CN110652856B - SNCR control system based on model - Google Patents
SNCR control system based on model Download PDFInfo
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- CN110652856B CN110652856B CN201910791006.8A CN201910791006A CN110652856B CN 110652856 B CN110652856 B CN 110652856B CN 201910791006 A CN201910791006 A CN 201910791006A CN 110652856 B CN110652856 B CN 110652856B
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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
- 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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- 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
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
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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
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D2258/00—Sources of waste gases
- B01D2258/02—Other waste gases
- B01D2258/0283—Flue gases
Abstract
The invention provides a model-based SNCR control system, comprising: a data acquisition device for acquiring process data related to a reaction process of the SNCR; the model parameter optimization unit comprises a model processing unit and a parameter optimization unit, wherein the model processing unit uses the process data as input variables to calculate output variables related to control parameters of the SNCR by using a dynamic flue gas processing model, the output variables predict flue gas emission data related to flue gas subjected to SNCR denitration treatment, and the parameter optimization unit calculates the optimized control parameters of the SNCR according to the output variables; and the automatic control module is used for automatically controlling the SNCR according to the optimized control parameters. According to the invention, the optimal control in the flue gas treatment process is realized, and the energy conservation and consumption reduction of the system are realized.
Description
Technical Field
The invention relates to the field of garbage treatment, in particular to an SNCR control system based on a model.
Background
The flue gas deacidification process for the waste incineration power generation comprises three methods, namely a dry method, a wet method and a semi-dry method, and the semi-dry method deacidification is widely applied to the waste incineration power generation process due to the advantages of high purification efficiency, simple process, less equipment, easiness in treatment of products, no secondary pollution, convenience in regulation and control and the like.
The high-temperature flue gas generated after the incineration of the garbage contains a large amount of NO2And NO, etc., it is necessary to perform denitration treatment. One typical denitration process is selective non-catalytic reduction (SNCR), which is a process of reducing nitrogen oxides in flue gas to harmless nitrogen and water by spraying a reducing agent within a "temperature window" suitable for a denitration reaction without the action of a catalyst. The technology generally adopts ammonia, urea or hydroammonia acid sprayed in a furnace as a reducing agent to reduce NOX。
The SNCR sprays ammonia water as a reducing agent into the flue gas of the 1 st channel of the incinerator, reduces the content of nitric oxide in the flue gas, and achieves the aim of denitration. Wherein, the adding amount and the distribution of the ammonia water are directly related to the content of the nitric oxide in the flue gas. In order to realize a good denitration effect and simultaneously improve the utilization rate of ammonia water and reduce the production cost, a control system is often adopted to control the running state of the SNCR. Specifically, the CEMS data at the tail of the chimney is used as a control basis, the control system generally adopts PID control, and then a control strategy is compiled according to operation experience, and the operation of the whole system under the optimal condition is difficult to guarantee under the control.
Therefore, it is necessary to provide a model-based SNCR control system to solve the problems of the prior art.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description. The summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The invention provides a SNCR control system based on a model, which comprises:
a data acquisition device for acquiring process data related to a reaction process of the SNCR;
a model parameter optimization unit which comprises a model processing unit and a parameter optimization unit, wherein the model processing unit calculates an output variable by using a dynamic flue gas processing model and taking the process data as an input variable, the output variable predicts flue gas emission data related to flue gas subjected to SNCR denitration treatment, the dynamic flue gas processing model is a calculation model established according to a correlation between the process data and the flue gas emission data, and the parameter optimization unit calculates an optimized control parameter of the SNCR according to the output variable;
and the automatic control module is used for automatically controlling the SNCR according to the optimized control parameters.
Illustratively, the dynamic flue gas treatment model comprises a time series model established using MATLAB.
Illustratively, the parameter optimization unit adaptively adjusts the optimization control parameter according to the output variable.
Illustratively, the parameter optimization unit calculates the optimized control parameter from the output variables using a machine learning model.
Illustratively, the data acquisition device also acquires the smoke emission data at a chimney outlet.
Illustratively, the parameter optimization unit further compares the output variable calculated by the model processing unit with the flue gas emission data at the chimney outlet to obtain a comparison result, and calculates the optimization control parameter according to the comparison result.
Illustratively, the input variables include main steam flow, flue gas volume, furnace temperature, flue gas oxygen content at the outlet of the waste heat boiler, and ammonia water flow.
