CN112598168A - Power station boiler NO based on monkey swarm algorithm and fast learning networkxEmission amount prediction method - Google Patents
Power station boiler NO based on monkey swarm algorithm and fast learning networkxEmission amount prediction method Download PDFInfo
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- 241000282693 Cercopithecidae Species 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000003062 neural network model Methods 0.000 claims abstract description 19
- 238000007599 discharging Methods 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 239000003245 coal Substances 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 7
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
- 230000009194 climbing Effects 0.000 claims description 4
- 230000009193 crawling Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 239000001301 oxygen Substances 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 4
- 238000013075 data extraction Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims 1
- 239000003546 flue gas Substances 0.000 claims 1
- 238000013528 artificial neural network Methods 0.000 abstract description 11
- 239000000779 smoke Substances 0.000 description 6
- 238000010248 power generation Methods 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N nitrogen Substances N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 241000218691 Cupressaceae Species 0.000 description 1
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- 230000001537 neural effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
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Abstract
The invention relates to a power station boiler NO based on a monkey swarm algorithm and a fast learning networkxAn emission amount prediction method, comprising the steps of: s1: preprocessing historical operating data of the power station boiler to obtain a model sample; s2: optimizing a fast learning net by using a model sample and a monkey group algorithm to obtain NO of the power station boilerxDischarging the neural network model; s3: obtaining actual power station boiler operation data, and utilizing NO of power station boilerxNO of power station boiler by discharging neural network modelxAnd (4) predicting the emission amount. Compared with the prior art, the obtained artificial neural network has stability, and NO of the power station boiler is ensuredxThe prediction of the discharge amount is more accurate.
Description
Technical Field
The invention relates to NO of a power station boilerxThe field of emission prediction, in particular to a power station boiler NO based on a monkey swarm algorithm and a fast learning networkxAn emission amount prediction method.
Background
Coal-fired power generation remains an important form of power generation in china and even globally. During power generation, a large amount of NO can be discharged by coal-fired power generationxThereby causing serious environmental pollution. The low-nitrogen combustion optimization technology has the advantages of simplicity, high efficiency and low cost. Establishing accurate NOxThe prediction model is weight of low-nitrogen combustion optimizationTo form a part[1]. Common modeling methods are support vector machine and artificial neural network modeling. Neural networks are always the research focus in the field of machine learning, but problems of slow overfitting and training and the like are slow to develop. Until the development of deep learning, the artificial neural network becomes the hot tide of research again. In 2004, GB Huang[2]A new type of single hidden layer feedforward neural network, namely Extreme Learning Machine (ELM), is proposed. The ELM overcomes the problems of iteration, long time consumption and easy falling into local optimal values of the traditional neural network algorithm.
Although extreme learning machines have many advantages, in some regression or classification applications, it requires more hidden layer neurons than traditional neural network learning algorithms, which may make the trained extreme learning machine response time to unknown test samples longer. Aiming at the problem, the Lizhong is strong[3]In 2013, a novel neural Network based on an extreme Learning machine, namely a Fast Learning Network (FLN), is provided, and the superiority of the FLN is proved. The input weight and the hidden layer threshold value generated randomly in the fast learning network can cause insufficient prediction accuracy and unstable performance of the fast learning network, so an optimization algorithm is needed to optimize the input weight and the hidden layer threshold value.
Reference documents:
[1] niupeng, lie enter cypress, Liu nan, Lizhong, Rongyan, based on the improved pollination algorithm and extreme learning machine boiler NOx emission optimization [ J ] power engineering report, 2018,38(10):782 and 787.
[2]Huang G-B,Zhu Q-Y,Siew C-K.Extreme learning machine:a new learning scheme of feedforward neural networks[C],2004:985-990.
[3]Li G,Niu P,Duan X,et al.Fast learning network:a novel artificial neural network with a fast learning speed[J].Neural Computing&Applications,2014,24(7-8):1683-1695.
Disclosure of Invention
Aiming at the defects of insufficient prediction accuracy and unstable performance of a fast learning network, the invention provides a power station boiler NO based on a monkey swarm algorithm and a fast learning networkxAn emission amount prediction method.
The purpose of the invention can be realized by the following technical scheme:
power station boiler NO based on monkey swarm algorithm and fast learning networkxAn emission amount prediction method, comprising the steps of:
s1: preprocessing historical operating data of the power station boiler to obtain a model sample;
s2: optimizing a fast learning net by using a model sample and a monkey group algorithm to obtain NO of the power station boilerxDischarging the neural network model;
s3: obtaining actual power station boiler operation data, and utilizing NO of power station boilerxNO of power station boiler by discharging neural network modelxAnd (4) predicting the emission amount.
The historical operation data of the power station boiler comprises boiler load, coal feeder rotating speed, primary air speed, secondary air speed, smoke exhaust oxygen quantity, smoke temperature, secondary air nozzle opening degree, over-fire air baffle opening degree, coal quality characteristics and the like.
