U.S. patent application number 15/344037 was filed with the patent office on 2017-05-11 for inferential sensor for internal heat exchanger parameters.
The applicant listed for this patent is Honeywell spol. s.r.o.. Invention is credited to Vladimir Havlena.
Application Number | 20170130982 15/344037 |
Document ID | / |
Family ID | 54541988 |
Filed Date | 2017-05-11 |
United States Patent
Application |
20170130982 |
Kind Code |
A1 |
Havlena; Vladimir |
May 11, 2017 |
INFERENTIAL SENSOR FOR INTERNAL HEAT EXCHANGER PARAMETERS
Abstract
Methods, devices, and systems for an inferential sensor for
internal heat exchanger parameters are described herein. One device
includes a memory, and a processor configured to execute executable
instructions stored in the memory to receive a number of measured
process variables of a heat exchanger, including a number of
measured inlet process variables and a number of measured outlet
process variables, predict, using a dynamic differential model
including the number of measured inlet process variables, internal
parameters of the heat exchanger and a number of outlet process
variables of the heat exchanger, compare the number of measured
outlet process variables with the number of predicted outlet
process variables, and update, based on the comparison, the
internal parameters of the heat exchanger.
Inventors: |
Havlena; Vladimir; (Prague,
CZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Honeywell spol. s.r.o. |
Prague |
|
CZ |
|
|
Family ID: |
54541988 |
Appl. No.: |
15/344037 |
Filed: |
November 4, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F24F 11/62 20180101;
G05B 13/048 20130101; F28F 2200/00 20130101; G01N 17/008 20130101;
F28F 27/00 20130101; F24F 11/63 20180101; F28G 15/003 20130101;
F24F 11/30 20180101; F28F 2200/005 20130101; F24F 11/52
20180101 |
International
Class: |
F24F 11/00 20060101
F24F011/00 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 6, 2015 |
EP |
15193498.1 |
Claims
1. An inferential sensor for internal heat exchanger parameters,
comprising: a memory; a processor configured to execute executable
instructions stored in the memory to: receive a number of measured
process variables of the heat exchanger, including: a number of
measured inlet process variables; and a number of measured outlet
process variables; predict, using a dynamic differential model
including the number of measured inlet process variables: internal
parameters of the heat exchanger; and a number of outlet process
variables of the heat exchanger; compare the number of measured
outlet process variables with the number of predicted outlet
process variables; and update, based on the comparison, the
internal parameters of the heat exchanger.
2. The inferential sensor of claim 1, wherein the number of
measured inlet process variables include: an inlet temperature of
the hot fluid of the heat exchanger; an inlet temperature of the
cold fluid of the heat exchanger; an inlet pressure of the hot
fluid of the heat exchanger; an inlet pressure of the cold fluid of
the heat exchanger; an inlet flow rate of the hot fluid of the heat
exchanger; and an inlet flow rate of the cold fluid of the heat
exchanger.
3. The inferential sensor of claim 1, wherein the number of
measured outlet process variables include: an outlet temperature of
the hot fluid of the heat exchanger; an outlet temperature of the
cold fluid of the heat exchanger; an outlet pressure of the hot
fluid of the heat exchanger; an outlet pressure of the cold fluid
of the heat exchanger; an outlet flow rate of the hot fluid of the
heat exchanger; and an outlet flow rate of the cold fluid of the
heat exchanger.
4. The inferential sensor of claim 1, wherein the internal
parameters of the heat exchanger include a heat transfer
coefficient of the heat exchanger.
5. The inferential sensor of claim 1, wherein the internal
parameters of the heat exchanger include metal temperatures of a
heat exchange surface of the heat exchanger.
6. The inferential sensor of claim 1, wherein the processor is
configured to execute the instructions calculate an efficiency of
the heat exchanger using the internal parameters of the heat
exchanger and the number of measured process variables of the heat
exchanger.
7. The inferential sensor of claim 1, wherein the predicted number
of outlet process variables include: a predicted outlet temperature
of the hot fluid of the heat exchanger; a predicted outlet
temperature of the cold fluid of the heat exchanger; a predicted
outlet pressure of the hot fluid of the heat exchanger; a predicted
outlet pressure of the cold fluid of the heat exchanger; a
predicted outlet flow rate of the hot fluid of the heat exchanger;
and a predicted outlet flow rate of the cold fluid of the heat
exchanger.
8. The inferential sensor of claim 1, wherein the dynamic
differential model includes a heat and mass balance of the number
of measured inlet process variables and the number of measured
outlet process variables, wherein the number of measured inlet
process variables and the number of measured outlet process
variables change with time, and wherein: the heat and mass balance
determines a metal temperature of the heat exchange surface of the
heat exchanger; and heat transfer from the hot fluid of the heat
exchanger to the cold fluid of the heat exchanger is determined by
a logarithmic mean temperature between a temperature of the hot
fluid, the metal temperature of the heat exchange surface of the
heat exchanger, and a temperature of the cold fluid of the heat
exchanger.
9. The inferential sensor of claim 1, wherein a nominal heat
exchanger has a nominal heat transfer coefficient.
10. The inferential sensor of claim 1, wherein the heat exchanger
has a heat transfer coefficient.