Illustratively, the output variable includes NO in the denitrated flue gasXNO of original flue gas of first flueXThe amount of NO in the denitrated flue gas is obtained by last calculation and is used as the output variableXThe amount and NO of the raw flue gas of the first flueXThe quantity is used as the input variable for the next calculation.
Illustratively, the optimized control parameter includes ammonia dosing amount.
The data acquisition device, the model parameter optimization unit and the automatic control module are communicated through the communication module.
According to the SNCR control system based on the model, the dynamic smoke processing model is used for predicting the smoke state after the SNCR processes the smoke, and the control parameters of the SNCR are optimally controlled based on the prediction data, so that the optimal control in the smoke processing process is realized, the SNCR processing efficiency is improved, the energy conservation and consumption reduction of the system are realized, and the discharged smoke reaches the optimal standard.
Drawings
The following drawings of the present invention are included to provide a further understanding of the invention. The drawings illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
In the drawings:
FIG. 1 is a block diagram of a model-based SNCR control system according to the present invention;
fig. 2 is a schematic diagram illustrating a control principle of a semi-dry process flue gas treatment control system according to an embodiment of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
In order to thoroughly understand the present invention, a detailed description will be provided in the following description to illustrate a method and an apparatus for treating late leachate in an old domestic garbage landfill according to the present invention. It will be apparent that the invention is not limited in its application to the specific details known to those skilled in the art of waste treatment. The following detailed description of the preferred embodiments of the invention, however, the invention can be practiced otherwise than as specifically described.
It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Exemplary embodiments according to the present invention will now be described in more detail with reference to the accompanying drawings. These exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. It is to be understood that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of these exemplary embodiments to those skilled in the art. In the drawings, the thicknesses of layers and regions are exaggerated for clarity, and the same elements are denoted by the same reference numerals, and thus the description thereof will be omitted.
The high-temperature flue gas generated after the incineration of the garbage contains a large amount of NO2And NO, etc., and denitration treatment is required. One typical denitration process is selective non-catalytic reduction (SNCR), which is a process of reducing nitrogen oxides in flue gas to harmless nitrogen and water by spraying a reducing agent within a "temperature window" suitable for a denitration reaction without the action of a catalyst. The technology generally adopts ammonia, urea or hydroammonia acid sprayed in a furnace as a reducing agent to reduce NOX。
The SNCR sprays ammonia water as a reducing agent into the flue gas of the 1 st channel of the incinerator, reduces the content of nitric oxide in the flue gas, and achieves the aim of denitration. Wherein, the adding amount and distribution of the ammonia water are directly related to the content of the nitric oxide in the flue gas. In order to realize the good denitration effect and simultaneously promote the utilization ratio of aqueous ammonia, reduce manufacturing cost, often adopt control system to control SNCR's running state. Specifically, the CEMS data at the tail of the chimney is used as a control basis, the control system generally adopts PID control, and then a control strategy is compiled according to operation experience, and the operation of the whole system under the optimal condition is difficult to guarantee under the control.
To solve the problems in the prior art, the present invention provides a model-based SNCR control system, including:
a data acquisition device for acquiring process data related to a reaction process of the SNCR;
the model parameter optimization unit comprises a model processing unit and a parameter optimization unit, wherein the model processing unit calculates an output variable by using a dynamic flue gas processing model and taking the process data as an input variable, the output variable predicts flue gas emission data related to flue gas subjected to SNCR (selective non-catalytic reduction) denitration treatment, the dynamic flue gas processing model is a calculation model established according to the correlation between the process data and the flue gas emission data, and the parameter optimization unit calculates the optimization control parameter of the SNCR according to the output variable;
and the automatic control module is used for automatically controlling the SNCR according to the optimized control parameters.
A model-based SNCR control system is exemplarily described below with reference to fig. 1 and 2, in which fig. 1 is a block diagram of the model-based SNCR control system according to the present invention, and fig. 2 is a schematic control principle diagram of a semi-dry flue gas treatment control system according to an embodiment of the present invention.
Referring first to fig. 1, the model-based SNCR control system includes a data acquisition device 101, a model parameter optimization unit 102, and an automatic control module 103.
The data acquisition device 101 is configured to acquire process data related to a reaction process of the SNCR.
