CN110263997A - A kind of cement producing line flue gas NO based on deep neural networkxConcentration prediction method - Google Patents

A kind of cement producing line flue gas NO based on deep neural networkxConcentration prediction method Download PDF

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CN110263997A
CN110263997A CN201910529701.7A CN201910529701A CN110263997A CN 110263997 A CN110263997 A CN 110263997A CN 201910529701 A CN201910529701 A CN 201910529701A CN 110263997 A CN110263997 A CN 110263997A
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郝晓辰
宋智星
赵彦涛
郭曈曈
杨跃
史鑫
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Yanshan University
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Abstract

The invention discloses the cement producing line flue gas NO based on deep neural networkxConcentration prediction method belongs to manufacture of cement flue gas NOxConcentration Testing field.First according to NOxMechanism of production combination cement producing line process flow filters out prediction NOxRequired correlated variables is downloaded the data of variable from cement database and is pre-processed；Then variable data is formed into input data in a manner of sliding window, so that input data implies the delay characteristics of each variable；Unsupervised training is carried out to extract delay characteristics, with the reversed Training DNN network of BP algorithm to extract corresponding relationship feature using the input layer of DNN network and hidden layer as DBN again；Finally, historical data binding model predicted value rolling forecast goes out the NO of following a period of timex.The method of the present invention is preferably solved because of flue gas NOxTest point is set to chimney, leads to NOxConcentration delay detection, to be difficult to set up NOxThe problem of prediction model.

Description

A kind of cement producing line flue gas NO based on deep neural networkxConcentration prediction method
Technical field
The invention belongs to manufacture of cement flue gas NOxConcentration Testing field, more particularly to a kind of based on deep neural network Cement producing line flue gas NOxConcentration prediction method.
Background technique
Nitrogen oxides (the NO of China's cement industryx) discharge amount accounts for national NOx10% or so of discharge amount is thermal power generation With the third-largest NO after vehicle exhaustxEmission source.A large amount of NOxDischarge can change the property of atmosphere, so as to cause acid rain, photochemical The atmosphere polluting problems such as pollution are learned, have significant damage to natural environment and environment for human survival." cement industry " 13 " development Planning " in explicitly point out, the year two thousand twenty NOxDischarge amount reduced 30% than 2015.Therefore, cement industry is as NOxThe weight of discharge One of industry is wanted, control and reduces NOxDischarge is of great significance.
Cement denitrating flue gas is mainly using selective non-catalytic reduction (Selective No Catalytic at present Reduction is abbreviated as SNCR, hereafter indicates selective non-catalytic reduction with " SNCR ") method.SNCR method is in suitable temperature Area sprays into ammonium hydroxide, by the NO in flue gas under conditions of not using catalystxQuickly it is reduced into nontoxic water and nitrogen, Jin Ershi Existing denitrating flue gas.The NO that cement industry generatesxIt is reduced in denitration reaction generating region, completes NOxThe reduction of concentration, later flue gas NOxGreat variety does not occur for concentration through process procedures such as mulitistage cyclone, conditioning Tower, dust-precipitators, flows to chimney discharge. Effectively detection flue gas NOxConcentration sensor setting chimney be discharged at atmospheric air port rather than practical denitration reaction generating region, cause not The NO of evitable process flowxConcentration detection delay.Cement production system has large dead time, time-varying, strong nonlinearity simultaneously The characteristics of, so that NOxThe time delay of Concentration Testing has uncertainty.Flue gas NOxThe Accurate Prediction of concentration can be NOxEmission reduction control It makes (the present invention is based on SNCR denitration controls) and effective data support is provided, so Accurate Prediction postpones the flue gas NO detectedx Concentration is of great significance.
Summary of the invention
For overcome the deficiencies in the prior art, the invention proposes one kind to be based on deep neural network (Deep Neural Network, DNN) prediction flue gas NOxThe method of concentration, being capable of the NO of instant prediction for a period of timexConcentration preferably solves cigarette Gas NOxThe delay issue of detection provides reliable data for the ammonia spraying amount calculating in SNCR denitration control process and supports.
