Journal of Scientific & Industrial Research
36
Vol. 67, January 2008, pp. 36-42
J SCI IND RES VOL 67 JANUARY 2008
DM make up water reduction in thermal power plants using Six Sigma
DMAIC methodology
Prabhakar Kaushik1* and Dinesh Khanduja2
Mechanical Engineering Deptt, N C College of Engineering, Israna, Panipat 132 107
Mechanical Engineering Deptt, National Institute of Technology, Kurukshetra 136 119
1
2
Received 11 May 2007; revised 22 October 2007; accepted 24 October 2007
Six Sigma DMAIC (define, measure, analysis, improve, control) methodology has been applied to a process industry
seeking energy conservation, taking a specific case of a thermal power plant. DM (De-mineralize) water in these plants is an
expensive input material. It has been found that 0.1% increase in DM make up water consumption increases generation cost by
Rs 82.82 lakhs per annum. In present study, implementation of Six Sigma project recommendations brought down mean make
up water from 0.90% to 0.54% of MCR (Maximum Continuous Rating), accruing with it a comprehensive energy savings of
nearly Rs 304.77 lakhs per annum.
Keywords: CTQ, DM, DMAIC, Process industry, Six Sigma
Introduction
Six Sigma (SS) methodologies improve quality and
produce large cost savings1-9. Kumar4 noted that SS has
found place primarily in manufacturing industries as a
quality tool. In process industries, no such convenience
is available. Working fluid in process industries may not
be visible and its quality is measured by pressure,
temperature and flow measurement. In manufacturing
industries, production is already operating at 1-2 sigma
level and by applying SS methodology, it can be raised
up to 5-6 sigma levels. In process industries, there are
many sub- processes that operate even at negative sigma
level because of being secondary in nature. So in process
industries, a quantum jump in sigma value by application
of SS tools cannot be expected and it is found that the
improvement potential is maximum up to 2-3 sigma
levels.
Present work is an initiative to implement SS in a
thermal power plant (TPP).
Six Sigma Application in Thermal Power Plants – A
Case Study
In TPP, optimisation of cycle make up water [Demineralize (DM) water] consumption process involves
substantial cost. Escalating water charges from water
supply department and cost of production of DM water
*Author for correspondence
E-mail: [email protected]
from raw water are substantial. Mostly, all gas based
TPPs are operating on one module of combined cycle
power plant, which consists of two gas turbines, two heat
recovery steam generators and a steam turbine (Fig. 1).
DM water is used for steam generation through gas based
combined cycle power plant. With in this closed cycle of
DM water, DM water make up cycle is required to
compensate for the losses incurred in water-steam cycle
due to evaporation, start up and shut down venting, valve
passing and blow downs. DM make up water enters in a
condenser at atmospheric temperature that is heated over
500°C for raising steam. Flow meter is used to measure
day cycle make up water as percentage of feed water
flow. Each 0.1% increase in cycle make up water
increases generation cost by Rs.82.82 lakhs per annum,
which includes cost of heat loss, extra water and
consumption of chemicals.
Hence, the main customer CTQ (Critical to quality)
selected for SS implementation is to conserve energy by
reducing DM makeup water requirement at TPP.
Presently, makeup water consumption at TPP is around
0.9-2.0% of MCR (Maximum Continuous Rating). In
comparison, other combined cycle power plants of the
same rating have been able to achieve DM water cycle
consumption of the order of 0.5-0.7%.
Methodology
To study all possible variations of water consumption,
6 months data of cycle make up water consumption has
KAUSHIK & KHANDUJA: SIX SIGMA DMAIC METHOD FOR THERMAL POWER PLANT
2
L.P. Steam 5kg /cm
~
570°C
GT - 1
Flue gases
GT Gener ator II
~
S team Turbine
Gene rator
156 MW
20 0°C
G as
570°C
GT
Ex h aust
GT - 2
H RSG - 1
VTB
H .P. Steam
L.P. Stea m
GT Gener ator I
76 k g / cm
52 8°C
~
2
H PS T
LPS T
H RSG - 2
VTB
37
D M W ater
Mak e u p
H .P. Steam
CO NDENS ER
ABBR.
D escription
GT
HRS G
VTB
H.P. S te am
L.P. S te am
HPS T
LPS T
G as T urbine
H eat Recovery St eam G enerat or
Vert ical T ube B oiler
H igh P ressure St ea m
Low P ressure St eam
H igh P ressure St ea m T urbine
Low P ressure St eam T urbine
C old Water
for
C ondensing
HO T W ELL
Hot
Water
C ondensate
Extra ction Pump
De ae rator
B oiler Fee d Pu mp
Fig. 1— Systematic block diagram of combined cycle power plant
Fig. 2— Flow diagram of methodology adopted
to be collected. Cycle make up water consumption has
to be converted in terms of percentage of MCR of feed
water flow so that this methodology can be applied to
other power plants. As it is not possible to reduce cycle
make up water consumption to zero and minimum is
the best, LST (lower specification limit) cannot be fixed
for water consumption. Hence, only USL (upper
specification limit) of 0.7% and target value of 0.5%
are specified and selected based on water consumption
pattern existing in the best power plants around.