After the garbage is incinerated by the garbage incinerator, high-temperature flue gas is discharged from an incineration hearth, and the high-temperature flue gas often contains NO2NO, etcXGas, which needs to be fed into SNCR for denitration. In the denitration process, NO in the flue gas is influenced by the burning working condition of the incineratorXThe amount of gas further influences the denitration effect, and meanwhile, the ammonia water flow of the SNCR is also an important factor influencing the denitration efficiency. In order for the model parameter optimization unit 102 to establish an accurate analysis model, the data collection device 101 collects as much process data related to the denitration reaction process as possible.
Exemplarily, the data acquisition device includes furnace temperature detection device, exhaust-heat boiler export oxygen content detection device that set up on burning furnace for detect the burning operating mode that burns burning furnace, judge the relevant data of flue gas that carries out SNCR denitration treatment according to the burning operating mode, and then predict SNCR's denitration effect. The data acquisition device also comprises a chimney outlet flue gas oxygen content detection device which is arranged at the chimney outlet and used for detecting the oxygen content in the flue gas. The data acquisition device also comprises an ammonia water flow detection device and the like which are arranged on the SNCR and used for detecting the flow of ammonia water. It should be appreciated that the data collection device may include any number of detection devices configured to collect any process data associated with the reaction process of the SNCR, and is not limited thereto.
With continued reference to fig. 1, the model parameter optimization unit 102 includes a model processing unit 1021 and a parameter optimization unit 1022.
The model processing unit 1021 calculates the flue gas emission data associated with the flue gas emitted from the SNCR using a dynamic flue gas processing model based on the process data collected by the data collection device 101. Wherein a dynamic flue gas treatment model calculates an output variable using the process data as an input variable, wherein the output variable predicts flue gas emission data associated with the flue gas output by the SNCR.
The dynamic flue gas treatment model is a calculation model established according to the correlation between the process data and the flue gas emission data. Illustratively, the dynamic flue gas treatment model comprises a time series model built using MATLAB. Illustratively, the flue gas emission data includes NO in the denitrated flue gasX。
Further illustratively, the step of building a timing model using MATLAB includes:
s1: analyzing the reaction mechanism of the SNCR, and establishing an original model of the SNCR; this step is carried out by theoretical analysis of the staff.
S2: carrying out an SNCR denitration test; this step is carried out at the site of waste incineration. Specifically, denitration tests under the conditions of different flue gas flow rates, different temperatures and different ammonia water flow rates are set.
S3: data from the SNCR denitration test were collected. Specifically, in step S2, process data related to the flue gas input into the SNCR and related to time variables, such as flow rate, temperature, water vapor content, and oxygen content of the flue gas, primary air volume of the incinerator, temperature of the incinerator furnace, and the like, are collected; flue gas emission data such as temperature of flue gas, water vapor, oxygen, NO after denitration, associated with flue gas discharged from the SNCRXAmount, and the like.
S4: and (4) performing correlation analysis on the process data and the flue gas emission data which are collected in the step S3 and are related to the time variable, and screening the process data and the flue gas emission data which are obviously related to the SNCR denitration efficiency. In one example according to the invention, the screened process data comprises main steam flow, flue gas amount, hearth temperature, flue gas oxygen content of an outlet of the waste heat boiler and ammonia water flow, and the flue gas emission data comprises NO of an outlet of a chimneyXNO of original flue gas of first flueXAmount of (a) and (b). Predicting NO of primary flue gas generated after incineration of incinerator by using main steam flow, flue gas amount, hearth temperature, oxygen content of flue gas at outlet of waste heat boiler and ammonia water flowXAmount of NO in flue gas subjected to SNCR (selective non-catalytic reduction) denitration treatmentXAmount according to NOXThe control parameters of the SNCR are optimized, and the denitration efficiency of the SCRN is obviously improved.
S5: and verifying the established original model according to the screened process data and the screened smoke emission data.
