fired and the outlet temperature of an air preheater and outputs a clean flue gas and dust emission value. The weighted accumulated value of the secondary voltages of all the electric fields of the secondary voltage electricity-taking and dust-removing equipment, the weighted accumulated value of the secondary currents of all the electric fields of the secondary current electricity-taking and dust-removing equipment and the rapping frequency of all the electric fields of the electricity-taking and dust-removing equipment are rapped every hour. And training by taking historical data as a sample, so that the fitting degree of the prediction model for the emission of the clean flue gas dust reaches over 95 percent.
Referring to fig. 3, in an embodiment of the present invention, an electric dust removal control method is provided, and in an actual operation process of an electric dust removal device, the method is used for controlling, and the method may include the following steps:
s31: when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and dust is larger than the preset emission threshold value of the clean flue gas and dust, the operation parameters of the electric dust removal equipment are adjusted for N times to obtain N operation parameter combinations.
N is an integer of not less than 2. The actual boiler working condition parameter changes, specifically, the actual boiler working condition parameter input as the clean flue gas and dust emission prediction model changes, and when the actual boiler working condition parameter not input by the clean flue gas and dust emission prediction model changes, subsequent processing is not performed.
In one embodiment, the operation parameters of the electric dust removal equipment are adjusted and optimized based on a particle swarm and genetic hybrid algorithm. Based on a particle swarm and genetic hybrid algorithm, the fast convergence characteristic of the empirical search of the particle swarm algorithm and the global search capability of the genetic algorithm cross variation are fused, and the efficient positioning of the operation parameters is realized.
S32: and aiming at each operation parameter combination, inputting all operation parameters in the operation parameter combination and actual boiler working condition parameters as input parameters into a net smoke dust emission prediction model obtained by pre-training to obtain a net smoke dust emission prediction value output by the net smoke dust emission prediction model.
Step S32 is executed to obtain a predicted value of the emission of the clean flue gas dust corresponding to the operation parameter combinations one to one.
S33: and calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination.
Step S33 is executed to obtain energy consumption values corresponding to the operation parameter combinations one to one.
S34: and taking the running parameter combination with the minimum energy consumption value and the predicted value of the emission of the clean flue gas dust not greater than the emission threshold of the clean flue gas dust as a target running parameter combination to control the electric dust removal equipment to run.
Referring to fig. 4, another electric dust removal control method provided for the embodiment of the present invention, compared to the method shown in fig. 3, after step S34, further includes the steps of:
s35: and optimizing the parameters of the prediction model of the emission of the clean flue gas and the dust by using the combination of the current actual emission value of the clean flue gas and the dust, the actual working condition parameters of the boiler and the operation parameters.
And step S35 is executed, and in the electric precipitation control process, real-time data is utilized to carry out continuous iterative optimization on the clean smoke dust emission prediction model, so that the prediction accuracy of the clean smoke dust emission prediction model is improved.
In one embodiment, the net flue gas dust emission prediction model employs a multi-layer neural network model. And training the multilayer neural network model by applying a forward propagation algorithm and a direction propagation algorithm.
While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present invention is not limited by the illustrated ordering of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 5, an electric dust removal control apparatus provided for an embodiment of the present invention includes: the device comprises an operation parameter combination generation unit, a clean flue gas and dust emission prediction unit, an energy consumption calculation unit and a parameter selection unit.
And the operation parameter combination generation unit is used for adjusting the operation parameters of the electric dust removal equipment for N times when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and the dust is greater than the preset emission threshold value of the clean flue gas and the dust, so as to obtain N operation parameter combinations, wherein N is an integer not less than 2.
And the clean flue gas and dust emission prediction unit is used for inputting all the operation parameters in the operation parameter combinations and the actual boiler working condition parameters as input parameters aiming at each operation parameter combination, inputting a clean flue gas and dust emission prediction model obtained by pre-training, and obtaining a clean flue gas and dust emission prediction value output by the clean flue gas and dust emission prediction model.
And the energy consumption calculation unit is used for calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination.
And the parameter selection unit is used for taking the running parameter combination with the minimum energy consumption value and the net smoke dust emission prediction value not larger than the net smoke dust emission threshold value as a target running parameter combination to control the electric dust removal equipment to run.
In the electric dust removal control device provided by this embodiment, the operation parameter combination generation unit and the clean flue gas and dust emission prediction unit use the clean flue gas and dust emission prediction model to obtain the corresponding clean flue gas and dust emission prediction values respectively for a plurality of operation parameter combinations under the current actual boiler operating condition parameters when the actual boiler operating condition parameters change or the actual clean flue gas and dust emission values are greater than the preset clean flue gas and dust emission threshold values; the energy consumption calculation unit calculates energy consumption values corresponding to the operation parameter combinations; and the parameter selection unit selects an operation parameter combination with the minimum energy consumption value and the net smoke dust emission prediction value not greater than a preset net smoke dust emission threshold value as a target operation parameter combination to control the operation of the electric dust removal equipment. And then the purposes of reducing energy consumption and improving control accuracy are achieved, manual operation is not needed in the whole process, and the control response speed is improved.
