CN112348920A - Data-driven dynamic characteristic thermodynamic diagram construction method for circulating fluidized bed boiler - Google Patents
Data-driven dynamic characteristic thermodynamic diagram construction method for circulating fluidized bed boiler Download PDFInfo
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- 239000011159 matrix material Substances 0.000 claims abstract description 10
- 238000001704 evaporation Methods 0.000 claims abstract description 5
- 230000008020 evaporation Effects 0.000 claims abstract description 5
- 230000004931 aggregating effect Effects 0.000 claims abstract description 4
- 238000009499 grossing Methods 0.000 claims abstract description 4
- 239000003245 coal Substances 0.000 claims description 20
- 239000002893 slag Substances 0.000 claims description 16
- 239000000779 smoke Substances 0.000 claims description 14
- 238000000034 method Methods 0.000 claims description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 12
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 9
- 229910052760 oxygen Inorganic materials 0.000 claims description 9
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- 238000004088 simulation Methods 0.000 description 1
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Abstract
The invention provides a dynamic characteristic thermodynamic diagram construction method of a circulating fluidized bed boiler based on data driving, which comprises the steps of selecting data corresponding to analysis factors as selected data; smoothing all the selected data; detecting and extracting a steady-state working condition section based on the trend quantitative change threshold limit to obtain a steady-state working condition section; aggregating the selected data corresponding to the analysis factors in the time interval of the steady-state working condition to obtain the aggregated analysis factor data: dividing a load interval by 5 to 10 percent of the maximum continuous evaporation capacity width of the boiler; clustering all steady-state working conditions of the boundary state marks in each load interval into a working condition cluster, and calculating a correlation coefficient among analysis factors in each working condition cluster; and constructing a correlation coefficient matrix, namely a thermodynamic diagram. The construction method can clearly and accurately display the correlation characteristics among the factors in the thermodynamic diagram, and can more accurately guide the selection and analysis of the control strategy and the related modeling work on the system input quantity.
Description
Technical Field
The invention belongs to the field of circulating fluidized bed boilers, relates to a dynamic characteristic thermodynamic diagram, and particularly relates to a data-driven method for constructing the dynamic characteristic thermodynamic diagram of a circulating fluidized bed boiler.
Background
In the actual operation process of the circulating fluidized bed boiler, a plurality of factors influencing the dynamic characteristics of the boiler are provided, wherein the factors comprise part of uncontrollable operation environmental factors such as environment temperature, load rate, coal quality and the like, and controllable operation parameters such as exhaust smoke oxygen content, air distribution mode and the like. A complex coupling relation exists among a plurality of factors, and certain research has been carried out on sensitive analysis of the energy efficiency of the coal-electric unit in some colleges and universities and electric power scientific research institutes. But most of the optimization methods are in a simulation stage and have certain application on actual optimization projects. If power supply coal consumption is selected as a measurement standard on a large coal-fired unit, an energy consumption analysis model based on a support vector machine is established, the sensitivity coefficient of each operation characteristic parameter of different load areas to the power supply coal consumption is calculated, different regulation measures are taken according to the difference of the sensitivity coefficients under different loads, but a single target such as boiler efficiency is often determined in the analysis process, then a certain amount of influence factors are selected, the correlation characteristics between each selected factor and the target are analyzed, in fact, complex coupling relations exist among the factors, when the sensitivity analysis determines the leading influence factor of a certain target, the independence of the factor to the target correlation coefficient needs to be determined, and the factor needs to be continuously used as the target to perform correlation analysis with other factors.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for constructing a dynamic characteristic thermodynamic diagram of a circulating fluidized bed boiler based on data driving so as to solve the technical problem of insufficient accuracy of the dynamic characteristic thermodynamic diagram in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a data-driven dynamic characteristic thermodynamic diagram construction method for a circulating fluidized bed boiler comprises the following steps:
