CN112418284A - Control method and system for SCR denitration system of full-working-condition power station - Google Patents
Control method and system for SCR denitration system of full-working-condition power station Download PDFInfo
- Publication number
- CN112418284A CN112418284A CN202011276325.4A CN202011276325A CN112418284A CN 112418284 A CN112418284 A CN 112418284A CN 202011276325 A CN202011276325 A CN 202011276325A CN 112418284 A CN112418284 A CN 112418284A
- Authority
- CN
- China
- Prior art keywords
- scr denitration
- denitration system
- individual
- predicted
- ammonia injection
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims abstract description 334
- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 164
- 238000002347 injection Methods 0.000 claims abstract description 109
- 239000007924 injection Substances 0.000 claims abstract description 109
- 230000002068 genetic effect Effects 0.000 claims abstract description 36
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 238000003062 neural network model Methods 0.000 claims abstract description 18
- 239000003546 flue gas Substances 0.000 claims description 23
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 22
- 210000002569 neuron Anatomy 0.000 claims description 18
- 239000004480 active ingredient Substances 0.000 claims description 12
- 239000000203 mixture Substances 0.000 claims description 12
- 238000005507 spraying Methods 0.000 claims description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 7
- 239000001301 oxygen Substances 0.000 claims description 7
- 229910052760 oxygen Inorganic materials 0.000 claims description 7
- 239000007789 gas Substances 0.000 claims description 6
- 230000006798 recombination Effects 0.000 claims description 6
- 238000005215 recombination Methods 0.000 claims description 6
- 239000010865 sewage Substances 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 231100001143 noxa Toxicity 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 6
- 238000013528 artificial neural network Methods 0.000 abstract description 3
- 230000007547 defect Effects 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 58
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 3
- 238000010531 catalytic reduction reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 239000003054 catalyst Substances 0.000 description 1
- 238000001311 chemical methods and process Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 231100000252 nontoxic Toxicity 0.000 description 1
- 230000003000 nontoxic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention discloses a control method and a system of an SCR denitration system of a full-working-condition power station, wherein the control method comprises the following steps: the denitration cost is added into an optimization objective function, a prediction control structure is adopted, and a neural network and a genetic algorithm are combined to establish a model and optimize the controlled quantity, so that the optimization control of the ammonia injection quantity is realized. Predicting outlet NO of SCR reactor by constructing neural network modelxThe future change trend of the concentration is optimized by adopting a genetic algorithm to determine the ammonia injection flow of the SCR denitration system and adjust the valve opening of an ammonia injection valve, so that the method can better overcome the defect of the SCR denitration systemThe defects of large inertia and large delay are overcome, the response speed of ammonia injection amount control on unit load change is increased, and the dynamic adjustment quality of the SCR denitration system is improved.
Description
Technical Field
The invention relates to the technical field of optimization control of coal-fired power stations, in particular to a control method and a control system of an SCR denitration system of a full-working-condition power station.
Background
In the process of energy structure transformation, the large-scale access of new energy electric power to a power grid puts forward the operation flexibility requirement on a coal-fired unit. The rapid depth variable load means that the operation condition of the unit is rapidly changed in a large range, and the change of the boiler condition can cause NO generated by combustionxThe fluctuation is intensified, which undoubtedly increases the realization of NO by the unitxThe difficulty of ultra-low emission. SCR (selective Catalytic reduction) -a selective Catalytic reduction method is the most mature and widely applied flue gas denitration technology in the international world at present. SCR utilizes a reducing agent NH under the action of a catalyst3Etc. to selectively react with NO in the flue gasxReacting and generating nontoxic and pollution-free N2And H2And O. SCR denitration is the mainstream smoke denitration technology at present, the reaction is a complex physical and chemical process, and more ammonia injection amount can reduce NOxEmission, but the economic cost is increased, ammonia escape is increased, and safe operation of the unit is affected.
The SCR control system applied on site at present mainly comprises two types, one type is a fixed molar ratio control mode, and the mode belongs to open loop control and cannot meet the requirement of ultralow emission; the other is fixed outlet NOxA concentration control mode is that a cascade PID control system is mostly adopted on site at present, parameters of the system are obtained by setting reactor characteristics under a designed (rated) working condition, when the working condition of a unit is greatly changed, if the SCR system cannot be effectively controlled, denitration efficiency is low or ammonia escape phenomenon is serious, optimal control is difficult to realize, and economic and environment-friendly operation of a thermal power plant is extremely unfavorable. Therefore, it is often difficult to achieve better control using open-loop control and cascaded PID controlThe control effect of (2).
Therefore, how to carry out optimal control to the deNOx systems, realize unit economic operation when guaranteeing discharge to reach standard is the urgent problem that awaits solution of coal-fired power plant.
Disclosure of Invention
The invention aims to provide a control method and a control system for an SCR denitration system of a full-working-condition power station, which are used for realizing the optimized control of the denitration system and realizing the economic operation of a unit while ensuring the emission reaching the standard.
In order to achieve the purpose, the invention provides the following scheme:
a control method of an SCR denitration system of a full-working-condition power station comprises the following steps:
acquiring historical operating data of an SCR denitration system;
dividing historical operating data into a plurality of load intervals by adopting a clustering algorithm to obtain historical operating data of the load intervals;
respectively training a neural network model by using historical operation data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NO of the SCR denitration system at each prediction time point in the prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time periodxConcentration and exit ammonia slip; the working state data comprises unit load and NO at the inlet of the SCR denitration systemxConcentration and flue gas flow;
according to a load instruction of the SCR denitration system, acquiring an SCR denitration system output prediction model of a load interval where a unit load corresponding to the load instruction is located;
according to the output prediction model of the SCR denitration system in the load interval of the unit load corresponding to the load instruction, determining the optimal ammonia injection amount of the multi-objective optimization function by adopting a genetic algorithm, and taking the optimal ammonia injection amount;
and controlling the SCR denitration system according to the optimal ammonia injection amount.