The whole modeling process is established by combining a mechanism model and a plurality of element linear regression equations, so that the established dynamic flue gas treatment model can accurately reflect the correlation between the process data and the flue gas emission data, and the model treatment unit can be improvedThe accuracy of the calculation. Meanwhile, the time sequence model established in the modeling process enables the model processing unit to reflect the current denitration state and the previous denitration state of the SNCR after processing the process data related to the time variable. In the established combustion oxygen amount model, the model input variables comprise: main steam flow, flue gas quantity, hearth temperature (a first flue, an outlet of a waste heat boiler and the like), oxygen content of flue gas at an outlet of the waste heat boiler, NO of original flue gas of the first flueXMeasuring and measuring NO in denitrated flue gasXQuantity, ammonia water flow; the output variable of the model comprises NO of the original smoke of the first flueXMeasuring and measuring NO in denitrated flue gasXAmount (v). In one example according to the present invention, the last calculation yields NO of the first flue raw flue gas as the output variableXMeasuring and measuring NO in denitrated flue gasXThe quantity is used as the input variable for the next calculation to perform the iterative calculation. And the model output variable is used as a relevant variable of the control parameter of the relevant controller of the SNCR to be used in the optimization process of the subsequent control parameter. Illustratively, the controller of the SNCR described above includes an ammonia controller.
With continued reference to fig. 1, the model parameter optimization unit 102 further comprises a parameter optimization unit 1022, and the parameter optimization unit 1022 outputs the optimized control parameters of the SNCR according to the smoke emission data calculated by the model processing unit 1021 as the output variable.
Illustratively, the parameter optimization unit 1022 adaptively adjusts the optimized control parameter according to the output variable.
Further, illustratively, the parameter optimization unit 1022 calculates the optimized control parameter from the output variable using a machine learning model.
Illustratively, the machine learning model comprises a neural network model. Specifically, in the parameter optimization unit 1022, the output variables calculated by the model processing unit 1021 are converted into calculable standardized data, and the neural network model is used to calculate the calculable standardized data to obtain the optimized control parameters. The data transformation process and the calculation process using the neural network model may be performed by methods known to those skilled in the art, and will not be described herein.
It should be understood that, in the present embodiment, the neural network is used as an example of the machine learning model to describe the parameter optimization unit, which is only exemplary, and other machine learning models, such as statistical learning based on a vector machine, deep learning, and the like, are all applicable to the present invention.
The machine learning model is adopted to optimize the control parameters, so that the control parameters can be adaptively optimized and adjusted while the optimized control parameters are accurately calculated and optimized. Specifically, in the process of calculating by using the machine learning model, the reaction result of the SNCR after being controlled and adjusted by using the optimal control parameter may be detected, and the machine learning model is corrected by the detected result to further optimize the machine learning model, thereby further adjusting the output result of the optimal control parameter. Meanwhile, according to the semi-dry flue gas treatment control system, the SNCR is completely automatically and adaptively adjusted and controlled, the manual control burden and errors are effectively reduced, and the control efficiency is improved.
In one example according to the present invention, the data collecting means further collects the flue gas emission data of flue gas emitted from an incinerator. The parameter optimization unit also compares the output variable used for predicting the smoke emission data and calculated by the model processing unit with the smoke emission data collected by the data collection device to obtain a comparison result, and calculates the optimization control parameter according to the comparison result.
Illustratively, the optimal control parameter includes an ammonia flow rate. The control of the SNCR reaction speed can be realized by controlling the flow of the ammonia water, so that the reaction efficiency of the SNCR is improved, and the energy consumption is saved.
In one example according to the present invention, the optimized control parameters further include ammonia water temperature, concentration, and the like. Those skilled in the art can increase or decrease specific control parameters according to actual needs to realize precise control of SNCR, which is not limited herein.
In one example according to the present invention, the calculation of the model processing unit and the calculation of the parameter optimization unit in the above-described model parameter control module are implemented on a PLC control panel.
As shown in fig. 1, the control parameter of the ammonia water flow rate calculated by the parameter optimization unit 1022 is transmitted to the automatic control module 103. The automatic control module 103 automatically controls the SNCR according to the optimized control parameters. Illustratively, the automated control module 103 includes executable program instructions and a controller that, when executed, enables control of the ammonia flow rate of the SNCR, and the like.
Referring to fig. 2, a control principle diagram of a semi-dry flue gas treatment control system according to an embodiment of the present invention is shown. Before control, the establishment of a model and a parameter optimization unit is realized, and the established model outputs a predicted value related to smoke emission data after model calculation is carried out by taking SNCR reaction process data as an input variable
The parameter optimization unit is used for predicting the value of the smoke emission data
And optimizing control parameters related to the control setting of the controller, and outputting a command for regulating the controller by combining the input e of the controller and the output u of the controller according to the optimized control parameters to serve as the SNCR regulation. Meanwhile, the SNCR is adjusted by combining a feedforward factor k under other interference in the adjustment process. The adjusted SNCR carries out denitration, and the detected fume emission data y and the model are processed to output a predicted value about the fume emission data
The comparison obtained by comparison is used as the self-learning reference of the parameter optimization unit, so that the optimization process of the parameters can be further controlled, and the optimal control of the SNCR is finally realized.
