CN112916189A - Optimization method of pulverizing system - Google Patents
Optimization method of pulverizing system Download PDFInfo
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- CN112916189A CN112916189A CN202011631804.3A CN202011631804A CN112916189A CN 112916189 A CN112916189 A CN 112916189A CN 202011631804 A CN202011631804 A CN 202011631804A CN 112916189 A CN112916189 A CN 112916189A
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- 238000010298 pulverizing process Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 239000003245 coal Substances 0.000 claims abstract description 242
- 238000000227 grinding Methods 0.000 claims abstract description 12
- 230000008901 benefit Effects 0.000 claims abstract description 8
- 238000003801 milling Methods 0.000 claims abstract description 5
- 238000012544 monitoring process Methods 0.000 claims abstract description 4
- 239000000843 powder Substances 0.000 claims description 18
- 238000002485 combustion reaction Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 11
- 238000002156 mixing Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012843 least square support vector machine Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 4
- 238000005303 weighing Methods 0.000 claims description 4
- 238000012512 characterization method Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 abstract description 3
- 239000000126 substance Substances 0.000 abstract description 3
- 238000004134 energy conservation Methods 0.000 abstract description 2
- 230000009467 reduction Effects 0.000 abstract description 2
- 238000012706 support-vector machine Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000013312 flour Nutrition 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C25/00—Control arrangements specially adapted for crushing or disintegrating
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Abstract
The invention discloses an optimization method of a pulverizing system, which comprises the following steps: the method comprises the following steps: the coal quality on-line monitoring module acquires coal quality parameter information through the coal quality on-line chemical examination equipment, is connected with a coal conveying program control system of a power plant, and acquires equipment running state information. Step two: and the operation optimization module of the coal pulverizing system is connected with the DCS control system of the power plant, and the read voltage signal and current signal calculate the power of each coal mill at the corresponding moment. Step three: and respectively identifying power consumption characteristic curves of the coal grinding through mutual compensation of different characteristic quantities of the coal mill load. Step four: and finding an optimal coal mill combined operation scheme under different loads. Step five: and judging the starting and stopping states of the coal mills by using the obtained coal mill combined operation scheme under different loads, and optimizing and distributing the output of the coal mills by using the obtained coal milling power consumption characteristic curve of each coal mill. The method can optimize and distribute the load of the coal mill in real time, and has great significance for energy conservation and emission reduction and benefit improvement of the thermal power generating unit.
Description
Technical Field
The invention relates to the technical field of thermal power control, in particular to an optimization method of a pulverizing system.
Background
Under the influence of social electricity consumption and coal markets, thermal power enterprises in the northeast region have the problems of low operation load rate and high raw coal price in the aspect of survival and development, so that the problem of further excavating the energy efficiency potential of a unit and improving the operation economy of the unit becomes increasingly prominent. The coal-fired power plant is an important component of the coal-fired power plant, and the power consumption of the coal-fired power plant accounts for about 15% -25% of the station power. In actual operation, because the coal mill start-stop strategy and the load distribution are improper, huge energy waste is caused, so that the coal mill needs to be deeply analyzed, an online load distribution optimization method is formulated, and the aims of saving energy and reducing emission of a coal-fired power plant and improving unit benefit are fulfilled.
The conventional technology divides the load into a high section and a low section to be respectively controlled, one coal mill is generally used as a spare in the high load section, and the other coal mills are all started and approximately equally divide the load; and in the low-load section, closing a plurality of coal mills, and starting the other coal mills to approximately equally divide the load. The method can only simply carry out load distribution, has simple start-stop strategy and poor energy-saving effect, and therefore needs an optimization method of the powder preparation system urgently.