Optionally, the neural network model includes an input layer, an output layer and a hidden layer, the input layer includes 4 neurons, the output layer includes 2 neurons, and the output layer includes 5 neurons.
Optionally, the method includes, according to the SCR denitration system output prediction model in the load interval where the unit load corresponding to the load instruction is located, determining an ammonia injection amount that optimizes a multi-objective optimization function by using a genetic algorithm, as an optimal ammonia injection amount, and specifically includes:
initializing a population of a genetic algorithm by taking the ammonia spraying amount of each control time point in a prediction time period as an individual;
judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each individual at each control time point in the population is between the lower limit value and the upper limit value of the valve opening or not, and obtaining a first judgment result;
if the first judgment result shows that the ammonia injection amount is not less than the lower limit value of the valve opening, replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system, and replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system, which is more than the upper limit value of the valve opening, with the ammonia injection amount corresponding to the upper limit value of the valve opening;
inputting each individual in the population into an SCR denitration system output prediction model to obtain a predicted outlet NO of each predicted time point in a prediction time period corresponding to each individualxConcentration and predicted outlet ammonia escape amount;
using formulas
Predicted outlet NO for each predicted time point within the corresponding predicted time period for each individualxThe concentration is corrected to obtain corrected predicted outlet NO of each predicted time point in the corresponding predicted time period of each individualxConcentration; wherein the content of the first and second substances,
is k + i.s1Predicted outlet NO after time correctionxThe concentration of the active ingredients in the mixture is,
is k + i.s1Predicted outlet NO of time of dayxThe concentration of the active ingredients in the mixture is,
predicted export NO for time kxThe concentration of the active ingredients in the mixture is,
actual outlet NO of SCR denitration system at moment kxConcentration, i denotes the ith prediction time point, s1R is a correction coefficient;
according to the predicted outlet NO of each predicted time point in the corresponding predicted time period of each individualxConcentration, predicted outlet ammonia slip and corrected predicted outlet NOxA concentration calculating unit for calculating a first objective function value and a second objective function value for each individual using the first objective function and the second objective function, respectively;
determining an individual with the smallest first objective function in individuals meeting the second objective function in the population as an optimal individual for the L-th iteration, and setting the optimal individual for the L-th iteration and an individual with a larger first objective function value in the global optimal individual for the L-1-th iteration as a global optimal individual for the L-th iteration;
judging whether the iteration times are larger than an iteration time threshold value or not, and obtaining a second judgment result;
if the second judgment result shows that the number of the iteration times is not 1, updating the population by adopting a genetic, variation and recombination mode in a genetic algorithm, and returning to the step of judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each control time point in each individual in the population is between the lower limit value and the upper limit value of the valve opening to obtain a first judgment result;
and if the second judgment result shows that the second judgment result is yes, outputting the global optimal individual of the L-th iteration as the optimal ammonia injection amount.
Optionally, the first objective function is:
wherein, J1A value representing a first value of an objective function,
is k + i.s1Predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is a second weight coefficient;
optionally, the second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Time of day outlet NOxDesired value of concentration, | Δ u (k + j · s)2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
A control system of an all-condition power station SCR denitration system, the control system comprising:
the historical operating data acquisition module is used for acquiring historical operating data of the SCR denitration system;
the clustering module is used for dividing the historical operating data into a plurality of load intervals by adopting a clustering algorithm to obtain the historical operating data of the load intervals;
the training module is used for training the neural network model by respectively utilizing the historical operating data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NOx concentration and the outlet ammonia escape of the SCR denitration system at each prediction time point in a prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time period; the working state data comprises unit load, NOx concentration at an inlet of the SCR denitration system and flue gas flow;
the SCR denitration system output prediction model selection module is used for acquiring an SCR denitration system output prediction model of a load interval where the unit load corresponding to the load instruction is located according to the load instruction of the SCR denitration system;
the optimal ammonia injection amount determining module is used for determining the ammonia injection amount which enables the multi-objective optimization function to be optimal as the optimal ammonia injection amount by adopting a genetic algorithm according to the output prediction model of the SCR denitration system in the load interval where the unit load corresponding to the load instruction is located;
and the control module is used for controlling the SCR denitration system according to the optimal ammonia injection amount.
Optionally, the neural network model includes an input layer, an output layer and a hidden layer, the input layer includes 4 neurons, the output layer includes 2 neurons, and the output layer includes 5 neurons.
