CN112733441A - Circulating fluidized bed boiler NOx emission concentration control system based on QGA-ELM network - Google Patents
Circulating fluidized bed boiler NOx emission concentration control system based on QGA-ELM network Download PDFInfo
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- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims abstract description 131
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims abstract description 34
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 21
- 239000004202 carbamide Substances 0.000 claims abstract description 21
- 239000003546 flue gas Substances 0.000 claims abstract description 21
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 239000000243 solution Substances 0.000 claims abstract description 19
- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 18
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 238000004891 communication Methods 0.000 claims abstract description 14
- 238000002347 injection Methods 0.000 claims abstract description 10
- 239000007924 injection Substances 0.000 claims abstract description 10
- 238000005507 spraying Methods 0.000 claims abstract description 7
- 238000001514 detection method Methods 0.000 claims description 16
- 239000003344 environmental pollutant Substances 0.000 claims description 11
- 231100000719 pollutant Toxicity 0.000 claims description 11
- 239000000779 smoke Substances 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 230000002068 genetic effect Effects 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 239000013618 particulate matter Substances 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- 239000000428 dust Substances 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 13
- 238000002485 combustion reaction Methods 0.000 description 6
- 239000000446 fuel Substances 0.000 description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 4
- 239000003245 coal Substances 0.000 description 4
- 239000001301 oxygen Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000007254 oxidation reaction Methods 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000010531 catalytic reduction reaction Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 150000003254 radicals Chemical class 0.000 description 2
- VHUUQVKOLVNVRT-UHFFFAOYSA-N Ammonium hydroxide Chemical compound [NH4+].[OH-] VHUUQVKOLVNVRT-UHFFFAOYSA-N 0.000 description 1
- 235000019738 Limestone Nutrition 0.000 description 1
- 235000011114 ammonium hydroxide Nutrition 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005338 heat storage Methods 0.000 description 1
- 239000006028 limestone Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- QJGQUHMNIGDVPM-UHFFFAOYSA-N nitrogen group Chemical group [N] QJGQUHMNIGDVPM-UHFFFAOYSA-N 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000005610 quantum mechanics Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
- B01D53/34—Chemical or biological purification of waste gases
- B01D53/46—Removing components of defined structure
- B01D53/54—Nitrogen compounds
- B01D53/56—Nitrogen oxides
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
- B01D53/34—Chemical or biological purification of waste gases
- B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
- B01D53/77—Liquid phase processes
- B01D53/78—Liquid phase processes with gas-liquid contact
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D2251/00—Reactants
- B01D2251/20—Reductants
- B01D2251/206—Ammonium compounds
- B01D2251/2067—Urea
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
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Abstract
The invention relates to a circulating fluidized bed boiler NOx emission concentration control system based on a QGA-ELM network, which comprises a urea solution delivery pump, a flue gas on-line monitoring system, a DCS control system and an external system, wherein the urea solution delivery pump is connected with a motor, and the motor is provided with a rotating speed sensor; the DCS control system is respectively connected with the motor, the rotating speed sensor, the flue gas online monitoring system and the plug-in system; the plug-in system is provided with a nitrogen oxide emission control module based on a QGA-ELM network, and the nitrogen oxide emission control module comprises four sub-modules: the device comprises a data acquisition and communication module, a preprocessing and parameter adjusting module, a nitrogen oxide prediction module and an ammonia spraying amount calculation module. The invention adopts a nitrogen oxide emission control module based on a QGA-ELM network, transmits the control quantity parameters to a DCS control system by calculating real-time data, controls the frequency of a urea solution delivery pump, achieves the aim of adjusting the ammonia injection quantity in advance, has no hysteresis in the control process, and improves the denitration efficiency.
Description
Technical Field
The invention belongs to the technical field of boilers, and particularly relates to a circulating fluidized bed boiler NOx emission concentration control system based on a QGA-ELM network.
