CN108121857A - A kind of method for predicting down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation - Google Patents
A kind of method for predicting down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation Download PDFInfo
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Abstract
The present invention relates to a kind of methods for predicting down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation, and the method comprising the steps of 1）To the Geometric Modeling of pulverized coal pyrolysis furnace apparatus；2）Using MP PIC methods to geometrical model mesh generation, initialization flow field and boundary condition are set；3）It is simulated using MP PIC methods and extracts instantaneous particle velocity and impact angle；4）The instantaneous rate of depreciation of radiant tube is calculated using wear model, and by data based on the rate of depreciation corresponding to particle speed and impact angle；5）The basic data is trained using neutral net, obtains rate of depreciation model；6）According to the rate of depreciation model, predict rate of depreciation, obtain instantaneous wear extent.The present invention couples Artificial Neural Network, the instantaneous wear extent of on-line prediction radiant tube using MP PIC methods, and monitor accumulative wear extent, judge the abrasion condition of radiant tube, timely and effectively instruct the maintenance of radiant tube, ensure the safe and stable operation of heat accumulating type fast pyrogenation stove.
Description
Technical field
The present invention relates to technical field of coal chemical industry more particularly to a kind of methods for predicting pyrolysis furnace radiating pipe rate of depreciation.
Background technology
China possesses abundant carbon containing low-grade energy and waste material, mainly including low-order coal, biomass, oily page
Rock, waste tire, sludge etc., and China's petroleum resources lacks, imported crude oil passs length every year, according to statistics crude oil in China in 2013
Up to 2.8 hundred million tons, crude oil external dependence degree is continuously increased import volume.Based on this, a kind of heat accumulating type downlink fast pyrolysis reactor,
Heat accumulation type radiant tube placement technique is taken without using heat carrier, it is simple for process, reliable, and enlargement easy to implement, energy
Enough meet the requirement of industry park plan.Meanwhile artificial petroleum, people can be extracted from carbon containing low-grade energy and waste material
Natural gas resource is made, China's oil, natural gas resource notch can not only be supplemented, and carbon containing low-grade energy can be solved and discarded
This world-famous puzzle of object recycling.
However, downlink pyrolysis oven is under long period hot environment in operational process, pyrolysis feed is powdery, such as coal dust,
There is erosion in the critical component radiant tube that may cause and provide heat source in pyrolysis oven that acutely moves downward of its interior pulverized coal particle
Wear phenomenon.When the wear extent for radiating pipe surface reaches a certain level, it is impossible to bear the particle in burner hearth to its surface
Impact, once radiant tube surfacing is worn, particle will transmitted radiation pipe, it is heavy then influence the efficient of fast pyrogenation furnace apparatus
Long period safe and stable operation causes insecurity and larger economic loss.
The content of the invention
To solve the above-mentioned problems, the present invention provides a kind of method for predicting pyrolysis furnace radiating pipe rate of depreciation, the party
Method comprises the following steps：
1) 1 is carried out to pulverized coal pyrolysis furnace apparatus using Three-dimensional Design Software:1 Geometric Modeling；
2) mesh generation is carried out to the pyrolysis oven geometrical model of structure using MP-PIC methods, initialization flow field and side is set
Boundary's condition calculates the conservation equation of each space micro unit；
3) using pyrolysis chemical reaction process in the pyrolysis oven under MP-PIC methods simulation different operating operating mode, extract instantaneous
Particle speed and impact angle；
4) the instantaneous rate of depreciation of radiant tube is calculated using wear model, and by the particle speed, impact angle, abrasion
Data based on the technological parameters such as rate；
5) basic data is trained using neutral net, obtains rate of depreciation model；
6) according to the rate of depreciation model, predict rate of depreciation, obtain instantaneous wear extent.
Further, the initialization flow field in the step 2) and boundary condition are according to pyrolysis oven operating condition and phase
Physical property is closed to set；The relevant physical properties include：Grain diameter, grain density, fluid properties, reaction temperature,
Grain inlet amount.
Further, simulated time is 100s in the step 3),
Preferably, the instantaneous particle velocity and impact angle between 10-100s are extracted.
Further, the calculation formula of the rate of depreciation model in the step 4) is：
In formula, erFor rate of depreciation；K is material relevant parameter；F (θ) is particle impacting function；θ values are (0, pi/2),
Unit is radian；urelRelative velocity between particle and wall surface, urefFor particle reference velocity constant；N is index.