Optionally, the optimal ammonia injection amount determining module specifically includes:
the initialization submodule is used for initializing the population of the genetic algorithm by taking the ammonia spraying amount of each control time point in the prediction time period as an individual;
the first judgment submodule is used for judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each individual in each control time point in the population is between the lower limit value and the upper limit value of the valve opening or not, and obtaining a first judgment result;
an individual updating submodule, configured to replace, if the first determination result indicates that the ammonia injection amount is not greater than the lower limit value of the valve opening, the ammonia injection amount in an individual having a valve opening of the SCR denitration system that is smaller than the lower limit value of the valve opening with the ammonia injection amount corresponding to the lower limit value of the valve opening, and replace, in an individual having a valve opening of the SCR denitration system that is greater than the upper limit value of the valve opening with the ammonia injection amount corresponding to the upper limit value of the valve opening;
the prediction submodule is used for inputting each individual in the population into the SCR denitration system output prediction model, and obtaining the predicted outlet NOx concentration and the predicted outlet ammonia escape amount of each predicted time point in the prediction time period corresponding to each individual;
a correction submodule for utilizing a formula
Correcting the predicted outlet NOx concentration of each predicted time point in the prediction time period corresponding to each individual to obtain the corrected predicted outlet NOx concentration of each predicted time point in the prediction time period corresponding to each individual; wherein the content of the first and second substances,
is k + i.s1The predicted outlet NOx concentration after the time correction,
is k + i.s1The predicted outlet NOx concentration at the time of day,
for the predicted outlet NOx concentration at time k,
i represents the ith predicted time, which is the actual outlet NOx concentration of the SCR denitration system at the time kPoint, s1R is a correction coefficient;
an objective function value calculation sub-module, configured to calculate, according to the predicted outlet NOx concentration, the predicted outlet ammonia escape amount, and the corrected predicted outlet NOx concentration at each prediction time point within the prediction time period corresponding to each individual, a first objective function value and a second objective function value of each individual by using the first objective function and the second objective function, respectively;
the optimal individual determining submodule is used for determining an individual with the minimum first objective function in individuals meeting the second objective function in the population as an optimal individual for the L-th iteration, and setting the optimal individual for the L-th iteration and an individual with a larger first objective function value in the global optimal individual for the L-1-th iteration as a global optimal individual for the L-th iteration;
the second judgment submodule is used for judging whether the iteration times are greater than the iteration time threshold value or not to obtain a second judgment result;
a population updating submodule, configured to increase the number of iteration times by 1 if the second determination result indicates no, update the population by using a genetic, variation, and recombination manner in a genetic algorithm, and return to the step of "determining whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount at each control time point in each individual in the population is between a lower limit value and an upper limit value of the valve opening, so as to obtain a first determination result";
and the optimal ammonia injection quantity output submodule is used for outputting the global optimal individual of the L-th iteration as the optimal ammonia injection quantity if the second judgment result shows that the global optimal individual of the L-th iteration is used as the optimal ammonia injection quantity.
Optionally, the first objective function is:
wherein, J1A value representing a first value of an objective function,
is k + i.s1Predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is a second weight coefficient;
optionally, the second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Expected value of outlet NOx concentration, | Δ u (k + j · s) at time2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a control method and a system of an SCR denitration system of a full-working-condition power station, wherein the control method comprises the following steps: adding the denitration cost into an optimization objective function, adopting a prediction control structure, combining a neural network and a genetic algorithm to establish a model and optimize the controlled quantity, and realizing the optimization of the ammonia injection quantityAnd (5) controlling. Predicting outlet NO of SCR reactor by constructing neural network modelxAccording to the future change trend of the concentration, the ammonia injection flow of the SCR denitration system is determined by optimizing through a genetic algorithm, and the valve opening of an ammonia injection valve is adjusted.
By adopting a multi-target control mode, the upper and lower limits of the valve opening and the outlet NO are considered in the prediction controlxThe emission concentration of concentration, ammonia escape and the economic index of a denitration system are prevented from influencing the system performance due to the saturation of an actuating mechanism, and the NO at the outlet can be ensuredxOn the basis that the concentration reaches the target value, the ammonia spraying amount is reduced as much as possible, and the operation cost and the ammonia escape rate are effectively reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a control method of an SCR denitration system of an all-condition power station, which is provided by the invention;
FIG. 2 is a schematic diagram illustrating a control method of an SCR denitration system of an all-condition power station according to the present invention;
FIG. 3 is a schematic diagram of the determination of the ammonia injection quantity for optimizing the multi-objective optimization function using a genetic algorithm according to the present invention
FIG. 4 is a flow chart for determining an amount of ammonia injection optimized for a multi-objective optimization function using a genetic algorithm, in accordance with the present invention.
Detailed Description
The invention aims to provide a control method and a control system for an SCR denitration system of a full-working-condition power station, which are used for realizing the optimized control of the denitration system and realizing the economic operation of a unit while ensuring the emission reaching the standard.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the embodiments and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
As shown in fig. 1-2, the present invention provides a control method for an SCR denitration system of an all-condition power station, which comprises the following steps:
The historical operation data comprises the unit load, the concentration of NOx at the inlet of the SCR denitration system, the flow rate of flue gas, the ammonia spraying amount, the concentration of NOx at the outlet of the SCR denitration system and ammonia escape.
The frequency of the invention when collecting historical operating data is once every 2 minutes.
And dividing the historical data according to the load interval by a clustering algorithm, and respectively recording the historical data as a high load interval, a medium load interval and a low load interval.
102, training a neural network model by respectively utilizing historical operating data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NO of the SCR denitration system at each prediction time point in the prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time periodxConcentration and exit ammonia slip(ii) a The working state data comprises unit load and NO at the inlet of the SCR denitration systemxConcentration and flue gas flow.
The neural network models are respectively trained according to historical data of different load intervals, different neural network models are selected according to real-time input data of the SCR denitration system in the process of running a control algorithm, the background of large-range change of the working condition of the unit can be better adapted, and the accuracy of model prediction is remarkably improved.
The neural network model is an LSTM neural network and comprises an input layer, an output layer and a hidden layer, wherein the input layer comprises 4 neurons, the output layer comprises 2 neurons, and the output layer comprises 5 neurons.
Before training, the invention also carries out normalization preprocessing on historical operating data. During training, the training times of the neural network model are set to be 1000 times, the learning rate is 0.05, and the minimum error is 10-3。
And 103, acquiring an SCR denitration system output prediction model of a load interval where the unit load corresponding to the load instruction is located according to the load instruction of the SCR denitration system.
And 104, determining the ammonia injection amount which enables the multi-objective optimization function to be optimal as the optimal ammonia injection amount by adopting a genetic algorithm according to the output prediction model of the SCR denitration system in the load interval of the unit load corresponding to the load instruction.
