CN112420133B - Modeling method and system for SCR denitration system of thermal power generating unit - Google Patents
Modeling method and system for SCR denitration system of thermal power generating unit Download PDFInfo
- Publication number
- CN112420133B CN112420133B CN202011302547.9A CN202011302547A CN112420133B CN 112420133 B CN112420133 B CN 112420133B CN 202011302547 A CN202011302547 A CN 202011302547A CN 112420133 B CN112420133 B CN 112420133B
- Authority
- CN
- China
- Prior art keywords
- scr denitration
- denitration system
- model
- system model
- scr
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005457 optimization Methods 0.000 claims abstract description 56
- 239000002245 particle Substances 0.000 claims abstract description 50
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 49
- 230000008569 process Effects 0.000 claims abstract description 14
- 238000006243 chemical reaction Methods 0.000 claims description 82
- 241000183024 Populus tremula Species 0.000 claims description 41
- 238000012549 training Methods 0.000 claims description 38
- 230000003993 interaction Effects 0.000 claims description 34
- 238000004088 simulation Methods 0.000 claims description 34
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 31
- 230000004913 activation Effects 0.000 claims description 27
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 21
- 239000003546 flue gas Substances 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 19
- 230000007246 mechanism Effects 0.000 claims description 15
- 229910021529 ammonia Inorganic materials 0.000 claims description 14
- 239000003054 catalyst Substances 0.000 claims description 8
- 238000005507 spraying Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 239000011800 void material Substances 0.000 claims description 6
- 230000000704 physical effect Effects 0.000 claims description 3
- 238000000053 physical method Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 2
- 238000002347 injection Methods 0.000 description 6
- 239000007924 injection Substances 0.000 description 6
- 238000001311 chemical methods and process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000010531 catalytic reduction reaction Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000010977 unit operation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000007385 chemical modification Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910000069 nitrogen hydride Inorganic materials 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
-
- 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
- B01D53/86—Catalytic processes
- B01D53/8621—Removing nitrogen compounds
- B01D53/8625—Nitrogen oxides
- B01D53/8631—Processes characterised by a specific device
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Health & Medical Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Biomedical Technology (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- General Chemical & Material Sciences (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- General Engineering & Computer Science (AREA)
- Crystallography & Structural Chemistry (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a modeling method and a modeling system for an SCR denitration system of a thermal power generating unit. The modeling method of the invention utilizes the strong data analysis capability of MATLAB, builds a particle swarm optimization algorithm in MATLAB, determines the optimal parameters of the SCR denitration system model based on the operation data, overcomes the technical defects of low parameter precision and model mismatch caused by the fact that factors in the actual operation process are not considered in the existing modeling method, does not need to carry out parameter identification according to a differential equation set, and reduces the complexity of parameter determination. The method reduces the complexity of building the SCR denitration system model of the thermal power unit and improves the accuracy of the built SCR denitration system model of the thermal power unit.
Description
Technical Field
The invention relates to the technical field of coal-fired power plant optimization control, in particular to a modeling method and system of an SCR denitration system of a thermal power unit.
Background
Along with the improvement of the flue gas emission standard of the coal-fired power plant, in order to ensure the denitration efficiency and avoid excessive ammonia injection, the ammonia injection amount of the SCR denitration system needs to be precisely controlled, and the establishment of an accurate SCR system model is a precondition of optimal control.
Aspen Plus is large-scale chemical process simulation software developed by Aspen Tech company, and a strong physical database provides reliable data analysis support for design and development of chemical process, modification and optimization of process equipment and the like. An Aspen Plus is used for constructing an SCR denitration reaction model, the operation is convenient, but certain model parameters are uncertain, such as denitration reaction activation energy E and denitration reaction rate constant k, and the parameters are different from the reaction temperature, the catalyst loading amount and the import and export NO X The concentration difference, the unit operation mode and other factors are related, and are generally difficult to solve and need manual adjustment.