To achieve the above object, the present invention is realized according to following technical scheme:
A kind of cement producing line flue gas NO based on deep neural networkxConcentration prediction method, includes the following steps:
Step S1: according to NOxMechanism of production combination cement producing line process flow, screening prediction NOxThe related of concentration becomes Amount；
Step S2: the variable data of screening is downloaded from cement production enterprise database and is pre-processed, sliding window is passed through Mode makes each variable data form ordered series of numbers, and the delay characteristics that each variable data includes are lain in the input ordered series of numbers of model；
Step S3: in such a way that unsupervised training and Training combine, by delay characteristics of input data and defeated The corresponding relationship feature extraction and preservation of data establish NO into the parameter of DNN network outxPrediction model；
Step S4: in conjunction with historical data and NOxPredicted value rolling forecast goes out the NO of following a period of timexConcentration value.
In above-mentioned technical proposal, correlated variables includes input variable and output variable, and wherein input variable includes feeding capacity X1, ammonium hydroxide flow X2, kiln current average X3, Coaling of Decomposing Furnace X4, secondary air temperature X5, level-one cylinder outlet temperature X6, kiln tail temperature Spend X7, level-one cylinder O2 content X8, denitration is for ammonia pump frequency X9, kiln hood hello coal amount X10, decomposition furnace outlet temperature X11, throat NOxIt is dense Spend X12；Output variable includes throat NOxConcentration Y.
It is using sliding window that selected variable data is defeated by formula (1)~(4) formation in step S2 in above-mentioned technical proposal Enter output data, each input variable ordered series of numbers indicates are as follows:
Xi(t)={ Xi(t-k-m, t-k) }, i=1,2,3 ... 11 (1)
X12(t)={ X12(t-n,t)} (2)
The input ordered series of numbers of network input layer are as follows:
X (t)={ X1(t),X2(t),X3(t),……,X12(t)} (3)
Network output layer are as follows:
Y (t+1)=X12(t+1) (4)
T is prediction time in formula (1)~(4), and X (t) and Y (t+1) respectively indicate the data of input layer, output layer, and k value is small In the minimal time delay time, Xi(t-k-m, t-k) indicates XiFor variable from the t-k-m moment to the time series at t-k moment, m is that the time is long Degree, X12(t-n, t) indicates X12Time series of the variable from t-n moment to t moment, n are time span, X12(t+1) it indicates under using The NO at one momentxThe label data that concentration value is inputted as t moment.
In above-mentioned technical proposal, step S3 is specifically included: the input data for enabling DNN network training process is X (t)={ X1 (t),X2(t),X3(t),……,X12(t) }, output data is Y (t+1)=X12(t+1)；The input layer of DNN and hidden layer are made Unsupervised training is carried out for DBN, to extract the delay characteristics for each variable for including in input data, and the feature of extraction is protected There are on the initial weight of DNN input layer and hidden layer；The DBN for saving input data delay characteristics is connect with output layer, DNN network parameter is reversely finely tuned by BP algorithm, so that the network for making the corresponding relationship feature of output data be stored in model is joined In number；Finally establish NOxPrediction model, the number of plies of DNN network and every layer of neuron according to the concrete condition of variable data into Row setting.
In above-mentioned technical proposal, step S4 is specifically included:
Using formula (5)~(7) historical data and NOxPredicted value combines, the NO of rolling forecast a period of time in futurexConcentration Value, each input variable ordered series of numbers are become from formula (1) (2):
Network input layer ordered series of numbers is become from formula (3):
In formula (6),For the NO at t+1 momentxPredicted value, by ordered series of numbers X1(t) NO is inputtedxPrediction model obtains t+2 The NO at momentxPredicted valueContinuous repetitive (5)~(7) process, obtains k+1 NOxPredicted value, to realize t to t+k+ The NO at 1 momentxConcentration prediction.
Compared with prior art, the present invention having the following beneficial effects:
1, DNN cement producing line flue gas NO proposed by the present inventionxConcentration prediction model, when can be extracted from input data Time-varying delay feature extracts corresponding relationship feature from output data, and the time delay of extraction and relationship characteristic is stored in network mould In the parameter of type.To predict following flue gas NO by the variable data of historyxValue.
2, the present invention passes through the transformation of prediction model input data, i.e. mode of the historical data in conjunction with prediction data, in advance Measure the NO of following a period of timexValue reaches and eliminates time-vary delay system Accurate Prediction flue gas NOxThe purpose of concentration.