SCR (selective catalytic reduction) outlet NO in denitration control system by using multi-objective optimization functionxOn the premise that the concentration meets the requirement, the NO outlet is consideredxAmmonia escape and related economic cost, and the safe and economic operation of the unit is realized on the premise of standard emission. And adding the upper and lower limits of the valve opening to the constraint objective function.
The invention carries out online rolling optimization in a prediction time domain, and optimizes and determines a control quantity sequence in an objective function by using a genetic algorithm, so that the prediction output of a neural network model can be close to an outlet NO to the maximum extentxConcentration and expected value of ammonia slip.
Compared with the traditional iterative algorithm, the genetic algorithm can well avoid the phenomenon of 'dead loop' caused by trapping in a local extremely small trap, and is a global optimization algorithm.
As shown in fig. 3 and 4, in step 103, determining an ammonia injection amount that optimizes the multi-objective optimization function by using a genetic algorithm according to the SCR denitration system output prediction model in the load section where the unit load corresponding to the load instruction is located, as an optimal ammonia injection amount, specifically includes:
And step 403, if the first judgment result shows that the ammonia injection amount is not greater than the lower limit value of the valve opening, replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system smaller than the lower limit value of the valve opening with the ammonia injection amount corresponding to the lower limit value of the valve opening, and replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system larger than the upper limit value of the valve opening with the ammonia injection amount corresponding to the upper limit value of the valve opening. The invention considers the upper and lower limits of the valve opening and the NO at the outletxThe emission concentration of concentration, ammonia escape and the economic index of a denitration system are prevented from influencing the system performance due to the saturation of an actuating mechanism, and the NO at the outlet can be ensuredxOn the basis that the concentration reaches the target value, the ammonia spraying amount is reduced as much as possible, and the operation cost and the ammonia escape rate are effectively reduced.
406, predicting the outlet NO according to each predicted time point in the corresponding predicted time period of each individualxConcentration, predicted outlet ammonia slip and corrected predicted outlet NOxAnd a concentration calculating unit for calculating a first objective function value and a second objective function value for each individual by using the first objective function and the second objective function, respectively.
and step 408, judging whether the iteration times are larger than the iteration time threshold value or not, and obtaining a second judgment result.
And 409, if the second judgment result shows that the number of the iteration times is not greater than 1, updating the population by adopting a genetic, variation and recombination mode in a genetic algorithm, and returning to the step of judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each control time point in each individual in the population is between the lower limit value and the upper limit value of the valve opening or not to obtain a first judgment result.
And step 410, if the second judgment result indicates yes, outputting the global optimal individual of the L-th iteration as the optimal ammonia spraying amount.
Wherein the first objective function is:
wherein, J1A value representing a first value of an objective function,
is k + i.s1Predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is a second weight coefficient;
the second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Time of day outlet NOxDesired value of concentration, | Δ u (k + j · s)2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
The objective function of the invention comprises ammonia escape amount, flue gas flow, flue gas oxygen content, nitrogen oxide discharge amount, discharge cost price, ammonia flow, liquid ammonia price, unit generating capacity and electricity price subsidy price.
And 105, controlling the SCR denitration system according to the optimal ammonia injection amount.
The invention also provides a control system of the SCR denitration system of the all-condition power station, which comprises the following components:
and the historical operating data acquisition module is used for acquiring historical operating data of the SCR denitration system.
And the clustering module is used for dividing the historical operating data into a plurality of load intervals by adopting a clustering algorithm to obtain the historical operating data of the load intervals.
The training module is used for training the neural network model by respectively utilizing the historical operating data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NO of the SCR denitration system at each prediction time point in the prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time periodxConcentration and exit ammonia slip; the working state data comprises unit load and NO at the inlet of the SCR denitration systemxConcentration and flue gas flow.
The neural network model comprises an input layer, an output layer and a hidden layer, wherein the input layer comprises 4 neurons, the output layer comprises 2 neurons, and the output layer comprises 5 neurons.
And the SCR denitration system output prediction model selection module is used for acquiring an SCR denitration system output prediction model of a load interval where the unit load corresponding to the load instruction is located according to the load instruction of the SCR denitration system.
And the optimal ammonia injection amount determining module is used for determining the ammonia injection amount which enables the multi-objective optimization function to be optimal by adopting a genetic algorithm according to the output prediction model of the SCR denitration system in the load interval where the unit load corresponding to the load instruction is located, and the optimal ammonia injection amount is used as the optimal ammonia injection amount.
