U.S. patent application number 16/311338 was filed with the patent office on 2019-08-01 for cleanroom control system and method.
The applicant listed for this patent is ENERGY EFFICIENCY CONSULTANCY GROUP LIMITED. Invention is credited to Shuji CHEN, Robert WALLACE.
Application Number | 20190234631 16/311338 |
Document ID | / |
Family ID | 56891618 |
Filed Date | 2019-08-01 |
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United States Patent
Application |
20190234631 |
Kind Code |
A1 |
WALLACE; Robert ; et
al. |
August 1, 2019 |
CLEANROOM CONTROL SYSTEM AND METHOD
Abstract
The control system for controlling air volume to maintain a
desired concentration of airborne contamination in a cleanroom
supplied by a HVAC system being operative to supply treated air to
the cleanroom includes a sensor for a concentration of non-viable
particles and/or viable particles in real time or near real time;
and a processor for comparing the sensed concentration of
non-viable particles and/or viable particles against the desired
concentration of airborne contamination. At least one control
signal is outputted to the HVAC system based on the comparison.
Inventors: |
WALLACE; Robert;
(Macclesfield, Cheshire, GB) ; CHEN; Shuji;
(Macclesfield, Cheshire, GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ENERGY EFFICIENCY CONSULTANCY GROUP LIMITED |
Macclesfield, Cheshire |
|
DE |
|
|
Family ID: |
56891618 |
Appl. No.: |
16/311338 |
Filed: |
June 23, 2017 |
PCT Filed: |
June 23, 2017 |
PCT NO: |
PCT/GB17/51837 |
371 Date: |
December 19, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F24F 11/46 20180101;
F24F 3/161 20130101; F24F 2110/50 20180101; F24F 11/30 20180101;
F24F 11/62 20180101; B01L 1/04 20130101; B01L 2200/147 20130101;
B01L 2200/146 20130101 |
International
Class: |
F24F 3/16 20060101
F24F003/16; F24F 11/62 20060101 F24F011/62; F24F 11/30 20060101
F24F011/30 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 27, 2016 |
GB |
1611107.2 |
Claims
1. A control system for controlling air volume to maintain a
desired concentration of airborne contamination in a cleanroom
supplied by a HVAC system being operative to supply treated air to
the cleanroom, the control system comprising: means for sensing a
concentration of non-viable particles and/or viable particles in
real time or near real time; and means for comparing the sensed
concentration of non-viable particles and/or viable particles
against the desired concentration of airborne contamination and
outputting at least one control signal to the HVAC system based on
the comparison.
2. The control system as claimed in claim 1, further comprising a
cleanroom having one or more zones or rooms, each of the zones or
rooms having a respective desired concentration of airborne
contamination.
3. The control system as claimed in claim 1, wherein the desired
concentration of airborne contamination is specified by the number
of non-viable particles per cubic meter having a particle size of
equal to or greater than 0.1 .mu.m, 0.2 .mu.m, 0.3 .mu.m, 0.5
.mu.m, 1 .mu.m and 5 .mu.m in diameter.
4. (canceled)
5. The control system as claimed in claim 1, wherein the HVAC
system comprises at least one HVAC air handling unit (AHU)
supplying treated air through a ducting system, and one or more
constant air volume devices and/or one or more variable air volume
devices positioned in the ducting and generally associated with
each respective zone or room of the cleanroom.
6. The control system as claimed in claim 1, wherein the air
treatment is selected from a group comprised of: filtration,
ventilation, heating, cooling, humidification, pressurisation,
occupancy, and combinations thereof.
7. (canceled)
8. The control system as claimed in claim 1, further comprising
means for secondary sensing an environmental condition and/or
process condition and/or HVAC system condition in real time or near
real time.
9. The control system as claimed in claim 8, wherein the secondary
sensing means further comprises one or more sensors selected from a
group being comprised of: temperature sensor, humidity sensor,
pressure sensor, differential pressure sensor, airborne molecular
contamination sensor, contaminant deposition sensor, air flow
sensor, proximity sensor, and combinations thereof.
10. The control system as claimed in claim 1, wherein the
processing means receiving energy price data and/or usage data.
11. The control system as claimed in claim 10, wherein the
processing means receiving the sensed environmental condition
and/or process condition and/or HVAC system condition and/or energy
price data and/or usage data and outputting one or more secondary
control signals to the HVAC system.
12. The control system as claimed in claim 11, wherein the one or
more secondary control signals are outputted without causing the
sensed concentration of particles to depart from the desired
concentration of airborne contamination.
13. The control system as claimed in claim 8, wherein the desired
concentration of airborne contamination and/or energy price data
and/or usage data being initially user configurable.
14. The control system as claimed in claim 1, wherein the at least
one control signal to the HVAC system controlling the air volume
supplied to the cleanroom.
15. The control system as claimed in claim 8, wherein the one or
more secondary control signals controlling the filtration,
ventilation, heating, cooling, humidification, pressurisation,
occupancy, and combinations thereof supplied to the cleanroom.
16. The control system as claimed in claim 1, wherein the
processing means comprises a model predictive control (MPC)
algorithm.
17. The control system as claimed in claim 16, wherein the model
predictive control algorithm being able to self-adapt.
18. The control system as claimed in claim 16, further comprising:
a model component that receives a HVAC system operating condition
from extrinsic data analysis and which models HVAC system
behaviour; and means for receiving the modelled HVAC system
behaviour and issuing a control action based on the modelled HVAC
system behaviour and a cost minimizing function and
constraints.
19-20. (canceled)
21. The control system as claimed in claim 1, further comprising
means for enabling communication and/or integration and/or
interoperability with third party building management systems
(BMSs).
22. The control system as claimed in claim 1, further comprising
means for monitoring the energy performance of the cleanroom and/or
particle contamination concentration within the cleanroom.
23. A method of controlling air volume to maintain a desired
concentration of airborne contamination in a cleanroom supplied by
a HVAC system being operative to supply treated air to the
cleanroom, comprising the steps of: sensing a concentration of
non-viable particles and/or viable particles in real time or near
real time; comparing the sensed concentration of non-viable
particles and/or viable particles against the desired concentration
of airborne contamination; and outputting at least one control
signal to the HVAC system based on the comparison.
24. A computer program product for controlling air volume to
maintain a desired concentration of airborne contamination in a
cleanroom supplied by a HVAC system being operative to supply
treated air to the cleanroom, comprising: computer program means
for sensing a concentration of non-viable particles and/or viable
particles in real time or near real time; computer program means
for comparing the sensed concentration of non-viable particles
and/or viable particles against the desired concentration of
airborne contamination; and computer program means for outputting
at least one control signal to the HVAC system based on the
comparison.
25-27. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] See Application Data Sheet.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
THE NAMES OF PARTIES TO A JOINT RESEARCH AGREEMENT
[0003] Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC
OR AS A TEXT FILE VIA THE OFFICE ELECTRONIC FILING SYSTEM
(EFS-WEB)
[0004] Not applicable.
STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT
INVENTOR
[0005] Not applicable.
BACKGROUND OF THE INVENTION
1. Field of the Invention
[0006] This invention relates to a cleanroom control system and
method. In particular, this invention relates to a cleanroom
control system which maintains the strict air cleanliness
requirements of cleanrooms, whilst optimising energy performance of
the equipment necessary for operations, which primarily includes
the cleanroom's heating, ventilation and air conditioning (HVAC)
system.
2. Description of Related Art Including Information Disclosed Under
37 CFR 1.97 and 37 CFR 1.98
[0007] A cleanroom is an environment, typically used in
manufacturing or scientific research, that has a low level of
environmental pollutants such as dust, airborne microbes, aerosol
particles and chemical vapours for critical environment
applications and research. More specifically, a cleanroom has a
controlled level of contamination that is specified by the number
of particles per cubic metre at a specified particle size. To put
this in some perspective, the ambient outside air in a typical
urban environment contains 35,000,000 particles per cubic metre
having a particle diameter greater than 0.5 .mu.m. This would be
classified as an International Standards Organization (ISO) 14644-1
Class 9 cleanroom. For the most critical environment applications,
an ISO Class 1 cleanroom is defined as allowing not more than 10
particles of 0.1 .mu.m diameter and greater per cubic metre.
[0008] The majority of cleanrooms that have been designed since the
1950s are based on a fixed air volume system that are generally
over-designed to supply more air than is required to meet the
relevant classification and cover the risk of not maintaining the
classification due to lack of continuous information. Whilst
cleanroom clothing and standard operating procedures have improved
greatly since the inception of cleanrooms, comparable advances in
control systems have hitherto not been made.
[0009] This results in much higher energy costs than is actually
needed for operating the cleanroom. There is a strong commercial
need for a control system which maintains the strict air
cleanliness requirements of the cleanroom, whilst optimising the
energy performance of the cleanroom's HVAC system. Any such control
system which addresses this problem serves two major purposes:
firstly, helping to reduce the energy costs of the cleanroom, and
secondly helping companies adopt a more sustainable stance boosting
their public image.
[0010] Energy efficiency activities are rare in cleanrooms, however
they present a very real opportunity in terms of energy savings.
The energy requirements of cleanrooms are immense: in some cases,
up to 80% of the energy consumed is required by the HVAC system to
control temperature and humidity as well as to filter out particles
and maintain pressure control. The integrity of the cleanroom
environment is also dependent upon maintaining a positive or
negative pressure, created by the HVAC system.
[0011] Until recently, energy efficiency has been of little concern
to cleanroom operations as energy prices were low. As Good
Manufacturing Practice (GMP) compliance is of the utmost importance
in the manufacture of food and pharmaceutical products, for
example, most companies in these sectors had been willing to accept
whatever energy is required to maintain the HVAC system performance
and ensure resulting compliance. This has made it hitherto
difficult for cleanroom operators to reduce energy costs in HVAC
systems.
[0012] It is estimated that high technology manufacturers in the UK
alone spend .English Pound.200 million on energy for their
cleanroom operations and very few pharmaceutical cleanroom
operations have any mitigation in place to reduce HVAC energy
consumption. However, with rising energy prices, and a desire for
more sustainable products, plant operators are very keen on finding
ways to reduce energy consumption without sacrificing plant
performance.
[0013] Several strategies have already been proposed for the
control of HVAC cleanroom systems. Existing control systems are
frequently independent of each other and are dedicated to
subsystems or groups of subsystems for example: ventilation,
heating and cooling, humidification and pressurisation.
[0014] One of the HVAC control systems available in the art is
described in US 2013/0324026 A1. US 2013/0324026 A1 provides a
cleanroom control system and method that reduces the energy
consumed by the air handling system of the cleanroom at times when
the cleanroom was not in use. It also provides a cleanroom control
system and method that enables the air handling system of the
cleanroom to return to an operation state (where the air handling
system operates at full capacity) from a low or reduced state upon
demand or at predetermined times.
[0015] There are still problems with known control systems of this
type. They do not provide the aforementioned control and
flexibility to maintain cleanroom integrity and significantly
reduce energy costs.
[0016] It is an object of the present invention to provide a
cleanroom control system and its method of use which overcomes or
reduces the drawbacks associated with known products of this type.
The present invention provides cleanroom control system that can be
used with, or retrofitted to, a HVAC cleanroom system, which can
save 50% or more of a cleanroom's energy costs whilst maintaining
the desired air quality levels. It is an object of the present
invention to provide a control system that integrates all of the
cleanroom's operations, including ventilation, heating, cooling,
room pressure, filtration and occupancy. Complex algorithms have
been developed to take into account cleanroom usage, demand and
user activities and/or energy prices. The present invention being
able to self-adapt to maintain the area or zone of the cleanroom in
the required condition in the most energy efficient and cost
effective manner. It is a further object of the present invention
to provide a cleanroom control system that will continuously
capture, and act upon, data from airborne particle counters,
temperature/humidity sensors, differential pressure sensors,
occupancy sensors, room pressure sensors, airborne molecular
contamination (AMC) sensors, particle deposition sensors and
microbiological sensors. Use of the present invention enabling
communication, integration and/or interoperability with other third
party products, including existing building management systems
(BMS). The present invention using open standards and application
programming interfaces (API) for communication. By using model
predictive control, variables such as occupancy, energy costs, past
monitoring and usage data can be utilised to create usage patterns
and forecasts for predictive control. This is key to accelerate the
system response time and guarantee air cleanliness and quality. Use
of the present invention provides a flexible, modular and scalable
system which can be suitable for retrofit and stand-alone
installations. The control system being flexible enough to be
expanded upon or altered as the cleanroom environment changes.
BRIEF SUMMARY OF THE INVENTION
[0017] The present invention is described herein and in the
claims.
[0018] According to the present invention there is provided a
control system for controlling air volume to maintain a desired
concentration of airborne contamination in a cleanroom supplied by
a HVAC system being operative to supply treated air to the
cleanroom, comprising: [0019] sensing means for sensing a
concentration of non-viable particles and/or viable particles in
real time or near real time; and [0020] processing means for
comparing the sensed concentration of non-viable particles and/or
viable particles against the desired concentration of airborne
contamination and outputting at least one control signal to the
HVAC system based on the comparison.
[0021] An advantage of the present invention is that it can be used
to maintain the cleanroom in the required condition in the most
energy efficient and cost effective manner both in-operation and at
rest. The control system can vary the control parameters based on a
proportion of the desired classification, as determined by a level
of acceptable risk.
[0022] Preferably, the cleanroom further comprises one or more
zones or rooms, each of the zones or rooms having a respective
desired concentration of airborne contamination.