Further, the calculation formula of the f (θ) is：
F (θ)=A θ+B θ2+Cθ3+Dθ4+Eθ5+Fθ6+Gθ7+Hθ8
In formula, A, B, C, D, E, F, G, H are constant.
Preferably, the urelCalculation formula is：
urel=us-uwall
In formula, usFor particle speed；uwallFor wall surface speed.
Further, neutral net is to basic data training flow in the step 5)：
Determine input layer, output layer, hidden layer,
Using particle speed, fire box temperature, furnace pressure, particulate charge amount and impact angle as input layer variable, mill
Rate is damaged as output layer variable；
Compare the mean square error that different node training patterns obtain, find out optimal node in hidden layer H；
Using tangent S type functions tansig as the transmission function between input layer and hidden layer, using selectively acting
Function Lin is as the transmission function between output layer and hidden layer；
Model training is carried out to training data using neutral net so that iteration result error is less than allowable error 0.001-
0.00001, model construction is completed.
Further, the reference formula of the node in hidden layer H is：
In formula, m is input number of nodes；N is output node number；L is constant.
Preferably, the iterations is 100-200 times, learning rate 0.1-0.5.
Further, according to pyrolysis fire box temperature, furnace pressure and particulate charge amount predicting radiation in the step 6)
Pipe abrasion rate.
A kind of method of prediction down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation of the present invention, advantage are：
1. a kind of method of prediction down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation of the present invention, quickly and accurately online
The instantaneous wear extent of predicting radiation pipe surface material, and accumulative wear extent is monitored, judge the abrasion condition of radiant tube, it is timely and effective
Ground instructs the maintenance of radiant tube, ensure that the safe and stable operation of fast pyrogenation stove, reduce the insecurity that thereby results in and
Economic loss.
2. the method for the prediction down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation of the present invention is easy, easy to operate, prediction essence
Degree is high, the strong applicability of model.
3. the method for the prediction down-flow fluidized bed using ECT fast pyrogenation furnace radiating pipe rate of depreciation of the present invention, which overcomes the prior art, not to be had
The shortcomings that consideration high temperature and high pressure environment is to radiating the influence of pipe abrasion, the result of calculation of wear extent differs farther out with actual conditions.
Description of the drawings
Fig. 1 is the method flow diagram of present invention prediction fast downlink bed fast pyrogenation furnace radiating pipe rate of depreciation.
Fig. 2 is neural network structure figure used in the present invention.
Fig. 3 is neural network algorithm flow chart of the present invention.
Fig. 4 is the axial distribution map of pyrolysis oven particle speed under three kinds of operation operating modes in the present invention.
Wherein, X1、X2、XnFor input layer variable；ωijThe transmission function between input layer and hidden layer；ωjkFor output layer
Transmission function between hidden layer；YmFor desired output.
Specific embodiment
Below by with reference to the accompanying drawings and specific embodiment to the present invention a kind of prediction fast downlink bed fast pyrogenation stove radiate
The method of pipe abrasion rate is further described in detail.
The flow of the method for present invention prediction fast downlink bed fast pyrogenation furnace radiating pipe rate of depreciation according to figure 1
Figure, the method for the present invention includes the following steps：
1) 1 is carried out to pulverized coal pyrolysis furnace apparatus using three-dimensional software:1 Geometric Modeling；
2) mesh generation is carried out to the pyrolysis oven geometrical model of structure using MP-PIC methods, according to pyrolysis oven operating condition
And relevant physical properties (grain diameter, grain density, fluid properties, reaction temperature, particulate charge amount etc.) are initial to set
Change flow field and boundary condition, calculate the mass-conservation equation of each space micro unit, momentum conservation equation, energy conservation equation,
Component conservation equation, and then obtain particle speed, impact angle, fire box temperature, furnace pressure etc.；
At this point, need to judge whether whole process meets convergence, step 3) is carried out if convergence；If conditions are not met, then
Back to the operation of step 2), until meeting convergence.
3) using the pyrolysis oven reaction under MP-PIC methods simulation different operating operating mode, simulated time 100s, and extract
The instantaneous particle velocity and impact angle of 10-100s；
4) the instantaneous rate of depreciation of radiant tube is calculated using wear model, and will be corresponding to particle speed and impact angle
Rate of depreciation based on data；
5) basic data is trained using neutral net, obtains rate of depreciation model；
6) according to the rate of depreciation model, predict rate of depreciation, obtain instantaneous wear extent.