11. The inferential sensor of claim 1, wherein the controller is
part of a heat pump.
12. The inferential sensor of claim 1, wherein the controller is
part of an air-conditioner.
13. The inferential sensor of claim 1, wherein the processor is
configured to execute the instructions while the heat exchanger is
operating.
14. A method for an inferential sensor for internal heat exchanger
parameters, comprising: receiving a number of measured process
variables of the heat exchanger, including: a number of measured
inlet process variables; and a number of measured outlet process
variables of the heat exchanger; predicting internal parameters of
the heat exchanger and a number of outlet process variables by a
dynamic differential model using a heat and mass balance of the
number of measured inlet process variables and the number of
measured outlet process variables of the heat exchanger; comparing
the number of measured outlet process variables with the number of
predicted outlet process variables; and updating, based on the
comparison, the internal parameters of the heat exchanger.
15. The method of claim 14, wherein updating the internal
parameters of the heat exchanger include updating a heat transfer
coefficient of the heat exchanger.
16. The method of claim 14, wherein updating the internal
parameters of the heat exchanger include updating metal
temperatures of a heat exchange surface of the heat exchanger.
17. The method of claim 14, wherein the method is continuously
repeated.
18. A system for an inferential sensor for internal heat exchanger
parameters, comprising: a heat pump; a heat exchanger; a controller
configured to: receive a number of measured process variables of
the heat exchanger, including: a number of measured inlet process
variables of the heat exchanger; a number of measured outlet
process variables of the heat exchanger; predict, using a dynamic
differential model including the number of measured inlet process
variables: internal parameters of the heat exchanger; and a number
of outlet process variables of the heat exchanger; compare the
number of measured outlet process variables with the number of
predicted outlet process variables; update, based on the
comparison, the internal parameters of the heat exchanger.
19. The system of claim 18, wherein the heat exchanger is a
liquid-liquid heat exchanger.
20. The system of claim 18, wherein the heat exchanger is a phase
change heat exchanger.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims foreign priority to EP
Application No. 15193498.1 filed Nov. 6, 2015, the specification of
which is herein incorporated by reference.
TECHNICAL FIELD
[0002] The present disclosure relates to methods, devices, and
systems for an inferential sensor for internal heat exchanger
parameters.
BACKGROUND
[0003] Adaptation of heat exchanger model parameters may be
necessary in order for precise model-based control and optimization
of thermal processes of the heat exchanger in heating and cooling
applications. Due to ageing phenomena like fouling, frost formation
etc. the actual parameters of heat exchangers can differ from
nominal parameters of nominal heat exchangers. For example, knowing
the actual parameters of a heat exchanger used in heat pump and/or
air-conditioning applications can allow for more efficient
operation of the heat pump and/or air-conditioning systems that can
be subject to ageing phenomena.
[0004] Steady state models based on the logarithmic mean
temperature difference concept can provide sufficient accuracy to
estimate the parameters of the heat exchanger in steady state
conditions. However, for practical applications it may be necessary
to estimate the internal state and parameters of the heat exchanger
continuously under time varying operating conditions. Continuous
estimation of the internal state and parameters under time varying
conditions requires an accurate dynamic model.
[0005] Dynamic models based on finite element or finite volume
approximation of distributed parameter models described by partial
differential equations require high order approximation to achieve
sufficient accuracy of the parameters and state estimates, as well
as consistency with the logarithmic mean temperature model in
steady state conditions. Therefore, dynamic models are not
applicable for embedded controllers and optimization methods
implemented in microcontrollers with limited computational power
and memory. Currently available methods, devices, and systems that
can provide sufficient steady state and dynamic accuracy may exceed
the processing and memory resource capabilities of current
microcontrollers.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates a system for an inferential sensor for
internal heat exchanger parameters, in accordance with one or more
embodiments of the present disclosure.
[0007] FIG. 2 illustrates a system for an inferential sensor for
internal heat exchanger parameters, in accordance with one or more
embodiments of the present disclosure.
[0008] FIG. 3 is a flow chart of a method for an inferential sensor
for internal heat exchanger parameters, in accordance with one or
more embodiments of the present disclosure.
[0009] FIG. 4 is a schematic block diagram of a controller for an
inferential sensor for internal heat exchanger parameters, in
accordance with one or more embodiments of the present
disclosure.
DETAILED DESCRIPTION
[0010] Methods, devices, and systems for an inferential sensor for
internal heat exchanger parameters are described herein. For
example, one or more embodiments include a memory, and a processor
configured to execute executable instructions stored in the memory
to receive a number of measured process variables of the heat
exchanger, including a number of measured inlet process variables
and a number of measured outlet process variables. The processor
can further execute executable instructions stored in the memory to
predict, using a dynamic differential model including the number of
measured inlet process variables, internal parameters of the heat
exchanger and a number of outlet process variables of the heat
exchanger. The processor can additionally execute executable
instructions stored in the memory to compare the number of measured
outlet process variables with the number of predicted outlet
process variables, and update, based on the comparison, the
internal parameters of the heat exchanger.