The model parameters of the SCR denitration system, namely the denitration reaction activation energy E and the denitration reaction rate constant k, are determined, parameters are identified according to a mechanism model differential equation set in a traditional method, but the modeling of the method is complex, factors in the actual operation process are not considered, and the problems of low parameter precision, model mismatch and the like often occur.
How to build an accurate thermal power generating unit SCR denitration system model becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a modeling method and a modeling system for an SCR denitration system of a thermal power unit, so as to reduce the complexity of building an SCR denitration system model of the thermal power unit and improve the accuracy of the built SCR denitration system model of the thermal power unit.
In order to achieve the above object, the present invention provides the following solutions:
a modeling method of an SCR denitration system of a thermal power generating unit, the modeling method comprising the steps of:
constructing an SCR denitration system model based on Aspen Plus software;
setting up a particle swarm optimization algorithm model in MATLAB software;
constructing a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model constructed in Aspen Plus software based on an Active X technology;
acquiring operation data of an SCR denitration system of the thermal power generating unit, and inputting an inlet NO of the SCR denitration system in the operation data X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X The concentration is used as the output quantity of the SCR denitration system model, and a training set is established;
based on the data interaction interface, the optimal denitration reaction activation energy parameter and the denitration reaction rate parameter of the SCR denitration system model are determined by utilizing the training set to adopt a particle swarm optimization algorithm model in MATLAB, and the trained SCR denitration system model is obtained.
Optionally, the construction of the SCR denitration system model based on Aspen Plus software specifically includes:
setting a type, a component and a physical method of SCR denitration reaction in Aspen Plus software according to a reaction mechanism of an SCR denitration system of the thermal power generating unit;
according to the physical parameters of the SCR denitration system of the thermal power generating unit, the type, the dimension and the pressure of the SCR reactor are set in Aspen Plus software, a simulation process flow is designed, the catalyst loading amount and the bed void ratio are designed, and the stoichiometry of the SCR denitration reaction is set.
Optionally, the reaction of SCR denitration system of thermal power generating unitThe mechanism satisfies the chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
Optionally, the determining, based on the data interaction interface, the optimal denitration reaction activation energy parameter and denitration reaction rate parameter of the SCR denitration system model by using the training set and adopting a particle swarm optimization algorithm model in MATLAB, so as to obtain a trained SCR denitration system model specifically includes:
inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model;
the simulation data output by the SCR denitration system model are transmitted to a particle swarm optimization algorithm model through a data interaction interface;
calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as a target function value;
updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by using the particle swarm optimization algorithm model;
transmitting the updated denitration reaction activation energy parameters and denitration reaction rate parameters to the SCR denitration system model through a data interaction interface, and updating the SCR denitration system model to obtain an updated SCR denitration system model;
repeating the steps for n times, and determining an SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model.
A modeling system of an SCR denitration system of a thermal power generating unit, the modeling system comprising:
the SCR denitration system model building module is used for building an SCR denitration system model based on Aspen Plus software;
the particle swarm optimization algorithm model building module is used for building a particle swarm optimization algorithm model in MATLAB software;
the data interaction interface building module is used for building a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model built in Aspen Plus software based on an Active X technology;
the training set establishment module is used for acquiring operation data of the SCR denitration system of the thermal power generating unit and enabling an inlet NO of the SCR denitration system in the operation data to be X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X The concentration is used as the output quantity of the SCR denitration system model, and a training set is established;
the training module is used for determining the optimal denitration reaction activation energy parameter and denitration reaction rate parameter of the SCR denitration system model by utilizing the training set to adopt a particle swarm optimization algorithm model in MATLAB based on the data interaction interface, so as to obtain the trained SCR denitration system model.
Optionally, the SCR denitration system model building module specifically includes:
the first setting submodule is used for setting the type, the component and the physical property method of the SCR denitration reaction in Aspen Plus software according to the reaction mechanism of the SCR denitration system of the thermal power generating unit;
and the second setting submodule is used for setting the type, the dimension and the pressure of the SCR reactor in Aspen Plus software according to the physical parameters of the SCR denitration system of the thermal power generating unit, designing a simulation process flow, the catalyst loading amount and the bed void ratio, and setting the stoichiometry of the SCR denitration reaction.