In one example according to the present invention, a communication module is further included. The communication module enables communication between the data acquisition device 101, the model parameter optimization unit 102 and the automatic control module 103. Specifically, the data acquisition device 101 sends acquired process data to the model parameter optimization unit through an I/O port of the communication module, the model processing unit 1021 of the model parameter optimization unit 102 calculates according to the process data to obtain flue gas emission data, the parameter optimization unit 1022 calculates according to the flue gas emission data to obtain optimization control parameters, the model parameter optimization unit 102 sends the optimization control parameters to the automatic control module through the communication module again, and the automatic control module automatically controls the electromagnetic valve and the regulating valve for SNCR to regulate the flow of ammonia water according to the optimization control parameters.
In one example according to the present invention, on the basis that the original SNCR already includes a control system for controlling the SNCR, the present invention may also be directly implemented on the original SNCR control system, that is, the communication between the original SNCR control system and the model parameter optimization unit of the present invention is implemented through a communication module, and in one example, the model parameter optimization unit according to the present invention is implemented on a PLC control board, and the communication between the PLC control board and the original SNCR control system is implemented through a communication network.
The present invention has been illustrated by the above embodiments, but it should be understood that the above embodiments are for illustrative and descriptive purposes only and are not intended to limit the invention to the scope of the described embodiments. Furthermore, it will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that many variations and modifications may be made in accordance with the teachings of the present invention, which variations and modifications are within the scope of the present invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A model-based SNCR control system for waste-incineration power generation, comprising:
the system comprises a data acquisition device, a data acquisition device and a data processing device, wherein the data acquisition device is used for acquiring process data related to the reaction process of the SNCR, and comprises a hearth temperature detection device arranged on an incinerator, an exhaust-heat boiler outlet oxygen content detection device, a chimney outlet flue gas oxygen content detection device arranged at a chimney outlet and used for detecting the oxygen content in flue gas, and an ammonia water flow detection device arranged on the SNCR and used for detecting the flow of ammonia water, and the process data comprise main steam flow, flue gas amount, hearth temperature, exhaust-heat boiler outlet flue gas oxygen content and ammonia water flow;
a model parameter optimization unit which comprises a model processing unit and a parameter optimization unit, wherein the model processing unit calculates an output variable by using a dynamic flue gas processing model and taking the process data as an input variable, the output variable predicts flue gas emission data related to flue gas subjected to SNCR denitration treatment, the dynamic flue gas processing model is a calculation model established according to a correlation between the process data and the flue gas emission data, the parameter optimization unit calculates an optimized control parameter of the SNCR according to the output variable, and the dynamic flue gas processing model comprises a time sequence model established by using MATLAB; the parameter optimization unit is also used for comparing the output variable calculated by the model processing unit with the smoke emission data at the outlet of the chimney to obtain a comparison result, and calculating the optimization control parameter according to the comparison result; the output variable comprises NO in the denitrated flue gasXNO of original flue gas of first flueXThe amount of NO in the denitrated flue gas is obtained by last calculation and is used as the output variableXThe amount and NO of the raw flue gas of the first flueXAmount as the input variable used in the next calculation;
the automatic control module is used for automatically controlling the SNCR according to the optimized control parameters;
and the data acquisition device, the model parameter optimization unit and the automatic control module are communicated through the communication module.
2. The model-based SNCR control system of claim 1, wherein the parameter optimization unit adaptively adjusts the optimized control parameter based on the output variable.
3. The model-based SNCR control system of claim 2, wherein the parameter optimization unit calculates the optimized control parameters from the output variables using a machine learning model.
4. The model-based SNCR control system of claim 1, wherein the data acquisition device further acquires smoke emission data at a stack outlet.
5. The model-based SNCR control system of claim 1, wherein the input variables include main steam flow, flue gas volume, furnace temperature, exhaust heat boiler outlet flue gas oxygen content, and ammonia water flow.
