International conference2017

Conference Proceeding of
7th International Conference on Engineering Technology, Science and Management Innovation (ICETSMI-2017)
at (IETE, Delhi) Institution of Electronics and Telecommunication Engineers, Lodhi Road, Delhi, India
on 16th
July 2017 ISBN: 978-81-934083-7-7
764 V.V.Potdar, V.K.Praveen, P.S.Karande
Evaluation of Optimum Room Ventilation Strategy Using
Taguchi Techniques
V.V.Potdar1
, V.K.Praveen2
, P.S.Karande3
1. Research Scholar, JNTUH, Hyderabad, India.
2. Professor, Mechanical Engineering Department, P.D.A.College of Engineering, Gulbarga, India.
3. Assistant Professor, Mechanical Engineering Department, A.G.Patil Institute of Technology, Solapur, India.
ABSTRACT
Fresh, contaminant free and healthy air is one of the important human needs. As per the estimates, people spend
relatively more time indoors, which necessitates requirement of proper ventilation system. It is observed that
contaminant particles affect Indoor Air Quality(IAQ) leading to variety of respiratory diseases which in turn affect
productivity. These adverse health outcomes due to particulate contaminants are constantly motivating scientific
research for past few decades. Selecting appropriate ventilation system and its parameters, for removal of contaminant
particles from the occupied zone, is a major challenge faced by HVAC engineers. In this paper, Taguchi’s method based
on Design of Experiments(DOE) is used for selecting proper ventilation parameters. Experimentations and CFD
simulations are carried out in a mixing ventilated “ Experimental Test Room “ (ETR). L9 Orthogonal Array with three
levels and three factors is used to determine the number of experiments to be conducted. Minitab 14 software is used to
obtain the main effect plots for the means for “Contaminant Removal Effectiveness in occupied zone”. For evaluation of
significance of various parameters on the Contaminant Removal Effectiveness, Analysis of Variance (ANOVA) was
performed with objective of “larger is better”. The input parameters considered were, Air Changes per Hour(ACH),
particle size and particle source location. The results will help in deciding influential parameters providing highest
Contaminant Removal Effectiveness, which can guide the HVAC system designers in selecting and maintaining
appropriate ventilation parameters for providing clean, healthy indoor air for the occupants.
KEYWORDS
Indoor Environment, Mixing Ventilation, Taguchi Method, Computational Fluid Dynamics, HVAC, Contaminant
Removal Effectiveness.
1. INTRODUCTION
Maintaining good indoor air quality is a very important issue as it was found that people were spending more
time indoors than ever with many spending 80% to 90% of their lifetime indoors (1). Indoor Air Quality
(IAQ) is downgraded because of air pollutants/contaminants which significantly affected people’s health,
comfort and workplace performance (2)(3). Airborne contaminants can be classified into three categories:
gaseous contaminants, particulate contaminants and biological contaminants. In the present work, focus is on
particulate contaminants and not on gaseous and biological contaminants. Many recent studies have shown
that increased concentration of environmental PM (Particulate Matter) is related to many respiratory diseases
(4).
The assessment of Indoor Air Quality(IAQ) in terms of indoor contaminant concentration is possible
through experimentation in “ Experimental Test Room (ETR)” or by using Computational Fluid Dynamics
(CFD) modeling. It is possible to assess the indoor climate by virtually constructing a room and testing
different system layouts to meet the predetermined design criteria. Typically, Indoor Air Quality is assessed
by means of total airflow supplied to the room, which should not be the only considered parameter in room
ventilation because the air supply method, contaminant particle size and contaminant source location have
considerable effects on concentration of solid contaminants in different parts of the room.

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For bringing in fresh air from outside and to remove particulate contaminants out of the room ventilation is
necessary. An important concept about ventilation is Contaminant Removal Effectiveness, which is the ratio
of the contaminant concentration at ventilation exhaust to the average contaminant concentration in occupied
zone. When ventilation rate is fixed according to industry standard, the greater the contaminant removal
effectiveness is , the cleaner the air in the occupied zone would be. The transport pattern of contaminants and
in turn value of contaminant removal effectiveness is affected by many factors like ventilation scheme, air
changes per hour, contaminant particle size and contaminant source location.
Traditionally, researchers and engineers applied judgmental methods for designing ventilation, which meant
predicting the ventilation performance with different design variables to find a scenario that has the best
agreement with the design objective. Researchers and engineers normally predicted or evaluated the
ventilation performance typically by analytical and empirical models, experimental measurements, and
computer simulations using Computational Fluid Dynamics(CFD). However, since these judgmental methods
require experimentations and CFD simulations for many scenarios to obtain an optimal design for a ventilated
space, which is time consuming and costly. Most importantly, because of the complexity of fluid flow, it is
less likely that the repeated trials in an interactive analysis and design procedure can lead to a truly optimal
design. The optimization approach, on the other hand, provides scope of using some statistical tools to decide
the influential parameters related to system design and help in reducing number of experiments required to
arrive at the most feasible solution. Taguchi’s DOE based methods help in arriving at appropriate/optimal
ventilation strategy in different room environments.
2. LITERATURE REVIEW
H. Brohus et al (5) studied the effect of renovating an office building on occupants’ Comfort and Health.
They conducted research using CFD modeling approach where model room was modified considering various
room layouts and furnitures. Their simulation based findings suggest that the thermal comfort and
contaminant dispersions are affected because of these obstacles.
P. Wargocki et al (6) carried out studies on the effects of outdoor air supply rate in an office on Perceived Air
Quality, Sick Building Syndrome (SBS) Symptoms and Productivity. As per this study, the indoor air
quality and productivity are affected by changes in supply air flow rate.
M. Wetter and J. Wright (7) evaluated the feasibility of application of genetic algorithm optimization methods
for issues related to indoor ventilation studies. They concluded that these methods provide useful insights
into air flow distribution and contaminant transport.
L. Lu et al (8) conducted elaborate studies concerning optimization of HVAC Systems in built
environment . They tried various ventilation configurations using CFD based simulation approach and
concluded that ventilation parameters can be optimized, to some extent, by using optimization methods.
S.W. Wang and X.Q. Jin (9) focused on operational aspects of HVAC systems and developed a model based
optimal control system using genetic algorithm. Their studies suggest that by using appropriate control
strategies related to air flow rate, temperature and humidity control it is possible to increase ventilation
efficiency in room environment.
J.A. Wright et al (10) used multi-criterion genetic algorithm for optimization of building thermal design.
They considered energy costs, operational costs, thermal comfort and ventilation efficiency in their
optimization studies.
2.1 SCOPE OF RESEARCH
Based on the above literature review it can be concluded that the research on evaluation of contaminant
particle removal effectiveness by using Taguchi method is not yet fully explored. Even-though efforts on
determining optimal ventilation strategy using other optimization methods are partially successful, there is
ample scope for carrying out research on determination of appropriate ventilation strategy using Taguchi
method.

