CN106202974A - A kind of thermal power unit boiler combustion control recruitment evaluation computational methods - Google Patents

Abstract

The invention discloses a kind of thermal power unit boiler combustion control effect evaluation method, this method comprises combustion control effectiveness indicator computing module, indicator-specific statistics analytical calculation module, the method simultaneously also disclosing that native system.To boiler combustion process real-time optimal control, it is the most direct, effective, the economic approach of electricity power enterprise's energy-saving and emission-reduction.Thermal power unit operation situation can be assessed objectively by the computational methods of the present invention, and when the operational effect after unit commitment optimal control.

Description

A kind of thermal power unit boiler combustion control recruitment evaluation computational methods

Technical field

The present invention relates to fired power generating unit and control technical field, particularly relate to a kind of thermal power unit boiler combustion control effect Fruit assessment computational methods.

Background technology

Boiler combustion process is complicated multifactor, a process for large time delay.Combustion Process Control affect exhaust gas temperature, Oxygen amount and lime-ash discharge capacity, these factors are closely related with boiler combustion efficiency.Combustion process has an effect on main steam simultaneously Temperature, reheat steam temperature, main steam spray water flux and reheated steam spray water flux, these factors unit Heat consumption rate.Meanwhile, boiler efficiency, thermal efficiency joint effect the net coal consumption rate of unit.

The main purpose of boiler combustion optimization is to improve boiler efficiency, the heat consumption rate of reduction unit.Traditional evaluation burning Effect after optimal control is implemented is mainly by the way of combustion adjustment test, pot when combustion optimizing system is put into and exited The difference of the efficiency of furnace determines the effect of combustion control, but the difference of this simple dependence index determines combustion control Effect is the most objective, it is impossible to take into account the stability of effect of optimization and the lifting of objective effect.

Summary of the invention

Because the drawbacks described above of prior art, the technical problem to be solved is to provide a kind of fired power generating unit pot Stove combustion control recruitment evaluation computational methods, to solve the deficiencies in the prior art.

For achieving the above object, the invention provides a kind of thermal power unit boiler combustion control recruitment evaluation calculating side Method, it is characterised in that include Combustion System effectiveness indicator computing module, indicator-specific statistics analytical calculation module:

Described Combustion System effectiveness indicator computing module: be responsible for calculating in combustion control input and exiting two kinds of feelings Under condition, load boiler efficiency, turbine heat rate rate and standard gross coal consumption rate in 50%～100% interval, wherein by machine The standard gross coal consumption rate of group is as the evaluation index of combustion optimizing system implementation result；

Described indicator-specific statistics analytical calculation module: combustion optimizing system is put into and the standard in the case of two kinds that exits is powered Coal consumption carries out statistical analysis, puts into and exit both standard difference of coal consumption of power supplies in the case of two kinds to combustion optimizing system simultaneously Row is analyzed, and is commented by the standard gross coal consumption rate difference analysis result to the input in a period of time and when exiting combustion optimizing system Valency combustion control effect.

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that include with Lower step:

S1, respectively in 50%, 60%, 70%, 80%, 90% and 100% load interval, put into burning optimization system System, unit operation Tn hour, observe from DCS and record corresponding data, being then log out combustion optimizing system, identical negative Unit operation Tn hour in lotus section, observes from DCS and records corresponding data；

S2, the principle that maximum tolerance is 0.25% according to step S1 each observation deviation observation meansigma methods are rejected with flat Mean bias is beyond the data record of maximum tolerance, and the most remaining data record can be as the original number being for further processing According to；

S3, utilize step S2 to process after data, calculate combustion optimizing system respectively and put into and in the case of exiting two kinds Standard net coal consumption rate, the computing formula of its Plays gross coal consumption rate is as follows:

$b_g=\frac{q_{cp}}{{\mathrm{\η}}_b\×{\mathrm{\η}}_p\×29.308}\×\frac{1}{1-{\mathrm{\ξ}}_{ap}}$

Wherein: b_gStandard net coal consumption rate；

q_cpTurbine heat rate rate；

η_pPipeline efficiency under this load.