6. The model-based SNCR control system of claim 1, wherein the optimal control parameter comprises an ammonia dosage.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103977705A (en) * | 2014-04-23 | 2014-08-13 | 浙江省环境保护科学设计研究院 | Reducing agent metering for cement clinker production line SNCR flue gas denitration and control system and method thereof |
| CN104707480A (en) * | 2015-02-12 | 2015-06-17 | 杭州天蓝环保设备有限公司 | CFD-based SNCR-SCR denitration process ammonia supplementing design method |
| CN104793651A (en) * | 2015-04-20 | 2015-07-22 | 四川大学 | Ammonia spraying amount controlling method for SNCR (selective no catalytic reduction) flue gas denitrating system for cement manufacturing |
| CN104826492A (en) * | 2015-04-23 | 2015-08-12 | 华北电力大学（保定） | Improvement method for selective catalytic reduction flue gas denitrification and ammonia injection control system |
| CN105938375A (en) * | 2016-05-31 | 2016-09-14 | 浙江省环境保护科学设计研究院 | Control method of flow of reducing agent in flue gas SNCR denitration process |
| CN106054608A (en) * | 2016-06-21 | 2016-10-26 | 光大环保技术研究院（深圳）有限公司 | Fuzzy control method and system for waste incineration flue gas denitration SNCR (Selective Non Catalytic Reduction) |
| CN106362561A (en) * | 2016-11-09 | 2017-02-01 | 山西华仁通电力科技有限公司 | Clustered SNCR (selective non-catalytic reduction) control method based on flow field in furnace |
| CN106681381A (en) * | 2017-01-03 | 2017-05-17 | 华北电力大学 | SCR denitration system ammonia spraying quantity optimal control system and method based on intelligent feedforward signals |
| CN106873381A (en) * | 2017-04-10 | 2017-06-20 | 内蒙古瑞特优化科技股份有限公司 | Spray ammonia control system |
| CN108786428A (en) * | 2018-06-07 | 2018-11-13 | 光大环保技术研究院（南京）有限公司 | A kind of SNCR denitration optimal control system and control method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060052902A1 (en) * | 2004-08-27 | 2006-03-09 | Neuco, Inc. | Method and system for SNCR optimization |
| US20160209031A1 (en) * | 2015-01-20 | 2016-07-21 | Alstom Technology Ltd | Model-based controls for a furnace and method for controlling the furnace |
-
2019
- 2019-08-26 CN CN201910791006.8A patent/CN110652856B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103977705A (en) * | 2014-04-23 | 2014-08-13 | 浙江省环境保护科学设计研究院 | Reducing agent metering for cement clinker production line SNCR flue gas denitration and control system and method thereof |
| CN104707480A (en) * | 2015-02-12 | 2015-06-17 | 杭州天蓝环保设备有限公司 | CFD-based SNCR-SCR denitration process ammonia supplementing design method |
| CN104793651A (en) * | 2015-04-20 | 2015-07-22 | 四川大学 | Ammonia spraying amount controlling method for SNCR (selective no catalytic reduction) flue gas denitrating system for cement manufacturing |
| CN104826492A (en) * | 2015-04-23 | 2015-08-12 | 华北电力大学（保定） | Improvement method for selective catalytic reduction flue gas denitrification and ammonia injection control system |
| CN105938375A (en) * | 2016-05-31 | 2016-09-14 | 浙江省环境保护科学设计研究院 | Control method of flow of reducing agent in flue gas SNCR denitration process |
| CN106054608A (en) * | 2016-06-21 | 2016-10-26 | 光大环保技术研究院（深圳）有限公司 | Fuzzy control method and system for waste incineration flue gas denitration SNCR (Selective Non Catalytic Reduction) |
| CN106362561A (en) * | 2016-11-09 | 2017-02-01 | 山西华仁通电力科技有限公司 | Clustered SNCR (selective non-catalytic reduction) control method based on flow field in furnace |
| CN106681381A (en) * | 2017-01-03 | 2017-05-17 | 华北电力大学 | SCR denitration system ammonia spraying quantity optimal control system and method based on intelligent feedforward signals |
| CN106873381A (en) * | 2017-04-10 | 2017-06-20 | 内蒙古瑞特优化科技股份有限公司 | Spray ammonia control system |
| CN108786428A (en) * | 2018-06-07 | 2018-11-13 | 光大环保技术研究院（南京）有限公司 | A kind of SNCR denitration optimal control system and control method |
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