CN113467392A - Coal-fired boiler open-loop combustion control optimization method - Google Patents
Coal-fired boiler open-loop combustion control optimization method Download PDFInfo
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- 238000002485 combustion reaction Methods 0.000 title claims abstract description 94
- 238000005457 optimization Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004140 cleaning Methods 0.000 claims abstract description 20
- 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 6
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 63
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 239000003245 coal Substances 0.000 claims description 18
- 238000010977 unit operation Methods 0.000 claims description 13
- 238000013461 design Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- 238000012935 Averaging Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 description 6
- 239000003550 marker Substances 0.000 description 4
- 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
- 230000033228 biological regulation Effects 0.000 description 2
- 230000000694 effects 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
- 238000003303 reheating Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
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- 230000008094 contradictory effect Effects 0.000 description 1
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- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction 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], 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], 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to a coal-fired boiler open-loop combustion control optimization method, which comprises the steps of dividing different load sections according to the working conditions of ultra-low load, medium load and high load, establishing an expert database in a grading manner, and initializing the expert database by adopting unit historical data; reading the operation parameters and the mode state of a boiler combustion unit in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring the corresponding grids of the expert database according to the clustering factors, calculating an optimization target according to the real-time operation parameters of the boiler combustion unit and comparing the optimization target with the corresponding grid parameters, and determining whether to perform unsteady state experience push, expert database grid update and benchmarking value optimization push according to different mode state judgment results and calculation results; a delay strategy is designed to solve the disturbance of switching of the unsteady/steady-state pushing scheme, the operation adjusting mode comprising the oxygen content of the flue gas and the opening degree of each layer of secondary air door is pushed to the distributed control system after the real-time operation parameters of the unit are analyzed, and the combustion control adjustment is carried out under the guidance of open loop, so that the stability of the optimized control system is ensured.
Description
Technical Field
The invention relates to a coal-fired boiler control technology of a thermal power plant, in particular to an open-loop combustion optimization control method of a coal-fired boiler.
Background
Along with the requirement of coal blending combustion of power generation enterprises and the situation development of deep peak regulation, the safe and economic operation of combustion of a boiler faces severe examination, the operation adjustment of the boiler is mainly adjusted by operators according to experience, and the boiler cannot be guaranteed to have higher economy, environmental protection and safety all the time due to the reasons of insufficient monitoring equipment, uneven levels of the operators, 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 power generation cost have important significance for saving energy and protecting 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, the improvement of the combustion heat efficiency is accompanied by the increase of the emission of the nitrogen oxides, and the mutual coordination and optimization of the boiler heat efficiency and the nitrogen oxide emission are required to be realized to meet the economic benefit and the social benefit of a power plant.
The power station boiler combustion process control system is a complex multivariable and strong coupling system, corresponding control means are required to be applied according to the measurement results of various physical quantities such as pressure, flow, temperature and the like, and the quality of the control effect of the power station boiler combustion process control system depends on the accuracy of the measurement results and the design rationality of a control optimization algorithm to a great extent.
In recent years, application research of intelligent algorithms such as neural networks and genetic algorithms in thermal process control optimization of a thermal power plant is continuously increased, massive operation data of a unit is used as a sample for learning and training so as to predict combustion heat efficiency and pollutant emission, results generated by different models are often greatly different, and unknown safety risks are brought due to the predictability of recommended adjustment schemes.
The combustion optimization of data mining is carried out based on a historical scheme, the combustion theory, the self-learning technology and the global optimization algorithm can be organically integrated, the recommended operation adjusting mode after optimization is also the historical scheme, and the combustion optimization and the unit operation safety are considered.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a coal-fired boiler open-loop combustion control optimization method, which obtains the historical optimal combustion working condition of the boiler and the corresponding optimal set value of each combustion parameter and pushes the optimal combustion working condition to DCS open loop to guide the combustion adjustment of operators. The aim is to realize autonomous optimization updating along with the continuous operation and adjustment of the unit, strengthen and consolidate the prior results, and finally realize the mutual coordination and the better and better emission of boiler combustion and nitrogen oxides.
The technical scheme adopted by the invention is as follows:
in order to achieve the aim, the invention provides a coal-fired boiler open-loop combustion control optimization method, which comprises the following steps:
step 1: dividing different load sections according to ultra-low load, medium load and high load working conditions, establishing an expert database in a grading manner, representing the combustion basic state of the boiler by adopting a unit load, low calorific value of coal, ambient temperature and different coal mill combination modes, establishing an expert database grid as a clustering factor, and initializing the expert database by adopting unit history data; and respectively setting step lengths for each clustering factor representing the combustion basic state of the boiler, dividing the clustering factors into a plurality of intervals and forming a multi-dimensional grid.
Step 2: reading real-time operation data and manual intervention/automatic mode state of boiler combustion, and turning to the step 3;
and step 3: cleaning the acquired real-time data, if not, turning to the step 2, and if not, turning to the step 4; data cleansing means: judging whether the data has default values, sudden increase and sudden decrease and exceeds upper and lower limit values;
and 4, 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 step 4.1 of unsteady-state experience push, and if so, turning to step 5;
step 4.1: and when the unstable working condition is represented by 2 clustering factors in a unit load and different coal mill combination modes, corresponding to a plurality of grids of the expert database. Averaging the parameters of the marker values in the grids to serve as experience values, pushing the experience values to the DCS, and then turning to the step 2;
and 5: starting to time the steady state accumulated time after the 1 st steady state working condition is judged, entering the step 6 after the preset time is reached, otherwise, turning to the step 4.1, and resetting the time counting during the period due to data abnormity or non-steady state working condition judgment;
step 6: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, turning to the step 2, and if so, turning to the step 7;
and 7: calculating an optimization target by adopting real-time parameters, comparing the optimization target with the optimization target value in the expert database grid, if the optimization target is more optimal, turning to the step 7.1, otherwise, turning to the step 8; 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;
step 7.1: executing expert database grid updating, updating the benchmark values in the original expert database grid into current real-time parameters, and turning to the step 2;
and 8: and (4) judging the current manual intervention/automatic mode state, if the current manual intervention/automatic mode state is the manual intervention mode, turning to the step 2, and if the current manual intervention/automatic mode state is not the manual intervention mode, turning to the step 9.
And step 9: and (5) executing the benchmarking value optimizing pushing, pushing the benchmarking values in the expert database grids to the DCS, and turning to the step 2.
According to the coal-fired boiler open-loop combustion control optimization method provided by the application, in the step 2, a manual intervention/automatic mode can be manually selected and switched, the difference between the manual intervention mode and the automatic mode is that the manual intervention mode does not push an operation adjustment mode to give operation guidance, a unit operator carries out self-combustion adjustment, and at the moment, the expert database grid updating can still be executed after the condition is met.
According to the coal-fired boiler open-loop combustion control optimization method provided by the application, the grid updating of the expert database is only carried out under the condition that the same grid is guaranteed, and the optimization target obtained by real-time data calculation is continuously superior to the grid value in the set time range and can be executed.
According to the coal-fired boiler open-loop combustion control optimization method provided by the application, in the step 4, the steady-state working condition judgment method is that after a series of key parameters representing unit operation are selected, the key parameters comprise coal-fired low-grade heating value, unit load, main steam pressure, main steam temperature, reheated steam temperature, flue gas oxygen content, secondary air door opening degree of each layer and hearth negative pressure, the average value of each parameter in a set time range is obtained, and if the real-time parameters are compared with each average value and then are in a fluctuation error range, the steady state can be considered.
According to the coal-fired boiler open-loop combustion control optimization method provided by the application, in the step 6, 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/reheating 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 desuperheating water flow of a reheater is in a set range, and the deviation of the feed water temperature and the design value is in a set range.
The invention has the beneficial effects that:
1. the boiler open-loop combustion control optimization method provided by the invention is characterized in that an expert database grid is established by combining a hierarchical load section based on a boiler combustion basic state as a clustering factor, the grid number is obviously reduced compared with a full load section, excellent schemes under historical working conditions can be absorbed, more excellent schemes which may appear in future operation can be updated and adopted, autonomous optimization updating is realized, and the existing results are strengthened and consolidated. After the unit operates, an optimized adjustment scheme can be pushed in a whole time period (including steady-state/unsteady-state working conditions), the working condition change stage is smoothly transited, and boiler combustion and nitrogen oxide emission are coordinated and better on the premise of safety.
2. The boiler open-loop combustion control optimization method is based on different loads to carry out primary classification, and working condition division bases with higher dimensionality (unit load, low calorific value of coal, ambient temperature and different coal mill combination modes) are used 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 heat efficiency of the boiler and the emission of nitrogen oxides are coordinated and optimized, and the economic benefit and the social benefit of a power plant are met.
3. The boiler open-loop combustion control optimization method adopts a big data mining algorithm based on an expert database system, has the functions of data cleaning, steady-state working condition judgment and constraint condition judgment and is provided with a set of guidance strategies aiming at steady state/unsteady state respectively. The quality of the boiler combustion control effect depends on the accuracy of a measuring result and the design rationality of a control optimization algorithm to a great extent, the invention carries out combustion optimization of data mining based on a historical scheme, organically integrates a combustion theory, a self-learning technology and a global optimization algorithm, and a recommended operation adjusting mode after optimization is also the historical scheme and gives consideration to combustion optimization and unit operation safety.
Drawings
FIG. 1 is a flow chart of a method for optimizing open-loop combustion control of a boiler according to the present invention;
FIG. 2 is an example of an expert database grid framework of the boiler open-loop combustion control optimization method of the present invention.
Detailed Description
In order to make the technical conception and advantages of the invention for realizing the purposes of the invention clearer, the technical scheme of the invention is further described in detail with reference to the accompanying drawings. It should be understood that the following examples are only for illustrating and explaining 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 invention discloses a coal-fired boiler open-loop combustion control optimization method, which comprises the following steps:
s1, dividing different load sections according to the working conditions of ultra-low load, medium load and high load, establishing an expert database in a grading way, representing the combustion basic state of the boiler by adopting the combination modes of unit load, low calorific value of coal, ambient temperature and different coal mills, establishing an expert database grid as a clustering factor, and initializing the expert database by adopting unit history data; for each clustering factor representing the combustion basic state of the boiler, setting step length respectively, dividing the clustering factors into a plurality of intervals and forming a multi-dimensional grid;
s2, designing a manual intervention/automatic mode, reading operation parameters and mode states of a boiler combustion unit in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring grids corresponding to an expert database according to clustering factors, calculating an optimization target by using real-time parameters, comparing the optimization target with corresponding grid parameters, and determining whether to perform unsteady state experience push, expert database grid update and benchmarking value optimization push according to different mode states, judgment results and calculation results; the benchmark value refers to a relatively excellent unit operation parameter stored in the expert database grid; 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;
s3, designing a delay strategy to solve disturbance of switching of the unsteady/steady-state pushing scheme, after analyzing real-time unit operation data, pushing a group of operation adjusting modes including the oxygen content of flue gas and the opening degree of each layer of secondary air door to a Distributed Control System (DCS), and guiding the combustion adjustment of operators by open loop to ensure the stability of the optimization control system.
According to the coal-fired boiler open-loop combustion control optimization method, a manual intervention mode/an automatic mode can be manually selected and switched, the difference between the manual intervention mode and the automatic mode is that under the manual intervention mode, an operation adjustment mode is not pushed to give operation guidance, a unit operator carries out self-combustion adjustment, and at the moment, the expert database grid updating is still executed after the condition is met; and the grid updating of the expert database can be executed only under the condition that the same grid is ensured, and the optimization target obtained by real-time data calculation is continuously superior to the grid value within a set time range.
Example 2
The coal-fired boiler open-loop combustion control optimization method of the embodiment is different from the embodiment 1 in that: in step S2, reading the operation data of boiler combustion and the state of manual intervention/automatic mode in real time, and cleaning the collected real-time data, namely judging whether the data has a default value, a sudden increase and a sudden decrease and exceeds an upper limit value and a lower limit value, if so, turning to step S2-1; if not, returning to reading the operation parameters and the mode state of the boiler combustion unit, 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 to carry out unsteady-state experience push, and if so, turning to a step S2-3;
step S2-2: and when the unstable working condition is represented by 2 clustering factors in a unit load and different coal mill combination modes, corresponding to a plurality of grids of the expert database. Respectively averaging the benchmark value parameters in the grids to serve as empirical values, pushing the empirical values to a DCS, returning to a set operation parameter and a mode state of boiler combustion, and performing data cleaning;
step S2-3: starting to time the steady-state accumulated time after the 1 st steady-state working condition is judged, entering the step S2-4 after the preset time is reached, otherwise, turning to the step S2-2, and resetting the time counting all when the data are abnormal or the non-steady-state working condition is judged;
step S2-4: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, returning to reading the unit operation parameters and mode states of the boiler combustion, and executing data cleaning; if yes, go to step S2-5;
step S2-5: calculating an optimization target by adopting real-time parameters, comparing the optimization target with the optimization target value in the expert database grid, if the optimization target is more optimal, turning to the step S2-6, otherwise, turning to the step S2-7;
step S2-6: executing expert database grid updating, updating the benchmark value in the original expert database grid into a current real-time parameter, returning to reading the unit operation parameter and mode state of boiler combustion, and executing data cleaning;
step S2-7: judging the current manual intervention/automatic mode state, if the current manual intervention/automatic mode state is the manual intervention mode, returning to the mode state of reading the unit operation parameters of boiler combustion, and executing data cleaning; otherwise, go to step S2-8;
step S2-8: and (4) executing the benchmarking value optimizing pushing, pushing the benchmarking values in the expert database grid to DCS, returning to the step of reading the operation parameters and the mode state of the boiler combustion unit, and executing data cleaning.
The invention discloses a coal-fired boiler open-loop combustion control optimization method, in step S2-1, the judgment method of the steady state working condition is that after a series of key parameters representing the operation of a unit are selected, the key parameters comprise low calorific value of coal, unit load, main steam pressure, main steam temperature, reheated steam temperature, flue gas oxygen content, secondary air door opening degree of each layer and hearth negative pressure, the average value of each parameter in a set time range is obtained, and if the real-time parameters are compared with each average value and then 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 slip pressure curve is in a set range, the deviation of the main/reheat steam temperature and the design value is in a set range, the concentration of nitrogen oxides at a denitration inlet is not too high, the desuperheater water flow of a reheater is in a set range, and the deviation of the feed water temperature and the design value is in a set range.
Example 3
In this embodiment, a 1000MW coal-fired unit boiler combustion system is taken as an example to specifically describe a specific implementation process of the coal-fired boiler open-loop combustion control optimization method of the present invention, as shown in fig. 1, the flow includes:
step 1: dividing different load sections according to ultra-low load, medium load and high load working conditions, establishing an expert database in a grading manner, representing the combustion basic state of the boiler by adopting a unit load, low calorific value of coal, ambient temperature and different coal mill combination modes, establishing expert database grids as clustering factors, totaling 67815 grids, and collecting historical data of related units to initialize the expert database, as shown in figure 2. According to the expert library grid framework, a database management system, such as MySQL, Oracle, Sybase and the like, can be selected for construction, and data storage and calling are performed.
Step 2: reading real-time operation data and manual intervention/automatic mode state of boiler combustion, and turning to the step 3;
and writing a manual intervention/automatic mode in the DCS configuration for the selection and switching of the unit operation personnel, wherein the manual intervention/automatic mode is different from the manual intervention mode in that the operation regulation mode is not pushed to give operation guidance, the unit operation personnel can automatically burn and regulate, and the unsteady experience pushing and the benchmarking value optimizing pushing are executed in the automatic mode.
And step 3: and (4) cleaning the collected real-time data, and turning to the step (2) when the data has the conditions of default, sudden increase and sudden decrease and exceeding the upper and lower limit values, or turning to the step (4).
And 4, step 4: analyzing and processing boiler operation real-time data, and judging whether the current boiler combustion accords with a steady-state working condition or not, wherein the conditions are as follows:
a. reading the data of the low-position calorific value of the fire coal which is about 10 minutes, and calculating the average value, wherein the difference value between the current value and the average value is within the range of +/-1000 kJ/kg, and the value is normal; otherwise, the operation is abnormal;
b. reading data of the unit load in about 10 minutes, calculating an average value, and determining that the difference value between the current value and the average value is normal within the range of +/-1% of a rated load value; otherwise, the operation is abnormal;
c. reading data of main steam pressure of about 10 minutes, calculating an average value, and determining that the difference value between the current value and the average value is normal within the range of +/-2% of a rated main steam pressure value; otherwise, the operation is abnormal;
d. reading data of the main steam temperature of about 10 minutes, and calculating an average value, wherein the difference value between the current value and the average value is normal within the range of +/-3 ℃; otherwise, the operation is abnormal;
e. reading data of reheated steam temperature of about 10 minutes, calculating an average value, and determining that the difference value between a current value and the average value is normal within a range of +/-4 ℃; otherwise, the operation is abnormal;
f. reading data of oxygen amount of about 10 minutes, and calculating an average value, wherein the difference value between the current value and the average value is within the range of +/-0.5 percent, and the value is normal; otherwise, the operation is abnormal;
g. reading data of each layer operation of the secondary air door for 10 minutes, and solving 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 +/-2 percent of the range, and the range is normal; otherwise, the operation is abnormal;
h. the negative pressure of the current hearth is nearly 3 seconds, the average value is calculated, and the difference value between the current value and the average value is normal within the range of +/-100 Pa; otherwise, it is abnormal.
If the steady-state working condition is not met, executing the step 4.1 of pushing the non-steady-state experience, and if the steady-state working condition is met, turning to the step 5;
step 4.1: when the unstable working condition is represented by 2 clustering factors in a unit load and different coal mill combination mode, corresponding to a plurality of grids of an expert database;
and (3) calling the benchmark value data stored in the database grid by adopting a global search algorithm, averaging all the parameters respectively to be used as experience values, pushing the experience values to the DCS, and then turning to the step 2.
And 5: and (4) starting to time the steady-state accumulated time after the 1 st steady-state working condition is judged, entering the step 6 after the preset time is reached, or turning to the step 4.1, and resetting the time counting due to data abnormity or judgment of an unsteady-state working condition.
Step 6: analyzing and processing boiler operation data, and judging whether the current boiler combustion meets constraint conditions, wherein the conditions are as follows:
a. the wall temperatures of all the sections of water-cooled walls, all the levels of superheaters and all the levels of reheaters do not exceed alarm values, and the operation is normal; otherwise, the operation is abnormal;
b. the difference value between the main steam pressure and the sliding pressure curve is within the range of +/-1 MPa, and the main steam pressure and the sliding pressure curve are normal; otherwise, the operation is abnormal;
c. the difference value between the main steam temperature and the design value is within the range of +/-10 ℃, and the main steam temperature is normal; otherwise, the operation is abnormal;
d. the difference value between the reheated steam temperature and the design value is within the range of +/-10 ℃, and the reheating steam temperature is normal; otherwise, the operation 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 operation is abnormal;
f. the flow of the reheater desuperheating water is not higher than the allowable value under the historical steady-state working condition and is normal; otherwise, the operation 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 data is normal; otherwise, it is abnormal.
And if the constraint condition is not met, turning to the step 2, and if the constraint condition is met, turning to the step 7.
And 7: calculating an optimization target value by adopting real-time parameters, and comparing the optimization target value with the optimization target value in the expert database grid, wherein the optimization target formula is as follows:
phi: optimizing the target value;
fNOx(x) The method comprises the following steps Denitrating the concentration of nitrogen oxides at an inlet in real time;
fη(x) The method comprises the following steps Real-time boiler thermal efficiency;
fNOx(xmax): the maximum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
fNOx(xmin): the minimum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
α: the concentration weight of nitrogen oxides at the denitration inlet;
beta: the weight of the thermal efficiency of the boiler, alpha + beta is 1;
fη(xmax): maximum value of boiler thermal efficiency under historical steady state working condition;
fη(xmin): minimum value of boiler thermal efficiency under historical steady state working condition;
if the former is more preferable, go to step 7.1, otherwise go to step 8;
step 7.1: and (5) executing the updating of the expert database grids, updating the benchmark values in the original expert database grids into current real-time parameters, and turning to the step 2.
And 8: judging the current manual intervention/automatic mode state, if the current manual intervention/automatic mode state is the manual intervention mode, turning to the step 2, and if the current manual intervention/automatic mode state is not the manual intervention mode, turning to the step 9;
under the manual intervention mode, operating personnel are allowed to automatically burn and adjust without depending on the operation adjusting mode pushed by the method, a combustion optimization scheme is actively explored, and the corresponding expert database grids are updated when a set of better schemes are found.
And step 9: and (3) carrying out optimizing pushing of the marker post values, reading the marker post values in the grid of the expert database, including the oxygen content of the flue gas and the opening degree of each layer of secondary air door, pushing the marker post values to DCS, guiding the combustion adjustment of operators by open loop, and turning to the step 2.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Other modifications of the invention will occur to those skilled in the art without the benefit of this disclosure and it is intended to cover within the scope of the invention any modifications that fall within the spirit and scope of the invention or the equivalents thereof which may be substituted by one of ordinary skill in the art without departing from the scope of the invention.
Claims (8)
1. An open-loop combustion control optimization method for a coal-fired boiler is characterized by comprising the following steps: the method comprises the following steps:
s1, dividing different load sections according to the working conditions of ultra-low load, medium load and high load, establishing an expert database in a grading way, representing the combustion basic state of the boiler by adopting the combination modes of unit load, low calorific value of coal, ambient temperature and different coal mills, establishing an expert database grid as a clustering factor, and initializing the expert database by adopting unit history data;
s2, designing a manual intervention/automatic mode, reading the operation parameters and mode states of a boiler combustion unit in real time, executing data cleaning, steady state judgment and constraint judgment, inquiring the corresponding grids of the expert database according to clustering factors, calculating an optimization target according to the real-time operation parameters of the unit, comparing the optimization target with the corresponding grid parameters, and determining whether to perform unsteady state experience push, expert database grid update and benchmarking value optimization push according to different mode states, judgment results and calculation results;
s3, designing a delay strategy to solve disturbance of switching of the unsteady/steady-state pushing scheme, pushing an operation adjusting mode comprising the oxygen content of the flue gas and the opening degree of each layer of secondary air door to the distributed control system after analyzing real-time operation parameters of the unit, and guiding operators to perform combustion control adjustment by open loop to ensure the stability of the optimized control system.
2. The coal-fired boiler open-loop combustion control optimization method of claim 1, characterized in that: in the step S2, reading the operation data of boiler combustion and the state of a manual intervention/automatic mode in real time, cleaning the collected real-time data, namely judging whether the data has a default value, a sudden increase and a sudden decrease and exceeds an upper limit value and a lower limit value, and if the data is normal, turning to the step S2-1; if the data is abnormal, returning to an initial state, reading the operation parameters and the mode state of the boiler combustion unit, and performing 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 to carry out unsteady-state experience push, and if so, turning to a step S2-3;
step S2-2: when the unit load and 2 clustering factors in different coal mill combination modes are adopted to represent unsteady-state working conditions, corresponding to a plurality of grids of an expert database, respectively averaging the benchmark value parameters in the grids to be used as experience values, pushing the experience values to DCS, then returning to an initial state, reading the unit operation parameters and the mode state of boiler combustion, and performing data cleaning;
step S2-3: starting to time the steady state accumulated time after the 1 st steady state working condition is judged, entering the step S2-4 after the preset time is reached, otherwise, turning to the step S2-2, and resetting and timing during the period when the data are abnormal or the non-steady state working condition is judged;
step S2-4: analyzing and processing boiler operation data, judging whether the current boiler combustion meets constraint conditions, if not, returning to reading the unit operation parameters and mode states of the boiler combustion, and executing data cleaning; if yes, go to step S2-5;
step S2-5: calculating an optimization target by adopting real-time parameters, comparing the optimization target with the optimization target value in the expert database grid, if the optimization target is more optimal, turning to the step S2-6, otherwise, turning to the step S2-7;
step S2-6: executing expert database grid updating, updating the benchmark value in the original expert database grid into a current real-time parameter, returning to an initial state, reading the unit operation parameters and the mode state of boiler combustion, and executing data cleaning;
step S2-7: judging the current manual intervention/automatic mode state, returning to the initial state if the current manual intervention/automatic mode state is the manual intervention mode, reading the operation parameters and the mode state of a boiler combustion unit, and performing data cleaning; otherwise, go to step S2-8;
step S2-8: and (4) executing the benchmarking value optimizing pushing, pushing the benchmarking values in the expert database grid to DCS, returning to the initial state, reading the operation parameters and the mode state of the boiler combustion unit, and executing data cleaning.
3. The coal-fired boiler open-loop combustion control optimization method of claim 2, characterized in that: in step S2-1, the method for determining the steady-state operating condition is: after a series of key parameters representing the operation of the unit are selected, including the low-grade calorific value of the coal, the load of the unit, the pressure of main steam, the temperature of reheated steam, the oxygen content of flue gas, the opening degree of secondary air doors at each layer and the negative pressure of a hearth, the average value of each parameter in a set time range is obtained, and if the real-time parameters are compared with each average value and are in a fluctuation error range, the stable state can be considered.
4. The coal-fired boiler open-loop combustion control optimization method of claim 2, characterized in that: in step S2-4, the constraint conditions are that the heat exchanger wall temperature is not alarmed, the difference between the main steam pressure and the slip pressure curve is within a set range, the deviation of the main/reheat steam temperature and the design value is within a set range, the concentration of nitrogen oxides at the denitration inlet is not too high, the reheater desuperheating water flow is within a set range, and the deviation of the feedwater temperature and the design value is within a set range.
5. The coal-fired boiler open-loop combustion control optimization method according to any one of claims 1 to 4, characterized by comprising: in step S1, step lengths are set for each clustering factor representing the boiler combustion base state, and the clustering factors are divided into a plurality of intervals to form a multidimensional grid.
6. The coal-fired boiler open-loop combustion control optimization method of claim 5, characterized in that: in step S2, the manual intervention/automatic mode may be manually selected and switched, and in the manual intervention mode, the combustion is automatically adjusted by the crew member, and at this time, the expert database grid updating is still executed after the conditions are satisfied; and the grid updating of the expert database can be executed only under the condition that the same grid is ensured, and the optimization target obtained by real-time data calculation is continuously superior to the grid value within a set time range.
7. The coal-fired boiler open-loop combustion control optimization method of claim 1, 2, 3, 4 or 6, characterized in that 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.
8. The coal-fired boiler open-loop combustion control optimization method of claim 7, wherein the optimization target value is calculated by using real-time parameters and compared with the optimization target value in the expert database grid, and the optimization target formula is as follows:
phi: optimizing the target value;
fNOx(x) The method comprises the following steps Denitrating the concentration of nitrogen oxides at an inlet in real time;
fη(x) The method comprises the following steps Real-time boiler thermal efficiency;
fNOx(xmax): the maximum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
fNOx(xmin): the minimum value of the concentration of nitrogen oxides at the denitration inlet under the historical steady-state working condition;
α: the concentration weight of nitrogen oxides at the denitration inlet;
beta: the weight of the thermal efficiency of the boiler, alpha + beta is 1;
fη(xmax): maximum value of boiler thermal efficiency under historical steady state working condition;
fη(xmin): minimum value of boiler thermal efficiency under historical steady state operating condition.
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