Background
Due to the advantages of low pollutant emission of a circulating fluidized bed boiler (CFB) and the like, the CFB boiler is developed rapidly in recent years, and according to statistics, the total capacity of the existing CFB boiler in China exceeds 1 hundred million kW, and the CFB boiler is the first to live in the world and exceeds the sum of other countries in the world. The circulating fluidized bed system is a high energy heat storage source that can be compared to a battery to quickly heat the newly added cold fuel to a sufficient ignition temperature. As long as the stable bed temperature is ensured in the combustion process, the stable operation can be realized, meanwhile, higher combustion efficiency can be kept, and due to the low combustion temperature, factors such as limestone can be added into the furnace, and the low-emission combustion furnace has the advantage of low emission.
The nitrogen oxides generated in the coal combustion process of a thermal power plant mainly comprise three types, namely thermal NOx, rapid NOx and fuel NOx. Thermal NOx is generated by oxidation reaction of N2 contained in air with oxygen at high temperature, and the reaction with oxygen at 1300 ℃ is remarkable. The fast NOx is produced through the reaction of CHi free radical in flame and nitrogen to produce intermediate HCN, the subsequent oxidation reaction of other radical and the oxidation of the intermediate HCN. The nitrogen-containing compounds in the coal are oxidized under certain conditions in the combustion process, and the generated NOx is fuel type NOx. In general, nitrogen oxides generated by CFB mainly come from N in fuel, that is, the generated nitrogen oxides are fuel type NOx.
With the increasing importance of the country on the environmental protection, the set pollutant emission index of the circulating fluidized bed is more and more strict, and the emission index of nitrogen oxides of the circulating fluidized bed is required to be lower than 50mg/m3 according to the requirements of environmental protection departments. At present, a selective non-catalytic reduction method (SNCR) and a selective catalytic reduction method (SCR) are generally adopted in a general flue gas denitration process of a thermal power plant.
But the existing denitration technology of the circulating fluidized bed boiler has the following defects:
(1) at present, the specific method for adjusting the concentration of the emitted nitrogen oxides is as follows: and measuring the concentration of the nitrogen oxides at the flue gas outlet, and adjusting the ammonia injection amount to adjust if the concentration of the nitrogen oxides exceeds the emission concentration standard. It is clear that the above control methods have severe hysteresis and cannot fundamentally play a role in regulation.
(2) As the national requirements on flue gas emission indexes are more and more strict, many power plants are controlled according to the method mentioned in the step (1), so that the emission of nitrogen oxides exceeds the standard, the denitration efficiency is low, the waste of ammonia water is caused, ammonia escapes, and certain influence is caused on the economy and the safety of a unit.
The concept of quantum computing was first proposed by the a roughs national laboratory. Ferman is also interested in this problem and is engaged in research outlining the vision to implement calculations with quantum phenomena. Many scholars introduce quantum concepts such as quantum bits, quantum gates, quantum state characteristics, probability amplitudes and the like in quantum mechanics into classical algorithms, and then the concepts of quantum algorithms appear. The problem that the calculation of the nitrogen oxide of the circulating fluidized bed is complex can be solved by utilizing a method of optimizing machine learning by using a quantum algorithm, and the prediction precision of the nitrogen oxide is improved.
Disclosure of Invention
The invention provides a circulating fluidized bed boiler NOx emission concentration control system based on a QGA-ELM network for solving the technical problems in the prior art, which has no hysteresis quality in controlling the concentration of the emitted nitric oxide, improves the denitration efficiency, saves the urea consumption and plays a role in regulation and control fundamentally.
The invention comprises the following technical scheme: a circulating fluidized bed boiler NOx emission concentration control system based on a QGA-ELM network comprises a urea solution delivery pump, a flue gas on-line monitoring system, a DCS control system and an external system, wherein the urea solution delivery pump is connected with a motor, and the motor is provided with a rotating speed sensor; the DCS control system is respectively connected with the motor, the rotating speed sensor, the flue gas online monitoring system and the plug-in system; the plug-in system is provided with a nitrogen oxide emission control module based on a QGA-ELM network, and the nitrogen oxide emission control module comprises four sub-modules: the device comprises a data acquisition and communication module, a preprocessing and parameter adjusting module, a nitrogen oxide prediction module and an ammonia spraying amount calculation module.