S1 includes:
s11: acquiring initial historical data;
s12: performing data preprocessing on the initial historical data to obtain a model sample, wherein the data preprocessing comprises data dead pixel elimination, steady-state data extraction and data normalization;
s13: and carrying out training sample and test sample division on the model sample.
70% of the model samples were selected as training samples and 30% of the model samples were selected as test samples.
In S2, before optimizing the fast learning net by using the model sample and the monkey group algorithm, the input weight of the fast learning net, the optimizing range of the hidden layer threshold and the number of hidden layer nodes of the fast learning net are set.
In S2, optimizing the input weight and hidden layer threshold of the fast learning net by using a monkey group algorithm to obtain the NO of the utility boilerxAn exhaust neural network model, the process comprising:
s21: setting initial parameters of a monkey group;
s22: optimizing the positions of the monkey groups according to the pseudo-gradient in the climbing process based on the initial parameters;
s23: updating the positions of the monkey group to a more optimal position through the hope-jump process based on the optimized position obtained in the crawling process, calculating the fitness (determining the fitness according to a cross validation method), and reserving parameters corresponding to the optimal fitness;
s24: forcing the monkey group to a new range through a flipping process based on the optimized position obtained by the look-and-jump process;
s25: checking whether the end condition is met, if so, obtaining a parameter corresponding to the optimal fitness to obtain the NO of the power station boilerxAnd (5) discharging the neural network model, and ending, otherwise, executing S22.
Compared with the prior art, the invention has the following advantages:
(1) optimizing input weight and hidden layer threshold of fast learning net by using monkey group algorithm, and establishing NO of power station boilerxAnd the neural network model is discharged, so that the performance of the prediction model is more accurate.
(2) Optimizing input weight and hidden layer threshold of fast learning net by using monkey group algorithm, and establishing NO of power station boilerxAnd the neural network model is discharged, so that the performance of the prediction model is more stable.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 shows a utility boiler NO according to an embodiment of the present inventionxAnd (4) a schematic diagram of an emission neural network model.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
This embodiment provides a power plant boiler NO based on monkey crowd's algorithm and quick learning netxAn emission amount prediction method, as shown in fig. 1, includes the steps of:
s1: preprocessing historical operating data of the power station boiler to obtain a model sample;
s2: optimizing a fast learning net by using a model sample and a monkey group algorithm to obtain NO of the power station boilerxEmission neural network model, power station boiler NOxThe emission neural network is the optimized fast learning network;
s3: obtaining actual power station boiler operation data, and utilizing NO of power station boilerxNO of power station boiler by discharging neural network modelxAnd (4) predicting the emission amount.
Specifically, the method comprises the following steps:
the historical operation data of the power station boiler comprises boiler load (MW), coal feeder rotating speed (r/min), primary air speed (m/s), secondary air speed (m/s), smoke exhaust oxygen quantity, smoke temperature (DEG C), secondary air nozzle opening degree (%), overfire air baffle opening degree (%) and coal quality characteristics. The historical operation data of the utility boiler is expressed as
And normalizing all data samples, wherein
xi∈Rn，
Representing the nth element in the input vector of the ith group of data samples, wherein n is the number of input variables; output of
ti∈Rm，
Representing the mth element in the parameter corresponding to the ith group of optimal fitness, wherein m is the number of the parameters;
at least 30 sets of plant boiler historical operating data are obtained.
S1 includes:
s11: acquiring initial historical data;
s12: performing data preprocessing on the initial historical data to obtain a model sample, wherein the data preprocessing comprises data dead pixel elimination, steady-state data extraction and data normalization;
s13: and carrying out training sample and test sample division on the model sample.
Including but not limited to selecting 70% of the model samples as training samples and 30% of the model samples as testing samples.
In S2, before optimizing the fast learning net by using the model sample and the monkey group algorithm, the input weight of the fast learning net, the optimizing range of the hidden layer threshold and the number of hidden layer nodes of the fast learning net are set
In S2, optimizing the input weight and hidden layer threshold of the fast learning net by using a monkey group algorithm to obtain the NO of the utility boilerxThe process of populating the neural network model includes:
s21: setting initial parameters of a monkey group;
s22: optimizing the positions of the monkey groups according to the pseudo-gradient in the climbing process based on the initial parameters;
s23: updating the positions of the monkey group to a more optimal position through the hope-jump process based on the optimized position obtained in the crawling process, calculating the fitness (determining the fitness according to a cross validation method), and reserving parameters corresponding to the optimal fitness;
s24: forcing the monkey group to a new range through a flipping process based on the optimized position obtained by the look-and-jump process;
s25: checking whether the end condition is met, if so, obtaining a parameter corresponding to the optimal fitness to obtain the NO of the power station boilerxDischarging the neural network model, and ending; otherwise, S22 is executed.