The optimal ammonia injection amount determining module specifically comprises: the initialization submodule is used for initializing the population of the genetic algorithm by taking the ammonia spraying amount of each control time point in the prediction time period as an individual; the first judgment submodule is used for judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each individual in each control time point in the population is between the lower limit value and the upper limit value of the valve opening or not, and obtaining a first judgment result; an individual update submodule for updating a valve of the SCR denitration system if the first judgment result indicates noReplacing the ammonia injection amount in the individual with the opening smaller than the lower limit value of the valve opening by the ammonia injection amount corresponding to the lower limit value of the valve opening, and replacing the ammonia injection amount in the individual with the valve opening larger than the upper limit value of the valve opening of the SCR denitration system by the ammonia injection amount corresponding to the upper limit value of the valve opening; the prediction submodule is used for inputting each individual in the population into the SCR denitration system output prediction model to obtain a predicted outlet NO of each predicted time point in a prediction time period corresponding to each individualxConcentration and predicted outlet ammonia escape amount; a correction submodule for utilizing a formula
Predicted outlet NO for each predicted time point within the corresponding predicted time period for each individualxThe concentration is corrected to obtain corrected predicted outlet NO of each predicted time point in the corresponding predicted time period of each individualxConcentration; wherein the content of the first and second substances,
is k + i.s1Predicted outlet NO after time correctionxThe concentration of the active ingredients in the mixture is,
is k + i.s1Predicted outlet NO of time of dayxThe concentration of the active ingredients in the mixture is,
predicted export NO for time kxThe concentration of the active ingredients in the mixture is,
actual outlet NO of SCR denitration system at moment kxConcentration, i denotes the ith prediction time point, s1R is a correction coefficient; an objective function value calculation sub-module for calculating the predicted outlet NO according to the predicted outlet NO of each predicted time point in the predicted time period corresponding to each individualxConcentration, predicted outlet ammonia slip and corrected predicted outlet NOxConcentration, calculating a first objective function for each individual using the first objective function and the second objective function, respectivelyA value and a second objective function value; the optimal individual determining submodule is used for determining an individual with the minimum first objective function in individuals meeting the second objective function in the population as an optimal individual for the L-th iteration, and setting the optimal individual for the L-th iteration and an individual with a larger first objective function value in the global optimal individual for the L-1-th iteration as a global optimal individual for the L-th iteration; the second judgment submodule is used for judging whether the iteration times are greater than the iteration time threshold value or not to obtain a second judgment result; a population updating submodule, configured to increase the number of iteration times by 1 if the second determination result indicates no, update the population by using a genetic, variation, and recombination manner in a genetic algorithm, and return to the step of "determining whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount at each control time point in each individual in the population is between a lower limit value and an upper limit value of the valve opening, so as to obtain a first determination result"; and the optimal ammonia injection quantity output submodule is used for outputting the global optimal individual of the L-th iteration as the optimal ammonia injection quantity if the second judgment result shows that the global optimal individual of the L-th iteration is used as the optimal ammonia injection quantity.
And the control module is used for controlling the SCR denitration system according to the optimal ammonia injection amount.
Wherein the first objective function is:
wherein, J1A value representing a first value of an objective function,
is k + i.s1Predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is the second weight coefficient.
The second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Time of day outlet NOxDesired value of concentration, | Δ u (k + j · s)2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
1. the method can better overcome the defects of large inertia and large delay of the SCR denitration system, improve the response speed of ammonia injection amount control on unit load change, and improve the dynamic regulation quality of the SCR denitration system;
2. by adopting a multi-target control mode, the upper and lower limits of the valve opening, the limit of the valve action rate and the outlet NO are considered in the prediction controlxThe emission concentration of concentration, ammonia escape and the economic index of a denitration system are prevented from influencing the system performance due to the saturation of an actuating mechanism, and the NO at the outlet can be ensuredxOn the basis that the concentration reaches a target value, the ammonia spraying amount is reduced as much as possible, and the operation cost and the ammonia escape rate are effectively reduced;
3. the prediction control technology of the invention has lower requirements on the model, is easy to calculate on line and has better control effect.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and the implementation manner of the present invention are explained by applying specific examples, the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof, the described embodiments are only a part of the embodiments of the present invention, not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.
Claims (10)
1. A control method of an SCR denitration system of a full-working-condition power station is characterized by comprising the following steps:
acquiring historical operating data of an SCR denitration system;
dividing historical operating data into a plurality of load intervals by adopting a clustering algorithm to obtain historical operating data of the load intervals;
respectively training a neural network model by using historical operation data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NO of the SCR denitration system at each prediction time point in the prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time periodxConcentration and exit ammonia slip; the working state data comprises unit load and NO at the inlet of the SCR denitration systemxConcentration and flue gas flow;
according to a load instruction of the SCR denitration system, acquiring an SCR denitration system output prediction model of a load interval where a unit load corresponding to the load instruction is located;
according to the output prediction model of the SCR denitration system in the load interval of the unit load corresponding to the load instruction, determining the optimal ammonia injection amount of the multi-objective optimization function by adopting a genetic algorithm, and taking the optimal ammonia injection amount;
and controlling the SCR denitration system according to the optimal ammonia injection amount.
2. The control method of the full regime power station SCR denitration system of claim 1, wherein the neural network model comprises an input layer, an output layer and an implied layer, the input layer comprising 4 neurons, the output layer comprising 2 neurons and the output layer comprising 5 neurons.
3. The control method of the full-condition power station SCR denitration system according to claim 1, wherein the step of determining the optimal ammonia injection amount of the multi-objective optimization function by using a genetic algorithm according to the SCR denitration system output prediction model of the load section where the unit load corresponding to the load instruction is located as the optimal ammonia injection amount specifically comprises the following steps:
initializing a population of a genetic algorithm by taking the ammonia spraying amount of each control time point in a prediction time period as an individual;
judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each individual at each control time point in the population is between the lower limit value and the upper limit value of the valve opening or not, and obtaining a first judgment result;
if the first judgment result shows that the ammonia injection amount is not less than the lower limit value of the valve opening, replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system, and replacing the ammonia injection amount in the individual with the valve opening of the SCR denitration system, which is more than the upper limit value of the valve opening, with the ammonia injection amount corresponding to the upper limit value of the valve opening;
inputting each individual in the population into an SCR denitration system output prediction model to obtain a predicted outlet NO of each predicted time point in a prediction time period corresponding to each individualxConcentration and predicted outlet ammonia escape amount;
using formulas
Predicted outlet NO for each predicted time point within the corresponding predicted time period for each individualxThe concentration is corrected to obtain corrected predicted outlet NO of each predicted time point in the corresponding predicted time period of each individualxConcentration; wherein the content of the first and second substances,
is k + i.s1Predicted outlet NO after time correctionxThe concentration of the active ingredients in the mixture is,
is k + i.s1Predicted outlet NO of time of dayxThe concentration of the active ingredients in the mixture is,
predicted export NO for time kxThe concentration of the active ingredients in the mixture is,
actual outlet NO of SCR denitration system at moment kxConcentration, i denotes the ith prediction time point, s1R is a correction coefficient;
according to the predicted outlet NO of each predicted time point in the corresponding predicted time period of each individualxConcentration, predicted outlet ammonia slip and corrected predicted outlet NOxA concentration calculating unit for calculating a first objective function value and a second objective function value for each individual using the first objective function and the second objective function, respectively;
determining an individual with the smallest first objective function in individuals meeting the second objective function in the population as an optimal individual for the L-th iteration, and setting the optimal individual for the L-th iteration and an individual with a larger first objective function value in the global optimal individual for the L-1-th iteration as a global optimal individual for the L-th iteration;
judging whether the iteration times are larger than an iteration time threshold value or not, and obtaining a second judgment result;
if the second judgment result shows that the number of the iteration times is not 1, updating the population by adopting a genetic, variation and recombination mode in a genetic algorithm, and returning to the step of judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each control time point in each individual in the population is between the lower limit value and the upper limit value of the valve opening to obtain a first judgment result;
and if the second judgment result shows that the second judgment result is yes, outputting the global optimal individual of the L-th iteration as the optimal ammonia injection amount.