Furthermore, the flue gas on-line detection system comprises a gaseous pollutant detection subsystem, a flue gas parameter monitoring subsystem, a particulate matter detection subsystem and a data acquisition and processing subsystem.
Further, the gaseous pollutant detection subsystem is used for continuously monitoring gaseous pollutants in the flue gas; the smoke parameter monitoring subsystem measures smoke state parameters such as temperature, pressure, flow rate and the like; and the particulate matter detection subsystem measures the smoke dust concentration in real time.
Furthermore, the DCS control system is in communication connection with the plug-in system through OPC.
Further, the data acquisition and communication module acquires required historical data from the DCS control system and returns a calculation result to the DCS control system.
Furthermore, the data preprocessing and parameter adjusting module performs data preprocessing on the data read from the DCS control system, eliminates abnormal values, performs normalization and standardization operation on the abnormal values, and avoids the influence of the abnormal data on the neural network training process; and the data preprocessing and parameter adjusting module adjusts parameters of the neural network.
Furthermore, the nitrogen oxide prediction module applies an extreme learning machine neural network (ELM) to improve the prediction effect and accuracy of the network and introduces a Quantum Genetic Algorithm (QGA) to optimize the connection weight and the threshold of the extreme learning machine; the quantum genetic algorithm has the characteristics of high operation speed, prevention of local optimization and the like, in order to improve the prediction precision of the neural network of the extreme learning machine, the quantum genetic algorithm is utilized for optimizing the model, the error between the test output and the model test input of the ELM-QGA model is used as the target function of the quantum genetic algorithm, the connection weight and the threshold of the neural network model of the extreme learning machine are optimized, meanwhile, the training input of the ELM-QGA model is from historical data obtained from a DCS control system, the model is trained by using the historical data of ammonia injection quantity, coal feeding quantity, primary air quantity, secondary air quantity, total air quantity, oxygen quantity and bed temperature, and the predicted value of nitrogen oxide is obtained by calculation according to real-time data.
Further, the ammonia injection amount calculation module calculates the flow of the urea solution to be injected into the hearth at the moment according to the predicted value of the nitrogen oxides, outputs a signal to the DCS control system and controls the frequency of the urea solution delivery pump.
Furthermore, the plug-in system comprises a plug-in system client and a plug-in system server, wherein a data acquisition and communication module and a preprocessing and parameter adjusting module are arranged in the plug-in system client, and a nitrogen oxide prediction module and an ammonia injection amount calculation module are arranged in the plug-in system server.
Further, the plug-in system client adopts a win7 operating system; the plug-in system server adopts an EIS-H2105 RCR-052U high-performance rack-mounted server, and in the using process, the client is used for operation and parameter adjustment, and calculation is carried out in the server.
The invention has the advantages and positive effects that: the invention controls the ammonia spraying amount of the urea solution delivery pump by using the motor, the rotating speed sensor measures the rotating speed of the urea solution delivery pump, the online smoke detection system measures the concentration of nitrogen oxides in smoke in real time, the DCS provides historical data to the external system, the external system carries a nitrogen oxide emission control module based on a QGA-ELM network, the control amount parameters are transmitted to the DCS by calculating real-time data, the frequency of the urea solution delivery pump is controlled, the aim of adjusting the ammonia spraying amount in advance is achieved, the control process has no hysteresis, the concentration of the discharged nitrogen oxides meets the emission standard, the denitration efficiency is improved, the urea consumption is saved, the regulation and control effects are fundamentally played, and the invention has good social and economic values.
Drawings
Fig. 1 is a schematic diagram of the control system structure of the present invention.
FIG. 2 is a schematic diagram of a NOx emission control module configuration.