Implementation of Six Sigma DMAIC Methodology
A five-step improvement cycle using SS organizations
(Define, Measure, Analyse, Improve, and Control;
DMAIC) has been successfully implemented in TPP to
reduce DM make up water reduction (Fig. 2).
38
J SCI IND RES VOL 67 JANUARY 2008
S up plier
In put
DM
P la n t
M a ke U p W a te r
C o n su m p tion
D a ta
P rocess
O utp ut
C u stom e r
O p e ra tion
and
M a in te n a n ce
p ra ctice s
R e d u ction in
M a ke U p
W a te r
C o n su m p tion
T h e rm a l
P o we r
M a n a ge m e n t
Th in kin g
6 S igm a
M e th o d olo gy
C u sto m e r
S a tisfactio n &
R e la tio n sh ip
Flow
Make up water before in %age
Make up water before in %age
Fig. 3— High-level process map for cycle make up water consumption
1 .5 0
1 .2 5
1 .0 0
0 .7 5
0 .5 0
1
20
40
N u m b e r o f r u n s a b o u t m e d ia n :
E x p e c te d n u m b e r o f r u n s:
L o n g e st r u n a b o u t m e d ia n :
A p p r o x P - V a lu e fo r C lu ste r in g :
A p p r o x P - V a lu e fo r M ix tu r e s:
60
92
91 .824 18
7
0 .510 45
0 .489 55
80
100
Ob s e r v a t io n
Observation
1 20
N u mb er o f ru n s u p o r d o w n :
E x p e c te d n u m b e r o f r u n s:
L o n g e st r u n u p o r d o w n :
A p p r o x P - V a lu e fo r T r e n d s:
A p p r o x P - V a lu e fo r O sc illa tio n :
1 40
1 60
1 80
119
12 1.00 000
4
0.36 191
0.63 809
Fig. 4— Run chart of make up water before
Define
In define phase, High level process map- a SIPOC
(Supplier, Input, Process, Output, Customer) diagram,
was drawn for cycle make up water consumption
(Fig. 3).
Measure
In cycle make up water consumption at TPP, make
up water flow is measured by a flow meter. To perform
Gauge R&R study9 on this process, another flow meter
of tested accuracy and characteristics needs to put in
series to the installed flow meter. Two persons (operators
in shift) were needed to perform this experiment. Sample
size was 10 and two readings were taken on each sample,
thereby making a total of 40 readings. From the results
of Gauge R & R study, repeatability and reproducibility
comes out to be 2.75% and 0.00% and put the percentage
study variation to be 2.75%, which is less than 10%,
indicating that flow meter was correct.
Analyse
Data is analysed and causes of problem are
discovered4 using following tools:
a) Run Chart
Run chart was drawn from data collected for day cycle
make up water from TPP measured through flow meter.
From the results found using Minitab, P-values (Fig. 4)
for clustering (0.51045), trend (0.36191), oscillation
(0.63809) and mixtures (0.48955) come out to be more
than the significance level (0.05), indicating not any
special cause of variation in data.
KAUSHIK & KHANDUJA: SIX SIGMA DMAIC METHOD FOR THERMAL POWER PLANT
Target
Process Data
LSL
Target
USL
Sample Mean
Sample N
StDev (Within)
StDev (O v erall)
USL
39
Within
Overall
*
0.50000
0.70000
0.90945
182
0.27796
0.26475
Potential (Within) C apability
Z.Bench -0.75
Z.LSL
*
Z.USL
-0.75
C pk
-0.25
C C pk
0.24
O v erall C apability
Z.Bench
Z.LSL
Z.USL
Ppk
C pm
0.4
O bserv ed Performance
PPM < LSL
*
PPM > USL 763736.26
PPM Total
763736.26
0.6
0.8
Exp. Within Performance
PPM < LSL
*
PPM > U SL 774435.70
PPM Total
774435.70
1.0
1.2
1.4
-0.79
*
-0.79
-0.26
0.14
1.6
Exp. O v erall Performance
PPM < LSL
*
PPM > USL 785564.80
PPM Total
785564.80
Fig. 5— Process capability analysis of make up water before implementing
DMAIC methodology
MAN
EQUIPMENT
Improper Adjustment of
S WAS Sampling Valves
Late closing of
Drain & Vent
Valves during Boiler Startup
Sampling Valve s remaining
open after collection of samples
Sample drains remained opened
during Shutdown boiler
Frequency of boiler
hydraulic tests
Higher no. of sample
collection in SWAS
Passing of Drain & vent valves
Vaccum pump overflow
Passing due to under
sizing of actuators
Leakages from HP/LP pipelines
flanges & piping s
MORE DM
CYCLE
MAKE UP
Passing of valves due to improper
limit switch setting
Longer running with boiler tube
Leakage
Blow down opening for Silica
& conductivity test
METHOD
Tube Leakages
Gland Leakages from
pump s
MATERIAL
Fig. 6— Fishbone diagram
b) Process Capability analysis
Process capability analysis was performed using
Minitab to draw curve for cycle make up water from
TPP measured through flow meter (Fig. 5). Z- bench
sigma value of process was found to be -0.75 and existing
DPMO level of the process comes out to be 774435.70,
which is remarkably high and shows that there are a lot
of opportunities for improvement in the process.