3, the present invention establishes the historical data that the data of model are cement production enterprise databases, without newly adding standby acquisition number According to.Therefore, cost is relatively low and has well adapting to property to different cement producing lines for model.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with Other attached drawings are obtained according to these attached drawings.
Fig. 1 is DNN cement flue gas NO proposed by the present inventionxConcentration prediction model structure chart；
Fig. 2 is DBN network structure；
Fig. 3 is limited Boltzmann machine structure chart；
Fig. 4 is that input ordered series of numbers shifts gears schematic diagram；
Fig. 5 is cement flue gas NO proposed by the present inventionxForecasting system flow chart.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is A part of the embodiment of the present invention, instead of all the embodiments.
The invention proposes one kind to predict cement producing line flue gas NO based on deep neural networkxThe method of concentration.First Variable is chosen, corresponding data are downloaded from cement production enterprise database, for training DNN network, another part is used for a part Examine the forecasting accuracy of network；Then the cement flue gas of the implicit time-vary delay system information of input based on deep neural network is established NOxConcentration prediction model, structure are shown in Fig. 1；The DNN cement flue gas NO proposed by the present invention for inputting implicit time-vary delay system informationxPrediction System flow chart is shown in Fig. 5；Finally, carrying out the reversed fine tuning of DNN network parameter by BP algorithm, i.e., by error to neuron Weight and biasing tuning, realize the foundation of DNN prediction model, the specific steps are as follows:
Step S1: according to NOxFormation mechanism combination cement production process chooses correlated variables and downloading data, to each variable Data carry out outlier processing and normalized；In step S1, sample data set is chosen from the database of cement production enterprise, and Outlier processing and normalized are carried out to the data of selection.Deep neural network can be by the feature and input of correlated variables Relationship between output is included in network parameter, therefore does not need to carry out variable data complicated pretreatment, as long as by shadow Ring the outlier processing of training.What input data was chosen is different variable, and the unit and variation range of each variable are respectively not It is identical, therefore input data is normalized before training.Variable data is set to form input using sliding window mode Data, it is therefore an objective to which the time response of variable is implicit in input data.It can extract according to deep neural network special between variable Sign relationship simultaneously makes it lie in the feature in network parameter, using the actual production data in cement production enterprise database, establishes Play the corresponding NO of a period of time variable dataxThe network structure of concentration value, to excavate premeasuring and each input variable Relationship reaches and eliminates time-vary delay system influence and then Accurate Prediction NOxThe purpose of concentration.
Specifically, NO in manufacture of cementxThere are mainly three types of types: burning type, heating power type, momentary type.Fuel type NOxIt is combustion Nitrogenous compound in material is first heated is cracked into the intermediate products such as N, CN, HCN in combustion, after be oxidized and generate NOx.? Using coal dust as in the cement kiln of fuel, fuel type NOxProduction quantity account for about the total NO of cement kilnx60% or more of production quantity.Heating power type NOxIt is that N2 and O2 in air reacts generation under the high temperature conditions, reaction temperature production quantity after being higher than 1800k can become rapidly Greatly.Momentary type NOxIt is that N molecule and the ion cluster that hydrocarbon combustion generates in air collides and be oxidized after reacting It is formed, momentary type NOxThe general very little of production quantity.
According to NOxMechanism of production it is found that NOxMainly generated in two production links of rotary kiln and dore furnace.Ammonium hydroxide is through dividing The spray ammonia equipment for solving furnace top sprays into, and restores NO under the conditions of suitable temperature on dore furnace topxComplete denitrification process.In conjunction with upper State analysis, following 12 input variables of DNN model selection: feeding capacity X1, ammonium hydroxide flow X2, kiln current average X3, dore furnace feed Coal amount X4, secondary air temperature X5, level-one cylinder outlet temperature X6, kiln end temperature X7, level-one cylinder O2 content X8, denitration is for ammonia pump frequency X9, Kiln hood feeds coal amount X10, decomposition furnace outlet temperature X11, throat NOxConcentration X12；The output variable of selection is throat NOxConcentration Y. DNN network model can propose the time-vary delay system feature implied in input data and each data variable and the relationship characteristic of premeasuring It takes and saves to the structure weight of network, therefore each variable data need to only be pre-processed, rear formation input and output come Training DNN model.