In the figure, a 1-urea solution delivery pump; 2-a flue gas on-line monitoring system; 3-DCS control system;
4-a plug-in system; 41-plug-in system client; 42-plug-in system server; 5-an electric motor; 6-a rotation speed sensor; 7-a nitrogen oxide emission control module; 71-a data acquisition and communication module; 72-data preprocessing and parameter adjusting module; 73-a nitrogen oxide prediction module; 74-ammonia injection amount calculation module.
Detailed Description
To further clarify the disclosure of the present invention, its features and advantages, reference is made to the following examples taken in conjunction with the accompanying drawings.
Example (b): referring to the attached drawings 1-2, the system for controlling the NOx emission concentration of the circulating fluidized bed boiler based on the QGA-ELM network comprises a urea solution delivery pump 1, a flue gas online monitoring system 2, a DCS control system 3 and an external system 4, wherein the urea solution delivery pump 1 is connected with a motor 5, and the motor 5 is provided with a rotating speed sensor 6; the DCS control system 3 is respectively connected with the motor 5, the rotating speed sensor 6, the flue gas online monitoring system 2 and the plug-in system 4; the DCS control system 3 is in communication connection with the plug-in system 4 through OPC.
The plug-in system 4 is provided with a nitrogen oxide emission control module 7 based on a QGA-ELM network, and the nitrogen oxide emission control module 7 comprises four sub-modules: a data acquisition and communication module 71, a preprocessing and parameter adjusting module 72, a nitrogen oxide prediction module 73 and an ammonia spraying amount calculation module 74.
The data acquisition and communication module 71 acquires required historical data from the DCS control system 3 and returns a calculation result to the DCS control system 3; the data preprocessing and parameter adjusting module 72 performs data preprocessing on the data read from the DCS control system 3, removes abnormal values, and performs normalization and standardization operations on the abnormal values, thereby avoiding the influence of the abnormal data on the neural network training process; the data preprocessing and parameter adjusting module 72 adjusts the neural network parameters.
The nitrogen oxide prediction module 73 applies an extreme learning machine neural network (ELM) to improve the prediction effect and accuracy of the network and introduces a Quantum Genetic Algorithm (QGA) to optimize the connection weight and the threshold of the extreme learning machine; the nitrogen oxide prediction module 73 acquires historical data from the DCS control system 3 to train the network, and calculates a nitrogen oxide prediction value according to real-time data; the quantum genetic algorithm has the characteristics of high operation speed, prevention of local optimization and the like, in order to improve the prediction precision of the neural network of the extreme learning machine, the quantum genetic algorithm is utilized for optimizing the model, the error between the test output and the model test input of the ELM-QGA model is used as the target function of the quantum genetic algorithm, the connection weight and the threshold of the neural network model of the extreme learning machine are optimized, meanwhile, the training input of the ELM-QGA model is from historical data obtained from the DCS control system 3, the model is trained by using the historical data of ammonia injection quantity, coal supply quantity, primary air quantity, secondary air quantity, total air quantity, oxygen quantity and bed temperature, and the predicted value of nitrogen oxides is obtained through calculation according to real-time data.
The ammonia injection amount calculation module 74 calculates the flow rate of the urea solution to be injected into the furnace at this time according to the predicted value of the nitrogen oxides, and outputs a signal to the DCS control system 3 to control the frequency of the urea solution delivery pump 1.
The flue gas online detection system 2 comprises a gaseous pollutant detection subsystem, a flue gas parameter monitoring subsystem, a particulate matter detection subsystem and a data acquisition and processing subsystem. The gaseous pollutant detection subsystem is used for continuously monitoring gaseous pollutants in the flue gas; the smoke parameter monitoring subsystem measures smoke state parameters such as temperature, pressure, flow rate and the like; and the particulate matter detection subsystem measures the smoke dust concentration in real time.
The plug-in system 4 comprises a plug-in system client 41 and a plug-in system server 42, wherein the plug-in system client 41 is internally provided with a data acquisition and communication module 71 and a preprocessing and parameter adjusting module 72, and the plug-in system server 42 is internally provided with a nitrogen oxide prediction module 73 and an ammonia injection amount calculation module 74. The plug-in system client 41 adopts a win7 operating system; the plug-in system server 42 adopts an EIS-H2105 RCR-052U high-performance rack-mounted server, and in the using process, the operation and parameter adjustment are carried out by using a client side, and the calculation is carried out in the server.