[0011] An inferential sensor for internal heat exchanger
parameters, in accordance with the present disclosure, can be based
on a dynamic low order model that can provide an accurate
approximation of a dynamic response of the heat exchanger in
transient conditions, as well as be consistent in steady state
conditions. The low order dynamic model can be used to estimate
changes of heat exchanger parameters (e.g., heat exchange surface
temperatures, heat transfer coefficient, etc.) and predict outlet
process variables.
[0012] Maintaining accurate inferential measurements of heat
exchanger parameters using the low order dynamic model of a heat
exchanger can bring significant economic benefits. For example, the
low order dynamic model can be used to ensure efficient heat
exchanger operation that may lead to utility cost savings, as well
as ensuring the heat exchanger is functioning properly.
[0013] In the following detailed description, reference is made to
the accompanying drawings that form a part hereof. The drawings
show, by way of illustration, how one or more embodiments of the
disclosure may be practiced.
[0014] These embodiments are described in sufficient detail to
enable those of ordinary skill in the art to practice one or more
embodiments of this disclosure. It is to be understood that other
embodiments may be utilized and that process, electrical, and/or
structural changes may be made without departing from the scope of
the present disclosure.
[0015] As will be appreciated, elements shown in the various
embodiments herein can be added, exchanged, combined, and/or
eliminated so as to provide a number of additional embodiments of
the present disclosure. The proportion and the relative scale of
the elements provided in the figures are intended to illustrate the
embodiments of the present disclosure, and should not be taken in a
limiting sense.
[0016] The figures herein follow a numbering convention in which
the first digit or digits correspond to the drawing figure number
and the remaining digits identify an element or component in the
drawing.
[0017] As used herein, "a" or "a number of" something can refer to
one or more such things. For example, "a number of process
variables" can refer to one or more process variables.
[0018] FIG. 1 illustrates a system 100 for an inferential sensor
for internal heat exchanger 104 parameters, in accordance with one
or more embodiments of the present disclosure. As shown in FIG. 1,
the system 100 can include heat pump 102, heat exchanger 104,
controller 106, a number of measured temperatures 108, a number of
measured pressures 110, and a number of measured flow rates
112.
[0019] Controller 106 can receive, from heat exchanger 104, a
number of measured process variables of heat exchanger 104. The
number of measured process variables can include measured
temperatures 108, measured pressures 110, and/or measured flow
rates 112. The measured temperatures 108 can include measured inlet
and outlet temperatures. Additionally, the measured pressures 110
can include measured inlet and outlet pressures. Further, the
measured flow rates 112 can include a measured inlet and/or outlet
flow rate.
[0020] As used herein, heat exchanger 104 can be a device that
allows for the process of heat exchange (e.g., the transfer of
thermal energy) between two media that are at different
temperatures and separated by a solid wall (e.g., a heat exchange
surface) to prevent the two media from mixing. For example, heat
exchanger 104 can be a concentric tube heat exchanger with a
parallel flow or a counter-flow arrangement. As another example,
heat exchanger 104 can be a cross-flow heat exchanger that can be
finned or un-finned.
[0021] As used herein, a heat exchange surface can be a medium
through which a hot fluid of heat exchanger 104 can transfer heat
to a cold fluid of heat exchanger 104. The heat transfer
coefficient between the cold fluid of heat exchanger 104 and the
hot fluid of heat exchanger 104 can be affected by the heat
transfer from the hot fluid to the heat exchange surface, thermal
resistance between the heat exchange surface, and heat transfer
from the heat exchange surface to the cold fluid of heat exchanger
104.
[0022] Although heat exchanger 104 is described as being a
concentric tube heat exchanger or a cross-flow heat exchanger,
embodiments of the present disclosure are not so limited. For
example, heat exchanger 104 can be any other type of heat
exchanger.
[0023] Controller 106 can be part of heat pump 102. As used herein,
heat pump 102 can be a device that moves thermal energy by
absorbing heat from a cold space and releasing it into a warmer
space. For example, heat pump 102 can utilize heat exchanger 104 to
move thermal energy from a cold space to a working fluid (e.g.,
cold fluid of heat exchanger 104) and release from a working fluid
in a different thermodynamic state to a warm space. Thermal energy
is typically transported using a medium such as a liquid, vapor,
and/or a mixture of the two.
[0024] Although controller 106 is shown in FIG. 1 as part of heat
pump 102, embodiments of the present disclosure are not so limited.
For example, controller 106 can be located remotely from heat pump
102 and can receive a number of process variables of heat exchanger
104 via a wired or wireless network relationship.
[0025] The wired or wireless network can be a network relationship
that connects heat pump 102 to controller 106. Examples of such a
network relationship can include a serial communication line,
and/or a local area network (LAN). Data from heat pump 102 can
further be communicated to a distributed computing environment
(e.g., a cloud computing environment), and/or the Internet using a
wide area network (WAN), among other types of network
relationships.
[0026] The number of measured inlet process variables can include
an inlet temperature of the hot fluid of heat exchanger 104 and an
inlet temperature of a cold fluid of heat exchanger 104. The number
of measured outlet process variables can include an outlet
temperature of the hot fluid of heat exchanger 104 and an outlet
temperature of the cold fluid of heat exchanger 104. The inlet
temperature of the hot fluid of heat exchanger 104 can be different
than the outlet temperature of the hot fluid of heat exchanger 104
as the hot fluid transfers heat to a cold fluid of heat exchanger
104 as the hot fluid moves the length of heat exchanger 104.
