CN111068919A - Energy-saving optimization method and device for electric dust removal device - Google Patents
Energy-saving optimization method and device for electric dust removal device Download PDFInfo
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Abstract
The invention provides a method and a device for energy-saving optimization of an electric dust removal device, which create an electric dust removal mechanism system, wherein the electric dust removal mechanism system meets the following formula:
wherein P is the ash removal efficiency of unit electric energy, U is the voltage of the electric field, I is the current of the electric field, T is the time spent by one-time operation of the electric dust removal device, η is the dust removal efficiency of the electric field, A is the anode area, K is the influence coefficient related to the electric dust removal structure and the position of the electric field, and the U and T of the electric field of the electric dust removal device are optimized and adjusted by adopting a genetic algorithm, wherein the genetic algorithm is used for optimizing and adjusting the U and the T of the electric field of theThe objective function of the algorithm is the minimum value of P, so that the electric dust removal device can be optimized, and the most dust can be removed by unit electric energy.
Description
Technical Field
The invention relates to the technical field of energy-saving optimization of electric dust collectors, in particular to an energy-saving optimization method and device of an electric dust collector.
Background
The mainstream technologies of flue gas dust removal of coal-fired power plants mainly include electrostatic dust removal, bag type dust removal and electric-bag composite dust removal, and because the electric dust removal technology is mature, the system has good controllability, and the electric dust removal technology is generally adopted in the coal-fired power plants in China at present and accounts for more than 95%. The electric dust collector is a dust collector which ionizes gas by using a strong electric field, namely, corona discharge is generated, so that dust is charged, and the dust is separated from the gas under the action of the electric field force. With the increasingly strict environmental regulations, many power plants adopt a high-power mode to operate the electric dust collector, so that the outlet dust concentration allowance is too large, the power consumption of the system is greatly improved, and the energy-saving optimized operation of the system is not facilitated.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for ensuring the freshness of fresh products.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an energy-saving optimization method of an electric dust removal device comprises the following steps:
creating an electric precipitation mechanism system, wherein the electric precipitation mechanism system meets the following formula:
and optimally adjusting the U and the T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the objective function of the genetic algorithm is the minimum value of P.
Optionally, the method further comprises the following steps: and fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ].
Optionally, the "optimizing and adjusting U and T of the electric field of the electric dust collector by using a genetic algorithm, where the objective function of the genetic algorithm is the minimum value of P" specifically includes: generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T; continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
generating a sub-population according to the initial population, and carrying out variation on individuals in the sub-population;
deleting all individuals in the initial population, and copying all individuals in the sub-population into the initial population.
Optionally, the "generating a sub-population according to the initial population" specifically includes:
the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1;
and forming the generated plurality of offspring individuals into a sub-population.
Optionally, the "mutating individuals in the sub-population" specifically includes: and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
The embodiment of the invention also provides an energy-saving optimization device of the electric dust removal device, which comprises the following modules:
the initialization module is used for creating an electric precipitation mechanism system, and the electric precipitation mechanism system meets the following formula:
wherein, P is the ash removal efficiency of unit electric energy, U is the voltage of the electric field, I is the current of the electric field, T is the time spent by one-time operation of the electric dust removal device, η is the dust removal efficiency of the electric field, A is the anode area, and K is the influence coefficient related to the electric dust removal structure and the position of the electric field;
and the optimization module is used for optimizing and adjusting the U and the T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the target function of the genetic algorithm is the minimum value of the P.
Optionally, the following modules are further included: and the fitting module is used for fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ].
Optionally, the optimization module is further configured to: generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T;
continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
generating a sub-population according to the initial population, and carrying out variation on individuals in the sub-population;
deleting all individuals in the initial population, and copying all individuals in the sub-population into the initial population.
Optionally, the optimization module is further configured to:
the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1;
and forming the generated plurality of offspring individuals into a sub-population.
Optionally, the optimization module is further configured to: and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
The invention has the beneficial effects that: the embodiment of the invention provides an energy-saving optimization method and device of an electric dust removal device, and an electric dust removal mechanism system is established, wherein the electric dust removal mechanism system meets the following formula:
Drawings
Fig. 1 is a schematic flow chart of a method for energy saving optimization according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
The embodiment of the invention provides an energy-saving optimization method of an electric dust removal device, which comprises the following steps as shown in figure 1:
step 101: creating an electric precipitation mechanism system, wherein the electric precipitation mechanism system meets the following formula:
in the practical use, when the electric dust collector is used for removing dust, dust can be continuously accumulated on the electrodes, so that when the using time exceeds a preset value, the dust on the electrodes needs to be cleaned, and then the time from the use to the cleaning is T, wherein the value of I is related to U and time T, namely I is f (U, T), and the understanding that the relationship between I and U is not linear generally exists, the dust removing efficiency η can be continuously reduced along with the increase of the dust on the electrodes
Then it is represented as [0, T ]]The electric energy consumed by the electric dust removing device in the period of time.
Step 102: and optimally adjusting I and T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the objective function of the genetic algorithm is that the value of P is minimum. Here, the objective function is to minimize the value of P, i.e., the more the amount/mass of dust that can be removed per unit of electric energy.
Preferably, the method further comprises the following steps: and fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ]. Here, the relationship between the current I and U, t is related to the actual electric dust removing device, so that the relationship between I and U, t can be obtained by fitting using field data.
Preferably, the "optimizing and adjusting U and T of the electric field of the electric dust collector by using a genetic algorithm, wherein the objective function of the genetic algorithm is the minimum value of P" specifically includes:
generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T; continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
Preferably, the "generating a sub-population according to the initial population" specifically includes: the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1; and forming the generated plurality of offspring individuals into a sub-population.