η_bBoiler efficiency；

ξ_apStation service power consumption rate.

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: in burning Optimization system put in the case of, calculate respectively 50% load, 60% load, 70% load, 80% load, 90% load and Standard net coal consumption rate value b under 100% load_goni；

In the case of combustion optimizing system exits, calculate respectively 50% load, 60% load, 70% load, 80% load, Standard net coal consumption rate value b under 90% load and 100% load_goffi。

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: by identical In load condition section, combustion optimizing system puts into and exits the standard gross coal consumption rate in the case of two kinds and does difference, and each load is interval Interior standard coal consumption difference is labeled as Δ b_gi。

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: count respectively Calculating under 50% load, 60% load, 70% load, 80% load, 90% load and 100% load, combustion optimizing system is thrown The meansigma methods of standard gross coal consumption rate when entering and exit:

$\stackrel{\‾}{bgon}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_{goni}/n$

$\stackrel{\‾}{bgoff}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_{goffi}/n$

The meansigma methods of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, again counts Calculation average:

$\stackrel{\‾}{\stackrel{\‾}{bgon}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{bgoni}/k$

$\stackrel{\‾}{\stackrel{\‾}{bgoff}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{bgoffi}/k$

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: under by When row formula calculates combustion optimizing system input and exits, the standard deviation of standard net coal consumption rate variable quantity:

${\mathrm{Sb}}_{gon}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(b_{goni}-\stackrel{\‾}{b_{gon}})}^2}$

${\mathrm{Sb}}_{goff}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(b_{goffi}-\stackrel{\‾}{b_{goff}})}^2}$

Sbgon represents when combustion optimizing system puts into, the standard deviation of standard net coal consumption rate variable quantity,

Sbgoff represents when combustion optimizing system exits, the standard deviation of standard net coal consumption rate variable quantity.

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: under by The meansigma methods of coal consumption difference when row formula calculates combustion optimizing system input and exits:

$\stackrel{\‾}{\mathrm{\Δ}bg}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(b_{goni}-b_{goffi})/n$

The meansigma methods of the difference of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, weight Newly calculate average:

$\stackrel{\‾}{\stackrel{\‾}{\mathrm{\Δ}bg}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{\mathrm{\Δ}bgi}/k$

Above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that: under by When row formula calculates combustion optimizing system input and exits, the standard deviation of the difference of standard net coal consumption rate:

${\mathrm{S\Δb}}_g=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{({\mathrm{\Δb}}_{gi}-\stackrel{\‾}{{\mathrm{\Δb}}_g})}^2}$

Wherein, △ bgi represents that when combustion optimizing system puts into and exits, coal consumption is poor,Combustion optimizing system puts into and moves back The meansigma methods of coal consumption difference when going out.

The invention has the beneficial effects as follows:

1, when fired power generating unit does not puts into combustion optimizing system, the current unit of patent objective appraisal of the present invention can be passed through Ruuning situation.

2, this method can be utilized when evaluating combustion optimizing system control effect when, intuitively burning optimization be thrown The mean-standard deviation figure of the standard gross coal consumption rate in a period of time before and after fortune, observes the fortune of combustion control system intuitively Row effect.

Below with reference to accompanying drawing, the technique effect of design, concrete structure and the generation of the present invention is described further, with It is fully understood from the purpose of the present invention, feature and effect.

Accompanying drawing explanation

Fig. 1 is the system flow chart of the present invention.

Fig. 2 is combustion optimizing system average value standard deviation figure of standard gross coal consumption rate when putting into operation.

The average value standard deviation figure of standard gross coal consumption rate when Fig. 3 combustion optimizing system puts into operation.

The average value standard deviation figure of standard gross coal consumption rate when Fig. 4 combustion optimizing system puts into operation.

