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1.1. INTRODUCTION
Indoor environmental quality (IEQ) of educational buildings not only affects health,
but may also the student learning, staff productivity and attendance. Good housekeeping
protocols that thoroughly removed dust from surfaces was found to have both health and
comfort benefits (Fogarty, 2000), (Wyon, 2000). Knowledge concerning the thermal climate
parameters, their influence on the occupants and the influence of buildings and systems on
these parameters is covered in international standards. When building occupants experience
mild symptoms of distress or discomfort (dry eyes, itchy or watery eyes, dry throat, lethargy,
headaches, chest tightness), they begin to perceive a loss in performance. This performance
loss ranged from 3-8% depending on the number of symptoms (Raw, Roy.M.S, & Leaman,A,
1998). In another study (Wargocki & Wyon, 1999), it was found that the exposure to a
reservoir of dust (an old carpet) had affected subjects’ such as typing, arithmetic, logical,
reasoning, memory, and creative thinking skills by 2-6% (DanTranter:, 2008). While
motivation can overcome small burdens of environmental stress, continued environmental
stress can drain a person’s physical and mental resources, which ultimately can affect
performance. Several factors influencing thermal comfort such as the use of increased air
velocity, humidity, adaptation to higher indoor temperatures during summer in naturally
ventilated (free running) buildings, and a whole-year evaluation of the indoor thermal
environment are now included in the revision of international standards, e.g. ISO EN 7730
(2005) and ASHRAE Standard-55-2004 (2004)
Under the Occupational Safety and Health Act 1984 and the Occupational Safety and
Health Regulations 1996, employers are required to provide working environment in which
employees are not exposed to hazards and thereby maintain atmospheric quality. In few
indoor work places the temperature and air quality may be less than ideal. Faculty and staff
in such places may experience discomfort as a result. These guidelines provide information
about what can be done locally to improve comfort levels in relatively warm or cool
indoor areas whilst acknowledging the environmental and economic impact associated with
the increased ventilation, utilization or non utilization of mechanical heating, cooling and air
conditioning systems. This understanding aids to plan and improve indoor thermal comfort
and ventilation.
2
Thermal comfort relates human sensation and perception with a number of
environmental and physical parameters (P.O.Fanger, 1970). It is by definition, the perception
of satisfaction a subject experiences in a given thermal environment (ANSI/ASHRAE55-
2004, 2004). Extensive studies have resulted in a number of thermal comfort equations as
proposed in some widely used design guides and standards (ANSI/ASHRAE55-2004, 2004)
(BSEN-ISO7730, 1995). Three indices have been derived based on Fanger’s comfort
equation: Predicted Mean Vote (PMV), Predicted Percentage Dissatisfied (PPD) and Lowest
Possible Percentage Dissatisfied (LPPD). The PMV and PPD indices are comparatively
common in practical applications (Fanger.P.O. 2002) (Han, Yang,W, Zhon,j, Zhang,G,
Zhang,Q, & Moschandreas,D,J, 2009). The former predicts the mean thermal comfort votes
among a large group of people; the latter, a quantitative measure of the number of thermally
dissatisfied persons in a group under particular thermal conditions. Field studies on the
thermal comfort of occupants working in an air-conditioned environment can be used to
examine the neutral temperature—a temperature associated with a neutral thermal sensation
(Oseland.N.A, 1995) (Fanger, 1995) (Mui,K.M & Wong,L.T, 2007). This temperature is a
key factor for selecting an appropriate air temperature set point for an indoor thermal
environment (Wong,L.T, Mui,K.W, Fong,N.K, & Hui,P.S, 2007). As climate, besides
occupant factors including lifestyle, economic status and adaptive behaviour, plays an
important role in affecting the indoor thermal environment (Yoshino, et al., 2006)
(Brager,G.S & Dedear,R.J, 1998), neutral temperatures for different climatic zones have been
studied (Mui,K.M & Wong,L.T, 2007), (Wang, 2005). In all the above work, the thermal
comfort and the indoor environmental quality is analysed but not optimized.