Optionally, the operation parameter combination includes: secondary voltage, secondary current and rapping frequency;
the actual boiler working condition parameters comprise: flue gas amount, boiler load, inlet dust concentration, coal as fired sulfur content, coal as fired moisture content and air preheater outlet temperature.
Optionally, the initial weight of the flue gas amount is 0.1, the initial weight of the boiler load is 0.2, the initial weight of the inlet dust concentration is 0.2, the initial weight of the sulfur content of the coal as fired is 0.05, the initial weight of the moisture content of the coal as fired is 0.05, the initial weight of the air preheater outlet temperature is 0.05, the initial weight of the secondary voltage is 0.2, the initial weight of the secondary current is 0.1, and the initial weight of the rapping frequency is 0.05.
Optionally, the electric precipitation control device further comprises: and the online optimization unit is used for optimizing the parameters of the clean flue gas and dust emission prediction model by using the current actual clean flue gas and dust emission value, the actual boiler working condition parameters and the operation parameter combination after the parameter selection unit is executed.
Optionally, the electric precipitation control device further comprises: the model training unit comprises a parameter screening subunit, a parameter acquisition subunit and a training subunit.
And the parameter screening subunit is used for screening out key parameters from the working condition parameters of the boiler and the operating parameters of the electric precipitation equipment according to the electric precipitation mechanism.
And the parameter acquisition subunit is used for acquiring historical data of the key parameters and the clean smoke dust emission values in one-to-one correspondence.
And the training subunit is used for training the clean smoke dust emission prediction model by taking the historical data as a sample.
Optionally, the prediction model for the emission of the clean flue gas dust specifically comprises:
and the multilayer neural network model is trained by applying a forward propagation algorithm and a direction propagation algorithm.
Referring to fig. 6, a schematic diagram of a controller according to an embodiment of the present invention is provided. The hardware structure of the controller may include: at least one processor 61, at least one communication interface 62, at least one memory 63 and at least one communication bus 64; and the processor 61, the communication interface 62 and the memory 63 are communicated with each other through a communication bus 64.
The communication interface 62 may include a standard wired interface, a wireless interface. Are commonly used to establish communication connections between the controller and other electronic devices or systems.
The memory 63 includes at least one type of readable storage medium. The readable storage medium may be an NVM (non-volatile memory) such as flash memory, hard disk, multimedia card, card-type memory, etc. The readable storage medium may also be a high-speed RAM (random access memory) memory. The readable storage medium may in some embodiments be an internal storage unit of the controller, for example a hard disk of the controller. In other embodiments, the readable storage medium may be an external storage device of the controller, such as a plug-in hard disk (hdd), a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the controller.
Wherein the memory 63 stores a computer program, and the processor 61 may call the computer program stored in the memory 63, the computer program being configured to:
when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and dust is larger than the preset emission threshold value of the clean flue gas and dust, adjusting the operation parameters of the electric dust removal equipment for N times to obtain N operation parameter combinations, wherein N is an integer not less than 2;
for each operation parameter combination, inputting a net flue gas and dust emission prediction model obtained by pre-training by taking all operation parameters in the operation parameter combination and actual boiler working condition parameters as input parameters to obtain a net flue gas and dust emission prediction value output by the net flue gas and dust emission prediction model;
calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and taking the running parameter combination with the minimum energy consumption value as a target running parameter combination to control the electric dust removal equipment to run, wherein the predicted value of the emission of the clean flue gas dust is not more than the emission threshold of the clean flue gas dust.
The refinement function and the extension function of the program may be referred to as described above.
FIG. 6 only shows the data verification device with components 61-64, but it is to be understood that not all of the shown components are required and that more or fewer components may alternatively be implemented.
An embodiment of the present invention further provides a readable storage medium, where the readable storage medium may store a program adapted to be executed by a processor, where the program is configured to:
when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and dust is larger than the preset emission threshold value of the clean flue gas and dust, adjusting the operation parameters of the electric dust removal equipment for N times to obtain N operation parameter combinations, wherein N is an integer not less than 2;
for each operation parameter combination, inputting a net flue gas and dust emission prediction model obtained by pre-training by taking all operation parameters in the operation parameter combination and actual boiler working condition parameters as input parameters to obtain a net flue gas and dust emission prediction value output by the net flue gas and dust emission prediction model;
calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and taking the running parameter combination with the minimum energy consumption value as a target running parameter combination to control the electric dust removal equipment to run, wherein the predicted value of the emission of the clean flue gas dust is not more than the emission threshold of the clean flue gas dust.
The refinement function and the extension function of the program may be referred to as described above.