Further, the inlet temperature of the cold fluid of heat exchanger
104 can be different than the outlet temperature of the hot fluid
of heat exchanger 104 as the cold fluid receives thermal energy
(e.g., heat) from the hot fluid as the cold fluid moves the length
of heat exchanger 104.
[0027] Although the number of measured inlet and outlet process
variables are described as including an inlet and outlet
temperature of the hot fluid of heat exchanger 104 and an inlet and
outlet temperature of a cold fluid of heat exchanger 104,
embodiments of the disclosure are not so limited. For example, the
number of measured inlet and outlet process variables can include
an inlet and outlet temperature of the hot fluid of heat exchanger
104, an inlet temperature of a cold fluid of heat exchanger 104,
but not an outlet temperature of a cold fluid of heat exchanger
104, an inlet temperature of the hot fluid of heat exchanger 104,
an inlet and outlet temperature of a cold fluid of heat exchanger
104, but not an outlet temperature of a hot fluid of heat exchanger
104, or any other combination thereof.
[0028] As used herein, the hot fluid of heat exchanger 104 can be
any fluid suitable to enable the transfer of heat from one medium
(e.g., hot fluid) to another medium (e.g., cold fluid). For
example, the hot fluid can be water, oil, ammonia, alcohol, and/or
any combination thereof in a liquid or vapor state, although
embodiments of the present disclosure are not so limited.
[0029] As used herein, the cold fluid of the heat exchanger can be
any fluid suitable to enable the transfer of heat from one medium
(e.g., hot fluid) to another medium (e.g., cold fluid). For
example, the cold fluid can be water, oil, ammonia, alcohol, and/or
any combination thereof in a liquid or vapor state, although
embodiments of the present disclosure are not so limited.
[0030] The number of measured inlet process variables can include
an inlet flow rate of the hot fluid of heat exchanger 104 and an
inlet flow rate of the cold fluid of heat exchanger 104. The number
of measured outlet process variables can include an outlet flow
rate of the hot fluid of heat exchanger 104 and an outlet flow rate
of the cold fluid of heat exchanger 104. For example, the flow
rates of the hot fluid and/or the cold fluid of heat exchanger 104
can be a flow rate for optimal heat transfer in heat exchanger
104.
[0031] Although the number of measured inlet and outlet process
variables are described as including an inlet flow rate of the hot
fluid of heat exchanger 104, an inlet flow rate of the cold fluid
of heat exchanger 104, an outlet flow rate of the hot fluid of heat
exchanger 104, and an outlet flow rate of the cold fluid of heat
exchanger 104, respectively, embodiments of the present disclosure
are not so limited. For example, the number of measured inlet and
outlet process variables can include an inlet flow rate of the hot
fluid of heat exchanger 104, an inlet flow rate of the cold fluid
of heat exchanger 104, and an outlet flow rate of the hot fluid of
heat exchanger 104, among other combinations of measured inlet and
outlet flow rates.
[0032] The number of measured inlet process variables can include
an inlet pressure of the hot fluid of heat exchanger 104 and an
inlet pressure of the cold fluid of heat exchanger 104. The number
of measured outlet process variables can include an outlet pressure
of the hot fluid of heat exchanger 104 and an outlet pressure of
the cold fluid of heat exchanger 104. For example, the pressures of
the hot fluid and/or the cold fluid of heat exchanger 104 can be
between 2 and 18 barA, although embodiments of the present
disclosure are not so limited.
[0033] Although the number of measured inlet and outlet process
variables are described as including an inlet and outlet pressure
of the hot fluid of heat exchanger 104 and an inlet and outlet
pressure of a cold fluid of heat exchanger 104, embodiments of the
disclosure are not so limited. For example, the number of measured
inlet and outlet process variables can include an inlet and outlet
pressure of the hot fluid of heat exchanger 104, an inlet pressure
of a cold fluid of heat exchanger 104, but not an outlet pressure
of a cold fluid of heat exchanger 104, an inlet pressure of the hot
fluid of heat exchanger 104, an inlet and outlet pressure of a cold
fluid of heat exchanger 104, but not an outlet pressure of a hot
fluid of heat exchanger 104, or any other combination thereof.
[0034] Controller 106 can predict, using a dynamic differential
model including the number of measured inlet process variables,
internal parameters of heat exchanger 104 and a number of outlet
process variables of heat exchanger 104.
[0035] Internal parameters of heat exchanger 104 can include a heat
transfer coefficient of heat exchanger 104. The heat transfer
coefficient can describe the heat transfer that can occur in heat
exchanger 104. The heat transfer coefficient of heat exchanger 104
may be less than a nominal heat transfer coefficient of a nominal
heat exchanger. As used herein, a nominal heat exchanger can be a
heat exchanger that does not experience losses due to fouling
and/or other environmental factors.
[0036] For example, heat exchanger 104 may experience fouling due
to rust or mineral deposits on the heat exchange surface of heat
exchanger 104 that can cause higher thermal resistance to heat
transfer in heat exchanger 104. As a result, a heat transfer
coefficient of heat exchanger 104 may be less than the nominal heat
transfer coefficient of the nominal heat exchanger.
