1.
0 Introduction
Today, every Industry is striving to achieve highest return value from its resources. This is called
resource utilization, and in a broader way, Capacity Utilization. There is a need of studying the
various reasons for the lower levels of Production in a Manufacturing firm and lower level of
eneration !of Power" in case of Power Plants. In India, average capacity utilization in industries has
been around #$% which is a dismal figure as compared to figures in the other nations. The average
economy&wide capacity utilization rate in the U' since ()#* was about $(.#%, according to the
+ederal ,eserve measure. The figure for -urope is not much different, for .apan being only slightly
higher. The average utilization rate of installed productive capacity in industry, in some ma/or areas of
the world, was estimated in 011230114 to be as follows5
United 'tates *).6% !7pril 011$ 8 +ederal ,eserve measure"
.apan $29$#% !:an; of .apan"
-uropean Union $0% !:an; of 'pain estimate"
7ustralia $1% !<ational :an; estimate"
:razil #19$1% !various sources"
India *1% !=indu business line"
China perhaps #1% !various sources"
Canada $*% !'tatistics Canada"
Capacity utilization is a concept in economics and managerial accounting which refers to the e>tent to
which an enterprise or a nation actually uses its installed productive capacity. Thus, it refers to the
relationship between actual output that ?is? produced with the installed e@uipment and the potential
output which ?could? be produced with it, if capacity was fully used.
1.1 Capacity Utilization
Engineering Definition:
7ccording to engineering or technical definition ,AButput represents the ma>imum amount of output
that can be produced in the short&run with the e>istent stoc; of capital. Thus, a standard definition of
capacity utilization is the !weighted" average of the ratios between the actual output of firms to the
ma>imum that could be produced per unit of time, with e>isting plant and e@uipment. Bbviously,
CoutputC could be measured in physical units or in mar;et values, but normally it is measured in
mar;et values.
=owever, as output increases and well before the absolute physical limit of production is reached,
most firms might well e>perience an increase in the average cost of production& even if there is no
change in the level of plant D e@uipment used. +or e>ample, higher average costs can arise, because
of the need to operate e>tra shifts, to underta;e additional plant maintenance, and so on.
Economic definition:
The CeconomicC utilization rate is to measure the ratio of actual output to the level of output beyond
which the average cost of production begins to rise. In this case, surveyed firms are as;ed by how
much it would be practicable for them to raise production from e>isting plant and e@uipment, without
raising unit costs. Typically, this measure will yield a rate around (1 percentage points lower than the
CengineeringC measure, but time series show the same movement over time.
Financial implications:
7 firmEs level of capacity utilization determines how much fi>ed costs should be allocated per unit, so
as a firmEs capacity utilization increases, the fi>ed costs !and therefore also, total costs" per unit will
decrease. +or e>ample, if the firm above had fi>ed costs of F(0,111 per month, the fi>ed costs per unit
would be F01 per unit at 61% capacity utilization, but only F(1 per unit at (11% capacity utilization.
It therefore follows that a firm should be most efficient if it is running at (11% capacity utilization.
=owever, if a firm is running at full capacity, there are a number of potential drawbac;s5
There may not be enough time for routine maintenance, so machine brea;downs may occur more
fre@uently and orders will be delayed.
It may not be possible to meet new or une>pected orders so the business cannot grow without
e>panding its scale of production.
'taff may feel under e>cessive pressure, leading to increased mista;es, absenteeism and labour
turnover.
If the factory space is overcrowded, wor; may become less efficient due to the untidy wor;ing
conditions. It may be necessary to spend more on staff overtime to satisfy orders, increasing labour
costs.
Government update on capacity utilization of poer plants in India:
The utilization of installed capacity of a generating unit is lin;ed to the type of power station. Ghile
the thermal units are meant to be utilized continuously as base&load units, hydro units are to be
utilized depending on availability of water 3 reservoir level. Thus, utilization of installed capacity is
effectively applicable to thermal !including nuclear" generating units and is e>pressed in terms of
Plant Hoad +actor !PH+". The PH+ of thermal and nuclear units mainly depends on a number of
factors such as vintage of the unit, forced and planned outages, availability of re@uired @uality and
@uantity of fuel, etc.