The following is a specific example:
a330 MW coal-fired boiler is taken as a research object, and boiler load (MW), 4 coal feeder rotating speeds (r/min), 4 primary wind speeds (m/s), 5 secondary wind speeds (m/s), smoke exhaust oxygen amount, smoke temperature (DEG C), secondary air nozzle opening degree (%), overfire air baffle opening degree (%) and coal quality characteristics are selected. A total of 30 test conditions were collected.
Optimizing the input weight and hidden layer threshold of the network by using a monkey group algorithm, wherein the algorithm flow is as follows:
s21: setting initial parameters of a monkey group;
s22: optimizing the positions of the monkey groups according to the pseudo-gradient in the climbing process based on the initial parameters;
s23: updating the positions of the monkey groups to more optimal positions through the hope-jump process based on the optimal positions obtained in the crawling process, calculating the fitness (determining the fitness according to a cross validation method), and reserving parameters corresponding to the optimal fitness, wherein the parameters are parameters of a fast learning network;
s24: forcing the monkey group to a new range through a flipping process based on the optimized position obtained by the look-and-jump process;
s25: checking whether the end condition is met, if so, obtaining a parameter corresponding to the optimal fitness to obtain the NO of the power station boilerxDischarging the neural network model, and ending; otherwise, S22 is executed.
By utility boiler NOxEmission neural network model (as shown in FIG. 2) for power station boiler NOxAnd (4) predicting the emission amount.
Claims (6)
1. Power station boiler NO based on monkey swarm algorithm and fast learning networkxAn emission amount prediction method, characterized by comprising the steps of:
s1: preprocessing historical operating data of the power station boiler to obtain a model sample;
s2: optimizing a fast learning net by using a model sample and a monkey group algorithm to obtain NO of the power station boilerxDischarging the neural network model;
s3: obtaining actual power station boiler operation data, and utilizing NO of power station boilerxNO of power station boiler by discharging neural network modelxAnd (4) predicting the emission amount.
2. The power station boiler NO based on monkey swarm algorithm and fast learning net according to claim 1xThe discharge capacity prediction method is characterized in that the historical operation data of the power station boiler comprise boiler load, coal feeder rotating speed, primary air speed, secondary air speed, exhaust oxygen amount, flue gas temperature and secondary air injectionMouth opening, overfire air baffle opening, coal quality characteristics and the like.
3. The power station boiler NO based on monkey swarm algorithm and fast learning net according to claim 1xThe emission amount prediction method is characterized in that S1 includes:
s11: acquiring initial historical data;
s12: performing data preprocessing on the initial historical data to obtain a model sample, wherein the data preprocessing comprises data dead pixel elimination, steady-state data extraction and data normalization;
s13: and carrying out training sample and test sample division on the model sample.
4. Power station boiler NO based on monkey swarm algorithm and fast learning net according to claim 3xThe emission prediction method is characterized in that 70% of model samples are selected as training samples, and 30% of the model samples are selected as testing samples.
5. The power station boiler NO based on monkey swarm algorithm and fast learning net according to claim 1xThe emission prediction method is characterized in that in S2, before optimizing the fast learning network by using the model sample and the monkey group algorithm, the input weight of the fast learning network, the optimization range of the hidden layer threshold and the number of hidden layer nodes of the fast learning network are set.
6. The power station boiler NO based on monkey swarm algorithm and fast learning net according to claim 1xThe emission prediction method is characterized in that in S2, the input weight and hidden layer threshold of the fast learning net are optimized by using a monkey group algorithm to obtain the NO of the utility boilerxAn exhaust neural network model, the process comprising:
s21: setting initial parameters of a monkey group;
s22: optimizing the positions of the monkey groups according to the pseudo-gradient in the climbing process based on the initial parameters;
s23: updating the positions of the monkey groups to a better position through the hope-jump process based on the optimized position obtained in the crawling process, calculating the fitness, and reserving the parameters corresponding to the optimal fitness;
s24: forcing the monkey group to a new range through a flipping process based on the optimized position obtained by the look-and-jump process;
s25: checking whether the end condition is met, if so, obtaining a parameter corresponding to the optimal fitness to obtain the NO of the power station boilerxAnd (5) discharging the neural network model, and ending, otherwise, executing S22.
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| CN113628694A (en) * | 2021-07-05 | 2021-11-09 | 国能国华(北京)电力研究院有限公司 | Method for predicting discharge amount of nitrogen oxides of boiler |
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| CN108038306A (en) * | 2017-12-11 | 2018-05-15 | 太原理工大学 | A kind of method of power boiler burning modeling and optimization towards magnanimity high dimensional data |
| CN109492807A (en) * | 2018-11-01 | 2019-03-19 | 大唐环境产业集团股份有限公司 | Based on the boiler NO for improving quanta particle swarm optimizationXPrediction model optimization method |
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| CN113628694A (en) * | 2021-07-05 | 2021-11-09 | 国能国华(北京)电力研究院有限公司 | Method for predicting discharge amount of nitrogen oxides of boiler |
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