CN109442465B - Fitting sample selection and metering method for instantaneous powder feeding amount of pulverized coal boiler - Google Patents
Fitting sample selection and metering method for instantaneous powder feeding amount of pulverized coal boiler Download PDFInfo
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- CN109442465B CN109442465B CN201811154450.0A CN201811154450A CN109442465B CN 109442465 B CN109442465 B CN 109442465B CN 201811154450 A CN201811154450 A CN 201811154450A CN 109442465 B CN109442465 B CN 109442465B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23K—FEEDING FUEL TO COMBUSTION APPARATUS
- F23K3/00—Feeding or distributing of lump or pulverulent fuel to combustion apparatus
- F23K3/02—Pneumatic feeding arrangements, i.e. by air blast
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23N—REGULATING OR CONTROLLING COMBUSTION
- F23N1/00—Regulating fuel supply
- F23N1/002—Regulating fuel supply using electronic means
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Abstract
The invention provides a fitting sample selecting and metering method for the instantaneous powder feeding amount of a pulverized coal boiler, which comprises the following steps: step 1, acquiring weight data of a pulverized coal bunker and powder feeding frequency data of m powder feeders in the operation process of a pulverized coal boiler, wherein m is a natural number; step 2, intercepting a plurality of sample sequences from the weight data of the coal powder bin and the powder feeding frequency data of the powder feeder, wherein each sample sequence is V (H)1,...,Hmδ T), where H1,...,HmThe frequencies of the m powder feeders are respectively expressed, and delta T represents the weight difference of the coal powder bin from the beginning to the end of each sample sequence.
Description
Technical Field
The invention belongs to the field of energy production, and particularly relates to a fitting sample selection and measurement method for instantaneous powder feeding amount of a pulverized coal boiler.
Background
In the field of energy production, a large number of thermal power plants, industrial steam production enterprises and civil heating enterprises are all using pulverized coal boilers to produce steam/hot water. The pulverized coal boiler heats water at normal temperature into steam or high-temperature water by burning pulverized coal to generate heat. In this type of production process, the main cost is derived from the consumption of pulverized coal, and the efficiency of production is evaluated using the pulverized coal consumption per unit of steam, the direct evaluation index is the boiler efficiency over a period of time, which is calculated by the following formula:
wherein: steam enthalpy value steam production (kg) steam hot break value (kj/kg)
Water supply enthalpy value (kg) water supply weight (kj/kg) water heat break value (kj/kg)
I.e. the proportion of the heat of the consumed coal powder converted into the water vapor heat in unit time.
The calculation of the boiler combustion efficiency has two purposes:
managed reports: and counting the average combustion efficiency of the boiler according to days, months and the like, and generating a report for management. Aiming at the use scene, the accumulated amount of the pulverized coal, the water and the steam in a large time interval such as days and months needs to be measured.
Boiler fuel efficiency optimization: and calculating the combustion efficiency of the boiler according to the minute level, and improving the combustion efficiency of the boiler by optimizing the control of the boiler. For the use scene, the accumulated amount of the pulverized coal, the water and the steam in a short time interval such as minutes needs to be measured.
Aiming at the measurement of water and steam, the accumulated quantity and the instantaneous quantity can be accurately measured through the corresponding flow meter, and the usage in a small interval such as minutes and the usage in a large interval such as days and months can be accurately obtained.
For the metering of coal dust, there are generally two ways: the coal powder consumption is accumulated by measuring the bin weight difference value of the coal powder bin and the instantaneous quantity is measured by a measuring instrument.
1) Bin weight metering
The pulverized coal bin is a closed container for containing pulverized coal, and the consumption of the pulverized coal in a period of time can be measured by measuring the bin weight variation of the pulverized coal bin. However, the bin weight measuring instrument of the coal powder bin aims at the weight of a hundred-ton powder bin, the measuring precision of the bin weight measuring instrument is about hundreds of kilograms, and the error is extremely large when the bin weight changes slightly; but also further increases the measurement error due to the different shapes of the coal powder in the coal powder bin. Therefore, the weight difference of the coal powder bin can only be used for measuring the weight change of the coal powder in a relatively long time (at least in an hour scale), and the weight difference of the coal powder bin in a minute scale cannot be relatively accurately measured.
2) Metering instrument measures
The pulverized coal is blown into the boiler through air, so the pulverized coal entering the boiler is a solid-gas two-phase flow, and the measurement method commonly used in the industry at present is a direct measurement method, namely, the flow rate and the density of the gas-gas two-phase flow are respectively measured, so that the instantaneous flow of the pulverized coal is calculated, and then the instantaneous flow is accumulated to obtain the pulverized coal amount in a time period.