c) Fish-bone Diagram
Using expert experience and critical analysis of actual
combined cycle at site, a fish bone diagram drawn
(Fig. 6) to find causes of more DM water consumption
during combined cycle.
d) Bar Chart
Actual DM water wastage from different points was
measured or approximated where no measurement was
40
J SCI IND RES VOL 67 JANUARY 2008
35
Percent of contribution
Contribution,
%
Percent
of contribution
30
25
20
15
10
5
0
SWAS
VA LVE P A SSING
BLO W DO W N VA C C UM P/P O VERF LO W
Causes
Causes
O THERS
Fig. 7— Bar chart to show causes percentage contribution
Target
P rocess D ata
LS L
Target
USL
S am ple M ean
S am ple N
S tD ev (Within)
S tD ev (O v erall)
USL
W ithin
O v erall
*
0.50000
0.70000
0.54066
61
0.09767
0.15382
P otential (Within) C apability
Z.B ench
1.63
Z.LS L
*
Z.U S L
1.63
C pk
0.54
C C pk
0.68
O v erall C apability
Z.B ench
Z.LS L
Z.U S L
P pk
C pm
0.32
O bserv ed P erform ance
P P M < LS L
*
P P M > U S L 65573.77
P P M T otal
65573.77
0.48
E xp. Within P erform ance
P P M < LS L
*
P P M > U S L 51389.17
P P M T otal
51389.17
0.64
0.80
0.96
1.04
*
1.04
0.35
0.42
1.12
E xp. O v erall P erform ance
P P M < LS L
*
P P M > U S L 150114.21
P P M Total
150114.21
Fig. 8— Process capability analysis of make up water after implementing DMAIC methodology
possible. Based upon measurement results, bar chart was
drawn and resultant causes with their percentage
contribution were found to have biggest impact on cycle
make up water consumption (Fig. 7).
gap between operation and chemistry staff and casual
approach of some of the staff were identified and action
plans were prepared to tackle such problems (Table 1).
Control
Improve
In SWAS (Steam water analysis system), periodic
awareness and training of lab analysts, communication
In this stage, new process considerations are
documented and frozen into systems so that the gains
are permanent. All possible related causes of specific
KAUSHIK & KHANDUJA: SIX SIGMA DMAIC METHOD FOR THERMAL POWER PLANT
41
Table 1—Action Plan (Improve and Control Phase)
Recommendation proposed
Status
1
All lab analysts to be individually interacted to emphasize the
importance of closure of SWAS valves after sample collection.
Implemented
2
Six month periodic training cum awareness program for lab analysts to
be conducted to make them aware of the importance of DM water loss.
First program
already
conducted
3
Instructions to be pasted on SWAS panel for closure of sample valve’s
each time after sample collection.
Implemented
Instructions
pasted
Operation staff to be instructed to cross check from time to time the
Implemented
position of SWAS sampling valves in their routine rounds
Instructions
being followed
5
As an improvement measure, the frequency of blow down opening to
be changed from weekly to fortnightly
Implemented
6
To avoid the loss of DM water due to vaccum pump overflow,
solenoid makeup valves of both the seal water tanks to be adjusted
properly for both low and high level settings.
Implemented
7
Q uarterly checking of solenoid valves of both seal water tanks to be
carried out.
Implemented
8
To detect the problem of seal water tank overflow at the earliest, in the
log sheet of the operator, the daily checking of seal water tanks to be
4
Implemented
included.
Included in the
log sheet
9
The leakages identified from HP/LP pipelines, valve passing to be
attended during next shutdown.
To be
implemented
10
The glands of all the pumps with excessive leakages to be tightened
optimally.
Implemente d
11
A schedule to be prepared to check/tighten (if required) the glands of
all the pumps fortnightly.