[0037] Internal parameters of heat exchanger 104 can include metal
temperatures of a heat exchange surface of heat exchanger 104.
Temperatures of a heat exchange surface within heat exchanger 104
may not be easily measured. Controller 106 can therefore predict
the metal temperatures of the heat exchange surface of heat
exchanger 104 to calculate an efficiency of heat exchanger 104, as
will be further described herein.
[0038] Controller 106 can determine an amount of heat transfer from
the hot fluid to the cold fluid of heat exchanger 104 using a
logarithmic mean temperature of the number of measured inlet
process variables of heat exchanger 104 and a number of predicted
outlet process variables of heat exchanger 104. For example,
controller 106 can utilize the measured inlet temperatures of the
hot and cold fluids of heat exchanger 104, the measured inlet
pressures of the hot and cold fluids of heat exchanger 104, the
measured inlet flow rates of the hot and cold fluids of heat
exchanger 104, predicted outlet temperatures of the hot and cold
fluids of heat exchanger 104, predicted outlet pressures of the hot
and cold fluids of heat exchanger 104, and predicted outlet flow
rates of the hot and cold fluids of heat exchanger 104 to determine
an amount of heat transfer from the hot fluid to the cold fluid of
heat exchanger 104 using a logarithmic mean temperature.
[0039] Differential equations for the inlet temperatures and outlet
temperatures of the hot fluid of heat exchanger 104, heat exchange
surface of heat exchanger 104, and the cold fluid of heat exchanger
104 can be developed based on the heat transfer during transient
conditions being characterized by the logarithmic mean temperature
of the hot fluid, the cold fluid, and the heat exchange surface of
heat exchanger 104, energy and mass conservation for the hot fluid
of heat exchanger 104 and the cold fluid of heat exchanger 104, as
well as energy conservation for the heat exchange surface of heat
exchanger 104. As used herein, transient conditions can refer to
internal state and parameters of heat exchanger 104 changing
continuously under time varying operating conditions.
[0040] The logarithmic mean temperature used in steady state models
can be described by equation 1:
.DELTA. T LMTD = .DELTA. T 1 - .DELTA. T 2 ln .DELTA. T 1 - ln
.DELTA. T 2 = LMTD ( .DELTA. T 1 , .DELTA. T 2 ) ( 1 )
##EQU00001##
[0041] where .DELTA.T.sub.LMTD describes the logarithmic mean
temperature, and .DELTA.T.sub.1 and .DELTA.T.sub.2 are mean
temperatures of the inlet and outlet of the hot cold and cold
fluids of heat exchanger 104.
[0042] Utilizing the logarithmic mean temperature equation (e.g.,
equation 1), the mean temperature of the hot fluid of heat
exchanger 104 (e.g., equation 2), the mean temperature of the metal
of the heat exchange surface of heat exchanger 104 (e.g., equation
3), and the mean temperature of the cold fluid of heat exchanger
104 (e.g., equation 4) can be obtained as a logarithmic mean
temperature between the inlet side and outlet side of heat
exchanger 104 and zero reference temperature:
T.sub.h=LMTD(T.sub.h1,T.sub.h2) (2)
T.sub.m=LMTD(T.sub.m1,T.sub.m2) (3)
T.sub.c=LMTD(T.sub.c1,T.sub.c2) (4)
where T.sub.h is the logarithmic mean temperature of the hot fluid
of heat exchanger 104, T.sub.m is the logarithmic mean temperature
of the metal of the heat exchange surface of heat exchanger 104,
and T.sub.c is the logarithmic mean temperature of the cold fluid
of heat exchanger 104. T.sub.h, T.sub.m, and T.sub.c can be
calculated as logarithmic mean temperature differences between the
fluid inlet and outlet temperatures, and a reference zero
temperature.
[0043] A heat and mass balance of the hot fluid, the cold fluid,
and the metal temperature of the heat exchange surface of heat
exchanger 104 results in the following differential equations
(e.g., equations 5-7) describing the internal parameters (e.g.,
boundary conditions for temperatures related to the hot fluid, heat
exchanger surface, and cold fluid of heat exchanger 104) T.sub.h1,
T.sub.h2, T.sub.m1, T.sub.m2, T.sub.c1, T.sub.c2 as they change
with time:
m h c h ( .differential. T h .differential. T h 1 T h 1 t +
.differential. T h .differential. T h 2 T h 2 t ) = F h c h ( T h 1
- T h 2 ) - .alpha. h A h ( T h - T m ) ( 5 ) m m c m (
.differential. T m .differential. T m 1 T m 1 t + .differential. T
m .differential. T m 2 T m 2 t ) = .alpha. h A h ( T h - T m ) -
.alpha. c A c ( T m - T c ) ( 6 ) m c c c ( .differential. T c
.differential. T c 1 T c 1 t + .differential. T c .differential. T
c 2 T c 2 t ) = F c c c ( T c 2 - T c 1 ) + .alpha. c A c ( T m - T
c ) ( 7 ) ##EQU00002##
where m.sub.h, m.sub.m, m.sub.c denote individual masses of the hot
fluid, heat exchange surface, and cold fluid of heat exchanger 104,
respectively, c.sub.h, c.sub.m, c.sub.c denote the specific heats
of the hot fluid, heat exchange surface, and cold fluid of heat
exchanger 104, respectively, F.sub.h, F.sub.c denote the mass flows
of the hot fluid and cold fluid of heat exchanger 104,
respectively, A.sub.h, A.sub.c are surface areas between the heat
exchange surface and hot fluid or cold fluid of heat exchanger 104,
and .alpha..sub.h, .alpha..sub.c are heat transfer coefficients
between the heat exchange surface and hot fluid or cold fluid of
heat exchanger 104.