[0023] Further preferably, the desired concentration of airborne
contamination is specified by the number of non-viable particles
per cubic meter having a particle size of equal to or greater than
0.1 .mu.m, 0.2 .mu.m, 0.3 .mu.m, 0.5 .mu.m, 1 .mu.m and 5 .mu.m in
diameter.
[0024] In use, the cleanroom can be classified by particle size
concentration as defined in ISO 14644-1 or any other classification
standard related to particle size concentration as determined by
the cleanroom user.
[0025] Further preferably, the control system will detect movement
and automatically change from an "at rest" to an "in-operation"
classification or mode of operation automatically.
[0026] Preferably, the HVAC system comprises at least one HVAC air
handling unit (AHU) supplying treated air through a ducting system,
and one or more constant air volume devices and/or one or more
variable air volume devices positioned in the ducting and generally
associated with each respective zone or room of the cleanroom.
[0027] Further preferably, the air treatment is selected from the
group consisting, but not limited to, any one of the following:
filtration, ventilation, heating, cooling, humidification,
pressurisation, occupancy, and combinations thereof.
[0028] In use, the sensing means may comprise one or more ISO
14644-1 calibrated laser particle counters and/or viable
particulate air monitoring sensors positioned in the cleanroom or
the ducting of the HVAC system.
[0029] Preferably, the control system further comprising one or
more secondary sensing means for sensing an environmental condition
and/or process condition and/or HVAC system condition in real time
or near real time.
[0030] Further preferably, the secondary sensing means further
comprises one or more sensors selected from the group consisting,
but not limited to, any one of the following: temperature sensor,
humidity sensor, pressure sensor, differential pressure sensor,
airborne molecular contamination sensor, contaminant deposition
sensor, air flow sensor, proximity sensor, and combinations
thereof.
[0031] In use, the processing means may receive energy price data
and/or usage data.
[0032] Preferably, the processing means receiving the sensed
environmental condition and/or process condition and/or HVAC system
condition and/or energy price data and/or usage data and outputting
one or more secondary control signals to the HVAC system.
[0033] Further preferably, the one or more secondary control
signals are outputted without causing the sensed concentration of
particles to depart from the desired concentration of airborne
contamination.
[0034] In use, the desired concentration of airborne contamination
and/or energy price data and/or usage data may be initially user
configurable.
[0035] Preferably, the at least one control signal to the HVAC
system controlling the air volume supplied to the cleanroom.
[0036] Further preferably, the one or more secondary control
signals controlling the filtration, ventilation, heating, cooling,
humidification, pressurisation, occupancy, and combinations thereof
supplied to the cleanroom.
[0037] Preferably, indication will be provided within the cleanroom
through a visual indication system to indicate status. A graphical
user interface may also be provided.
[0038] In use, the processing means may comprise a model predictive
control (MPC) algorithm.
[0039] Preferably, the model predictive control algorithm being
able to self-adapt.
[0040] Further preferably, the control system further comprising:
[0041] a model component that receives a HVAC system operating
condition from extrinsic data analysis and which models HVAC system
behaviour; and [0042] means for receiving the modelled HVAC system
behaviour and issuing a control action based on the modelled HVAC
system behaviour and a cost minimizing function and
constraints.
[0043] Preferably, the control system is implemented in a
programmable logic controller (PLC).
[0044] Further preferably, the control system further comprising
display means.
[0045] In use, the control system may further comprise means for
enabling communication and/or integration and/or interoperability
with third party building management systems (BMSs).
[0046] Preferably, the control system further comprising monitoring
the energy performance of the cleanroom and/or performance to the
particle contamination concentration within the cleanroom.
[0047] Also according to the present invention there is provided a
method of controlling air volume to maintain a desired
concentration of airborne contamination in a cleanroom supplied by
a HVAC system being operative to supply treated air to the
cleanroom, comprising the steps of: [0048] sensing a concentration
of non-viable particles and/or viable particles in real time or
near real time; [0049] comparing the sensed concentration of
non-viable particles and/or viable particles against the desired
concentration of airborne contamination; and [0050] outputting at
least one control signal to the HVAC system based on the
comparison.
[0051] Further according to the present invention there is provided
a computer program product for controlling air volume to maintain a
desired concentration of airborne contamination in a cleanroom
supplied by a HVAC system being operative to supply treated air to
the cleanroom, comprising: [0052] computer program means for
sensing a concentration of non-viable particles and/or viable
particles in real time or near real time; [0053] computer program
means for comparing the sensed concentration of non-viable
particles and/or viable particles against the desired concentration
of airborne contamination; and [0054] computer program means for
outputting at least one control signal to the HVAC system based on
the comparison.
[0055] It is believed that a cleanroom control system and its
method of use in accordance with the present invention at least
addresses the problems outlined above.
[0056] It will be obvious to those skilled in the art that
variations of the present invention are possible and it is intended
that the present invention may be used other than as specifically
described herein.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0057] The present invention will now be described by way of
example only, and with reference to the accompanying drawings.
[0058] FIG. 1 is a schematic illustration of a typical cleanroom in
which the control system of the present invention is used to
monitor and maintain the air cleanliness and other controlled
variables including temperature, humidity, occupancy, pressure
etc.
[0059] FIG. 2 shows a schematic illustration of how the control
system of the present invention can be utilised to maintain the
required air cleanliness of a cleanroom;
[0060] FIG. 3 is a high level flow diagram showing the
multivariable inputs and outputs of the control system of the
present invention.
[0061] FIG. 4 illustrates a block diagram of a model predictive
controller (MPC) for a cleanroom HVAC system of the present
invention.
[0062] FIG. 5 shows a flow diagram illustrating how the system
model for the MPC controller of the present invention is
obtained.
[0063] FIG. 6 is a schematic illustration of a typical cleanroom
supplied by two separate HVAC air handling units and controlled by
the MPC controller of the present invention.
[0064] FIGS. 7 and 8 show graph illustrations of comparative data
obtained from the cleanroom of FIG. 6 and shows particle
concentrations measured in various zones of the cleanroom to the
experimental test defined in Table 1, the test data showing the
response of a known BMS control system which is based on a
Proportional-Integral (PI) control algorithm.
[0065] FIG. 9 shows a graph illustration of the dynamic response of
the cleanroom control system of the present invention in response
to the same experimental test of FIGS. 7 and 8, based on a first
optimal setting value.
[0066] FIG. 10 shows a graph illustration of the dynamic response
of the cleanroom control system of the present invention in
response to the same experimental test of FIGS. 7 and 8, based on a
second optimal setting value.
[0067] FIG. 11 shows a graph illustration of the power consumed by
a known BMS system at various air change rates obtained from the
cleanroom of FIG. 6 as well as comparative dynamic power
measurements obtained by the cleanroom control system of the
present invention and shows that model predictive control
significantly reduces the power consumption of the cleanroom HVAC
system.