According to an embodiment of the invention, the calculation formula of the rate of depreciation model is：
In formula, erFor rate of depreciation；K is material relevant parameter；F (θ) is particle impacting function；θ values are (0, pi/2),
Unit is radian；urelRelative velocity between particle and wall surface, urefFor particle reference velocity constant；N is index.
According to an embodiment of the invention, the calculation formula of the f (θ) is：
F (θ)=A θ+B θ2+Cθ3+Dθ4+Eθ5+Fθ6+Gθ7+Hθ8
In formula, A, B, C, D, E, F, G, H are constant.
According to an embodiment of the invention, the urelCalculation formula is：
urel=us-uwall
In formula, usFor particle speed；uwallFor wall surface speed.
Due to BP (Back Propagation) neutral net have good non-linear quality, high fitting precision and
Extensive function is a kind of multilayer feedforward neural network of one way propagation, is mainly characterized by signal propagated forward, and error reversely passes
It broadcasts.Therefore, we utilize BP neural network model, using tens groups in above-mentioned basic data or hundreds of groups of data as training number
According to remainder data obtains the relation of fire box temperature, furnace pressure, particulate charge amount and rate of depreciation as data are verified.
It should be noted that training data is only randomly selected without particular/special requirement in basic database.
According to it is shown in Fig. 2 be neural network algorithm structure chart of the present invention, wherein, X1、X2、XnFor input layer variable；ωij
The transmission function between input layer and hidden layer；ωjkTransmission function between output layer and hidden layer；YmFor desired output.
And by the use of tangent S type functions tansig as transmission function between input layer and hidden layer in the present invention, with selectivity
Action function Lin is as the transmission function between output layer and hidden layer.
According to the structure chart of neutral net used in the present invention shown in Fig. 3, the neutral net is to basic data
Training flow be：
Determine input layer, output layer, hidden layer,
Using particle speed, fire box temperature, furnace pressure, particulate charge amount and impact angle as input layer variable, mill
Rate is damaged as output layer variable；
Compare the mean square error that training pattern obtains under different nodes, find out optimal node in hidden layer H；
Using tangent S type functions tansig as the transmission function between input layer and hidden layer, using selectively acting
Function Lin is as the transmission function between output layer and hidden layer；
Model training is carried out to training data using neutral net so that iteration result error is less than allowable error 0.001-
0.00001, model construction is completed.
Shown in Fig. 3 is the flow chart that 80 groups of basic datas are trained, and selects number based on 60 groups of data therein
According to remaining 20 groups of data are as training data.
According to an embodiment of the invention, the reference formula of the node in hidden layer H is：
In formula, m is input number of nodes；N is output node number；L is constant.
Set iterations is 100-200 time, learning rate 0.1-0.5 in the present invention, desired value is 0.00001~
0.0001；When the error of iteration result is less than allowable error 0.001~0.00001, system finishing iterates to calculate, and model construction is complete
Into.
According to the axial distribution map of pyrolysis oven particle speed under three kinds of operations operating mode of the invention shown in Fig. 4, wherein, three kinds
Operation operating mode is respectively Case 1, Case 2 and Case 3, wherein,
The particulate charge of Case 1 is 1.2t/h, and radiant tube temperature is 600 DEG C, and the grain size of coal dust is 4mm, the stove of pyrolysis oven
Gun pressure power is 0.1MPa；
The particulate charge of Case 2 is 3t/h, and radiant tube temperature is 700 DEG C, and the grain size of coal dust is 2mm, the burner hearth of pyrolysis oven
Pressure is 0.2MPa；
The particulate charge of Case 3 is 4t/h, and radiant tube temperature is 800 DEG C, and the grain size of coal dust is 1mm, the burner hearth of pyrolysis oven
Pressure is 0.6MPa.
As seen from Figure 4, operating condition influences particle speed distribution.
Particle impact angle is comprehensive effect of the particle speed on x, tri- directions of y, z, and angle is led in simulation process
Cross x, velocity amplitude and its direction calculating automatic calculation obtain on tri- directions of y, z.
When simulated time is less than 10s, Gas-particle Flows are not up to the stabilization sub stage in pyrolysis oven, and therefore, 0-10s data do not have
Representative, analog result differs larger with the stabilization sub stage, it is impossible to reflect actual experiment situation, therefore, be carried in extraction process
Take data based on the data of 10-100s.
