costing small cleanrooms
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doi:10.1016/j.bu
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Building and Environment 42 (2007) 743–751
www.elsevier.com/locate/buildenv
Costing small cleanrooms
Liu Yanga,�, Cheong Eng Ganb
aShanghai ForteLand Co., Ltd., No. 2-513, East Fuxing Road, Shanghai 200010, PR ChinabDepartment of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
Received 27 July 2005; accepted 13 September 2005
Abstract
The quick development of high technology and life sciences industries nowadays sees the need for more and better cleanrooms in
modern laboratories expanding dramatically. Cleanrooms, as mechanically intensive facilities that consume large amount of energy to
maintain its defined environment demand high capital and operating costs. The owners also expect the cleanrooms to be constructed with
less money, less time, yet with higher performance standards and lower running costs. Most cleanroom designs and construction are
customized for a wide-ranging scale of operations. To date, not only there is no complete study on cost of the entire lifetime of
cleanroom, but also no standard cost system available, which makes it fairly difficult to verify and project the costs of cleanroom design
and construction. In order to obtain accurate costing, it is necessary to establish a thoughtful cost framework based on the critical
parameters of the facility. This paper defines the critical elements of cleanroom design that would significantly impact the costs of its
construction. It also presents the relationships among the elements within the cost model. The cost model is applied to small cleanrooms,
which has an increasing demand from entrepreneurs and small and medium enterprises. Such small, standard and modular cleanrooms
are suitable for individual inventors, small high-tech manufacturers and school laboratories.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Cleanroom; Life cycle cost; Regression model; Singapore
1. Background
Along with the development of high-technology indus-tries nowadays, the need for more and better cleanroomshas been expanding dramatically. This increasing demandcan be attributed to a greater number of semiconductormanufacturing industry, research laboratories, pharmaceu-tical facilities, hospitals, and other new product manufac-turers. They require their operations in a specialenvironment to prevent contamination, because the pro-cesses or products are sensitive to environmental factorsincluding temperature, relative humidity, electrostaticdischarges, airborne particles, chemical contamination,electromagnetic fields, oxygen, and vibration. To achievethis special environment as required, cleanrooms aredesigned and built to control these environmental factors.
e front matter r 2005 Elsevier Ltd. All rights reserved.
ildenv.2005.09.010
ing author. Tel.: +8621 633 200 55X5023;
251 92.
ess: [email protected] (L. Yang).
The growing need for cleanroom is most notable in thesemiconductor manufacturing industry because of theincredible progress in this field. As a result there are higherdemands and stricter requirements for cleanroom facilitiesto provide the suitable manufacturing space for thesemiconductor production. The steady increasing in suchadvanced technology facilities over the past few years canbe seen from the number of new and remodeled manu-facturing plants for semiconductor devices around theworld.Various research labs contribute to another significant
demand for comparatively small-scale cleanrooms, amongwhich the micro-/nano-technology research laboratoriesare the most prevailing ones. Besides, the medical/biotechlaboratories and other newly booming life sciencesresearch laboratories also take an important seat andeven demand the cleanroom design to be more specia-lized and complex. Modern cleanroom technology isaccordingly being pushed to reach a higher level tokeep up with the rapid development in its supportingindustries.
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751744
The cost of cleanrooms keeps skyrocketing in the recentyears. Capital estimates can range from US$1800 to asmuch as US$4000 per square foot of process cleanroomspace that includes the cleanroom (building shell, clean-room walls, floors and ceilings), and the supporting utilitysystems (high purity gases and liquids, effluent treatment)[1]. ‘‘A Class 10 environment typically costs about US$2000per square foot to build and US$1 million a year tooperate,’’ said Lloyd Crosthwait, cleanroom expert at theUniversity of Texas at Dallas’ NanoTech Institute [2].
Cleanrooms, as mechanically intensive facilities, consumelarge amount of power and other kinds of energy to maintainthe defined environment. Air conditioning, along with cleanair handling and circulation, accounts for more than 60% ofthe power required to operate a cleanroom [3]. The largepower consumption has made the energy-efficiency animportant consideration in designing cleanrooms. By choos-ing equipments of high efficiency, minimizing the airflow rateand exhaust air volume, or adopting the mini-environmentlayout, scientists have explored many feasible ways and arestill searching for optimum cost-efficiency.