DETAILED DESCRIPTION OF THE INVENTION
[0068] The present invention has adopted the approach of utilising
a cleanroom control system that can be used with, or retrofitted
to, a HVAC cleanroom system, which can save 50% or more of a
cleanroom's energy costs whilst maintaining the desired air quality
levels. Advantageously, the present invention provides a control
system that integrates all of the cleanroom's operations, including
ventilation, heating, cooling, room pressure, filtration and
occupancy. Complex algorithms have been developed to take into
account cleanroom usage, demand and user activities and/or energy
prices. The present invention being able to self-adapt to maintain
the area or zone of the cleanroom in the required condition in the
most energy efficient and cost effective manner. Further
advantageously, the present invention provides a cleanroom control
system that will continuously capture, and act upon, data from
airborne particle counters, temperature/humidity sensors,
differential pressure sensors, occupancy sensors, room pressure
sensors, airborne molecular contamination (AMC) sensors, particle
deposition sensors and microbiological sensors. Use of the present
invention enabling communication, integration and/or
interoperability with other third party products, including
existing building management systems (BMS). The present invention
using open standards and application programming interfaces (API)
for communication. Further advantageously, by using predictive
control, variables such as occupancy, energy costs, past monitoring
and usage data can be utilised to create usage patterns and
forecasts for predictive control. This is key to accelerate the
system response time and guarantee air cleanliness and quality.
Further advantageously, use of the present invention provides a
flexible, modular and scalable system which can be suitable for
retrofit and stand-alone installation. The control system being
flexible enough to be expanded upon or altered as the cleanroom
environment changes.
[0069] Referring now to the drawings, FIG. 1 is illustrative of a
typical cleanroom 100 for which the control system 10 of the
present invention can be utilised to maintain the required air
cleanliness. The cleanroom 100 shown in FIG. 1 is for illustrative
purposes only and the control system 10 of the present invention
can be used to control multiple zones or rooms in multiple
configurations according to the requirements of the facility.
[0070] As can be seen a typical cleanroom 100 comprises a number of
zones or rooms usually of varying cleanliness ISO classifications,
or other as required. The cleanroom 100 in the example of FIG. 1
has its highest rated zone or room, in this case zone 108, which is
an ISO Class 5 cleanroom at the furthest point from the main door
entry 110. It is adjoined to a "dirtier" less clean cleanliness
classification room or zone 104, which in this example is an ISO
Class 7 cleanroom, via a gown/ungown room 106. Entry to room 104
being made through airlock entry 102.
[0071] The skilled person will appreciate that the ISO Class 5
cleanroom is kept at a higher air pressure (known as a "pressure
cascade") to prevent contaminants from, say, the adjacent ISO Class
7 cleanroom 104 entering through the gown/ungown room 106. This
pressure differential is maintained by the supply of filtered and
conditioned air, which flows through the inflows 112.
Exfiltration/exhaust air is taken from outflows 114. The inflows
112 and outflows 114 are controlled by the HVAC cleanroom control
system 10, as described in more detail below.
[0072] FIG. 2 shows how a HVAC cleanroom system can be controlled
utilising the control unit or system 10 of the present invention.
In order to aid clarification, only a single central HVAC air
handling unit (AHU) 12 is depicted, although the skilled person
will appreciate that any number of such HVAC air handling units 12
can be controlled by the control unit 10 according to the size,
capacity and/or cleanliness requirements of the cleanroom 100.
[0073] As shown in FIG. 2, fresh air is drawn through the inlet 14
of the air handling unit 12. This is controlled by a series of
baffles 16. The incoming air can be mixed with the air returning
from the cleanroom 100 generally in the mixing area 18 behind the
baffles 16. If needed, returning air from the cleanroom 100 can be
directly vented outside of the air handling unit 12 via discharge
outlet 20.
[0074] The air is then filtered, firstly through a pre-filter 22a
and a secondary filter 22b before passing through a series of
heating and cooling elements 24, 26 being drawn by the main air
blower 28. The output of the main air blower 28 passes through the
main high-efficiency particulate air (HEPA) filter element 30
before being transferred through ducting 32 to a series of
proprietary constant air volume (CAV) devices 36. It is necessary
to regulate the pressure variations in the air duct system 36 in
order to achieve the desired airflow in the room or zones 102, 104,
106, 108. The outflow of the air into the room or zones 102, 104,
106, 108 is through distribution grilles 38.
[0075] The air to be recirculated is drawn through grilles 40 and
the control unit 10 modulates a plurality of variable air volume
(VAV) devices 42 before returning the exhaust air through ducting
44 and return or check valve 46.
[0076] The control unit 10 of the present invention is used to
monitor and control each and every operation of the HVAC cleanroom
system. As shown in FIG. 2, the control unit 10, which is typically
implemented as microcontroller, receives a number of sensor inputs
48 indicated generally at the left hand side of the control unit
10. The microcontroller 100 can be considered a self-contained
system with a processor, memory and peripherals and can be used to
control all of the cleanroom's 100 operations, including
ventilation, heating, cooling, filtration via a number of outputs
indicated generally at the right hand side of the control unit
10.
[0077] For reasons of clarity in FIG. 2, the skilled person will
appreciate that there are a significant number of sensors and
transducers which are inputted to the control unit 10. These have
been shown schematically as sensor inputs 48 in FIG. 2. This
drawing is a schematic diagram and, in order to aid clarification,
many other circuit elements are not shown. For example, although
not shown in FIG. 2, the analogue signal received from any one or
more of the sensors is first converted to a digital form by any
suitable type of analogue-to-digital convertor (ADC) available in
the art. Equally, one or more of the digital outputs of the
microprocessor 100 can be converted to analogue form using any form
of digital-to-analogue convertor (DAC) available in the art. For
example, such an analogue output signal could be used to energise
the heating element 24. In operation, a set of instructions or
algorithm written in software in the microcontroller is configured
to program the control unit 10. The control unit 10 processes the
input signals using complex algorithms to provide control outputs
to multiple HVAC devices, including the central HVAC air handling
unit 12, constant air volume devices 36 and variable air volume 42
devices to maintain a supply of filtered and conditioned air within
the cleanroom 100, whilst taking into account cleanroom
classification, usage and occupancy, and other activities within
the cleanroom 100 environment.
[0078] The control unit 10 provides predictive sensor-based dynamic
control of the HVAC cleanroom system to maintain the required air
cleanliness while maximising energy efficiency. The unit 10 is a
modular, retrofit control solution, easily expanded as the
cleanroom 100 environment changes. It is able to communicate with
third-party products for complete integration with, for example, a
building energy management system. Bespoke control algorithms have
been developed based on real-world cleanroom applications in the
applicant's own HVAC cleanroom test facility.
