CN104199290A - Circulating fluidized bed boiler operation optimizing method based on consumption difference analysis - Google Patents
Circulating fluidized bed boiler operation optimizing method based on consumption difference analysis Download PDFInfo
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Abstract
The invention provides a circulating fluidized bed boiler operation optimizing method based on consumption difference analysis. According to the method, an expert system that is built based on a circulating fluidized bed boiler system can achieve simulation on energy efficiency analysis of human experts, and perform inference on boiler operation and guiding optimal operation by using expert knowledge in the industry; a critical parameter consumption difference analysis model is built, so that consumption difference computation of real-time operation critical parameters of the boiler system can be achieved, and influence of change of various parameter on boiler efficiency can be obtained; an intelligent algorithm of an expert system is used for obtaining a boiler operation optimizing strategy under a specific load working condition, and thereby boiler efficiency is improved; various losses and boiler efficiencies of the boiler system before and behind optimization are computed and compared to intuitively reflect optimizing effect of the boiler system; the platform of a knowledge base is opened that system operators correct, add or remove facts, rules and corresponding decisions and the like in real time, and thereby sensitivity and precision rate of system diagnosis are improved.
Description
Technical field
The present invention relates to the technical field of burning in circulating fluid bed boiler, particularly a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis.
Background technology
Circulating Fluidized Bed Boiler (CFB) combustion technology is the clean coal combustion technology of a kind of novel efficient, low pollution, due to the unique advantage that it has in coal adaptability, varying load adjustment capability and pollutant emission, be widely used in recent years.Although fluidized-bed combustion can be alleviated the pollution problem that power plant's burning inferior coal brings greatly, but in the evolution of large capacity, high parameter, high automation, still there is the problems such as burning efficiency is low, fire box temperature variation is large, heating surface wear, unburned carbon in flue dust height in its operation in Circulating Fluidized Bed Boiler.Circulating Fluidized Bed Boiler be a multiparameter, non-linear, time become and the closely-coupled system of multivariate, make the raising of its efficiency more complicated and difficult than general boiler, how to improve monitoring boiler level, operation and management level, and to realize economic optimization operation be one of key issue of facing of fluidized-bed combustion boiler.
Set up Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis, than the economic Examination of Small Indicators method of boiler operatiopn generally adopting at present, have larger comprehensive and scientific.Calculate and the method such as historical data reorganization analysis is obtained each operational factor reference value, i.e. the desired value of each parameter operating process in operational process by variable working condition.Quantitative effect by the each parameter drift-out reference value of real-time calculating to boiler efficiency, namely consumption is poor, can more clear judgement affect the parameter of boiler efficiency, thereby can accurately instruct boiler optimization operation.
The main application thinking of expert system is according to steam generator system basic data and the history run situation obtained, simulation expert thinking inference mode, use in industry expertise knowledge to steam generator system operation carry out reasoning and and guide optimization.Artificial intelligence technology is expert system particularly, all be widely used in industries such as space flight, aviation, electric power, boats and ships, chemical industry, create huge economic benefit, be particularly useful for the large scale dynamic system of this type of multiparameter of fluidized-bed combustion boiler system, Multivariable Coupling.
Summary of the invention
The shortcoming that the object of the invention is to overcome prior art, with not enough, provides a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis, realizes the operation of typical recycling fluidized-bed combustion boiler entire system and optimizes.
Object of the present invention is achieved through the following technical solutions:
A Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis, comprises the following steps:
S1, create expert system, comprise underlying parameter storehouse, inference machine, factbase, rule base and the explanation module of Circulating Fluidized Bed Boiler;
S2, set up the poor computation model of Circulating Fluidized Bed Boiler special parameter consumption;
S3, parameter benchmark value are determined, by obtaining Circulating Fluidized Bed Boiler history data, adopt data digging method, determine parameter benchmark value in particular job situation;
S4, obtain boiler real-time running data, and carry out pre-service;
S5, real-time running data and contrast when desired value under preload, true according to rule output, start expert system and carry out reasoning, and export energy consumption diagnosis;
S6, output parameter corresponding to conclusion consumed to poor calculating, the influence value of variable to boiler efficiency gets parms;
S7, according to expert system reasoning conclusion and the poor result of calculation of consumption, provide the suggestion of optimization run action and parameter adjustment and should reach value, and application adjustment parameter afterwards carries out energy consumption analysis calculating and parameter current energy consumption analysis result contrasts, and confirms accessible effect of optimization.