4. The control method of the full-service-condition power station SCR denitration system of claim 3, wherein the first objective function is:
wherein, J1A value representing a first value of an objective function,
predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is the second weight coefficient.
5. The control method of the full-service-condition power station SCR denitration system of claim 3, wherein the second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Time of day outlet NOxDesired value of concentration, | Δ u (k + j · s)2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
6. The utility model provides a control system of full operating mode power station SCR deNOx systems which characterized in that, control system includes:
the historical operating data acquisition module is used for acquiring historical operating data of the SCR denitration system;
the clustering module is used for dividing the historical operating data into a plurality of load intervals by adopting a clustering algorithm to obtain the historical operating data of the load intervals;
the training module is used for training the neural network model by respectively utilizing the historical operating data of each load interval to obtain an SCR denitration system output prediction model of each load interval; the SCR denitration system output prediction model is used for predicting the outlet NOx concentration and the outlet ammonia escape of the SCR denitration system at each prediction time point in a prediction time period according to the current working state data of the SCR denitration system and the ammonia injection amount at each control time point in the prediction time period; the working state data comprises unit load, NOx concentration at an inlet of the SCR denitration system and flue gas flow;
the SCR denitration system output prediction model selection module is used for acquiring an SCR denitration system output prediction model of a load interval where the unit load corresponding to the load instruction is located according to the load instruction of the SCR denitration system;
the optimal ammonia injection amount determining module is used for determining the ammonia injection amount which enables the multi-objective optimization function to be optimal as the optimal ammonia injection amount by adopting a genetic algorithm according to the output prediction model of the SCR denitration system in the load interval where the unit load corresponding to the load instruction is located;
and the control module is used for controlling the SCR denitration system according to the optimal ammonia injection amount.
7. The control system of the full regime power station SCR denitration system of claim 6, wherein the neural network model comprises an input layer, an output layer and an implied layer, the input layer comprising 4 neurons, the output layer comprising 2 neurons and the output layer comprising 5 neurons.
8. The control system of the SCR denitration system of the full-condition power station as claimed in claim 6, wherein the optimal ammonia injection amount determining module specifically comprises:
the initialization submodule is used for initializing the population of the genetic algorithm by taking the ammonia spraying amount of each control time point in the prediction time period as an individual;
the first judgment submodule is used for judging whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount of each individual in each control time point in the population is between the lower limit value and the upper limit value of the valve opening or not, and obtaining a first judgment result;
an individual updating submodule, configured to replace, if the first determination result indicates that the ammonia injection amount is not greater than the lower limit value of the valve opening, the ammonia injection amount in an individual having a valve opening of the SCR denitration system that is smaller than the lower limit value of the valve opening with the ammonia injection amount corresponding to the lower limit value of the valve opening, and replace, in an individual having a valve opening of the SCR denitration system that is greater than the upper limit value of the valve opening with the ammonia injection amount corresponding to the upper limit value of the valve opening;
the prediction submodule is used for inputting each individual in the population into the SCR denitration system output prediction model, and obtaining the predicted outlet NOx concentration and the predicted outlet ammonia escape amount of each predicted time point in the prediction time period corresponding to each individual;
a correction submodule for utilizing a formula
Correcting the predicted outlet NOx concentration of each predicted time point in the prediction time period corresponding to each individual to obtain the corrected predicted outlet NOx concentration of each predicted time point in the prediction time period corresponding to each individual; wherein the content of the first and second substances,
is k + i.s1The predicted outlet NOx concentration after the time correction,
is k + i.s1The predicted outlet NOx concentration at the time of day,
for the predicted outlet NOx concentration at time k,
the actual outlet NOx concentration of the SCR denitration system at the moment k, i represents the ith predicted time point, s1R is a correction coefficient;
an objective function value calculation sub-module, configured to calculate, according to the predicted outlet NOx concentration, the predicted outlet ammonia escape amount, and the corrected predicted outlet NOx concentration at each prediction time point within the prediction time period corresponding to each individual, a first objective function value and a second objective function value of each individual by using the first objective function and the second objective function, respectively;
the optimal individual determining submodule is used for determining an individual with the minimum first objective function in individuals meeting the second objective function in the population as an optimal individual for the L-th iteration, and setting the optimal individual for the L-th iteration and an individual with a larger first objective function value in the global optimal individual for the L-1-th iteration as a global optimal individual for the L-th iteration;
the second judgment submodule is used for judging whether the iteration times are greater than the iteration time threshold value or not to obtain a second judgment result;
a population updating submodule, configured to increase the number of iteration times by 1 if the second determination result indicates no, update the population by using a genetic, variation, and recombination manner in a genetic algorithm, and return to the step of "determining whether the valve opening of the SCR denitration system corresponding to the ammonia injection amount at each control time point in each individual in the population is between a lower limit value and an upper limit value of the valve opening, so as to obtain a first determination result";
and the optimal ammonia injection quantity output submodule is used for outputting the global optimal individual of the L-th iteration as the optimal ammonia injection quantity if the second judgment result shows that the global optimal individual of the L-th iteration is used as the optimal ammonia injection quantity.