[0079] The present invention at its core intelligently handles
particulate levels in the cleanroom 100 by monitoring viable and/or
non-viable particles of varying sizes. The control system 10
controls air volume to maintain below a desired concentration of
both viable (particles containing living micro-organisms) and
non-viable (particles that do not contain living micro-organisms
but acts as transportation for viable particles) particles using
real time or near real time viable and non-viable particle
counters, and other sensors and transducers inputted to the control
system. The control system 10 being able to vary the control signal
outputted to the HVAC cleanroom system as a percentage under the
desired class limit as a variable set point or weighting. The
control system 10 will also detect occupancy within the cleanroom
100 environment to determine the particulate limit being controlled
between an "at rest" or "in-operation" mode of operation and bring
the system out of the "at rest" state to aid speed of response, as
required.
[0080] FIG. 3 shows systematically how the control steps of the
unit 10 are followed using the logic flow shown in FIG. 3. In the
following description each step of FIG. 3 will be referred to as
"S" followed by a step number, e.g. S52, S54 etc.
[0081] FIG. 3 also shows that the control unit 10 can be
implemented as part of, or integrated within, a building management
system 50 which is computer-based control system installed in
buildings that controls and monitors the building's mechanical and
electrical equipment such as ventilation, lighting, power systems,
fire systems, and security systems.
[0082] In its broadest sense the control system 10 of the present
invention will monitor, process and control all variables,
including particulate sensors, on a continuous real time basis to
ensure the HVAC equipment responds to demands, occupancy and
changes within the cleanroom 100 environment and other associated
areas served by the HVAC cleanroom system. The control system 10
will either control the air volume as a secondary function to
maintain a correct air temperature and/or humidity directly or send
and receive data to the existing BMS system 50, as required.
[0083] The sensor and control arrangement of the present invention
is such that it provides a level of redundancy to ensure fail safe
operation of HVAC equipment in the event of sensor failure or
control system failure. In use, the sensor arrangement continuously
captures data from the cleanroom 100 environment (including
particulate count, temperature, humidity, occupancy, pressure) and
sends that data in real time to the control unit 10 for processing.
These "fail safe" modes of operation will ensure that the control
unit 10 maximises the risk to the product in the cleanroom 100.
[0084] In a preferred embodiment, the control system 10 will be
installed with a control panel (not shown) local to the cleanroom
100. There will be an option for a touchscreen graphical user
interface on the control panel. The external devices, such as the
various sensors, CAVs 36, VAVs 42 and AHUs 12 will be hardwired
directly to the control system 10, although the system 10 will be
able to control existing HVAC equipment via an Open Platform
Communications (OPC) server in an existing BMS system 50. In
addition, one or more of the various sensor inputs 48 which are
remote to the control unit 10 can be inputted via wireless
communication protocols, such as, for example, Wi-Fi (IEEE 802.11
standard), Bluetooth or a cellular telecommunications network would
also be appropriate.
[0085] The BMS 50 or control panel of the control unit 10 can be
used to set the reference inputs for the rooms or zones of the
cleanroom 100. These will include the temperature and humidity and
the desired cleanroom classification for the various zones. The
cleanroom classifications for particulates are defined in ISO
14644-1, or equivalent, but the skilled person will understand that
all classifications will be selectable or programmable in the
software. The amount of air supplied to meet the cleanroom
classification within a desired level of margin or comfort is also
a selectable parameter, and will need to be a risked-based decision
by each particular cleanroom facility operator.
[0086] In addition to the particulate contamination level or class,
the pressure cascade within the cleanroom 100 needs to be
maintained to achieve the desired cascade based on the room
classifications and adjacent rooms. This will be a selectable and
controllable parameter as part of the control system 10.
[0087] Once the various input variables have been initially set,
the cleanroom control system 10 will continuously capture, and act
upon, data from airborne counters, temperature/humidity sensors
etc. and be able to self-adapt to maintain the area or zone of the
cleanroom 100 in the required condition in the most energy
efficient and cost effective manner.
[0088] At S52, the primary sensor input inputted to the control
system 10 to maintain the area or zone of the cleanroom 100 in the
required condition or class is the real time continuous monitoring
of non-viable particles detected in the various rooms or zones of
the cleanroom 100 or the extraction ducting 44. The particles that
will primarily be the control measure will be non-viable, in the
size range of 0.1 .mu.m, 0.2 .mu.m, 0.3 .mu.m, 0.5 .mu.m, 1 .mu.m
and 5 .mu.m diameter, but any particle size measurable by a
particle counter could be selected as the primary control measure.
Non-viable particles in the size range of 0.5 .mu.m and 5 .mu.m are
the preferred particulates used for pharmaceutical cleanrooms
100.
[0089] The control system 10 will also be able to monitor viable
particulates using one or more viable particulate counting devices.
The non-viable and viable particle counters are positioned in the
room space or within the extraction ductwork 44 serving the
controlled zone in the cleanroom 100.
[0090] The predictive control algorithm will follow the required
particle counting methodology defined by ISO 14644, but will also
be configurable to other standards and requirements. The measuring
device will be a calibrated instrument, as defined in ISO
14644.
[0091] Non-viable particles are inert particles of varying sizes.
Particle sizes for classifying cleanrooms are 0.1 .mu.m, 0.2 .mu.m,
0.3 .mu.m, 0.5 .mu.m, 1 .mu.m to 5 .mu.m. The measurement of these
non-viable particles in the size range 0.5 .mu.m to 5 .mu.m will be
the primary control measure of the control system 10. Other
particle sizes can be selectable should they be required.
[0092] Viable particles are those that could carry pathogens and
bacteria. The control system 10 is capable of controlling the
ventilation rates to viable counts utilising appropriate viable
particle counting equipment. This will be the secondary control
function or measure for the control of the cleanroom 100.
[0093] The control system 100 will also need to be capable of
controlling the air volume as a secondary function to maintain a
correct air temperature and/or humidity. This could be measured via
a connected temperature/humidity sensor, but could also be via the
remote BMS 50. As mentioned, temperature and humidity are a
secondary control function either measured via connected sensors or
via the external BMS 50 input.
[0094] At S52, the AHU 12 also can be monitored with equipment
sensors measuring pressure, temperature, humidity, power, filter
pressure etc. and which are all measured as secondary input
parameters but that are still part of the control unit sensor
input. Each of these variables forming a part of the multivariable
control system.
[0095] At S54, the various input sensors are continuously
interrogated to ensure that the rooms or zone of the cleanroom 100
are within the bounds initially set by operator or as modified by
the predictive control algorithm. As mentioned, the system 10 will
be capable of controlling directly or interfacing with the BMS 50
for the following additional parameters: fan static pressure
control, temperature and/or humidity.
[0096] In addition, pressure cascade between areas or zones of
differing classification are a key requirement for cleanrooms 100.
The control system 10 will maintain a pressure set point for each
room or zone being controlled to either absolute pressure or
differential pressure to the adjacent rooms. The pressure control,
at S58, will be achieved with suitable proprietary pressure sensors
and mechanical dampers capable of acting and stabilising
quickly.