Disclosure of Invention
The present invention aims to provide an optimization method of a pulverizing system to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: the optimization method of the powder process system comprises the following steps:
the method comprises the following steps: the coal quality on-line monitoring module acquires coal quality parameter information through coal quality on-line chemical examination equipment, is simultaneously connected with a coal conveying program control system of a power plant, acquires equipment operation state information, monitors and judges coal quality parameters output to each coal mill, analyzes a coal pulverizing system by utilizing the nonlinear characteristic of a least square support vector machine, establishes an independent least square support vector machine model of the relationship between coal pulverizing unit consumption and related operation parameters and between coal powder fineness and related operation parameters, optimizes the working condition by adopting a hybrid genetic algorithm on the basis of establishing the coal pulverizing unit consumption model, and obtains an optimized operation calculation coal powder fineness prediction result of the minimum coal pulverizing unit consumption under different working conditions;
step two: the coal pulverizing system operation optimizing module is connected with a power plant DCS control system, the coal feeding amount of each coal mill, the primary total air temperature of a mill inlet, the primary total air quantity of the mill inlet, the primary air temperature of a mill outlet, the current of the coal mill, the voltage of the coal mill and the belt weight of the corresponding coal feeder are obtained in real time, and the power of each coal mill at the corresponding moment is calculated by using the read voltage signal and the read current signal;
step three: for each coal mill, based on a belt weighing signal and a power signal of the corresponding coal feeder in the preset time, comprehensively measuring the coal storage amount of the coal mill by adopting the current, the noise and the differential pressure of the coal mill, mutually compensating through different characterization quantities of the load of the coal mill, and respectively identifying a coal grinding power consumption characteristic curve;
step four: searching an optimal coal mill combined operation scheme under different loads under the condition of limited output of the coal mills by using the obtained coal-grinding power consumption characteristic curve of each coal mill;
step five: and judging the starting and stopping states of the coal mills by using the obtained coal mill combined operation scheme under different loads, and optimizing and distributing the output of the coal mills by using the obtained coal milling power consumption characteristic curve of each coal mill. And intelligently calculating and automatically judging according to the combustion condition in the furnace, the unit load and the coal quality condition, and giving the number of the coal mills in operation, the combination mode of the coal mills and the coal feeding amount distribution proportion of each coal mill so as to match the investment of the coal pulverizing system with the combustion system in the furnace.
Preferably, the coal feeding amount distribution proportion of the coal mill is to set the current coal feeding amount according to the operation of the coal pulverizing system in the previous period. In the system, the current coal feed amount is estimated. The actual coal feeding output is adjusted by taking the estimated coal feeding amount as the center, slow adjustment is adopted when the adjustment deviates from the estimated value, and fast adjustment is adopted when the estimated value is regressed.
Preferably, the power of each coal mill at the corresponding moment is used for analyzing the control performance of the system according to system experimental data and historical data, a comprehensive fuzzy control system is formed by utilizing special graphical fuzzy control software, the comprehensive variable parameter decoupling control, the coal feeding amount estimation control, the fuzzy discrimination and calculation of the coal mill load are carried out, and the optimal control fixed value corresponding to the current system is calculated.
Preferably, the optimal coal mill combined operation scheme under different loads determines the upper and lower limits of the output of the coal mill according to the design indexes of the coal mill, the loads are divided into 1-N coal mill working sections according to the upper and lower limits, and the started optimal combination of the coal mills is determined in each working section, wherein N refers to the number of the coal mills put into operation in the coal-fired power plant.
Preferably, according to the characteristics of different coal types and different coal pulverizing systems, the safe operation boundary of the coal pulverizing system is optimized for guiding the reasonable operation mode of the coal pulverizing system, and the method comprises the steps of calculating the economic benefits under different blending combustion proportions on the premise that the safe and environment-friendly emission indexes can meet the requirements, finding out the optimal blending combustion proportion for obtaining the maximum blending combustion benefit, and guiding the optimized operation of the coal pulverizing system of the boiler.
Compared with the prior art, the invention has the beneficial effects that: the invention discloses an optimization method of a powder process system, which focuses on searching the minimum coal mill operation mode under different load working conditions from the aspect of optimization operation, further reduces the plant power consumption rate on the premise of ensuring safe production, and achieves the purposes of saving energy, reducing consumption and improving the operation economy of a unit. Through the comparative analysis to actual operating parameters, the coal mill operating coal quantity under the relatively wide range raw coal calorific value variation range has been adjusted, improves the operation mode of powder process system, and fundamentally has solved the problem that the power consumption rate is on the high side that the powder process system operation quantity is too much caused, great improvement the economic nature of unit operation. The method has the advantages of high processing speed (the time is in the second level), outstanding energy-saving effect, low additional cost, strong system adaptability, high robustness and capability of being used for various working conditions and automatically updated along with the change of system parameters. The method can optimize and distribute the load of the coal mill in real time, and has great significance for energy conservation and emission reduction and benefit improvement of the thermal power generating unit.
Drawings
FIG. 1 is a flow chart of the optimization control of the present invention;
FIG. 2 is a model of the consumption per unit of pulverizing of the present invention;
FIG. 3 is a model of the fineness of the flour of the present invention;
FIG. 4 is a coal feed regulation loop of the pulverizing system of the present invention;
FIG. 5 is a schematic illustration of a dead zone of the coal pulverizer of the present invention;
FIG. 6 is a graph illustrating the load distribution of the coal pulverizer of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-6, the present invention provides a technical solution: the optimization method of the powder process system comprises the following steps:
the method comprises the following steps: the coal quality on-line monitoring module acquires coal quality parameter information through coal quality on-line chemical examination equipment, is simultaneously connected with a coal conveying program control system of a power plant, acquires equipment operation state information, monitors and judges coal quality parameters output to each coal mill, analyzes a coal pulverizing system by utilizing the nonlinear characteristic of a least square support vector machine, establishes an independent least square support vector machine model of the relationship between coal pulverizing unit consumption and related operation parameters and between coal powder fineness and related operation parameters, optimizes the working condition by adopting a hybrid genetic algorithm on the basis of establishing the coal pulverizing unit consumption model, and obtains an optimized operation calculation coal powder fineness prediction result of the minimum coal pulverizing unit consumption under different working conditions; if the result is in the specified fineness range, the scheme is selected by the optimization control decision of the powder making system; if the prediction result is not in the specified fineness range, returning to the optimization model, and recalculating optimization until an optimization scheme is obtained; two independent multi-input and single-output coal pulverizing unit consumption models and coal powder fineness prediction models.
The trained unit consumption model of the powder process optimizes the powder process system, and the training process is from input to output and the optimization process is from output to input, namely, the working condition corresponding to each input parameter when the unit consumption of the powder process reaches the minimum under the given condition is solved. Therefore, the optimization problem of the pulverizing system is converted into the optimization problem of solving the minimum value of the pulverizing unit consumption Y of the objective function under the given condition.
min Z＝f(Si,αi,βi,σ)
0<Xi<1
In the formula: z, Si are all normalized parameters; f is a mapping relation established by the trained support vector machine model; z is the unit consumption of milling; si is the ith variable of the network input layer, i is 1,2, …, 50; alpha i is a Lagrange multiplier of the support vector machine; β i is the deviation of the support vector machine; σ is a nuclear parameter.
Step two: the coal pulverizing system operation optimizing module is connected with a power plant DCS control system, the coal feeding amount of each coal mill, the primary total air temperature of a mill inlet, the primary total air quantity of the mill inlet, the primary air temperature of a mill outlet, the current of the coal mill, the voltage of the coal mill and the belt weight of the corresponding coal feeder are obtained in real time, and the power of each coal mill at the corresponding moment is calculated by using the read voltage signal and the read current signal; the power of each coal mill at the corresponding moment is realized according to the system and historical data, the system control performance is analyzed, a comprehensive fuzzy control system is formed by utilizing special graphical fuzzy control software, the omnibearing variable parameter decoupling control, the coal feeding amount estimation control, the fuzzy discrimination and calculation of the coal mill load are carried out, the optimal control fixed value corresponding to the current system is calculated, the historical operation data of one month before the current moment is read from a DCS control system real-time database, and the method specifically comprises the following steps by taking four coal mills as an example: the A-D coal mill current corresponds to the coal feeding mass flow of the A-D coal mill, 10kV bus voltage of the section IA and 10kV bus voltage of the section IB, wherein the section IA is responsible for power supply of the A/C coal mill, and the section IB is responsible for power supply of the B/D coal mill. The power factor of each coal mill is 0.85, and the power of each coal mill at the corresponding moment is calculated respectively.
Step three: for each coal mill, based on a belt weighing signal and a power signal of the corresponding coal feeder in the preset time, comprehensively measuring the coal storage amount of the coal mill by adopting the current, the noise and the differential pressure of the coal mill, mutually compensating through different characterization quantities of the load of the coal mill, and respectively identifying a coal grinding power consumption characteristic curve; and D, identifying a coal grinding power consumption characteristic curve of each coal grinding machine by using the one-month historical coal feeding mass flow and power data of each coal grinding machine obtained in the step two. The power consumption characteristic curves of the grinding coal are set as quadratic curves, and polynomial coefficients including quadratic coefficients, first order coefficients and constant terms are identified by a least square method.
Step four: searching an optimal coal mill combined operation scheme under different loads under the condition of limited output of the coal mills by using the obtained coal-grinding power consumption characteristic curve of each coal mill; the optimal coal mill combined operation scheme under different loads determines the upper and lower limits of the output of the coal mill according to the design indexes of the coal mill, divides the load into 1-N coal mill working sections according to the upper and lower limits, and determines the optimal combination of the started coal mills in each working section, wherein N refers to the number of coal mills put into operation in a coal-fired power station, and a load optimization model is as follows:
<mrow><mi>m</mi><mi>i</mi><mi>n</mi><munderover> <mo>&Sigma；</mo><mrow><mi>j</mi><mo>＝</mo><mn>1</mn></mrow> <msub><mi>k</mi><mi>i</mi></msub></munderover><msub><mi>f</mi> <mrow><mi>i</mi><mi>j</mi></mrow></msub><mrow><mo>(</mo><msub> <mi>P</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>)</mo> </mrow><mo>＝</mo><mi>m</mi><mi>i</mi><mi>n</mi><munderover> <mo>&Sigma；</mo><mrow><mi>j</mi><mo>＝</mo><mn>1</mn></mrow> <msub><mi>k</mi><mi>i</mi></msub></munderover><msub><mi>a</mi> <mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>*</mo><msup><msub> <mi>P</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mn>2</mn> </msup><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>j</mi> </mrow></msub><mo>*</mo><msub><mi>P</mi><mrow><mi>i</mi> <mi>j</mi></mrow></msub><mo>+</mo><msub><mi>c</mi><mrow> <mi>i</mi><mi>j</mi></mrow></msub></mrow>
<mrow><munderover><mo>&Sigma；</mo><mrow><mi>j</mi><mo>＝</mo> <mn>1</mn></mrow><msub><mi>k</mi><mi>i</mi></msub></munderover> <msub><mi>P</mi><mrow><mi>i</mi><mi>j</mi><mo>_</mo> <mi>min</mi></mrow></msub><mo>&le；</mo><msub><mi>P</mi> <mi>i</mi></msub><mo>&le；</mo><munderover><mo>&Sigma；</mo><mrow> <mi>j</mi><mo>＝</mo><mn>1</mn></mrow><msub><mi>k</mi><mi>i</mi> </msub></munderover><msub><mi>P</mi><mrow><mi>i</mi><mi>j</mi> <mo>_</mo><mi>max</mi></mrow></msub></mrow>
wherein: pij _ min, Pij _ max (mw) are the lower limit and the upper limit of the load of the j unit in the ith unit respectively.
And D, adding the coal feeding mass flow of each coal mill by using the monthly history coal feeding mass flow of each coal mill obtained in the step two to obtain the total coal feeding mass flow, counting the frequency occupied by the total output, namely the different total coal feeding mass flows, and fitting a frequency curve by using a Gaussian function.
The coal mill start-stop strategy adopts a dead zone to avoid frequent start-stop at boundary conditions. In order to ensure a certain margin, the output boundary of a single coal mill is set to be 40 t/h-95 t/h and the dead zone is set to be 5t/h by combining historical data analysis and coal mill design indexes, and the dead zone with 90t/h as the center is formed, wherein t/h is the output unit of the coal mill and ton per hour. Taking 90t/h of a coal mill as standard output, and sequentially dividing the output into N grinding working sections of (90 x (N-1)) t/h to (90 x N) t/h, wherein N is 1-6. And respectively calculating the optimal coal mill starting combination in each section, taking 4 grinding working section calculations as an example for explanation, and calculating methods of other sections are similar.
In the existing coal mill starting scheme, each scheme is optimized by an interior point method at each point of selected 6 points to obtain an optimal coal mill load distribution scheme, and the power consumption of the scheme is calculated, so that an objective function is minimized as follows, and the optimal coal mill starting scheme in each section is obtained, and the method comprises the following steps:
<math><mrow><mi>J</mi><mo>＝</mo><munder><mi>min</mi> <mi>j</mi></munder><munderover><mi>&Sigma；</mi><mrow><mi>i</mi> <mo>＝</mo><mn>1</mn></mrow><mn>8</mn></munderover><msub> <mi>&alpha；</mi><mi>i</mi></msub><mo>×；</mo><msub> <mi>W</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub> <mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>4</mn> <mo>)</mo></mrow></mrow></math>
in the formula, J is total electricity consumption; i represents the ith point in each section and is 1-6; j represents the j-th scheme and is 1 to 14.
Step five: and judging the starting and stopping states of the coal mills by using the obtained coal mill combined operation scheme under different loads, and optimizing and distributing the output of the coal mills by using the obtained coal milling power consumption characteristic curve of each coal mill. And intelligently calculating and automatically judging according to the combustion condition in the furnace, the unit load and the coal quality condition, and giving the number of the coal mills in operation, the combination mode of the coal mills and the coal feeding amount distribution proportion of each coal mill so as to match the investment of the coal pulverizing system with the combustion system in the furnace. The coal feeding quantity batch proportion of the coal mill is that the current coal feeding quantity is set according to the operation of the coal pulverizing system in the previous period. In the system, the current coal feed amount is estimated. The actual coal feeding output is adjusted by taking the estimated coal feeding amount as the center, slow adjustment is adopted when the adjustment deviates from the estimated value, and fast adjustment is adopted when the estimated value is regressed.
Judging whether to switch the coal mill starting combination by utilizing the dead zone characteristic, taking 4 coal mill operation stages as an example, if the 4 mills work at 360t/h and are in a load increasing stage near a switching point, the load increasing stage is required to be switched to the (360+4 x 5) t/h to be switched to the 5 mill operation stages, if the 4 mills work at 270t/h and are in a load reducing stage, the load reducing stage is required to be switched to the (270-4 x 5) t/h to be switched to the 3 mill operation stages, and if the other output conditions are met, the 4 mills are kept to work. And D, determining the starting state of the coal mill according to the optimal coal mill starting combination of each stage obtained in the step three, and calculating the optimal coal mill load distribution scheme at the working point by using an interior point method.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The optimization method of the powder process system is characterized by comprising the following steps:
the method comprises the following steps: the coal quality on-line monitoring module acquires coal quality parameter information through coal quality on-line testing equipment, is simultaneously connected with a power plant coal conveying program control system, acquires equipment operation state information, monitors and judges coal quality parameters output to each coal mill, analyzes a coal pulverizing system by utilizing the nonlinear characteristic of a least square support vector machine, establishes an independent least square support vector machine model of the relationship between coal pulverizing unit consumption and related operation parameters and between coal powder fineness and related operation parameters, optimizes the working condition by adopting a hybrid genetic algorithm on the basis of establishing the coal pulverizing unit consumption model, and obtains the coal powder fineness prediction result of the optimized operation with the minimum coal pulverizing unit consumption under different working conditions.
Step two: the coal-feeding quantity, the primary total wind temperature of a mill inlet, the primary total wind quantity of a mill inlet, the primary wind temperature of a mill outlet, the current of the coal mill, the voltage of the coal mill and the belt weighing of the corresponding coal feeder of each coal mill are obtained in real time by the coal-pulverizing system operation optimization module, and the power of each coal mill at the corresponding moment is calculated by the read voltage signal and the read current signal.
Step three: for each coal mill, based on the belt weighing signal and the power signal of the corresponding coal feeder in the preset time, the coal storage amount of the coal mill is comprehensively measured by adopting the current, the noise and the differential pressure of the coal mill, and the power consumption characteristic curves of the coal mill are respectively identified by mutually compensating different characterization quantities of the load of the coal mill.
Step four: and searching an optimal coal mill combined operation scheme under different loads under the condition of limited output of the coal mills by using the obtained coal-grinding power consumption characteristic curve of each coal mill.
Step five: and judging the starting and stopping states of the coal mills by using the obtained coal mill combined operation scheme under different loads, and optimizing and distributing the output of the coal mills by using the obtained coal milling power consumption characteristic curve of each coal mill. And intelligently calculating and automatically judging according to the combustion condition in the furnace, the unit load and the coal quality condition, and giving the number of the coal mills in operation, the combination mode of the coal mills and the coal feeding amount distribution proportion of each coal mill so as to match the investment of the coal pulverizing system with the combustion system in the furnace.
2. The method of optimizing a pulverizing system of claim 1, further comprising: the coal feeding quantity distribution proportion of the coal mill is to set the current coal feeding quantity according to the operation of the coal pulverizing system in the previous period; in the system, the current coal feeding amount is estimated, the actual coal feeding output is adjusted by taking the estimated coal feeding amount as a center, slow adjustment is adopted when the adjustment deviates from the estimated value, and fast adjustment is adopted when the estimated value is regressed.
3. The method of optimizing a pulverizing system of claim 1, further comprising: the power of each coal mill at the corresponding moment is analyzed for the control performance of the system according to system experimental data and historical data, a comprehensive fuzzy control system is formed by utilizing special graphical fuzzy control software, the omnibearing variable parameter decoupling control, the coal feeding amount estimation control, the fuzzy discrimination and calculation of the coal mill load are carried out, and the optimal control fixed value corresponding to the current system is calculated.
4. The method of optimizing a pulverizing system of claim 1, further comprising: the optimal coal mill combined operation scheme under different loads determines the upper and lower limits of the output of the coal mill according to the design indexes of the coal mill, the loads are divided into 1-N coal mill working sections according to the upper and lower limits, and the optimal combination of the started coal mills is determined in each working section, wherein N refers to the number of the coal mills put into operation in a coal-fired power station.
5. The method of optimizing a pulverizing system of claim 1, further comprising: according to the characteristics of different coal types and different coal pulverizing systems, the safe operation boundary of the coal pulverizing system is optimized for guiding the reasonable operation mode of the coal pulverizing system, the economic benefits under different blending combustion proportions are calculated on the premise that the safety and environmental-protection emission indexes can meet the requirements, the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found out, and the optimal blending combustion proportion is used for guiding the optimized operation of the boiler coal pulverizing system.
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