CN108038561A - A kind of Multipurpose Optimal Method of SCR denitration preformed catalyst - Google Patents
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Abstract
The present invention discloses a kind of Multipurpose Optimal Method of SCR denitration preformed catalyst, comprises the following steps：The structural parameters to be optimized of SCR denitration preformed catalyst are selected, and determine optimization section；Using highest denitration efficiency, minimum pressure drop and minimal wear speed as optimization aim, fitness function is constructed；Variable working condition numerical experiment is carried out, the data sample of RBF neural is made；RBF neural is trained based on training sample, establishes the RBF neural agent model of SCR denitration reaction；SCR denitration preformed catalyst structural parameters are optimized using chaotic optimization algorithm, determine the optimal value of structural parameters to be optimized.The present invention overcomes the deficiency of the existing optimization method of SCR denitration preformed catalyst, there is provided a kind of agent model precision is high, and global optimization ability is strong, has the Multipurpose Optimal Method of high robust.
Description
Technical field
The invention belongs to the Air Pollution Control field of discharged from coal-fired boiler, and in particular to a kind of selective catalytic reduction
The Multipurpose Optimal Method of (Selective catalytic reduction, SCR) denitration preformed catalyst.
Background technology
Basis of the power industry as Chinese national economy, will adhere to the general layout based on coal fired power generation for a long time.Thermoelectricity
Nitrogen oxides in factory's discharge flue gas is both the main source of nitric acid type acid rain and forms photochemical fog, destroys ozone layer
Important substance.Nitrogen oxides has very strong toxicity, has great harm to human body, environment and ecology, it has also become thermoelectricity
One of factory's major pollutants discharged.Selective catalytic reduction (Selective catalytic reduction, SCR) technology
It is one of denitration technology that China's Thermal Power Generation Industry generally uses, its core is SCR denitration.Denitration performance, pressure drop and
Wear resistance be evaluate SCR denitration preformed catalyst leading indicator, but the lifting of denitration performance often with increase flow resistance and
Wear for cost, such as to reduce catalyst aperture can improve denitration efficiency, but can cause increase and the wear resistance of pressure drop
Deteriorate.How for SCR denitration preformed catalyst collaboration to realize denitration performance, pressure drop and wear resistance is optimized most
It is excellent to have important practical significance.
Radial basis function (Radial basis function, RBF) neutral net is a kind of excellent feed-forward type nerve net
Network, learning algorithm is relatively easy, has multidimensional nonlinear mapping ability, very strong generalization ability, cluster analysis ability, in theory
It is upper that there is any approximation capability and optimal approximation capability, and exist without Local Minimum problem.Chaotic optimization algorithm is a kind of utilization
The method of the ergodic search optimal solution of chaotic model, it is possible to prevente effectively from tradition optimization algorithm is easy when solving more pole problems
The defects of being absorbed in local extremum.
The content of the invention
The object of the present invention is to provide a kind of Multipurpose Optimal Method of SCR denitration preformed catalyst, overcome SCR denitration into
The deficiency of the existing optimization method of type catalyst, there is provided a kind of agent model precision is high, and global optimization ability is strong, has high robust
Multipurpose Optimal Method.
To achieve the above object, the present invention uses following technical scheme：
A kind of Multipurpose Optimal Method of SCR denitration preformed catalyst, comprises the following steps：
Step 1, the structural parameters to be optimized of SCR denitration preformed catalyst are selected, and determine optimization section；
Step 2, using highest denitration efficiency, minimum pressure drop and minimal wear as optimization aim, fitness function is constructed；
Step 3, variable working condition numerical experiment is carried out, the data sample of RBF neural is made, data sample is by training sample
With detection sample composition；
Step 4, RBF neural parameter is trained based on training sample, establishes the RBF god of SCR preformed catalysts
Verified through network agent model, and by detecting sample；
Step 5, SCR denitration preformed catalyst is optimized using chaotic optimization algorithm, determines parameter to be optimized most
The figure of merit.
Further, in step 1, the parameter selection to be optimized includes catalyst aperture (S), catalyst wall thickness (D), urges
The distance between agent floor height (H), catalyst layer (L).The optimization range in catalyst aperture is 5~13mm, catalyst wall thickness
Optimization range is 0.8~1.1mm, and the optimization range of catalyst height is the excellent of the distance between 800~1500mm, catalyst layer
Change scope is 200-700mm.