The instruments for measuring the flow velocity mainly comprise a pitot tube, a hot wire anemometer, a laser Doppler velocimeter and the like. The density measurement methods are relatively many, and there are direct measurement methods such as a microwave method, a photoelectric detection method, an ultrasonic method and the like; there are also indirect measurement methods such as a temperature method, a velocity-pressure difference method, and an energy method [2].
The use of this metering method has the following disadvantages:
high installation/modification/maintenance costs, great construction difficulties: the flow velocity and the density are respectively measured, the installation and maintenance process of each measuring instrument is quite complex, the construction difficulty is high, and the cost of initial installation and subsequent maintenance is extremely high.
Large error: the instantaneous quantities of the flow rate and the density are respectively measured by using a separate instrument, and some measuring methods are calculated by indirect measurement; and integrating the instantaneous quantity in a short time to calculate the accumulated quantity. Each type of measuring instrument has measurement errors, the deviation of the measured value is larger and larger along with the increase of time, and the final result obtained by using a multi-layer indirect calculation mode leads to the cumulative amplification of the errors and large final result errors.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for measuring the instantaneous powder feeding amount of a pulverized coal boiler, and solve the technical problem that the instantaneous powder feeding amount of the pulverized coal boiler cannot be accurately estimated in the prior art.
In order to solve the technical problem, the application adopts the following technical scheme:
a fitting sample selection method for the instantaneous powder feeding amount of a pulverized coal boiler comprises the following steps:
step 1, acquiring weight data of a pulverized coal bunker and powder feeding frequency data of m powder feeders in the operation process of a pulverized coal boiler, wherein m is a natural number;
step 2, intercepting a plurality of sample sequences from the weight data of the coal powder bin and the powder feeding frequency data of the powder feeder, wherein each sample sequence is V (H)1,...,Hmδ T), where H1,...,HmRespectively representing the frequencies of the m powder feeders, wherein delta T represents the weight difference value of the coal powder bin from the beginning to the end of each sample sequence;
each sample sequence intercepted meets the following conditions:
(1) the weight of the pulverized coal bin in each sample sequence is in a continuous descending stage;
(2) the powder feeding frequency of the m powder feeders in each sample sequence is kept constant;
(3) the sampling time of each sample sequence is greater than or equal to 30 minutes.
The invention also provides a method for metering the instantaneous powder feeding amount of the pulverized coal fired boiler, which comprises the following steps:
step 1, selecting a plurality of sample sequences according to the fitting sample selection method of claim 1, dividing the plurality of sample sequences into two completely disjoint sample sets, and obtaining a training sample set SL1And a test sample set SL2Where L1 is a training sample set SL1L2 is the test sample set SL2The number of sample sequences of (a);
step 2, respectively carrying out comparison on training sample sets S by utilizing N regression algorithmsL1Training to obtain N primary learners f1......fN；
Step 3, testing the sample set SL2Inputting N number of primary learners f1......fNIn (1), obtaining a test sample set SL2Predicted result V of1...Vx...VL2In which V isx＝(δTxf1...δTxfy...δTxfN)，δTxfyRepresents the primary learner fy versus the test sample set SL2The predicted result of the xth test sample, x 1.. L2, y 1.. N;
step 4, testing the sample set SL2Predicted result V of1...Vx...VL2As input quantities, a test sample set SL2Taking the observed value of the powder feeding amount of each sample sequence as an output amount, training a high-level learner F, wherein the learner F is the relation between the powder feeding frequency of the powder feeder and the powder discharging amount of the pulverized coal boiler;
and 6, integrating the instantaneous powder feeding amount of each powder feeder to obtain the powder feeding amount of any micro time period.
Compared with the prior art, the invention has the beneficial technical effects that:
(1) the invention does not need to be additionally provided with any new metering equipment, only depends on the powder bin weight metering equipment of the powder bin, and has no additional implementation and maintenance cost.
(2) The invention only depends on one metering device, and has low precision requirement on the metering device, so the measurement precision error of the metering device has little influence on the metering effect, and the higher accuracy can be achieved through a data test.
Drawings
FIG. 1 is a graph of the frequency of a powder feeder versus the weight of a powder bin as a function of time;
FIG. 2 is a sample sequence diagram taken in the plateau.
The details of the present invention are explained in further detail below with reference to the drawings and examples.