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July 2017 ISBN: 978-81-934083-7-7
There are many parameters that govern the indoor air quality. It is essential to find out the influential
parameters from the point of view of contaminant particle removal effectiveness. Various ventilation methods
have variable impacts on indoor air quality. Further, Air Changes per Hour(ACH), particle source locations
and particle sizes also have influence on quality of indoor air.
Thus, it becomes useful to evaluate influential parameters with the objective of improving contaminant
particle removal effectiveness using combination of statistical methods, simulation based methods and
experimental methods. This research gap is explored to arrive at appropriate/optimal ventilation strategy.
The major objective of this research is to study the effects of various input parameters i.e., Air Changes per
Hour(ACH), Contaminant source location and Contaminant Particle size on the Contaminant Particle
Removal Effectiveness in a mixing type ventilated room
3. METHODOLOGY
For fulfilling the objectives mentioned above, experiments were carried out in “ Experimental Test
Room” which was well-equipped with appropriate pre-calibrated instruments. Air velocity and temperatures
were measured by using constant temperature hot wire anemometer and particle concentrations are measured
by using Particle counter. The contaminant particle removal effectiveness was computed from the measured
data at various measurement points.
3.1 Taguchi Method:
Taguchi’s approach was used for problem formulation, identification of performance characteristics,
control factors, selection of factors and levels, selection of Orthogonal Array, preparation and conduct of
experiments, collection of data, statistical analysis of data, interpretation of experimental results and finally
arriving at influential parameters and optimal solution. The details of present studies related to room
ventilation studies are summarized below.
Input Parameters and their Levels for the study of various types of ventilation are shown below in Table 1.
The Orthogonal Array for the present study is given in Table 2.
Input Parameter Symbol Unit
Levels
Level 1 Level 2 Level 3
Air Changes per Hour ACH -1
Hr 3.6 6.0 8.4
Particle size PS µm 0.5 2.5 10
Particle source location SL m 0.5 2.4 3.5
Table 1 Ventilation Input parameters
Experiment ACH (-1
Hr) Particle size(µm)
Particle source
location(m)
1 3.6 0.5 0.5
2 3.6 2.5 2.4
3 3.6 10.0 3.5
4 6.0 0.5 2.4
5 6.0 2.5 3.5
6 6.0 10.0 0.5
7 8.4 0.5 3.5
8 8.4 2.5 0.5
9 8.4 10.0 2.4
Table 2 Taguchi Orthogonal Array Design for Ventilation study.

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3.2 Experimental Method:
The experimentation is carried out in Experimental Test Room which is equipped with hot wire anemometer
for air velocity measurement and particle counter for contaminant concentration measurements. These
equipments were pre-calibrated to measure these parameters correctly.
The experimental set-up is as shown in figure A.
Figure A: Experimental set-up for Mixing Ventilation Studies
Air is supplied to the Experimental Test Room through appropriate ducting. In the present case
mixing ventilation strategy is adopted. The measured contaminant particles are supplied in the test room
through particle generator. By using a traversing unit particle concentrations at various locations in the room
are carried out. The data so collected is analyzed to arrive at useful conclusions.
Three different ACH, particle source locations and particle sizes are used. Nine test runs are
carried out. Contaminant Particle Removal Effectiveness is computed for each test run. The results of these
trials are tabulated in Table 3.
Table 3 Details of test runs
Test Runs
Parameters
ACH Particle size
Particle source
location
Contaminant
Removal
Effectiveness
1 3.6 0.5 0.5 1.1083
2 3.6 2.5 2.4 1.1140
3 3.6 10.0 3.5 1.1380
4 6.0 0.5 2.4 1.1750
5 6.0 2.5 3.5 1.1910
6 6.0 10.0 0.5 1.1950
7 8.4 0.5 3.5 1.1790
8 8.4 2.5 0.5 1.1130
9 8.4 10.0 2.4 1.2010

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4. RESULTS AND DISCUSSIONS
These experiments are conducted according to Taguchi Design method by using proper
instrumentation in a Experimental Test Room.. Experiments are varied to complete 9 altered trials with
parameters like ACH, Particle Source Location, Particle Size, which are varied to compute Contaminant
Particle Removal Effectiveness for mixing ventilation configuration.
The objective of this research was to study the effect of various input parameters like Air Changes per
Hour(ACH), Particle Source Location, Particle Size on the contaminant particle removal Effectiveness. The
discussions regarding most influential input parameter is provided.
4.1 Influences on Contaminant Particle Removal Effectiveness
A) Taguchi Analysis
The S/N ratios for contaminant particle removal effectiveness are calculated as given in the following
equation (1). Taguchi method is used to analyze the results of response of parameter considering “Larger is
better” criteria.
………………………. (1)
Where S/N ratios are calculated from observed values, yi represents the experimentally observed value of the
ith
experiment and n=1 is the repeated number of each experiment in L-9 Orthogonal Array . (Table: 4)
ACH
Particle
size
Particle
source
location
Contaminant
Removal
Effectiveness
SNRA1 MEAN1
3.6 0.5 0.5 1.1083 0.89315 1.1083
3.6 2.5 2.4 1.1140 0.93770 1.1140
3.6 10.0 3.5 1.1380 1.12285 1.1380
6.0 0.5 2.4 1.1750 1.40076 1.1750
6.0 2.5 3.5 1.1910 1.51824 1.1910
6.0 10.0 0.5 1.1950 1.54736 1.1950
8.4 0.5 3.5 1.1790 1.43028 1.1790
8.4 2.5 0.5 1.1130 0.92990 1.1130
8.4 10.0 2.4 1.2010 1.59086 1.2010
Table 4: S/N ratios of observed values
Chart shows the optimum solution of the given set of parameters is given by the value having S/N ratio is
largest i.e 1.59086.
4.2 Minitab Results and Graphs:
From Figures 1 and 2, it can be observed that the parameters ACH, Particle Source Location, Particle Size
affect Contaminant Particle Removal Effectiveness.

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MeanofMeans
8.46.03.6
1.18
1.16
1.14
1.12
10.02.50.5
3.52.40.5
1.18
1.16
1.14
1.12
ACH PARTICLE SIZE
SOURCE LOCATION
Main Effects Plot (data means) for Means
Fig 1: Main Effects Plot of Means for Contaminant Removal Effectiveness
MeanofSNratios
8.46.03.6
1.4
1.2
1.0
10.02.50.5
3.52.40.5
1.4
1.2
1.0
ACH PARTICLE SIZE
SOURCE LOCATION
Main Effects Plot (data means) for SN ratios
Signal-to-noise: Larger is better
Fig 2: Main Effects Plot of S/N Ratios for Contaminant Removal Effectiveness
From Table 5 it is concluded that ACH with rank 1 is a more influencing parameter on Contaminant
Particle Removal Effectiveness than Particle Source Location and Particle Size.
Level ACH Particle size Particle source location
1 0.9846 1.2414 1.1235
2 1.4888 1.1286 1.3098
3 1.3170 1.4204 1.3571
Delta 0.5042 0.2917 0.2336
Rank 1 2 3
Table 5: Response table for Signal to Noise Ratios (Larger is better)
B ) Regression Analysis: It is carried our for Contaminant Removal Effectiveness in Occupied Zone(εoz)
versus ACH, Particle Size, Source Location.
The regression equation(2) is as given below-
(εoz) = 1.07 + 0.00922 ACH + 0.00324 Particle Size + 0.0105 Source Location ............(2)
The predictor table is given in Table 6.

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Predictor Coef SE Coef T P
Constant
1.06544 0.04327 24.62
0.000
ACH
0.009215 0.005807 1.59 0.173
Particle Size
0.003239 0.002783
1.16
0.297
Source Location
0.010490 0.009183
1.14
0.305
Table 6 Predictor table
B) Analysis Of Variance(ANOVA)
Source DF SS MS F P
Regression 3 0.006035 0.002012 1.73 0.277
Residual Error 5 0.005827 0.001165
Total 8 0.011862
Table 7 Table for Analysis of Variance
Residual
Percent
0.0500.0250.000-0.025-0.050-0.075
99
95
90
80
70
60
50
40
30
20
10
5
1
Normal Probability Plot of the Residuals
(response is eff o.z.)
Fitted Value
Residual
1.201.181.161.141.121.10
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
-0.05
Residuals Versus the Fitted Values
Figure 3 : Residuals vs Fits for (εOZ) Figure 4: Residual Histogram for (εOZ)

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Residual
Frequency
0.040.020.00-0.02-0.04
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Histogram of the Residuals
Observation Order
Residual
987654321
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
-0.05
Residuals Versus the Order of the Data
Figure 5: Residuals vs Order for (εOZ) Figure 6: One-way ANOVA: (εOZ) versus ACH
C) Analysis of Variance for S/N Ratio of ACH:
Analysis of variance for contaminant removal effectiveness is given in Table: 8. These values are obtained
from MINITAB 14 software.
ACH
Contaminant
Removal
Effectiveness (εOZ)
RESI1 FITS1
3.6 1.1083 -0.0118000 1.12010
3.6 1.1140 -0.0061000 1.12010
3.6 1.1380 0.0179000 1.12010
6.0 1.1750 -0.0120000 1.18700
6.0 1.1910 0.0040000 1.18700
6.0 1.1950 0.0080000 1.18700
8.4 1.1790 0.0146667 1.16433
8.4 1.1130 -0.0513333 1.16433
8.4 1.2010 0.0366667 1.16433
Table: 8 Analysis of Variance for S- N ratios of ACH.
Source DF SS MS F P
Particle source location 2
0.008018 0.004009 5.29 0.047
Residual Error 6
0.004550 0.000758
Total 8
0.012568
Table: 9 One way ANOVA for Contaminant Removal Effectiveness v/s Particle source location

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Residual
Percent
0.0500.0250.000-0.025-0.050
99
95
90
80
70
60
50
40
30
20
10
5
1
Normal Probability Plot of the Residuals
(response is oz)
Fitted Value
Residual
1.191.181.171.161.151.141.131.121.11
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
Residuals Versus the Fitted Values
(response is oz)
Figure 7: Normplot of Residuals for Occupied Zone Figure 8 : Residuals vs Fits for Occupied Zone
Residual
Frequency
0.030.020.010.00-0.01-0.02-0.03
2.0
1.5
1.0
0.5
0.0
Histogram of the Residuals
(response is oz)
Observation Order
Residual
987654321
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
Residuals Versus the Order of the Data
(response is oz)
Figure 9: Residual Histogram for Occupied Zone Figure 10: Residuals vs Order for Occupied Zone
5. CONCLUSIONS:
From results provided in figures 1 to 10 and tables 5 to 9, following conclusions can be drawn.
1. Air Changes per Hour, Contaminant particle source location and particle size are found to be the
influential parameters for Contaminant Particle Removal Effectiveness.
2. Maximum Contaminant Particle Removal Effectiveness in occupied zone is obtained at ACH of 8.4. This
suggests that if particle source location and particle size are not controllable, in mixing ventilation system,
it is recommended to use ACH of 8.4 for better contaminant particle removal.
3. Maximum Contaminant Particle Removal Effectiveness in occupied zone is obtained at source location
of 1.00 m distance from opposite wall of the Experimental Test Room. This leads to the conclusion that if
ACH and particle size are not controllable, better contaminant particle removal is obtained for source
location of 1.00m.
4. Maximum Contaminant Particle Removal Effectiveness in occupied zone is obtained when the
contaminant particle size is 10µm. This provides us the guideline that if ACH and particle source location
are not controllable, good contaminant removal can be obtained for particle size of 10 µm.
Thus, for determination of optimal ventilation strategy from the point of view of contaminant particle
removal, these studies provide useful guidelines for HVAC system engineers and operators. This, in turn,
will help in providing cleaner and healthier indoor environment.

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REFERENCES
1. Gee, I. (2001). Monitoring indoor air pollution. Indoor Built Environment 10:123-124.
2. Wolkoff, P. (2013). Indoor air pollutants in office environments: Assessment of comfort, health, and
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3. Franklin, P.J. (2007). Indoor air quality and respiratory health of children. Paediatric Respiratory Reviews
8: 281–286.
4. Ormstad, H., (2000). Suspended particulate matter in indoor air: adjuvants and allergen carriers.
Toxicology, 152, 53-68.
5. J. Petersen, H. Brohus and C.E. Hyldaard (1999) “The Effect of Renovating an Office Building on
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Supply Rate in an Office on Perceived Air Quality, Sick Building Syndrome (SBS) Symptoms and
Productivity”, Indoor Air, 10, 222-237.
7. M. Wetter and J. Wright (2003) “Comparison of a Generalized Pattern Search and a Genetic Algorithm
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8. L. Lu, W.J. Cai, L.H. Xie, S.J. Li and Y.C. Soh (2005) “HVAC System Optimization—in Building
Section”, Energy and Buildings, 37, 11-22.
9. S.W. Wang and X.Q. Jin (2000) “Model-based Optimal Control of VAV Air-conditioning System Using
Genetic Algorithm”, Building and Environment, 35, 471-478.
10. J.A. Wright, H.A. Loosemore and R. Farmani (2002), “Optimization of Building Thermal Design and
Control by Multi-criterion Genetic Algorithm”, Energy and Buildings, 43, 959-972.

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