Further, in step 2, for three highest denitration efficiency, minimum pressure drop and minimal wear optimization aims, difference
Assign 0.5,0.3 and 0.2 weight.
Further, in step 3, the input vector of RBF neural is catalyst aperture, catalyst wall thickness, catalyst
The distance between floor height and catalyst layer；RBF neural output vector is denitration efficiency, pressure drop and the wear extent of catalyst.
Make RBF neural data sample the step of be：
Step 3.1, carried out for the distance between catalyst aperture, catalyst wall thickness, catalyst height and catalyst layer
Random combine, carries out variable working condition numerical experiment, obtains some groups of numerical experiment data；
Step 3.2, instruction of the part group data as RBF neural is randomly selected from some groups of numerical experiment data
Practice sample, detection sample of the remainder grouped data as RBF neural；
Step 3.3, it is normalized for experimental data, method for normalizing is as follows：
In formula, xmaxFor the maximum of input data, xminFor the minimum value of input data, x is input data,For normalizing
Data after change.
Further, in step 4, the RBF neural agent model is expressed as：
RBF neural topological structure is divided into input layer, hidden layer and output layer；In i-th of neuron node of hidden layer
The input value of the heart is represented by：
Hi=| | X-Ci||
In formula, X=(x1,x2,…,xm) it is net input vector, Ci=(c1i,c2i,…cmi) it is i-th of neuron node
Center, m is neutral net input vector dimension, and ‖ ‖ represent European norm.J-th of neuron node output valve of hidden layer
It is represented by：
In formula, δ represents RBF neural expansion rate.The output valve of output layer neuron is represented by：
In formula：W=(w1,w2,…,wn) to connect the weights of hidden layer and output layer, n is hidden layer neuron quantity.
Further, in step 4, the RBF neural agent model, expansion rate is determined by trial-and-error method.
Further, step 5 comprises the concrete steps that：
Step 5.1, object function and the optimization section of optimization are determined：
Step 5.2, global search and fine searching greatest iteration step number N are determined1And N2；
Step 5.3, for (t1 0,t2 0,t3 0,t4 0) in 0~1 section random assignment, and based on Logistic models generation two
Tie up time series (t1 i,t2 i,t3 i,t4 i)：
ti+1=4.0ti(1-ti) i=1,2 ..., N1
Step 5.4, by (t1 i,t2 i,t3 i,t4 i) be converted to (t1 i*,t2 i*,t3 i*,t4 i*)：
Step 5.5, if (S, D, H, L)=(t1 i*,t2 i*,t3 i*,t4 i*), calculation optimization object function Fi, by object function
The optimal value of corresponding (S, D, H, L) as global optimization when minimum；
Step 5.6, the initial value (r using the optimal value of global optimization as fine searching1 0,r2 0,r3 0,r4 0), and be based on
Logistic models generate two-dimensional time sequence
Step 5.7, willBe converted to
In formula, φ is contraction factor, the value between 0-0.5；
Step 5.8, ifCalculation optimization object function Fj；
Step 5.9, by object function (S, D, H, L) corresponding when minimum as the optimal value during fine searching, optimization
Process terminates.
Further, in step 5.2, N1=2000, N2=3000.
The beneficial effects of the invention are as follows：
The technical solution adopted by the present invention has the following effects that compared with prior art：
1) optimization aim of the present invention is wide：SCR is unfolded by assigning different weights to denitration efficiency, pressure drop and wear rate
The multiple-objection optimization of denitration preformed catalyst, the catalyst structure after optimization will realize denitration performance, flow resistance performance and wear-resistant
The collaboration of performance is optimal.
2) agent model precision of the present invention is high：RBF neural is a kind of machine learning method, can be directed to the non-of system
Linear relationship carries out the data mining of depth, has the advantages of calculated load is small, and global approximation capability is strong.
3) robustness of the present invention is high：Chaotic motion can not repeatedly travel through all shapes by its own rule within the specific limits
State, thus chaotic model is applied to optimize algorithm can reduce susceptibility to initial value, and effectively avoid more extreme values excellent
The problem of local extremum is absorbed in during change.
