CN108803309A - It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and model adaptation - Google Patents
It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and model adaptation Download PDFInfo
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- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 title claims abstract description 152
- 229910021529 ammonia Inorganic materials 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 238000005259 measurement Methods 0.000 title claims abstract description 20
- 230000006978 adaptation Effects 0.000 title abstract 2
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims abstract description 236
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 38
- 230000003044 adaptive effect Effects 0.000 claims abstract description 25
- 238000002485 combustion reaction Methods 0.000 claims abstract description 22
- 238000005507 spraying Methods 0.000 claims abstract description 14
- 238000002347 injection Methods 0.000 claims description 54
- 239000007924 injection Substances 0.000 claims description 54
- 239000003245 coal Substances 0.000 claims description 13
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 9
- 239000003546 flue gas Substances 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 7
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 claims description 6
- 238000012843 least square support vector machine Methods 0.000 claims description 5
- 238000013135 deep learning Methods 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 4
- 238000000513 principal component analysis Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 238000010531 catalytic reduction reaction Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 5
- GQPLMRYTRLFLPF-UHFFFAOYSA-N Nitrous Oxide Chemical class [O-][N+]#N GQPLMRYTRLFLPF-UHFFFAOYSA-N 0.000 abstract 2
- 238000007619 statistical method Methods 0.000 description 7
- 238000000738 capillary electrophoresis-mass spectrometry Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000006722 reduction reaction Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000003111 delayed effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010298 pulverizing process Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Classifications
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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
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
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Abstract
Ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and model adaptation the invention discloses a kind of.This method is：1) operation data based on fossil-fired unit combustion system establishes inlet nitrogen oxides concentration model；Then the operation data for utilizing the model and currently acquiring, obtains the predicted value of inlet nitrogen oxides concentration；2) SCR denitration system is sent to as feed-forward signal according to ammonia spraying amount under the current operating condition of predictor calculation；3) measured value for exporting nitrous oxides concentration and outlet nitrous oxides concentration setting value are done into input adaptive PID controller after deviation, obtains ammonia spraying amount feedback control signal and is sent to SCR denitration system；Wherein, self-adaptive PID controller is using the pid parameter under Adaptive PID Control algorithm adjustment current working；4) SCR denitration system controls the ammonia spraying amount of SCR denitration reactor according to the ammonia spraying amount feed-forward signal and ammonia spraying amount feedback control signal.
Description
Technical Field
The invention belongs to the technical field of denitration of coal-fired thermal power plants, and particularly relates to an SCR (selective catalytic reduction) denitration intelligent ammonia injection optimization method and system based on soft measurement and model self-adaptation.
Background
At present, in order to realize ultralow emission of nitrogen oxides, most coal-fired thermal power plants are provided with SCR denitration devices, a CEMS (flue gas on-line monitoring system) system is adopted to collect the concentrations of the nitrogen oxides at an inlet and an outlet, and then PID (proportion-integration-differentiation) feedback control is carried out.
The PID feedback control is shown in fig. 1, the calculated value of ammonia flow is (inlet nitrogen oxide concentration measurement value-outlet nitrogen oxide concentration measurement value) x flue gas volume x ammonia nitrogen mole ratio, wherein the ammonia nitrogen mole ratio is basically a fixed value, and the inlet nitrogen oxide concentration, the outlet nitrogen oxide concentration and the flue gas volume are measured by instruments.
The above scheme has the following problems:
1. the concentration of nitrogen oxides at the inlet and the outlet is measured by a CEMS system, the sampling pipeline of the CEMS system is long, so that the measurement purity is delayed greatly, and the measured value is delayed for 2-3 minutes;
2. the existing CEMS system adopts single-point sampling measurement, so that the measurement data cannot represent the average concentration of the whole section;
3. by adopting PID control, PID parameters are not changed after initial setting, so that when the load operation condition of the unit changes, the denitration system is not adjusted timely, and standard-exceeding emission is easy to occur;
4. in order to ensure the emission reaching the standard, the operator of the power plant sets the set value of the PID parameter to be very low, so that the ammonia spraying amount is too large, the overshoot of the control system is large, and the response rate of the system is low. Not only is reductant wasted, but also the risk of subsequent equipment plugging is increased.