Optionally, the reaction mechanism of the SCR denitration system of the thermal power generating unit meets the chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
Optionally, the training module specifically includes:
the simulation data calculation sub-module is used for inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model;
the simulation data transmission sub-module is used for transmitting the simulation data output by the SCR denitration system model to the particle swarm optimization algorithm model through the data interaction interface;
the objective function value calculation sub-module is used for calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as an objective function value;
the parameter updating sub-module is used for updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by utilizing the particle swarm optimization algorithm model;
the parameter transmission submodule is used for transmitting the updated denitration reaction activation energy parameter and the denitration reaction rate parameter to the SCR denitration system model through the data interaction interface, updating the SCR denitration system model and obtaining an updated SCR denitration system model;
and the trained SCR denitration system model output submodule is used for repeatedly calling the submodule for n times and outputting the SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a modeling method of an SCR denitration system of a thermal power generating unit, which comprises the following steps: constructing an SCR denitration system model based on Aspen Plus software; setting up a particle swarm optimization algorithm model in MATLAB software; constructing a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model constructed in Aspen Plus software based on an Active X technology; acquiring operation data of an SCR denitration system of the thermal power generating unit, and inputting an inlet NO of the SCR denitration system in the operation data X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X The concentration is used as the output quantity of the SCR denitration system model, and a training set is established; based on the data interaction interface, the optimal denitration reaction activation energy parameter and the denitration reaction rate parameter of the SCR denitration system model are determined by utilizing the training set to adopt a particle swarm optimization algorithm model in MATLAB, and the trained SCR denitration system model is obtained. The invention utilizes the strong data analysis capability of MATLAB, builds a particle swarm optimization algorithm in MATLAB, and is based onThe operation data are used for determining the optimal parameters of the SCR denitration system model, the technical defects of low parameter precision and model mismatch caused by the fact that factors in the actual operation process are not considered in the existing modeling method are overcome, parameter identification is not needed according to a differential equation set, and the complexity of parameter determination is reduced. The method reduces the complexity of building the SCR denitration system model of the thermal power unit and improves the accuracy of the built SCR denitration system model of the thermal power unit.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a modeling method of an SCR denitration system of a thermal power generating unit;
FIG. 2 is a schematic diagram of a modeling method of an SCR denitration system of a thermal power generating unit;
FIG. 3 is a schematic diagram of determining optimal denitration reaction activation energy parameters and denitration reaction rate parameters of an SCR denitration system model according to the present invention;
FIG. 4 is a model diagram of an SCR denitration system built in Aspen Plus;
fig. 5 is a graph showing the change of objective function values of global optimum particles in the process of optimizing the denitration reaction activation energy parameter and the denitration reaction rate parameter of the SCR denitration system model provided by the present invention.
Detailed Description
The invention aims to provide a modeling method and a modeling system for an SCR denitration system of a thermal power unit, so as to reduce the complexity of building an SCR denitration system model of the thermal power unit and improve the accuracy of the built SCR denitration system model of the thermal power unit.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
MATLAB is advanced numerical analysis solving software developed by MathWorks company, and has wide application in the fields of data calculation, deep learning, control systems and the like. Based on the strong data analysis capability of MATLAB, the parameters of the SCR denitration system model can be optimized through the combination of Aspen Plus and MATLAB, and the denitration system model can be obtained.
Aiming at the problems of complex modeling and low precision of a selective catalytic reduction (Selective Catalytic Reduction, SCR) denitration system, the method for constructing an SCR denitration system model in Aspen Plus is provided, an interface for data interaction between Aspen Plus and MATLAB is established, and a particle swarm intelligent optimization algorithm based on MATLAB is adopted to determine model parameters, so that the SCR denitration system model with high accuracy is obtained.
As shown in fig. 1 and 2, the invention provides a modeling method of an SCR denitration system of a thermal power generating unit, the modeling method comprises the following steps:
and step 101, building an SCR denitration system model based on Aspen Plus software.