Detailed Description
The following embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention are within the protection scope of the present invention.
Example 1:
the embodiment provides a method for selecting a fitting sample of the instantaneous powder feeding amount of a pulverized coal boiler, which comprises the following steps:
step 1, acquiring weight data of a pulverized coal bunker and powder feeding frequency data of m powder feeders in the operation process of a pulverized coal boiler, wherein m is a natural number;
the acquired pulverized coal bunker weight data and the powder feeding frequency data of m powder feeders in the operation process of the pulverized coal fired boiler in this embodiment are historical data during stable operation of the boiler, and fig. 1 is a graph showing the variation of the weight of one pulverized coal bunker and the frequency of two powder feeders, wherein the increase of the weight of the pulverized coal bunker is the process of adding powder to the pulverized coal bunker;
step 2, intercepting a plurality of sample sequences from the weight data of the coal powder bin and the powder feeding frequency data of the powder feeder, wherein each sample sequence is V (H)1,...,Hmδ T), where H1,...,HmRespectively representing the frequencies of m powder feeders, and delta TThe weight difference of the coal powder bin from the beginning to the end of each sample sequence;
the specific operation of intercepting the plurality of sample sequences comprises:
(1) the weight of the pulverized coal bin in each sample sequence is in a continuous descending stage;
(2) the powder feeding frequency of the m powder feeders in each sample sequence is kept constant;
(3) the sampling time of each sample sequence is more than or equal to 30 minutes;
the sample sequence thus selected has the following advantages, as shown in fig. 2 for the sample sequence truncated according to the specific operation of truncating the sample sequence described above:
(1) the measurement accuracy of the pulverized coal bunker is usually 0.5 ton, and the bunker weight change value of the pulverized coal bunker is relatively large in a relatively long time, such as 100 ton, in this case, the measurement error of the pulverized coal bunker has little influence on the measurement of the bunker weight change value;
(2) all the powder feeder frequencies are in a constant state, namely the descending sections of the pulverized coal bins are a straight line, the descending rate of the pulverized coal bin weight can be obtained by dividing the bin weight change value by the time period, and the powder feeding rate under the current powder feeder frequency is expressed by using the descending rate;
(3) a large number of training samples are obtained from historical data to carry out model training, and statistical errors of single samples can be eliminated sufficiently.
Example 2:
the embodiment provides a method for measuring the instantaneous powder feeding amount of a pulverized coal boiler, which comprises the following steps:
step 1, selecting a plurality of sample sequences according to the fitting sample selection method provided by the invention, and dividing the plurality of sample sequences into two completely disjoint sample sets to obtain a training sample set SL1And a test sample set SL2Where L1 is a training sample set SL1L2 is the test sample set SL2The number of sample sequences of (a);
step 2, respectively carrying out comparison on training sample sets S by utilizing N regression algorithmsL1Training to obtain N primary learners f1......fN；
The regression algorithm in this embodiment may adopt any one of a linear regression algorithm, a Ridge regression algorithm, or a lasso regression algorithm, which is specifically referred to in the following references.
Step 3, testing the sample set SL2Inputting N number of primary learners f1......fNIn (1), obtaining a test sample set SL2Predicted result V of1...Vx...VL2In which V isx＝(δTxf1...δTxfy...δTxfN)，δTxfyRepresents the primary learner fy versus the test sample set SL2The predicted result of the xth test sample, x 1.. L2, y 1.. N;
step 4, testing the sample set SL2Predicted result V of1...Vx...VL2As input quantities, a test sample set SL2Taking the observed value of the powder feeding amount of each sample sequence as an output amount, training a high-level learner F, wherein the learner F is the relation between the powder feeding frequency of the powder feeder and the powder discharging amount of the pulverized coal boiler;
and 6, integrating the instantaneous powder feeding amount of each powder feeder to obtain the powder feeding amount of any micro time period.
As shown in table 1, the powder feeding data of a certain coal powder bin in a short period of time:
TABLE 1 powder feeding data of a certain coal powder bin
Wherein:
the powder feeder frequency is 1-4, and represents the frequency opening of all 4 powder feeders of the coal powder bin;
the bin weight is the metering data of the pulverized coal bin weighing at the current moment;
the bin weight change is a change value of the weight of the coal powder bin at the current time point, a positive number represents the reduction amount of the weight of the coal powder bin, and a negative number and zero are caused by a bin weight measurement error;
the model prediction is a metric value calculated by data simulation using the present invention.
