EP3475625B1 - Cleanroom control system and method - Google Patents

Cleanroom control system and method Download PDF

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Publication number
EP3475625B1
EP3475625B1 EP17734432.2A EP17734432A EP3475625B1 EP 3475625 B1 EP3475625 B1 EP 3475625B1 EP 17734432 A EP17734432 A EP 17734432A EP 3475625 B1 EP3475625 B1 EP 3475625B1
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EP
European Patent Office
Prior art keywords
cleanroom
hvac system
control
hvac
air
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EP17734432.2A
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German (de)
English (en)
French (fr)
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EP3475625A1 (en
Inventor
Robert Wallace
Shuji CHEN
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Energy Efficiency Consultancy Group Ltd
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Energy Efficiency Consultancy Group Ltd
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Priority to PL17734432T priority Critical patent/PL3475625T3/pl
Publication of EP3475625A1 publication Critical patent/EP3475625A1/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L1/00—Enclosures; Chambers
    • B01L1/04—Dust-free rooms or enclosures
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
    • F24F3/12—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling
    • F24F3/16—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling by purification, e.g. by filtering; by sterilisation; by ozonisation
    • F24F3/167—Clean rooms, i.e. enclosed spaces in which a uniform flow of filtered air is distributed
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L2200/00—Solutions for specific problems relating to chemical or physical laboratory apparatus
    • B01L2200/14—Process control and prevention of errors
    • B01L2200/143—Quality control, feedback systems
    • B01L2200/146—Employing pressure sensors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L2200/00—Solutions for specific problems relating to chemical or physical laboratory apparatus
    • B01L2200/14—Process control and prevention of errors
    • B01L2200/143—Quality control, feedback systems
    • B01L2200/147—Employing temperature sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties

Definitions

  • This invention relates to a cleanroom control system and method.
  • 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.
  • HVAC heating, ventilation and air conditioning
  • 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 ⁇ 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 ⁇ m diameter and greater per cubic metre.
  • ISO International Standards Organization
  • 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.
  • 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.
  • Use of the present invention enabling communication, integration and/or interoperability with other third party products, including existing building management systems (BMS).
  • BMS building management systems
  • the present invention using open standards and application programming interfaces (API) for communication.
  • API application programming interfaces
  • 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.
  • 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.
  • the desired concentration of airborne contamination is specified by the number of non-viable particles per cubic meter having a particle size equal to or greater than 0.1 ⁇ m, 0.2 ⁇ m, 0.3 ⁇ m, 0.5 ⁇ m, 1 ⁇ m and/or 5 ⁇ m in diameter.
  • 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.
  • control system will detect movement and automatically change from an “at rest” to an "in-operation” classification or mode of operation automatically.
  • the air treatment is selected from the group consisting of any one of the following: filtration, ventilation, heating, cooling, humidification, pressurisation, occupancy, and combinations thereof.
  • 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.
  • 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.
  • the secondary sensing means further comprises one or more sensors selected from the group consisting of 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.
  • the processing means may receive energy price data and/or usage data.
  • 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.
  • 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.
  • the desired concentration of airborne contamination and/or energy price data and/or usage data may be initially user configurable.
  • the at least one control signal to the HVAC system controlling the air volume supplied to the cleanroom.
  • the one or more secondary control signals controlling the filtration, ventilation, heating, cooling, humidification, pressurisation, occupancy, and combinations thereof supplied to the cleanroom.
  • indication will be provided within the cleanroom through a visual indication system to indicate status.
  • a graphical user interface may also be provided.
  • the model predictive control algorithm being able to self-adapt.
  • control system is implemented in a programmable logic controller (PLC).
  • PLC programmable logic controller
  • control system further comprising display means.
  • control system may further comprise means for enabling communication and/or integration and/or interoperability with third party building management systems (BMSs).
  • BMSs building management systems
  • control system further comprising monitoring the energy performance of the cleanroom and/or performance to the particle contamination concentration within the cleanroom.
  • 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 which comprises one or more zones or rooms, each of the zones or rooms having a respective desired concentration of airborne contamination
  • the HVAC system comprises at least one HVAC air handling unit 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 a ducting generally associated with each respective zone or room of the cleanroom, the method comprising the steps of:
  • 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 which comprises one or more zones or rooms, each of the zones or rooms having a respective desired concentration of airborne contamination, and the HVAC system comprises at least one HVAC air handling unit 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 a ducting generally associated with each respective zone or room of the cleanroom, the computer program product comprising:
  • 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.
  • 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.
  • 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).
  • BMS building management systems
  • the present invention using open standards and application programming interfaces (API) for communication.
  • API application programming interfaces
  • 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 installation. The control system being flexible enough to be expanded upon or altered as the cleanroom environment changes.
  • Figure 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 Figure 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.
  • 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 Figure 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.
  • 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.
  • FIG. 2 shows how a HVAC cleanroom system can be controlled utilising the control unit or system 10 of the present invention.
  • AHU central HVAC air handling unit
  • 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.
  • 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.
  • HEPA high-efficiency particulate air
  • CAV constant air volume
  • VAV variable air volume
  • the control unit 10 of the present invention is used to monitor and control each and every operation of the HVAC cleanroom system.
  • 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.
  • FIG. 2 For reasons of clarity in Figure 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 Figure 2 .
  • This drawing is a schematic diagram and, in order to aid clarification, many other circuit elements are not shown.
  • 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.
  • ADC analogue-to-digital convertor
  • DAC digital-to-analogue convertor
  • 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.
  • 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.
  • 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.
  • FIG. 3 shows systematically how the control steps of the unit 10 are followed using the logic flow shown in Figure 3 .
  • each step of Figure 3 will be referred to as "S" followed by a step number, e.g. S52, S54 etc.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • OPC Open Platform Communications
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 ⁇ m, 0.2 ⁇ m, 0.3 ⁇ m, 0.5 ⁇ m, 1 ⁇ m and 5 ⁇ 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 ⁇ m and 5 ⁇ m are the preferred particulates used for pharmaceutical cleanrooms 100.
  • 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.
  • Non-viable particles are inert particles of varying sizes.
  • Particle sizes for classifying cleanrooms are 0.1 ⁇ m, 0.2 ⁇ m, 0.3 ⁇ m, 0.5 ⁇ m, 1 ⁇ m to 5 ⁇ m.
  • the measurement of these non-viable particles in the size range 0.5 ⁇ m to 5 ⁇ m will be the primary control measure of the control system 10.
  • Other particle sizes can be selectable should they be required.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • MPC model predictive control
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 MEMUTM.
  • 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.
  • 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.
  • 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.
  • the main values the MPC controller 10 uses are sensors 70 and drivers 71.
  • 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.
  • 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.
  • 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.
  • the cost functions 66 are the sum of the difference between the current and past measured output and the desired set-point, w y is a weighting coefficient; the sum of the increment of the inputs, w ⁇ u is a weighting coefficient; and the sum of the input and a particular value, w u is a weighting coefficient.
  • the constraints 68 are the upper limit and lower limits of the input u, the output y and the increment rate of the input.
  • 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.
  • 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.
  • 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.
  • 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 Figure 6 .
  • S system model 62 for a MPC controller 10 of the present invention
  • FIG. 5 each step of Figure 5 will be referred to as "S" followed by a step number, e.g. S76, S78 etc.
  • 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.
  • OPC Open Platform Communications
  • SISO single-input single-output
  • 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.
  • any sets of input and output data can be used to identify the mathematical model.
  • 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.
  • 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.
  • 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.
  • 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 ToolboxTM and Simulink ® blocks of Matlab ® .
  • 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).
  • PLC programmable logic controller
  • Figure 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.
  • the cleanroom 100 of Figure 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.
  • 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 Figure 2 , the Class 5 cleanroom 130 is operated at higher pressure than the Class 7 cleanroom 124.
  • the cleanroom 100 in the example of Figure 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.
  • 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.
  • a pressure cascade air pressure
  • headgear should totally 2 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
  • Figure 7 shows comparative data obtained from the cleanroom of Figure 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.
  • PI Proportional-Integral
  • 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).
  • 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.
  • Room 124 has one particle counter
  • room 130 has two particle counters, PC2 and PC3.
  • Figures 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 Figures 7 to 10 :
  • Figure 7(a) shows the ISO 7 room 124 0.5 ⁇ m particle concentration
  • Figure 7(b) shows the ISO 7 room 124 5 ⁇ m particle concentration
  • Figure 7(c) shows the ISO 5 room 130 0.5 ⁇ m particle concentration
  • Figure 7(d) shows the ISO 5 room 130 5 ⁇ m particle concentration.
  • PI Proportional-Integral
  • Figure 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.
  • ACR4 ACR4 in Table 2
  • PI Proportional-Integral
  • Figures 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 ⁇ m particles and 29,000 5 ⁇ m particles per cubic meter.
  • Room 130 which is designed as a class 5 cleanroom, has a limitation of 352,000 0.5 ⁇ m particles and 2,900 5 ⁇ m particles per cubic meter.
  • Figures 9(a) and 10(a) show the ISO 7 room 124 0.5 ⁇ m and 5 ⁇ m particle concentrations; and Figures 9(b) and 10(b) show the ISO 5 room 130 0.5 ⁇ m and 5 ⁇ m particle concentrations, and it is clear from both that an improved dynamic response is obtained.
  • Figures 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.
  • Figures 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.
  • Figures 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.
  • Figure 11 shows the power consumed by a known BMS 50 system at various air change rates (ACR) obtained from the typical cleanroom 100 of Figure 6 , as set out in Table 2.
  • FIG. 11 The right hand portion of Figure 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.
  • 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 Figure 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.
  • ACR air change rate
  • 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.
  • HVAC heating, ventilation and air conditioning

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EP17734432.2A 2016-06-27 2017-06-23 Cleanroom control system and method Active EP3475625B1 (en)

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BE1027785B1 (nl) * 2019-11-25 2021-06-22 Advipro Bvba Werkwijze voor het regelen van drukverschillen, temperatuur en luchtvochtigheid in een stofvrije ruimte
BE1027790B1 (nl) * 2019-11-25 2021-06-23 Advipro Bvba Inrichting voor het monitoren en regelen van een stofvrije ruimte
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JP7487034B2 (ja) 2020-07-28 2024-05-20 清水建設株式会社 設計方法、設計システム、及びプログラム
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AU2017289701B2 (en) 2020-12-03
GB201611107D0 (en) 2016-08-10
WO2018002589A1 (en) 2018-01-04
US20190234631A1 (en) 2019-08-01
GB2551714A (en) 2018-01-03
EP3475625A1 (en) 2019-05-01
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PL3475625T3 (pl) 2022-04-19
AU2017289701A1 (en) 2019-01-31

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