[0097] What is key to the present invention that provides advances
over other continuously based sensor control of cleanrooms is that
integrates all cleanroom 100 operations (ventilation, heating,
cooling, filtration, pressure) in a complex control algorithm or
multiple control algorithms that takes into account cleanroom
usage, occupancy and/or user activities. The number and complexity
of the variables to monitor and control, and their constant
evolution, means that the algorithm must self-adapt to keep the
area in the required condition in the most energy efficient and
cost effective manner.
[0098] The output response of the control system 10 is determined
by the predictive control algorithm at S56. The algorithm is
automatically and continuously adaptive and self-learning in that
it will process and analyse to make a predictive control action
based on past environment conditions and equipment operation, in
order to approach optimum cleanliness conditions and equipment
performance according to the criteria defined by the facility
operator.
[0099] The control algorithms embedded in the control unit 10
utilises a model predictive control (MPC) algorithm to maximise the
control of the inputs and outputs. As mentioned, the control system
10 receives the data from the particle counters, pressure sensors,
temperature sensors and/or any external BMS 50 signals. It is
envisaged that energy prices and the data collected can also be
used to create usage patterns and forecasts for predictive control.
The MPC algorithm will process all parameters to provide the
optimal control output whilst optimising energy performance of the
equipment necessary for HVAC operations.
[0100] At S58, the air volume will be controlled utilising
proprietary CAV devices 36 and VAV devices 42 readily available in
the marketplace with the required capabilities. The central HVAC
air handling unit 12 can also be controlled directly from the
control system 10 if required to optimise the system energy
consumption and control.
[0101] The control system 10 can modulate the CAV 36, VAV 42 and
AHU 12 to achieve the optimal air volumes and minimise energy
consumption and will maintain the desired margin to the cleanroom
100 classification. The controller outputs at S58 alter the
conditions in the cleanroom 100 and these are again continually
monitored at S60, as described above.
[0102] The skilled person will appreciate that the control system
10 can also provide out of condition alarming and reporting. This
can be via traffic light signals within the cleanroom 100, or local
to control panel, e-mail, cellular messaging or via a remote web
dashboard.
[0103] Offsite monitoring and alarming will also be available to
allow the system 10 be monitored remotely. The cleanroom 100 and
its energy performance can be monitored by the use of the
applicant's GSM-based remote energy monitoring systems under the
trade mark MEMU.TM.. These remote monitoring units feed information
back to a dashboard and can include monitored variables such as
temperature, airflow velocities, fan speeds, energy drawn, filter
pressures etc. Predictive and planned maintenance and alarm
conditions can all be set and accessed on the dashboard by the
plant operator.
[0104] The software embedded in the control system 10 of the
present invention is capable of being CRF11 Part 2 compliant. The
system will be supplied complete and with a standard validation
protocol to ensure that.
[0105] As mentioned, the control algorithms embedded in the control
unit 10 utilise a model predictive control (MPC) algorithm to
exploit the control of the inputs and outputs. FIG. 4 shows a block
diagram of an illustrative model predictive controller (MPC) 10 for
a cleanroom HVAC system. In essence, model predictive control is a
multivariable control algorithm that uses a dynamic model 62 to
predict future process outputs, based on the past and current
values and on proposed optimal future control actions. These
actions are calculated by an optimizer 64 that takes into account a
cost minimizing function 66 as well as various constraints 68.
[0106] As shown in FIG. 4, the main values the MPC controller 10
uses are sensors 70 and drivers 71. In the illustrative embodiment
of FIG. 4, the MPC controller 10 controlling a cleanroom 100 HVAC
system receives the sensed 70 airflow rate, air pressure,
concentration of non-viable and viable particles, temperature,
humidity and occupancy. These give the past outputs of the model
62.
[0107] The other main values the MPC controller 10 refers to are
drivers 70. The drivers 70 are devices used to implement or
manipulate the control action, e.g. blowers 28 to achieve a
particular fan speed, and/or CAVs 36 and VAVs 42 set to various
damper positions and/or cooling 26 or heating coils 24 to deliver a
proper air temperature, and/or humidifiers to humidify the air, if
necessary. These give the past inputs to the model 62.
[0108] The model 62 uses these past inputs and outputs, and future
inputs from the optimizer 64 to predict the future outputs. Known
control algorithms, such as Proportional Integral (PI) control, do
not have this predictive ability. The difference 74 between the
predicted future outputs and a reference trajectory 72, is defined
as future errors which are inputted to the optimizer 62. The
optimizer 62 limits the inputs and outputs using the constraints
68. It minimises the cost function 66 to make the output approach
the set-point (target), the input to achieve a particular value,
and the increment rate of the input to the calculated level.
[0109] The cost functions 66 are the sum of the difference between
the current and past measured output and the desired set-point, wy
is a weighting coefficient; the sum of the increment of the inputs,
w.DELTA.u is a weighting coefficient; and the sum of the input and
a particular value, wu is a weighting coefficient.
[0110] The constraints 68 are the upper limit and lower limits of
the input u, the output y and the increment rate of the input.
[0111] The skilled person will understand that the process model 62
plays a crucial role in the realisation of the MPC controller 10.
The chosen model must be able to capture the process dynamics to
precisely predict the future outputs and be simple to implement and
understand. As model predictive control is not a "one size fits
all" approach, but rather a set of different methodologies, and
there are many types of models that could be used to predict the
system behaviour.
[0112] The optimizer 64 is a fundamental part of the control
strategy as it provides the control actions. If the cost function
66 is quadratic, its minimum can be obtained as an explicit
function (linear) of past inputs and outputs and the future
reference trajectory. In the presence of inequality constraints,
the solution must be obtained by more complex numerical algorithms.
The size of the optimisation problems depends on the number of
variables and the prediction horizons used, and which usually turns
out to be a relatively modest optimisation problem which does not
require solving by sophisticated computer programs.
[0113] FIG. 5 is a flow diagram illustrating how the system model
62 for a MPC controller 10 of the present invention can be obtained
for the particular typical cleanroom 100 shown in FIG. 6. In the
following description each step of FIG. 5 will be referred to as
"S" followed by a step number, e.g. S76, S78 etc.
[0114] To determine an appropriate mathematical model of the
cleanroom 100 of FIG. 6, the process involves, at S76, running a
series of operational measurements from the HVAC equipment of the
cleanroom 100 under the control of the existing BMS system 50.
These operational measurements of the cleanroom 100 can be
collected via the Open Platform Communications (OPC) server on the
existing BMS system 50 which operates using several single-input
single-output (SISO) PI controllers. Data is collected at S78 from
the OPC server from the results of these several experimental tests
to derive the inputs and outputs of the model. As noted below, the
measured data for model identification can be collected in a
variety of ways, such as open-loop testing by applying a step
signal (or other kind of signal) input and collecting the measured
output, or closed-loop testing by PI or other control methods, etc.