Thus there is a need for this study seeking to fill this gap by providing empirical
thermal comfort data from Karunya University which is situated near Siruvani hills of
Coimbatore in Tamil Nadu. Data for Occupants’ perception about the present indoor
Environment were collected by four different types of questionnaire and the required
parameter values collected using various instruments.
Questionnaires were prepared to collect the data and the informants were asked to
provide their personal details like name, age, sex and the number of years of their living in
this place and other details pertaining to the study of thermal comfort and indoor
environmental quality. The questionnaire contained dichotomous assessment scale; when a
question has two possible responses, it is called dichotomous. This scale was used for a
direct feedback of acceptability with the question ‘Is the thermal environment / indoor air
quality/noise level/illumination level being perceived in the office and residence environment
3
acceptable to you?’ being asked. The ranks ‘(1) Yes, acceptable’ and ‘(0) No, not acceptable’
were self-explanatory. In order to confirm the validity of their responses, each respondent had
to use a semantic differential evaluation scale for the subjective assessment of the first two
aspects, and a visual analogue assessment scale for the evaluation of the aural and visual
comfort. At the end of the survey, occupants’ present status was identified.
A Visual Analogue Scale (VAS) is a tool that tries to measure a characteristic or
attitude that is believed to range across a continuum of values and cannot easily be directly
measured. Operationally a VAS is usually a horizontal line, 100 mm in length, anchored by
word descriptors at each end. The patient marks on the line the point that they feel represents
their perception of their current state. The VAS score was determined by measuring in
millimeters from the left hand end of the line to the point that the patient had marked.
Natural ventilation is a ventilation strategy that uses wind, heat and pressure
differential to move fresh outside air through an interior space. Using operable windows as
well as other façade elements to provide ventilation air is an intuitive manner of improving
the atmosphere within built environment. There are several ways to provide natural
ventilation and minimize the use of mechanical force, thereby conserving electrical energy.
1.2. SIGNIFICANCE OF THE PROJECT
1.2.1. Thermal comfort
Thermal comfort for a human being is one of the major problems in present. Thermal
comfort is important both for one's well-being and for productivity. It can be achieved only
when the air temperature, humidity and air movement are within the specified range often
referred to as the "comfort zone". To have "thermal comfort" means that a person wearing a
normal amount of clothing feels neither too cold nor too warm. Providing thermal comfort in
buildings is really a challenging task. The environmental parameters that constitute the
thermal environment are: temperature (air, radiant, surface), humidity, air velocity and
personal parameters (clothing together with activity level), these entire six variables play a
major role in providing thermal comfort. A criterion for an acceptable thermal climate is
dependent on PMV-PPD index.
Temperature preferences vary greatly among individuals and there is no one
temperature that can satisfy everyone. Nevertheless, an office which is too warm makes its
occupants feel tired; on the other hand, one that is too cold causes the occupants' attention to
4
drift, making them restless and easily distracted. Maintaining constant thermal conditions in
the offices is important. Even minor deviation from comfort may be stressful and affect
performance and safety. Workers already under stress are less tolerant of uncomfortable
conditions. Many large-scale building studies show that increased volumes of outdoor air,
natural ventilation, air ventilation rates, and filtration of air and improved cleaning and
maintenance of systems are correlated with reduced sick building syndrome (SBS) symptoms
(Brightman & Moss, 2000)..Lawrence Berkeley National Laboratory, Berkeley,. Performance
assessments in work settings are rare because of the difficulty of capturing actual
performance measures and linking them to specific environmental features. Nonetheless, a
field experiment in Denmark shows that workers performed better on a typing task and
perceived themselves as able to think more clearly with increased vent (Wargocki, Wyon, &
Fanger, 2000). A field experiment looked at the link between SBS symptoms associated with
ventilation rates and work performance. They found that workers reporting SBS symptoms
worked 7.2% more slowly on a vigilance task and made 30% more errors on a numerical
task. Elevated temperatures are associated with increases in illness symptoms (Wyon, 1996).