Therefore, the development of the SCR intelligent optimization ammonia injection system has important significance for safe and economic operation of the SCR denitration device by realizing accurate ammonia injection.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention aims to provide an SCR denitration intelligent ammonia injection optimization method and system based on soft measurement and model self-adaptation.
The technical scheme of the invention is as follows:
an SCR denitration intelligent ammonia injection optimization method based on soft measurement and model self-adaptation comprises the following steps:
1) establishing an inlet nitrogen oxide concentration model based on the operation data of the combustion system of the coal-fired thermal power generating unit; then, obtaining a predicted value of the concentration of the inlet nitrogen oxide by using the inlet nitrogen oxide concentration model and the currently collected operation data;
2) calculating the ammonia injection amount under the current operation working condition according to the inlet nitrogen oxide concentration predicted value to serve as a feedforward signal, and sending the feedforward signal of the ammonia injection amount to the SCR denitration system;
3) measuring an actual measurement value of the concentration of the nitrogen oxide at the outlet of a combustion system of the coal-fired thermal power generating unit, inputting the actual measurement value and a set value of the concentration of the nitrogen oxide at the outlet into an adaptive PID controller after deviation is made, and obtaining an ammonia injection amount feedback control signal by the adaptive PID controller based on the deviation and sending the feedback control signal to an SCR (selective catalytic reduction) denitration system; the adaptive PID controller adopts an adaptive PID control algorithm to adjust PID parameters under the current working condition;
4) and the SCR denitration system controls the ammonia injection amount of the SCR denitration reactor according to the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal.
Further, dividing the operating data through a principal component analysis algorithm to determine parameters closely related to the inlet nitrogen oxide; and then, taking the determined parameters closely related to the inlet nitrogen oxide as training data, and establishing an inlet nitrogen oxide concentration model by adopting a deep learning algorithm or a least square support vector machine algorithm.
Further, the operation data comprises unit load, primary air volume, secondary air volume, total coal volume and the number of running coal mills.
Further, the method for obtaining the ammonia injection amount feedforward signal comprises the following steps: and multiplying the predicted value by the flue gas amount, and then multiplying by the ammonia nitrogen molar ratio to calculate the ammonia gas amount demand, wherein the ammonia gas amount demand is used as the ammonia injection amount feedforward signal of the SCR denitration system.
Further, the self-adaptive PID controller establishes an SCR ammonia spraying system model according to the concentration of the inlet nitrogen oxide and the concentration of the outlet nitrogen oxide; and then optimizing the model parameters of the SCR ammonia spraying system by adopting a genetic algorithm or a PSO optimization algorithm to obtain the PID optimal parameters of the self-adaptive PID controller.
Further, the method for adjusting the PID parameter under the current working condition by the adaptive PID controller using the adaptive PID control algorithm is as follows: during initial debugging, obtaining characteristic parameters of a combustion system of the coal-fired thermal power generating unit under different working conditions according to field test, establishing an SCR denitration input and output system model, and determining PID parameters of the adaptive PID controller through the SCR denitration input and output system model; in the operation process, the operation data of the combustion system of the coal-fired thermal power generating unit is collected in real time, the SCR denitration input and output system model is corrected, and then the optimal PID parameter under the current working condition is obtained based on the corrected SCR denitration input and output system model.