Brief description of the drawings
Fig. 1 is SCR denitration preformed catalyst multiple-objection optimization flow chart；
Fig. 2 is SCR denitration preformed catalyst geometrical model；Fig. 2 (a) is the 3-D view of SCR denitration preformed catalyst；Figure
2 (b) is the vertical section view of SCR denitration preformed catalyst；Fig. 2 (c) is the viewgraph of cross-section of SCR denitration preformed catalyst；
Fig. 3 is radial base neural net structure.
Specific embodiment
SCR preformed catalysts Multipurpose Optimal Method provided by the invention is carried out with reference to figure and specific embodiment detailed
Describe in detail bright.
Step 1, between selecting catalyst aperture (S), catalyst wall thickness (D), catalyst height (H), catalyst layer away from
It is parameter to be optimized from (L).The optimization range in catalyst aperture is 5~13mm, the optimization range of catalyst wall thickness for 0.8~
1.1mm, the optimization range that the optimization range of catalyst height is the distance between 800~1500mm, catalyst layer is 200-
700mm。
Step 2, using highest denitration efficiency, minimum pressure drop and minimal wear as optimization aim, 0.5,0.3 and is assigned respectively
0.2 weight, constructs fitness function.
Step 3, according to the experimental program in table 1 for catalyst aperture, catalyst wall thickness, catalyst height, catalyst
The distance between layer is combined, and carries out the variable working condition numerical experiment of SCR denitration, is obtained 60 groups of experimental datas, is randomly selected 45
Training sample of the group as RBF neural, 15 groups of detection samples for RBF neural.The input of RBF neutral nets to
Measure as the distance between catalyst aperture, catalyst wall thickness, catalyst height, catalyst layer；Output quantity be respectively denitration efficiency,
Pressure drop and rate of depreciation.And training sample and detection sample are normalized, method for normalizing is as follows：
In formula, xmaxFor the maximum of input data, xminFor the minimum value of input data, x is input data,For normalizing
Data after change.
Step 4, call the newrbe orders in Matlab Neural Network Toolbox to be trained RBF neural, adjust
RBF neural is trained with the sim orders in Matlab Neural Network Toolbox, neutral net expansion rate passes through examination
Wrong method determines.
Step 5, by carrying out self-programming on Matlab platforms, using chaotic optimization algorithm to SCR preformed catalysts into
Row multiple-objection optimization, comprises the following steps that：
Step 5.1, object function and the optimization section of optimization are determined：
Step 5.2, global search and fine searching greatest iteration step number N are determined1And N2：N1=2000, N2=3000；
Step 5.3, for (t1 0,t2 0,t3 0,t4 0) in 0~1 section random assignment, and based on Logistic models generation two
Tie up time series (t1 i,t2 i,t3 i,t4 i)：
ti+1=4.0ti(1-ti) i=1,2 ..., N1
Step 5.4, by (t1 i,t2 i,t3 i,t4 i) be converted to (t1 i*,t2 i*,t3 i*,t4 i*)：
Step 5.5, if (S, D, H, L)=(t1 i*,t2 i*,t3 i*,t4 i*), calculation optimization object function Fi, by object function
The optimal value of corresponding (S, D, H, L) as global optimization when minimum；
Step 5.6, the initial value (r using the optimal value of global optimization as fine searching1 0,r2 0,r3 0,r4 0), and be based on
Logistic models generate two-dimensional time sequence
Step 5.7, willBe converted to
In formula, φ is contraction factor, the value between 0-0.5；
Step 5.8, ifCalculation optimization object function Fj；
Step 5.9, by object function (S, D, H, L) corresponding when minimum as the optimal value during fine searching, optimization
Process terminates.
By optimization, it is (8.6,1.0,1350,587) mm to obtain optimal (S, D, H, L), with initial reference value (S, D,
H, L) the result of calculation contrast of=(6.2,0.9,1480,692) mm finds that denitration efficiency improves 21%, pressure drop reduces
10%, abrasion have dropped 23%.
1 training sample of table and test sample design
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (9)
1. a kind of Multipurpose Optimal Method of SCR denitration preformed catalyst, it is characterised in that comprise the following steps：
Step 1, the structural parameters to be optimized of SCR denitration preformed catalyst are selected, and determine optimization section；
Step 2, using highest denitration efficiency, minimum pressure drop and minimal wear speed as optimization aim, fitness function is constructed；
Step 3, variable working condition numerical experiment is carried out, the data sample of RBF neural is made, data sample is by training sample and inspection
This composition of test sample；
Step 4, RBF neural parameter is trained based on training sample, establishes the RBF neural of SCR denitration reaction
Agent model, and verified by detecting sample；
Step 5, SCR denitration preformed catalyst is optimized using chaotic optimization algorithm, determines structural parameters to be optimized most
The figure of merit.
2. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：It is described to treat
Optimum structural parameter includes the distance between catalyst aperture (S), catalyst wall thickness (D), catalyst height (H), catalyst layer
(L)。
3. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 2, it is characterised in that：It is described to urge
The optimization section in agent aperture is 5~13mm, and the optimization section of catalyst wall thickness is 0.8~1.1mm, the optimization of catalyst height
Section is 800~1500mm, and the optimization interval range of distance is 200-700mm between catalyst layer.
4. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：For most
Three high denitration efficiency, minimum pressure drop and minimal wear speed optimization aims, assign 0.5,0.3 and 0.2 weight respectively.
5. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：Step 3
In, the input vector of RBF neural is catalyst aperture, between catalyst wall thickness, catalyst height, catalyst layer away from
From；Output vector is denitration efficiency, pressure drop and rate of depreciation.
6. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：Step 3
In, make RBF neural data sample the step of be：
Step 3.1, carried out for the distance between catalyst aperture, catalyst wall thickness, catalyst height and catalyst layer random
Combination, carries out variable working condition numerical experiment, obtains some groups of numerical experiment data；
Step 3.2, training sample of the part group data as RBF neural is randomly selected from some groups of numerical experiment data
This, detection sample of the remainder grouped data as RBF neural；
Step 3.3, it is normalized for numerical experiment data, method for normalizing is as follows：
In formula, xmaxFor the maximum of input data, xminFor the minimum value of input data, x is input data,After normalization
Data.
7. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：Step 3
In, RBF neural topological structure is divided into input layer, hidden layer and output layer；I-th of neuron node center of hidden layer it is defeated
Enter value to be represented by：
Hi=| | X-Ci||
In formula, X=(x1,x2,…,xm) it is net input vector, Ci=(c1i,c2i,…cmi) in i-th of neuron node
The heart, m are neutral net input vector dimension, and ‖ ‖ represent European norm；J-th of neuron node output valve of hidden layer can represent
For：
In formula, δ represents RBF neural expansion rate；The output valve of output layer neuron is represented by：
In formula, w=(w1,w2,…,wn) to connect the weights of hidden layer and output layer, n is hidden layer neuron quantity.
8. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 1, it is characterised in that：Step 4
In, the RBF neural agent model, expansion rate is determined by trial-and-error method.
9. the Multipurpose Optimal Method of SCR denitration preformed catalyst according to claim 2, it is characterised in that：Step 5
Comprise the concrete steps that：
Step 5.1, object function and the optimization section of optimization are determined：
Step 5.2, global search and fine searching greatest iteration step number N are determined1And N2；
Step 5.3, for (t1 0,t2 0,t3 0,t4 0) in 0~1 section random assignment, and based on Logistic models generation two dimension when
Between sequence (t1 i,t2 i,t3 i,t4 i)：
ti+1=4.0ti(1-ti) i=1,2 ..., N1
Step 5.4, by (t1 i,t2 i,t3 i,t4 i) be converted to (t1 i*,t2 i*,t3 i*,t4 i*)：
Step 5.5, if (S, D, H, L)=(t1 i*,t2 i*,t3 i*,t4 i*), calculation optimization object function Fi, by object function it is minimum when
Optimal values of corresponding (S, D, H, the L) as global optimization；
Step 5.6, the initial value (r using the optimal value of global optimization as fine searching1 0,r2 0,r3 0,r4 0), and it is based on Logistic
Model generates two-dimensional time sequenceJ=1,2 .., N2；
Step 5.7, willBe converted to
In formula, φ is contraction factor, the value between 0-0.5；
Step 5.8, ifCalculation optimization object function Fj；
Step 5.9, by object function (S, D, H, L) corresponding when minimum as the optimal value during fine searching, optimization process
