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
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- 238000010298 pulverizing process Methods 0.000 title claims abstract description 67
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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 vector
I.e. the position of the ith particle in the D dimension search space is
In other words, the position of each particle is a potential solution to the optimization problem. Will be provided with
Substituting into the objective Function (Fitness Function) to calculate its adaptive value, and measuring according to the adaptive value
The 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 as
Noting the best bit searched so far for the ith particleIs arranged as
The 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 particle
And 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)；
b. Updating the optimal position of each particle searched so far
c. Updating the optimal location of the entire population of particles searched thus far
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 the content is less than 40 percent,
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 temperature
As 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.
Power supply and coal consumption:
wherein, Lfcy is the plant power rate.
c)
d) And (3) calculating the material consumption of the desulfurization system:
the cost is increased:
unit increase cost:
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 the content is less than 40 percent,
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, will
As 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.
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