CN113467392B - Open-loop combustion control optimization method for coal-fired boiler - Google Patents
Open-loop combustion control optimization method for coal-fired boiler Download PDFInfo
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- 238000002485 combustion reaction Methods 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005457 optimization Methods 0.000 title claims abstract description 33
- 238000004140 cleaning Methods 0.000 claims abstract description 21
- 239000003550 marker Substances 0.000 claims abstract description 10
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 8
- 239000001301 oxygen Substances 0.000 claims abstract description 8
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 8
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 7
- 239000003546 flue gas Substances 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 60
- 230000002159 abnormal effect Effects 0.000 claims description 20
- 239000003245 coal Substances 0.000 claims description 19
- 238000010977 unit operation Methods 0.000 claims description 13
- 238000013461 design Methods 0.000 claims description 10
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 238000012935 Averaging Methods 0.000 claims description 9
- 238000010438 heat treatment Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 8
- 238000010606 normalization Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 description 5
- 238000007418 data mining Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000003344 environmental pollutant Substances 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
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- 230000008094 contradictory effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
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- 238000013500 data storage Methods 0.000 description 1
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- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23N—REGULATING OR CONTROLLING COMBUSTION
- F23N5/00—Systems for controlling combustion
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention relates to a coal-fired boiler open-loop combustion control optimization method, which divides different load sections according to ultralow load, low load, medium load and high load working conditions, establishes expert libraries in a grading manner, and adopts unit history data to initialize the expert libraries; reading the running parameters and the mode states of a unit of boiler combustion in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring a corresponding grid of an expert database according to a clustering factor, calculating an optimizing target according to the real-time running parameters of the unit, comparing the parameters of the corresponding grid, and determining whether unsteady state experience pushing, expert database grid updating and marker post value optimizing pushing are carried out according to different mode state judgment results and calculation results; the delay strategy is designed to solve the disturbance of the switching of the unsteady state/steady state pushing scheme, the operation adjustment mode comprising the oxygen content of the flue gas and the opening degree of the secondary air valve of each layer is pushed to the distributed control system after the real-time operation parameters of the unit are analyzed, and the open loop guidance is used for combustion control adjustment, so that the stability of the optimized control system is ensured.
Description
Technical Field
The invention relates to a control technology of a coal-fired boiler of a thermal power plant, in particular to an open-loop combustion optimization control method of the coal-fired boiler.
Background
Along with the development of the requirements of coal blending and combustion of power generation enterprises and the situation of deep peak regulation, the combustion safety and economic operation of the boiler face serious tests, the operation adjustment of the boiler is mainly carried out through the operation personnel according to experience, and the boiler can not be ensured to have higher economical efficiency, environmental protection and safety all the time due to the reasons of insufficient monitoring equipment, uneven level of the operation personnel, untimely adjustment and the like.
The improvement of the combustion efficiency of the coal-fired boiler, the reduction of pollutant emission and the reduction of the power generation cost are significant for saving energy and protecting the environment. The improvement of the combustion heat efficiency of the boiler and the reduction of the emission of pollutants such as nitrogen oxides are contradictory, and the increase of the emission of the nitrogen oxides is often accompanied with the improvement of the combustion heat efficiency, so that the mutual coordination and the optimal optimization of the heat efficiency of the boiler and the emission of the nitrogen oxides are required to be realized, and the economic benefit and the social benefit of a power plant are met.
The utility boiler combustion process control system is a complex multivariable and strong coupling system, and a corresponding control means is needed to be applied according to the measurement results of a plurality of physical quantities such as pressure, flow, temperature and the like, and the quality of the control effect depends on the accuracy of the measurement results and the design rationality of a control optimization algorithm to a great extent.
In recent years, the application research of intelligent algorithms such as neural networks, genetic algorithms and the like in thermal process control optimization of thermal power plants is continuously increased, massive operation data of units are used as samples for learning and training so as to predict combustion heat efficiency and pollutant emission, the results generated by different models often have larger differences, and unknown safety risks can be brought due to the predictability of recommended adjustment schemes.
The combustion optimizing method based on the historical scheme for data mining can organically integrate combustion theory, self-learning technology and global optimizing algorithm, and the recommended operation adjusting mode after optimizing is also the historical scheme, so that combustion optimizing and unit operation safety are considered.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an open-loop combustion control optimization method for a coal-fired boiler, which is used for obtaining the historical optimal combustion working condition of the boiler and the corresponding optimal setting values of all combustion parameters, and pushing the optimal setting values to a DCS to open-loop guide the combustion adjustment of operators. The purpose is to realize autonomous optimizing updating, strengthen and consolidate the existing achievements along with continuous running adjustment of the unit, finally realize mutual coordination of boiler combustion and nitrogen oxide emission, and is more excellent.
The invention adopts the technical scheme that:
in order to achieve the above object, the present invention provides a method for optimizing open-loop combustion control of a coal-fired boiler, comprising the steps of:
step 1: dividing different load sections according to ultralow load, low load, medium load and high load working conditions, establishing an expert database in a grading manner, characterizing the combustion basic state of a boiler by adopting a combination mode of unit load, low-level heating value of coal, environmental temperature and different coal mills, establishing an expert database grid by taking the basic state as a clustering factor, and initializing the expert database by adopting unit historical data; and setting step sizes of clustering factors representing the combustion basic state of the boiler respectively, and dividing the step sizes into a plurality of sections to form a multi-dimensional grid.
Step 2: reading real-time operation data of boiler combustion and a manual intervention/automatic mode state, and turning to step 3;
step 3: carrying out data cleaning on the collected real-time data, if not, turning to the step 2, and if not, turning to the step 4; data cleaning refers to: judging whether the data has a shortage value, a sudden increase and a sudden decrease and exceeds an upper limit value and a lower limit value;
step 4: analyzing and processing boiler operation real-time data, judging whether the current boiler combustion accords with a steady-state working condition, if not, executing the non-steady-state experience pushing of the step 4.1, and if so, turning to the step 5;
step 4.1: when the unsteady state working condition is represented by adopting the unit load and 2 cluster factors of different coal mill combination modes, a plurality of grids of the expert database are corresponding. Averaging the value parameters of each marker post in the grids to obtain experience values, pushing the experience values to the DCS, and then turning to the step 2;
step 5: starting to count the steady state accumulated time after judging the 1 st steady state working condition, and entering the step 6 after reaching the preset time, otherwise, turning to the step 4.1, and resetting the count when the data is abnormal or judging the unsteady state working condition;
step 6: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, turning to step 2, and if so, turning to step 7;
step 7: calculating an optimizing target by adopting real-time parameters, comparing the optimizing target with an optimizing target value in an expert database grid, and if the optimizing target value is better, turning to the step 7.1, otherwise turning to the step 8; the optimizing target adopts a normalization method, and the concentration of nitrogen oxides at a denitration inlet and the thermal efficiency of a boiler are comprehensively considered;
step 7.1: performing expert database grid updating, namely updating the marker post values in the original expert database grids into current real-time parameters, and turning to the step 2;
step 8: judging the current manual intervention/automatic mode state, if the manual intervention mode is the manual intervention mode, turning to the step 2, otherwise turning to the step 9.
Step 9: and (3) performing target value optimizing pushing, pushing the target values in the expert database grids to the DCS, and turning to the step (2).
According to the open-loop combustion control optimization method for the coal-fired boiler, in the step 2, the manual intervention/automatic mode can be manually selected and switched, and the difference is that in the manual intervention mode, the operation adjustment mode is not pushed to give operation guidance, but the operation is automatically adjusted by the crew member, and the expert library grid updating is still performed after the condition is met.
According to the open-loop combustion control optimization method for the coal-fired boiler, the optimization target obtained by calculation of real-time data is continuously superior to the grid value in the set time range and can be executed only under the condition that the same grid is ensured by grid updating of the expert database.
According to the open-loop combustion control optimization method for the coal-fired boiler, in the step 4, the judging method of the steady-state working condition is that after a series of key parameters representing the operation of a unit including low-level heating value of coal, unit load, main steam pressure, main steam temperature, reheat steam temperature, oxygen content of flue gas, opening degree of secondary air gates of each layer and negative pressure of a hearth are selected, average values of all parameters in a set time range are obtained, and if the real-time parameters are compared with all average values and are in a fluctuation error range, the steady-state can be considered.
According to the open-loop combustion control optimization method for the coal-fired boiler, in the step 6, constraint conditions are that the wall temperature of the heat exchanger is not alarmed, the difference value of the main steam pressure and the sliding pressure curve is in a set range, the deviation of the main/reheat steam temperature and a design value is in the set range, the concentration of nitrogen oxides at a denitration inlet is not too high, the flow of the reheater desuperheating water is in the set range, and the deviation of the feedwater temperature and the design value is in the set range.
The invention has the beneficial effects that:
1. according to the open-loop combustion control optimization method for the boiler, the expert library grids are established based on the basic state of boiler combustion as the clustering factor and combined with the hierarchical load section, the number of grids is obviously reduced compared with that of the full load section, the excellent scheme of the historical working condition can be absorbed, the possible optimal scheme in future operation can be adopted in an updating mode, the autonomous optimizing updating is realized, and the existing achievements are strengthened and consolidated. After the unit operates, an optimized adjustment scheme can be pushed in a full period (including steady state/unsteady state working conditions), the change stage of the working conditions is smoothly transited, and the mutual coordination of the combustion of the boiler and the emission of nitrogen oxides is realized on the premise of safety, so that the method is more excellent.
2. The open-loop combustion control optimization method of the boiler carries out primary classification based on different loads, and has higher-dimension working condition classification basis (unit load, low-level heating value of coal, environment temperature and different coal mill combination modes) as secondary classification. According to the measurement results of various physical quantities such as pressure, flow, temperature and the like, corresponding control means are applied, so that the mutual coordination and optimization of the thermal efficiency of the boiler and the emission of nitrogen oxides are realized, and the economic benefit and the social benefit of the power plant are met.
3. The open-loop combustion control optimization method of the boiler adopts a big data mining algorithm based on an expert database system, has the function of cleaning data, judging steady-state working conditions and judging constraint conditions, and provides a set of guiding strategies for each steady-state and unsteady-state. The invention performs combustion optimization of data mining based on a historical scheme, organically integrates combustion theory, self-learning technology and global optimization algorithm, and the recommended operation adjustment mode after optimization is also the historical scheme, thereby considering combustion optimization and unit operation safety.
Drawings
FIG. 1 is a flow chart of an optimization method of open-loop combustion control of a boiler according to the present invention;
FIG. 2 shows an example of an expert database grid framework for the open-loop combustion control optimization method of the boiler of the present invention.
Detailed Description
In order to make the technical conception and advantages of the invention to achieve the object of the invention more clear, the technical scheme of the invention is further described in detail below with reference to the accompanying drawings. It is to be understood that the following examples are provided only for the purpose of illustrating and describing the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention.
Example 1
Referring to fig. 1, the open-loop combustion control optimization method of the coal-fired boiler comprises the following steps:
s1, dividing different load sections according to ultralow load, low load, medium load and high load working conditions, establishing an expert database in a grading manner, adopting a combination mode of unit load, low-level heating value of coal, environment temperature and different coal mills to represent the combustion basic state of a boiler, establishing an expert database grid as a clustering factor, and adopting unit historical data to initialize the expert database; step sizes are respectively set for clustering factors representing the combustion basic state of the boiler and divided into a plurality of intervals to form a multi-dimensional grid;
s2, designing a manual intervention/automatic mode, reading unit operation parameters and mode states of boiler combustion in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring corresponding grids of an expert database according to clustering factors, calculating an optimizing target by using real-time parameters, comparing the optimizing target with the corresponding grid parameters, and determining whether unsteady state experience pushing, expert database grid updating and marker post value optimizing pushing are carried out according to different mode states, judging results and calculating results; the marker post value refers to more excellent unit operation parameters stored in the expert database grid; the optimizing target adopts a normalization method, and the concentration of nitrogen oxides at a denitration inlet and the thermal efficiency of a boiler are comprehensively considered;
s3, designing a delay strategy to solve the disturbance of the switching of an unsteady state/steady state pushing scheme, pushing a group of operation adjustment modes comprising the oxygen content of the flue gas and the opening degree of the secondary air valve of each layer to a Distributed Control System (DCS) after analyzing the operation data of the real-time unit, and guiding an operator to burn and adjust in an open loop manner to ensure the stability of an optimized control system.
The invention relates to an open-loop combustion control optimization method for a coal-fired boiler, wherein a manual intervention/automatic mode can be manually selected and switched, and the two modes are different in that in the manual intervention mode, an operation adjustment mode is not pushed to give operation guidance, but a crew member can automatically burn and adjust the operation mode, and then the expert database grid is still updated after the conditions are met; the updating of the expert database grid is only executable under the condition of ensuring the same grid, and the optimizing target obtained by calculation of real-time data is continuously superior to the grid value in the set time range.
Example 2
The method for optimizing open-loop combustion control of a coal-fired boiler of this embodiment is different from that of embodiment 1 in that: in the step S2, the operation data of boiler combustion and the manual intervention/automatic mode state are read in real time, the collected real-time data are subjected to data cleaning, namely whether the data have a shortage value, a sudden increase and a sudden decrease and exceed an upper limit value and a lower limit value or not is judged, and if the data are normal, the step S2-1 is carried out; if not, returning to read the operation parameters and the mode state of the unit burnt by the boiler, and executing data cleaning;
step S2-1: analyzing and processing boiler operation real-time data, judging whether the current boiler combustion accords with a steady-state working condition, if not, executing a step S2-2, performing unsteady-state experience pushing, and if so, turning to the step S2-3;
step S2-2: when the unsteady state working condition is represented by adopting the unit load and 2 cluster factors of different coal mill combination modes, a plurality of grids of the expert database are corresponding. Taking average values of all the marker post values in the grids as experience values respectively, pushing the experience values to the DCS, returning to reading the running parameters and the mode state of the boiler combustion unit, and executing data cleaning;
step S2-3: starting to count the steady state accumulated time after judging the 1 st steady state working condition, entering a step S2-4 after reaching the preset time, otherwise, turning to the step S2-2, and resetting the count according to abnormal data or judging the unsteady state working condition;
step S2-4: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, returning to reading unit operation parameters and mode states of the boiler combustion, and executing data cleaning; if yes, turning to step S2-5;
step S2-5: calculating an optimizing target by adopting real-time parameters, comparing the optimizing target with an optimizing target value in an expert database grid, and if the optimizing target value is better, turning to the step S2-6, otherwise turning to the step S2-7;
step S2-6: updating the expert database grid, updating the standard pole value in the original expert database grid into the current real-time parameter, returning to reading the unit operation parameter and the mode state of boiler combustion, and executing data cleaning;
step S2-7: judging the current manual intervention/automatic mode state, if the manual intervention mode is adopted, returning to reading the unit operation parameters and the mode state of boiler combustion, and executing data cleaning; otherwise, turning to the step S2-8;
step S2-8: and performing target value optimizing pushing, pushing the target values in the expert database grids to the DCS, returning to reading the unit operation parameters and the mode state of boiler combustion, and performing data cleaning.
In the step S2-1, the judging method of the steady-state working condition is that after a series of key parameters representing the operation of a unit, including low-level heating value of coal, unit load, main steam pressure, main steam temperature, reheat steam temperature, oxygen content of flue gas, opening degree of secondary air gates of each layer and negative pressure of a hearth are selected, average values of all parameters in a set time range are calculated, and if the real-time parameters are compared with all average values and are in a fluctuation error range, the steady-state can be considered.
In the step S2-4, the constraint conditions are that the wall temperature of the heat exchanger is not alarmed, the difference value of the main steam pressure and the sliding pressure curve is in a set range, the deviation of the main/reheat steam temperature and a design value is in the set range, the concentration of nitrogen oxides at a denitration inlet is not too high, the flow of the desuperheater water is in the set range, and the deviation of the feed water temperature and the design value is in the set range.
Example 3
In this embodiment, taking a boiler combustion system of a certain 1000MW coal-fired unit as an example, a specific implementation process of the open-loop combustion control optimization method of the coal-fired boiler of the present invention is specifically described, as shown in fig. 1, and the flow includes:
step 1: different load sections are divided according to ultralow load, low load, medium load and high load working conditions, an expert database is built in a grading mode, the combustion basic state of a boiler is represented by adopting a unit load, low-level heating value of coal, environment temperature and different coal mill combination modes, grids of the expert database are built as cluster factors, the total number of the grids is 67815, and relevant unit historical data are collected for expert database initialization, as shown in fig. 2. According to the expert database grid framework, a database management system, such as MySQL, oracle, sybase and the like, can be selected for construction, and data storage and calling can be performed.
Step 2: reading real-time operation data of boiler combustion and a manual intervention/automatic mode state, and turning to step 3;
the DCS configuration is written with a manual intervention/automatic mode for the selection and switching of a unit operator, wherein the manual intervention/automatic mode is different in that in the manual intervention mode, an operation adjustment mode is not pushed to give operation guidance, the unit operator can automatically burn and adjust the operation mode, and in the automatic mode, unsteady experience pushing and marker post value optimizing pushing are executed.
Step 3: and (3) carrying out data cleaning on the acquired real-time data, and when the data has the conditions of deficiency, sudden increase and decrease and exceeds the upper limit value and the lower limit value, turning to the step (2), otherwise turning to the step (4).
Step 4: analyzing and processing boiler operation real-time data, and judging whether the current boiler combustion accords with steady-state working conditions or not, wherein the conditions are as follows:
a. reading data of the coal burning low-level heating value for about 10 minutes, and averaging, wherein the difference between the current value and the average value is within the range of +/-1000 kJ/kg and is normal; otherwise, the result is abnormal;
b. reading data of the unit load of nearly 10 minutes, and averaging, wherein the difference between the current value and the average value is within a range of +/-1% of a rated load value, and is normal; otherwise, the result is abnormal;
c. reading data of the main steam pressure for about 10 minutes, and averaging, wherein the difference between the current value and the average value is within the range of +/-2% of the rated main steam pressure value, and is normal; otherwise, the result is abnormal;
d. reading data of the main steam temperature of approximately 10 minutes, and averaging, wherein the difference between the current value and the average value is normal within the range of +/-3 ℃; otherwise, the result is abnormal;
e. reading data of reheat steam temperature of approximately 10 minutes, averaging, and determining that the difference between the current value and the average value is normal within the range of +/-4 ℃; otherwise, the result is abnormal;
f. reading data of oxygen content of about 10 minutes, averaging, wherein the difference between the current value and the average value is within a range of +/-0.5 percent, and is normal; otherwise, the result is abnormal;
g. reading data of each layer operation of the secondary air door for approximately 10 minutes, and calculating an average value of each layer operation, wherein the difference value between the current value and the average value of each layer operation is within a range of +/-2% and is normal; otherwise, the result is abnormal;
h. the data of the current hearth negative pressure of approximately 3 seconds are averaged, and the difference value between the current value and the average value is within the range of +/-100 Pa and is normal; otherwise, the result is abnormal.
If the running state is not consistent with the steady-state working condition, executing the unsteady-state experience pushing in the step 4.1, and if the running state is consistent with the steady-state working condition, turning to the step 5;
step 4.1: when the unsteady state working condition is represented by adopting the unit load and 2 cluster factors of different coal mill combination modes, a plurality of grids of the expert database are corresponding;
and (2) taking the marker post value data stored in the database grid by adopting a global search algorithm, respectively averaging all parameters to obtain experience values, pushing the experience values to the DCS, and then transferring to the step (2).
Step 5: and (4) starting to count the steady state accumulated time after judging the 1 st steady state working condition, and entering a step (6) after reaching the preset time, otherwise, turning to a step (4.1), and resetting the count due to abnormal data or judging the unsteady state working condition.
Step 6: analyzing and processing boiler operation data, and judging whether the current boiler combustion meets constraint conditions or not, wherein the conditions are as follows:
a. the wall temperatures of the water cooling walls, the superheaters and the reheaters of all stages do not exceed alarm values, and the water cooling walls, the superheaters and the reheaters of all stages are normal; otherwise, the result is abnormal;
b. the difference value between the main steam pressure and the sliding pressure curve is within the range of +/-1 MPa, and is normal; otherwise, the result is abnormal;
c. the difference between the temperature of the main steam and the design value is within the range of + -10 ℃, which is normal; otherwise, the result is abnormal;
d. the difference between the reheat steam temperature and the design value is within the range of + -10 ℃, which is normal; otherwise, the result is abnormal;
e. the concentration of nitrogen oxides at the inlet of the denitration device is not higher than the maximum value under the historical steady-state working condition, and the denitration device is normal; otherwise, the result is abnormal;
f. the flow rate of the reheater desuperheating water is not higher than the allowable value under the historical steady-state working condition, and is normal; otherwise, the result is abnormal;
g. reading real-time data of the water supply temperature, wherein the difference value between the real-time data and the design value is within +/-10 ℃, and the water supply temperature is normal; otherwise, the system is abnormal.
If the constraint condition is not satisfied, the step 2 is shifted to, and if the constraint condition is satisfied, the step 7 is shifted to.
Step 7: calculating an optimizing target value by adopting real-time parameters, and comparing the optimizing target value with an optimizing target value in an expert database grid, wherein the optimizing target formula is as follows:
phi: optimizing a target value;
f NOx (x) The method comprises the following steps Real-time denitration inlet nitrogen oxide concentration;
f η (x) The method comprises the following steps Real-time boiler thermal efficiency;
f NOx (x max ): denitration inlet nitrogen oxidation under historical steady-state working conditionMaximum value of the concentration of the substance;
f NOx (x min ): the minimum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
alpha: a denitration inlet nitrogen oxide concentration weight;
beta: boiler thermal efficiency weight, α+β=1;
f η (x max ): maximum value of boiler thermal efficiency under the history steady-state working condition;
f η (x min ): minimum value of boiler thermal efficiency under the history steady-state working condition;
if the former is better, the step is transferred to the step 7.1, otherwise, the step 8 is transferred;
step 7.1: and (3) updating the expert database grids, updating the marker post values in the original expert database grids into current real-time parameters, and turning to the step (2).
Step 8: judging the current manual intervention/automatic mode state, if the manual intervention mode is the manual intervention mode, turning to the step 2, otherwise turning to the step 9;
in the manual intervention mode, operators are allowed to automatically burn and adjust without depending on an operation adjustment mode pushed by the method, a combustion optimization scheme is actively explored, and a corresponding expert library grid is updated when a set of better scheme is found.
Step 9: and (3) performing optimizing pushing of the target rod values, reading the target rod values in the expert database grids, including the oxygen content of the flue gas and the opening degree of secondary air gates of each layer, pushing the target rod values to a DCS, guiding an operator to burn and adjust the target rod values through an open loop, and turning to the step (2).
The above description is only a preferred embodiment of the present invention, and does not limit the present invention. Other modifications of the practice of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention without the need for inventive faculty, and any modification or substitution of equivalents which fall within the spirit and principles of the invention, or which are obvious to those skilled in the art, are intended to be encompassed within the scope of the invention.
Claims (7)
1. A method for optimizing open-loop combustion control of a coal-fired boiler is characterized by comprising the following steps of: the method comprises the following steps:
s1, dividing different load sections according to ultralow load, low load, medium load and high load working conditions, establishing an expert database in a grading manner, adopting a combination mode of unit load, low-level heating value of coal, environment temperature and different coal mills to represent the combustion basic state of a boiler, establishing an expert database grid as a clustering factor, and adopting unit historical data to initialize the expert database;
s2, designing a manual intervention/automatic mode, reading unit operation parameters and mode states of boiler combustion in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring corresponding grids of an expert database according to clustering factors, calculating an optimizing target according to the unit real-time operation parameters, comparing the optimizing target with the corresponding grid parameters, and determining whether unsteady state experience pushing, expert database grid updating and marker post value optimizing pushing are carried out according to different mode states, judging results and calculating results;
s3, designing a delay strategy to solve the disturbance of the switching of an unsteady state/steady state pushing scheme, pushing an operation adjustment mode comprising the oxygen content of the flue gas and the opening degree of the secondary air valve of each layer to a distributed control system after analyzing real-time operation parameters of a unit, and guiding an operator to perform combustion control adjustment in an open loop manner to ensure the stability of an optimized control system;
in the step S2, the operation data of the boiler combustion and the manual intervention/automatic mode state are read in real time, the collected real-time data are subjected to data cleaning, namely whether the data have a shortage value, a sudden increase and a sudden decrease and exceed an upper limit value and a lower limit value is judged, and if the data are normal, the step S2-1 is carried out; if the data are abnormal, returning to an initial state, reading the running parameters and the mode state of the unit burnt by the boiler, and executing data cleaning;
step S2-1: analyzing and processing boiler operation real-time data, judging whether the current boiler combustion accords with a steady-state working condition, if not, executing a step S2-2, performing unsteady-state experience pushing, and if so, turning to the step S2-3;
step S2-2: when the unsteady state working condition is represented by adopting the unit load and 2 cluster factors of different coal mill combination modes, corresponding to a plurality of grids of an expert database, respectively averaging the values of the benchmarks in the grids to be used as experience values, pushing the experience values to a DCS, returning to an initial state, reading the unit operation parameters and the mode state of boiler combustion, and executing data cleaning;
step S2-3: starting to count the steady state accumulated time after judging the 1 st steady state working condition, entering a step S2-4 after reaching a preset time, otherwise, turning to the step S2-2, and resetting the count according to abnormal data or judging the unsteady state working condition;
step S2-4: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, returning to reading unit operation parameters and mode states of the boiler combustion, and executing data cleaning; if yes, turning to step S2-5;
step S2-5: calculating an optimizing target by adopting real-time parameters, comparing the optimizing target with an optimizing target value in an expert database grid, and if the optimizing target value is better, turning to the step S2-6, otherwise turning to the step S2-7;
step S2-6: updating the expert database grid, updating the standard pole value in the original expert database grid into the current real-time parameter, returning to the initial state, reading the running parameter and the mode state of the unit of boiler combustion, and executing data cleaning;
step S2-7: judging the current manual intervention/automatic mode state, if the manual intervention mode is the manual intervention mode, returning to the initial state, reading the unit operation parameters and the mode state of boiler combustion, and executing data cleaning; otherwise, turning to the step S2-8;
step S2-8: and performing target value optimizing pushing, pushing the target values in the expert database grids to the DCS, returning to an initial state, reading the unit operation parameters and the mode state of boiler combustion, and performing data cleaning.
2. The optimization method for open-loop combustion control of a coal-fired boiler according to claim 1, characterized by: in the step S2-1, the judging method of the steady-state working condition is as follows: after a series of key parameters representing the operation of the unit, including low-level heating value of coal, unit load, main steam pressure, main steam temperature, reheat steam temperature, flue gas oxygen content, opening of secondary air gates of each layer and hearth negative pressure are selected, the average value of each parameter in a set time range is obtained, and if the real-time parameter is compared with each average value and is in a fluctuation error range, the steady state can be considered.
3. The optimization method for open-loop combustion control of a coal-fired boiler according to claim 1, characterized by: in the step S2-4, constraint conditions are that the wall temperature of the heat exchanger is not alarmed, the difference value of the main steam pressure and the sliding pressure curve is in a set range, the deviation of the main/reheat steam temperature and a design value is in a set range, the concentration of nitrogen oxides at a denitration inlet is not too high, the flow of the desuperheating water of the reheater is in the set range, and the deviation of the feed water temperature and the design value is in the set range.
4. The coal-fired boiler open-loop combustion control optimization method according to any one of claims 1 to 3, characterized in that: in step S1, step sizes are respectively set for each clustering factor representing the basic state of boiler combustion and divided into a plurality of sections to form a multi-dimensional grid.
5. The optimization method for open-loop combustion control of a coal-fired boiler according to claim 4, wherein: in step S2, the manual intervention/automatic mode can be manually selected and switched, and in the manual intervention mode, the air crew operators burn and adjust the air crew automatically, and the expert database grid updating is still executed after the conditions are met; the updating of the expert database grid is only executable under the condition of ensuring the same grid, and the optimizing target obtained by calculation of real-time data is continuously superior to the grid value in the set time range.
6. The optimization method for open-loop combustion control of a coal-fired boiler according to claim 1, 2, 3 or 5, wherein the optimization target adopts a normalization method, and the concentration of nitrogen oxides at the denitration inlet and the thermal efficiency of the boiler are comprehensively considered.
7. The method for optimizing open-loop combustion control of a coal-fired boiler according to claim 6, wherein the optimizing target value is calculated by using real-time parameters and compared with the optimizing target value in an expert database grid, and the optimizing target formula is as follows:
optimizing a target value;
f NOx (x) The method comprises the following steps Real-time denitration inlet nitrogen oxide concentration;
f η (x) The method comprises the following steps Real-time boiler thermal efficiency;
f NOx (x max ): maximum value of nitrogen oxide concentration of a denitration inlet under a historical steady-state working condition;
f NOx (x min ): the minimum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
alpha: a denitration inlet nitrogen oxide concentration weight;
beta: boiler thermal efficiency weight, α+β=1;
f η (x max ): maximum value of boiler thermal efficiency under the history steady-state working condition;
f η (x min ): minimum value of boiler thermal efficiency under historical steady state conditions.
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| CN113467392B (en) * | 2021-06-18 | 2024-03-26 | 中国大唐集团科学技术研究院有限公司中南电力试验研究院 | Open-loop combustion control optimization method for coal-fired boiler |
| CN113834091B (en) * | 2021-10-12 | 2022-11-11 | 中国矿业大学 | Control method for combustion optimization air supply system of gas boiler |
| CN114118726A (en) * | 2021-11-09 | 2022-03-01 | 华能秦煤瑞金发电有限责任公司 | Scientific coal blending co-combustion system of coal-fired power plant based on coal full-value chain management |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634459A (en) * | 2009-08-24 | 2010-01-27 | 陶晓鹏 | Thermal power generation boiler intelligent combustion optimizing system and realizing method thereof |
| KR20160104481A (en) * | 2015-02-26 | 2016-09-05 | 두산중공업 주식회사 | System for controlling optimized combustion on boiler |
| CN106200565A (en) * | 2015-04-30 | 2016-12-07 | 通用电气公司 | Combustion optimizing system and method |
| CN110486749A (en) * | 2019-08-29 | 2019-11-22 | 国网河南省电力公司电力科学研究院 | A kind of thermal power unit boiler optimized control method of combustion and system |
| CN110989360A (en) * | 2019-12-23 | 2020-04-10 | 武汉博晟信息科技有限公司 | Thermal power generating unit steady-state history optimizing method based on full data |
| CN111199304A (en) * | 2018-11-19 | 2020-05-26 | 天津市职业大学 | Multi-target combustion optimization method based on data-driven fusion strategy |
| CN111219733A (en) * | 2018-11-26 | 2020-06-02 | 斗山重工业建设有限公司 | Apparatus for managing combustion optimization and method thereof |
| CN111260107A (en) * | 2018-11-30 | 2020-06-09 | 斗山重工业建设有限公司 | Boiler combustion optimization system and method |
| CN112859780A (en) * | 2021-01-07 | 2021-05-28 | 西安西热锅炉环保工程有限公司 | Thermal power plant intelligent combustion control method based on cloud data and cloud computing |
| CN113467392A (en) * | 2021-06-18 | 2021-10-01 | 中国大唐集团科学技术研究院有限公司中南电力试验研究院 | Coal-fired boiler open-loop combustion control optimization method |
| WO2022142264A1 (en) * | 2020-12-31 | 2022-07-07 | 深圳市深能环保东部有限公司 | Method for online rapid calculation of garbage incineration calorific value |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7039555B2 (en) * | 1998-03-24 | 2006-05-02 | Exergetic Systems Llc | Method for detecting heat exchanger tube failures and their location when using input/loss performance monitoring of a recovery boiler |
-
2021
- 2021-06-18 CN CN202110680104.1A patent/CN113467392B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634459A (en) * | 2009-08-24 | 2010-01-27 | 陶晓鹏 | Thermal power generation boiler intelligent combustion optimizing system and realizing method thereof |
| KR20160104481A (en) * | 2015-02-26 | 2016-09-05 | 두산중공업 주식회사 | System for controlling optimized combustion on boiler |
| CN106200565A (en) * | 2015-04-30 | 2016-12-07 | 通用电气公司 | Combustion optimizing system and method |
| CN111199304A (en) * | 2018-11-19 | 2020-05-26 | 天津市职业大学 | Multi-target combustion optimization method based on data-driven fusion strategy |
| CN111219733A (en) * | 2018-11-26 | 2020-06-02 | 斗山重工业建设有限公司 | Apparatus for managing combustion optimization and method thereof |
| CN111260107A (en) * | 2018-11-30 | 2020-06-09 | 斗山重工业建设有限公司 | Boiler combustion optimization system and method |
| CN110486749A (en) * | 2019-08-29 | 2019-11-22 | 国网河南省电力公司电力科学研究院 | A kind of thermal power unit boiler optimized control method of combustion and system |
| CN110989360A (en) * | 2019-12-23 | 2020-04-10 | 武汉博晟信息科技有限公司 | Thermal power generating unit steady-state history optimizing method based on full data |
| WO2022142264A1 (en) * | 2020-12-31 | 2022-07-07 | 深圳市深能环保东部有限公司 | Method for online rapid calculation of garbage incineration calorific value |
| CN112859780A (en) * | 2021-01-07 | 2021-05-28 | 西安西热锅炉环保工程有限公司 | Thermal power plant intelligent combustion control method based on cloud data and cloud computing |
| CN113467392A (en) * | 2021-06-18 | 2021-10-01 | 中国大唐集团科学技术研究院有限公司中南电力试验研究院 | Coal-fired boiler open-loop combustion control optimization method |
Non-Patent Citations (1)
| Title |
|---|
| 基于动态标杆值的电站锅炉燃烧控制优化;廖彭伟;热能动力工程;第38卷(第5期);全文 * |
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