In essence, any sets of input and output data can be used to
identify the mathematical model.
[0115] The skilled person appreciates that whilst a closed-loop
measurement of the system has been described, it is also possible
for the model structure and parameters to be obtained in open-loop
systems without having any feedback. For the present invention,
since the HVAC system can be operated by BMS 50 with PI control,
closed-loop data is easier to collect.
[0116] Disturbances affecting the process will highly influence the
modelling and therefore a priori assumptions on the noise are
required to describe the process. The main disturbance of this
system is the disturbance affecting the process internally, such as
the air leakage, the distribution of the hardware, hysteresis and
time delay of the sensors 70 etc. A white noise signal is therefore
generated and integrated in the input to overcome such
uncertainties.
[0117] At S80, the model is then determined using a variety of
techniques available in the art. This step involves applying
methodologies for computationally modelling the structure and
parameterisation. This skilled person will understand that various
sets of software tools and applications can be utilised to
systematically analyse and design the system model. For the MPC
control of the typical cleanroom 100 shown in FIG. 6 a black-box
modelling approach was applied to allow a judicious selection from
three model structures: including Auto-Regressive with eXogenous
input (ARX) models, State Space (SS) model and Transfer Function
(TF) models. A criterion function is specified to measure the
fitness between the outputs of the identified model and the
operational measurements.
[0118] The estimated model is evaluated at S82 to decide if the
resulting model is accurate enough to be used in MPC controller 10.
It is possible to adjust the performance of the controller 10 as it
runs by tuning disturbance models, horizons, constraints, and
weights. In the preferred embodiment, these steps were undertaken
using the Model Predictive Control Toolbox.TM. and Simulink.RTM.
blocks of Matlab.RTM..
[0119] After the evaluation, at S84, the robust mathematical model
can be used to support the design of the MPC controller 10 and the
system model design can be embedded in a programmable logic
controller (PLC).
[0120] FIG. 6 is illustrative of a typical cleanroom 100 supplied
by two separate HVAC air handling units 12a, 12b and controlled by
the MPC controller 10, and which has been used to develop the
methodology of the present invention. Unlike FIG. 2, the cleanroom
100 of FIG. 6 has two separate AHUs 12a, 12b which allow a wide
variety of performance testing options. The testing experiments are
taken in the cleanroom 100 via the HVAC system. The HVAC system
cleans and circulates the air drawn from outside of the cleanroom
100, the functionality of which is achieved by the operation of
hardware including AHUs 12a, 12b, VAVs 42, extract ductwork 44,
sensors, grilles 38 and diffusers 40, as described previously.
[0121] This typical cleanroom 100 is configured having an entrance
120 which leads into an ISO Class 7 change room 122. From the
change room 122 is a zone or small room 124 which is an ISO Class 7
cleanroom 124. Between the Class 7 cleanroom 124 and a larger ISO
Class 5 cleanroom 130 are a series of material pass rooms and
airlock 126 and a large lab change room 128 which is a Class 5
change room. As with FIG. 2, the Class 5 cleanroom 130 is operated
at higher pressure than the Class 7 cleanroom 124. The cleanroom
100 in the example of FIG. 6 has its highest rated room, in this
case the larger room 130, at the furthest point from the main door
entry 110. It is adjoined to the "dirtier" cleanliness
classification smaller room 124, via a change room 122.
[0122] The skilled person will appreciate that the Class 5
cleanroom 130 is kept at a higher air pressure (known as a
"pressure cascade") to prevent contaminants from, say, the adjacent
Class 7 cleanroom 124. Such a configuration has been used to
validate the model 62 and gives significant improvement in terms of
dynamic response and efficiency, as described and shown in FIGS. 7
to 11.
[0123] A simple test was devised to challenge the standard BMS 50
cleanroom control against the particle-based MPC based control
system 10. All the following dynamic test results are obtained
following the same test protocol as set out in Table 1.
TABLE-US-00001 TABLE 1 Experimental test protocol; personnel
donning cleanroom garb No. Timeline (minutes) Behaviour of
personnel 0 Class 7 level guard up and enter the 3 room 124, stay
and walk around. Note: hair and, where relevant beard and
moustache, should be covered. A two-piece trouser suit, gathered at
the wrists and with high neck and appropriate overshoes should be
worn. They should shed virtually no fibres or particulate matter.
15 Class 5 level guard up and enter the 2 room 130, stay and walk
around. Note: headgear should totally enclose hair and, where
relevant, beard and moustache. A boiler suit is worn with face mask
to prevent the shedding of droplets. Appropriate sterilized,
non-powdered rubber or plastic gloves should be worn. Bootees
should be worn with the trouser leg tucked in. Garment sleeves
should be tucked into the gloves. The protective clothing should
shed virtually no fibres or particulate matter and retain particles
shed by the body. Stay in room 124, walk around 1 30 Leave the
cleanroom 3
[0124] FIG. 7 shows comparative data obtained from the cleanroom of
FIG. 6, and shows particle concentrations measured in various rooms
of the cleanroom 100 in accordance with the experimental test
defined in Table 1, the test data showing the response of a known
BMS 50 control system which is based on a Proportional-Integral
(PI) control algorithm.
[0125] The PI controllers implemented in the BMS 50 maintain the
air change rate (ACR) for each room 124, 130 at a steady state. The
ACR rates were fixed at 17 ACR/h for the ISO 7 room 124, and 40
ACR/h for the ISO 5 room 130 (and termed ACR1 in Table 2). At same
time, the air pressure in each lab is kept constant at 15 Pa in the
ISO 7 room 124, and 30 Pa in the ISO 5 room 130.
[0126] Two particle sizes are analysed: 0.5 .mu.m and 5 .mu.m. Room
124 has one particle counter, and room 130 has two particle
counters, PC2 and PC3.
[0127] FIGS. 7 to 10 also make reference to interval data and
rolling data. This is obtained as described below: The particle
counters continuously sample air at a fixed sampling rate. The size
of the air sample is therefore determined by the length of the
measurement interval. The standard flow rate is 1.0 cubic feet per
minute, which limits the allowable concentration of particles to 1
million per cubic foot (CF) or 35.3 million per cubic meter (CM).
The sample volume can be collected in CF mode or CM mode. The
sample time for the CF mode is 1 minute whereas the sample time for
the CM mode is 35.3 minutes, such that in FIGS. 7 to 10: [0128]
Interval data--60 times more frequently than the full sample
volume, based on 1/60 of the total sample volume, updated every
35.3 s; and
[0129] Rolling data--the totalized counts, particle concentration
over a continuous sample volume, not an increasing number of
particles for the current sample, updated every 35.3 s.