Terminate.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034457A (en) * | 2018-06-28 | 2018-12-18 | 杭州电子科技大学 | A kind of modeling of coal-burning power plant's pollutant low cost cooperation-removal and optimization method |
| CN109145988A (en) * | 2018-08-22 | 2019-01-04 | 广东电网有限责任公司 | Determination method, apparatus, equipment and the storage medium of the target operating condition of denitrating system |
| CN110298142A (en) * | 2019-07-19 | 2019-10-01 | 国电环境保护研究院有限公司 | A kind of denitrating catalyst operational characteristic index prediction technique based on measured data |
| CN111027207A (en) * | 2019-12-09 | 2020-04-17 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Method for visualizing abrasion of guide plate of SCR (Selective catalytic reduction) denitration system |
| CN111179463A (en) * | 2019-12-09 | 2020-05-19 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | SCR deNOx systems guide plate wearing and tearing visual system |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130054500A1 (en) * | 2011-08-22 | 2013-02-28 | King Fahd University Of Petroleum And Minerals | Robust controller for nonlinear mimo systems |
| CN103574580A (en) * | 2013-11-15 | 2014-02-12 | 神华集团有限责任公司 | Thermal power generating unit NOx discharge monitoring method and system |
| CN104331736A (en) * | 2014-11-13 | 2015-02-04 | 国家电网公司 | RBF (Radial Basis Function) neural network based supercritical boiler nitric oxide discharging dynamic predication method |
| CN105629738A (en) * | 2016-03-24 | 2016-06-01 | 内蒙古瑞特优化科技股份有限公司 | SCR (Selective Catalytic Reduction) flue gas denitration system control method and apparatus |
| CN106777642A (en) * | 2016-12-08 | 2017-05-31 | 南京航空航天大学 | A kind of Forecasting Methodology of film cooling structure discharge coefficient |
-
2017
- 2017-09-21 CN CN201710858682.3A patent/CN108038561A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130054500A1 (en) * | 2011-08-22 | 2013-02-28 | King Fahd University Of Petroleum And Minerals | Robust controller for nonlinear mimo systems |
| CN103574580A (en) * | 2013-11-15 | 2014-02-12 | 神华集团有限责任公司 | Thermal power generating unit NOx discharge monitoring method and system |
| CN104331736A (en) * | 2014-11-13 | 2015-02-04 | 国家电网公司 | RBF (Radial Basis Function) neural network based supercritical boiler nitric oxide discharging dynamic predication method |
| CN105629738A (en) * | 2016-03-24 | 2016-06-01 | 内蒙古瑞特优化科技股份有限公司 | SCR (Selective Catalytic Reduction) flue gas denitration system control method and apparatus |
| CN106777642A (en) * | 2016-12-08 | 2017-05-31 | 南京航空航天大学 | A kind of Forecasting Methodology of film cooling structure discharge coefficient |
Non-Patent Citations (1)
| Title |
|---|
| 赵乾: "SCR烟气脱硝系统模拟优化及喷氨量最优控制", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034457A (en) * | 2018-06-28 | 2018-12-18 | 杭州电子科技大学 | A kind of modeling of coal-burning power plant's pollutant low cost cooperation-removal and optimization method |
| CN109034457B (en) * | 2018-06-28 | 2022-03-25 | 杭州电子科技大学 | Low-cost collaborative removal modeling and optimization method for pollutants of coal-fired power plant |
| CN109145988A (en) * | 2018-08-22 | 2019-01-04 | 广东电网有限责任公司 | Determination method, apparatus, equipment and the storage medium of the target operating condition of denitrating system |
| CN110298142A (en) * | 2019-07-19 | 2019-10-01 | 国电环境保护研究院有限公司 | A kind of denitrating catalyst operational characteristic index prediction technique based on measured data |
| CN110298142B (en) * | 2019-07-19 | 2023-01-03 | 国电环境保护研究院有限公司 | Denitration catalyst process characteristic index prediction method based on measured data |
| CN111027207A (en) * | 2019-12-09 | 2020-04-17 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Method for visualizing abrasion of guide plate of SCR (Selective catalytic reduction) denitration system |
| CN111179463A (en) * | 2019-12-09 | 2020-05-19 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | SCR deNOx systems guide plate wearing and tearing visual system |
| CN112949175A (en) * | 2021-02-26 | 2021-06-11 | 南京航空航天大学 | Multi-objective optimization method for turbine gas collection cavity structure |
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