The effectiveness of the method of the invention can be evaluated from this data in two ways:
1) comparing the variation of the bin weights at the starting time and the ending time with the accumulated value of the model predicted quantity at each time to obtain the accumulated error of the model in the time period;
total errorModel predictionBin weight loss-SUM (all model predicted values) | ÷ bin weight loss × 100%
＝|0.520152–0.516456|÷0.520152×100％
＝0.7％
Total errorVariation of bin weightBin weight loss-SUM (all bin weight change) | ÷ bin weight loss × 100%
＝|0.520152–0.446886|÷0.520152×100％
＝14.1％
The total error of the model prediction is only 5% of the total error of the bin weight variation.
2) Except the last row in table 1, the frequency of the powder feeder is kept constant in other time, so the change of the bin weight should be uniform, and the random error measured by the model method can be evaluated by calculating the standard deviation of each time period;
random errorModel predictionStandard deviation ÷ mean × 100%
＝0.00154÷0.032419×100％
＝4.8％
Random errorVariation of bin weightStandard deviation ÷ mean × 100%
＝0.02433÷0.030597×100％
＝79.5％
The random error of the model prediction is only 6% of the total error of the bin weight variation.
[ REFERENCE ] to
[1] The statistical learning method 1.10 regression problem, Lihangi, Qinghua university Press;
[2] chapter 3, machine learning, linear model p53, university press, zhou shihua, qing;
[3] foundation of linear regression analysis, william d. bei (usa), ge press;
[4] ridge regression analysis and its application, http:// www.doc88.com/p-9803114479516.html
[5] Norm regularized in machine learning, the norm of L0, L1, and L2, https:// blog, csdn. net/zoxy 09/article/details/24971995
[6] Ridge regression and lasso, http:// f.dataguru.cn/thread-598486-1. html
[7] Regularized linear regression-ridge regression and Lasso regression, https:// www.cnblogs.com/Belter/p/8536939.html
Claims (2)
1. A fitting sample selection method for the instantaneous powder feeding amount of a pulverized coal boiler is characterized by comprising the following steps:
step 1, acquiring weight data of a pulverized coal bunker and powder feeding frequency data of m powder feeders in the operation process of a pulverized coal boiler, wherein m is a natural number;
step 2, intercepting a plurality of sample sequences from the weight data of the coal powder bin and the powder feeding frequency data of the powder feeder, wherein each sample sequence is V (H)1,...,Hmδ T), where H1,...,HmRespectively representing the frequencies of the m powder feeders, wherein delta T represents the weight difference value of the coal powder bin from the beginning to the end of each sample sequence;
each sample sequence intercepted meets the following conditions:
(1) the weight of the pulverized coal bin in each sample sequence is in a continuous descending stage;
(2) the powder feeding frequency of the m powder feeders in each sample sequence is kept constant;
(3) the sampling time of each sample sequence is greater than or equal to 30 minutes.
2. A method for measuring the instant powder feeding amount of a pulverized coal fired boiler is characterized by comprising the following steps:
step 1, according to the claims1 the fitting sample selection method selects a plurality of sample sequences, divides the sample sequences into two completely disjoint sample sets, and obtains a training sample set SL1And a test sample set SL2Where L1 is a training sample set SL1L2 is the test sample set SL2The number of sample sequences of (a);
step 2, respectively carrying out comparison on training sample sets S by utilizing N regression algorithmsL1Training to obtain N primary learners f1......fN；
Step 3, testing the sample set SL2Inputting N number of primary learners f1......fNIn (1), obtaining a test sample set SL2Predicted result V of1...Vx...VL2In which V isx＝(δTxf1...δTxfy...δTxfN)，δTxfyRepresents the primary learner fy versus the test sample set SL2The predicted result of the xth test sample, x 1.. L2, y 1.. N;
step 4, testing the sample set SL2Predicted result V of1...Vx...VL2As input quantities, a test sample set SL2Taking the observed value of the powder feeding amount of each sample sequence as an output amount, training a high-level learner F, wherein the learner F is the relation between the powder feeding frequency of the powder feeder and the powder discharging amount of the pulverized coal boiler;
step 5, substituting the instantaneous frequency of each powder feeder in the pulverized coal boiler into the relationship between the powder feeding frequency of the powder feeder and the powder outlet quantity of the pulverized coal boiler to obtain the instantaneous powder feeding quantity of each powder feeder;
and 6, integrating the instantaneous powder feeding amount of each powder feeder to obtain the powder feeding amount of any micro time period.
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