Implemented
12
For on line sealing of HP steam leakages, annual maintenance contract
to be awarded
To be
implemented
identified problem from analysis phase were tackled and
shut out in control phase (Table 1).
Results
Cycle make up water consumption was 0.9% MCR,
which is equivalent to Rs 745 lakhs (Rs.82.82×0.9%)
per annum (Appendix I). Application of project
recommendation brought up the sigma level to 1.63 with
DPMO level of 51389.17 (an improvement of
723046.53) and mean of the process reduced to
0.54066% (an improvement of 0.368% mean), which is
equivalent to monitory saving of Rs 304.77 lakhs per
annum (Fig. 8). A few more agreed recommendations
are still to be implemented during plant shutdown.
42
J SCI IND RES VOL 67 JANUARY 2008
Estimated saving from the project after implementation
of all recommendations is expected to be Rs 331.2 lakhs
per annum with mean make up water expected to come
down (< 0.5%), which is substantial for any organization.
Conclusions
Study proves that firms that successfully implement
Six Sigma perform better in virtually every business
category, including return on scales, return on
investment, employment growth and stock value growth.
Higher consumption of DM water is found to be a big
problem in a thermal power plant. The causes for more
DM water consumption are SWAS, problem of valve
passing, vacuum pump overflow etc. SWAS makes a
big impact having 33% contribution for DM water
consumption. Further, some actions are recommended
to reduce the consumption of DM water. Application of
Six Sigma project recommendations brought up the
sigma level to 1.63. Estimated saving from the project
after implementation of all recommendations is expected
to be around Rs 331.2 lakhs per annum with mean make
up water is expected to come down below 0.5%, which
is substantial for any organization.
References
1
2
3
4
5
6
7
8
9
Coronado R & Antony J, Critical success factors for the
implementation of six sigma projects in organization, TQM
Mag, 14 (2002) 92-99.
Henderson K M & Evans J R, Successful implementation of
Six Sigma: benchmarking: general electric company,
Benchmarking Int J, 7 (2000) 260-282.
Kapur K C & Feng Q, Integrated optimisation models and
strategies for the improvement of the Six Sigma process, Int J
Six Sigma and Comp adv, 1 (2005) 210-228.
Kumar P, Six Sigma in manufacturing, Prod J, 43 (2002) 196202.
Mahanti R & Antony J, Confluence of Six Sigma simulation
and software development, Manag Aud J, 20 (2005) 739-762.
Mathew H, Barth B & Sears B, Leveraging Six Sigma discipline
to drive improvement, Int J Six Sigma Comp Adv, 1 (2005)
121-133.
Pandey P S, Neuman R & Cavanagh R R, The Six Sigma Way:
How GE, Motorola and Other Top Companies are Honing their
Performance (McGraw Hill, New York) 2000.
Park S H, Six Sigma for productivity improvement: Korean
business corporations, Prod J, 43 (2002) 173-183.
Raisinghani M S, Ette H, Pierce R, Cannon G & Dariply P,
Six Sigma: concepts, tools, and applications, Ind Manag Data
Sys, 105 (2005) 491-505.
Appendix-1
Cost calculations of loss on account of 0.1% make up water
Loss due to make up water consumption
Water is heated in boiler from 27°C at atmosphere pressure to superheated steam at 528°C and 76kg/cm2
Heat loss
Enthalpy of water at 27°C (atmosphere pressure) = 113.25 KJ/kg
Enthalpy of superheated steam at 528°C and 76kg/cm2 = 3472.74 KJ/kg
Loss in enthalpy = 3472.74 -113.25 = 3459.49 KJ/kg = (3459.49 × 1000) / 4.18 Kcal/m3 = 827629.1866 Kcal/m3
Equivalent loss in power = 827629.1866 / 1965 = 421.185 KWh/m3
(Considering Heat Rate as 1965 Kcal/ KWh for combined cycle)
Equivalent loss in monetary term = Rs 4.00 × 421.185 = Rs 1684.74 per m3
(Considering Rs. 4.00 per unit (KWh))
Cost of DM water = Rs 22.00 per m3
Total loss on account of make up water =Heat loss + water loss = Rs 1684.74 per m3 + Rs 22.00 per m3 = Rs 1706.741 per
m3
Losses on account of 0.1% make up water
Total flow in boiler per annum = [(231(HP) + 46 (LP)) × 2 (Boiler)] ×24 h ×365 days = 4853040 m3
Water quantity for 0.1% make up = (0.1 × 4853040)/ 100 = 4853.04 m3 per annum
Therefore, loss on account of 0.1% make up water = 4853.04 × Rs 1706.74 = Rs 8282882.343 = Rs 82.82 lakhs approx per
annum