Iuring the last three years, the average PH+ of thermal power plants has declined from **.6% !011)&
(1" to *2.2% !01((&(0", primarily due to shortage of coal and delay in stabilization of newly
commissioned units, the average PH+ of nuclear power plants has increased from 6(.(% !011)&(1" to
*#.)% !01((&(0".
'uppose the capacity of (,111 megawatts !MG" power plant produces #4$,111 megawatt&hours
!MGh" in a 21&day month. The number of megawatt&hours that would have been produced had the
plant been operating at full capacity can be determined by multiplying the plant?s ma>imum capacity
by the number of hours in the time period i.e. (,111 MG J 21 days J 04 hours3day is *01,111 MGh.
The capacity factor is determined by dividing the actual output with the ma>imum possible output. In
this case, the capacity factor is 1.) !)1%".
Capacity +actor K 7ctual Butput ! Ma>imum Possible Butput
= (648000) = 0.9
(30*24*1000)
Br, Capacity +actor is "0#.
LIndustrial 7geE of last century has paved the way for LInformation 7geE in 0(
st
century and this
revolutionary transformation has rendered several business practices, approaches and concepts as
obsolete. In this conte>t 'i> 'igma is evolving into a powerful business improvement strategy and its
importance is growing everyday !Mote and =uston, 0116". 'i> 'igma enables companies to use simple
but powerful statistical methods to define measure, analyse, improve and control processes for
achieving and sustaining operational e>cellence. Ioing things rightly and ;eeping them consistent is
the basic fundamental idea behind 'i> 'igma !'ingh, 01(1". Hiterature shows that industries in India
are still not convinced that 'i> 'igma can be effectively implemented with their e>isting shop floor
constraints. The present study shall attempt to address this issue 9 Can 'i> 'igma be effectively
implemented in industries to reduce capacity waste and so improve productivityN Oeeping the
fundamentals of 'i> 'igma in mind, the present study plans to e>plore the synergy of 'i> 'igma and
capacity waste management for Indian industries.
$.0 %i& %igma
'i> 'igma is a process improvement set of tools and strategies, originally developed by Motorola in
()$#. 'i> 'igma became well ;nown after .ac; Gelch made it a central focus of his business strategy
at eneral -lectric in ())6 and today it is used in different sectors of industry.
$.1 %i& %igma: Concept and Evolution
'i> 'igma as a measurement standard in product variation can be traced bac; to the ()01, when
Galter 'hewhart showed that there is a three sigma from the mean and it is the point where a process
re@uires correction. Motorola originally developed 'i> 'igma in the ()$1Es and its implementation
helped the company to win the ()$$ Malcolm :aldrige Puality 7ward. Table&( depicts certain
themes whose synergy with 'i> 'igma has created considerable interest among the researchers during
the last (6 years.
'a(le 1: ')emes *elated to %i& %igma
%+ ')emes *eferences
(. Puality
7pproaches
=ild et al. !0111"Q Olefs/o et al. !011("Q 'adagopan et al. !0116"Q
.ohnson et al !011$
0. Methodology :ayle et al. !011("Q Ouei and Madu !0112"Q 7ntony !011#"Q
:andyopadhyay !011*"
2. Tools3Techni@ues =endric;s and Oelbaugh !())$"Q :anuelas et al. !0116"Q 7ntony
!011*"Q
4. :elt 'ystem =enderson and -vans !0111"Q Ingle and ,oe !011("Q
6. :enefits :ehara et al. !())6"Q .ohnson !0110"Q Ouei and Madu !0112"Q
#. Challenges +eld and 'tone !0110"Q oh and Rie !0114"Q oh et al. !011#"Q
*. 'uccess +actors 7ntony and :anuelas !0110"Q 7ntony et al !011*"Q Chung et al.
!011$"
'i> 'igma see;s to improve the @uality of process outputs by identifying and removing the causes of
defects !errors" and minimizing variability in manufacturing and business processes. It uses a set of
@uality management methods, including statistical methods, and creates a special infrastructure of
people within the organization who are e>perts in these very comple> methods. -ach 'i> 'igma
pro/ect carried out within an organization follows a defined se@uence of steps and has @uantified
financial targets !cost reduction and3or profit increase".