Also, it is found by analog study early period, simulated time reaches 10s or so, and Gas-particle Flows reach steady in pyrolysis oven
Determine state, in order to obtain more analog results, have chosen simulated time as 100s.How much simulated time does not influence to simulate if being chosen
As a result, but simulated time it is too short, obtain data volume it is small；Simulated time is long, and time-consuming for calculating, considers simulated time and is set to
100s。
Embodiment one
A kind of method for predicting vertical type square heat-storage type coal power fast pyrogenation furnace radiating pipe rate of depreciation, including following step
Suddenly：
1) 1 is carried out using the vertical square heat-storage type coal power fast pyrogenation furnace apparatus of Solidworks softwares:1 geometry is built
Mould, a height of 12m of pyrolysis oven, top are the rectangle of 2.0 × 3.0m, and bottom is the rectangle of 1.0 × 3m, internal 40 spokes of arranging
Pipe is penetrated, is arranged 20 layers altogether, two radiant tubes of every layer of arrangement；
The a diameter of 180mm of radiant tube used, horizontal and vertical spacing is respectively 300mm to radiant tube two-by-two.
2) mesh generation is carried out to it using MP-PIC methods, according to actual physical property setting pulverized coal particle, gas property；Institute
The average grain diameter of the coal dust used is 0.5mm, real density 1400kg/m3；Coal dust is from roof of the furnace uniform feeding, in pyrolysis oven
It inside reacts, is discharged from bottom；Particulate charge amount is 3t/h；During initialization, full of nitrogen, no granulation mass inside entire burner hearth
Product；Radiant tube surface temperature is 700-900 DEG C, and the furnace pressure for being pyrolyzed furnace apparatus is 0.1Mpa.
3) the above-mentioned real reaction time is calculated as 100s, extracts 20-100s, and time interval is the particle speed u under 1ssWith
Impact angle θ；
Bring above-mentioned data into rate of depreciation formula：
Wherein, K=2.0e-9,
F (θ)=A θ+B θ2+Cθ3+Dθ4+Eθ5+Fθ6+Gθ7+Hθ8
Wherein, A, B, C, D, E, F, G, H are constant, value is respectively 9.37, -42.295,110.864, -175.804,
170.137th, -98.398,31.211 and -4.170；θ values are (0, pi/2), and unit is radian；
urelRelative velocity between particle and wall surface,
urel=us-uwall
urefFor particle reference velocity constant, default value 1m/s；N is index, default value 2.6；uwallFor 0.
4) corresponding rate of depreciation is calculated by rate of depreciation formula.
5) using BP neural network model, using wantonly 60 groups of data in above-mentioned data as training data, remaining 20 groups of number
According to as data are verified, the relation of fire box temperature, furnace pressure, particulate charge amount and rate of depreciation is obtained.
BP neural network training concretely comprises the following steps：By particle speed, impact angle, fire box temperature, furnace pressure and particle
Inlet amount technological parameter is as input layer variable, and rate of depreciation is as output layer variable；Node in hidden layer selects reference formula
In formula, m is input number of nodes, is preferably that 20, n is output node number, is preferably 1, is respectively compared under different nodes
The mean square error that training pattern obtains, finds out optimal node in hidden layer H；Tangent S type functions are used between input layer and hidden layer
Tansig, by the use of selectively acting function Lin as the transmission function between output layer and hidden layer, uses function as transmission function
Inputoutput data trains BP neural network, and trained network fit non-linear function is enable to export.Utilize BP nerve nets
Network carries out training sample model training, and the iterations of setting is 200 times, learning rate 0.5, desired value 0.00001；When
The error of iteration result is less than allowable error 0.001-0.00001, system finishing iterative calculation, and model construction is completed.
6) model based on structure, according to online pyrolysis oven fire box temperature, furnace pressure and particulate charge amount data,
The instantaneous wear extent of on-line prediction radiant tube and the accumulative wear extent of monitoring, are calculated radiant tube wear extent as 0.027mm/a.
The wear extent 0.027mm/a for the radiant tube that above example one is calculated for this patent and the spoke of experiment detection
It penetrates pipe abrasion thickness to compare, error has higher precision in 2% or so, thus explainable the method applied in the present invention.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment of the present invention or example.In the present specification, schematic expression of the above terms is not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
It is combined in an appropriate manner in a or multiple embodiments or example.In addition, without conflicting with each other, the technology of this field
Different embodiments described in this specification or example and different embodiments or exemplary feature can be combined by personnel
And combination.