An SCR denitration intelligent ammonia injection optimization system based on soft measurement and model self-adaptation is characterized by comprising an ammonia injection amount feedforward signal generation unit, a self-adaptation PID controller and an SCR denitration system; wherein,
the ammonia injection amount feedforward signal generation unit is connected with the coal-fired thermal power unit combustion system operation data sensor, and an inlet nitrogen oxide concentration model is established based on operation data acquired by the coal-fired thermal power unit combustion system operation data sensor; obtaining a predicted value of the concentration of the inlet nitrogen oxide by using the inlet nitrogen oxide concentration model and the currently acquired operation data, and then calculating an ammonia injection amount feedforward signal under the current operation working condition according to the predicted value of the concentration of the inlet nitrogen oxide;
the self-adaptive PID controller is connected with the outlet nitrogen oxide concentration measuring instrument and is used for obtaining an ammonia injection amount feedback control signal according to the deviation of the measured value of the outlet nitrogen oxide concentration of the combustion system of the coal-fired thermal power generating unit and the set value of the outlet nitrogen oxide concentration; the adaptive PID controller adopts an adaptive PID control algorithm to adjust PID parameters under the current working condition;
and the SCR denitration system is respectively connected with the ammonia injection amount feedforward signal generation unit and the self-adaptive PID controller to receive the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal and is used for controlling the ammonia injection amount of the SCR denitration reactor according to the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal.
The method comprises the steps of firstly collecting data such as primary air, secondary air and load of a combustion system of the coal-fired thermal power generating unit, then establishing an inlet nitrogen oxide concentration model based on the collected data, then obtaining an inlet nitrogen oxide concentration predicted value according to the currently collected data and the inlet nitrogen oxide concentration model, then calculating ammonia injection amount feedforward under the current operation working condition according to the inlet nitrogen oxide concentration predicted value, and adding the feedforward into a nitrogen oxide concentration feedback control loop to realize accurate ammonia injection. In addition, an SCR ammonia spraying system model is established based on the operation data, and PID parameters are adjusted in a self-adaptive mode when the change of the operation condition is detected, so that the optimization control is realized.
An inlet nitrogen oxide concentration prediction method comprises the following steps:
according to the boiler operation data, unit load, primary air volume, secondary air volume, total coal volume, coal mill operation number parameters and corresponding inlet nitrogen oxide concentration are selected preliminarily, the selected parameters are analyzed through a principal component analysis algorithm, parameters closely related to inlet nitrogen oxide are determined, and namely main parameters and secondary parameters are determined. And then, establishing an inlet nitrogen oxide concentration model by taking the determined parameters closely related to the inlet nitrogen oxide as training data and adopting a deep learning algorithm or a least square support vector machine algorithm, so that the inlet nitrogen oxide concentration model is utilized to realize the prediction of the inlet nitrogen oxide concentration. Wherein the parameters closely related to the inlet nitrogen oxide comprise total air volume, total coal volume, air-coal ratio, primary air ratio and secondary air ratio.
Then, an SCR ammonia spraying system model is established based on the operation data, and PID parameters are adjusted in a self-adaptive mode when the change of the operation condition is detected, so that the optimization control is realized.
The SCR ammonia spraying system model is a denitration model established according to the concentration of inlet nitrogen oxides and the concentration of outlet nitrogen oxides, and is established based on the combination of mechanism and data (for example, a structural formula of the model is assumed through mechanism analysis, and model parameters, a, b and c, are determined through actual operation data), wherein the data are the concentration of the inlet nitrogen oxides and the concentration of the outlet nitrogen oxides. Based on the current ammonia injection system model parameters, the optimal PID control parameters under the current working condition can be obtained by adopting a genetic algorithm or a PSO optimization algorithm. In actual operation, the system continuously identifies the SCR ammonia spraying model on line, and when the monitored model changes greatly, the PID parameter is recalculated, so that the PID parameter self-adaptive adjustment is realized, and better control parameters are always kept.
The invention can also obtain the system characteristic parameters of typical working conditions through field test. Then, several sets of PID controller parameters are determined according to different typical conditions. In actual operation, the operation condition is automatically detected, and then the optimal PID parameter corresponding to the current condition is determined, namely, switching is carried out according to different operation conditions, so that segmented PID is realized.