[0044] The differential equations represented by equations 5-7 can
be used to evaluate internal parameters of heat exchanger 104. The
internal parameters of heat exchanger 104 can correspond to
individual inlet and outlet temperatures of the hot fluid, heat
exchange surface, and cold fluid of heat exchanger 104.
[0045] The dynamic model is fully consistent with the logarithmic
mean temperature model in steady state and transient conditions, as
well as observes mass and energy conservation laws. The dynamic
model can be used to predict internal parameters of heat exchanger
104, such as a heat transfer coefficient and other parameters for
optimization (e.g., thermal cycle performance optimization) in
steady state, as well as during transient conditions.
[0046] Controller 106 can calculate an efficiency of heat exchanger
104 using the predicted internal parameters of heat exchanger 104
and the number of measured process variables of heat exchanger 104.
That is, the efficiency of heat exchanger 104 can be calculated
using the internal parameters of heat exchanger 104 and the number
of measured inlet process variables and the number of measured
outlet process variables.
[0047] Controller 106 can compare the number of measured outlet
process variables with the number of predicted outlet process
variables. For example, the measured outlet temperatures of the hot
and cold fluids of heat exchanger 104 can be compared to the
predicted outlet temperatures of the hot and cold fluids of heat
exchanger 104. Additionally, the measured outlet pressures of the
hot and cold fluids of heat exchanger 104 can be compared to the
predicted outlet pressures of the hot and cold fluids of heat
exchanger 104. Further, the measured outlet flow rates of the hot
and cold fluids of heat exchanger 104 can be compared to the
predicted outlet flow rates of the hot and cold fluids of heat
exchanger 104. The internal parameters of heat exchanger 104 may
change during operation of heat exchanger 104, resulting in a
change in the measured outlet process variables. The change can
result from a change of the internal parameters of heat exchanger
104.
[0048] In some embodiments, fouling can result in a change in the
internal parameters of heat exchanger 104. Fouling can occur when
impurities, rust, and/or other deposits occur on the heat exchange
surface of a heat exchanger (e.g., heat exchanger 104). For
example, the hot fluid and/or cold fluid of heat exchanger 104 can
include impurities such as minerals and/or other contaminants that
can deposit onto a heat exchange surface of heat exchanger 104,
causing a decrease in the amount of heat transferred from the hot
fluid to the cold fluid. The decrease in heat transfer is due to a
higher thermal resistance of the heat transfer surface as a result
of fouling. As another example, frost can occur on the heat
exchange surface between the working fluid and air when air
moisture condenses on the heat exchange surface and freezes. The
frost can act as an insulator that may cause a decrease in the
amount of heat transfer of heat exchanger 104.
[0049] Controller 106 can update, based on the comparison of the
number of measured outlet process variables with the number of
predicted outlet process variables, the internal parameters of heat
exchanger 104. That is, if the actual internal parameters of heat
exchanger 104 have changed (e.g., due to fouling), the predicted
internal parameters of heat exchanger 104 can be updated based on
the comparison.
[0050] For example, if the actual heat transfer coefficient of heat
exchanger 104 is smaller than the predicted heat transfer
coefficient, the low order dynamic model can predict a lower outlet
temperature of the cold fluid of heat exchanger 104. Knowing this
difference, the low order dynamic model can update the heat
transfer coefficient of heat exchanger 104.
[0051] In some embodiments, heat exchanger 104 can be a
liquid-liquid heat exchanger. For example, the hot fluid and cold
fluid of heat exchanger 104 can remain in a liquid state throughout
the heat exchange process. That is, no boiling and/or evaporation
of the hot and/or cold fluid of heat exchanger 104 occurs during
the heat exchange process in heat exchanger 104. The liquid-liquid
heat exchanger can be accurately modeled using the number of
measured process variables of heat exchanger 104.
[0052] In some embodiments, heat exchanger 104 can be a phase
change heat exchanger. A phase change heat exchanger can include
partial boiling and/or evaporation of a liquid of heat exchanger
104 (e.g., the hot fluid or the cold fluid).
[0053] Heat exchanger 104 can additionally be accurately modeled as
a phase change heat exchanger using a logarithmic mean temperature
of the liquid portion of heat exchanger 104 and a logarithmic mean
temperature of the a boiling and/or evaporating portion of heat
exchanger 104. The implementation of the inferential sensor for
internal heat exchanger parameters can utilize other thermodynamic
state variables, such as enthalpies of individual fluids (e.g., hot
fluid and cold fluids) of heat exchanger 104.