Besides the high energy cost, major components ofcleanroom operating cost also include the maintenancecost, replacement cost, consumable cost and cleaning cost.Since all these costs are recurring, the accumulated sum ofoperating cost at the end of the cleanroom lifetime wouldmultiply the initial cost many times. Therefore it is morereasonable to consider life cycle cost rather than the initialcost when evaluating a cleanroom project. The life cyclecost herein refers to the total facility-related costs over theentire lifetime of a cleanroom, and is expressed as adiscounted value at certain point of time by applying thepresent value technique.
So far, there are much dissertations about how life cyclecosts should be modeled and calculated [4], and someattention had been focused on the need to optimize the sumof capital and operating costs [5]. However, much literaturehad concentrated on the mechanics of the calculation,especially the application of discounted cash flow. As aresult, quantitative, practical solutions remain elusive,principally because of the continuing absence of reliabledata [6]. In view of these deficiencies, it is meaningful toestablish an appropriate model for cleanroom life cycle costbased on an in-depth survey of available cleanroomprojects. Through interviews, questionnaire survey andstatistical analysis, this study established a feasible costmodel to define the relationship among the life cycle costand those critical variables of cleanroom design that wouldimpact significantly on the cost of acquiring, owning andoperating facilities.
2. Objectives
With the high and ever increasing cost of cleanroomprojects due to the increasing tool cost, the complexity ofthe advanced technology facilities, and also with thesubstantial expense to maintain cleanrooms operating
regularly due to the level of cleanliness required, aninvestment budget with consideration of the total life cyclecost for this kind of facility is necessary in the feasibilityresearch stage. The objective of this study was to develop, asimple cost estimation model to facilitate and encouragedata collection and analysis. Such a model would serve toimprove understanding of how the capital cost andoperating cost arise, to identify which design parameterscontribute most to the cleanroom life cycle cost, and toprovide a rational framework for project cost estimating,evaluation of alternative designs and final decision-makingby the facilities owners.
3. Research methodology and data acquisition
3.1. Survey method
The cost of cleanroom is an important factor that wouldinfluence the profit in manufacturing high-tech products.Because of the competition, the related data is always kept inhighly confidential. A detailed survey is necessary on thosenew construction cleanroom projects. The primary source isespecially cleanroom contractors, and if possible, throughtheir contacts, to project-corresponding facility owners. Sincethe research topic is quite sensitive and highly dependent onsuccessful data acquisition, this survey method to be adoptedin this phase is crucial to the whole research.This study adopts a mixed-mode method: survey coupled
with interview. This combination allows the strengths ofone method to compensate for the weaknesses of another,make up the investigation more feasible for the study. Awell-prepared structured interview with a deliberatelydesigned questionnaire is greatly helpful to obtain asuccessful response.The procedure for this mixed-mode interview process is
as follows:
1.
Prior to the survey, send the respondents a brief letterthat notifies them of the importance of the survey theyare to receive, and followed by a call to confirm aninterview appointment.2.
During the interview, explain clearly the objectives,significance and benefits of the research, convince therespondents of the survey’s credibility and ask them toparticipate. After getting the approval, hand theprescribed questionnaire to the respondents. The ques-tionnaire is packaged in an envelope with a well-designed cover letter, and a self-addressed stampedreturn envelope.3.
Make a reminder and enquiry phone call, thankingrespondents, reminding nonrespondents and enquiringproblems or difficulties they may encounter, to allsample members 2 weeks after the initial handing.4.
Conduct an interview to nonrespondents two to fourweeks after the initial questionnaire handing, to dealwith those problems or difficulties, which could not beexplained or overcome via phone or email communication.
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751 745
5.
The final step is taken after the questionnaire isreturned. Check blanks or obvious faults in the returnedquestionnaire. Clarify them to improve data validityover a phone enquiry or a third interview, if needed.These special emphasize the importance of the surveyand encourage people to response.The questionnaire is a structured technique for collectingprimary data in a survey. It is a series of written questionsfor which the respondent provides answers. A well-designed questionnaire motivates the respondent to pro-vide complete and accurate information. When designingthe questionnaire, guidance was under the leading work of[7], whereas the structure follows the life cycle costbreakdown of cleanroom as shown in Fig. 1.
3.2. Data collection
The survey was conducted in Singapore within 3 months.According to the information provided by Singapore BCAContractors Registry, a total 396-registered companies that
D
Floor System
Partition Wall System
Recirculation Air System
Cooling System
Fire Protection System
Electrical Systems
Air shower, Air Lock and Pass-boxes
Inst
Initial Costs
Annual
Annual Ma
Annual Co
Intermittent R
Annual C
Operating cos
Salvage Va
Cleanroom Life Cycle Cost
Fig. 1. Framework of cleanroom
majored in air-conditioning, refrigeration & ventilationwork was selected. Through phone or email enquiries, 15 ofthem run a business in contracting cleanroom projects.Subsequently, these 15 companies were selected as therespondents of this survey and were sent interviewenquiries respectively. Following the survey procedurementioned above, 5 companies finally participated in thesurvey and provided information of 12 projects. Theresponse rate, say 33%, is better than expected, as it wasinitially considered to be very low because of the sensitiveproperty of the research topic. Yet the amount of casesobtained is a little less than expected mainly because two ofthe five companies do not have much experience on newconstructed cleanroom projects and could only provide onequalified data set each. In the end, 12 data sets were finallycollected for model developing.These 12 projects are all built for purpose of laboratory
research or small-scale high-tech product manufacturing,and the floor areas of these cleanrooms range from 100 to4000 square meters, much smaller than the larger ones forwafer fabrication, which usually cover more than ten
esign
Filter Ceiling System
Make-up Air System
Process Exhaust Systems
Heating System
Lighting System
Monitoring Control & Alarm Systems
Gowning Furnitures, etc
allation
Energy Cost
intenance cost
nsumable cost
eplacement cost
leaning cost
ts
lue
life cycle cost breakdown.
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751746
thousand square meters. Unlike large wafer Fabs, of whichcleanrooms should be designed and built. Integrating withthe framework structure, small cleanrooms could be easilyinstalled within existing building. Due to the data avail-ability, the paper was confined in studying the life cyclecost for this kind of small cleanrooms.
4. Modeling life cycle cost of cleanrooms
4.1. Method of modeling
The method used in this research is to apply regressionanalysis techniques to develop a cost estimation model. Themodeling approach was performed using the statisticalpackage SAS. The approach consists of the five steps, namely:
�
Identify dependent variable and potential independentvariables—cost factors for modeling; � Use backward selection method to remove insignificantvariables;
� Run the regression procedure using variables left; � Analyze the regression results; and � If the model is acceptable, conclude. Otherwise, revisethe model, determine a corrective action, and go back tothe first step.
4.2. Modeling preparation
The first step in developing the cost model wasidentifying a feasible dependent variable. Since six out 12projects were constructed in 1998, base year was set at 1998to simplify the calculation. For all the 12 cases, the capitalcost data are actual in current value dollars, and theoperation & maintenance (O&M) costs data are estimatedamounts in constant value dollars based on the marketprice at the year of construction. The dependent variableadopted in the model is the life cycle cost at base year of1998. To eliminate the influence of time value, two steps fordata normalizing are followed: Firstly, the life cycle costfor each project was calculated by accumulating the presentvalue of all the cash flows during its economic life, which isassumed to be 15 years in this study. The cleanroomstudied here is confined to its internal space, which includesfloor, ceiling, wall, air shower/lockers, gowning furniture,HVAC system, electrical system, fire protection system,and other utility facilities’ monitoring control and alarmsystems. The external structural enclosure and the processsupporting systems such as DI water, chemical supply andprocess gas piping, etc. are not covered in the study.
Likewise, as it was applied to other building types, thebasic costing equation for the life cycle cost of a cleanroomis expressed as follow:
Life Cycle Costðyear of constructionÞ
¼ Initial costsþ PVðOperating costsÞ
� PVðSalvage valueÞ; ð1Þ
where
�
Initial costs, are composed of cleanroom design andinstallation fees, and assumed to occur at the beginningof the year of construction. � Operating costs, i.e., the future costs appear in clean-room operation, are the integration of energy cost,maintenance cost, consumable cost, cleaning cost andreplacement cost. The cost data for the first four itemsare estimated on annual basis whereas that of the lastitem is estimated intermittently. All cash flows areassumed to happen at the beginning of the year. Tomaintain the constant clean environment as required,cleanrooms are normally operating 24 h per day withoutstop.
� Salvage value, value of an asset at the end of economiclife, is assumed as zero here.
� PV, is the present value derived from the followingformula:For intermittent cash flows, single present value formulais used:
PVðyear of constructionÞ ¼ CFt �1
ð1þ dÞt. (2)
For annual cash flows, uniform present value formulais used:
PVðyear of constructionÞ ¼ A0 �ð1þ dÞn � 1
dð1þ dÞn. (3)
Note: CFt is the cash flow occurs in year t, t is the timeelapsed since construction, A0 the annual amount atprice of year of construction, and d ¼ 2:7%, the realdiscount rate. It is derived from the formula:
d ¼1þD
1þ i� 1, (4)
where D is the nominal discount rate and i the rate ofinflation.
Based on the data from year 1977 to year 2000 providedby International Finance Statistics, Singapore, applyingthe 3-month inter-bank rate as nominal discount rate, andconverting the consumer price index into inflation rate(yearly % change in consumer price index), an averagevalue of real discount rate was obtained as 2.7%.Secondly, for the remaining six projects constructed after
1998, the final amounts of life cycle cost at the year ofconstruction were further discounted to the values at 1998using the formula of single present value:
Life Cycle Costðbase year 1998Þ
¼ Life Cycle Costðyear of constructionÞ
�1
ð1þ dÞr, ð5Þ
where r is the time difference between the base year 1998and year of construction.
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751 747
Thus, after the data normalizing, the time effect wasremoved and the cost data of all the 12 cases wereexpressed in equivalent value and became comparable.
After the dependent variable was identified, the next stepwas to determine the cost factors for cleanroom projects.The selection for cost factors was based on four sources:literature review of cleanroom technology from source oflibrary and internet, a close consultation with the profes-sionals including cleanroom designer, constructors andfacility managers, a review and analysis of bid documentsfrom completed cleanroom projects and finally the result ofthe questionnaire survey. Table 1 shows the initial list ofcost factors selected to be considered as independentvariables in the model.
Among these 10 cost factors, the clean-zone area (CA) isthe common one that applies to all types of projects.Average filter coverage rate (AFC) is a derivative variable(AFC ¼ FCA/CA), where FCA is the filter ceiling areaexpressed as the sum of each clean-zone area multiplies itscorresponding filter coverage ratio, which reflects the effectof different cleanliness classes. The general adoptedpercentage of filter coverage required in practice to achievethe desired air classification is presented in Table 2.
Supply air velocity (SAV), make-up air volume (MAV),recirculated air volume (RAV), exhaust air volume (EAV)and the type of chiller adopted (TC) are all basicparameters for the design of cleanroom HVAC system,which have an significant influence on both capital cost andthe long term operating cost. Type of air ventilation(TAV), type of air return (TAR) and type of high efficiencyfilters (THF) are mostly determined by the function of
Table 1
Initial list of variables considered in the cost model
Item Variable description Variable
1 Clean-zone area (M2) CA
2 Average filter coverage rate (%) AFC
3 Supply air velocity (M/S) SAV
4 Make-up air volume (CMH) MAV
5 Recirculated air volume (CMH) RAV
6 Exhaust air volume (CMH) EXA
7 Type of air ventilation TAV
8 Type of air return TAR
9 Type of high efficiency filters THF
10 Type of chiller TC
Table 2
Filter coverage for desired cleanliness classification
Classification Filter coverage (%)
Class 1 100
Class 10 90
Class 100 70
Class 1000 50
Class 10,000 25
Class 100,000 10
cleanrooms and also reflect the difference on overall plan,which is normally decided by capital budget available atthe initial design stage.Some other cost factors like the application of clean-
room, the location of cleanroom, airflow direction and typeof cleanroom layout, etc. However in the research, the total12 projects are all applied in semiconductor-related field,located in Singapore, with vertical airflow and ballroomtype layout. Hence, these cost factors were excluded fromthe cost modeling.Besides, the cleanroom internal environment parameters
such as temperature, relative humidity, noise and vibrationlevel, etc., which were determined by the personnelconsiderations and criteria of each process in the clean-room would to some extent influence the life cycle cost. Inthis paper, due to absence of data, these cost factors werealso not considered in the model.Furthermore, due to the difficulties of collecting
information on cleanroom operation scheme and quantify-ing this potential cost factor, the effect of cleanroomoperation scheme is also ignored in the paper.The categorical variables TAV, TAR, THF, TC in the
regression model is described in Table 3.
4.3. Model developing
At this stage, 11 variables, which represent the 10 initialcost factors, were considered in the model. An inspection ofcorrelation matrix of the variables in Table 4 shows anextremely high coefficient value 0.9705 between thevariable CA and RAV, which indicates a high correlationbetween the variable CA and RAV. To eliminate theinfluence of this multi-colinearity problem, the variableRAV is removed from modeling.To set up the model with only those useful explanatory
variables, the number of explanatory variables should bekept as low as possible especially in case only a smallnumber of data sets are available. Applying the partial F-test criterion, as realized in SAS system by processing thebackward selection of variables in regression, the potentialinsignificant variables could be removed step by step. Sincethe variable RAV was removed already, there were 10explanatory variables left to be inputted and 12 data setsavailable for modeling, multiply regression with interceptcould be adopted. Given a 90 percent confidence level, thebackward elimination result was shown in Table 5.
4.4. Model testing
Using the six independent variables: CA, MAV, EAV,TAV1, TAR and AFC, which were left after the backwardselection, the regression results were shown in Table 6. Theindependent variables were able to significantly representthe variation of the model and were able to explain muchabout it, as reported by the high-adjusted R2.The p-value of F was far below the 0.1, which indicates
that the linear regression model is meaningful. The p-value
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Table 3
Definition of dummy variables
Variable description X ¼ 0 X ¼ 1
TAV1 Type of air ventilation FFU Pressurize circulation fan or ducted filter
TAV2 Type of air ventilation Pressurize circulation fan FFU or ducted filter
TAR Type of air return Raised floor Vents in wall
THF Filter efficiency ULPA filter HEPA filter
TC Type of chiller Air cooled Water cooled
Table 4
Correlation matrix for 11 initial explanatory variables
The CORR procedure
Pearson correlation coefficients, N ¼ 12
CA AFC SAV MAV RAV EAV TAV1 TAV2 TAR THF TC
CA 1.0000 0.3199 0.3418 0.9218 0.9705 0.5423 �0.3662 0.2455 �0.3884 0.0909 0.0632
AFC 0.3199 1.0000 0.9202 0.4316 0.4588 0.5085 0.2335 �0.3663 �0.7719 �0.6185 �0.2147
SAV 0.3418 0.9202 1.0000 0.3567 0.4480 0.3414 0.1841 �0.3758 �0.9206 �0.5502 �0.3758
MAV 0.9218 0.4316 0.3567 1.0000 0.9416 0.7150 �0.2931 0.1798 �0.3827 �0.0465 0.2421
RAV 0.9705 0.4588 0.4480 0.9416 1.0000 0.6437 �0.3045 0.1892 �0.4905 �0.0647 0.0880
EAV 0.5423 0.5085 0.3414 0.7150 0.6437 1.0000 �0.4003 0.2263 �0.2915 �0.3610 0.3721
TAV1 �0.3662 0.2335 0.1841 �0.2931 �0.3045 �0.4003 1.0000 �0.8165 �0.1250 �0.1195 0.0000
TAV2 0.2455 �0.3663 �0.3758 0.1798 0.1892 0.2263 �0.8165 1.0000 0.4083 0.2928 0.1111
TAR �0.3884 �0.7719 �0.9206 �0.3827 �0.4905 �0.2915 �0.1250 0.4083 1.0000 0.5976 0.4083
THF 0.0909 �0.6185 �0.5502 �0.0465 �0.0647 �0.3610 �0.1195 0.2928 0.5976 1.0000 0.2928
TC 0.0632 �0.2147 �0.3758 0.2421 0.0880 0.3721 0.0000 0.1111 0.4083 0.2928 1.0000
Table 5
Results of backward elimination of model variables
Summary of backward elimination
Step Variable removed Number vars in Partial R2 Model R2 C(p) F-value Pr4F
1 TC 9 0.0000 0.9998 9.0214 0.0214 0.9074
2 SAV 8 0.0000 0.9998 7.1426 0.2372 0.6744
3 THF 7 0.0000 0.9998 5.1655 0.0600 0.8222
4 TAV2 6 0.0001 0.9997 3.7199 1.9028 0.2399
Dependent variable: LCCyear1998
Variable Parameter estimate Standard error Type II SS F-value Pr4F
Intercept �1645051 629,500 4.789667E11 6.83 0.0475
CA 5156.56632 246.35863 3.072723E13 438.11 o.0001
MAV 68.83196 4.60069 1.569902E13 223.84 o.0001
EAV 33.98916 7.49339 1.442985E12 20.57 0.0062
TAV1 514,211 243,259 3.133888E11 4.47 0.0882
TAR �742442 285,694 4.736553E11 6.75 0.0483
AFC 2,996,627 076,379 5.435913E11 7.75 0.0387
Bounds on condition number: 14.519, 240.77.All variables left in the model are significant at the 0.1000 Level.
L. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751748
of F represents the probability of obtaining a higherF-value than that shown. If a 90 percent confidence level isused to test the hypothesis that all coefficients ofindependent variables are zero (i.e. not meaningful), thenthe p-value should be less than 0.1 in order to reject thehypothesis.
The p-values in the last column of Table 6 were all wellbelow the 0.1, which indicates that all the respectivevariables are significant at the 90% confidence level. Thesep-values represent the probability of obtaining a highert-value than that shown. If a 90 percent confidence level isused to test the hypothesis that a coefficient of an
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Table 6
Results for regression model using 6 explanatory variables
Dependent variable: LCCyear1998
Source DF Sum of squares Mean square F-value Pr4F
Model 6 1.261834E15 2.103057E14 2998.56 o.0001
Error 5 3.506774E11 70,135,486,685
Corrected total 11 1.262185E15 0.0387
Variable DF Parameter estimate Standard error t-value Pr4|t|
Intercept 1 �1,645,051 629,500 �2.61 0.0475
CA 1 5156.56632 246.35863 20.93 o.0001
MAV 1 68.83196 4.60069 14.96 o.0001
EAV 1 33.98916 7.49339 4.54 0.0062
TAV1 1 514,211 243,259 2.11 0.0882
TAR 1 �742,442 285,694 �2.60 0.0483
AFC 1 2,996,627 1,076,379 2.78 0.0387
Root MSE 264,831 R2 0.9997
Dependent mean 9,619,644 Adj R2 0.9994
Coeff var 2.75302
40000000
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
4000000035000000300000002500000020000000150000001000000050000000
LCC
yea
r 19
98
Predicted Value
Fig. 2. Observed values versus predicted values of LCC(base year 1998).
L. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751 749
independent variable is zero (i.e. not significant), then thep-value should be less than 0.1 in order to reject thehypothesis.
Fig. 2 shows a plot of the observed values versus thepredicted values of LCCbase year 1998. The plot indicatesa nice fit between the regression line and the observedvalues.
Before the model can be considered acceptable, theassumptions of regression analysis should be verified. If theassumptions are violated, it will mean that this will not bethe best rule for fitting a line through a scatter plot [8].Regression analysis is based on four assumptions:
�
Linear relationship between the dependent variable andthe explanatory variables;�
Homoscedasticity. The variance of the error terms(residuals) of a regression line is constant; � The residuals are independent; � The residuals are normally distributed.To verify the regression assumptions, the commonlyadopted method is to plot the distribution of residuals.By checking the pattern that the residuals exhibit, thefirst three assumptions could be verified accordingly, thatis, the residuals should show random scatter about theresidual’s mean value if the assumptions are satisfied. Onthe contrary, the appearance of a pattern would indicatethat at least one of the assumptions is violated. Fig. 3shows the plot of residuals against the predicted values. Itis noted that no pattern can be discerned from the plot, so
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751750
that the first three assumptions can be assumed to besatisfied.
By plotting the normal probability of the residuals, thefourth assumption regarding residuals being normallydistributed can be checked. Fig. 4 shows a plot of thecumulative distribution of residuals versus the normalcumulative distribution. If the residuals are normallydistributed, a straight line could be drawn through thedata points. The data points in Fig. 4 are generally alignedabout a straight line, indicating no violation of thenormality assumption. The normal distribution of theresiduals also corroborates that the first three assumptionsare satisfied as found out previously.
Generally, the model matches well with the data sets andit is proven that all of the regression assumptions have beensatisfied. Therefore, the model can be considered as areliable tool whereby estimation for cleanroom life cycle
1.0
0.8
0.6
0.4
0.2
0.0
Nor
mal
Cum
ulat
ive
Dis
trib
utio
n
0.0 0.1 0.2 0.3 0.4
Cumulative Distr
LCC year 1998 = -1.65E6 + 56156.6 CA +68.832 MA
Fig. 4. Normal probability pl
400000
300000
200000
100000
0
-100000
-200000
-300000
Res
idua
l
0 5000000 10000000 15000000 2
Predic
LCC year 1998 = -1.65E6 + 5156.6 CA + 68.832 MA
Fig. 3. Plot of the model residual
cost could be established at the price level of base year1998. The regression equation to be used is:
LCCbase year 1998 ¼ � 1645051þ 5156:6 CAþ 68:8MAV
þ 34EAVþ 514211TAV1
� 742442TARþ 2996627AFC: ð6Þ
5. Research findings and limitations
5.1. Findings
The regression model uses six significant factors toestimate the cleanroom life cycle cost at price of base year1998. The change in floor area (CA), MAV, EAV andcleanliness level, which is reflected by AFC rate would
0.5 0.6 0.7 0.8 0.9 1.0
ibution of Residual
V +33.989 EAV + 514211 TAV1 -742442 TAR +3E6 AFC
ot of the model residuals.
0000000 25000000 30000000 35000000 40000000
ted Value
V + 33.989 EAV + 514211 TAV1 - 742442 TAR + 3E6 AFC
s versus the predicted values.
ARTICLE IN PRESSL. Yang, C. Eng Gan / Building and Environment 42 (2007) 743–751 751
appear to change the cleanroom life cycle cost in the samedirection since the model coefficients of these variables areall positive ones. In view of the whole life time ofcleanroom, the positive coefficient of variable TAV1indicates that air ventilation using FFUs would cost lessthan using pressurized plenum or ducted filter, and thenegative coefficient of variable TAR means that air returnadopting raised floor would cost more than vents in wall.
Highly correlated with the floor area, the influence ofRAV to life cycle cost may be explained by that of floorarea. It is seen that in the process of modeling, the otherfour initial variables: THF, SAV, TC, Type of airventilation that using pressurized plenum or ducted filter(TAV2) were removed. The possible reasons might be two:
�
Comparing with those six variables left, their influencesto the life cycle cost were not so significant. � The variables used in the model cannot represent theentire effect of the respective variables.
Generally, the regression equation presents reasonablythe relationships between each significant variable and thecleanroom life cycle cost, and that the whole provides ameaningful result.
5.2. Limitations
Though identified as potential variables that would affectcost, many of the proposed variables are still omitted fromthe model because of three main reasons: first, the smallsamples available confines those important variables to beconsidered since the number of variables inputted should beless than the number of samples for regression modeling;second, these available samples could not represent a widerange of population since they are of many identicalcharacteristics; third, many important variables turn out tobe practically unobtainable or immeasurable in quantifiedforms, and any inability to enable such variables to enterregression will clearly be a limitation on the accuracy of themodel developed. Fortunately, the effect of some unobtain-able variables may sometimes be contained in anotherquantifiable one, although the user is unaware of this inprecise terms, thus alleviating to some extent what mightotherwise be a serious omission.
Once an important variable was omitted, the predictiveability of the equation will be inadequate while theinclusion of unnecessary variables only makes the equationcumbersome without improving its predictive capabilities.The success of a regression analysis depends greatly on twopoints: one is that the model containing only thosevariables that significantly affect cost, and the other isthat the model have enough samples well representing thepopulation. Confined by the small sample, the availabilityand precision of the data collected, exhaustive developmentof this model was not possible and the outcome appears tobe acceptable at a comparable lower confidence level, say,only 90 percent here. Further work to obtain a more
precise model may be possible given more time to continuethe survey to get more samples and integrated projectinformation as well.
6. Conclusion
The cost model is applied to small cleanrooms which areincreasingly demanded by entrepreneurs and small andmedium enterprises. Such small, standard and modularcleanrooms are suitable for individual inventors, small high-tech manufacturers and school laboratories. Final researchfindings stated and the regression equation of cleanroomlife cycle cost obtained in this paper would definitelycontribute to the investment budgeting, design alternativescomparison and decision-making by facility owners. Also inthis research, survey methodology used and cost frameworkdeveloped through understanding the mechanisms ofcleanroom life cycle will be useful when establishing anew protocol for the collection of statistics and applicationof data, and development of database for different productdemands; Finding of factors that significantly influence lifecycle cost in the paper will also benefit further studies oncost efficiency of cleanroom projects.Finally, since owners expect the cleanrooms to be
constructed in less time, less money, while with higherperformance standards and lower future running costs, theproblem confronting most owners now is a lack of astandard by which the performance of a cleanroom projectcan be gauged. By attempting to identify and quantifythose significant costs involved in the whole life of acleanroom, and presenting the internal relationship amongthe life cycle cost and cleanroom critical elements, theestimation cost model established in this paper will alsocontribute to the establishment of such standard, especiallyin the cost performance aspect.
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