The innovation of the scheme comprises three points:
first, the inlet nox concentration of conventional control schemes is measured by a CEMS meter with a large measurement lag. The scheme is obtained based on mechanism and data modeling, and has good real-time performance.
Secondly, when the boiler combustion system changes, the traditional scheme is passive adjustment, and the scheme introduces a combustion system change feedforward signal into a control scheme, so that the adjustment can be carried out in advance.
And thirdly, the PID control parameters in the traditional scheme are kept unchanged after being set, and the PID control parameters are adjusted in real time according to the system operation condition to realize control parameter optimization.
Compared with the prior art, the invention has the following beneficial effects:
1. the concentration deviation of nitrogen oxides at the denitration outlet can be controlled to be 5-10mg/Nm3And stable standard emission under the condition of variable working conditions can be ensured.
2. The denitration control system has the advantages of high system response rate, low investment and short modification period.
Drawings
FIG. 1 is a flow chart of a prior art system control;
FIG. 2 is a control flow diagram of the present invention;
FIG. 3 is a diagram of the system control stage architecture of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to specific examples and the accompanying drawings.
The scheme mainly provides a denitration optimization control method combining inlet nitrogen oxide concentration soft measurement and working condition self-adaptive PID.
The method comprises the steps of firstly, acquiring data of primary air, secondary air, load and the like of a combustion system of the coal-fired thermal power generating unit and inlet nitrogen oxide concentrations corresponding to the parameters, and establishing an inlet nitrogen oxide concentration model.
As shown in fig. 2, based on parameters such as the primary air volume, the secondary air volume, the coal feeding amount, the number of operating units of the pulverizing system, and the like of the boiler, an inlet nitrogen oxide concentration predicted value is obtained according to the inlet nitrogen oxide concentration model, and then multiplied by the flue gas volume, and then multiplied by the ammonia nitrogen molar ratio to calculate the ammonia gas demand, and the ammonia gas demand is used as a feed-forward signal (namely, an ammonia injection amount rapid tracking signal) of the SCR denitration system, and when the feed-forward signal can ensure load change, the ammonia injection amount can be rapidly adjusted. And after deviation between the set value of the concentration of the nitrogen oxides at the outlet and the measured value, the set value of the concentration of the nitrogen oxides at the outlet enters a self-adaptive PID controller to be used as a feedback control signal of the ammonia injection amount. And the parameters of the self-adaptive PID controller are self-adaptively adjusted according to the combustion condition of the boiler.
The method for calculating the predicted value of the concentration of the nitrogen oxides at the inlet comprises the following steps: and establishing an inlet nitrogen oxide concentration model by adopting a machine learning algorithm or a least square support vector machine model based on the operation parameters and the inlet nitrogen oxide concentration historical data, and acquiring input data in real time and inputting the data into the model in actual operation to obtain an inlet nitrogen oxide concentration predicted value.
The feedback control loop PID adopts an adaptive PID control algorithm. The self-adaptive PID control algorithm comprises the following steps:
1) during initial debugging, characteristic parameters of the system under different working conditions are obtained according to field test, an SCR denitration input and output system model is established, and PID parameters can be determined through the model;
2) and in the operation process, acquiring operation data in real time, correcting the model, and then obtaining the optimal PID parameter under the current working condition by adopting an optimization algorithm based on the corrected model so as to realize self-adaptive PID control.
Wherein, the flue gas amount refers to (the actually measured boiler flue gas amount) and the unit is (NM)3H). The ammonia nitrogen molar ratio refers to (the ratio of the concentration of ammonia gas to the concentration of nitrogen oxide in denitration is a common concept in denitration and is generally 0.7-0.9). The calculation formula of the ammonia gas demand is as follows: (ammonia demand-inlet)Nitrogen oxides times flue gas mass times ammonia nitrogen mole ratio).