[0054] FIG. 2 illustrates a system 214 for an inferential sensor
for internal heat exchanger 218 parameters, in accordance with one
or more embodiments of the present disclosure. As shown in FIG. 2,
the system 214 can include air-conditioner 216, heat exchanger 218,
controller 206, a number of measured temperatures 220, a number of
measured pressures 222, and a number of measured flow rates
224.
[0055] Similar to the embodiment described in FIG. 1, controller
206 can receive, from heat exchanger 218, a number of process
variables of heat exchanger 218. The number of measured process
variables of heat exchanger 218 can include a measured temperatures
220, measured pressures 222, and/or measured flow rates 224. The
measured temperatures 220 can include measured inlet and outlet
temperatures. Additionally, the measured pressures 222 can include
measured inlet and outlet pressures. Further, the measured flow
rates 224 can include a measured inlet and/or outlet flow rate.
[0056] Controller 206 can be part of air-conditioner 216. As used
herein, air-conditioner 216 can be a device that lowers the air
temperature of a space. Air-conditioner 216 can lower the air
temperature using heat exchanger 218.
[0057] Although controller 206 is shown in FIG. 2 as part of
air-conditioner 216, embodiments of the present disclosure are not
so limited. For example, controller 206 can be located remotely
from air-conditioner 216 and can receive a number of process
variables of heat exchanger 218 via a wired or wireless network
relationship.
[0058] The wired or wireless network can be a network relationship
that connects air-conditioner 216 to controller 206. Examples of
such a network relationship can include a serial communication
line, and/or a local area network (LAN). Data from air-conditioner
216 can further be communicated to a distributed computing
environment (e.g., a cloud computing environment), and/or the
Internet using a wide area network (WAN), among other types of
network relationships.
[0059] Controller 206 can determine an amount of heat transfer from
the hot fluid to the cold fluid of air-conditioner 216 using a
logarithmic mean temperature of the number of measured inlet
process variables of air-conditioner 216 and a number of predicted
outlet process variables of air-conditioner 216. For example,
controller 206 can utilize the measured inlet temperatures of the
hot and cold fluids of air-conditioner 216, the measured inlet
pressures of the hot and cold fluids of air-conditioner 216, the
measured inlet flow rates of the hot and cold fluids of
air-conditioner 216, predicted outlet temperatures of the hot and
cold fluids of air-conditioner 216, predicted outlet pressures of
the hot and cold fluids of air-conditioner 216, and predicted
outlet flow rates of the hot and cold fluids of air-conditioner 216
to determine an amount of heat transfer from the hot fluid to the
cold fluid of heat exchanger 218 using a logarithmic mean
temperature.
[0060] Controller 206 can compare the number of measured outlet
process variables with the number of predicted outlet process
variables. For example, the measured outlet temperatures of the hot
and cold fluids of heat exchanger 218 can be compared to the
predicted outlet temperatures of the hot and cold fluids of heat
exchanger 218. Additionally, the measured outlet pressures of the
hot and cold fluids of heat exchanger 218 can be compared to the
predicted outlet pressures of the hot and cold fluids of heat
exchanger 218. Further, the measured outlet flow rates of the hot
and cold fluids of heat exchanger 218 can be compared to the
predicted outlet flow rates of the hot and cold fluids of heat
exchanger 218. The internal parameters of heat exchanger 218 may
change during operation of heat exchanger 218, resulting in a
change in the measured outlet process variables. The change can
result from a change of the internal parameters of heat exchanger
218.
[0061] Controller 206 can update, based on the comparison of the
number of measured outlet process variables with the number of
predicted outlet process variables, the internal parameters of heat
exchanger 218. For example, the internal parameters of heat
exchanger 218 can be updated based on the comparison.
[0062] FIG. 3 is a flow chart of a method 325 for an inferential
sensor for internal heat exchanger parameters, in accordance with
one or more embodiments of the present disclosure. Method 325 can
be performed by, for example, controllers 106, 206, and 406, as
described in connection with FIGS. 1, 2, and 4, respectively.
[0063] At block 326 of method 325, the controller can receive a
number of measured process variables of the heat exchanger (e.g.,
heat exchanger 104, 218, previously described in connection with
FIGS. 1 and 2, respectively). For example, the controller can
receive a number of measured temperatures (e.g., measured
temperatures 108, 220, previously described in connection with
FIGS. 1 and 2, respectively) of the heat exchanger, a number of
measured pressures (e.g., number of measured pressures 110, 222,
previously described in connection with FIGS. 1 and 2,
respectively) of the heat exchanger, and a number of measured flow
rates (e.g., number of measured flow rates 112, 224, as previously
described in connection with FIGS. 1 and 2, respectively).
[0064] At block 328 of method 325, the controller can predict
internal parameters of the heat exchanger and a number of outlet
process variables by a dynamic differential model using a heat and
mass balance of the number of measured inlet process variables and
the number of measured outlet process variables of the heat
exchanger. For example, controller 106 can utilize a heat and mass
balance (e.g., equations 5-7, previously described in connection
with FIG. 1) of the measured inlet temperatures of the hot and cold
fluids of the heat exchanger, measured inlet pressures of the hot
and cold fluids of the heat exchanger, measured inlet flow rates of
the hot and cold fluids of the heat exchanger, predicted outlet
temperatures of the hot and cold fluids of the heat exchanger,
predicted outlet pressures of the hot and cold fluids of the heat
exchanger, and predicted outlet flow rates of the hot and cold
fluids of the heat exchanger to predict internal variables of the
heat exchanger.
[0065] At block 330 of method 325, the controller can compare the
number of measured outlet process variables with the number of
predicted outlet process variables. That is, the controller can use
the heat exchanger model to compare the measured outlet process
variables with the values predicted by the model. The controller
can use the difference to update model parameters that define the
heat transfer of the heat exchanger.
[0066] At block 332 of method 325, the controller can update, based
on the comparison of the measured outlet process variables with the
values predicted by the model, the internal parameters of the heat
exchanger. For example, if the actual internal parameters of heat
exchanger 104 have changed (e.g., due to fouling, etc.), the
internal parameters of the heat exchanger can be updated based on
the comparison.
[0067] The controller can update a heat transfer coefficient of the
heat exchanger. For example, a predicted heat transfer coefficient
of the heat exchanger can be updated in response to a change in the
actual heat transfer coefficient of the heat exchanger.
[0068] The controller can update metal temperatures of a heat
exchange surface of the heat exchanger. For example, a predicted
metal temperature of the heat exchange surface can be updated in
response to a change in the actual metal temperature of the heat
exchange surface of the heat exchanger.
[0069] Method 325 can be performed while the heat exchanger is
operating. For example, method 325 can be performed during
operation of the heat exchanger. That is, the heat exchanger does
not need to be removed from service while method 325 is performed.
Further, method 325 can be continuously repeated during operation
of the heat exchanger. That is, the method 325 can be continuously
repeated to dynamically model and predict changes of the heat
exchanger, as well as continuously update predicted values (e.g.,
internal parameters and/or outlet process variables).
[0070] FIG. 4 is a schematic block diagram of a controller 406 for
an inferential sensor for internal heat exchanger parameters (e.g.,
heat exchanger 104, 218, previously described in connection with
FIGS. 1 and 2, respectively), in accordance with one or more
embodiments of the present disclosure. Controller 406 can be, for
example, controllers 106 and 206, previously described in
connection with FIGS. 1 and 2, respectively. Controller 406 can
include a memory 436 and a processor 434 configured for an
inferential sensor for internal heat exchanger parameters in
accordance with the present disclosure.
[0071] The memory 436 can be any type of storage medium that can be
accessed by the processor 434 to perform various examples of the
present disclosure. For example, the memory 436 can be a
non-transitory computer readable medium having computer readable
instructions (e.g., computer program instructions) stored thereon
that are executable by the processor 434 to receive a number of
process variables of the heat exchanger. Further, processor 434 can
execute the executable instructions stored in memory 436 to predict
internal parameters of a heat exchanger and a number of outlet
process variables of the heat exchanger, compare a number of
measured outlet process variables with the number of predicted
outlet process variables, and update, based on the comparison, the
internal parameters of the heat exchanger.
[0072] The memory 436 can be volatile or nonvolatile memory. The
memory 436 can also be removable (e.g., portable) memory, or
non-removable (e.g., internal) memory. For example, the memory 436
can be random access memory (RAM) (e.g., dynamic random access
memory (DRAM) and/or phase change random access memory (PCRAM)),
read-only memory (ROM) (e.g., electrically erasable programmable
read-only memory (EEPROM) and/or compact-disc read-only memory
(CD-ROM)), flash memory, a laser disc, a digital versatile disc
(DVD) or other optical storage, and/or a magnetic medium such as
magnetic cassettes, tapes, or disks, among other types of
memory.
[0073] Further, although memory 436 is illustrated as being located
within controller 406, embodiments of the present disclosure are
not so limited. For example, memory 436 can also be located
internal to another computing resource (e.g., enabling computer
readable instructions to be downloaded over the Internet or another
wired or wireless connection).
[0074] Although specific embodiments have been illustrated and
described herein, those of ordinary skill in the art will
appreciate that any arrangement calculated to achieve the same
techniques can be substituted for the specific embodiments shown.
This disclosure is intended to cover any and all adaptations or
variations of various embodiments of the disclosure.
[0075] It is to be understood that the above description has been
made in an illustrative fashion, and not a restrictive one.
Combination of the above embodiments, and other embodiments not
specifically described herein will be apparent to those of skill in
the art upon reviewing the above description.
[0076] The scope of the various embodiments of the disclosure
includes any other applications in which the above structures and
methods are used. Therefore, the scope of various embodiments of
the disclosure should be determined with reference to the appended
claims, along with the full range of equivalents to which such
claims are entitled.
[0077] In the foregoing Detailed Description, various features are
grouped together in example embodiments illustrated in the figures
for the purpose of streamlining the disclosure. This method of
disclosure is not to be interpreted as reflecting an intention that
the embodiments of the disclosure require more features than are
expressly recited in each claim.
[0078] Rather, as the following claims reflect, inventive subject
matter lies in less than all features of a single disclosed
embodiment. Thus, the following claims are hereby incorporated into
the Detailed Description, with each claim standing on its own as a
separate embodiment.
* * * * *