Preferably, the "mutating individuals in the sub-population" specifically includes: and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
The embodiment of the invention also provides an energy-saving optimization device of the electric dust removal device, which comprises the following modules:
the initialization module is used for creating an electric precipitation mechanism system, and the electric precipitation mechanism system meets the following formula:
wherein, P is the ash removal efficiency of unit electric energy, U is the voltage of the electric field, I is the current of the electric field, T is the time spent by one-time operation of the electric dust removal device, η is the dust removal efficiency of the electric field, A is the anode area, and K is the influence coefficient related to the electric dust removal structure and the position of the electric field;
and the optimization module is used for optimizing and adjusting the U and the T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the target function of the genetic algorithm is the minimum value of the P.
Preferably, the following modules are also included:
and the fitting module is used for fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ].
Preferably, the optimization module is further configured to:
generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T;
continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
generating a sub-population according to the initial population, and carrying out variation on individuals in the sub-population;
deleting all individuals in the initial population, and copying all individuals in the sub-population into the initial population.
Preferably, the optimization module is further configured to:
the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1;
and forming the generated plurality of offspring individuals into a sub-population.
Preferably, the optimization module is further configured to:
and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. The energy-saving optimization method of the electric dust removal device is characterized by comprising the following steps of:
creating an electric precipitation mechanism system, wherein the electric precipitation mechanism system meets the following formula:
wherein, P is the ash removal efficiency of unit electric energy, U is the voltage of the electric field, I is the current of the electric field, T is the time spent by one-time operation of the electric dust removal device, η is the dust removal efficiency of the electric field, A is the anode area, and K is the influence coefficient related to the electric dust removal structure and the position of the electric field;
and optimally adjusting the U and the T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the objective function of the genetic algorithm is the minimum value of P.
2. The method of energy efficient optimization according to claim 1, further comprising the steps of:
and fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ].
3. The energy-saving optimization method according to claim 1, wherein the "optimizing and adjusting the U and T of the electric field of the electric dust collector by using a genetic algorithm, wherein the objective function of the genetic algorithm is that the value of P is minimum" specifically comprises:
generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T;
continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
generating a P value corresponding to each individual, sequencing the individuals in the initial population according to the P value of each individual, and deleting the individuals in a preset proportion at the tail end from the initial population;
generating a sub-population according to the initial population, and carrying out variation on individuals in the sub-population;
deleting all individuals in the initial population, and copying all individuals in the sub-population into the initial population.
4. The energy-saving optimization method according to claim 3, wherein the "generating sub-populations according to the initial population" specifically comprises:
the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1;
and forming the generated plurality of offspring individuals into a sub-population.
5. The energy-saving optimization method according to claim 4, wherein the "mutating individuals in the sub-population" specifically comprises:
and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
6. The utility model provides an energy-conserving optimization's of electrostatic precipitator device which characterized in that includes following module:
the initialization module is used for creating an electric precipitation mechanism system, and the electric precipitation mechanism system meets the following formula:
wherein, P is the ash removal efficiency of unit electric energy, U is the voltage of the electric field, I is the current of the electric field, T is the time spent by one-time operation of the electric dust removal device, η is the dust removal efficiency of the electric field, A is the anode area, and K is the influence coefficient related to the electric dust removal structure and the position of the electric field;
and the optimization module is used for optimizing and adjusting the U and the T of the electric field of the electric dust removal device by adopting a genetic algorithm, wherein the target function of the genetic algorithm is the minimum value of the P.
7. The apparatus for energy saving optimization according to claim 6, further comprising the following modules:
and the fitting module is used for fitting according to field data to obtain a functional relation I between the secondary voltage and the current, wherein the functional relation I is F (U, T), and T belongs to [0, T ].
8. The apparatus for energy conservation optimization according to claim 6, wherein the optimization module is further configured to:
generating an initial population comprising a plurality of different individuals, each individual in the initial population comprising a voltage and a time T;
continuously executing the following preset optimization steps until a preset condition is reached, wherein the preset optimization steps specifically comprise:
depending on the voltage U and time T in each individual in the initial population,
generating a P value corresponding to each individual, sequencing the individuals in the initial population according to the P value of each individual, and deleting the individuals in a preset proportion at the tail end from the initial population;
generating a sub-population according to the initial population, and carrying out variation on individuals in the sub-population;
deleting all individuals in the initial population, and copying all individuals in the sub-population into the initial population.
9. The apparatus for energy conservation optimization according to claim 3, wherein the optimization module is further configured to:
the sub-populations are generated according to the following criteria: a first individual and a second individual belong to an initial population, the first individual has a voltage of U1 and a time of T1, the second individual has a voltage of U2 and a time of T2, then at least offspring individuals are generated, the first offspring individual has a voltage of U1 and a time of T2, the second offspring individual has a voltage of U2 and a time of T1, the third offspring individual has a voltage of U3 and a time of T3, U3 ═ kU1+ (1-k) U2, T3 ═ jT1+ (1-j) T2, 0 ≦ k ≦ 1, 0 ≦ j ≦ 1;
and forming the generated plurality of offspring individuals into a sub-population.
10. The apparatus for energy conservation optimization according to claim 9, wherein the optimization module is further configured to:
and randomly selecting a plurality of offspring individuals from the sub population, and modifying the voltage U and/or T of the plurality of offspring individuals.
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Application publication date: 20200428 |