In order to ensure that the algorithm can be applied on site and keep the control algorithm secret, the implementation of the scheme is realized on the basis of a denitration optimization control platform. The core of the platform is a high-performance controller, parameters (load, primary air quantity, secondary air quantity, total coal quantity and other boiler operation parameters) required by calculation are obtained from the DCS through the data acquisition card, and after calculation, the parameters are returned to the original DCS, so that closed-loop control is realized. The controller is communicated with the original DCS system in a modbus, RS485 and other communication modes, and can be communicated with a domestic mainstream DCS system in a two-way mode. The denitration optimization controller and the DCS field controller can realize undisturbed switching. As shown in fig. 3, the optimization controller mainly includes a system communication module and a core algorithm calculation module, and the system communication module is mainly responsible for implementing data input and output with the DCS. The core algorithm module is mainly used for realizing an optimization control algorithm. When the system runs, firstly, the running data is collected through the system communication module and then input into the core algorithm module, and then the core algorithm module is output to the DCS, so that closed-loop control is realized.
Table 1 shows the comparison between the method of the present invention and the prior art in actual operation, and it can be seen from these comparisons that the method of the present invention can realize accurate ammonia injection of the SCR denitration device, improve the system operation efficiency, and reduce the cost.
Table 1 shows comparison of actual operation results
Dynamic working condition operation effect comparative analysis
1 Effect of operation before transformation
【1】 Load-up working condition of unit
Reactor outlet NO under variable working conditionsxThe dynamic characteristic of the content is to investigate the automatic control of the denitration systemOne of the most important parts of the effect. This section analyzes reactor outlet NO during the load rampxDynamic nature of the content.
In the process that the load is increased from 365MW to 395MW, the NO is discharged from the first side reactor and the second side reactor of the denitration systemxThe statistical analysis of the contents is shown in Table 2.
Table 2 shows denitration performance data of 365MW rising to 395MW load section
【2】 Load reduction working condition of unit
The following is the reactor outlet NO during the load sheddingxAnalysis of the dynamic properties of the contents.
In the process that the load is reduced to 330MW from 500MW or so, the NO at the outlet of the first side reactor and the second side reactor of the denitration systemxThe statistical analysis of the contents is shown in Table 3.
TABLE 3 Denitrification Performance data for 500MW to 330MW load segment
2 operating effect after transformation
【1】 Load-up working condition of unit
The following is the operation process when the load of the unit is increased from 400MW to 500MW, and generally, in the load increasing interval, a coal mill is started. NO to reactor inlet during start-stop of millxShort term effects of the content, NO at the reactor outletxThe excessive content is mostly influenced by the excessive content.
In the process that the load is increased from 400MW to 500MW, the outlet NO of the first side reactor and the second side reactor of the denitration systemxThe statistical analysis of the contents is shown in Table 4.
Table 4 shows the denitration performance data of the operation interval with the load of increasing from 400MW to 500MW
The load is increased to 600MW operation interval from 550MW, and NO is discharged from the side A and side B reactors of the denitration systemxThe statistical analysis of the contents is shown in Table 5.
Table 5 shows the denitration performance data of the operation interval with the load increasing from 550MW to 600MW
【2】 Load reduction working condition of unit
This section will be analyzed in the same manner for the performance of the denitration reactor during the load reduction.
The load is reduced to 550MW operation interval from 600MW, and NO is discharged from the side A and side B reactors of the denitration systemxThe statistical analysis of the contents is shown in Table 6.
Table 6 shows the denitration performance data of the operation interval of reducing the load from 600MW to 550MW
The load is reduced to 500MW operation interval from 550MW, and NO is discharged from the side reactor A and the side reactor B of the denitration systemxThe statistical analysis of the contents is shown in Table 7.
Table 7 shows the denitration performance data of the operation interval with the load reduced from 550MW to 500MW
The load is reduced from 500MW to 400MW operation interval, and the load is removedNitric system side A and side B reactor outlet NOxThe content statistical analysis is shown in Table 8.
Table 8 shows the denitration performance data of the operation interval of reducing the load from 500MW to 400MW
The above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person skilled in the art can modify the technical solution of the present invention or substitute the same without departing from the spirit and scope of the present invention, and the scope of the present invention should be determined by the claims.