9. The control system of the full service power station SCR denitration system of claim 8, wherein the first objective function is:
wherein, J1A value representing a first value of an objective function,
is k + i.s1Predicted outlet ammonia slip at time, M2For liquid ammonia price, P is the number of predicted time points of the predicted time period, QgasAs the flow rate of the flue gas,
is the oxygen content of the flue gas, M1In order to pay the price of the sewage discharge,
is k + j · s2The amount of sprayed ammonia at the time, j being the jth control time point, s2For controlling the step length, M is the number of control time points in a prediction time period, N is the generating capacity of the unit, M is the generating capacity of the unit3Subsidizing the price, omega, for the price of electricity1Is a first weight coefficient, ω2Is the second weight coefficient.
10. The control system of the full service power station SCR denitration system of claim 8, wherein the second objective function is:
wherein, J2Is a second objective function value, P is the number of prediction time points of the prediction time period, r (k + i · s)1) Is k + i.s1Expected value of outlet NOx concentration, | Δ u (k + j · s) at time2) I is k + j.s2The difference between the ammonia injection amount at the moment and the expected ammonia injection amount, j is the jth control time point, s2For the control step size, M is the number of control time points within the prediction period, ω3Is the third weight coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011276325.4A CN112418284A (en) | 2020-11-16 | 2020-11-16 | Control method and system for SCR denitration system of full-working-condition power station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011276325.4A CN112418284A (en) | 2020-11-16 | 2020-11-16 | Control method and system for SCR denitration system of full-working-condition power station |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112418284A true CN112418284A (en) | 2021-02-26 |
Family
ID=74831225
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011276325.4A Pending CN112418284A (en) | 2020-11-16 | 2020-11-16 | Control method and system for SCR denitration system of full-working-condition power station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112418284A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113380338A (en) * | 2021-06-16 | 2021-09-10 | 哈电发电设备国家工程研究中心有限公司 | Method for measuring, correcting and predicting NOx concentration at inlet of cyclone separator of circulating fluidized bed unit |
| CN113433911A (en) * | 2021-06-30 | 2021-09-24 | 浙江大学 | Denitration device ammonia injection accurate control system and method based on concentration accurate prediction |
| CN113485106A (en) * | 2021-07-07 | 2021-10-08 | 山西大学 | Method for controlling concentration of nitrogen oxide in thermal power plant |
| CN114053865A (en) * | 2021-11-03 | 2022-02-18 | 华能铜川照金煤电有限公司 | Generalized predictive control method suitable for SCR denitration control system of coal-fired boiler |
| CN114609986A (en) * | 2022-03-16 | 2022-06-10 | 中国中材国际工程股份有限公司 | Cement decomposing furnace denitration regulation and control optimization system and method based on predictive control |
| CN115309129A (en) * | 2022-10-11 | 2022-11-08 | 华电电力科学研究院有限公司 | SCR denitration efficiency automatic optimization regulation and control method and system |
| CN116832614A (en) * | 2023-05-24 | 2023-10-03 | 华能国际电力股份有限公司上海石洞口第二电厂 | Ammonia spraying amount control method and system for SCR denitration system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060045801A1 (en) * | 2004-08-27 | 2006-03-02 | Alstom Technology Ltd. | Model predictive control of air pollution control processes |
| CN106599586A (en) * | 2016-12-19 | 2017-04-26 | 北京国能中电节能环保技术股份有限公司 | Neural network-based SCR intelligent ammonia-spraying optimization method and apparatus |
| 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 |
| CN107243257A (en) * | 2017-05-08 | 2017-10-13 | 浙江大学 | It is adapted to the intelligence spray ammonia control system of full load |
| CN107544288A (en) * | 2017-09-26 | 2018-01-05 | 天津拓科思科技有限公司 | A kind of denitration optimal control method and system |
| CN109062053A (en) * | 2018-08-31 | 2018-12-21 | 江苏国信靖江发电有限公司 | A kind of denitration spray ammonia control method based on multivariate calibration |
| CN111522290A (en) * | 2020-04-24 | 2020-08-11 | 大唐环境产业集团股份有限公司 | Denitration control method and system based on deep learning method |
| CN111581581A (en) * | 2020-04-23 | 2020-08-25 | 大唐环境产业集团股份有限公司 | Method and system for detecting NOx concentration at SCR inlet under multi-boundary condition |
| CN111921377A (en) * | 2020-09-25 | 2020-11-13 | 大唐环境产业集团股份有限公司 | SCR denitration ammonia injection control method and system based on mechanism and data driving |
-
2020
- 2020-11-16 CN CN202011276325.4A patent/CN112418284A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060045801A1 (en) * | 2004-08-27 | 2006-03-02 | Alstom Technology Ltd. | Model predictive control of air pollution control processes |
| CN106599586A (en) * | 2016-12-19 | 2017-04-26 | 北京国能中电节能环保技术股份有限公司 | Neural network-based SCR intelligent ammonia-spraying optimization method and apparatus |
| 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 |
| CN107243257A (en) * | 2017-05-08 | 2017-10-13 | 浙江大学 | It is adapted to the intelligence spray ammonia control system of full load |
| CN107544288A (en) * | 2017-09-26 | 2018-01-05 | 天津拓科思科技有限公司 | A kind of denitration optimal control method and system |
| CN109062053A (en) * | 2018-08-31 | 2018-12-21 | 江苏国信靖江发电有限公司 | A kind of denitration spray ammonia control method based on multivariate calibration |
| CN111581581A (en) * | 2020-04-23 | 2020-08-25 | 大唐环境产业集团股份有限公司 | Method and system for detecting NOx concentration at SCR inlet under multi-boundary condition |
| CN111522290A (en) * | 2020-04-24 | 2020-08-11 | 大唐环境产业集团股份有限公司 | Denitration control method and system based on deep learning method |
| CN111921377A (en) * | 2020-09-25 | 2020-11-13 | 大唐环境产业集团股份有限公司 | SCR denitration ammonia injection control method and system based on mechanism and data driving |
Non-Patent Citations (4)
| Title |
|---|
| YOU LV等: "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants", 《ENERGY》, vol. 129, pages 1 - 40 * |
| 付忠广等: "基于改进模糊聚类与IPSO-SVM的燃煤电站NOx排放多模型预测", 《动力工程学报》, vol. 39, no. 05, pages 387 - 393 * |
| 杨婷婷等: "SCR脱硝系统多目标优化控制研究", 《中国电机工程学报》, vol. 41, no. 14, pages 4905 - 4911 * |
| 肖运启等: "一种火电厂SCR系统的级联型机理仿真模型", 《动力工程学报》, vol. 39, no. 06, pages 468 - 473 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113380338A (en) * | 2021-06-16 | 2021-09-10 | 哈电发电设备国家工程研究中心有限公司 | Method for measuring, correcting and predicting NOx concentration at inlet of cyclone separator of circulating fluidized bed unit |
| CN113380338B (en) * | 2021-06-16 | 2022-06-10 | 哈电发电设备国家工程研究中心有限公司 | Method for measuring, correcting and predicting NOx concentration at inlet of cyclone separator |
| CN113433911A (en) * | 2021-06-30 | 2021-09-24 | 浙江大学 | Denitration device ammonia injection accurate control system and method based on concentration accurate prediction |
| CN113433911B (en) * | 2021-06-30 | 2022-05-20 | 浙江大学 | Accurate control system and method for ammonia spraying of denitration device based on accurate concentration prediction |
| CN113485106A (en) * | 2021-07-07 | 2021-10-08 | 山西大学 | Method for controlling concentration of nitrogen oxide in thermal power plant |
| CN114053865A (en) * | 2021-11-03 | 2022-02-18 | 华能铜川照金煤电有限公司 | Generalized predictive control method suitable for SCR denitration control system of coal-fired boiler |
| CN114609986A (en) * | 2022-03-16 | 2022-06-10 | 中国中材国际工程股份有限公司 | Cement decomposing furnace denitration regulation and control optimization system and method based on predictive control |
| CN115309129A (en) * | 2022-10-11 | 2022-11-08 | 华电电力科学研究院有限公司 | SCR denitration efficiency automatic optimization regulation and control method and system |
| WO2023221446A1 (en) * | 2022-10-11 | 2023-11-23 | 华电电力科学研究院有限公司 | Scr denitrification efficiency automatic optimization regulation and control method and system |
| CN116832614A (en) * | 2023-05-24 | 2023-10-03 | 华能国际电力股份有限公司上海石洞口第二电厂 | Ammonia spraying amount control method and system for SCR denitration system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112418284A (en) | Control method and system for SCR denitration system of full-working-condition power station | |
| CN107243257B (en) | It is suitble to the intelligence spray ammonia control system of full load | |
| CN104826492B (en) | Improvement method for selective catalytic reduction flue gas denitrification and ammonia injection control system | |
| CN105629736B (en) | The fired power generating unit SCR denitration Disturbance Rejection forecast Control Algorithm of data-driven | |
| CN109062053B (en) | Denitration ammonia injection control method based on multivariate correction | |
| CN105629738B (en) | SCR flue gas denitrification systems control method and equipment | |
| CN107482688B (en) | Scheduling optimization method for carbon capture virtual power plant | |
| CN106599586B (en) | SCR neural network based intelligently sprays ammonia optimization method and device | |
| CN112580250A (en) | Thermal power generating unit denitration system based on deep learning and optimization control method | |
| CN111897373B (en) | Model prediction-based ammonia injection flow adjusting method for SCR denitration device | |
| CN104607042A (en) | Selective catalytic reduction (SCR) denitration system and method based on constraint predictive control | |
| CN105786035A (en) | Thermal power set SCR denitration optimization control system based on heuristic prediction control technology | |
| CN111968708B (en) | SCR denitration ammonia injection amount prediction method based on random forest and LSTM neural network | |
| CN111522290A (en) | Denitration control method and system based on deep learning method | |
| CN111552175B (en) | Overall optimization scheduling and rapid variable load control method for supercritical coal-fired power plant-carbon capture system after chemical adsorption combustion | |
| CN110188383A (en) | A kind of power station SCR denitration modeling method based on selective ensemble model | |
| CN111013370A (en) | Wet desulphurization slurry supply amount prediction method based on deep neural network | |
| CN114558447A (en) | Ammonia injection amount optimization control device and method based on SCR denitration system | |
| CN114928049A (en) | Feedforward predictive control method and system for thermal power generating unit | |
| CN111428970A (en) | Large-scale hydropower station group trans-provincial delivery capacity analysis model and solving method | |
| CN109359830B (en) | Power trading plan electric quantity decomposition method for hydropower station | |
| CN116345450A (en) | Intelligent scheduling method of wind-light-water complementary system based on deep reinforcement learning | |
| CN114053865A (en) | Generalized predictive control method suitable for SCR denitration control system of coal-fired boiler | |
| CN112627953A (en) | Ship SCR system ammonia injection amount control method based on ARMAX and MMPC | |
| CN206848713U (en) | A kind of fuzzy rule adjustable adaptive fuzzy controller online |
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 |