Recent advances in bio-medical technology and vaccine production necessitate regular reevaluation of the Heating, Ventilating and Air-Conditioning (HVAC) needs for biomedical cleanroom facilities. The purpose of the HVAC system for a bio-cleanroom is not only to achieve thermo-stabilized environment but also to remove airborne contamination. It is vital to perform the vaccine production in a particle-free and thermo-stabilized environment as well as maintain the minimization of energy consumption. However, little knowledge or quantitative information about energy-efficient HVAC system is available on how to control the environmental parameter in the bio-cleanroom for vaccine storage effectively. With cost of rising, value of conservation grows. Since, HVAC systems of bio-cleanroom operate continuously, it is vital and significant to consider energy-efficient strategies as well as to achieve an acceptable performance for environmental parameter contaminations control.
Thermo-stability is a critical biophysical property of viruses that has direct applicability to the delivery of live viral vaccines. The more thermostable a live viral vaccine, the easier it is to maintain infectivity under the conditions of clinical was (Ikizler and Wright, 2002). It is vital to keep the thermo-stabilization of egg grown influenza viruses during the production process. Influenza stocks usually have to be held at 4°C for a couple of hours for stability storage. Besides, the licensed influenza vaccines in the U.S. are currently produced in embrocated chicken eggs. However, Lu et al. (2006) investigated another option for H3N2 vaccine production to increase virus growth in embrocated chicken eggs. It is essential to provide a cold room at 4°C for vaccine production and storage. Furthermore, the experiment and modeling of airflow pattern as well as the diffusion of contaminants in an operating room was performed by Woloszyn et al. (2004).
Computational Fluid Dynamics (CFD) simulation technique is a well-known and
widely-accepted scientific technique that allows improvement of airflow distribution
for cleanroom configuration (Wang et al., 2009). They also identified
some option under a limited budget, as well as reduced trial-and-error effort
when modifications of cleanrooms have to be conducted. Besides, Wang et al.
(2008) also evaluated thermal comfort and contamination control for a cleanroom
by CFD simulation technique and field measurement data as well. Moreover, the
CFD codes were successfully used to simulate the air distribution and contamination
decay as well as comparison of indoor particle concentration in different rooms
by Zhao et al. (2004). Besides, the biological contaminant control strategies
under different ventilation models in the hospital operating room have been
proposed by using CFD simulation (Zhang et al., 2008). Results showed
that improving air flow distribution could reduce particle deposition on certain
critical surface. Furthermore, the effect of diffuser discharge velocity on
ventilation performance in a cleanroom has been investigated by Chow et al.
(2006) through CFD analysis as well.
Field-measurement is essential to assure the cleanroom performs correctly and achieves the contamination standards. The biological contamination control strategies under different models in operating room has been simulated and then compared with filed measurement to check the acceptable level of consistency (Zhang et al., 2008). Besides, bioaerosol characteristics related to human dispersion were evaluated extensively based on field tests data in bio-cleanroom with different class levels (Li and Hou, 2003). Some valuable information describing cleanroom measurement to evaluate the overall performance of cleanroom can be found in literature (IEST, 1993). General principles and methods on bio-contamination control of cleanroom are described extensively in the standard of ISO 14698 (2003). Besides, the essential information on design consideration, equipments and comprehensive procedures for certified testing of cleanrooms were reported in NEBB (1996).
Although, some research has been done on CFD simulation as well as for field measurement for cleanroom, little quantitative information is available on compromise of contamination control and energy saving potential. In this study, the strategic approach on performance improvement of the HVAC system for an bio-cleanroom for vaccine production will be investigated. Both numerical simulation and field measurement of a full-scale bio-cleanroom will be carried out in cold room of a bio-cleanroom for vaccine production in Taiwan. Different velocity and temperature of supply air could be assessed extensively not only by airflow and temperature distribution but also by transient simulation to achieve the design specification for vaccine storage.
SYSTEM DESCRIPTION AND FIELD MEASUREMENT
The investigated bio-cleanroom is part of a newly-constructed CGMP (current good manufacturing practice) level manufacturing plant for vaccine production. It provides the clean environment at 4°C for vaccine storage after virus growth in embrocated chicken eggs. The layout of HVAC system including the supply and return air arrangement for the investigated operating room is shown in Fig. 1. Supply air flow at 0°C (273 K) from air handling unit (with brine cooling coils) is provided to the bio-cleanroom through 6 pieces (1.5x0.75 m) of supply air grilles. Return air flow is recirculated through the return air grilles located at the opposite of the cleanroom to produce horizontal by laminar air flow and stable temperature at 4°C for vaccine storage. An inverter with variable frequency drives was installed to examine the performance variation and energy-saving potential under different supply air temperature and face velocity. The investigated bio-cleanroom for vaccine storage with the dimension of length 5.6 m, width 4.7 m and height 2.7 m, respectively.
This bio-cleanroom with cleanliness level class 10,000 (ISO class 7) is equipped
with high efficiency particulate air (HEPA) filters at the filtration efficiency
over 99.97% (above 0.5 μm) in the air handling unit. Specified design conditions
are temperature 4±1°C, humidity 45±5 (%RH) and the pressurization
of 10±2.5 Pa. The colony forming unit (cfu) for microbial counts less
than 175 cfu m-3 is specified. To establish the exact geometrical
model for simulation, 30 chicken eggs racks with 6 rowsx5 columns for vaccine
storage were evenly arranged in the bio-cleanroom. The cross-section geometric
schematic diagram of the investigated bio-cleanroom with eggs racks is shown
in Fig. 2. There are four spacing room at each egg rack and
each layer are denoted as first (1st), second (2nd) third (3rd) and fourth (4th),
respectively along the z-axis.
||Layout of the investigatal biocleanroom for vaccine production
||Schematic diagram of the egg rack in the bio-cleanroom with
supply and return grille
||Dimension of chicken eggs racks in the bio-cleanroom
The schematic diagram with detail dimension of the chicken eggs racks for vaccine
growth and storage is displayed in Fig. 3. Each egg rack is
arranged side by side along x-axis and y-axis of the cold storage room to have
a maximum production for vaccine. There are four spacing room (25 cm) for chicken
egg holder at each egg rack. Dimension of the egg rack is adopted from the actual
measurement. The full-scale geometric model along with all of the eggs racks
is assumed to investigate the performance improvement of air flow and temperature
Field tests were conducted not only to provide reliable data for simulation but also to verify the simulation results for assessment of performance improvement of the bio-cleanroom. The particle counts of ten sampling locations were carried out at specified position of the operating room. Quantities tests of airborne particle counts were performed with a Met-One Model 3313 particle counter, sensitive to particles larger then 0.5 μm. Three times of measuring at each sampling location were conducted for accuracy and repeatability. The sampling flow rate for particle counter operates at 28.3 l min-1 (1 ft3 min-1) with sampling period of 1 min. Furthermore, to provide reliable data as the boundary condition of CFD simulation, the temperature and face velocity of each Fan-Filter Unit (FFU) were tested with an ALNOR Model 8585 hot-wired anemometer. A TSI Model 8386A digital manometer was employed to monitor the pressure difference of the cleanroom closure for contamination control concern during reducing the face velocity. The variation of temperature and humidity at return air grille were recorded by a multi-channel data logger (YOKOGAWA, Model MV100) with several temperature and humidity transmitters. Tests of temperature at accuracy of 0.2°C and humidity at accuracy of 2% RH were performed continuously for at least three hours under different measurement case. All instruments used in this study were calibrated regularly according to the manufactures instructions.
The Computational Fluid Dynamics (CFD) technique has been proven to be very powerful and efficient in parametric studies of airflow distribution and temperature profile. In this study, a commercial CFD code, STAR-CD (CD Adapoc Group, 2001), was used to simulate the temperature airflow distribution of the bio-cleanroom accordingly. It will examine the improvement of bio-cleanroom configuration and energy saving potential without interfering any normal processes. The governing equations solved by STAR-CD include the three-dimensional time-dependent incompressible Navier-Stokes equation, time dependent convection diffusion equation and k-å turbulence equations. They are the continuity equation and the Reynolds-averaged Navier-Stokes equations which can be expressed as:
where, x is the coordinate, subscripts i and j are the index of Cartesian component, u is the fluid velocity, ρ is the mass density, τij is the stress tensor, p is the static pressure and si is the momentum source. The well-known finite control volume method with a Pressure Implicit with Splitting of Operator (PISO) algorithm was adopted to solve all the governing equations simultaneously. After solving the velocity field, the transient simulation of concentration field was conducted and concentration decay method based on mass concentration equation accordingly. The comparison of grid test for 335,836 cells and 590,592 cells possesses the relative error at 0.279%. Therefore, a grid with approximately 335,836 cells was used for numerical simulation it this study. The increase of cell number will provide more favorable information; however, it will accompany by a significant increase of computation resources.
To evaluate the energy-saving potential under different air temperature and face velocity of supply air grille, the performance improvement strategies with reduction of face velocity and increase the supply air temperature were proposed and analyzed by numerical simulation. It was assumed that the air flow field is homogenous, isotropic and three-dimensional. The temperature and face velocity of the supply air grille have been measured through field tests using a multi-function hot-wired anemometer to provide reliable measurement data as the boundary conditions of CFD simulation. Furthermore, all of the boundary conditions for solution domain were clearly defined according to the actual field tests data to carry out the accurate solutions. The face velocities of supply air grille were kept at 1.5 m sec-1 initially and then reduce to 1.0 and 0.5 m sec-1 to examine the variation of environmental parameter of the bio-cleanroom. It could be achieved by on-site adjusting the frequency of inverter at field test stage. The supply air temperature was maintained at 273 K (0°C) initially and then increase to 274 K (1°C) and 275 K (2°C), respectively. Typically, the no-slip condition was applied on the solid walls since they were not permeable.
RESULTS AND DISCUSSION
Figure 4 shows the airborne particle counts of the 10 sampling
locations in the bio-cleanroom for particle size larger than 0.5 and 0.3 μm,
respectively. The bio-cleanroom meets the specified class 10,000 (ISO class
7) and it even reached a higher class of class 100 (ISO class 5). It reveals
that the possibility for over design and the energy saving potential is accessible
for HVAC system of the bio-cleanroom.
||Particle counts of sampling locations in the bio-cleanroom
||Field tests of temperature and relative humidity, (a) temperature
and (b) relative humidity
On the other hand, as shown in Fig. 5a, the field measurement
data of temperature variation during 24 h for 15 sampling location specified
in the cleanroom are about 2°C. It represents the cooling capacity of HVAC
system can meet the temperature requirement for thermo-environment control of
the cleanroom under the supply air temperature of 0°C and face velocity
of 1.5 m sec-1. Besides, as it depicts in Fig. 5b,
the variation of relative humidity during 24 h of these 15 sampling locations
also reveals that the HVAC system provides satisfactory compliance with the
design specification of 45±5%(RH). Furthermore, Fig. 6
depicts the field measurement of air velocity at different rows of eggs racks.
It also represents good agreement of the specification set at the design stage.
||Field measurement of air velocity at different rows of eggs
||Velocity in the bio-cleanroom at y = 1.975 m
Although, the field data provide satisfactory compliance with the design specification
of the bio-cleanroom, it is vital and significant to evaluate energy-efficient
strategies, especially judging from the field measurement data of particle counts.
The CFD simulation has been conducted to investigate the temperature and airflow
distribution as well as the energy saving potential of the bio-cleanroom. Figure
7 shows the velocity vectors in the bio-cleanroom at the y = 1.975 m under
the supply air temperature 0°C (273 K) and face velocity of 1.5 m sec-1.
The uniform and favorable velocity vectors can be obtained in the vicinity of
eggs racks even though there are some vortices above the eggs rack.
Since, the HVAC systems of the bio-cleanroom operate continuously, different
velocity and temperature of supply air can be assessed by the CFD simulation
to identify the energy-saving strategies for the bio- cleanroom.
||Temperature distribution at different layers of eggs racks,
(a) 1st layer and (b) 3rd layer
||Temperature distribution under different supply air velocity,
(a) 1st layer and (b) 3rd layer
As shown in Fig. 8a and b, the temperature distribution
at different layers (1st layer and 3rd layer) of eggs racks is presented by
transient simulation for 4 h at supply air temperature of 273 K (0°C) and
face velocity of 1.5 m sec-1. It displays the favorable simulation results for temperature distribution at
both eggs rocks during four hours. The first column (X1) of eggs
racks presents the lowest temperature because they are close to the low temperature
supply air mostly. Besides, the fifth column(X5) of eggs racks can
achieve the design specification under 4°C easily as well. It also reveals
the feasibility of energy saving strategies by increase the supply air temperature
or reduction of face velocity.
Figure 9a and b show the temperature distribution
of the bio-cleanroom with fixed supply air temperature at 0°C (273 K) under
different supply air velocity of 1.5, 1.0 and 0.5 m sec-1. The results
present the feasibility of reducing the supply air velocity from 1.5 to 1.0
m sec-1. It will be possible to reduce the face velocity to 0.5 m
sec-1 even though there exists about 50-60 min of risk for over the
design specification at 4°C.
||Temperature distribution under different supply air temperature,
(a) 1st layer and (b) 3rd layer
Another scheme for energy saving potential is to increase the supply air temperature.
Figure 10a and b show the temperature
distribution of the bio-cleanroom with fixed supply air velocity at 1.5 m sec-1
under different supply air temperature of 273, 274 and 275 K. The simulation
results for temperature distribution are similar for first layer and third layer
of eggs racks under different supply air temperature. It is feasible to increase
the supply air temperature from 273 to 274 K to reduce energy consumption for
HVAC system. However, it may be inappropriate to raise the supply air temperature
from 274 K to 275 K because it will take more time (over 1.5 h) to cool down
the airflow in the vicinity of eggs racks for the bio-cleanroom.
It is essential to provide clean and thermo-stabilized environment and cost-effective operation conditions for bio-tech industries as well as for vaccine production and storage field measurement and numerical simulation of a full-scale bio-cleanroom has carried out an vaccine plant in Taiwan. The CFD simulation has been conducted extensively to investigate the temperature and airflow distribution as well as the energy saving potential of the bio-cleanroom. Different velocity and temperature of supply air could be assessed extensively not only by airflow and temperature distribution but also by transient simulation to achieve the design specification of 4°C for vaccine storage. The results from computer simulation revealed that the improvement of energy consumption could be achieved satisfactorily by reduction of supply airflow rate and increase of supply air temperature. Results in this study should provide valuable information to the facility engineer facing the compromise between energy saving strategy and environmental control consideration in the bio-cleanroom. It was also expected that CFD aided simulation could identify strategies for best practice at design stage as well as reduce running cost at full operation.
The authors would like to express their great appreciation to the financial support by the National Science Council under the grant No. NSC-96-2628-E-167-016-MY2.