Claims (10)
1. An SCR denitration intelligent ammonia injection optimization method based on soft measurement and model self-adaptation comprises the following steps:
1) establishing an inlet nitrogen oxide concentration model based on the operation data of the combustion system of the coal-fired thermal power generating unit; then, obtaining a predicted value of the concentration of the inlet nitrogen oxide by using the inlet nitrogen oxide concentration model and the currently collected operation data;
2) calculating the ammonia injection amount under the current operation working condition according to the inlet nitrogen oxide concentration predicted value to serve as a feedforward signal, and sending the feedforward signal of the ammonia injection amount to the SCR denitration system;
3) measuring an actual measurement value of the concentration of the nitrogen oxide at the outlet of a combustion system of the coal-fired thermal power generating unit, inputting the actual measurement value and a set value of the concentration of the nitrogen oxide at the outlet into an adaptive PID controller after deviation is made, and obtaining an ammonia injection amount feedback control signal by the adaptive PID controller based on the deviation and sending the feedback control signal to an SCR (selective catalytic reduction) denitration system; the adaptive PID controller adopts an adaptive PID control algorithm to adjust PID parameters under the current working condition;
4) and the SCR denitration system controls the ammonia injection amount of the SCR denitration reactor according to the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal.
2. The method of claim 1, wherein the operational data is partitioned by a principal component analysis algorithm to determine parameters closely related to inlet nitrogen oxides; and then, taking the determined parameters closely related to the inlet nitrogen oxide as training data, and establishing an inlet nitrogen oxide concentration model by adopting a deep learning algorithm or a least square support vector machine algorithm.
3. The method of claim 1 or 2, wherein the operational data comprises unit load, primary air volume, secondary air volume, total coal volume, number of coal mills operating.
4. The method of claim 1, wherein the ammonia injection amount feed forward signal is obtained by: and multiplying the predicted value by the flue gas amount, and then multiplying by the ammonia nitrogen molar ratio to calculate the ammonia gas amount demand, wherein the ammonia gas amount demand is used as the ammonia injection amount feedforward signal of the SCR denitration system.
5. The method of claim 1, wherein the adaptive PID controller models the SCR ammonia injection system based on an inlet nox concentration and an outlet nox concentration; and then optimizing the model parameters of the SCR ammonia spraying system by adopting a genetic algorithm or a PSO optimization algorithm to obtain the PID optimal parameters of the self-adaptive PID controller.
6. The method of claim 1, wherein the adaptive PID controller adjusts the PID parameters under the current operating condition by adopting an adaptive PID control algorithm by: during initial debugging, obtaining characteristic parameters of a combustion system of the coal-fired thermal power generating unit under different working conditions according to field test, establishing an SCR denitration input and output system model, and determining PID parameters of the adaptive PID controller through the SCR denitration input and output system model; in the operation process, the operation data of the combustion system of the coal-fired thermal power generating unit is collected in real time, the SCR denitration input and output system model is corrected, and then the optimal PID parameter under the current working condition is obtained based on the corrected SCR denitration input and output system model.
7. An SCR denitration intelligent ammonia injection optimization system based on soft measurement and model self-adaptation is characterized by comprising an ammonia injection amount feedforward signal generation unit, a self-adaptation PID controller and an SCR denitration system; wherein,
the ammonia injection amount feedforward signal generation unit is connected with the coal-fired thermal power unit combustion system operation data sensor, and an inlet nitrogen oxide concentration model is established based on operation data acquired by the coal-fired thermal power unit combustion system operation data sensor; obtaining a predicted value of the concentration of the inlet nitrogen oxide by using the inlet nitrogen oxide concentration model and the currently acquired operation data, and then calculating an ammonia injection amount feedforward signal under the current operation working condition according to the predicted value of the concentration of the inlet nitrogen oxide;
the self-adaptive PID controller is connected with the outlet nitrogen oxide concentration measuring instrument and is used for obtaining an ammonia injection amount feedback control signal according to the deviation of the measured value of the outlet nitrogen oxide concentration of the combustion system of the coal-fired thermal power generating unit and the set value of the outlet nitrogen oxide concentration; the adaptive PID controller adopts an adaptive PID control algorithm to adjust PID parameters under the current working condition;
and the SCR denitration system is respectively connected with the ammonia injection amount feedforward signal generation unit and the self-adaptive PID controller to receive the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal and is used for controlling the ammonia injection amount of the SCR denitration reactor according to the ammonia injection amount feedforward signal and the ammonia injection amount feedback control signal.