And constructing a main structure of the SCR denitration system model by using flow simulation software Aspen Plus according to the reaction mechanism and entity parameters of the SCR denitration system of a certain thermal power generating unit.
Step 101, building an SCR denitration system model based on Aspen Plus software, specifically including:
setting a type, a component and a physical method of SCR denitration reaction in Aspen Plus software according to a reaction mechanism of an SCR denitration system of the thermal power generating unit; the reaction mechanism of the SCR denitration system of the thermal power generating unit meets the chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
According to the physical parameters of the SCR denitration system of the thermal power generating unit, the type, the dimension and the pressure of the SCR reactor are set in Aspen Plus software, a simulation process flow is designed, the catalyst loading amount and the bed void ratio are designed, and the stoichiometry of the SCR denitration reaction is set.
And 102, building a particle swarm optimization algorithm model in MATLAB software.
Design in MATLABParticle swarm intelligent optimization algorithm program, setting the denitration reaction activation energy E optimization variable range to be 5-10 kcal/mol, and setting the denitration reaction rate constant k optimization variable range to be 2000-5000 m 3 /(mol.s) for actually operating the SCR denitration system inlet NO of the unit X The concentration, the ammonia injection amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as inputs of an SCR denitration system model in Aspen Plus, and NO output by the model X Concentration and actual SCR denitration system outlet NO X The concentration mean square error is minimized as an objective function.
And step 103, building a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model built in Aspen Plus software based on an Active X technology.
Step 104, obtaining operation data of the SCR denitration system of the thermal power generating unit, and inputting an inlet NO of the SCR denitration system in the operation data X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X And (5) taking the concentration as the output quantity of the SCR denitration system model, and establishing a training set.
The invention collects the actual operation data of the SCR denitration system of the thermal power generating unit with the time length of 1 month or more continuously. That is, the actual operation data of the SCR system of the thermal power generating unit is obtained continuously for 1 month or longer, and the collected variables include: SCR denitration system inlet NO X Concentration, ammonia spraying amount of SCR denitration system, inlet flue gas flow of SCR denitration system, inlet flue gas temperature of SCR denitration system and outlet NO of SCR denitration system X Concentration.
And 105, determining optimal denitration reaction activation energy parameters and denitration reaction rate parameters of the SCR denitration system model by using a particle swarm optimization algorithm model in MATLAB by using the training set based on the data interaction interface, and obtaining the trained SCR denitration system model.
As shown in fig. 3 and 4, in fig. 4, GAS represents an SCR system inlet flue GAS, NH3 represents an SCR denitration system ammonia injection amount, GASFLOW represents a mixed GAS, emisistion represents an SCR denitration system outlet flue GAS, and S1 represents ammonia GAS passing through an ammonia injection valve. VALVE indicates an ammonia injection VALVE, MIX indicates a mixer, and SCR indicates a primary site where SCR denitration occurs. The invention discloses a collaborative optimization solution based on MATLAB and Aspen Plus, which comprises the following specific steps: using the model main body structure established in the step 101, selecting actual data as an inlet parameter of the model, and obtaining simulation data of an outlet; the simulation data is input to a MATLAB algorithm program through a data interaction interface, and iteration optimization is realized by taking the minimum mean square error of the simulation and actual data as an objective function; the optimized result is transmitted to the main structure of the model through the data interaction interface; repeating the above process for multiple iterations to obtain a group of parameters with minimum root mean square error with the actual model; the specific optimization algorithm can adopt a particle swarm intelligent optimization algorithm. And (3) bringing the optimized parameters k and E into a main structure in Aspen Plus, and establishing an SCR denitration system model.
Step 105, based on the data interaction interface, determining an optimal denitration reaction activation energy parameter and a denitration reaction rate parameter of the SCR denitration system model by using the training set and adopting a particle swarm optimization algorithm model in MATLAB, so as to obtain a trained SCR denitration system model, which specifically comprises: inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model; the simulation data output by the SCR denitration system model are transmitted to a particle swarm optimization algorithm model through a data interaction interface; calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as a target function value; updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by using the particle swarm optimization algorithm model; transmitting the updated denitration reaction activation energy parameters and denitration reaction rate parameters to the SCR denitration system model through a data interaction interface, and updating the SCR denitration system model to obtain an updated SCR denitration system model; repeating the steps for n times, and determining an SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model. In the training process, as the iteration number increases, the trend of the objective function value change of the global optimal particle is shown in fig. 5.