Preferably, the underlying parameter storehouse in described step S1 comprises the each device structure parameter of recirculating fluidized bed, design and operation parameter and nameplate parameter;
Factbase in described step S1 comprises that boiler combustion system, air and gas system and auxiliary system measurable parameter runtime value and reference value contrast the fact higher or on the low side;
Rule base in described step S1: comprise the rule that the each system of boiler reduces based on difference operation operational energy efficiency true and that cause;
Inference machine in described step S1, for carrying out reasoning based on forward reasoning strategy;
Explanation module in described step S1, for realizing the output matching fact, the conclusion of matched rule, and suggestion optimization action corresponding to conclusion.
Preferably, the form of described rule is IF and THEN, and the boiler operating parameter that the prerequisite of rule is various combination departs from the fact collection of reference value.
Preferably, in described step S2, the important parameter about Circulating Fluidized Bed Boiler of the poor computation model of consumption comprises: exhaust gas temperature, boiler oxygen amount, feed temperature, environment temperature, enter stove coal net calorific value, main steam temperature, reheat temperature, main steam pressure, unburned carbon in flue dust, boiler slag carbon content, air preheater air leakage amount.
Preferably, described step S3 parameter benchmark value is definite comprises the following steps: again
S31, choose more than 6 typical load operating mode historical data;
S32, calculate boiler efficiency under different operational factors, choose under each load condition boiler efficiency minimum as optimum operating condition sample;
S33, oxygen content in exhaust gas, fire box temperature parameter adopt design load to carry out variable working condition calculating, obtain its load-reference value relational expression: y
i=a
ix
2+ b
ix+c
i, wherein x is boiler load value, y is operational factor reference value, parameter a
i, b
i, c
iadopt mathematical tool to carry out matching and obtain above-mentioned load-reference value relational expression.
Preferably, in described step S4, boiler real-time running data is by power station SIS system acquisition, get under same load operation operating mode taking 1 minute 10 groups of parameter as the cycle, reject wherein maximal value and minimum value, get the arithmetical mean of remaining 8 groups of parameters as effective real-time running data.
Preferably, in described step S6, steam generator system is carried out to energy consumption analysis calculating, obtain boiler efficiency, and flue gas loss, incomplete combustion loss.
Preferably, in described step S5, by real-time running data and after under preload, desired value contrasts, all initial evidence corresponding to output also calculates its degree of confidence.
Preferably, after in described step S5, expert system starts, first inference machine carries out rule match according to initial true in rule base, and exports rule conclusion, causes the true of conclusion and suggestion Optimal Decision-making.
Preferably, described inference engine of expert system carries out after rule match in rule base according to initial evidence and execution degree thereof, obtains diagnosis of energy saving conclusion and decision-making thereof by degree of confidence pass-algorithm, and wherein degree of confidence pass-algorithm is specially:
If Ai is true, Ci is its degree of confidence, and B is conclusion, the degree of confidence that b is conclusion, and i=1,2 ..., n, in system, Rule Expression is:
IF?A1(C1)A2(C2)…An(Cn)
THEN?B(b)
If have true A1 ' (C1 '), A2 ' (C2 ') ... An ' (Cn '), mate with regular prerequisite, also must meet the following conditions and just export this rule conclusion:
Max{0, C1-C1 ' }+Max{0, C2-C2 ' }+... + Max{0, Cn-Cn ' } <=λ, wherein λ is the given threshold value of expert;
The degree of confidence b ' of the conclusion B of output is determined by following formula:
b’＝(1-Max{0，C1-C1’})*(1-Max{0，C2-C2’})*…*(1-Max{0，Cn-Cn’})，
When being greater than the given threshold value of expert, true matched rule success and conclusion degree of confidence export conclusion; By consuming poor calculating, obtain each fact to unit power supply heat consumption rate influence value; Exporting all conclusions sorts to heat consumption rate influence value size that unit is powered according to it.
The present invention has following advantage and effect with respect to prior art:
1, the present invention is based on the Expert System Model that circulating fluidized bed boiler systems is set up, can realize the thinking of simulating human expert Energy Efficiency Analysis, use in industry expertise knowledge to steam generator system operation carry out reasoning and and guide and optimize operation; Set up Circulating Fluidized Bed Boiler important parameter power consumption analysis model, can realize the poor calculating of steam generator system real time execution parameter consumption.
The Circulating Fluidized Bed Boiler optimizing operation method of the power consumption analysis that 2, the present invention proposes, can realize the poor calculating of consumption of steam generator system important parameter, obtains the impact of parameter drift-out reference value on every loss and boiler efficiency; Adopt the intelligent algorithm of expert system, obtain the boiler operatiopn Optimal Decision-making under specific load operating mode, improve boiler efficiency; And the every loss of steam generator system and boiler efficiency calculating contrast before and after being optimized, intuitively embody steam generator system effect of optimization.
Brief description of the drawings
Fig. 1 is the circulating fluidized bed boiler systems optimizing operation method Technology Roadmap based on power consumption analysis in the present invention;
Fig. 2 is expert system diagnosis of energy saving reasoning process flow diagram in the present invention.
Embodiment
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.
Embodiment mono-
The technical scheme of technical solution problem of the present invention comprises the following steps:
One, the establishment of expert system
Underlying parameter storehouse: comprise the each device structure parameter of recirculating fluidized bed, design and operation parameter and nameplate parameter etc.;
Factbase: comprise the fact that boiler combustion system, air and gas system and auxiliary system measurable parameter runtime value and reference value contrast are higher or on the low side;
Rule base: comprise the rule that steam generator system reduces based on difference operation boiler efficiency true and that cause, be that regular form is [IF (), THEN ()], the fact collection of the steam generator system parameter drift-out reference value that the prerequisite of rule is various combination;
Inference machine: carry out reasoning based on forward reasoning strategy;
Explanation module: output matching [fact], [conclusion] of matched rule, and suggestion optimization action corresponding to conclusion.
Two, the poor computation model of consumption is set up
Adopt heating power calculating, equivalent enthalpy drop method and variable working condition to calculate the poor computation model of consumption of setting up exhaust gas temperature, boiler oxygen amount, feed temperature, environment temperature, entering 11 circulating fluidized bed boiler systems important parameters such as stove coal net calorific value, main steam temperature, reheat temperature, main steam pressure, unburned carbon in flue dust, boiler slag carbon content, air preheater air leakage amount.
Three, parameter benchmark value is determined
Choose 6 typical load operating mode historical datas, calculate boiler efficiency under different operational factors, choose under each load condition boiler efficiency minimum as optimum operating condition sample; The parameter such as oxygen content in exhaust gas, fire box temperature adopts design load to carry out variable working condition calculating, obtains its load-reference value relational expression and is: y
i=a
ix
2+ b
ix+c
i(x is unit load value, and y is operational factor reference value), other parameters adopt mathematical tool to carry out matching and obtain above-mentioned load-reference value relational expression.
Four, valid data obtain in real time
Effectively real-time running data obtains from power station SIS system (SIS in Thermal Power PlantQ Supervisory Information System is called for short SIS); Getting 1min under same load operation operating mode is cycle 10 groups of parameters, rejects maximal value and minimum value, gets the arithmetical mean of 8 groups of parameters as effective real-time running data.
Five, Energy Efficiency Analysis
Steam generator system is carried out to energy consumption analysis calculating, obtain the each loss distribution of steam generator system and boiler efficiency value; Steam generator system is carried out to energy consumption analysis calculating, and calculating can be obtained result and be comprised boiler efficiency, flue gas loss, incomplete combustion loss.
Six, start expert system
Valid data and reference value contrast in real time, output difference starts expert system and carries out reasoning, in rule base, carry out rule match according to the initial fact, and output rule [conclusion] (being diagnosis of energy saving conclusion), [fact] that cause conclusion and [action is optimized in suggestion] (suggestion Optimal Decision-making).
Seven, consumption is poor calculates
Consume poor calculating to exporting true corresponding straggling parameter, obtain the quantitative effect of each parameter drift-out reference value to boiler efficiency.
Eight, optimize operation
According to expert system reasoning conclusion and the poor result of calculation of consumption, provide the suggestion of optimization run action and parameter adjustment and should reach value, and application adjustment parameter afterwards carries out energy consumption analysis calculating and parameter current energy consumption analysis result contrasts, and confirms accessible effect of optimization.
Embodiment bis-
The present embodiment is in conjunction with for a more detailed description to the present invention to the efficiency optimization of certain two circulating fluidized bed boiler systems of power plant.
One, the foundation of expert system knowledge base
It is the typical recycling fluidized bed generating plant level of factory optimization system based on expert system that this Circulating Fluidized Bed Boiler is optimized operational system.The foundation of expert system knowledge base comprises:
1) foundation in device parameter storehouse
Device parameter storehouse comprises structure and the design parameter of this boiler of power plant system major equipment and subsidiary engine thereof; Comprise after major overhaul in October, 2013～2014 operation history data in year March, from SIS system introducing.
2) foundation of factbase
This factbase comprises steam generator system while carrying out expert reasoning needs and all facts that produce;
3) foundation of rule base
The rule of the relative influence steam generator system efficiency that this rule base comprises the each apparatus field expert of circulating fluidized bed boiler systems; The form of rule base adopts:
The true n of IF
True m
……
THEN diagnosis---decision-making
As follows:
Rule?6
IF make-up water flow increases
Exhaust gas temperature reduces
THEN pipe leakage
Output decision-making: pipeline maintenance, reduces to leak.
Rule?11
IF exhaust gas temperature reduces
Air preheater outlet O2 increases
Air preheater outlet CO2 reduces
Boiler export O2 is normal
THEN air preheater leaks
Decision-making: air preheater leak test and maintenance
……
Two, the obtaining of reference value under steam generator system specific load operating mode;
Steam generator system operation history data is carried out to efficiency calculating, obtain under each typical load operating mode historical optimum operating condition sample; Adopt mathematical tool to carry out matching, the each operational factor load-reference value of matching relational expression is: y
i=a
ix
2+ b
ix+c
i(x is boiler load value, and y is operational factor reference value).
Example: the reference value curve of exhaust gas temperature (DEG C):
Y1=0.00093x
2-0.32299x+157.37637 (X is load, and unit is MW, lower same)
Three, real-time data acquisition;
Obtain current service data, with reference value contrast, the corresponding all initial evidences of output also adopt following method to calculate its degree of confidence:
Setting parameter reference value is X
c, upper limit X
sR, lower limit X
sL, parameter currency is X, the definition of degree of confidence function C (X) divides 3 kinds of situations:
1, only there is upper limit X in parameter
sR,
2, only there is lower limit X in parameter
sL,
3, there is upper limit X in parameter
sR, lower limit X
sL,
Four, setup rule storehouse, carries out steam generator system diagnosis of energy saving;
Start inference machine, in rule base, carry out rule match according to initial evidence and execution degree thereof, and obtain diagnosis of energy saving conclusion and decision-making thereof by degree of confidence pass-algorithm:
If Ai is true, Ci is its degree of confidence, and B is conclusion, the degree of confidence that b is conclusion, and i=1,2 ..., n,
In system, Rule Expression is:
IF?A1(C1)A2(C2)…An(Cn)
THEN?B(b)
If have true A1 ' (C1 '), A2 ' (C2 ') ... An ' (Cn '), mate with regular prerequisite, also must meet the following conditions and just export this rule conclusion:
Max{0, C1-C1 ' }+Max{0, C2-C2 ' }+... + Max{0, Cn-Cn ' } <=λ (λ is the given threshold value of expert)
The degree of confidence b ' of the conclusion B of output is determined by following formula:
b’＝(1-Max{0，C1-C1’})*(1-Max{0，C2-C2’})*…*(1-Max{0，Cn-Cn’})
When being greater than the given threshold value of expert, true matched rule success and conclusion degree of confidence export conclusion; By consuming poor calculating, obtain each fact to boiler efficiency influence value; Exporting all conclusions sorts to boiler efficiency influence value size according to it.
With reference to the accompanying drawings shown in 2, the basic thought of forward reasoning is: from the known initial evidence of steam generator system, (while is true) forward service regeulations also, match by the fact known in regular precondition and factbase, if the match is successful, activate this rule, add in factbase regular conclusion part as new fact, repeat said process, till knowing the diagnostic rule that there is no coupling.Because the initial evidence of this expert system is to obtain according to operational factor, operations staff is judged to steam generator system running status has succinct effect directly perceived, therefore initial evidence and the rule conclusion that the match is successful at every turn are all exported to user.
Five, energy consumption analysis, comparing calculation effect of optimization.
Energy consumption analysis module, calculates respectively according to expert system and provides every loss and the boiler efficiency of decision-making before and after being optimized, and with to running Optimization effect, the energy efficiency indexes of calculating comprises: boiler efficiency, flue gas loss, incomplete combustion loss.。
Above-described embodiment is preferably embodiment of the present invention; but embodiments of the present invention are not restricted to the described embodiments; other any do not deviate from change, the modification done under Spirit Essence of the present invention and principle, substitutes, combination, simplify; all should be equivalent substitute mode, within being included in protection scope of the present invention.
Claims (10)
1. the Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis, is characterized in that, comprises the following steps:
S1, create expert system, comprise underlying parameter storehouse, inference machine, factbase, rule base and the explanation module of Circulating Fluidized Bed Boiler;
S2, set up the poor computation model of Circulating Fluidized Bed Boiler special parameter consumption;
S3, parameter benchmark value are determined, by obtaining Circulating Fluidized Bed Boiler history data, adopt data digging method, determine parameter benchmark value in particular job situation;
S4, obtain boiler real-time running data, and carry out pre-service;
S5, real-time running data and contrast when desired value under preload, true according to rule output, start expert system and carry out reasoning, and export energy consumption diagnosis;
S6, output parameter corresponding to conclusion consumed to poor calculating, the influence value of variable to boiler efficiency gets parms;
S7, according to expert system reasoning conclusion and the poor result of calculation of consumption, provide the suggestion of optimization run action and parameter adjustment and should reach value, and application adjustment parameter afterwards carries out energy consumption analysis calculating and parameter current energy consumption analysis result contrasts, and confirms accessible effect of optimization.
2. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that:
Underlying parameter storehouse in described step S1 comprises the each device structure parameter of recirculating fluidized bed, design and operation parameter and nameplate parameter;
Factbase in described step S1 comprises that boiler combustion system, air and gas system and auxiliary system measurable parameter runtime value and reference value contrast the fact higher or on the low side;
Rule base in described step S1: comprise the rule that the each system of boiler reduces based on difference operation operational energy efficiency true and that cause;
Inference machine in described step S1, for carrying out reasoning based on forward reasoning strategy;
Explanation module in described step S1, for realizing the output matching fact, the conclusion of matched rule, and suggestion optimization action corresponding to conclusion.
3. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 2, is characterized in that:
The form of described rule is IF and THEN, and the boiler operating parameter that the prerequisite of rule is various combination departs from the fact collection of reference value.
4. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that,
In described step S2, the important parameter about Circulating Fluidized Bed Boiler of the poor computation model of consumption comprises: exhaust gas temperature, boiler oxygen amount, feed temperature, environment temperature, enter stove coal net calorific value, main steam temperature, reheat temperature, main steam pressure, unburned carbon in flue dust, boiler slag carbon content, air preheater air leakage amount.
5. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that, described step S3 parameter benchmark value is definite to be comprised the following steps: again
S31, choose more than 6 typical load operating mode historical data;
S32, calculate boiler efficiency under different operational factors, choose under each load condition boiler efficiency minimum as optimum operating condition sample;
S33, oxygen content in exhaust gas, fire box temperature parameter adopt design load to carry out variable working condition calculating, obtain its load-reference value relational expression: y
i=a
ix
2+ b
ix+c
i, wherein x is boiler load value, y is operational factor reference value, parameter a
i, b
i, c
iadopt mathematical tool to carry out matching and obtain above-mentioned load-reference value relational expression.
6. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that:
In described step S4, boiler real-time running data is by power station SIS system acquisition, get under same load operation operating mode taking 1 minute 10 groups of parameter as the cycle, reject wherein maximal value and minimum value, get the arithmetical mean of remaining 8 groups of parameters as effective real-time running data.
7. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that:
In described step S6, steam generator system is carried out to energy consumption analysis calculating, obtain boiler efficiency, and flue gas loss, incomplete combustion loss.
8. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that:
In described step S5, by real-time running data and after under preload, desired value contrasts, all initial evidence corresponding to output also calculates its degree of confidence.
9. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 1, is characterized in that:
After in described step S5, expert system starts, first inference machine carries out rule match according to initial true in rule base, and exports rule conclusion, causes the true of conclusion and suggestion Optimal Decision-making.
10. a kind of Circulating Fluidized Bed Boiler optimizing operation method based on power consumption analysis according to claim 9, is characterized in that,
Described inference engine of expert system carries out after rule match in rule base according to initial evidence and execution degree thereof, obtains diagnosis of energy saving conclusion and decision-making thereof by degree of confidence pass-algorithm, and wherein degree of confidence pass-algorithm is specially:
If Ai is true, Ci is its degree of confidence, and B is conclusion, the degree of confidence that b is conclusion, and i=1,2 ..., n, in system, Rule Expression is:
IF?A1(C1)A2(C2)…An(Cn)
THEN?B(b)
If have true A1 ' (C1 '), A2 ' (C2 ') ... An ' (Cn '), mate with regular prerequisite, also must meet the following conditions and just export this rule conclusion:
Max{0, C1-C1 ' }+Max{0, C2-C2 ' }+... + Max{0, Cn-Cn ' } <=λ, wherein λ is the given threshold value of expert;
The degree of confidence b ' of the conclusion B of output is determined by following formula:
b’＝(1-Max{0，C1-C1’})*(1-Max{0，C2-C2’})*…*(1-Max{0，Cn-Cn’})，
When being greater than the given threshold value of expert, true matched rule success and conclusion degree of confidence export conclusion; By consuming poor calculating, obtain each fact to unit power supply heat consumption rate influence value; Exporting all conclusions sorts to heat consumption rate influence value size that unit is powered according to it.
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