While the preferred embodiments of the present invention have been illustrated and described, it will be appreciated by those skilled in the art that the foregoing embodiments are illustrative and not limiting, and that many changes may be made in the form and details of the embodiments of the invention without departing from the spirit and scope of the invention as defined in the appended claims. All falling within the scope of protection of the present invention.
Claims (10)
1. The utility model provides a circulating fluidized bed boiler NOx discharges concentration control system based on QGA-ELM network includes urea solution delivery pump, flue gas on-line monitoring system, DCS control system and external system, urea solution delivery pump connects the motor, its characterized in that: the motor is provided with a rotating speed sensor, and the DCS control system is respectively connected with the motor, the rotating speed sensor, the flue gas online monitoring system and the plug-in system; the plug-in system is provided with a nitrogen oxide emission control module based on a QGA-ELM network, and the nitrogen oxide emission control module comprises four sub-modules: the device comprises a data acquisition and communication module, a preprocessing and parameter adjusting module, a nitrogen oxide prediction module and an ammonia spraying amount calculation module.
2. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: the flue gas on-line detection system comprises a gaseous pollutant detection subsystem, a flue gas parameter monitoring subsystem, a particulate matter detection subsystem and a data acquisition and processing subsystem.
3. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 2, wherein: the gaseous pollutant detection subsystem is used for continuously monitoring gaseous pollutants in the flue gas; the smoke parameter monitoring subsystem measures smoke state parameters; and the particulate matter detection subsystem measures the smoke dust concentration in real time.
4. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: the DCS is connected with the plug-in system through OPC communication.
5. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: the data acquisition and communication module acquires required historical data from the DCS control system and returns a calculation result to the DCS control system.
6. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: the data preprocessing and parameter adjusting module is used for preprocessing data read from the DCS, eliminating abnormal values and carrying out normalization and standardization operation on the abnormal values; and the data preprocessing and parameter adjusting module adjusts parameters of the neural network.
7. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: the nitrogen oxide prediction module applies an extreme learning machine neural network (ELM) to improve the prediction effect and accuracy of the network and introduces a Quantum Genetic Algorithm (QGA) to optimize the connection weight and the threshold of the extreme learning machine; and the nitrogen oxide prediction module acquires historical data from the DCS to train the network, and calculates the nitrogen oxide prediction value according to real-time data.
8. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 1, wherein: and the ammonia spraying amount calculation module calculates the flow of the urea solution to be sprayed into the hearth at the moment according to the predicted value of the nitrogen oxides and outputs a signal to the DCS control system to control the frequency of the urea solution delivery pump.
9. A circulating fluidized bed boiler NOx emission concentration control system based on QGA-ELM network according to any of claims 1-8, characterized by: the plug-in system comprises a plug-in system client and a plug-in system server, wherein a data acquisition and communication module and a preprocessing and parameter adjusting module are arranged in the plug-in system client, and a nitrogen oxide prediction module and an ammonia injection amount calculation module are arranged in the plug-in system server.
10. The QGA-ELM network-based circulating fluidized bed boiler NOx emission concentration control system of claim 9, wherein: the plug-in system client adopts a win7 operating system; the plug-in system server adopts a high-performance rack server.
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| CN114859841A (en) * | 2022-05-16 | 2022-08-05 | 西安热工研究院有限公司 | Thermal power plant NOx emission monitoring control system and method |
| CN116062971A (en) * | 2023-02-15 | 2023-05-05 | 郑州市天之蓝环保科技有限公司 | Automatic control method and system for ammonia water throwing of glass kiln |
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| CN113908673A (en) * | 2021-09-30 | 2022-01-11 | 湖北华电襄阳发电有限公司 | Wet desulphurization efficiency prediction system and method based on extreme learning machine |
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Application publication date: 20210430 |