Detailed description of the invention

As it is shown in figure 1, a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that bag Include Combustion System effectiveness indicator computing module, indicator-specific statistics analytical calculation module:

Described Combustion System effectiveness indicator computing module: be responsible for calculating in combustion control input and exiting two kinds of feelings Under condition, load boiler efficiency, turbine heat rate rate and standard gross coal consumption rate in 50%～100% interval, wherein by machine The standard gross coal consumption rate of group is as the evaluation index of combustion optimizing system implementation result；

Described indicator-specific statistics analytical calculation module: combustion optimizing system is put into and the standard in the case of two kinds that exits is powered Coal consumption carries out statistical analysis, puts into and exit both standard difference of coal consumption of power supplies in the case of two kinds to combustion optimizing system simultaneously Row is analyzed, and is commented by the standard gross coal consumption rate difference analysis result to the input in a period of time and when exiting combustion optimizing system Valency combustion control effect.

In the present embodiment, above-mentioned a kind of thermal power unit boiler combustion control recruitment evaluation computational methods, its feature It is, comprises the following steps:

S1, respectively in 50%, 60%, 70%, 80%, 90% and 100% load interval, put into burning optimization system System, unit operation Tn hour, observe from DCS and record corresponding data, being then log out combustion optimizing system, identical negative Unit operation Tn hour in lotus section, observes from DCS and records corresponding data；

S2, the principle that maximum tolerance is 0.25% according to step S1 each observation deviation observation meansigma methods are rejected with flat Mean bias is beyond the data record of maximum tolerance, and the most remaining data record can be as the original number being for further processing According to；

S3, utilize step S2 to process after data, calculate combustion optimizing system respectively and put into and in the case of exiting two kinds Standard net coal consumption rate, the computing formula of its Plays gross coal consumption rate is as follows:

$b_g=\frac{q_{cp}}{{\mathrm{\η}}_b\×{\mathrm{\η}}_p\×29.308}\×\frac{1}{1-{\mathrm{\ξ}}_{ap}}$

Wherein: b_gStandard net coal consumption rate；

q_cpTurbine heat rate rate；

η_pPipeline efficiency under this load.

η_bBoiler efficiency；

ξ_apStation service power consumption rate.

In the present embodiment, in the case of combustion optimizing system puts into, calculate 50% load, 60% load, 70% negative respectively Standard net coal consumption rate value b under lotus, 80% load, 90% load and 100% load_goni。

In the case of combustion optimizing system exits, calculate respectively 50% load, 60% load, 70% load, 80% load, Standard net coal consumption rate value b under 90% load and 100% load_goffi。

In the present embodiment, by same load operating mode section, combustion optimizing system puts into and exits the standard in the case of two kinds Gross coal consumption rate does difference, and the standard coal consumption difference in each load interval is labeled as Δ b_gi。

In the present embodiment, calculate respectively 50% load, 60% load, 70% load, 80% load, 90% load and Under 100% load, the meansigma methods of standard gross coal consumption rate when combustion optimizing system puts into and exits:

$\stackrel{\‾}{bgon}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_{goni}/n$

$\stackrel{\‾}{bgoff}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_{goffi}/n$

The meansigma methods of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, again counts Calculation average:

$\stackrel{\‾}{\stackrel{\‾}{bgon}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{bgoni}/k$

$\stackrel{\‾}{\stackrel{\‾}{bgoff}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{bgoffi}/k$

In the present embodiment, when being calculated combustion optimizing system input by following equation and exited, standard net coal consumption rate becomes The standard deviation of change amount:

${\mathrm{Sb}}_{gon}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(b_{goni}-\stackrel{\‾}{{\mathrm{\Δb}}_{gon}})}^2}$

${\mathrm{Sb}}_{goff}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(b_{goffi}-\stackrel{\‾}{{\mathrm{\Δb}}_{goff}})}^2}$

Sbgon represents when combustion optimizing system puts into, the standard deviation of standard net coal consumption rate variable quantity,

Sbgoff represents when combustion optimizing system exits, the standard deviation of standard net coal consumption rate variable quantity.

In the present embodiment, the meansigma methods of coal consumption difference when calculating combustion optimizing system input by following equation and exit:

$\stackrel{\‾}{\mathrm{\Δ}bg}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(b_{goni}-b_{goffi})/n$

The meansigma methods of the difference of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, weight Newly calculate average:

$\stackrel{\‾}{\stackrel{\‾}{\mathrm{\Δ}bg}}=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\stackrel{\‾}{\mathrm{\Δ}bgi}/k$

In the present embodiment, calculate combustion optimizing system by following equation and put into and when exiting, standard net coal consumption rate it The standard deviation of difference:

${\mathrm{S\Δb}}_g=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{({\mathrm{\Δb}}_{gi}-\stackrel{\‾}{{\mathrm{\Δb}}_g})}^2}$

Wherein, △ bgi represents that when combustion optimizing system puts into and exits, coal consumption is poor,Combustion optimizing system puts into and moves back The meansigma methods of coal consumption difference when going out.

Fig. 2～Fig. 4 is that combustion control system puts into and unit standard gross coal consumption rate improved situation when exiting and shows It is intended to.Aforesaid computing formula is wherein utilized to draw the average value standard deviation of standard gross coal consumption rate when combustion optimizing system puts into operation Figure is as in figure 2 it is shown, wherein the computing formula of upper and lower limit can be according to practical situation adjustment design parameter；Combustion optimizing system exits The average value standard deviation figure of standard gross coal consumption rate during operation, as shown in Figure 3.By observing the standard coal consumption of the unit in a period of time Fall into number of times reactive combustion in upper and lower interval and optimize when system puts into and exits the lifting feelings of the stability to unit allocation Condition；And the average value standard deviation figure of standard gross coal consumption rate difference that combustion optimizing system puts into and exits, as shown in Figure 4.Pass through In observation a period of time, combustion optimizing system puts into and exits the standard gross coal consumption rate of Practical Calculation in the case of two kinds at average mark The distribution situation of quasi-difference figure judges effect of optimization.

The preferred embodiment of the present invention described in detail above.Should be appreciated that those of ordinary skill in the art without Need creative work just can make many modifications and variations according to the design of the present invention.Therefore, all technology in the art Personnel are available by logical analysis, reasoning, or a limited experiment the most on the basis of existing technology Technical scheme, all should be in the protection domain being defined in the patent claims.

Claims (8)

1. thermal power unit boiler combustion control recruitment evaluation computational methods, it is characterised in that including:

Combustion System effectiveness indicator computing module, is responsible for calculating in the case of combustion control puts into and exits two kinds, load Boiler efficiency, turbine heat rate rate and standard gross coal consumption rate in 50%～100% interval, wherein sends out the standard of unit Electricity coa consumption rate is as the evaluation index of combustion optimizing system implementation result；

Indicator-specific statistics analytical calculation module, puts into combustion optimizing system and exits the standard net coal consumption rate in the case of two kinds and carry out Statistical analysis, puts into combustion optimizing system simultaneously and exits both standard difference of coal consumption of power supplies in the case of two kinds and be analyzed, Burning is evaluated by the standard gross coal consumption rate difference analysis result to the input in a period of time and when exiting combustion optimizing system Optimal control effect.

2. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 1, its feature exists In, comprise the following steps:

S1, respectively in 50%, 60%, 70%, 80%, 90% and 100% load interval, put into combustion optimizing system, machine Group is run Tn hour, observes and records corresponding data, being then log out combustion optimizing system, in same load section from DCS Interior unit operation Tn hour, observes from DCS and records corresponding data；

S2, the principle that maximum tolerance is 0.25% rejecting observing meansigma methods according to the deviation of step S1 each observation and meansigma methods Deviation is beyond the data record of maximum tolerance, and the most remaining data record can be as the initial data being for further processing；

S3, utilize step S2 process after data, respectively calculate combustion optimizing system put into standard net coal consumption rate bgon and Standard net coal consumption rate bgoff when exiting, the computing formula of its Plays gross coal consumption rate is as follows:

$b_g=\frac{q_{cp}}{{\mathrm{\η}}_b\×{\mathrm{\η}}_p\×29.308}\×\frac{1}{1-{\mathrm{\ξ}}_{ap}}$

Wherein: b_gStandard net coal consumption rate；

q_cpTurbine heat rate rate；

η_pPipeline efficiency under this load.

η_bBoiler efficiency；

ξ_apStation service power consumption rate.

3. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 2, its feature exists In: combustion optimizing system put in the case of, calculate respectively 50% load, 60% load, 70% load, 80% load, 90% Load and standard net coal consumption rate value b in 100% load lower a period of time_goni, use the computing formula described in S3；

In the case of combustion optimizing system exits, calculate respectively 50% load, 60% load, 70% load, 80% load, 90% Load and standard net coal consumption rate value b in 100% load lower a period of time_goffi, use the computing formula described in S3.

4. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 3, its feature exists In: by same load operating mode section, combustion optimizing system puts into and exits the standard gross coal consumption rate in the case of two kinds and does difference, each Standard coal consumption difference in load interval is labeled as Δ b_gi.Computing formula is as follows:

Δb_gi=b_goni-b_goffi。

5. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 3, its feature exists In: calculate respectively under 50% load, 60% load, 70% load, 80% load, 90% load and 100% load, burn excellent The standard gross coal consumption rate that change system puts intoWith standard gross coal consumption rate when exitingMeansigma methods:

$\stackrel{\‾}{bgon}=\underset{i=1}{\overset{n}{\Σ}}{b}_{goni}/n$

$\stackrel{\‾}{bgoff}=\underset{i=1}{\overset{n}{\Σ}}{b}_{goffi}/n$

The meansigma methods of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, recalculates all Value:

$\stackrel{\‾}{\stackrel{\‾}{bgon}}=\underset{i=1}{\overset{k}{\Σ}}\stackrel{\‾}{bgoni}/k$

$\stackrel{\‾}{\stackrel{\‾}{bgoff}}=\underset{i=1}{\overset{k}{\Σ}}\stackrel{\‾}{bgoffi}/k.$

6. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 2, its feature exists In: calculate standard deviation S b of standard net coal consumption rate variable quantity when combustion optimizing system puts into by following equation_gonWith move back Standard deviation S b of standard net coal consumption rate variable quantity when going out_goff:

${\mathrm{Sb}}_{gon}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{(b_{goni}-\stackrel{\‾}{b_{gon}})}^2}$

${\mathrm{Sb}}_{goff}=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{(b_{goffi}-\stackrel{\‾}{b_{goff}})}^2}$

Sbgon represents when combustion optimizing system puts into, the standard deviation of standard net coal consumption rate variable quantity,

Sbgoff represents when combustion optimizing system exits, the standard deviation of standard net coal consumption rate variable quantity.

7. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 3, its feature exists In: the meansigma methods of coal consumption difference when calculating combustion optimizing system input by following equation and exit:

$\stackrel{\‾}{\mathrm{\Δ}bg}=\underset{i=1}{\overset{n}{\Σ}}(b_{goni}-b_{goffi})/n$

The meansigma methods of the difference of standard gross coal consumption rate when combustion optimizing system is put into and exited divides some deciles, again counts Calculation average:

$\stackrel{\‾}{\stackrel{\‾}{\mathrm{\Δ}bg}}=\underset{i=1}{\overset{k}{\Σ}}\stackrel{\‾}{\mathrm{\Δ}bgi}/k.$

8. a kind of thermal power unit boiler combustion control recruitment evaluation computational methods as claimed in claim 8, its feature exists In: when calculating combustion optimizing system input by following equation and exit, the standard deviation of the difference of standard net coal consumption rate:

${\mathrm{S\Δb}}_g=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{({\mathrm{\Δb}}_{gi}-\stackrel{\‾}{{\mathrm{\Δb}}_g})}^2}$

Wherein, △ bgi represents that when combustion optimizing system puts into and exits, coal consumption is poor,When combustion optimizing system puts into and exits The meansigma methods of coal consumption difference.