U.S. patent application number 14/559052 was filed with the patent office on 2015-06-11 for multistage hrsg control in a combined cycle unit.
The applicant listed for this patent is HONEYWELL INTERNATIONAL INC.. Invention is credited to Lubomir BARAMOV, Ondrej BASUS, Vladimir HAVLENA.
Application Number | 20150159518 14/559052 |
Document ID | / |
Family ID | 49880392 |
Filed Date | 2015-06-11 |
United States Patent
Application |
20150159518 |
Kind Code |
A1 |
BARAMOV; Lubomir ; et
al. |
June 11, 2015 |
MULTISTAGE HRSG CONTROL IN A COMBINED CYCLE UNIT
Abstract
A system and method include receiving waste heat from a gas
turbine, adding heat via duct firing, using the received waste heat
and added heat via duct firing to create steam at multiple stages
of a heat recovery steam generator, and controlling the multiple
stages of the heat recovery steam generator utilizing parameters
representative of heat input to each stage.
Inventors: |
BARAMOV; Lubomir; (Praha 5,
CZ) ; BASUS; Ondrej; (Prague, CZ) ; HAVLENA;
Vladimir; (Prague 8, CZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HONEYWELL INTERNATIONAL INC. |
Morristown |
NJ |
US |
|
|
Family ID: |
49880392 |
Appl. No.: |
14/559052 |
Filed: |
December 3, 2014 |
Current U.S.
Class: |
60/645 ;
60/670 |
Current CPC
Class: |
F22B 1/1815 20130101;
F01K 19/00 20130101; Y02P 80/154 20151101; Y02P 80/15 20151101;
F01K 27/02 20130101; F22B 35/007 20130101; F22B 1/1861 20130101;
Y02E 20/16 20130101; F01K 23/108 20130101; F01K 23/105
20130101 |
International
Class: |
F01K 27/02 20060101
F01K027/02; F01K 19/00 20060101 F01K019/00 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 11, 2013 |
EP |
13196771.3 |
Claims
1. A method comprising: receiving waste heat from a gas turbine;
adding heat via duct firing; using the received waste heat and
added heat via duct firing to create steam at multiple stages of a
heat recovery steam generator; and controlling the multiple stages
of the heat recovery steam generator utilizing parameters
representative of heat input to each stage.
2. The method of claim 1 wherein a first stage of the heat recovery
steam generator is a high pressure stage, HP, and a second stage is
a medium pressure stage, MP.
3. The method of claim 2 wherein the HP and MP stages are cascaded
and represented by parallel models.
4. The method of claim 3 wherein the parallel models comprise:
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2) where O.sub.2 is oxygen
concentration, .alpha..sub.HP and .alpha..sub.MP are efficiency
coefficients, and Q.sub.in is input heat, and Q.sub.steam,HP,
Q.sub.steam,MP correspond to heat in steam for respective
stages.
5. The method of claim 4 and further comprising adapting the
parallel models by including multiplicative factors to account for
environmental changes.
6. The method of claim 5 wherein the multiplicative factors account
for heat exchanger fouling and variations in feed-water
temperature.
7. The method of claim 4 and further comprising modeling
environmental changes as multiplicative perturbations in accordance
with: {circumflex over (F)}.sub..alpha.,HP(Q.sub.in, O.sub.2,
.theta..sub.HP)=.theta..sub.HPF.sub..alpha.,HP(Q.sub.in, O.sub.2)
{circumflex over (F)}.sub..alpha.,MP(Q.sub.in, O.sub.2,
.theta..sub.MP)=.theta..sub.MPF.sub..alpha.,MP(Q.sub.in, O.sub.2).
where F.sub..alpha.,HP and F.sub..alpha.,MP are functions and
.theta..sub.HP and .theta..sub.mP are perturbation parameters
estimated from process data.
8. The method of claim 7 wherein .theta..sub.HP and .theta..sub.mP
are estimated using a recursive estimator with inequality
bounds.
9. A system comprising: a duct to receive waste heat; a heat
recovery steam generator having multiple stages to receive the
waste heat and add heat via duct firing to create steam; sensors to
sense heat parameters at an input to each of the multiple stages;
and a controller to control the multiple stages of the heat
recovery steam generator using the parameters representative of
heat input to each stage.
10. The system of claim 9 wherein a first stage of the heat
recovery steam generator is a high pressure stage, HP, and a second
stage is a medium pressure stage, MP, wherein the HP and MP stages
are cascaded and represented in the controller by parallel models
comprising: Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in,
O.sub.2)Q.sub.in=: F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2) where O.sub.2 is oxygen
concentration, .alpha..sub.HP and .alpha..sub.MP are efficiency
coefficients, and Q.sub.in is input heat, and Q.sub.steam,HP,
Q.sub.steam,MP correspond to heat in steam for respective
stages.
11. The system of claim 10 wherein the controller models
environmental changes as multiplicative perturbations in accordance
with: {circumflex over (F)}.sub..alpha.,HP(Q.sub.in, O.sub.2,
.theta..sub.HP)=.theta..sub.HPF.sub..alpha.,HP(Q.sub.in, O.sub.2)
{circumflex over (F)}.sub..alpha.,MP(Q.sub.in, O.sub.2,
.theta..sub.MP)=.theta..sub.MPF.sub..alpha.,MP(Q.sub.in, O.sub.2).
where F.sub..alpha.,HP and F.sub..alpha.,MP are functions and
.theta..sub.HP and .theta..sub.mP are perturbation parameters
estimated from process data.
12. The system of claim 11 wherein .theta..sub.HP and
.theta..sub.mP are estimated using a recursive estimator with
inequality bounds.
13. The system of claim 11 wherein the multiplicative perturbations
account for heat exchanger fouling and variations in feed-water
temperature.
14. A computer readable storage device having code to cause a
computer to perform a method, the method comprising: receiving
waste heat from a gas turbine; adding heat via duct firing; using
the received waste heat and added heat via duct firing to create
steam at multiple stages of a heat recovery steam generator; and
controlling the multiple stages of the heat recovery steam
generator utilizing parameters representative of heat input to each
stage.
15. The computer readable storage device of claim 14 wherein a
first stage of the heat recovery steam generator is a high pressure
stage, HP, and a second stage is a medium pressure stage, MP,
wherein the HP and MP stages are cascaded and represented by
parallel models, and wherein the parallel models comprise:
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.In=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2) where O.sub.2 is oxygen
concentration, .alpha..sub.HP and .alpha..sub.MP are efficiency
coefficients, and Q.sub.in is input heat, and Q.sub.steam,HP,
Q.sub.steam,MP correspond to heat in steam for respective stages.
Description
BACKGROUND
[0001] Combined cycle (CC) units consisting of gas turbines (GT)
and heat recovery steam generator MSG) with duct firing are
increasingly used in industrial energy plants as they provide
significantly higher efficiency compared to the classical
boiler-turbine cycle. Their importance becomes even higher after
significant market and energy price changes caused by rapidly
growing shale gas production. Advanced regulatory control
techniques can significantly help to operate CC plants optimally.
To be able to do that, mathematical models satisfactorily
describing real behavior of strongly interacting individual
components have been used.
SUMMARY
[0002] A system and method include receiving waste heat from a gas
turbine, adding heat via duct firing, using the received waste heat
and added heat via duct firing to create steam at multiple stages
of a heat recovery steam generator, and controlling the multiple
stages of the heat recovery steam generator utilizing parameters
representative of heat input to each stage.
[0003] A system includes a duct to receive waste heat. A heat
recovery steam generator having multiple stages is coupled to the
duct to receive the waste heat and add heat via duct firing to
create steam. Sensors may be provided to sense heat parameters
representative of heat input to each of the multiple stages. A
controller controls the multiple stages of the heat recovery steam
generator using the parameters representative of heat input to each
stage.
[0004] A computer readable storage device has code to cause a
computer to perform a method. The method includes receiving waste
heat from a gas turbine, adding heat via duct firing, using the
received waste heat and added heat via duct firing to create steam
at multiple stages of a heat recovery steam generator, and
controlling the multiple stages of the heat recovery steam
generator utilizing parameters representative of heat input to each
stage,
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a block diagram combined cycle unit having a
cascaded multiple stage heat recovery steam generator with parallel
stage heat input based model control according to an example
embodiment.
[0006] FIG. 2 is a block diagram illustrating further detail of a
duct burner with a multiple stage heat recovery steam generator
according to an example embodiment.
[0007] FIG. 3 is a block diagram representation of heat transfer
between stages of a multiple stage heat recovery steam generator
according to an example embodiment.
[0008] FIG. 4 is a graphical representation of heat distribution in
a multiple stage heat recovery steam generator according to an
example embodiment.
[0009] FIG. 5 is a graphical representation of a parallel heat
recovery steam generator according to an example embodiment.
[0010] FIG. 6 is a graph illustrating efficiency versus heat
exhaust for multiple stages of a heat recovery steam generator
according to an example embodiment.
[0011] FIGS. 7A and 7B are graphs illustrating estimated parameters
for parallel and prior models according to an example
embodiment.
[0012] FIGS. 8A and 8B illustrate residuals of a parallel model for
parameters estimated and fitted to a partial efficiencies model
according to an example embodiment.
[0013] FIG. 9 is table illustrating a statistical evaluation of
residuals according to an example embodiment.
[0014] FIG. 10 is a block diagram of a computer system to implement
methods and a controller according to an example embodiment.
DETAILED DESCRIPTION
[0015] In the following description, reference is made to the
accompanying drawings that form a part hereof, and in which is
shown by way of illustration specific embodiments which may be
practiced. These embodiments are described in sufficient detail to
enable those skilled in the art to practice the invention, and it
is to be understood that other embodiments may be utilized and that
structural, logical and electrical changes may be made without
departing from the scope of the present invention. The following
description of example embodiments is, therefore, not to be taken
in a limited sense, and the scope of the present invention is
defined by the appended claims.
[0016] The functions or algorithms described herein may be
implemented in software or a combination of software and human
implemented procedures in one embodiment. The software may consist
of computer executable instructions stored on computer readable
media such as memory or other type of storage devices. Further,
such functions correspond to modules, which are software, hardware,
firmware or any combination thereof. Multiple functions may be
performed in one or more modules as desired, and the embodiments
described are merely examples. The software may be executed on a
digital signal processor, ASIC, microprocessor, or other type of
processor operating on a computer system, such as a personal
computer, server or other computer system.
[0017] A method establishes proper mathematical models of
individual stages in a multistage heat recovery steam generator
(HRSG) used for control. In contrast to usual practice, where
efficiency of a boiler (or a HRSG stage) is parameterized in the
models by steam flow of the particular component, various
embodiments parameterize by overall heat flow at the inlet to the
HRSG for all individual stages. This new approach provides better
and more stable control models. Moreover, the models provide a
convenient framework for direct physical interpretation and
examination of contributions of individual components to overall
operation. The new model or representation of HRSG is in terms of
non-interacting non-linear scalar-valued functions sharing the same
input. Thus possible model uncertainty is not propagated to the
downstream stages. In contrast, the traditional representation
based on partial efficiencies uses a cascaded representation where
the output heat flows from a stage depend both on the heat input
flow as well as on the output flows of stages upstream.
[0018] Static unit models are subject to changes due to
environmental changes as well as component degradation. In the case
of HRSG, efficiency loss may occur due to heat exchanger fouling,
variations in feed-water temperature, etc. It is useful to adapt
the models by including multiplicative factors modifying slightly
models of individual stages. The multiplicative factor can capture
these process changes and can be estimated on-line from measured
process data.
[0019] A parallel representation simplifies identification of the
HRSG steady-state model, its on-line adaptation as well as its
integration to the overall process optimizer optimizing the overall
economy of the power utility, using ELA (Economic Load Allocation).
In further embodiments, other suitable optimizers may be
utilized.
[0020] FIG. 1 is a block diagram of a combined cycle system 100
utilizing a heat recovery steam generator using heat flow at inlets
for stages for modeling and control according to an example
embodiment. A gas turbine 110 burns gas 115 to turn a first
generator 117 and produce electricity 120. Excess heat 122 from the
gas turbine is provided to a duct 125 to heat water in a heat
exchanger 127 to provide steam at 130 to a steam turbine 135. An
optional fan 128 may be used to provide air to the duct 125 when
needed. Additional heat is provided via a duct burner 140 that
burns gas 142 to produce additional heat 145 in the duct 125 to
produce additional steam.
[0021] An HRSG high pressure (HP) stage 146 follows the duct burner
140, and is in turn followed by a HRSG middle pressure (MP) stage
147. In further embodiments, duct burner may include additional
stages if desired. In a typical combined cycle power plant (CCCP),
roughly 30% of the heat content in the filet is transferred to
electrical energy in the generator 117, leaving the other roughly
70% as input to the HRSG stages 146 and 147 which is then
transferred to steam.
[0022] The steam turbine 135 receives the steam generated in HRSG
stages 146 and 147, and uses the steam to turn a second generator
150 that produces electricity 152. Together, the generation of
electricity from the gas turbine and the steam turbine represent
the combined cycle. Steam 154 from the steam turbine 135 is
provided to a condenser 155 where it is condensed to water 157 and
pumped via a pump 160 back via 162 to the heat exchanger 127 to be
turned back into steam.
[0023] A controller 170 receives information from a plurality of
sensors 175 and is coupled to control the duct burner 140 in
accordance with a control algorithm utilizing parameters describing
heat flow at each stage input for the stages of system 100 that
generate electricity from steam
[0024] In one embodiment, the sensors 175 are coupled to obtain
information about the temperature and corresponding heat provided
at various input stages of HRSG 146 and 147, such as the waste
heat, and heat produced by duct firing. In some embodiments not all
measurements at all inputs are provided. In some embodiments,
sensors may be positioned both in the flue gas channel (leads to
indirect efficiency calculation method) and in the steam channel
(leads to direct efficiency calculation method) to provide data
representative of heat input to each stage. Either way, they help
us build and maintain the mathematical models needed for better
control. Further sensors may be used to sense the amount of steam
produced at various positions in the system 100 to control various
aspects of system 100.
[0025] In one example embodiment, a combined cycle unit may consist
of several gas turbines (GTs) and one or more HRSG units. The
controller may be used to optimize the load allocated to system 100
against the other generators available (other boilers/turbines and
units) to optimize the overall steam plant performance (economic
load allocation).
[0026] FIG. 2 provides further detail of the duct burner 140 with
HRSG 146 and HRSG 147. The reference numbers are consistent with
FIG. 1. Heat entering stage HRSG 146 may be measured by sensors
represented at 210, and heat entering stage HRSG 147 may be
measured by sensors represented at 212. In a further embodiment,
sensor 214 may be positioned to measure heat entering a flue gas
stage 220. Sensors 210, 212, and 214, as well as sensors measuring
heat entering optional further stages are logically represented as
sensors 175 in FIG. 1, providing parameter measurements to
controller 170. The positioning of the sensors may be done to best
capture or estimate heat input at each stage, and wilt likely be
dependent on the particular structures used to implement each
stage. One of average skill in the art will be able to
appropriately place the sensors to capture accurate heat
measurements.
[0027] In some embodiments, sensors may be positioned to calculate
efficiencies of the individual stages. In the case of indirect
efficiency calculation (calculation based on enthalpy lost in flue
gas), flue gas temperatures at outlet of each stage and at least
one oxygen measurement point for the entire HRSG may be used. In
the case of direct efficiency calculation (calculation based on
enthalpy transferred to steam channel, i.e. enthalpy difference
between feedwater and steam the parameters of feedwater
(temperature, optionally pressure) and steam (temperature and
pressure) together with steam (optionally feed water) mass flow may
be provided by sensors.
[0028] FIG. 3 illustrates heat transfer in the HRSG units at 300.
The heat is input at 310 to HRSG 146 and is transferred to steam
315. The remaining heat from HRSG 145 is indicated at 320, and is
transferred to steam 325 in HRSG stage 147. Finally, the remaining
heat 330 is lost in the stack. The output of each stage may be
calculated from the input heat and the heat transferred to steam,
which output may then serve as the heat input to a following stage.
Steam produced at each stage may be used in a steam turbine, and
may also be used e.g. for heating, in a chemical reaction etc; the
topology of the plant steam flows can vary based on plant overall
purpose, geographical position etc.
[0029] An example of heat distribution in a typical HRSG can be
seen in FIG. 4, which represents inlet heat 400 being transferred
to steam in both unit 146 and 147. Heat in steam for HRSG 146 is
represented by flow 410. Heat in steam for HRSG 147 is indicated by
flow 415. Stack heat loss is indicated by flow 420. The flows are
represented by arrows, with a width of each arrow corresponding to
the amount of heat in the flow. For an example inlet heat of 90.1
MW, flow 410 is 57.3%, flow 415 comprises 12.2%, and flow 420
comprises 30.5% or 27 MW. The heat input and heat utilized at each
stage may vary significantly in further embodiments. As seen in
FIG. 1, even the stack heat is used to drive a further steam
turbine 135 and generator 150.
[0030] In one embodiment, HRSG heat input Q.sub.in is used for all
stage models; thus, instead of partial efficiencies as previously
used, coefficients .alpha. relating the stage heat output to this
HRSG heat input are used
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2)
Coefficients .alpha. are typically obtained from data acquired
during the steady state experiments on the real process. The model
can be fine-tuned on-line, during the controller operation using a
suitable adaptation mechanism.
[0031] O.sub.2 is the oxygen concentration in flue gas, which is an
auxiliary parameter not subject to optimization in one embodiment.
It simply parameterizes the model. The above representation does
not need a stage coupling condition. It is a parallel form of the
model as illustrated in FIG. 5. Each stage is treated separately
even though physically they are concatenated. The relation between
efficiencies .eta. and coefficients .alpha. are given by
.alpha..sub.HP(Q.sub.in,
O.sub.2)=.eta..sub.HP(F.sub..alpha.,HP(Q.sub.steam,HP, O.sub.2),
O.sub.2)
.alpha..sub.MP(Q.sub.in,
O.sub.2)=.eta..sub.MP(F.sub..alpha.,HP(Q.sub.steam,HP, O.sub.2),
O.sub.2).times.(1-.eta..sub.HP(F.sub..alpha.,HP(Q.sub.steam,HP,
O.sub.2), O.sub.2)).
[0032] FIG. 6 is a graph 600 illustrating the efficiency models of
HRSG stages 146 at 610 and 147 at 615 in the form of efficiency
versus heat input flow. Minimum and maximum steam flows are
illustrated as the same for each HRSG stage.
[0033] Note that for the first HRSG 146 stage, the alpha
coefficient and the efficiency eta are fully equivalent. The prior
cascade representation was usually used in single-stage boiler
models. The reason was that the output steam flow (or its
equivalent heat flow) was used as a decision variable in an
optimization problem of Economic Load Allocation for multiple
parallel boilers. It was advantageous, because the decision
variables entered linearly in the balance equations, which is
important for solving a nonlinear programming problem. In the case
of HRSG, however, non-linear balance equations cannot be avoided
and thus the prior representation with efficiencies parameterized
by the output heat flow does not have this advantage any
longer.
[0034] The parallel approach utilized in various described
embodiments seems to be the better option, because it provides a
common basis for both stages (heat inlet to HRSG), allowing
parallel and independent calculation of model outputs; there is no
uncertainty propagation to downstream stages. Moreover, it allows a
physical interpretation of a coefficients and a straightforward
examination of individual contributions of each stage to overall
HRSG operation. Controller 170 may use the models to control the
multiple stages of the heat steam generator to obtain desired
efficiencies and power output. Models thus obtained are used in a
steady-state optimizer of the overall process involving heat and
power generation (as is, e.g., ELA, the Economic Load Allocation
unit). Further, it can be used for obtaining a correct gain
information for the linear model of the dynamic model-based
predictive controller which stabilizes pressure in multiple steam
headers. Efficiency curves in the parallel representation are
illustrated at 610 and 615 in FIG. 6.
[0035] The parallel HRSG model is subject to changes. For example,
changes may occur due to heat exchangers fouling and fluctuations
of feed-water temperature. These changes and others can be captured
by changing the shapes of functions F.sub..alpha.,HP and
F.sub..alpha.,MP. Most of the changes may be modeled as
multiplicative perturbations
{circumflex over (F)}.sub..alpha.,HP(Q.sub.in, O.sub.2,
.theta..sub.HP)=.theta..sub.HPF.sub..alpha.,HP(Q.sub.in,
O.sub.2)
{circumflex over (F)}.sub..alpha.,MP(Q.sub.in, O.sub.2,
.theta..sub.MP)=.theta..sub.MPF.sub..alpha.,MP(Q.sub.in,
O.sub.2).
[0036] More complex shape variations can be considered, but the
simple multiplicative one is adequate in most cases. The
perturbation parameters .theta..sub.HP and .theta..sub.mP can be
estimated on-line from process data, using a suitable recursive
estimator with inequality bounds.
[0037] In one example, calculated models were tested on
experimental process data from a combined cycle power plant
consisting of several boilers, GTs and HRSGs. The models allow
straightforward analysis of contribution of individual parts to
overall HRSG efficiency pattern. The amount of heat transferred to
steam in the HP and MP stages may he easily calculated and shown
graphically. Similarly, heat lost in the stack may be easily
calculated and shown graphically. Note that sum of all .alpha.
coefficients in the whole HRSG (including stack heat loss) sums up
to 1. Calculated models well represented real operation of the HRSG
and can be directly used in the optimization engines, such as
Honeywell ELA (Economic Load Allocation, part of the Honeywell
Advanced Energy Solutions (AES) suite).
[0038] Parameter variations may be compensated by an adaptation
mechanism. Model adaptation considers the following input-output
balance equation
0 = .theta. 1 dh HP S HP .alpha. HP ( Q in ) - Q in ##EQU00001## 0
= .theta. 2 dh MP S MP .alpha. MP ( Q in ) - Q in
##EQU00001.2##
[0039] Where, S.sub.HP and S.sub.MP are mass flows of high and
mid-pressure steam, respectively. Variables
dh.sub.HP(.DELTA.h.sub.HP) and dh.sub.MP(.DELTA.h.sub.MP) denote
enthalpy increase. Hence, heat passed to high pressure steam in the
exchanger is given by Q.sub.steam,HP=dh.sub.HPS.sub.HP
[0040] Parameters .theta..sub.1 and .theta..sub.2 are for model
adaptation to uncertainties from alpha-coefficient degradation
(exchanger fouling), or to changes in feed-water temperature
(perturbing enthalpy increase). Nominal value for these
multiplicative parameters is thus equal to one.
[0041] An representation with partial efficiencies .eta. is
considered as
0 = .theta. 1 dh HP S HP .eta. HP ( S HP ) - Q in ##EQU00002## 0 =
.theta. 2 dh MP S MP .eta. MP ( S MP ) - ( Q in - .theta. 3 d h HP
S HP ) ##EQU00002.2## 0 = .theta. 1 - .theta. 3 ##EQU00002.3##
[0042] The variables are as above; parameter .theta..sub.3 serves
to enforce the coupling condition. Although parameters
.theta..sub.1 and .theta..sub.2 are the same in both
representations as illustrated in FIGS. 7A and 7B at 710 and 715
respectively, their estimated values from data may not be the same,
as the formulation of the estimation problem is different. The
parameters in representations 710 and 715 are estimated for the
parallel representation (with alpha coefficients) and are labeled
as `native`, where those for the prior cascade representation (with
partial efficiencies eta) as `imported`. It needs to be noted that
alpha and eta parameters were obtained independently and their
consistency is thus not guaranteed. In this case, the inconsistency
is reflected in the parameter offset in FIGS. 7A and 7B.
[0043] FIGS. 8A and 8B at 810 and 815 illustrate residuals of the
parallel model for parameters estimated (alpha coefficients,
`native`) and those fitted to the partial efficiencies model
(efficiencies eta, `imported` parameters). In the latter case the
residuals have an offset, but are equivalent otherwise; this offset
is due to the offset in parameters, caused by the inconsistency
between the model representations.
[0044] A statistical evaluation of the residuals is in a table 900
in FIG. 9. It can be seen that the imported parameters indeed cause
a biased estimate, but the variability is nearly the same. Table
900 illustrates mean and standard deviations for the residuals with
rows comprising the HRSG stages and columns corresponding to native
parameters and imported parameters. Mean and standard deviation are
shown for each set of parameters.
[0045] Table 900 shows that the proposed parallel representation of
HRSG is equivalent to the serial one. The benefit is its
simplicity, enabling identifying and adapting the HRSG model as a
set of independent SISO models.
[0046] FIG. 10 is a block diagram of a computer system to implement
methods and the controller, according to an example embodiment. In
the embodiment shown in FIG. 10, a hardware and operating
environment is provided that is applicable to any specifically
programmed device capable of performing one or more of the methods
and control functions described. Many components may be eliminated
as they perform functions that may not be relevant to such methods
and control functions in various embodiments. Much simpler micro
controllers or other electronic processing device may be used in
some embodiments.
[0047] As shown in FIG. 10, one embodiment of the hardware and
operating environment includes a general purpose computing device
in the form of a computer 1000 (e.g., a personal computer,
workstation, or server), including one or more processing units
1021, a system memory 1022, and a system bus 1023 that operatively
couples various system components including the system memory 1022
to the processing unit 1021. There may be only one or there may be
more than one processing unit 1021, such that the processor of
computer 1000 comprises a single central-processing unit (CPU), or
a plurality of processing units, commonly referred to as a
multiprocessor or parallel-processor environment. In various
embodiments, computer 1000 is a conventional computer, a
distributed computer, or any other type of computer.
[0048] The system bus 1023 can be any of several types of bus
structures including a memory bus or memory controller, a
peripheral bus, and a local bus using any of a variety of bus
architectures. The system memory can also be referred to as simply
the memory, and, in some embodiments, includes read-only memory
(ROM) 1024 and random-access memory (RAM) 1025. A basic
input/output system (BIOS) program 1026, containing the basic
routines that help to transfer information between elements within
the computer 1000, such as during start-up, may be stored in RUM
1024. The computer 1000 further includes a hard disk drive 1027 for
reading from and writing to a hard disk, not shown, a magnetic disk
drive 1028 for reading from or writing to a removable magnetic disk
1029, and an optical disk drive 1030 for reading from or writing to
a removable optical disk 1031 such as a CD ROM or other optical
media.
[0049] The hard disk drive 1027, magnetic disk drive 1028, and
optical disk drive 1030 couple with a hard disk drive interface
1032, a magnetic disk drive interface 1033, and an optical disk
drive interface 1034, respectively. The drives and their associated
computer-readable media provide non volatile storage of
computer-readable instructions, data structures, program modules
and other data for the computer 1000. It should be appreciated by
those skilled in the art that any type of computer-readable media
which can store data that is accessible by a computer, such as
magnetic cassettes, flash memory cards, digital video disks,
Bernoulli cartridges, random access memories (RAMS), read only
memories (ROMs), redundant arrays of independent disks (e.g., RAID
storage devices) and the like, can be used in the exemplary
operating environment.
[0050] A plurality of program modules can be stored on the hard
disk, magnetic disk 1029, optical disk 1031, ROM 1024, or RAM 1025,
including an operating system 1035, one or more application
programs 1036, other program modules 1037, and program data 1038.
Programming for implementing one or more processes or method
described herein may be resident on any one or number of these
computer-readable media.
[0051] A user may enter commands and information into computer 1000
through input devices such as a keyboard 1040 and pointing device
1042. Other input devices (not shown) can include a microphone,
joystick, game pad, satellite dish, scanner, or the like. These
other input devices are often connected to the processing unit 1021
through a serial port interface 1046 that is coupled to the system
bus 1023, but can be connected by other interfaces, such as a
parallel port, game port, or a universal serial bus (USB). A
monitor 1047 or other type of display device can also be connected
to the system bus 1023 via an interface, such as a video adapter
1048. The monitor 1047 can display a graphical user interface for
the user. In addition to the monitor 1047, computers typically
include other peripheral output devices (not shown), such as
speakers and printers.
[0052] The computer 1000 may operate in a networked environment
using logical connections to one or more remote computers or
servers, such as remote computer 1049. These logical connections
are achieved by a communication device coupled to or a part of the
computer 1000; the invention is not limited to a particular type of
communications device. The remote computer 1049 can be another
computer, a server, a router, a network PC, a client, a peer device
or other common network node, and typically includes many or all of
the elements described above I/O relative to the computer 1000,
although only a memory storage device 1050 has been illustrated.
The logical connections depicted in FIG. 10 include a local area
network (LAN) 1051 and/or a wide area network (WAN) 1052. Such
networking environments are commonplace in office networks,
enterprise-wide computer networks, intranets and the internet,
which are all types of networks.
[0053] When used in a LAN-networking environment, the computer 1000
is connected to the LAN 1051 through a network interface or adapter
1053, which is one type of communications device. In some
embodiments, when used in a WAN-networking environment, the
computer 1000 typically includes a modem 1054 (another type of
communications device) or any other type of communications device,
e.g., a wireless transceiver, for establishing communications over
the wide-area network 1052, such as the internet. The modem 1054,
which may be internal or external, is connected to the system bus
1023 via the serial port interface 1046. In a networked
environment, program modules depicted relative to the computer 1000
can be stored in the remote memory storage device 1050 of remote
computer, or server 1049. It is appreciated that the network
connections shown are exemplary and other means of, and
communications devices for, establishing a communications link
between the computers may be used including hybrid fiber-coax
connections, T1-T3 lines, DSL's, OC-3 and/or OC-12, TCP/IP,
microwave, wireless application protocol, and any other electronic
media through any suitable switches, routers, outlets and power
lines, as the same are known and understood by one of ordinary
skill in the art.
EXAMPLES
[0054] 1. A method comprising:
[0055] receiving waste heat from a gas turbine;
[0056] adding heat via duct firing;
[0057] using the received waste heat and added heat via duct firing
to create steam at multiple stages of a heat recovery steam
generator; and
[0058] controlling the multiple stages of the heat recovery steam
generator utilizing parameters representative of heat input to each
stage.
[0059] 2. The method of example 1 wherein a first stage of the heat
recovery steam generator is a high pressure stage, HP, and a second
stage is a medium pressure stage, MP.
[0060] 3. The method of example 2 wherein the HP and MP stages are
cascaded and represented by parallel models.
[0061] 4. The method of example 3 wherein the parallel models
comprise:
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2)
[0062] where O.sub.2 is oxygen concentration, .alpha..sub.HP and
.alpha..sub.MP are efficiency coefficients, and Q.sub.in is input
heat, and Q.sub.steam,HP, Q.sub.steam,MP correspond to heat in
steam for respective stages.
[0063] 5. The method of example 4 and further comprising adapting
the parallel models by including multiplicative factors to account
for environmental changes.
[0064] 6. The method of example 5 wherein the multiplicative
factors account for heat exchanger fouling and variations in
feed-water temperature.
[0065] 7. The method of example 4 and further comprising modeling
environmental changes as multiplicative perturbations in accordance
with:
{circumflex over (F)}.sub..alpha.,HP(Q.sub.in, O.sub.2,
.theta..sub.HP)=.theta..sub.HPF.sub..alpha.,HP(Q.sub.in,
O.sub.2)
{circumflex over (F)}.sub..alpha.,MP(Q.sub.in, O.sub.2,
.theta..sub.MP)=.theta..sub.MPF.sub..alpha.,MP(Q.sub.in,
O.sub.2).
where F.sub..alpha.,HP and F.sub..alpha.,MP are functions and
.theta..sub.HP and .theta..sub.mP perturbation parameters estimated
from process data.
[0066] 8. The method of example 7 wherein .theta..sub.HP and
.theta..sup.mP are estimated using a recursive estimator with
inequality bounds.
[0067] 9. A system comprising:
[0068] a duct to receive waste heat;
[0069] a heat recovery steam generator having multiple stages to
receive the waste heat and add heat via duct firing to create
steam;
[0070] sensors to sense heat parameters at an input to each of the
multiple stages; and
[0071] a controller to control the multiple stages of the heat
recovery steam generator using the parameters representative of
heat input to each stage.
[0072] 10. The system of example 9 wherein a first stage of the
heat recovery steam generator is a high pressure stage, HP, and a
second stage is a medium pressure stage, MP, wherein the HP and MP
stages are cascaded and represented in the controller by parallel
models comprising:
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2)
[0073] where O.sub.2 is oxygen concentration, .alpha..sub.HP and
.alpha..sub.MP are efficiency coefficients, and Q.sub.in is input
heat, and Q.sub.steam,HP, Q.sub.steam,MP correspond to heat in
steam for respective stages.
[0074] 11. The system of example 10 wherein the controller models
environmental changes as multiplicative perturbations in accordance
with:
{circumflex over (F)}.sub..alpha.,HP(Q.sub.in, O.sub.2,
.theta..sub.HP)=.theta..sub.HPF.sub..alpha.,HP(Q.sub.in,
O.sub.2)
{circumflex over (F)}.sub..alpha.,MP(Q.sub.in, O.sub.2,
.theta..sub.MP)=.theta..sub.MPF.sub..alpha.,MP(Q.sub.in,
O.sub.2).
where F.sub..alpha.,HP and F.sub..alpha.,MP are functions and
.theta..sub.HP and .theta..sub.mP are perturbation parameters
estimated from process data.
[0075] 12. The system of example 11 wherein .theta..sub.HP and
.theta..sub.mP are estimated using a recursive estimator with
inequality bounds.
[0076] The system of example 11 wherein the multiplicative
perturbations account for heat exchanger fouling and variations in
feed-water temperature.
[0077] 14. A computer readable storage device having code to cause
a computer to perform a method, the method comprising:
[0078] receiving waste heat from a gas turbine;
[0079] adding heat via duct firing;
[0080] using the received waste heat and added heat via duct firing
to create steam at multiple stages of a heat recovery steam
generator; and
[0081] controlling the multiple stages of the heat recovery steam
generator utilizing parameters representative of heat input to each
stage.
[0082] 15. The computer readable storage device of example 14
wherein a first stage of the heat recovery steam generator is a
high pressure stage, HP, and a second stage is a medium pressure
stage, MP, wherein the HP and MP stages are cascaded and
represented by parallel models, and wherein the parallel models
comprise:
Q.sub.steam,HP=.alpha..sub.HP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,HP(Q.sub.in, O.sub.2)
Q.sub.steam,MP=.alpha..sub.MP(Q.sub.in, O.sub.2)Q.sub.in=:
F.sub..alpha.,MP(Q.sub.in, O.sub.2)
[0083] where O.sub.2 is oxygen concentration, .alpha..sub.HP and
.alpha..sub.MP are efficiency coefficients, and Q.sub.in is input
heat, and Q.sub.steam,HP, Q.sub.steam,MP correspond to heat in
steam for respective stages.
[0084] Although a few embodiments have been described in detail
above, other modifications are possible. For example, the logic
flows depicted in the figures do not require the particular order
shown, or sequential order, to achieve desirable results. Other
steps may be provided, or steps may be eliminated, from the
described flows, and other components may be added to, or removed
from, the described systems. Other embodiments may be within the
scope of the following claims.
* * * * *