Step S2: pretreated variable data is formed into input data by way of sliding window, by each variable The delay characteristics that data include are lain in the input ordered series of numbers of model, while using the throat NO of subsequent timexConcentration normalizing Change numerical value as output data；
The X of m (s) duration is used first1~X11The X of variable data and n (s) duration12Variable data is in a manner of sliding window Input layer is constructed, input layer structure is as shown in Figure 1.Wherein, each row of data indicates different input variables, and such as feeding capacity feeds coal Amount etc.；Each column data indicate different moments each variable value.
Each input variable ordered series of numbers indicates are as follows:
Xi(t)={ Xi(t-k-m, t-k) }, i=1,2,3 ... 11 (1)
X12(t)={ X12(t-n,t)} (2)
Network input layer ordered series of numbers are as follows:
X (t)={ X1(t),X2(t),X3(t),……,X12(t)} (3)
Network output layer are as follows:
Y (t+1)=X12(t+1) (4)
T is prediction time in above formula, and X (t) and Y (t+1) respectively indicate the data of input layer, output layer, and k value is slightly less than most Small decay time, Xi(t-k-m, t-k) indicates XiFor variable from the t-k-m moment to the time series at t-k moment, m is time span, X12(t-n, t) indicates X12Time series of the variable from t-n moment to t moment, n are time span, X12(t+1) it indicates with next The NO at momentxThe label data that concentration value is inputted as t moment.
Step S3: in such a way that unsupervised training and Training combine, by delay characteristics of input data and defeated The corresponding relationship feature extraction and preservation of data establish NO into the parameter of DNN network outxPrediction model；
In step s3, input layer, the output layer neuron number of DNN network are determined according to input data, output data； According to NOxThe industrial data feature of concentration correlated variables determines the number of plies and every layer of neuron number of DBN network.The maximum instruction of setting Practice number, learning rate, iteration sample size size.Using the input layer of DNN network and hidden layer as DBN network, greedy nothing is carried out The forward direction training of supervision, the initial value for determining weight w, biasing a and b, so that can be realized data characteristics by DBN extracts sum number The characteristics of according to dimensionality reduction, the delay characteristics that input data implies were extracted and preserved.Having the anti-of supervision is carried out to entire DNN network again To training, the adjustment of whole network parameter is realized, the corresponding relationship feature with output data is stored in network parameter.Most Eventually, a period of time variable data prediction single-point NO is establishedxThe DNN model of value.
DNN network architecture proposed by the present invention is set as 4 layers, and three first layers are by two limited Boltzmann machines (RBM) It stacks, an independent neuron is as the 4th layer.That is, three first layers as DBN, then connect with the output layer of single neuron It connects.Every layer of neuron number of DNN model is followed successively by 672,100,50,1, and learning rate is set as 0.1, and by the company of DBN network structure It connects weight and biasing is set as 0.
The unsupervised training process of DBN network is that each RBM is successively trained since bottom.First RBM is with entirely The input layer training of DNN network；RBM above uses the output layer of previous RBM to complete to train as input layer, and all RBM are complete At obtaining entire DBN network parameter, DBN network structure such as Fig. 2 after training.It is illustrated, is tied with the training process of single RBM Structure is shown in Fig. 3.
RBM model is a kind of energy model, and utilisable energy function and probability are described and calculate.If aobvious layer nerve First v has n, and hidden neuron h has m, then the energy function of a RBM given two state of value may be defined as
In formula, wijTo show layer neuron viWith hidden neuron hjThe weight of connection, aiFor neuron viBiasing, bjFor Neuron hjBiasing.θ=(w, a, b) indicates to constitute the parameter of energy function.The probability distribution of energy function is
In formula, Z is normalization factor.
According to above formula, the activation probability of hidden layer is calculated by showing layer:
Aobvious layer is calculated by hidden layer and activates probability:
In formula,For sigmiod function.The contrast divergence algorithm proposed according to professor Hinton (Contrastive Divergence, CD) trains RBM, and the thought of the algorithm is to calculate hidden layer to the sampling of aobvious layerAobvious layer is updated to hidden layer sampling againθ=(w, a, b) each parameter calculation formula It is as follows:
bj=P (hj=1 | v(0))-P(hj=1 | v(k)) (12)
In contrast divergence algorithm, k value, which generally takes, 1 can obtain preferably result.
So that input layer ordered series of numbers X is entered aobvious layer, the activation probability P (h of hidden layer is obtained by formula (8)I=|1V), then with formula (9) Reverse goes out the activation probability of aobvious layer, obtains weight and biasing finally by formula (10)~(12), to complete primary unsupervised Training.
It repeats the above process, training is completed to the RBM of top, obtains weight and the biasing of whole DBN.
On the basis of obtaining DBN network parameter, optimized entirely according to exemplar using the reversed error correction algorithms of BP DNN network parameter w, a, b have the reversed fine tuning in supervision ground to network, complete the foundation of DNN model.
Composition DNN network is connect before completing entirely with output layer to the DBN of unsupervised training, using BP algorithm to DNN network Parameter carries out reversed layer-by-layer correction, realizes the reversed fine tuning of DNN network model.The mesh of network parameter is reversely adjusted using BP algorithm Be, the corresponding relationship feature extraction of exemplar and input data and save into the parameter of network.Due to the step Exemplar is needed to carry out error calculation, therefore the step has supervision.
Using trained DBN network parameter as three first layers structure initial parameter, the output layer of single neuron is then connected, Connection weight takes random value, and single neuron biasing takes 0.The reversed micro- of DNN network parameter is realized with the reversed error correction algorithms of BP It adjusts.
Error function:
In formula, p is neural network forecast value, and y is exemplar value.
It enablesIndicate l layers of network of i neuron output, f () indicates that activation primitive, η indicate learning rate.Accidentally Difference function is to j-th of neuron weight in output layer (L layers) k-th of neuron and L-1 layersLocal derviation:
To the local derviation of biasing:
To L-1 hidden layer, local derviation of the error to weight:
Local derviation of the error to biasing:
It enables
So
The then variable quantity of l layers of weight and biasing are as follows:
Finally, the more new formula of weight and biasing:
wl=wl-η*△wl (24)
bl=bl-η*△bl (25)
The reversed trim process of DNN network model proposed by the present invention is introduced according to above-mentioned formula.Pass through formula (22) first (23) weight of output layer (L layers) and preceding layer (L-1 layers) and the variable quantity of biasing are calculated, then is completed by formula (24) (25) The update of weight and biasing.Reversed successively realize to all weights of DNN network and biasing that repeat the above steps updates tuning, from And the corresponding relationship feature extraction that exemplar includes is stored in whole network and is completed in parameter.After the completion of reverse train, cigarette Gas NOxConcentration value prediction model just establishes.
Step S4: with historical data combination NOxPredicted value rolling forecast goes out the flue gas NO of following a period of timexConcentration value.
Make to enter data into prediction model and obtains next sampling instant NOxPredicted value, after by input data include it is each Variable series moves back a sampling instant, in conjunction with NOxPredicted value form new input data, next sampling instant NO under completionxValue Prediction.It repeats the above steps and predicts the NO of following a period of timexValue.
Specifically, the input variable ordered series of numbers in step S1 is made to enter step trained NO in S3xIn concentration prediction model, Obtain the NO of subsequent timexConcentration prediction valueBy variable X in step S1i(t) data move back a sampling instant and are formed New input ordered series of numbersData move back a sampling instant also to by NOxConcentration prediction valueInclude, it is defeated Enter ordered series of numbers and shift gears to see Fig. 4, input ordered series of numbers expression formula is as follows:
Each input variable ordered series of numbers is become from formula (1) (2)
Network input layer ordered series of numbers is become from formula (3)
By X1(t) ordered series of numbers input prediction model obtains the NO at t+2 momentxConcentration prediction valueIt repeats the above process To the NO at t+3 momentxConcentration prediction valueAnd so on can obtain total k+1 NOxConcentration prediction value, realization pass through depth Neural network prediction t moment is to t+k+1 moment flue gas NOxThe function of concentration value.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto, In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art, It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims Subject to.

Claims (5)

1. a kind of cement producing line flue gas NO based on deep neural networkxConcentration prediction method, which is characterized in that including as follows Step:
Step S1: according to NOxMechanism of production combination cement producing line process flow, screening prediction NOxThe correlated variables of concentration；
Step S2: the variable data of screening is downloaded from cement production enterprise database and is pre-processed, by way of sliding window So that each variable data is formed ordered series of numbers, the delay characteristics that each variable data includes are lain in the input ordered series of numbers of model；
Step S3: in such a way that unsupervised training and Training combine, by the delay characteristics of input data and output number According to corresponding relationship feature extraction and save into the parameter of DNN network, establish NOxPrediction model；
Step S4: in conjunction with historical data and NOxPredicted value rolling forecast goes out the NO of following a period of timexConcentration value.