step one, data selection:
selecting main steam pressure sp, main steam temperature st, main steam flow sf, hearth outlet negative pressure np, hearth upper differential pressure dp, air chamber pressure wpo, primary-secondary air ratio q12, smoke exhaust oxygen content o, smoke exhaust temperature smt, secondary fan outlet temperature w2t, slag cooler frequency lz, coal feeding amount c, slag discharge ratio hf, separator temperature difference tc and boiler relative efficiency eta as analysis factors;
selecting data corresponding to the analysis factors as selected data;
step two, clustering extraction of the circulating fluidized bed boiler steady-state working condition sections:
step 201, smoothing all selected data;
step 202, detecting and extracting a steady-state working condition section limited by a trend quantitative change threshold value to obtain a steady-state working condition section;
the limiting constraint condition of the trend quantitative change threshold is as follows:
the extreme difference of the main steam pressure is less than 2% of the mean value;
the main steam flow extreme difference is less than 3% of the mean value;
the extreme value difference of the coal feeding amount is less than 3 percent of the mean value;
the difference of extreme bed temperatures is less than 1% of the mean value;
the difference of the extreme values of the bed pressure is less than 2 percent of the mean value;
the difference of the extreme value of the oxygen content of the discharged smoke is less than 5 percent of the mean value;
the data continuous time length is more than 10min and less than 60 min;
step 203, aggregating the selected data corresponding to the analysis factors within the time interval of the steady-state working condition obtained in step 202, and repeatedly participating in the multi-stage aggregation by using single data to obtain aggregated analysis factor data:
step 204, dividing a load interval by 5-10% of the maximum continuous evaporation capacity width of the boiler according to the change of the boundary state mark parameter main steam flow; all the steady-state working conditions of the boundary state marks in each load interval are clustered into a working condition cluster, and all the aggregated analysis factor data corresponding to all the steady-state working conditions of the boundary state marks in each load interval are analysis factor data corresponding to the working condition cluster;
step three, constructing a thermodynamic diagram representing the dynamic characteristics of the circulating fluidized bed boiler:
step 301, calculating a correlation coefficient between analysis factors in each working condition cluster by using Pearson correlation based on analysis factor data corresponding to each working condition cluster;
step 302, constructing a correlation coefficient matrix;
constructing a correlation coefficient matrix corresponding to each working condition cluster according to the correlation coefficient between the analysis factors in each working condition cluster obtained in the step 301;
and 303, representing the correlation degree of each two of the analysis factors by using the data parameters and the color parameters of the correlation coefficient matrix, namely the dynamic characteristic thermodynamic diagram of the circulating fluidized bed boiler.
The invention also has the following technical characteristics:
in the first step, the specific process of data selection comprises the following steps:
step 101, selecting coal feeding quantity, water feeding flow, water feeding temperature, water feeding pressure, main steam flow, main steam temperature and main steam pressure to calculate operation parameters of relative efficiency of a boiler;
the relative efficiency calculation method of the boiler is a ratio eta of the difference of the enthalpy value of the total steam and the enthalpy value of the total feed water and the coal feed quantity;
step 102, selecting a feedback ratio of primary air volume to secondary air volume as a primary air volume to secondary air volume q 12;
103, selecting the ratio of the frequency of the slag cooler to the coal feeding quantity as a slag discharging ratio hf;
104, taking the difference value of the outlet temperature of the hearth and the return temperature as the temperature difference tc of the separator;
and 105, selecting main steam pressure sp, main steam temperature st, main steam flow sf, hearth outlet negative pressure np, hearth upper differential pressure dp, air chamber pressure wpo, primary and secondary air ratio q12, smoke exhaust oxygen content o, smoke exhaust temperature smt, secondary fan outlet temperature w2t, slag cooler frequency lz, coal feeding amount c, slag discharge ratio hf, separator temperature difference tc and boiler relative efficiency eta as analysis factors.
In the first step, the time period for selecting the data is 60-90 days.
Compared with the prior art, the invention has the following technical effects:
the construction method comprises the steps of (I) selecting operation parameters capable of representing the dynamic characteristics of the boiler, converting the operation parameters into influence factors, and eliminating the influence of boiler heat storage change and the like on the dynamic characteristics among the factors through characteristic working condition treatment. The construction method can clearly and accurately display the correlation characteristics among the factors in the thermodynamic diagram, and can more accurately guide the selection and analysis of the control strategy and the related modeling work on the system input quantity.
And (II) the method guides technicians to analyze the correlation among multiple factors more intuitively through a graphical visual display mode.
The invention (III) can analyze the actual operation all working conditions of the boiler, reduce the influence of the operation characteristics of the boiler along with the change of the working conditions, and can be applied to the optimization of the actual production process.
(IV) the invention is based on the boiler operation historical data to analyze, and avoids the extra huge workload brought by online performance test adjustment.
Drawings
FIG. 1 is a thermodynamic diagram of the dynamic characteristics of a circulating fluidized bed boiler corresponding to a 260 t/h-270 t/h working condition cluster.
FIG. 2 is a thermodynamic diagram of the dynamic characteristics of the circulating fluidized bed boiler corresponding to the working condition clusters of 270t/h to 280 t/h.
FIG. 3 is a thermodynamic diagram of dynamic characteristics of a circulating fluidized bed boiler corresponding to a 280 t/h-290 t/h working condition cluster.
FIG. 4 is a thermodynamic diagram of the dynamic characteristics of the circulating fluidized bed boiler corresponding to 290 t/h-300 t/h working condition clusters.
The present invention will be explained in further detail with reference to examples.
Detailed Description
The present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention fall within the protection scope of the present invention.
Example (b):
it should be noted that in this embodiment, a circulating fluidized bed boiler is configured in a self-contained thermal power plant of a certain chemical industry, the rated evaporation capacity is 300t/h, the rated steam pressure is 9.8MPa, the rated steam temperature is 540 ℃, and a single steam drum is horizontally arranged, a single hearth is arranged, natural circulation is performed, and all steel is erected.
The embodiment provides a method for constructing a dynamic characteristic thermodynamic diagram of a circulating fluidized bed boiler based on data driving, which is carried out according to the following steps:
step one, data selection:
the specific process of data selection comprises the following steps:
step 101, selecting coal feeding quantity, water feeding flow, water feeding temperature, water feeding pressure, main steam flow, main steam temperature and main steam pressure to calculate operation parameters of relative efficiency of a boiler;
the relative efficiency calculation method of the boiler is a ratio eta of the difference of the enthalpy value of the total steam and the enthalpy value of the total feed water and the coal feed quantity;
step 102, selecting a feedback ratio of primary air volume to secondary air volume as a primary air volume to secondary air volume q 12;
103, selecting the ratio of the frequency of the slag cooler to the coal feeding quantity as a slag discharging ratio hf;
104, taking the difference value of the outlet temperature of the hearth and the return temperature as the temperature difference tc of the separator;
and 105, selecting main steam pressure sp, main steam temperature st, main steam flow sf, hearth outlet negative pressure np, hearth upper differential pressure dp, air chamber pressure wpo, primary and secondary air ratio q12, smoke exhaust oxygen content o, smoke exhaust temperature smt, secondary fan outlet temperature w2t, slag cooler frequency lz, coal feeding amount c, slag discharge ratio hf, separator temperature difference tc and boiler relative efficiency eta as analysis factors.
Selecting data corresponding to the analysis factors as selected data;
in this embodiment, the continuous operation history data of the unit is selected, the data selection frequency interval is 5 seconds, the data is stored in a CSV file format, and the time period for selecting the data is 60 days.
Step two, clustering extraction of the circulating fluidized bed boiler steady-state working condition sections:
step 201, smoothing all selected data;
step 202, detecting and extracting a steady-state working condition section limited by a trend quantitative change threshold value to obtain a steady-state working condition section;
the limiting constraint condition of the trend quantitative change threshold is as follows:
the extreme difference of the main steam pressure is less than 2% of the mean value;
the main steam flow extreme difference is less than 3% of the mean value;
the extreme value difference of the coal feeding amount is less than 3 percent of the mean value;
the difference of extreme bed temperatures is less than 1% of the mean value;
the difference of the extreme values of the bed pressure is less than 2 percent of the mean value;
the difference of the extreme value of the oxygen content of the discharged smoke is less than 5 percent of the mean value;
the data continuous time length is more than 10min and less than 60 min;
in this embodiment, 1593 stable conditions are obtained in the selected time period.
Step 203, aggregating the selected data corresponding to the analysis factors within the time interval of the steady-state working condition obtained in step 202, and repeatedly participating in the multi-stage aggregation by using single data to obtain aggregated analysis factor data:
in this embodiment, the aggregated analysis factor data is stored in a CSV file format.
Step 204, dividing a load interval by the evaporation capacity width of 10t/h according to the change of the boundary state mark parameter main steam flow; all the steady-state working conditions of the boundary state marks in each load interval are clustered into a working condition cluster, and all the aggregated analysis factor data corresponding to all the steady-state working conditions of the boundary state marks in each load interval are analysis factor data corresponding to the working condition cluster;
in the embodiment, the load of the boiler operates at 250 t/h-300 t/h within a selected time period, wherein the stable working condition section in the 250 t/h-260 t/h working condition cluster is 39 sections, the stable working condition section in the 260 t/h-270 t/h working condition cluster is 189 sections, the stable working condition section in the 270 t/h-280 t/h working condition cluster is 461 sections, the stable working condition section in the 280 t/h-290 t/h working condition cluster is 538 sections, and the stable working condition section in the 290 t/h-300 t/h working condition cluster is 366 sections, wherein the data section in the 250 t/h-260 t/h working condition cluster is less, the working condition is an unusual working condition, the data volume is insufficient, and the working condition cluster is ignored.
Step three, constructing a thermodynamic diagram representing the dynamic characteristics of the circulating fluidized bed boiler:
step 301, calculating a correlation coefficient between analysis factors in each working condition cluster by using Pearson correlation based on analysis factor data corresponding to each working condition cluster;
it should be noted that, the calculation formula of the pearson correlation in the present invention is:
in the formula: e is the mathematical expectation and X and Y are the two factors to be analyzed, respectively.
The calculation results ranged from-1 to 1. -1 represents a negative correlation and a 1 ratio represents a positive correlation. The real measure of pearson correlation is whether two random variables are increasing or decreasing. If two random variables are sampled simultaneously, when one of the random variables obtains a larger value and the other random variable also obtains a larger value, and when one of the random variables is smaller, the other random variable also obtains a smaller value, the correlation is positive, the calculated correlation degree is close to 1, otherwise, the calculated correlation degree is close to-1.
Step 302, constructing a correlation coefficient matrix;
constructing a correlation coefficient matrix corresponding to each working condition cluster according to the correlation coefficient between the analysis factors in each working condition cluster obtained in the step 301;
and 303, representing the correlation degree of each two of the analysis factors by using the data parameters and the color parameters of the correlation coefficient matrix, namely the dynamic characteristic thermodynamic diagram of the circulating fluidized bed boiler. The dynamic thermodynamic diagram of the circulating fluidized bed boiler obtained in this example is specifically shown in fig. 1 to 4.
Claims (3)
1. A data-driven dynamic thermodynamic diagram construction method for a circulating fluidized bed boiler is characterized by comprising the following steps:
step one, data selection:
selecting main steam pressure sp, main steam temperature st, main steam flow sf, hearth outlet negative pressure np, hearth upper differential pressure dp, air chamber pressure wpo, primary-secondary air ratio q12, smoke exhaust oxygen content o, smoke exhaust temperature smt, secondary fan outlet temperature w2t, slag cooler frequency lz, coal feeding amount c, slag discharge ratio hf, separator temperature difference tc and boiler relative efficiency eta as analysis factors;
selecting data corresponding to the analysis factors as selected data;
step two, clustering extraction of the circulating fluidized bed boiler steady-state working condition sections:
step 201, smoothing all selected data;
step 202, detecting and extracting a steady-state working condition section limited by a trend quantitative change threshold value to obtain a steady-state working condition section;
the limiting constraint condition of the trend quantitative change threshold is as follows:
the extreme difference of the main steam pressure is less than 2% of the mean value;
the main steam flow extreme difference is less than 3% of the mean value;
the extreme value difference of the coal feeding amount is less than 3 percent of the mean value;
the difference of extreme bed temperatures is less than 1% of the mean value;
the difference of the extreme values of the bed pressure is less than 2 percent of the mean value;
the difference of the extreme value of the oxygen content of the discharged smoke is less than 5 percent of the mean value;
the data continuous time length is more than 10min and less than 60 min;
step 203, aggregating the selected data corresponding to the analysis factors within the time interval of the steady-state working condition obtained in step 202, and repeatedly participating in the multi-stage aggregation by using single data to obtain aggregated analysis factor data:
step 204, dividing a load interval by 5-10% of the maximum continuous evaporation capacity width of the boiler according to the change of the boundary state mark parameter main steam flow; all the steady-state working conditions of the boundary state marks in each load interval are clustered into a working condition cluster, and all the aggregated analysis factor data corresponding to all the steady-state working conditions of the boundary state marks in each load interval are analysis factor data corresponding to the working condition cluster;
step three, constructing a thermodynamic diagram representing the dynamic characteristics of the circulating fluidized bed boiler:
step 301, calculating a correlation coefficient between analysis factors in each working condition cluster by using Pearson correlation based on analysis factor data corresponding to each working condition cluster;
step 302, constructing a correlation coefficient matrix;
constructing a correlation coefficient matrix corresponding to each working condition cluster according to the correlation coefficient between the analysis factors in each working condition cluster obtained in the step 301;
and 303, representing the correlation degree of each two of the analysis factors by using the data parameters and the color parameters of the correlation coefficient matrix, namely the dynamic characteristic thermodynamic diagram of the circulating fluidized bed boiler.
2. The method for constructing the thermodynamic diagram of the circulating fluidized bed boiler based on the data driving as claimed in claim 1, wherein in the first step, the specific process of data selection comprises the following steps:
step 101, selecting coal feeding quantity, water feeding flow, water feeding temperature, water feeding pressure, main steam flow, main steam temperature and main steam pressure to calculate operation parameters of relative efficiency of a boiler;
the relative efficiency calculation method of the boiler is a ratio eta of the difference of the enthalpy value of the total steam and the enthalpy value of the total feed water and the coal feed quantity;
step 102, selecting a feedback ratio of primary air volume to secondary air volume as a primary air volume to secondary air volume q 12;
103, selecting the ratio of the frequency of the slag cooler to the coal feeding quantity as a slag discharging ratio hf;
104, taking the difference value of the outlet temperature of the hearth and the return temperature as the temperature difference tc of the separator;
and 105, selecting main steam pressure sp, main steam temperature st, main steam flow sf, hearth outlet negative pressure np, hearth upper differential pressure dp, air chamber pressure wpo, primary and secondary air ratio q12, smoke exhaust oxygen content o, smoke exhaust temperature smt, secondary fan outlet temperature w2t, slag cooler frequency lz, coal feeding amount c, slag discharge ratio hf, separator temperature difference tc and boiler relative efficiency eta as analysis factors.
3. The method for constructing the thermodynamic diagram based on the dynamic characteristics of the data-driven circulating fluidized bed boiler according to claim 1, wherein in the first step, the time period of the selected data is 60-90 days.
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