The term 'i> 'igma originated from terminology associated with manufacturing, specifically terms
associated with statistical modeling of manufacturing processes. The maturity of a manufacturing
process can be described by a sigma rating indicating its yield or the percentage of defect&free
products it creates. 7 si> sigma process is one in which "".""",,# of the products manufactured are
statistically e>pected to be free of defects !-.. defects per million", although, this defect level
corresponds to only a 4.6 sigma level. Motorola set a goal of Csi> sigmaC for all of its manufacturing
operations, and this goal became a byword for the management and engineering practices used to
achieve it.
Table 2: Sigma and Corresponding PPM
$.$ %i& %igma /et)odologies
Two methods are used to use the techni@ue of si> sigma5
(. IM7IM
0. IM7IC
1. D/0D1 or DF%%:
The IM7IM pro/ect methodology, ;nown as I+'' !CDesign For %i> %igmaC" features five phases5
Define design goals that are consistent with customer demands and the enterprise strategy.
/easure and identify CTPs !characteristics that are Critical 'o 2uality", product
capabilities, production process capability, and ris;s.
0nalyze to develop and design alternatives, create a high&level design and evaluate design
capability to select the best design.
Design details, optimize the design, and plan for design verification. This phase may re@uire
simulations.
1erify the design, set up pilot runs, implement the production process and hand it over to the
process owner!s".
$. D/0IC:
The traditional 'i> 'igma includes five steps5 Iefine, Measure, 7nalyse, Improve and Control
!shortly and commonly ;nown as IM7IC".
Figure 1: Six Sigma Process
(" Define: The main ob/ective of this first phase is to define the pro/ect goals, the customer needs,
the @uality levels to be achieved, the most important variables and the @uantities to be monitored.
+or the e>ample described above, in this phase the metal sheet thic;ness, the material strength and
the piece weight ma>imum variability should be identified as process variables and desired output
respectively.
0" /easure: In this phase all the available data concerning the product or process under study have
to be collected and categorized to obtain a reliable view of the actual situation. The metal sheet
thic;ness and the metal strength have to be measured and related to the supplier, the period, the
stoc; and so on. The final products have to weighted and the data have to be collected in a
profitable way.
2" 0nalyse: The intent of this phase is to provide a deep insight of the process allowing the
identification of the problems and the causes that generates them. 7nalysing the data it seems that
a supplier provides sheets of worst @uality, that a wor;ing machine tolerances are going to
deteriorate and so on.
4" Improve: Iuring this step solutions are developed and changes are done to improve the @uality
level. The maintenance fre@uency for a given machine has to be changed, some process operations
have to be re&organized or some personnel has to opportunely trained.
6" Control: The goal of this last step is to monitor and control the results produced by the previous
steps. Bbviously, the e>pected result is an improvement of the @uality level. If so, it has to be
improved and maintained in the future, otherwise a new improve phase has to be performed. In the
worst case a new pro/ect, starting from the analysis phase has to be planned.
Iuring all this phases graphical, statistical and data control tools can be effectively used. The most
;nown are the5
Time table, where the timing of all the pro/ect phases is defined,
Gor; flow diagrams, by means of which the process can be described and controlled. They are
very useful especially in the first phase when defining the process,
Gor;sheets and data bases, where all the data are collected and organized ma;ing them available
in a profitable way,
Cause&and&effect diagrams !the fishbone or Ishi;awa diagram" which allow to identify the causes
of a given phenomenon, +M-7 !+ailure Mode and -ffects 7nalysis" to identify failure modes and
their possible causes, etc.,
Pareto diagrams, which allow to identify the main causes, according to the $1301 empirical law,
control charts of different nature !histograms, apple&pie diagram, graphs, etc." which are used to
visualize and interpret distributions,
Time history charts, which are essentially used to monitor the evolution of a variable,
'tatistical tools able to highlight relations between variables !the regression analysis for e>ample",
T&'tudent analysis, etc.,
Iesign of e>periment techni@ues !IB-", used to eventually plan an optimal campaign of
e>periments !obtain the ma>imum information from a system with the minimum number of
e>periments".
Figure 2: Six Sigma Meaning
Bbviously, all these instruments have to be used in a profitable way to obtain the ma>imum result. To
this aim, one appreciable aspect for the practitioner is to have all them under a uni@ue environment
where the data can be shared and easily treated once loaded.
$.- 2uality /anagement tools and /et)ods used in %i& %igma
Githin the individual phases of a IM7IC or IM7IM pro/ect, 'i> 'igma utilizes many
established @uality&management tools that are also used outside 'i> 'igma. +ollowing are the
most used tools and methods in si> sigma5
Process Capability
Pareto Chart
,egression 7nalysis
Control Chart
Correlation
=istogram
Cause D effects diagram
$.. %oftares Used for %i& %igma
MATLAB
Mathematica
Minitab
R language
S! T""l#
Sigma $L
S"%t&a'e A( &eb Meth")#
S*+ $L
STATA
Stat g'a,hic#
STAT!ST!+A
$.3 %i& %igma: 0pplications
'everal authors have dealt with the famous L'i> CompaniesE cases. Table 2 summarizes the
organizations, benefits and savings after implementing the 'i> 'igma process in these large
companies. These research wor;s have contributed immensely in popularizing the concept of 'i>
'igma.
'a(le -: %i& %igma 4enefits for %i& 5arge Companies
Company 6ear /etric!/easure 4enefits %aving
Motorola ()$#&011( In&process defect level 011× reduction S(6 billion
eneral
-lectric
())#&())) Turnaround time at
repair shops
#0% reduction S0 billion in
()))
=oneywell ())$&0111 Concept to shipment
cycle time
,educed from ($ to
$ months
S(.0 billion
+ord 0111&0110 Mehicle rollovers, recalls
and production delays
,educed defects by
*1%
S( billion
Caterpillar 0111&0110 Puality improvement,
,educed cost structure
,educed defects by
*6%
<ot reported
Bur lady of
HM Centre
()))&0110 =ospital bed availability
delays
,educed from 0#*
to 026 min
S##111
In India for global competitiveness, Indian industries need overall operational and service e>cellence
and are e>tensively engaged in Puality Circles, TPM and I'B Certification. =owever, these methods
have failed to deliver re@uired performance over the last decade or so. Ma/ority of Indian industries
yet to e>perience the 'i> 'igma initiative for a number of reasons, yet there are some evidences on
application of 'i> 'igma at different organizations.
-.0Industrial Capacity: Concept
In a macroscopic sense, industrial capacity is Lcapacity of an enterprise to produce and this capability
is measurable in physical units. This capability to produce basically represents the capability of factors
of production li;e labor, machines and e@uipment !tools, /igs fi>tures, material handling devices etc."
in con/unction with technology and management support. 7ccording to 7sian Productivity
Brganization !()$1", the capacity of a production unit represents a ceiling on the ma>imum load the
unit can handle. Puestions on the amount of capacity that is appropriate arise in relation to planning of
new facilities, e>pansion of e>isting facilities, buying or leasing e@uipment, introducing new products
and services etc. =ence, the utilization of the available production capacity has become an issue of
ma/or concern for all industries, and so for our economy. 'ince machines used in production represent
a significant part of the production capacity, the intensity of the production process, and therefore, the
production effort of the company as a whole, depends on their utilization level. Therefore, LCapacityE
in a more microscopic sense, refers to machines&hours available for productive activity i.e., at a wor;
centre. Time during which a machine centre is not involved in productive activity, its idle time, is
synonymous with capacity waste and all efforts should be to minimize this capacity waste if
elimination is not possible to achieve. 7 large number of misleading concepts and fallacies e>ist in
perception, measurement and management of LcapacityE. Marious perceptions of capacity used by
researchers are5
C17 ')eoretical installed capacity: This perception assumes 2#6 wor;ing days with all three wor;ing
shifts !i.e. 2#6>04K$*#1 hours per year" for each e@uipment and operational time or product cycle
time is based on collaboratorEs time, or established through appropriate techni@ues.
C$7 ')eoretical rated capacity: This capacity also assumes $*#1 hours per year but operational time
is considered after being down rated due to poor method applications, faulty wor; measurement or low
labour productivity etc. This capacity is also called LIesign CapacityE.
C-7 8lanned capacity: This capacity is less than theoretical rated capacity as labor is employed to
wor; for less number of shifts or less than $*#1 hours per year, depending upon the load planned.