2. the cement producing line flue gas NO according to claim 1 based on deep neural networkxConcentration prediction method, it is special Sign is that correlated variables includes input variable and output variable, and wherein input variable includes feeding capacity X1, ammonium hydroxide flow X2, kiln electricity Levelling mean value X3, Coaling of Decomposing Furnace X4, secondary air temperature X5, level-one cylinder outlet temperature X6, kiln end temperature X7, level-one cylinder O2 content X8, denitration is for ammonia pump frequency X9, kiln hood hello coal amount X10, decomposition furnace outlet temperature X11, throat NOxConcentration X12；Output variable packet Include throat NOxConcentration Y.
3. the cement producing line flue gas NO according to claim 2 based on deep neural networkxConcentration prediction method, it is special Sign is, in step S2, selected variable data is formed inputoutput data by formula (1)~(4) using sliding window, each to input Variable series indicates are as follows:
Xi(t)={ Xi(t-k-m, t-k) }, i=1,2,3 ... 11 (1)
X12(t)={ X12(t-n,t)} (2)
The input ordered series of numbers of network input layer are as follows:
X (t)={ X1(t),X2(t),X3(t),……,X12(t)} (3)
Network output layer are as follows:
Y (t+1)=X12(t+1) (4)
T is prediction time in formula (1)~(4), and X (t) and Y (t+1) respectively indicate the data of input layer, output layer, and k value is less than most Small decay time, Xi(t-k-m, t-k) indicates XiFor variable from the t-k-m moment to the time series at t-k moment, m is time span, X12(t-n, t) indicates X12Time series of the variable from t-n moment to t moment, n are time span, X12(t+1) it indicates with next The NO at momentxThe label data that concentration value is inputted as t moment.
4. the cement producing line flue gas NO according to claim 3 based on deep neural networkxConcentration prediction method, it is special Sign is that step S3 is specifically included: the input data for enabling DNN network training process is X (t)={ X1(t),X2(t),X3 (t),……,X12(t) }, output data is Y (t+1)=X12(t+1)；Nothing is carried out using the input layer of DNN and hidden layer as DBN The feature of extraction to extract the delay characteristics for each variable for including in input data, and is stored in DNN input by supervised training On the initial weight of layer and hidden layer；The DBN for saving input data delay characteristics is connect with output layer, it is anti-by BP algorithm To fine tuning DNN network parameter, so that the corresponding relationship feature of output data be made to be stored in the network parameter of model；It is final to establish NOxPrediction model, the number of plies of DNN network are configured with every layer of neuron according to the concrete condition of variable data.
5. the cement producing line flue gas NO according to claim 4 based on deep neural networkxConcentration prediction method, it is special Sign is that step S4 is specifically included:
Using formula (5)~(7) historical data and NOxPredicted value combines, the NO of rolling forecast a period of time in futurexConcentration value, respectively Input variable ordered series of numbers is become from formula (1) (2):
Network input layer ordered series of numbers is become from formula (3):
In formula (6),For the NO at t+1 momentxPredicted value, by ordered series of numbers X1(t) NO is inputtedxPrediction model obtains the t+2 moment NOxPredicted valueContinuous repetitive (5)~(7) process, obtains k+1 NOxPredicted value, to realize t to the t+k+1 moment NOxConcentration prediction.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111174824A (en) * 2019-12-27 2020-05-19 北京首钢自动化信息技术有限公司 Control platform that acid mist discharged
CN111266004A (en) * 2020-03-20 2020-06-12 大唐环境产业集团股份有限公司 SCR denitration reactor inlet NOx delay estimation system
CN111898867A (en) * 2020-07-05 2020-11-06 西北工业大学 Airplane final assembly production line productivity prediction method based on deep neural network