Claims (10)
1. a kind of method for predicting pyrolysis furnace radiating pipe rate of depreciation, this method comprise the following steps：
1) 1 is carried out to pulverized coal pyrolysis furnace apparatus using Three-dimensional Design Software:1 Geometric Modeling；
2) mesh generation is carried out to the pyrolysis oven geometrical model of structure using MP-PIC methods, initialization flow field and perimeter strip is set
Part calculates the conservation equation of each space micro unit；
3) using pyrolysis chemical reaction process in the pyrolysis oven under MP-PIC methods simulation different operating operating mode, instantaneous particle is extracted
Speed and impact angle；
4) the instantaneous rate of depreciation of radiant tube is calculated using wear model, and by the instantaneous particle velocity, impact angle, abrasion
Data based on the technological parameters such as rate；
5) basic data is trained using neutral net, obtains rate of depreciation model；
6) according to the rate of depreciation model, predict rate of depreciation, obtain instantaneous wear extent.
2. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 1, which is characterized in that the step 2)
In initialization flow field and boundary condition be to be set according to pyrolysis oven operating condition and relevant physical properties；
The relevant physical properties include：Grain diameter, grain density, fluid properties, reaction temperature, particulate charge amount.
3. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 1, which is characterized in that the step 3)
Middle simulated time is 100s,
Preferably, the instantaneous particle velocity and impact angle between 10-100s are extracted.
4. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 1, which is characterized in that the step 4)
In the calculation formula of rate of depreciation model be：
<mrow>
<msub>
<mi>e</mi>
<mi>r</mi>
</msub>
<mo>=</mo>
<mi>K</mi>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>&theta;</mi>
<mo>)</mo>
</mrow>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<msub>
<mi>u</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>l</mi>
</mrow>
</msub>
<msub>
<mi>u</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>f</mi>
</mrow>
</msub>
</mfrac>
<mo>)</mo>
</mrow>
<mi>n</mi>
</msup>
</mrow>
In formula, erFor rate of depreciation；K is material relevant parameter；F (θ) is particle impacting function；θ values are (0, pi/2), and unit is
Radian；urelRelative velocity between particle and wall surface, urefFor particle reference velocity constant；N is index.
5. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 4, which is characterized in that the f (θ)
Calculation formula is：
F (θ)=A θ+B θ2+Cθ3+Dθ4+Eθ5+Fθ6+Gθ7+Hθ8
In formula, A, B, C, D, E, F, G, H are constant.
6. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 4, which is characterized in that the urel's
Calculation formula is：
urel=us-uwall
In formula, usFor particle speed；uwallFor wall surface speed.
7. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 1, which is characterized in that the step 5)
Middle neutral net to basic data training flow be：
Determine input layer, output layer, hidden layer,
Using particle speed, fire box temperature, furnace pressure, particulate charge amount and impact angle as input layer variable, abrasion speed
Rate is as output layer variable；
Compare the mean square error that different node training patterns obtain, find out optimal node in hidden layer H；
Using tangent S type functions tansig as the transmission function between input layer and hidden layer, using selectively acting function
Lin is as the transmission function between output layer and hidden layer；
Model training is carried out to training data using neutral net so that iteration result error is less than allowable error 0.001-
0.00001, model construction is completed.
8. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 7, which is characterized in that the hidden layer
The reference formula of number of nodes H is：
<mrow>
<mi>H</mi>
<mo>=</mo>
<msqrt>
<mrow>
<mi>m</mi>
<mo>+</mo>
<mi>n</mi>
</mrow>
</msqrt>
<mo>+</mo>
<mi>L</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>&le;</mo>
<mi>L</mi>
<mo>&le;</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, m is input number of nodes；N is output node number；L is constant.
9. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 7, which is characterized in that the iteration time
Number is 100-200 times, learning rate 0.1-0.5.
10. the method for prediction pyrolysis furnace radiating pipe rate of depreciation according to claim 1, which is characterized in that the step
6) according to pyrolysis fire box temperature, furnace pressure and particulate charge amount predicting radiation pipe abrasion rate in.
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| CN114781285A (en) * | 2022-04-29 | 2022-07-22 | 浙江大学 | Biomass large particle pyrolysis simulation method based on ball cluster hypothesis and Laguerre-Voronoi structure |
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