The above-described embodiments of the apparatus are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are mainly described as different from other embodiments, the same and similar parts in the embodiments may be referred to each other, and the features described in the embodiments in the present description may be replaced with each other or combined with each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An electric dust removal control method is characterized by comprising the following steps:
when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas and dust is larger than the preset emission threshold value of the clean flue gas and dust, adjusting the operation parameters of the electric dust removal equipment for N times to obtain N operation parameter combinations, wherein N is an integer not less than 2;
for each operation parameter combination, inputting a net flue gas and dust emission prediction model obtained by pre-training by taking all operation parameters in the operation parameter combination and actual boiler working condition parameters as input parameters to obtain a net flue gas and dust emission prediction value output by the net flue gas and dust emission prediction model;
calculating to obtain an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and taking the running parameter combination with the minimum energy consumption value as a target running parameter combination to control the electric dust removal equipment to run, wherein the predicted value of the emission of the clean flue gas dust is not more than the emission threshold of the clean flue gas dust.
2. The electric precipitation control method of claim 1, wherein the operating parameter combination comprises: secondary voltage, secondary current and rapping frequency;
the actual boiler working condition parameters comprise: flue gas amount, boiler load, inlet dust concentration, coal as fired sulfur content, coal as fired moisture content and air preheater outlet temperature.
3. The electric precipitation control method of claim 2, wherein the initial weight of the flue gas amount is 0.1, the initial weight of the boiler load is 0.2, the initial weight of the inlet dust concentration is 0.2, the initial weight of the coal as fired sulfur content is 0.05, the initial weight of the coal as fired moisture is 0.05, the initial weight of the air preheater outlet temperature is 0.05, the initial weight of the secondary voltage is 0.2, the initial weight of the secondary current is 0.1, and the initial weight of the rapping frequency is 0.05.
4. The electric precipitation control method according to claim 1, further comprising, after the step of controlling the electric precipitation equipment to operate by using the operation parameter combination with the minimum energy consumption value and the predicted value of the emission of the clean flue gas dust being not greater than the emission threshold of the clean flue gas dust as a target operation parameter combination, the step of:
and optimizing the parameters of the prediction model of the emission of the clean flue gas and the dust by using the combination of the current actual emission value of the clean flue gas and the dust, the actual working condition parameters of the boiler and the operation parameters.
5. The electric precipitation control method of claim 1, wherein the training process of the prediction model for net flue gas dust emission comprises:
screening out key parameters from working condition parameters of a boiler and operation parameters of electric precipitation equipment according to an electric precipitation mechanism;
acquiring historical data of the key parameters and the clean flue gas dust emission values in one-to-one correspondence;
and taking the historical data as a sample, and training the clean flue gas dust emission prediction model.
6. The electric precipitation control method according to any one of claims 1 to 5, wherein the prediction model for net flue gas and dust emission is specifically:
and the multilayer neural network model is trained by applying a forward propagation algorithm and a direction propagation algorithm.
7. An electric dust removal control device is characterized by comprising:
the operation parameter combination generating unit is used for adjusting the operation parameters of the electric dust removal equipment for N times when the actual working condition parameters of the boiler change or the actual emission value of the clean flue gas dust is larger than the preset emission threshold value of the clean flue gas dust to obtain N operation parameter combinations, wherein N is an integer not less than 2;
the clean flue gas and dust emission prediction unit is used for inputting all the operation parameters in the operation parameter combinations and actual boiler working condition parameters as input parameters for each operation parameter combination, inputting a clean flue gas and dust emission prediction model obtained by pre-training, and obtaining a clean flue gas and dust emission prediction value output by the clean flue gas and dust emission prediction model;
the energy consumption calculation unit is used for calculating an energy consumption value according to all the operation parameters in the operation parameter combination aiming at each operation parameter combination;
and the parameter selection unit is used for taking the running parameter combination with the minimum energy consumption value, with the predicted value of the emission of the clean flue gas dust being not more than the emission threshold value of the clean flue gas dust, as a target running parameter combination to control the electric dust removal equipment to run.
8. The electric precipitation control device of claim 7, further comprising: the model training unit comprises a parameter screening subunit, a parameter acquisition subunit and a training subunit;
the parameter screening subunit is used for screening out key parameters from the working condition parameters of the boiler and the operation parameters of the electric precipitation equipment according to an electric precipitation mechanism;
the parameter acquisition subunit is used for acquiring historical data corresponding to the key parameters and the emission values of the clean flue gas dust one by one;
and the training subunit is used for training the clean smoke dust emission prediction model by taking the historical data as a sample.
9. A readable storage medium on which a program is stored, wherein the program, when executed by a processor, implements the steps of the electric dust removal control method according to any one of claims 1 to 6.
10. A controller comprising a memory and a processor, wherein the memory is configured to store a program;
the processor is used for executing the program and realizing the steps of the electric precipitation control method as claimed in any one of claims 1 to 6.
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