CN112163335A (en) * 2020-09-25 2021-01-01 广东电科院能源技术有限责任公司 Training method, prediction method and device of NOx concentration prediction model
CN113268871A (en) * 2021-05-21 2021-08-17 燕山大学 Cement chimney NOX prediction method based on multivariable time sequence depth network model
CN113470763A (en) * 2021-07-14 2021-10-01 中国航发贵阳发动机设计研究所 System for measuring and calculating components of thermal dissociation gas generated by combustion of hydrocarbon fuel
CN115245735A (en) * 2022-06-16 2022-10-28 浙江省生态环境科学设计研究院 Cement kiln flue gas iSNCR control method based on predictive control model
JP7471105B2 (en) 2020-02-27 2024-04-19 Ｕｂｅ三菱セメント株式会社 CEMENT CLINKER MANUFACTURING SUPPORT SYSTEM, CEMENT CLINKER MANUFACTURING SUPPORT METHOD, AND MANUFACTURING SUPPORT PROGRAM

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104793651A (en) * 2015-04-20 2015-07-22 四川大学 Ammonia spraying amount controlling method for SNCR (selective no catalytic reduction) flue gas denitrating system for cement manufacturing
CN107679671A (en) * 2017-10-23 2018-02-09 燕山大学 A kind of manufacture of cement power consumption Forecasting Methodology of implicit time series depth belief network
US20180149094A1 (en) * 2016-11-30 2018-05-31 General Electric Company Emissions modeling for gas turbine engines
CN109165798A (en) * 2018-10-19 2019-01-08 燕山大学 A kind of Free Calcium Oxide Contents in Cement Clinker on-line prediction method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104793651A (en) * 2015-04-20 2015-07-22 四川大学 Ammonia spraying amount controlling method for SNCR (selective no catalytic reduction) flue gas denitrating system for cement manufacturing
US20180149094A1 (en) * 2016-11-30 2018-05-31 General Electric Company Emissions modeling for gas turbine engines
CN107679671A (en) * 2017-10-23 2018-02-09 燕山大学 A kind of manufacture of cement power consumption Forecasting Methodology of implicit time series depth belief network
CN109165798A (en) * 2018-10-19 2019-01-08 燕山大学 A kind of Free Calcium Oxide Contents in Cement Clinker on-line prediction method and system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
刘博文: "燃煤电厂脱硝系统入口NOx浓度测量修正及喷氨量优化控制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 *
李剑波 等: "《大数据挖掘技术与应用》", 31 July 2018, 延边大学出版社 *
谭思超 等: "《海洋条件下反应堆热工水力特性研究》", 31 August 2018, 国防工业出版社 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111174824A (en) * 2019-12-27 2020-05-19 北京首钢自动化信息技术有限公司 Control platform that acid mist discharged
CN111174824B (en) * 2019-12-27 2022-04-12 北京首钢自动化信息技术有限公司 Control platform that acid mist discharged
JP7471105B2 (en) 2020-02-27 2024-04-19 Ｕｂｅ三菱セメント株式会社 CEMENT CLINKER MANUFACTURING SUPPORT SYSTEM, CEMENT CLINKER MANUFACTURING SUPPORT METHOD, AND MANUFACTURING SUPPORT PROGRAM
CN111266004A (en) * 2020-03-20 2020-06-12 大唐环境产业集团股份有限公司 SCR denitration reactor inlet NOx delay estimation system
CN111898867A (en) * 2020-07-05 2020-11-06 西北工业大学 Airplane final assembly production line productivity prediction method based on deep neural network
CN111898867B (en) * 2020-07-05 2023-04-07 西北工业大学 Airplane final assembly production line productivity prediction method based on deep neural network
CN112163335A (en) * 2020-09-25 2021-01-01 广东电科院能源技术有限责任公司 Training method, prediction method and device of NOx concentration prediction model
CN113268871A (en) * 2021-05-21 2021-08-17 燕山大学 Cement chimney NOX prediction method based on multivariable time sequence depth network model
CN113470763A (en) * 2021-07-14 2021-10-01 中国航发贵阳发动机设计研究所 System for measuring and calculating components of thermal dissociation gas generated by combustion of hydrocarbon fuel
CN113470763B (en) * 2021-07-14 2023-03-14 中国航发贵阳发动机设计研究所 System for measuring and calculating components of thermal dissociation gas generated by combustion of hydrocarbon fuel
CN115245735A (en) * 2022-06-16 2022-10-28 浙江省生态环境科学设计研究院 Cement kiln flue gas iSNCR control method based on predictive control model

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