C.7 4udgeted capacity: This capacity is established after ta;ing care of the demand trends,
availability of power and other inputs. This is basically L-ffective CapacityE
C3: *eal capacity: This capacity refers to actual production levels achieved after tac;ling
brea;downs, absenteeism, power failure, material shortages, scheduling inade@uacies etc.
C(5 Theoretical Installed Capacity
C05 Theoretical ,ated Capacity
C25 Planned Capacity
C45 :udgeted Capacity
C65 ,eal Capacity
CG5 Capacity Gaste
7 5 Poor Method 7pplication, +aulty Gor; Measurement, How Habour Productivity
: 5 Gor; in Hess 'hifts, Hess Gor;ing =ours
C 5 Iemand Trends, Poor Power 7vailability, Poor Input 7vailability
I 5 Material 'hortages, Machine :rea; downs, 7bsenteeism, Idleness, 'cheduling Problems
Figure 3: Capacit Perceptions
Capacity 9aste
:etter capacity utilization is regarded as a precondition for accelerating the tempo of industrial growth,
improving the rates of return on capital and generating additional resources. The term industrial
capacity is sub/ect to several interpretations, but in practice, the three concepts that are widely used
are5
(" Iesign Capacity
0" -ffective Capacity
2" 7ctual Capacity
Design capacity is the ma>imum rate of output that might be achieved under almost ideal conditions.
Effective capacity is planned after compensating for certain essential unavoidable delays.
0ctual Capacity arises because Iespite best efforts on the part of the management, effective capacity
is never achieved and the actual output obtained is determined by the occurrence of causal delays
which are basically due to deficiencies, inade@uacies and fallacies on the part of the wor;ers as well as
the management. These can be minimized substantially with strenuous efforts and sincere participation
of wor;er and management ali;e. These different measures of capacity are useful in defining two
measures of system effectiveness i.e. -fficiency and Utilization. -fficiency is the ratio of actual
capacity to effective capacity while utilization is the ratio of actual capacity to design capacity.
Thu#-
./cienc0 = Actual +a,acit0
.1ecti2e +a,acit0
+a,acit0 utili3ati"n = Actual +a,acit0
e#ign +a,acit0
S"-
+a,acit0 4a#te = 1 5 +a,acit0 6tili3ati"n
= 1 5 Actual +a,acit0
e#ign +a,acit0
=ence, efficiency and effectiveness of the production processes are reflected in levels of utilization of
capacity available and this optimum utilization is one of the most important tools to measure
productivity which is so essential to gain a competitive edge in the e>isting global scenario.
..0 Capacity Utilization of a ')ermal 8oer 8lant
..1 Define 8)ase
7 Thermal Power Plant seldom produces the pea; Power Butput regularly. There are a lot of reasons
responsible for the lower levels of generation of Power, and thus, there is a need to study all those
factors and ta;e measures to improve the Power eneration levels. There are times when the higher
power generation levels are not re@uired and that situation is called :ac;ing Iown. :ut, there are
times !as in the month of .une D Iecember" when the pea; generation is desired and if the production
is not up there, Capacity Hoss arises. Thus, Capacity Utilization is a very important term and its
higher level is desired. 7nalyzing the factors which account for higher levels of Capacity Hoss, and
results in lower Capacity Utilization, is a very important and decisive way to increase the
Performance of the plant, and thereby, Improved Capacity Utilization levels.
%I8:C diagram for Capacity Utilization
!C"TPP
#dministra
ti$e Cell
Po%er
&enerati
on Stats
Po%er
Plant
'peratio
ns (or t)e
stated
*ig)er
Capacit
+tili,ation
and
t)ereb-
"educing
"ecommendat
ions made to
!C"TPP
P"'C.S
S
'+TP+T C+ST'M." S+PP/0.
"
01P+T
The causes which are responsible for the Capacity Gaste in the thermal powerplant are numerous and
their analysis and their contribution to the total loss is e>tremely important
The causes for capacity loss can be associated with5
B"ile'#
+0cle (Ran7ine +0cle)
Tu'bine (ene'at"'
Stati"n Au8illa'ie#
The c"nt'ibuti"n "% all the l"##e# a##"ciate) &ith the#e element# "% *"&e'
(ene'ati"n S0#tem can be #tate) in a gene'ali#e) meth") a# #h"&n in 9gu'e 4:
Figure 2: 3arious Cause (or Capacit 4aste in t)e Plant
..$ /easure 8)ase
The Unit&0 of IC,TPP, Tamunanagar has had a whole lot to troubles in the past. The Turbine rotor
got bent with some deflection and the :alancing issues arose. The Coal mill rollers are also causing
problems off late and the Mill overhauling is being done regularly. The complete Unit&0 Pre&
maintenance program is also going.
Power eneration Iata for the last two years of Unit&0 is used for the purpose of analyzing the stats
with the Iesigned Capacity of the Unit. The data is from the official records of the Plant?s
administrative cell, and thus, is accurate.
Installed Capacity5 211MG !Unit&0"
*"&e' (ene'ati"n ca,acit0 in a )a0 = 300 $ 24 M4h ,e' )a0
*"&e' (ene'ati"n ca,acit0 in a m"nth = 300 $ 24 $ 30 M4h ,e' m"nth
= 216000000 ;4h ,e' m"nth
= 216 Milli"n 6nit# ,e' m"nth
'a(le .: 8oer Generation of Unit71
'a(le 3: 8oer Generation of Unit7$
+or Unit&0, the tripping is prominent factor for the Capacity Hoss, as indicated by zero level of
generation for a considerable time. The Turbine 'haft :ending is also one of the main reason.
..- 0nalysis 8)ase
7nalysis phase includes the analysis of the data ta;en as input !Power eneration 'tats in this case"
and then comparing it with the designed values is going to give us the actual insight in to the situation.
The 7nalysis of last 2 years of generated Mega Units !MU" is going to give us the deviation of the
7ctual Capacity of the Plant from Iesigned Capacity. Thus, let us first analyze the data input and find
out the Capacity Gaste in the Plant.
5+nit617: 0nstalled Capacit: 300M4
*"&e' (ene'ati"n ca,acit0 in a )a0 = 300 $ 24 M4h ,e' )a0
*"&e' (ene'ati"n ca,acit0 in a 0ea' =300 $ 24 $ 36< M4h ,e' 0ea'
= 2628000000 ;4h ,e' 0ea'
= 2628 Milli"n 6nit# ,e' 0ea'
e#igne) +a,acit0 = !n#talle) +a,acit0 = Au8illia'0
+"n#um,ti"n
= 2628 = 8>:8
= 2<38:2 Milli"n 6nit# ,e' 0ea'
Actual +a,acit0 = 2338:<3 Milli"n 6nit# ,e' 0ea'
S"- +a,acit0 4a#te = (2<38:2 = 2338:<3) ? 2<38:2 = 8.9:: ;
5+nit627: 0nstalled Capacit: 300M4
*"&e' (ene'ati"n ca,acit0 in a )a0 = 300 $ 24 M4h ,e' )a0
*"&e' (ene'ati"n ca,acit0 in a 0ea'=300 $ 24 $ 36< M4h ,e' 0ea'
= 2628000000 ;4h ,e' 0ea'
= 2628 Milli"n 6nit# ,e' 0ea'
e#igne) +a,acit0 = !n#talle) +a,acit0 = Au8illia'0
+"n#um,ti"n
= 2628 = 8>:8
= 2<38:2 Milli"n 6nit# ,e' 0ea'
Actual +a,acit0 (A2e'age) = 1230:3 Milli"n 6nit# ,e' 0ea'
S"- +a,acit0 4a#te = (2<38:2 = 1230:3) ? 2<38:2 = <1.<: ;
The Power eneration stats for the 2 years depicts that Unit71 is )aving Capacity 9aste of ;.<,,#
and the Unit7$ is )aving a Capacity 9aste of 31.3,#. Thus, Unit&0 is having lower Capacity
Utilization as compared to the +irst one.
The Power eneration statistics for Unit ( for the years 011)&(1, 01(1&((, 01((&(0 are shown with
respect to time in the figure 6, #, * respectively.
200
1<0
100
<0
0
Mont)
M
+
&eneration C)art o( 2009610 +nit615Total=21: M +7
Figure <: Po%er &eneration C)art o( 2009610 5+10T617
200
1<0
100
<0
0
Mont)
M
+
&eneration C)art o( 2010611 +nit615Total =21: M+7
Figure :: Po%er &eneration C)art o( 2010611 5+10T617
200
1<0
100
<0
0
Mont)
M
+
&eneration c)art o( 2011612 +nit615Total=21:7
Figure 8: Po%er &eneration C)art o( 2011612 5+10T617
The graphs depict that there is consistency in the generation over the span of three years and thus,
performance of the Unit is good, and that is why, Capacity Hoss is *.$##%.
The Power eneration statistics for Unit 0 for the years 011)&(1, 01(1&((, 01((&(0 are shown with
respect to time in the figure $,),(1 respectively. Unit 0 is having a varying generation level which
depicts that the Capacity loss is considerable for the Unit and so it is. The Unit is having troubles
because of the low levels of Pre&maintenance and carelesmess e>ercised while running the e@uipment.
200
1<0
100
<0
0
Mont)
M
+
C)art o( &eneration o( +nit625096107
Figure 9: Po%er &eneration C)art o( 2009610 5+10T627
200
1<0
100
<0
0
Mont)
M
+
C)art o( &eneration o( +nit625106117
Figure 9: Po%er &eneration C)art o( 2010611 5+10T627
200
1<0
100
<0
0
Mont)
M
+
C)art o( &eneration o( +nit625116127
Figure 10: Po%er &eneration C)art o( 2011612 5+10T627
The graphs depict that there is inconsistency in the generation over the span of three years and thus,
performance of the Unit is average, and that is why, Capacity Hoss is 6(.6#%.
This high level of capacity los is also because of the continuous output of Uero levels of eneration of
Power because of the maintenance going on in the plant of the whole unit&0 and the repairing of the
turbine shaft which had deflection.
Capacity loss, as stated earlier, is the difference between the actual capacity and the Iesigned
capacity.
Thu#- +a,acit0 L"## = e#igne) +a,acit0 = Actual +a,acit0
@"&- %"' the *"&e' *lant- the +a,acit0 l"## i# calculate) b0 #ubt'acting the
(ene'ate) ,"&e' %'"m the *"&e' &hich c"ul) ha2e been ,'")uce) b0 the ,lant-
that i#- 216 M6?m"nth:
The'e%"'e- +a,acit0 L"## %"' a m"nth = 216 M6 = (ene'ate) ,"&e' in that m"nth
'a(le 1: Capacity 5oss in Unit71
Capacity Hoss in the Unit&0 is very high and it had never been the case that the Unit achieved Pea;
production level, which Unit&( achieved in ) months out of the 2# months in total.
'a(le $: Capacity 5oss in Unit7$
The Capacity Hoss statistics of Unit&( for the years 011)&(1, 01(1&((, 01((&(0 are shown with respect
to time in the figure ((, (0, (2 respectively.
120
100
80
60
40
20
0
Mont)
M
+
C)art o( capacilt loss +nit61 520096107
Figure 11: Capacit 4aste C)art o( 2009610 5+10T617
200
1<0
100
<0
0
Mont)
c
a
p
a
c
i
t
l
o
s
s
5
2
0
1
0
6
1
1
7
C)art o( capacit loss +nit61 520106117
Figure 12: Capacit 4aste C)art o( 2010611 5+10T617
<0
40
30
20
10
0
Mont)
c
a
p
a
c
i
t
l
o
s
s
5
2
0
1
1
6
1
2
7
C)art o( capacit loss +nit61520116127
Figure 13: Capacit 4aste C)art o( 2011612 5+10T617
L"& 2alue# "% the +a,acit0 l"## a'e "b2i"u# becau#e "% the high le2el# "% *"&e'
(ene'ati"n in the 6nit51:
The Capacity Hoss statistics of Unit&0 for the years 011)&(1, 01(1&((, 01((&(0 are shown with respect
to time in the figure (4, (6, (# respectively.
160
140
120
100
80
60
40
20
0
Mont)
M
+
C)art o( Capacit loss in +nit625096107
Figure 12: Capacit 4aste C)art o( 2009610 5+10T627
120
100
80
60
40
20
0
Mont)
M
+
C)art o( Capacit /oss in unit 25106117
Figure 1<: Capacit 4aste C)art o( 2010611 5+10T627
200
1<0
100
<0
0
Mont)
M
+
C)art o( Capacit /oss in unit 25116127
Figure 1:: Capacit 4aste C)art o( 2011612 5+10T627
Capacity loss is high and the reasons are the less reliability of the Unit operations which account for
high levels of Capacity loss and thereby, lower levels of Capacity Utilization. The first and the second
years are showing somewhat acceptable values, but, for the year 01((&(0, the Power Butput stats are
very dismal and are not acceptable by any means.
3.0 Case %tudy
Thi# ca#e #tu)0 i# being ca''ie) "ut at een Ban)hu +hh"tu Ram The'mal *"&e'
*lant (+RT**)- Aamunanaga' (Ba'0ana- !n)ia): Thi# ,lant &a# #et u, b0 Shangai
.lect'ic +"',"'ati"n- +hina in 2008: !t i# a c"al59'e) ,lant an) ha# t&" unit# "%
300M4 each: The gene'ating ca,acit0 "% thi# ,lant i# 14:4M6 (milli"n unit#) ,e'
)a0: 6nit52 ha# been #electe) %"' the )etaile) #tu)0 "n Si8 Sigma
im,lementati"n: The #electe) ,e'i") %"' the #tu)0 an) anal0#i# ha# been )eci)e)
a# 3 0ea'# (%'"m 1 A,'il- 200> t" 31 Ma'ch- 2012): ata "n *"&e' (ene'ati"n &a#
c"llecte) an) anal03e) %"' %u'the' 'e#ea'ch: The )ata c"llecte) i# #h"&n in Table5
4 %"' 6nit51 an) in Table5< %"' 6nit52:
0nstalled Capacit:
6nit51 (300M4)
6nit52 (300M4)
Figure 1;: Cause and Effect Diagram for Capacity 9aste at DC*'88= 6amunanagar
3.1 /a>or energy saving potential areas in ')ermal 8oer 8lant
Most thermal power plants use 21&41% of energy value of primary fuels. The remaining #1&
*1% is lost during generation, transmission and distribution of which ma/or loss is in the
form of heat. Thermal power consist of various sub cycles 3 systems li;e air D flue gas cycle,
main steam, feed water D condensate cycle, fuel D ash cycle, -@uipment cooling water
!-CG", au>iliary cooling water !7CG" system, Compressed air system, -lectrical au>iliary
power D lighting system etc. There is tremendous scope of improvement in each
system3cycle which is given below5
1. 0ir ? flue gas cycle
a@ :ptimizing e&cess air ratio
It reduces +I fan D II fan loading and helps prevent plant brea;down and smooth running is
ensured.
(@ *eplacement of oversize FD and 80 fan
Many thermal power plants have oversize fan causing huge difference between design D
operating point leads to lower efficiency. =ence fan efficiency can be improved by replacing
correct size of fan.
c@ 0ttending t)e air ? flue gas leaAages
Hea;ages in air D flue gas path increases fan loading. Use of Thermo vision monitoring can
be adopted to identify lea;ages in flue gas path. 7ir preheater performance is one crucial
factor in lea;age contribution.
$. Fuel ? as) Cycle
a@ Use of 9as) Coal or 4lending it) 07 grade coal
+&grade coal has high ash content. Bverall performance can be improved by using Gash coal
or blending of +&grade coal with 7& grade coal instead of only using +& grade coal.
(@ 0voiding idle running of conveyors ? crus)er
c@ Use of Dry as) Evacuation instead of 9E' de7as)ing %ystem
Iry de&ashing system consumes less power D also minimizes waste reduction.
-. Compressed air system:7
a@ :ptimizing disc)arge air pressure (y tuning loading!unloading cycle: 7
It helpful to reduce sp. Power consumption
(@ Use of )eat of compression air dryer instead of electrically )eated air dryer: 7
=eat of compression air dryer use heat generated in compression cycle, thus reduces
sp. Power consumption
c@ Use of scre compressor instead reciprocating compressor: 7
'p. Power consumption of screw compressor is less than reciprocating air compressor
leads to reduce au>. power consumption.
.. Cooling toer performance improvement7
Installing absorption refrigeration system instead of vapor compression system