8. The system of claim 7, wherein the operational data is partitioned by a principal component analysis algorithm to determine parameters closely related to inlet nitrogen oxides; then, taking the determined parameters closely related to the inlet nitrogen oxide as training data, and establishing an inlet nitrogen oxide concentration model by adopting a deep learning algorithm or a least square support vector machine algorithm; the operation data comprises unit load, primary air quantity, secondary air quantity, total coal quantity and the number of coal mills in operation.
9. The system of claim 7, wherein the adaptive PID controller models the SCR ammonia injection system based on an inlet nitrogen oxide concentration and an outlet nitrogen oxide concentration; and then optimizing the model parameters of the SCR ammonia spraying system by adopting a genetic algorithm or a PSO optimization algorithm to obtain the PID optimal parameters of the self-adaptive PID controller.
10. The system of claim 7, wherein the adaptive PID controller establishes an SCR denitration input output system model based on characteristic parameters of the coal-fired thermal power generating unit combustion system under different working conditions during initial debugging, and determines PID parameters of the adaptive PID controller through the SCR denitration input output system model; in the operation process, the SCR denitration input and output system model is corrected based on operation data of the coal-fired thermal power generating unit combustion system collected in real time, and then the optimal PID parameter under the current working condition is obtained based on the corrected SCR denitration input and output system model.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111522290A (en) * | 2020-04-24 | 2020-08-11 | 大唐环境产业集团股份有限公司 | Denitration control method and system based on deep learning method |
| CN111540412A (en) * | 2020-04-24 | 2020-08-14 | 大唐环境产业集团股份有限公司 | SCR reactor inlet flue gas soft measurement method based on least square method |
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| CN111540412A (en) * | 2020-04-24 | 2020-08-14 | 大唐环境产业集团股份有限公司 | SCR reactor inlet flue gas soft measurement method based on least square method |
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| CN112185475A (en) * | 2020-09-28 | 2021-01-05 | 齐鲁工业大学 | Aiming at SCR denitration system distortion NOxHigh-precision intelligent concentration prediction method |
| CN112619394A (en) * | 2020-11-24 | 2021-04-09 | 呼和浩特科林热电有限责任公司 | Denitration ammonia injection self-adaptive control method and device and denitration system |
| CN113325693A (en) * | 2021-05-25 | 2021-08-31 | 国网新疆电力有限公司电力科学研究院 | Improved PID control method and device for SCR denitration system |
| CN113433911A (en) * | 2021-06-30 | 2021-09-24 | 浙江大学 | Denitration device ammonia injection accurate control system and method based on concentration accurate prediction |
| CN113485106A (en) * | 2021-07-07 | 2021-10-08 | 山西大学 | Method for controlling concentration of nitrogen oxide in thermal power plant |
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| CN114225662A (en) * | 2021-12-07 | 2022-03-25 | 国网河北能源技术服务有限公司 | Flue gas desulfurization and denitrification optimization control method based on hysteresis model |
| CN114326387A (en) * | 2021-12-07 | 2022-04-12 | 江苏方天电力技术有限公司 | Denitration control device and method for thermal power generating unit |
| CN114225662B (en) * | 2021-12-07 | 2024-02-13 | 国网河北能源技术服务有限公司 | Hysteresis model-based flue gas desulfurization and denitrification optimal control method |
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