[0130] FIG. 7(a) shows the ISO 7 room 124 0.5 .mu.m particle
concentration; FIG. 7(b) shows the ISO 7 room 124 5 .mu.m particle
concentration; FIG. 7(c) shows the ISO 5 room 130 0.5 .mu.m
particle concentration; and FIG. 7(d) shows the ISO 5 room 130 5
.mu.m particle concentration. It can be clearly seen that the known
BMS 50 control system, which is based on a Proportional-Integral
(PI) control algorithm, takes a significant time lag to bring the
particle count down in the various rooms 124, 130.
[0131] FIG. 8 shows the same BMS 50 control system operating at
another ACR (termed ACR4 in Table 2) and being fixed at 3 ACR/h for
the ISO 7 room 124 and 10 ACR/h for the ISO 5 room 130. Again, the
Proportional-Integral (PI) control algorithm takes a significant
time to reduce the particle count down in rooms 124, 130.
[0132] FIGS. 9 and 10 show the dynamic response of the MPC
controller 10 of the present invention to the same experimental
test protocol as set out in Table 1, when the desired particle
concentration set-points are set at 20% and 50%, respectively.
These dynamic test results were obtained with the MPC controller 10
implemented in a PLC platform. The measured values from the
particle counters are transferred into percentage values which is
calculated against the particle limitations defined in the
classifications. Room 124, which is designed as a class 7
cleanroom, has a limitation of 3,520,000 0.5 .mu.m particles and
29,000 5 .mu.m particles per cubic meter. Room 130, which is
designed as a class 5 cleanroom, has a limitation of 352,000 0.5
.mu.m particles and 2,900 5 .mu.m particles per cubic meter.
[0133] FIGS. 9(a) and 10(a) show the ISO 7 room 124 0.5 .mu.m and 5
.mu.m particle concentrations; and FIGS. 9(b) and 10(b) show the
ISO 5 room 130 0.5 .mu.m and 5 .mu.m particle concentrations, and
it is clear from both that an improved dynamic response is
obtained.
[0134] FIGS. 9(c) and 10(c) show the dynamic control of the air
change rates in the ISO 7 room 124 and ISO 5 room 130, and again it
can be seen that the ACR ramp ups rapidly when there are particles
in the rooms 124, 130, as expected.
[0135] FIGS. 9(d) and 10(d) show the static room pressure for the
ISO 7 room 124 (15 Pa) and the ISO 5 room 130 (30 Pa). The
pressures are controlled within the process range .+-.5 Pa, except
when the door 110 is open and close. The minimum differential
pressure (DP) is monitored and alarmed in this system 10 and is
determined to be 5 Pa for the ISO 7 room 124 and 15 Pa for the ISO
5 room 130, separated with airlocks 126, 128 to maintain DP during
personal and material transitions. DP values higher than 5 Pa
provide sufficient overflow on one side. The static pressure
set-points of the cleanrooms are designed as 15 Pa in the ISO 7
room 124 and 30 Pa in the ISO 5 room 130. The system recovers from
the peak to steady state in a very short time.
[0136] FIGS. 9(e) and 10(e) show dynamic control of the AHU 12a
(AHU1) supply fan and the supply VAV 42 of each room 124, 130 and
shows a good dynamic response when the particle concentration is
higher than the set-point.
[0137] The dynamic response of the MPC controller (FIGS. 9 and 10)
is much better that is obtained from the known BMS 50 control
system (FIGS. 7 and 8).
[0138] FIG. 11 shows the power consumed by a known BMS 50 system at
various air change rates (ACR) obtained from the typical cleanroom
100 of FIG. 6, as set out in Table 2.
TABLE-US-00002 TABLE 2 Air change rates of typical cleanroom 100 as
depicted in FIG. 11 ISO 7 ISO 5 room room No. ACR (/h) ACR (/h)
ACR1 17 40 ACR2 13 30 ACR3 8 20 ACR4 3 10
[0139] All the fans are controlled in steady state which give
steady powers, and the figures demonstrate the average power
consumed at each ACR of the known BMS 50 system.
[0140] The right hand portion of FIG. 11 is comparative dynamic
power measurements obtained by the MPC controller 10 of the present
invention and shows that model predictive control significantly
reduces the power consumption of the cleanroom HVAC system. It can
be clearly seen that the power drawn by the MPC controller 10 is
significantly less the steady state ACR of the known BMS 50
system.
TABLE-US-00003 TABLE 3 Consumed energy for MPC and BMS 50 control,
as depicted in FIG. 11 20%, 50%, Set- Set-point point Duration
(hours) 2.27 2.43 Dynamic Energy (KWh) 2.82 3.14 ACR1 energy (KWh)
8.52 9.14 ACR2 energy (KWh) 5.38 5.78 ACR3 energy (KWh) 3.98 4.27
ACR4 energy (KWh) 3.03 3.25
[0141] The consumed energy for each test is calculated as shown in
Table 3. The energy consumption of the dynamic control is
calculated by the integral of power (from the power curve in FIG.
11) against time. Since the BMS 50 system operates in steady state,
the power is assumed to be static. The energy consumption of the
known BMS 50 system is calculated by the multiplication of the
static power and the time duration of the dynamic control. As shown
in Table 3, the dynamic control consumes lower energy than the
known BMS 50 system whatever the air change rate (ACR) the system
maintains.
[0142] The system of the present invention is flexible enough to be
expanded, and/or altered as the cleanroom 100 requirements change.
The control system 10 is completely scalable for a single cleanroom
100 to multiple rooms or zones within multiple cleanrooms 100.
Furthermore, no use of a system of this nature has ever been
produced or hinted at in any printed publication of a system of the
purpose generally for industrial use within existing cleanrooms or
bespoke cleanrooms and which provides advances in continuously
based sensor control of cleanrooms.
[0143] The use of the letters HVAC (heating, ventilation and air
conditioning) are intended to be used with their ordinary English
language meaning and this is generally speaking accepted as the
words heating, ventilation and air conditioning, as used previously
in the document.
[0144] The invention is not intended to be limited to the details
of the embodiments described herein, which are described by way of
example only. Various additions and alternations may be made to the
present invention without departing from the scope of the
invention. For example, although particular embodiments refer to
implementing the present invention as a HVAC cleanroom control
system this is in no way intended to be limiting as, in use, the
present invention can be used with many types of industrial
environments. It will be understood that features described in
relation to any particular embodiment can be featured in
combination with other embodiments.
[0145] When used in this specification and claims, the terms
"comprises" and "comprising" and variations thereof mean that the
specified features, steps or integers are included. The terms are
not to be interpreted to exclude the presence of other features,
steps or components.
[0146] The features disclosed in the foregoing description, or the
following claims, or the accompanying drawings, expressed in their
specific forms or in the terms of a means for performing the
disclosed function, or a method or process for attaining the
disclosed result, as appropriate, separately, or in any combination
of such features, can be utilised for realising the invention in
diverse forms thereof.
* * * * *