As researchers systematically increased indoor temperatures from 68ºF to 76.2ºF, they found
increased incidents of headache and other SBS symptoms. Wyon (1996) also reports
increased incidence of headache and fatigue as indoor temperatures increase from 68o
F to
76o
F. At 76o
F, 60% of the workers experienced headache compared with 10% at 68o
F. A
field study of lighting conditions in a government office building in England found that
headache incidence decreased significantly with increased access to daylight (Wilkins &
Grois, 1989). People who suffer from Seasonal Affective Disorder (SAD) may also benefit
from access to daylight. Because people with SAD prefer more brightly lighted spaces than
people who do not suffer from seasonal variation in mood and well-being, being adjacent to a
window where light levels are higher than interior spaces may have therapeutic effects
(Heerwagen, 1990).
1.2.2. PMV – PPD Index
Predicted mean vote (PMV): An index that predicts the mean value of the votes of a large
group of persons on the seven point thermal sensation scale. PMV model uses heat balance
principles to relate the six key factors for thermal comfort to the average response of
people on a seven point scale.
Predicted percentage of dissatisfied (PPD): An index that establishes a quantitative
prediction of the percentage of thermally dissatisfied people determined as a function of
5
PMV.In general thermal comfort can be calculated by an equation called Fanger’s ‘Predicted
Mean Vote’ (PMV) given by Fanger and this equation gives the optimal thermal comfort for
any environment
Thermal comfort is vogue and cannot get any crisp values, so a new approach based
on fuzzy logic is introduced to estimate the thermal comfort level depending on the state of
the following six variables: the air temperature, the mean radiant temperature, the relative
humidity, the air velocity, the activity level of occupants and their clothing insulation.
PMV= (0.028 + 0.3033e-0.036m
). {M-W} – 0.000699(M-W) - Pa] –
0.42[(M-W) – 58.15] – 0.0173M (5.867 – PA) – 0.0014M (34 – Ta) –
3.96.10-8
fcl [(Tcl + 273)4 – (Tmrt + 273)
4] – fcl. hc (Tcl –Ta)}
Tcl = 35.7 – 0.28(M – W)-0.155Icl [3.9610-3
fcl [(Tcl + 2273)4 - (Tmrt + 273)
4]
- fcl.hc (Tcl – Ta)
hc= 2.38(Tcl-Ta)0.25
for 2.38(Tcl –Ta)0.25
≥12.1 √vair
12.1 √vair for 2.38(Tcl –Ta) 0.25
≤ 12.1 √vair
The parameters are defined as follows:
PMV: Predicted mean vote.
M: Metabolism (W/m2).
W: External work, equal to zero for most activity (W/ m2).
Icl: Thermal resistance of clothing (Clo).
Fcl: Ratio of body’s surface area when fully clothed to body’s surface area when
nude.
Ta: Air temperature (0C).
Tmrt: Mean radiant temperature (0C).
6
Vair: Relative air velocity (m/s).
Pa: Partial water vapour pressure (Pa).
hc: Convection heat transfer coefficient (W/m2 k)
Tcl: Surface temperature of clothing (0C).
New fuzzy thermal sensation index is calculated implicitly as the consequence of
linguistic rules that describe human’s comfort level as the result of the interaction of the
environmental variables with the occupant’s personal parameters. The fuzzy comfort model is
deduced on the basis of learning Fanger’s ‘Predicted Mean Vote’ (PMV) equation. The new
fuzzy PMV calculation does not require an iterative solution like Fanger’s PMV and can be
easily adjusted depending on the specific thermal sensation of users.
For most thermal parameters it has been possible to establish a relationship between
the parameter and PPD. People may be dissatisfied due to general thermal comfort and/or
local thermal comfort parameters.
1.2.3. ASHRAE Thermal Sensation Scale
The ASHRAE thermal sensation scale, which was developed for use in quantifying
people's thermal sensation, is defined as follows
Table 1.1 − The meanings of the 7-point thermal sensation scale
It is based on the assumption that people voting +2, +3, –2, or –3 on the thermal sensation
scale are dissatisfied, and the simplification that PPD is symmetric around a neutral PMV.
Predicted Percentage of Dissatisfied (PPD) <10%
Predicted Mean Vote (PMV) >-0.5 to <+0.5
−3 Cold
−2 Cool
−1 Slightly cool
0 Neutral
1 Slightly warm
2 Warm
3 Hot
7
This defines the recommended PPD and PMV range for typical applications
PMV equation and a fitted curve to Fanger’s PPD data, is illustrated in Figure 2.1., have been
widely accepted by ASHRAE and ISO as a standard for the thermal satisfaction of human
occupancy. If the resulting PMV value generated by the model is within the recommended
range, the conditions are within the comfort zone.
Figure 1.1 – Predicted Percentage of Dissatisfied as a function of Predicted Mean Vote
1.2.4. Indoor environmental quality
The overall IEQ acceptance for an office and resident environment perceived by an occupant
expressed by a multivariate logistic regression model was proposed by (Wong, Mui,K.W, &
Hui,P.S, 2008) (Lai,A.C.K, Mui,K.W, Wong,L.T, & Law,L.Y, 2009). A range of acceptance
in typical office and resident environmental conditions and its dependence on the four
parameters are determined. The proposed overall IEQ acceptance can be used as a
quantitative assessment criterion for these environments.
The overall IEQ acceptance (φ0) can be expressed by a multivariate logistic regression model
as shown below
8
where C0,0 and Ci,0 are the regression constants which can be determined from field
measurements, φi is the occupant acceptance correlated with the thermal sensation vote ξ1 ,
the Co2 concentration ξ2 (ppm), equivalent sound pressure level ξ3 (dBA)and the horizontal
illumination equivalent sound pressure level ξ4 (lux)
The thermal environment acceptance Ø1(ξ1) is given with regression coefficients C0,0 and Ci,0
The acceptance Ø2(ξ2), Ø3(ξ3) and Ø4(ξ4) are expressed by logistic regression models with
regression coefficients C0,j and C1,j , where the Logistic regression models is explained in the
chapter II.
The report based on the survey summarizes a project on the health by optimizing the Thermal
comfort and indoor environmental quality of an office and residential building by using 4
different types of questionnaires among faculty working in the University and also from
people staying in the university quarters addressing the quality of the environment.
1.3. COIMBATORE CITY:
Coimbatore City is one of the top 10 fastest growing cities of India. Coimbatore district has a
population of about 42.25 Lakhs (Census in 2001).
Coimbatore is also a district capital. There are more than
25,000 small, medium, large scale industries and textile mills.
Coimbatore District has six taluks and two revenue divisions
of Coimbatore and Pollachi.
Coimbatore has a well-developed educational infrastructure,
with 7 Universities, 2 medical colleges and over 54
Engineering Colleges and 70 Arts and Science colleges
9
(www.wikipedia.com). The city is also a major health care and an emerging medical tourism
destination with many super specialty hospitals and as many as 750 hospitals and medical
centers in total. The hill stations of Ooty, Coonoor and Valparai are close to the city, making
it a good tourist attraction throughout the year. The city is situated about 500 kilometers
(311 miles) southwest of state capital Chennai, on the banks of the Noyyal River and is close
to the Siruvani Waterfalls.
1.4. ABOUT KARUNYA UNIVERSITY
Karunya University (declared under section 3 of the UGC Act, 1956 Vide Notification No. 9-
3-2000-U3 dated. 26.6.2004 of the Govt. of India) was founded in 1986. Located about 30
Kms drive from Coimbatore city in a picturesque 700-acre campus in Western Ghates
Siruvani foot hills. Today, more than 8500 students (both boys and girls) study in
Karunya, hailing from various parts of the country and abroad. The University recognizes the
need for a friendly residential atmosphere for the students
Fig 1.2. The landscape of office buildings of Karunya University
Karunya University has ranked seventh in infrastructure in all over India. The weather
is cool and breezy.
10
Karunya is thus set in a natural environment ideal for a residential institution.
Fig.1.3. The landscape of Residential buildings of Karunya University
The purpose of this study is to evaluate the thermal comfort and Indoor environmental
quality of both office and residential buildings of the Karunya University, whether it is
optimum or within the acceptable range. If it is not in the acceptable range, suggestions can
be given for better thermal comfort.
1.5. DATA COLLECTION:
The basic study is in the office rooms of the faculty working in the Karunya
University and faculty staying on the campus quarters. Four different questionnaires were
for Thermal comfort in the office and residences and Indoor environmental quality for office
and residences. The office consists of faculty belonging to
Mechanical and Aero space,
ECE and media,
EEE and EIE,
BIO Tech, Bio Info and Food processing,
Science and Humanities
Civil,
MBA,
and CST
Departments of the Karunya University.
11
The residences were Alpha, Bethel, Elim, Cannan, Camel, Kidron, Pat Robertson,
Hebron, Frankinson, Sinai and Tabor
Table 1.2. Details about the department, residence and area
Office Area(m2) Residence Area
MECHANICAL 233.3 ALPHA 726.87
ECE/ MEDIA 684.64 BETHEL 628.55
EIE/EEE 285.6 ELIM 330
BIO TECH, BIO INFO AND
FOOD PROCESSING 726.57
CANNAN 429.27
CST 629.69 CARMEL 1053.46
S & H 947.77 KIDRON 885.24
MBA 291.02 PAT ROBERTSON 616.54
CIVIL 296.2 FRANKINSON 833
HEBRON 538.37
SINAI 1053.6
TABOR 1336.17
This document summarizes a review of office and residential building of our University
with 220 from office and 102 people from our residences. The presented information is
drawn from expert contributions and discussions.
1.6. OBJECTIVES
The objectives identified are:
a) To observe and analyze the existing environmental quality of offices and residential
buildings in Karunya University with a focus on thermal comfort and indoor
environmental quality due to natural ventilation.
b) To optimize the encoded values of variables obtained from Karunya University using
fuzzy and nontraditional optimization techniques.
c) To analyse and arrive at an effective optimization technique that could yield an
appropriate indoor environmental variable value for optimum comfort. The variables
that the indoor environmental quality depends on are the following
Thermal comfort
Indoor air temperature or carbon dioxide
Lighting
12
sound
Thermal comfort is dependent on the combination of environmental and personal factors.
Environmental factors include:
o air temperature
o humidity
o radiant temperature
o air movement
o level of activity
o clothing worn
Individual responses also vary depending on:
o perception
o physical fitness
o clothing
o occupant’s activity level
o Acclimatization.
1.7. CHOSEN OPTIMIZATION TECHNIQUES
There are many optimization techniques available both traditional and non-traditional. But in
this study only non-traditional optimization techniques are considered. The traditional
optimization techniques fail to converge on a feasible solution in many cases
(Prof.Dr.B.V.Babu, 1992). The Non-traditional optimization techniques differ from the
conventional traditional optimization techniques in that it produces optimal results in a short
period of time (Kokilavani,T & Dr.George Amalarethinam, 2010). Most of the traditional
optimization techniques based on gradient methods have the possibility of getting trapped at
local optimum depending upon the degree of non-linearity (Prof.Dr.B.V.Babu, 1992). Hence,
these traditional optimization techniques do not ensure global optimum and also have limited
applications. The problems considered are Non Linear optimization which is single objective
where the constraints are only bounds for environmental variables and the problems are
continuous. So the following non-traditional optimization methods are used to solve the
problems, as these techniques are used widely in technical computation.
(www.mathworks.com)
The methods are
13
1. Genetic algorithm
2. Simulated annealing
3. Pattern search
4. Particle swarm optimization
5. GODLIKE- hybrid
6. Fmincon (Non Linear optimization)
7. Differential evolution (Numerical optimization method)
8. Lipschitz global optimisation (LGO)
9. glcCluster
10. glcSolve
In these problems to find the optimum value of thermal comfort and indoor environmental
quality of a building, ten solvers are used. The characteristics lead to different solutions and
run times. The results are compared on various bases and appropriate solver is identified.
1.8. DATA PROCESSING AND ANALYSIS
The collected data was processed and classified on the basis of the thermal comfort
and indoor environmental quality.
The programs were coded using MATLAB with TOMLAB programming and all the
experiments executed in a 2.2 GHz Intel laptop machine. The performances of the
techniques were compared and results were analysed.
1.9. LIMITATION OF THE STUDY
As the present study has its wide range of application, the area is restricted only to
faculty working in office and staying in the quarters alone.
1.10. METHODLOGY
The performance of the non-traditional algorithms will vary for every run and it is
assured that the solution is global optimum. So for every problem twenty trial runs were
performed in all the algorithms and the average value of the solution was obtained from all
the trials (Elbeltagi, Tarek Hegazy, & Grierson, (2005) ).
14
1.11. DIVISION OF THE RESEARCH WORK
The rest of this dissertation is organized as follows:
Chapter II gives brief description of the Multivariate Logistic Regression mathematical
model. A detailed and descriptive account of the optimization techniques methods are fuzzy
logic, Genetic algorithm, Simulated annealing, Pattern Search, Particle swarm optimization
and a hybrid algorithm known as GODLIKE algorithm and Non-Linear optimization,
Differential Evolution, and solvers like LGO ( Lipschitz-Continuous Global Optimizer),
glcCluster and glcSolve which are based on Direct algorithm.
Chapter III gives the available literature on Thermal fuzzy, Thermal comfort of office and
residential buildings separately. Summarizes the evaluation thermal comfort using Fuzzy
logic technique and optimizing both thermal comfort of office and residential building using
the optimization techniques described in Chapter II. For every problem twenty trial runs
were performed in all the algorithms and the average value of the solution was obtained
from all the trials. All the results are tabulated with the comparative graph.
The PMV index which is found using fuzzy PMV varies from -3 to +3 as Fanger’s PMV
index without any iteration. It will be acceptable only when PMV lies between -0.5 and +0.5
(ANSI/ASHRAE55-2004, 2004). The bibliography used for the thermal comfort is attached.
Chapter IV gives the available literature on Indoor environmental quality of office and
residential buildings. The equations are derived using multivariate logistic regression model
and optimized using the nontraditional optimization methods described in Chapter II. For
every problem twenty trial runs were performed in all the algorithms and the average value
of the solution is obtained from all the trials. All the results are tabulated and the solutions
are compared in a graph. The bibliography referred for IEQ is attached.
Chapter V presents the consolidated optimized results for both thermal and indoor
environmental quality of the University buildings. The entire coding of the optimization
methods for different problems is furnished in the Annexure.
1.12. Future scope of work
While there are many research needs related to the subjects discussed here, the following are
suggested as high priority research needs which can be taken as future work
15
In continuation, optimization of thermal comfort and indoor environmental quality of
class room and hostels for students of Karunya University could be calculated using
questionnaire and checked whether it is optimum otherwise improvement can be
suggested.
The relationship between performance of the employees and students of the
University and indoor environmental quality should be assessed.
1.13. Conclusion
(i) Thermal comfort or sensation (PMV) is considered to be acceptable if the value lies
between -0.5 to + 0.5. In the experiment conducted using fuzzy logic and ten non-
traditional optimization techniques the thermal sensation takes the value 0.129 and -0.5
respectively. Hence the thermal comfort of the office and residential buildings of Karunya
University is found to be optimum. The percentage of people dissatisfied (PPD) should be
less than 10. The experiment shows that the PPD is 5 in all the optimization techniques
and 5.3447 in the fuzzy logic. The indoor environmental quality of a building is
acceptable if its value is 1. In the problem of both office and residence, the IEQ value is
1. From the above we can conclude that the thermal sensation, the percentage of people
dissatisfied and indoor environmental quality is within the acceptable range. All the
buildings are only naturally ventilated. So the natural ventilation is more than sufficient to
maintain good health of the inmates who are working and living here in the Karunya
University. It is concluded that among the ten algorithms, the appropriate algorithm, for
optimization of thermal comfort as well as IEQ, is Direct search algorithm & the solver
is PATTERN SEARCH.
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