CN103438444B - CFBB minimize energy losses system and method - Google Patents
CFBB minimize energy losses system and method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000002485 combustion reaction Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 30
- 230000002596 correlated effect Effects 0.000 claims description 24
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 12
- 239000005864 Sulphur Substances 0.000 claims description 12
- 239000003245 coal Substances 0.000 claims description 12
- 239000000203 mixture Substances 0.000 claims description 12
- 239000000779 smoke Substances 0.000 claims description 12
- 239000002802 bituminous coal Substances 0.000 claims description 6
- 230000000875 corresponding effect Effects 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 6
- 230000017105 transposition Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 238000012423 maintenance Methods 0.000 abstract 1
- 238000005457 optimization Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
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Abstract
The invention discloses a kind of CFBB minimize energy losses system and method, system comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with CFBB; Field intelligent instrument is connected with control station, database and host computer, host computer comprises: energy loss prediction module, for prediction loop fluidized-bed combustion boiler under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce; Constraint judge module, for judging whether the performance variable preset is under current working, meet the technological requirement of CFBB; Optimizing module, for finding optimum performance variable; Constraint judge module; Signal acquisition module; Result display module.The present invention, under maintenance steam parameter and steam production meet the prerequisite of production requirement, by the optimization of performance variable, fully reduces energy loss, improves boiler operating efficiency.
Description
Technical field
The present invention relates to energy project field, especially, relate to a kind of CFBB minimize energy losses system and method.
Background technology
CFBB has the advantages such as pollutant emission is few, fuel tolerance wide, Load Regulation ability is strong, obtains in recent years applying more and more widely in the industry such as electric power, heat supply.Along with the growing tension of the energy and the continuous enhancing of people's energy-conserving and environment-protective consciousness, user, in the urgent need to excavating the operation potentiality of boiler controller system, improves the operational efficiency of unit.But current most of CFBB all to there is automaticity low, operation relies on the feature of artificial experience, makes the energy-saving potential of boiler be difficult to be taped the latent power fully.Set up effective minimize energy losses system, fully improve the energy efficiency of CFBB, there is important practical significance.
Summary of the invention
The object of the invention is to for the deficiencies in the prior art, a kind of CFBB heat loss due to exhaust gas prognoses system and method are provided.
The technical solution adopted for the present invention to solve the technical problems is: a kind of CFBB minimize energy losses system, comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with CFBB; Field intelligent instrument is connected with control station, database and host computer, and described host computer comprises:
Energy loss prediction module, for prediction loop fluidized-bed combustion boiler under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent i-th training sample value of First air total blast volume (Nm3/h), Secondary Air total blast volume (Nm3/h), main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%) respectively; y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number.
1.2) prediction coefficient matrix β is asked by following formula:
β=(X
TX)
-1X
TY （2）
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix.
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]=[1 x
1x
2... x
11]β （3）
Wherein, x
1, x
2represent performance variable respectively: First air total blast volume (Nm
3/ h), Secondary Air total blast volume (Nm
3/ h) current preset value; x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A).
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f=f
p(y
5,y
6,y
7,x
3) （6）
Wherein, K
1, K
2for the design factor relevant with coal, for common bituminous coal, get K
1=3.35, K
2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat.
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all=f(X)=q
2+q
4+k
ep
f（7）
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat; k
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X.
Constraint judge module, for judging whether the performance variable preset is under current working, meet the technological requirement of CFBB, adopts following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax（8）
f
smin≤x
2≤f
smax（9）
t
bmin≤y
2≤t
bmax（10）
x
4+y
3≥t
pmin（11）
x
1+x
2≥k
fminx
3（12）
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio.
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible.
Optimizing module, for finding optimum performance variable, adopts following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1=f
pmin+r
1(f
pmax-f
pmin) （13）
x
2=f
smin+r
2(f
smax-f
smin) （14）
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval.
3.2) whether feasible by constraint judge module judging point X, if some X is feasible, then make initial point X
(i)=X, otherwise go to step 3.1) produce new random point, till the random point X produced is feasible.
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3).
3.4) by energy loss prediction module future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H).
3.5) initial optimizing coefficient gamma=1.6 are made.
3.6) new exploration point X is produced by following formula
(R):
3.7) by constraint judge module judging point X
(R)whether can, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible.
3.8) by energy loss prediction module future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8).
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| <1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing.
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow.
As preferred a kind of scheme: described host computer also comprises:
Signal acquisition module, for the sampling time interval by setting, gathers real time data from field intelligent instrument, and gather historical data from database.
Result display module, for reading parameters from control station, and passes to control station show the primary air flow of optimum, Secondary Air value, so that control station staff, according to suggestion for operation, adjust operating condition in time, reduce boiler energy loss, improve boiler operating efficiency.
A kind of CFBB minimize energy losses method, described minimize energy losses method comprises the following steps:
1) prediction loop fluidized-bed combustion boiler is under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent First air total blast volume (Nm respectively
3/ h), Secondary Air total blast volume (Nm
3/ h), i-th training sample value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number.
1.2) prediction coefficient matrix β is asked by following formula:
β=(X
TX)
-1X
TY （2）
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix.
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]=[1 x
1x
2... x
11]β （3）
Wherein, x
1, x
2represent performance variable respectively: First air total blast volume (Nm
3/ h), Secondary Air total blast volume (Nm
3/ h) current preset value; x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A).
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f=f
p(y
5,y
6,y
7,x
3) （6）
Wherein, K
1, K
2for the design factor relevant with coal, for common bituminous coal, get K
1=3.35, K
2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat.
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all=f(X)=q
2+q
4+k
ep
f（7）
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat; k
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X.
2) judge whether default performance variable is under current working, meet the technological requirement of CFBB, adopt following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax（8）
f
smin≤x
2≤f
smax（9）
t
bmin≤y
2≤t
bmax（10）
x
4+y
3≥t
pmin（11）
x
1+x
2≥k
fminx
3（12）
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio.
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible.
3) find optimum performance variable, adopt following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1=f
pmin+r
1(f
pmax-f
pmin) （13）
x
2=f
smin+r
2(f
smax-f
smin) （14）
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval.
3.2) by step 2) whether judging point X feasible, if some X is feasible, then makes initial point X
(i)=X, otherwise go to step
3.1) new random point is produced, till the random point X produced is feasible.
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3).
3.4) by step 1) future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H).
3.5) initial optimizing coefficient gamma=1.6 are made.
3.6) new exploration point X is produced by following formula
(R):
3.7) by step 2) judging point X
(R)whether can, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible.
3.8) by step 1) future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8).
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| <1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing.
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow.
As preferred a kind of scheme: in described step 3), parameters is read from control station, and the primary air flow of optimum, Secondary Air value are passed to control station and shown, so that control station staff, according to suggestion for operation, timely adjustment operating condition, reduces boiler energy loss, improves boiler operating efficiency.
Beneficial effect of the present invention is mainly manifested in: be optimized the performance variable of CFBB, and suggestion and guides production operation, reduces boiler energy loss, excavates device energy-saving potential, improve productivity effect.
Accompanying drawing explanation
Fig. 1 is the hardware structure diagram of system proposed by the invention.
Fig. 2 is the functional block diagram of host computer of the present invention.
Detailed description of the invention
Below in conjunction with drawings and Examples, the invention will be further described.
Embodiment 1
With reference to Fig. 1, Fig. 2, a kind of CFBB minimize energy losses system, comprise the field intelligent instrument 2, data-interface 3, database 4, control station 5 and the host computer 6 that are connected with CFBB 1, field intelligent instrument 2 is connected with fieldbus, data/address bus is connected with data-interface 3, data-interface 3 is connected with database 4, control station 5 and host computer 6, and described host computer 6 comprises:
Energy loss prediction module 7, for prediction loop fluidized-bed combustion boiler under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent First air total blast volume (Nm respectively
3/ h), Secondary Air total blast volume (Nm
3/ h), i-th training sample value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number.
1.2) prediction coefficient matrix β is asked by following formula:
β=(X
TX)
-1X
TY （2）
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix.
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]=[1 x
1x
2... x
11]β （3）
Wherein, x
1, x
2represent performance variable respectively: First air total blast volume (Nm
3/ h), Secondary Air total blast volume (Nm
3/ h) current preset value; x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A).
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f=f
p(y
5,y
6,y
7,x
3) （6）
Wherein, K
1, K
2for the design factor relevant with coal, for common bituminous coal, get K
1=3.35, K
2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat.
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all=f(X)=q
2+q
4+k
ep
f（7）
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat; k
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X.
Constraint judge module 9, for judging whether the performance variable preset is under current working, meet the technological requirement of CFBB, adopts following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax（8）
f
smin≤x
2≤f
smax（9）
t
bmin≤y
2≤t
bmax（10）
x
4+y
3≥t
pmin（11）
x
1+x
2≥k
fminx
3（12）
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio.
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible.
Optimizing module 8, for finding optimum performance variable, adopts following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1=f
pmin+r
1(f
pmax-f
pmin) （13）
x
2=f
smin+r
2(f
smax-f
smin) （14）
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval.
3.2) whether feasible by constraint judge module 9 judging point X, if some X is feasible, then make initial point X
(i)=X, otherwise go to step 3.1) produce new random point, till the random point X produced is feasible.
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3).
3.4) by energy loss prediction module 7 future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H).
3.5) initial optimizing coefficient gamma=1.6 are made.
3.6) new exploration point X is produced by following formula
(R):
3.7) by constraint judge module 9 judging point X
(R)whether can, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible.
3.8) by energy loss prediction module 7 future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8).
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| <1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing.
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow.
Described host computer 6 also comprises: signal acquisition module 11, for the sampling time interval by setting, gathers real time data, and gather historical data from field intelligent instrument from database.
Described host computer 6 also comprises: result display module 10, for reading parameters from control station, and the primary air flow of optimum, Secondary Air value are passed to control station and shown, so that control station staff, according to suggestion for operation, timely adjustment operating condition, reduces boiler energy loss, improves boiler operating efficiency.
The hardware components of described host computer 6 comprises: I/O element, for the collection of data and the transmission of information; Data storage, the data sample needed for storage running and operational factor etc.; Program storage, stores the software program of practical function module; Arithmetic unit, performing a programme, realizes the function of specifying; Display module, parameter, operation result that display is arranged, and provide suggestion for operation.
Embodiment 2
With reference to Fig. 1, Fig. 2, a kind of CFBB minimize energy losses method, described minimize energy losses method comprises the following steps:
1) prediction loop fluidized-bed combustion boiler is under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent First air total blast volume (Nm respectively
3/ h), Secondary Air total blast volume (Nm
3/ h), i-th training sample value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number.
1.2) prediction coefficient matrix β is asked by following formula:
β=(X
TX)
-1X
TY （2）
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix.
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]=[1 x
1x
2... x
11]β （3）
Wherein, x
1, x
2represent performance variable respectively: First air total blast volume (Nm
3/ h), Secondary Air total blast volume (Nm
3/ h) current preset value; x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A).
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f=f
p(y
5,y
6,y
7,x
3) （6）
Wherein, K
1, K
2for the design factor relevant with coal, for common bituminous coal, get K
1=3.35, K
2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat.
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all=f(X)=q
2+q
4+k
ep
f（7）
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat; k
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X.
2) judge whether default performance variable is under current working, meet the technological requirement of CFBB, adopt following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax（8）
f
smin≤x
2≤f
smax（9）
t
bmin≤y
2≤t
bmax（10）
x
4+y
3≥t
pmin（11）
x
1+x
2≥k
fminx
3（12）
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio.
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible.
3) find optimum performance variable, adopt following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1=f
pmin+r
1(f
pmax-f
pmin) （13）
x
2=f
smin+r
2(f
smax-f
smin) （14）
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval.
3.2) by step 2) whether judging point X feasible, if some X is feasible, then makes initial point X
(i)=X, otherwise go to step
3.1) new random point is produced, till the random point X produced is feasible.
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3).
3.4) by step 1) future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H).
3.5) initial optimizing coefficient gamma=1.6 are made.
3.6) new exploration point X is produced by following formula
(R):
3.7) by step 2) judging point X
(R)whether can, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible.
3.8) by step 1) future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8).
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| <1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing.
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow.
Described method also comprises: in described step 3), parameters is read from control station, and the primary air flow of optimum, Secondary Air value are passed to control station and shown, so that control station staff, according to suggestion for operation, timely adjustment operating condition, reduces boiler energy loss, improves boiler operating efficiency.
CFBB minimize energy losses system and method proposed by the invention, be described by above-mentioned concrete implementation step, person skilled obviously can not depart from content of the present invention and scope apparatus and method as herein described are changed or suitably change with combination, realize the technology of the present invention.Special needs to be pointed out is, all similar replacements and change apparent to one skilled in the art, they all can be deemed to be included in spirit of the present invention, scope and content.
Claims (2)
1. a CFBB minimize energy losses system, is characterized in that, comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with CFBB; Field intelligent instrument is connected with control station, database and host computer, and described host computer comprises:
Energy loss prediction module, for prediction loop fluidized-bed combustion boiler under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent i-th training sample value of First air total blast volume (Nm3/h), Secondary Air total blast volume (Nm3/h), main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%) respectively; y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number;
1.2) prediction coefficient matrix β is asked by following formula:
β＝(X
TX)
-1X
TY (2)
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix;
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]＝[1 x
1x
2... x
11]β (3)
Wherein, x
1, x
2represent performance variable respectively: the current preset value of First air total blast volume (Nm3/h), Secondary Air total blast volume (Nm3/h); x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A);
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f＝f
p(y
5,y
6,y
7,x
3) (6)
Wherein, K1, K2 are the design factor relevant with coal, for common bituminous coal, get K1=3.35, K2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat;
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all＝f(X)＝q
2+q
4+k
ep
f(7)
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat; k
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X;
Constraint judge module, for judging whether the performance variable preset is under current working, meet the technological requirement of CFBB, adopts following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax(8)
f
smin≤x
2≤f
smax(9)
t
bmin≤y
2≤t
bmax(10)
x
4+y
3≥t
pmin(11)
x
1+x
2≥k
fminx
3(12)
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio;
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible;
Optimizing module, for finding optimum performance variable, adopts following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1＝f
pmin+r
1(f
pmax-f
pmin) (13)
x
2＝f
smin+r
2(f
smax-f
smin) (14)
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval;
3.2) whether feasible by constraint judge module judging point X, if some X is feasible, then make initial point X
(i)=X, otherwise go to step 3.1) produce new random point, till the random point X produced is feasible;
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3);
3.4) by energy loss prediction module future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H);
3.5) initial optimizing coefficient gamma=1.6 are made;
3.6) new exploration point X is produced by following formula
(R):
3.7) by constraint judge module judging point X
(R)whether feasible point, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible;
3.8) by energy loss prediction module future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8);
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| < 1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing;
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow;
Described host computer also comprises:
Signal acquisition module, for the sampling time interval by setting, gathers real time data from field intelligent instrument, and gather historical data from database;
Result display module, for reading parameters from control station, and passes to control station show the primary air flow of optimum, Secondary Air value, so that control station staff, according to suggestion for operation, adjust operating condition in time, reduce boiler energy loss, improve boiler operating efficiency.
2., by the minimize energy losses method that CFBB minimize energy losses system according to claim 1 realizes, it is characterized in that, described minimize energy losses method comprises the following steps:
1) prediction loop fluidized-bed combustion boiler is under current operating condition, if run by the performance variable value preset, the total energy loss rate that boiler will produce, adopts following process:
1.1) from database, gather the historical record of operation variable and service condition variable, the training sample matrix X of composition independent variable, gathers corresponding energy loss correlated variables, composition dependent variable training sample matrix Y, note:
Wherein x
i, 1, x
i, 2..., x
i, 11represent First air total blast volume (Nm respectively
3/ h), Secondary Air total blast volume (Nm
3/ h), i-th training sample value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
i, 1, y
i, 2..., y
i, 7represent i-th training sample value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A) respectively; N is training sample number;
1.2) prediction coefficient matrix β is asked by following formula:
β＝(X
TX)
-1X
TY (2)
Wherein, transposition, the inverse of a matrix of subscript: T ,-1 difference representing matrix;
1.3) gather the live signal of service condition variable from field intelligent instrument, the current preset value of binding operation variable, ask the predicted value of energy loss correlated variables by following formula:
[y
1y
2... y
7]＝[1 x
1x
2... x
11]β (3)
Wherein, x
1, x
2represent performance variable respectively: First air total blast volume (Nm
3/ h), Secondary Air total blast volume (Nm
3/ h) current preset value; x
3, x
4..., x
11represent service condition variable respectively: the instantaneous value of main steam flow (t/h), environment temperature (DEG C), feed temperature (DEG C), combustion chamber draft (kPa), bed pressure (kPa), coal-fired moisture (%), coal-fired volatile matter (%), coal-fired ash content (%), coal-fired sulphur content (%); y
1, y
2..., y
7represent energy loss correlated variables respectively: the predicted value of excess air coefficient, bed temperature (DEG C), the smoke evacuation temperature difference (DEG C), flying marking percentage (%), primary air fan electric current (A), overfire air fan electric current (A), air-introduced machine electric current (A);
1.4) predicted value of each subitem rate of energy loss is asked for by (4) ~ (6) formula:
p
f＝f
p(y
5,y
6,y
7,x
3) (6)
Wherein, K
1, K
2for the design factor relevant with coal, for common bituminous coal, get K
1=3.35, K
2=0.44; q
2for heat loss due to exhaust gas rate predicted value, namely heat loss due to exhaust gas accounts for the predicted value that boiler always inputs the ratio of heat; x
12for the net calorific value as received basis (kJ/kg) of fire coal; q
4for solid-unburning hot loss rate predicted value, namely solid-unburning hot loss accounts for the predicted value that boiler always inputs the ratio of heat; f
p() is each blower fan electric current of determining according to physical device and the functional relation between boiler load and blower fan power consumption rate; p
ffor blower fan power consumption rate predicted value, namely blower fan power consumption accounts for the predicted value that boiler always inputs the ratio of heat;
1.5) predicted value of total energy loss rate is asked for by following formula:
q
all＝f(X)＝q
2+q
4+k
ep
f(7)
Wherein, q
allfor total energy loss rate predicted value, namely the total energy loss of boiler accounts for the predicted value that boiler always inputs the ratio of heat;
K
efor the value ratio of electric energy and heat energy; F (X) represents that total energy loss rate is the function of performance variable collection X;
2) judge whether default performance variable is under current working, meet the technological requirement of CFBB, adopt following process:
2.1) by the energy loss correlated variables that service condition variable, default performance variable and prediction obtain, (8) ~ (12) formula is substituted into:
f
pmin≤x
1≤f
pmax(8)
f
smin≤x
2≤f
smax(9)
t
bmin≤y
2≤t
bmax(10)
x
4+y
3≥t
pmin(11)
x
1+x
2≥k
fminx
3(12)
Wherein, f
pminfor minimum primary air flow, f
pmaxfor maximum primary air flow, f
sminfor minimum secondary air flow, f
smaxfor maximum secondary air quantity, t
bminfor minimum operation bed temperature, t
bmaxfor the highest operation bed temperature, t
pminfor minimum exhaust gas temperature, k
fminfor minimum wind load ratio;
2.2) if (8) ~ (12) formula is all set up, then judge that the performance variable preset is as feasible, otherwise judge that the performance variable preset is as infeasible;
3) find optimum performance variable, adopt following process:
3.1) in the two dimensional surface being reference axis with primary air flow, secondary air flow, a random point X (x is produced by (13), (14) formula
1, x
2):
x
1＝f
pmin+r
1(f
pmax-f
pmin) (13)
x
2＝f
smin+r
2(f
smax-f
smin) (14)
Wherein, r
1, r
2for 2 pseudo random numbers in (0,1) interval;
3.2) by step 2) whether judging point X feasible, if some X is feasible, then makes initial point X
(i)=X, otherwise go to step 3.1) produce new random point, till the random point X produced is feasible;
3.3) step 3.1 is returned) produce next feasible random point, until the random point that generation 3 is feasible, be designated as X respectively
(1), X
(2), X
(3);
3.4) by step 1) future position X
(1), X
(2), X
(3)total energy loss rate: f (X
(1)), f (X
(2)), f (X
(3)), compare f (X
(1)), f (X
(2)), f (X
(3)), by f (X
(i)) minimum, middle, maximum point, be designated as X respectively
(L), X
(G), X
(H);
3.5) initial optimizing coefficient gamma=1.6 are made;
3.6) new exploration point X is produced by following formula
(R):
3.7) by step 2) judging point X
(R)whether feasible point, if X
(R)for feasible point, then turn 3.8), otherwise order
return step 3.6), until the exploration point X produced
(R)till feasible;
3.8) by step 1) future position X
(R)total energy loss rate, if f (X
(R))>=f (X
(H)), then make
return step 3.6), otherwise order X
(1)=X
(L), X
(2)=X
(G), X
(3)=X
(R), turn 3.8);
3.9) if meet the condition of convergence: || X
(1)-X
(2)||+|| X
(2)-X
(3)||+|| X
(1)-X
(3)|| < 1 × 10
-10, i.e. X
(1), X
(2), X
(3)enough close, then turn 3.10), otherwise return step 3.4) carry out next step optimizing;
3.10) X
(1)be optimum point, the component of its correspondence
be optimum primary air flow, optimum secondary air flow;
Described method also comprises: in described step 3) in, parameters is read from control station, and the primary air flow of optimum, Secondary Air value are passed to control station and shown, so that control station staff, according to suggestion for operation, timely adjustment operating condition, reduces boiler energy loss, improves boiler operating efficiency.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS56108012A (en) * | 1980-01-29 | 1981-08-27 | Kawasaki Heavy Ind Ltd | Circulating method for high-temperature exhaust gas in fluidized-bed combustion furnace |
| US6418866B1 (en) * | 1998-06-16 | 2002-07-16 | Mitsubishi Heavy Industries, Ltd. | Operating method of fluidized-bed incinerator and the incinerator |
| CN101556038A (en) * | 2009-05-27 | 2009-10-14 | 北京和隆优化控制技术有限公司 | Optimization control system for stable operation and economical combustion of circulating fluidized-bed boiler |
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2013
- 2013-08-05 CN CN201310335777.9A patent/CN103438444B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS56108012A (en) * | 1980-01-29 | 1981-08-27 | Kawasaki Heavy Ind Ltd | Circulating method for high-temperature exhaust gas in fluidized-bed combustion furnace |
| US6418866B1 (en) * | 1998-06-16 | 2002-07-16 | Mitsubishi Heavy Industries, Ltd. | Operating method of fluidized-bed incinerator and the incinerator |
| CN101556038A (en) * | 2009-05-27 | 2009-10-14 | 北京和隆优化控制技术有限公司 | Optimization control system for stable operation and economical combustion of circulating fluidized-bed boiler |
Non-Patent Citations (2)
| Title |
|---|
| 循环流化床锅炉效率相关参数的建模研究;吴家标等;《计算机与应用化学》;20121028;第29卷(第10期);第1163-1166页 * |
| 循环流化床锅炉节能的操作优化;吴家标等;《江南大学学报》;20120831;第11卷(第4期);第396-405页 * |
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