The invention also provides a modeling system of the SCR denitration system of the thermal power generating unit, which comprises:
the SCR denitration system model building module is used for building the SCR denitration system model based on Aspen Plus software.
The SCR denitration system model building module specifically comprises: the first setting submodule is used for setting the type, the component and the physical property method of the SCR denitration reaction in Aspen Plus software according to the reaction mechanism of the SCR denitration system of the thermal power generating unit; and the second setting submodule is used for setting the type, the dimension and the pressure of the SCR reactor in Aspen Plus software according to the physical parameters of the SCR denitration system of the thermal power generating unit, designing a simulation process flow, the catalyst loading amount and the bed void ratio, and setting the stoichiometry of the SCR denitration reaction. The reaction mechanism of the SCR denitration system of the thermal power generating unit meets the chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
The particle swarm optimization algorithm model building module is used for building a particle swarm optimization algorithm model in MATLAB software;
the data interaction interface building module is used for building a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model built in Aspen Plus software based on an Active X technology;
the training set establishment module is used for acquiring operation data of the SCR denitration system of the thermal power generating unit and enabling an inlet NO of the SCR denitration system in the operation data to be X The method comprises the steps of taking the concentration, the ammonia spraying amount of an SCR denitration system, the temperature of flue gas at an inlet of the SCR denitration system and the flow of flue gas at an inlet of the SCR denitration system as input amounts of an SCR denitration system model, taking the concentration of NOx at an outlet of the SCR denitration system in operation data as output amounts of the SCR denitration system model, and establishing a training set;
the training module is used for determining the optimal denitration reaction activation energy parameter and denitration reaction rate parameter of the SCR denitration system model by utilizing the training set to adopt a particle swarm optimization algorithm model in MATLAB based on the data interaction interface, so as to obtain the trained SCR denitration system model.
The training module specifically comprises: the simulation data calculation sub-module is used for inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model; the simulation data transmission sub-module is used for transmitting the simulation data output by the SCR denitration system model to the particle swarm optimization algorithm model through the data interaction interface; the objective function value calculation sub-module is used for calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as an objective function value; the parameter updating sub-module is used for updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by utilizing the particle swarm optimization algorithm model; the parameter transmission submodule is used for transmitting the updated denitration reaction activation energy parameter and the denitration reaction rate parameter to the SCR denitration system model through the data interaction interface, updating the SCR denitration system model and obtaining an updated SCR denitration system model; and the trained SCR denitration system model output submodule is used for repeatedly calling the submodule for n times and outputting the SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention simplifies the modeling process. The traditional SCR denitration system model can be generally divided into a mechanism model based on a dynamic differential equation set and a data model based on field actual data, and the method provides an SCR denitration system model establishment method based on a chemical process simulation platform Aspen Plus, so that the operation is more convenient.
The invention improves the model precision. Because the parameters of the SCR denitration model, namely the denitration reaction activation energy E and the denitration reaction rate constant k, have a plurality of influencing factors, including reaction temperature, catalyst loading, inlet and outlet NOx concentration difference, unit operation mode and the like, the numerical value of a single Aspen Plus is generally difficult to determine.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, which are intended to be only illustrative of the methods and concepts underlying the invention, and not all examples are intended to be within the scope of the invention as defined by the appended claims.
Claims (6)
1. The modeling method of the SCR denitration system of the thermal power generating unit is characterized by comprising the following steps of:
constructing an SCR denitration system model based on Aspen Plus software;
setting up a particle swarm optimization algorithm model in MATLAB software;
constructing a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model constructed in Aspen Plus software based on an Active X technology;
acquiring operation data of an SCR denitration system of the thermal power generating unit, and inputting an inlet NO of the SCR denitration system in the operation data X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X The concentration is used as the output quantity of the SCR denitration system model, and a training set is established;
based on the data interaction interface, determining optimal denitration reaction activation energy parameters and denitration reaction rate parameters of the SCR denitration system model by using a particle swarm optimization algorithm model in MATLAB by using the training set, and obtaining the trained SCR denitration system model specifically comprises the following steps:
inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model;
the simulation data output by the SCR denitration system model are transmitted to a particle swarm optimization algorithm model through a data interaction interface;
calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as a target function value;
updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by using the particle swarm optimization algorithm model;
transmitting the updated denitration reaction activation energy parameters and denitration reaction rate parameters to the SCR denitration system model through a data interaction interface, and updating the SCR denitration system model to obtain an updated SCR denitration system model;
repeating the steps for n times, and determining an SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model.
2. The modeling method of the thermal power generating unit SCR denitration system according to claim 1, wherein the modeling method based on Aspen Plus software is characterized by comprising the following steps:
setting a type, a component and a physical method of SCR denitration reaction in Aspen plus software according to a reaction mechanism of an SCR denitration system of the thermal power generating unit;
according to the physical parameters of the SCR denitration system of the thermal power generating unit, the type, the dimension and the pressure of the SCR reactor are set in Aspen Plus software, a simulation process flow is designed, the catalyst loading amount and the bed void ratio are designed, and the stoichiometry of the SCR denitration reaction is set.
3. The modeling method of the SCR denitration system of the thermal power generating unit according to claim 2, wherein the reaction mechanism of the SCR denitration system of the thermal power generating unit satisfies a chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
4. Modeling system of a thermal power generating unit SCR denitration system, characterized in that the modeling system comprises:
the SCR denitration system model building module is used for building an SCR denitration system model based on Aspen Plus software;
the particle swarm optimization algorithm model building module is used for building a particle swarm optimization algorithm model in MATLAB software;
the data interaction interface building module is used for building a data interaction interface of a particle swarm optimization algorithm model in MATLAB and an SCR denitration system model built in Aspen Plus software based on an Active X technology;
the training set establishment module is used for acquiring operation data of the SCR denitration system of the thermal power generating unit and enabling an inlet NO of the SCR denitration system in the operation data to be X The concentration, the ammonia spraying amount of the SCR denitration system, the temperature of flue gas at the inlet of the SCR denitration system and the flow of flue gas at the inlet of the SCR denitration system are used as the input amount of an SCR denitration system model, and the NO at the outlet of the SCR denitration system in the operation data X The concentration is used as the output quantity of the SCR denitration system model, and a training set is established;
the training module is used for determining the optimal denitration reaction activation energy parameter and denitration reaction rate parameter of the SCR denitration system model by utilizing the training set to adopt a particle swarm optimization algorithm model in MATLAB based on the data interaction interface, so as to obtain a trained SCR denitration system model;
the training module specifically comprises:
the simulation data calculation sub-module is used for inputting the input quantity in the training set into the SCR denitration system model to obtain simulation data output by the SCR denitration system model;
the simulation data transmission sub-module is used for transmitting the simulation data output by the SCR denitration system model to the particle swarm optimization algorithm model through the data interaction interface;
the objective function value calculation sub-module is used for calculating the mean square error of the simulation data output by the SCR denitration system model and the output quantity in the training set by using the particle swarm optimization algorithm model as an objective function value;
the parameter updating sub-module is used for updating the denitration reaction activation energy parameter and the denitration reaction rate parameter by utilizing the particle swarm optimization algorithm model;
the parameter transmission submodule is used for transmitting the updated denitration reaction activation energy parameter and the denitration reaction rate parameter to the SCR denitration system model through the data interaction interface, updating the SCR denitration system model and obtaining an updated SCR denitration system model;
and the trained SCR denitration system model output submodule is used for repeatedly calling the submodule for n times and outputting the SCR denitration system model corresponding to the minimum objective function value as the trained SCR denitration system model.
5. The modeling system of an SCR denitration system of a thermal power generating unit according to claim 4, wherein the model building module of the SCR denitration system specifically comprises:
the first setting submodule is used for setting the type, the component and the physical property method of the SCR denitration reaction in Aspen Plus software according to the reaction mechanism of the SCR denitration system of the thermal power generating unit;
and the second setting submodule is used for setting the type, the dimension and the pressure of the SCR reactor in Aspen Plus software according to the physical parameters of the SCR denitration system of the thermal power generating unit, designing a simulation process flow, the catalyst loading amount and the bed void ratio, and setting the stoichiometry of the SCR denitration reaction.
6. The modeling system of the SCR denitration system of the thermal power generating unit according to claim 5, wherein the reaction mechanism of the SCR denitration system of the thermal power generating unit satisfies a chemical equation: 4NO+4NH 3 +O 2 ＝4N 2 +6H 2 O。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011302547.9A CN112420133B (en) | 2020-11-19 | 2020-11-19 | Modeling method and system for SCR denitration system of thermal power generating unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011302547.9A CN112420133B (en) | 2020-11-19 | 2020-11-19 | Modeling method and system for SCR denitration system of thermal power generating unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112420133A CN112420133A (en) | 2021-02-26 |
| CN112420133B true CN112420133B (en) | 2024-01-26 |
Family
ID=74774212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011302547.9A Active CN112420133B (en) | 2020-11-19 | 2020-11-19 | Modeling method and system for SCR denitration system of thermal power generating unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112420133B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011150462A1 (en) * | 2010-06-02 | 2011-12-08 | Co2Crc Technologies Pty Ltd | Optimising objectives of a plant and a carbon dioxide capture facility |
| CN103400196A (en) * | 2013-07-02 | 2013-11-20 | 中国科学院沈阳自动化研究所 | Method for modeling and optimizing cleaner production of cement clinker sintering process |
| CN106971049A (en) * | 2017-04-17 | 2017-07-21 | 武汉理工大学 | A kind of new multi objective optimization method of catalytic cracking piece-rate system |
| CN109636048A (en) * | 2018-12-18 | 2019-04-16 | 上海交通大学 | A kind of non-homogeneous spray ammonia convex quadratic programming optimum design method of SCR denitration system |
| CN109753702A (en) * | 2018-12-21 | 2019-05-14 | 哈尔滨工程大学 | A kind of emulation mode of gas turbine chemical back heating system |
| CN109933884A (en) * | 2019-03-08 | 2019-06-25 | 华北电力大学（保定） | A method of towards coal unit SCR denitration system Neural network inverse control |
| CN111489605A (en) * | 2020-04-21 | 2020-08-04 | 大唐环境产业集团股份有限公司 | Ammonia spraying optimization control simulation system based on Simulink and WinCC |
| KR20220070365A (en) * | 2020-11-22 | 2022-05-31 | 서울대학교산학협력단 | Method for fault diagnosis of engine emission aftertreatment system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011022653A2 (en) * | 2009-08-20 | 2011-02-24 | Reilly Timothy J | Recuperative combustion system |
-
2020
- 2020-11-19 CN CN202011302547.9A patent/CN112420133B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011150462A1 (en) * | 2010-06-02 | 2011-12-08 | Co2Crc Technologies Pty Ltd | Optimising objectives of a plant and a carbon dioxide capture facility |
| CN103400196A (en) * | 2013-07-02 | 2013-11-20 | 中国科学院沈阳自动化研究所 | Method for modeling and optimizing cleaner production of cement clinker sintering process |
| CN106971049A (en) * | 2017-04-17 | 2017-07-21 | 武汉理工大学 | A kind of new multi objective optimization method of catalytic cracking piece-rate system |
| CN109636048A (en) * | 2018-12-18 | 2019-04-16 | 上海交通大学 | A kind of non-homogeneous spray ammonia convex quadratic programming optimum design method of SCR denitration system |
| CN109753702A (en) * | 2018-12-21 | 2019-05-14 | 哈尔滨工程大学 | A kind of emulation mode of gas turbine chemical back heating system |
| CN109933884A (en) * | 2019-03-08 | 2019-06-25 | 华北电力大学（保定） | A method of towards coal unit SCR denitration system Neural network inverse control |
| CN111489605A (en) * | 2020-04-21 | 2020-08-04 | 大唐环境产业集团股份有限公司 | Ammonia spraying optimization control simulation system based on Simulink and WinCC |
| KR20220070365A (en) * | 2020-11-22 | 2022-05-31 | 서울대학교산학협력단 | Method for fault diagnosis of engine emission aftertreatment system |
Non-Patent Citations (6)
| Title |
|---|
| Matlab 与Aspen Plus 软件交互实现和应用;陆佳伟等;《计算机与应用化学》;第35卷(第1期);第59页右栏5 结论 * |
| 一种基于Aspen Plus和MATLAB协同仿真的SCR脱硝系统建模方法;刘一卓等;《热能动力工程》;第37卷(第11期);第122-130页 * |
| 基于Aspen Plus 的燃煤电厂烟气污染控制单元模拟;于荆鑫等;《过程工程学报》;第19卷(第2期);第330页右栏第2段、第331页右栏2.1.4 SCR脱硝模型 * |
| 火电厂SCR脱硝系统建模与优化控制研究;何瞳;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;第19页最后1段、第13页最后1段至第14页第1段、第17页2.4.2 机理参数辨识 * |
| 火电机组瞬态特性研究现状综述;谢天等;《中国电机工程学报》(第19期);第213-221页 * |
| 联合循环电站余热锅炉全工况烟气脱硝运行分析;任洪运等;《广东电力》;第29卷(第07期);第29-34页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112420133A (en) | 2021-02-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107243257B (en) | It is suitble to the intelligence spray ammonia control system of full load | |
| CN101842148B (en) | Adaptive control system for reagent distribution control in scr reactors | |
| CN104826493B (en) | A kind of control method of selective catalytic reduction flue gas denitrification system | |
| US9650934B2 (en) | Engine and aftertreatment optimization system | |
| CN106599586B (en) | SCR neural network based intelligently sprays ammonia optimization method and device | |
| KR20160122649A (en) | System and method for injection control of urea in selective catalyst reduction | |
| CN113689917A (en) | Visual ammonia injection optimization method and device based on SCR outlet NOx concentration | |
| Zhang et al. | MPC case study on a selective catalytic reduction in a power plant | |
| CN109499364B (en) | Catalyst aided design method based on digital mirror image | |
| CN109636048B (en) | SCR denitration system non-uniform ammonia injection convex quadratic programming optimization design method | |
| CN111489605B (en) | Ammonia spraying optimization control simulation system based on Simulink and WinCC | |
| CN106368772A (en) | Urea injection control system of SCR system | |
| CN116189791A (en) | Ammonia spraying optimization method, device and storage medium for ammonia nitrogen double control of SCR system | |
| CN112420133B (en) | Modeling method and system for SCR denitration system of thermal power generating unit | |
| WO2024021943A1 (en) | Ammonia injection optimization method and apparatus based on scr reaction kinetic model, and medium | |
| CN105498530B (en) | The method that search gas instructs spray ammonia adjustment is introduced in SCR system simulation | |
| JP4557912B2 (en) | Process control system | |
| CN106762049A (en) | Dinuclear used urea SCR system discharge control method based on NMPC | |
| CN109933884B (en) | Neural network inverse control method for SCR denitration system of coal-fired unit | |
| CN110244565B (en) | SCR system partition control method and device | |
| EP3184768B1 (en) | Emission control network for hybrid power plants | |
| CN114444295A (en) | NOx exhaust gas concentration prediction method based on combined time sequence prediction | |
| CN113868984B (en) | Intelligent optimization method and equipment for flow field of SCR (Selective catalytic reduction) denitration system | |
| Qin et al. | Dynamic modeling for SCR system of coal fired power plant | |
| Yang et al. | Selective catalytic reduction (SCR) modeling using parallel way of identification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |