CN108764604B - Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit - Google Patents
Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit Download PDFInfo
- Publication number
- CN108764604B CN108764604B CN201810300690.0A CN201810300690A CN108764604B CN 108764604 B CN108764604 B CN 108764604B CN 201810300690 A CN201810300690 A CN 201810300690A CN 108764604 B CN108764604 B CN 108764604B
- Authority
- CN
- China
- Prior art keywords
- coal
- pulverizing
- pulverizing system
- mill
- blending
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000010298 pulverizing process Methods 0.000 title claims abstract description 67
- 238000005457 optimization Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 22
- 239000003245 coal Substances 0.000 claims abstract description 191
- 238000002156 mixing Methods 0.000 claims abstract description 52
- 238000002485 combustion reaction Methods 0.000 claims abstract description 41
- 230000008901 benefit Effects 0.000 claims abstract description 15
- 238000011217 control strategy Methods 0.000 claims abstract description 10
- 230000007246 mechanism Effects 0.000 claims abstract description 7
- 239000002245 particle Substances 0.000 claims description 44
- 238000004364 calculation method Methods 0.000 claims description 13
- 239000002274 desiccant Substances 0.000 claims description 9
- 239000000843 powder Substances 0.000 claims description 9
- 229910000831 Steel Inorganic materials 0.000 claims description 8
- 238000007664 blowing Methods 0.000 claims description 8
- 239000000203 mixture Substances 0.000 claims description 8
- 239000010959 steel Substances 0.000 claims description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 239000002802 bituminous coal Substances 0.000 claims description 6
- 239000003077 lignite Substances 0.000 claims description 6
- 238000006477 desulfuration reaction Methods 0.000 claims description 4
- 230000023556 desulfurization Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 238000010977 unit operation Methods 0.000 claims description 3
- 238000000498 ball milling Methods 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 8
- 238000010248 power generation Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000001035 drying Methods 0.000 description 3
- 239000000428 dust Substances 0.000 description 3
- 239000000446 fuel Substances 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- RHZUVFJBSILHOK-UHFFFAOYSA-N anthracen-1-ylmethanolate Chemical compound C1=CC=C2C=C3C(C[O-])=CC=CC3=CC2=C1 RHZUVFJBSILHOK-UHFFFAOYSA-N 0.000 description 2
- 239000003830 anthracite Substances 0.000 description 2
- 238000000227 grinding Methods 0.000 description 2
- 230000017525 heat dissipation Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000011234 economic evaluation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 239000001115 mace Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Computational Linguistics (AREA)
- Water Supply & Treatment (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Disintegrating Or Milling (AREA)
Abstract
The invention relates to a pulverizing optimization control method of a large coal-fired unit based on an intelligent evolutionary algorithm, which comprises the following steps: establishing a mechanism model of pulverizing system control coupled with coal quality characteristics when various coal types are ground by combining expert knowledge according to the heat balance and operation safety boundary of the pulverizing system; and finding out an optimal control strategy for guiding the operation of the coal mill based on an evolutionary algorithm by utilizing the established mechanism model for controlling the coal pulverizing system. According to the invention, the safe operation boundary of the coal pulverizing system can be calculated in advance according to the characteristics of different coal types and different coal pulverizing systems, 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, and the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found out and is used for guiding the optimal operation of the boiler coal pulverizing system.
Description
Technical Field
The invention belongs to the technical field of thermal power, and particularly relates to a pulverizing optimization control method of a large coal-fired unit based on an intelligent evolutionary algorithm.
Background
Because coal resources in China are not uniformly distributed and coal markets change frequently, the coal types for combustion and the designed coal types of coal power enterprises are often greatly different. The domestic scholars have carried out a lot of researches on the blended coal blending, mainly carry out the blending of a certain proportion according to the requirements of calorific capacity and volatile matter, have obtained certain effect on coal type adaptability, but still have many problems in the aspects of combustion efficiency, slagging and dust deposition, pollutant emission, etc. At present, most of power plant coal blending systems are incomplete, lack of sufficient scientific basis, and have strong blindness and randomness of coal blending, so that the quality of the coal blending cannot be ensured.
In fact, the blending process of the mixed coal is complex, the whole process from coal purchase to furnace burning of power generation enterprises is involved, and the coordination requirement on each link of the whole process is high. In an information-based modern society, a digital coal yard built by a plurality of power generation enterprises is an effective mode for replacing old-fashioned artificial coal yard management, and a series of problems of complicated coal sources, difficult management, storage of various coals, control of storage time and the like in the manual management mode in the past are well solved. However, at present, most of the work in this aspect focuses on statistics concerning coal storage amount of the coal yard, and the influence on the downstream link of the power generation is not considered basically. The coal feeding process, the control process of a coal pulverizing system, even the combustion process in a furnace and the like are often cracked to carry out independent management and control, so that the problems of mismatching of equipment and output, unstable boiler combustion or slagging and the like in the various coal blending combustion processes are caused, and the safety and the economical efficiency of a unit and equipment are seriously influenced.
At present, aiming at the optimization of a coal pulverizing system under the condition of blending combustion of various coals, a safety limit, equipment output, a blending combustion ratio and the like are determined by adopting a method combining experimental research with the manual experience of experts, for example, the current change of a coal mill, the pressure drop change of an inlet and an outlet of the coal mill, the vibration and the sound of local equipment are generally used for judging whether the equipment is normal or not, whether the blending reaches the maximum ratio or not and the like, the judgment of accurate quantification is lacked, the control and the adjustment of the operation parameters of the coal pulverizing system are fuzzy and approximate, and the method belongs to extensive management and optimization. The disadvantages of this optimization are: the manual experience of experts has deviation and uncertainty, the tracking and guidance cannot be carried out in real time, the adjustment of a coal pulverizing system also has obvious hysteresis, and the adjustment cannot be accurately matched with the current situation of the existing coal source and coal type structure.
Disclosure of Invention
The invention aims to provide a pulverizing optimization control method of a large coal-fired unit based on an intelligent evolutionary algorithm, which is used for calculating the safe operation boundary of a pulverizing system in advance according to the characteristics of different coal types and different pulverizing systems, 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 optimization operation of the pulverizing system of a boiler.
The invention provides a pulverizing optimization control method of a large coal-fired unit based on an intelligent evolutionary algorithm, which comprises the following steps: establishing a mechanism model of pulverizing system control coupled with coal quality characteristics when various coal types are ground by combining expert knowledge according to the heat balance and operation safety boundary of the pulverizing system;
finding out an optimal control strategy for guiding the operation of the coal mill based on an evolutionary algorithm by utilizing the established mechanism model for controlling the coal pulverizing system;
the optimal control strategy comprises:
according to the unit load prediction, optimizing the coal feeding strategy of different raw coal bins and coal mills in advance, and keeping the output of the pulverizing system adapted to the unit load at all times;
according to the characteristics of different coal types and different coal pulverizing systems, optimizing the safe operation boundary of the coal pulverizing system for guiding the reasonable operation mode of the coal pulverizing system; the safe operation boundary of the coal pulverizing system comprises the upper and lower limits of the temperature of a mixture at the outlet of a single coal mill, the maximum output of the single coal mill, the mixing proportion, the combination mode of the coal mills and the limit processing capacity of a desulfurization system;
optimizing and making coal mill wind-coal ratio curves and control strategies under different coal qualities according to a unit operation historical database and an expert knowledge system;
and carrying out intelligent calculation and automatic judgment according to the combustion condition in the furnace, the unit load and the coal quality condition, and giving optimization suggestions of the number of the coal mills in operation, the combination mode of the coal mills and the distribution proportion of the coal feeding amount of each coal mill so as to match the investment of the coal pulverizing system with the combustion system in the furnace.
Further, according to the characteristics of different coal types and different coal pulverizing systems, the safe operation boundary of the coal pulverizing system is optimized, and the reasonable operation mode for guiding the coal pulverizing system comprises the following steps:
on the premise that the safety and environmental-protection emission indexes can meet the requirements, the economic benefits under different blending combustion proportions are calculated, and the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found out and used for guiding the optimized operation of the boiler pulverizing system.
Further, the evolutionary algorithm is a particle swarm optimization algorithm.
By means of the scheme, the safe operation boundary of the coal pulverizing system can be calculated in advance according to the characteristics of different coal types and different coal pulverizing systems through the intelligent evolution algorithm-based large coal-fired unit coal pulverizing optimization control method, the economic benefits under different blending combustion proportions are calculated on the premise that the safety and environment-friendly emission indexes can meet the requirements, the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found, and the optimal blending combustion proportion is used for guiding the optimized operation of the boiler coal pulverizing system.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a particle swarm optimization algorithm;
FIG. 2 is a flow chart of multi-objective optimization based on particle swarm optimization.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The embodiment provides a pulverizing optimization control method of a large coal-fired unit based on an intelligent evolutionary algorithm, which is characterized by establishing a mechanism model for pulverizing system control coupled with coal quality characteristics during pulverizing multiple coal types according to the heat balance and operation safety boundary of a pulverizing system and combining expert knowledge, and then finding an optimal control strategy for guiding the operation of a coal pulverizer based on the evolutionary algorithm.
This optimal control strategy satisfies:
1) according to the unit load prediction, the strategy of feeding coal to different raw coal bins and coal mills is optimized in advance, the output of the coal pulverizing system is kept to be adapted to the unit load, and the problem of the common insufficient output of the unit during blending coal is solved.
2) According to the characteristics of different coal types and different coal pulverizing systems, the safe operation boundary of the coal pulverizing system is optimized, such as the temperature range of a mixture at the outlet of a coal mill, the maximum output of a single coal mill, the mixing proportion and the like, and the safe operation boundary is used for guiding the reasonable operation mode of the coal pulverizing system.
3) According to the unit operation historical database and the expert knowledge system, the coal mill wind-coal ratio curves and the control strategies under different coal qualities are optimized and formulated, and the optimal state of operation of the coal mill is realized.
4) The method comprises the steps of carrying out intelligent calculation and automatic judgment according to conditions such as combustion conditions in a furnace, unit loads, coal quality conditions and the like, giving optimization suggestions such as the number of running coal mills, a coal mill combination mode, coal feeding amount distribution proportions of the coal mills and the like, matching the investment of a coal pulverizing system with a combustion system in the furnace, and improving the capacity of the coal pulverizing system for blending low-price and low-quality coal.
The optimization Fitness Function (Fitness Function) used in the embodiment is a boiler economic evaluation model based on multi-coal blending, the blending economic benefits under different blending proportions are calculated in real time on the premise that safety and environmental emission indexes can meet requirements, and the work of advanced coal blending, coal feeding, coal pulverizing and the like of the boiler can be guided according to the finally calculated maximum benefit.
The evolutionary algorithm used in this embodiment is a Particle Swarm Optimization algorithm (Particle Swarm Optimization), and how to successfully solve the Optimization control problem of the pulverizing system by using the evolutionary algorithm is described below.
The present invention is described in further detail below.
1. And (4) analyzing the principle of a particle swarm optimization algorithm.
In practical applications of particle optimization algorithms, a potential solution of each optimization problem can be thought of as a particle in a D-dimensional search space, and all particles have a Fitness Value (Fitness Value) determined by an objective Function (Fitness Function), and fly at a certain speed in the search space, and the magnitude and direction of the speed are dynamically adjusted according to the flight experience of the particle itself and the flight experience of the entire population. Then, all the particles will follow the current optimal particle to search in the solution space.
Assuming that in a minimum finding problem, an optimal solution x needs to be found such that the multidimensional objective function f (x) satisfies the following equation,
x=argminf(x);
in a D-dimension target search space, N particles form a group, wherein the ith particle is expressed as a D-dimension vectorI.e. the position of the ith particle in the D dimension search space isIn other words, the position of each particle is a potential solution to the optimization problem. Will be provided withSubstituting into the objective Function (Fitness Function) to calculate its adaptive value, and measuring according to the adaptive valueThe quality of (1) is good. Let the Fitness value of each particle be Fitnessi(i∈[1,N]). The flight velocity of the ith particle is also a D-dimensional vector, which is recorded asNoting the best bit searched so far for the ith particleIs arranged asThe optimal position searched by the whole particle swarm so far is gbest=(g1,g2,...,gD). The mode of operation of each particle is not only dependent on its own flight experience (i.e. p)best) It is also influenced by the flight experience of the entire population (i.e., g)best). Therefore, the particle swarm optimization algorithm can ensure that the final result is a global optimum value rather than a local optimum value. The particle swarm optimization algorithm is as follows:
1) setting initial values of N particles, each xiRepresents a potential solution to the optimization problem, i ∈ [1, N ∈ ]]. For the minimum optimization problem, the Fitness value Fitness of each particleiOptimum position of each particleAnd the optimal position g of the whole populationbestAre set to infinity.
2) Reaching the set maximum iteration number t at the iteration number tmaxPreviously, or in case some termination condition is not met, the following steps are repeated in each iteration:
a. calculating an adaptation value, Fitness, for each particlei=f(xi);
d. The particles are moved according to the following formula,
xi,t+1=xi,t+ui,t+1,
wherein u in the formulai,t+1Is defined as
Wherein u isi,tRepresenting the flight speed, u, of the ith particle during a time period ti,t+1Represents the flight speed of the ith particle in the next time period t +1, and omega is a constant less than 1 and is used for feeding back the influence of the flight speed of the particle in the time period t on the flight speed of the next time period t + 1. x is the number ofi,tRepresenting the current position of the ith particle. Learning factor c1And c2Are the weight values that these variables have on determining the flight speed. r is1And r2Is between [0,1 ]]Random constants between, adding random factors to the algorithm.
t=t+1。
3) After the iteration is finished, the optimal solution x meeting the multidimensional objective function f (x) can be obtained.
The flow chart of the particle swarm optimization algorithm is shown in fig. 1.
2. And calculating the blending ratio by applying a PSO multi-objective optimization method.
(1) Each particle represents a potentially viable dosing ratio.
(2) For each particle, i.e. each blending ratio, the coal mill outlet temperature t can be calculated2Upper and lower limits of (3).
i. The upper limit calculation formula is as follows:
a) direct-blowing type (after separator) for medium-speed coal mill:
when V isdafWhen t is more than or equal to 40 percent2=60~70℃。
b) Storage type for steel ball mill (after mill):
lean coal, 100-130 ℃;
bituminous coal, 70-90 ℃;
brown coal, 60-70 ℃.
c) Double-inlet and double-outlet steel ball milling direct blowing type (after coal mill):
lean coal, 100-130 ℃;
bituminous coal, 70-90 ℃;
brown coal, 60-70 ℃.
The lower limit calculation formula is as follows:
a) coal mill outlet temperature t2Should be above the dew point temperature tdpAnd must not be lower than 60 ℃, i.e. both values are high.
For the bin pulverizing system: t is t2min=tdp+5℃;
For a direct-fired pulverizing system: t is t2min=tdp+2℃。
In the formula: t is tdpDew point temperature, ° c.
b) Dew point temperature calculation
For the dew point temperature calculation at the outlet of the coal mill, the moisture content in the air should contain the external moisture in the raw coal at this time, since the external moisture in the raw coal has already entered the wind-dust mixture (it can be simply considered that the external moisture has entered the wind-dust mixture in its entirety).
When d is2When the total weight is 3.8g/kg to 60g/kg,
when d is2When the weight is 61g/kg to 825g/kg,
in the formula: pa-local atmospheric absolute pressure, kPa;
d 2-moisture content per kg of desiccant (air) in the air-powder mixture (i.e.: the moisture content already contained in the moisture in the raw coal), g/kg.
When air alone is used as the desiccant, the following is calculated:
in the formula: g1The amount of drying agent fed to the coal mill can be calculated by calculating the dew point temperatureAs a dry dose;
Klethe air leakage rate of the powder making system takes the following values: the steel ball coal mill has a storage bin type of 0.2-0.4 and a direct blowing type of 0.25; a medium speed coal mill, wherein the negative pressure direct blowing mode is 0.2;
d-air moisture content, usually taken as d ═ 10 g/kg;
Δ M — the amount of water evaporated per kg of raw coal being dried.
In the formula: mar-receiving base moisture for raw coal,%;
Mpcthe water content of coal powder at the outlet of the coal mill is percent.
(3) The mathematical model of the optimization control problem of the powder process system is as follows:
max mixed combustion income (each mixed combustion proportion particle can be calculated out corresponding mixed combustion income)
min|qin-qout(for each kind of mixed burning proportion particle, the corresponding q can be calculatedinAnd q isout)
And s.t. the upper limit corresponding to each blending proportion is less than or equal to the outlet temperature t2 of the coal mill and is less than or equal to the lower limit corresponding to each blending proportion.
(4) The multi-objective optimization flow based on the particle swarm optimization algorithm is shown in FIG. 2.
The correlation calculation formula is as follows:
1. calculation of drying output in coal mill
1) Total heat input qin(kJ/kg)
qin=qag1+qmac;
In the formula: q. q.sag1-drying agentHeat treatment, kJ/kg;
qmacmechanical heat from the coal mill operation, kJ/kg.
qag1=cag1t1g1;
In the formula: t is t1The initial temperature (which can be regarded as the coal mill inlet air temperature) of the drying agent of each component after mixing is DEG C;
cag1at t1The mass specific heat capacity after weighted average of all the drying agents at the temperature is kJ/(kg DEG C);
qmac=Kmace;
in the formula: kmac-mechanical thermal conversion coefficient; for a steel ball coal mill, the weight is 0.7; taking the mass of the medium-speed coal mill to be 0.6;
e-unit coal grinding power consumption; the coal mill with the steel balls is 90-110% for anthracite burning, 55-90% for bituminous coal burning, 35-65% for lignite burning, 30-58% for shale coal burning, 22-36% for E-type medium-speed and HP (RP) type medium-speed coal mill, 20-30% for MPS (ZGM) type coal mill, and kJ/kg.
2) Heat q taken out and consumed by drying and grinding 1kg coal by powder making systemout(kJ/kg)
qout=qev+qag2+qf+q5;
In the formula: q. q.sev-heat consumed by evaporation of water from the raw coal, kJ/kg;
qag2-kJ/kg of heat brought out by the dead steam desiccant;
qf-the heat consumed by the heating fuel, kJ/kg;
q5-equipment heat dissipation loss, kJ/kg.
qev=ΔM(2500+c″H2Ot2-4.187trc);
In the formula: c ″)H2OAt t with water vapour2At temperatureAverage specific heat capacity at constant pressure, kJ/(kg. DEG C);
t-steam temperature, in this case the coal mill outlet medium temperature t2,℃;
trcTemperature of raw coal, pair(Qnet,arThe unit is MJ/kg, MtFull moisture of raw coal) of high moisture fuel trc20 ℃ for other fuels trc=0℃,℃。
In the formula: c. Ca2At a temperature t2Specific heat capacity of wet air in the hour, kJ/(kg. DEG C);
cdathe specific heat capacity of dry air, kJ/(kg. DEG C);
t-temperature of the dry air, in this case the coal mill outlet medium temperature t2,℃;
c″H2O-specific heat capacity of water vapour at the same temperature as dry air, kJ/(kg. DEG C);
d-water content of air, g/kg.
When bituminous coal is used: c. Cdc=0.0034t+0.8796
When the lean coal is used: c. Cdc=0.0032t+0.8136
When brown coal is combusted: c. Cdc=0.0031t+0.9332
When anthracite is combusted: c. Cdc=0.0011t+0.7684
When shale coal is used: c. Cdc=0.0014t+0.8562
In the formula: c. CdcThe specific heat capacity of the dried coal is determined by the sum of the inlet and outlet temperatures of the coal, kJ/(kg. DEG C.).
Equipment heat dissipation loss q5Can be calculated as follows:
when a silo-type system is used, q5=0.05qin;
When a direct blow system is used, q5=0.02qin。
2. Blended combustion revenue calculation
a) And (3) calculating the coal price:
coal type i list:wherein Qnet' ar is the low calorific value of the raw coal; the standard coal calorific value was 7000kcal/kg or 29308kJ/kg, and P was a manually input value.
b) Calculating the power supply coal consumption:
power generation coal consumption:wherein, the heat consumption of the steam turbine is calculated according to 8000 kg/kwh; the pipe efficiency was calculated to be 0.99.
d) And (3) calculating the material consumption of the desulfurization system:
the cost is increased:
according to the invention, the safe operation boundary of the coal pulverizing system, such as the upper and lower limits of the outlet mixture temperature of a single coal mill, the maximum output of the single coal mill, the combination mode of the coal mills, the limit processing capacity of the desulfurization system and the like, is calculated in advance according to the characteristics of different coal types and different coal pulverizing systems. On the premise that the safety and environmental-protection emission indexes can meet the requirements, the economic benefits under different blending combustion proportions are calculated, and the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found out and used for guiding the optimized operation of the boiler pulverizing system. Therefore, although the coal price and the coal characteristics are changed at any time, the optimal dynamic blending combustion proportion can be finally determined according to the blending combustion benefit.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (1)
1. A pulverizing optimization control method of a large coal-fired unit based on an intelligent evolution algorithm is characterized by comprising the following steps: establishing a mechanism model of pulverizing system control coupled with coal quality characteristics when various coal types are ground by combining expert knowledge according to the heat balance and operation safety boundary of the pulverizing system;
finding out an optimal control strategy for guiding the operation of the coal mill based on an evolutionary algorithm by utilizing the established mechanism model for controlling the coal pulverizing system; the evolutionary algorithm is a particle swarm optimization algorithm;
the optimal control strategy comprises:
according to the unit load prediction, optimizing the coal feeding strategy of different raw coal bins and coal mills in advance, and keeping the output of the pulverizing system adapted to the unit load at all times;
according to the characteristics of different coal types and different coal pulverizing systems, the safe operation boundary of the coal pulverizing system is optimized, and the reasonable operation mode of the coal pulverizing system is guided, and the method comprises the following steps: under the premise that the safety and environmental-protection emission indexes can meet the requirements, the economic benefits under different blending combustion proportions are calculated, and the optimal blending combustion proportion for obtaining the maximum blending combustion benefit is found out and used for guiding the optimized operation of a boiler pulverizing system; the safe operation boundary of the coal pulverizing system comprises the upper and lower limits of the temperature of a mixture at the outlet of a single coal mill, the maximum output of the single coal mill, the mixing proportion, the combination mode of the coal mills and the limit processing capacity of a desulfurization system;
optimizing and making coal mill wind-coal ratio curves and control strategies under different coal qualities according to a unit operation historical database and an expert knowledge system;
carrying out intelligent calculation and automatic judgment according to the combustion condition in the furnace, the unit load and the coal quality condition, and giving optimization suggestions of the number of coal mills in operation, the coal mill combination mode and the coal feeding amount distribution proportion of each coal mill so as to match the investment of a coal pulverizing system with the combustion system in the furnace;
the method for calculating the blending ratio by utilizing the particle swarm optimization algorithm comprises the following steps:
for each particle, i.e. each blending ratio, the coal mill outlet temperature t can be calculated2Upper and lower limits of (2):
the upper limit calculation formula is as follows:
a) direct-blowing type for medium-speed coal mill:
when V isdafWhen t is more than or equal to 40 percent2=60~70℃;
b) Storage type for steel ball coal mill:
lean coal, 100-130 ℃;
bituminous coal, 70-90 ℃;
60-70 ℃ of brown coal;
c) the double-inlet and double-outlet steel ball milling direct blowing type:
lean coal, 100-130 ℃;
bituminous coal, 70-90 ℃;
60-70 ℃ of brown coal;
the lower limit calculation formula is as follows:
a) coal mill outlet temperature t2Above dew point temperature tdpAnd the temperature can not be lower than 60 ℃, namely the temperature of the two is high;
for the bin pulverizing system: t is t2min=tdp+5℃;
For a direct-fired pulverizing system: t is t2min=tdp+2℃;
In the formula: t is tdp-dew point temperature, ° c;
b) dew point temperature calculation
When d is2When the total weight is 3.8g/kg to 60g/kg,
when d is2When the weight is 61g/kg to 825g/kg,
in the formula: pa-local atmospheric absolute pressure, kPa;
d 2-moisture content per kg of desiccant in the air-powder mixture, g/kg;
when air alone is used as the desiccant, the following is calculated:
in the formula: g1The amount of drying agent entering the coal mill, when calculating the dew point temperature, willAs a dry dose;
K1ethe air leakage rate of the powder making system takes the following values:
the steel ball coal mill has a storage bin type of 0.2-0.4 and a direct blowing type of 0.25; a medium speed coal mill, wherein the negative pressure direct blowing mode is 0.2;
d is the air moisture content, and d is 10 g/kg;
Δ M — the amount of water evaporated per kg of raw coal dried;
in the formula: marReceiving base moisture for raw coal,%;
Mpcthe water content of coal powder at the outlet of the coal mill is percent;
the mathematical model for optimizing and controlling the powder process system is as follows:
max blending combustion income, each blending combustion proportion particle, calculating the corresponding blending combustion income;
min|qin-qoutcalculating corresponding q for each kind of mixed burning proportion particlesinAnd q isout;
qinkJ/kg for total heat;
qoutheat brought out and consumed for 1kg of coal is dried and ground by a pulverizing system, kJ/kg;
s.t. the upper limit corresponding to each blending proportion is less than or equal to the outlet temperature t of the coal mill2The lower limit of each mixing proportion is less than or equal to.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810300690.0A CN108764604B (en) | 2018-04-04 | 2018-04-04 | Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810300690.0A CN108764604B (en) | 2018-04-04 | 2018-04-04 | Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit |
Publications (2)
Publication Number | Publication Date |
---|---|
CN108764604A CN108764604A (en) | 2018-11-06 |
CN108764604B true CN108764604B (en) | 2021-10-19 |
Family
ID=63980974
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810300690.0A Active CN108764604B (en) | 2018-04-04 | 2018-04-04 | Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108764604B (en) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112183924B (en) * | 2020-08-25 | 2022-05-24 | 华能国际电力股份有限公司上安电厂 | Coal blending and blending combustion method for thermal power generating unit |
CN112418527B (en) * | 2020-11-24 | 2023-04-07 | 西安热工研究院有限公司 | Optimal coal blending ratio calculation and judgment method based on boiler side index and fuel price |
CN113654075B (en) * | 2021-07-06 | 2024-03-26 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Method and device for predicting lignite blending combustion proportion of coal-fired boiler |
CN114415601A (en) * | 2021-11-25 | 2022-04-29 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Boiler overall coordination real-time intelligent optimization system and method for thermal power generating unit |
CN114647191A (en) * | 2022-03-28 | 2022-06-21 | 华北电力大学 | Optimized scheduling method of pulverizing system based on boiler heat load balanced distribution |
CN114997529A (en) * | 2022-07-18 | 2022-09-02 | 西安热工研究院有限公司 | Full life cycle management method, system, equipment and storage medium for powder process system |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
CN107301477A (en) * | 2017-06-22 | 2017-10-27 | 湖南华润电力鲤鱼江有限公司 | A kind of coal-fired procurement decisions method based on many coal coal mixing combustion optimizing models |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2369433A1 (en) * | 2010-03-24 | 2011-09-28 | ABB Research Ltd. | Computer-based method and device for automatically providing control parameters for a plurality of coal mills supplying coal powder to a plant |
CN102645523B (en) * | 2012-05-10 | 2015-02-11 | 北京华电天仁电力控制技术有限公司 | Moisture as received coal on-line identification method based on heat balance of powder process system |
CN103345213B (en) * | 2013-06-09 | 2015-09-02 | 华电电力科学研究院 | Fire coal management under the changeable condition of fossil-fired unit ature of coal and combustion strategies optimization method |
CN107016176A (en) * | 2017-03-24 | 2017-08-04 | 杭州电子科技大学 | A kind of hybrid intelligent overall boiler burning optimization method |
CN107316104A (en) * | 2017-06-07 | 2017-11-03 | 西安西热锅炉环保工程有限公司 | The coal mixing combustion forecast system of assessment system after a kind of band |
-
2018
- 2018-04-04 CN CN201810300690.0A patent/CN108764604B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
CN107301477A (en) * | 2017-06-22 | 2017-10-27 | 湖南华润电力鲤鱼江有限公司 | A kind of coal-fired procurement decisions method based on many coal coal mixing combustion optimizing models |
Non-Patent Citations (2)
Title |
---|
1000MW机组锅炉低热值煤种掺烧的经济性分析;梁学东 等;《热力发电》;20141130;第43卷(第11期);第1-5页 * |
火电机组混煤掺烧全程动态优化***开发与应用;陈刚 等;《中国电力》;20010430;第44卷(第4期);第50-54页 * |
Also Published As
Publication number | Publication date |
---|---|
CN108764604A (en) | 2018-11-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108764604B (en) | Intelligent evolution algorithm-based pulverizing optimization control method for large coal-fired unit | |
CN102425807B (en) | Combustion feedforward and feedback composite optimization controlling method for pulverized coal fired boiler | |
CN108800191B (en) | A kind of Dynamic Optimum method of tangential firing boiler Secondary Air air distribution | |
CN107016176A (en) | A kind of hybrid intelligent overall boiler burning optimization method | |
CN102840593B (en) | Fume dried lignite medium speed mill powder-making system | |
CN103576655A (en) | Method and system for utility boiler combustion subspace modeling and multi-objective optimization | |
CN111881554B (en) | Optimization control method for boiler changing along with air temperature | |
CN102840595A (en) | Fume pre-dried lignite medium speed mill straight-blowing powder-making system | |
CN104598761A (en) | Method for analyzing impact of changes of multifuel fired boiler operating parameters on unit power generation coal consumption | |
CN102750424B (en) | Method for optimizing combustion of biomass furnace | |
CN202195493U (en) | Medium-speed milling pulverization system for flue gas-dried lignite | |
CN105808945B (en) | A kind of hybrid intelligent boiler efficiency burning optimization method | |
JP6990593B2 (en) | Semi-carbonization treatment condition determination device and semi-carbonization treatment condition determination method | |
CN103886370A (en) | Power station boiler combustion performance neural network model suitable for different coal mill combinations | |
CN112862632B (en) | Method and system for blending and burning coal in thermal power plant | |
CN112418664B (en) | Particle swarm optimization-based binning combination blending method and system | |
Wijayapala et al. | Co-firing of biomass with coal in pulverized coal fired boilers at Lakvijaya Power Plant: A case study | |
CN204240391U (en) | Superheat steam drying powder process type coal generating system | |
CN204100278U (en) | The pulverized coal preparation system of direct feed pulverized coal-fan mill and the warehouse style combination of fan mill | |
Zhao et al. | Constrained optimization of combustion at a coal-fired utility boiler using hybrid particle swarm optimization with invasive weed | |
CN113836729B (en) | Method for reducing combustible content in fly ash of boiler of thermal power plant | |
CN111735038B (en) | System and method for improving low-load operation performance of coal-fired boiler | |
Andria | Indramayu 3 x 330 MW CFPP Coal Yard Management Optimization with K-Means Clustering | |
CN103301921A (en) | Energy-saving and emission-reducing device and method for industrial vertical mill | |
Chong et al. | The development of a neural network based system for the optimal control of chain-grate stoker-fired boilers |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |