Ijmet 06 09_006

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 6, Issue 9, Sep 2015, pp. 51-60, Article ID: IJMET_06_09_006
Available online at
http://www.iaeme.com/IJMET/issues.asp?JTypeIJMET&VType=6&IType=9
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
________________________________________________________________________
PIPE NETWORK ANALYSIS OF A
COMPLEX FLOW SYSTEM USING
PIPENET – A CASE STUDY
Hrishikesh Epte and R. S. Maurya
Sardar Patel College of Engineering, Mumbai, India,
ABSTRACT
A complex water pipe network is an integral part of a process industry
which serves as its lifeline. For an optimum operation of many of its key
devices such as cooling towers, pumps, heat exchangers etc., correct
functioning of the network is essential. Present case study deals with a
processing industry which is fighting with water flow network problem due to
its prolonged used and expansion activities. These changes caused improper
flow distribution reaching to key devices such as heat exchanger. Identifying
the problem and providing proper suggestion for improvement is a marathon
task. In order to handle this problem pipe flow analysis software PIPENET
has been used along with a systematic method of execution supported by
theoretical analysis. It is based on a lot of live data collection, analysis and
theoretical investigations. This software solves the network related problems
considering the entire network at once. Exercise includes creating virtual
prototype of a petrochemical industry pipe flow network, its analysis and
identifying problems, and recommending solutions. Investigation results in
providing improved pipe network model for better flow distribution. Results
show an improvement in flow distribution by 15% saving in cooling water and
27% savings in energy. Work successfully illustrates problem solving
methodology of a complex pipe flow network.
Key words: Flow mal-distribution, PIPENET and Pipe water network
Cite this Article: Epte, H. and Maurya, R. S. Pipe Network Analysis of a
Complex Flow System Using Pipenet – A Case Study. International Journal
of Mechanical Engineering and Technology, 6(9), 2015, pp. 51-60.
http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=6&IType=9
1. INTRODUCTION
In chemical and processing industries, a complex pipe network carrying fluids to
desired location at required condition is common for proper functioning of the system.
Improper flow distribution leads to systems overheating, large pressure drop and

2. Hrishikesh Epte and R. S. Maurya
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process related problems. Cooling water system is an integral part of process
industries, which includes cooling towers, piping network and equipment like heat
exchangers and pumps. A simplified schematic diagram of cooling water network is
shown in Figure 1. Over-design of these is a common practice to satisfy the field
standards, which absorbs all the fluctuations in power and flow rate. Result of over
design is that, it causes variations like flow mal-distribution, increased pressure
resistance in the network, fluctuations in flow velocity and temperature in heat
exchangers, and fouling due to prolonged use. CWN is complex in nature and is
similar to that of electric circuit in parallel arrangement. A change in any part of
network results in affecting other part of network. Piping network involves many
physical parameters like flow rate, temperature at inlet and outlet of heat exchanger,
pipe velocity, pressures at various locations, etc. which vary as per conditions in
network and are always interdependent on each other. A complex interdependence of
parameters makes the exercise complex and their complexity goes on increasing with
network complexities. This has always been a prime area of investigation among
researchers due to their uniqueness where problem varies from case to case. Literature
review shows several studies regarding piping network. Modification of CWN is very
important to increase cooling tower capacity and performance [1]. Analytical,
Graphical and Numerical are the three techniques in which piping network
optimization are generally carried out. Thought leaders explain some analytical
methods to optimize flow in CW network. In order to maintain constant pressure drop
in all the branches of network, individual components of CWN was considered while
developing model for given load conditions one at a time [2]. But when these
components were put together in system network, results were varying since it
redistributed flow throughout the system network depending on resistance of network.
Another analytical method in which CWN model was synthesized and series-parallel
arrangement for exchangers was obtained, leading to a 40% reduction in cooling
tower load and consequently lower operating costs and water consumption [3].
Similarly, design of CWN was developed in series arrangement [4] where water was
reused between different cooling duties which will increase cooling tower capacity
and performance. Another method for CWN optimization can be Graphical technique
in which single cooling source was developed, debottlenecking of CW system was
carried out [5]. Use of graphs to identify pinch point of operating system and
modelled them accordingly. Although this method offers powerful optimization on
the basis of simplified linear model they cannot replicate reality models.
Lot of work using numerical approach has been observed. Popularity of this approach
is due to time saving in performing calculations and level of accuracy obtained compared
to other non-numerical methods. Matlab has been used to develop cooling system
network consisting of cooling tower and heat exchangers [6]. Mathematical formulation
was performed considering two practical scenarios; Non-linear Programming and Mixed
Non-Linear Programming, but this model was valid for limited number of heat
exchangers. Hybrid approach has been considered for simultaneous layout and pipe size
optimization of branched pipe networks to minimize cost [7].
This new approach is based on combination of a pipe size optimizer (LIDM) with a
layout optimizer for joint layout and pipe size optimization. Mathematical model was
developed to reduce water consumption using wet and dry cooling tower in combination
[8]. Above numerical technique though cost efficient but are very constraint to their cases,
it may not be possible to replicate their behaviour for similar problem faced in industries.
So, effort was taken to develop a methodology which can be used to optimize water usage
and save energy by most of the industries which are the victims of CWN problems.

3. Pipe Network Analysis of a Complex Flow System Using Pipenet – A Case Study
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Figure 1 Cooling Water Network
This paper uses commercial software PIPENET [9] as a mathematical tool, it is
possible to create an entirepiping model and analyse the network [10]. Retro-fit
solutions is our main motive which can be obtained using this technique, thus we can
reduce cost, optimize operation and ensure smooth start-ups.
2. PROBLEM DEFINITION
Cooling water system discussed here is a parallel circuit where cold water is passed
through heat exchangersthat absorbs heat and returns to cooling tower. Generally,
pipe network is designed as per initial requirement of plants, considering heat duties
which are needed to be satisfied. Prolonged use, expansion activities and increase in
water demand parallel to process requirements resulted in unbalanced network.
Throttling of valves or installing new booster pumps acts as a temporary solution to
satisfy heat duties of new equipment. These solutions may increase the cost of energy
and water usage. Main reason for above temporary solution is to supply cooling water
at required flow and pressure to the exchangers. If we size the pipes considering the
pipe flow velocity then usage of booster pumps can be reduced. Overall flow can be
regulated; saving excess water flowing in the network. Redesign of existing network
with little modification is more economical, than changing entire network which is
suggested by some authors. Increase or decrease of flow by sizing of pipes for an
exchanger causes relative effect on other exchanger. To tackle this relative effect
problem, entire network has to be analyzed while performing changes in the network.
Objective of present investigation is to revise CWN with economical suggestions
and improved network efficiency.
3. METHODOLOGY
In order to fulfil objective a methodology has been developed to reduce complexity of
investigation for analysing and solving problems which are common in the flow
network. Optimized network can be determined by following three steps.

4. Hrishikesh Epte and R. S. Maurya
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1. Obtaining real-time data and calculating current heat duties of exchangers.
2. Finding optimum flow required to satisfy existing heat duty by fixing temperature
change.
3. Synthesizing cooling water network based on optimum flow.
Methodology used is represented in Flow diagram shown in Figure 2.
Existing CWN was studied by collecting current data such as flow and
temperature at inlet and outlet.
After collecting live data heat duty for all exchangers was calculated using
following heat transfer equation.
(1)
Now, for the exchangers to perform at higher efficiency design temperature
change of 8–12 °C is required. Heat duty in the exchangers is not constant always; it
varies as per process requirement. Margin is kept so that temperature of CW outlet
should not exceed 12 °C. Thus value of 8 °C is used as optimum temperature
difference. Optimum flow for every exchanger is found by using earlier heat duty and
optimum temperature difference substituted in heat transfer equation.
To achieve this optimum flow in the network, sizing has to be done. As mentioned
in problem definition relative effect takes place while changing pipe size for particular
exchanger. To ease our laborious work, help of network analysing mathematical tool
PIPENET can be taken. PIPENET uses series of flow modelling equations while
simulating, it considers entire network at once.
Figure 2 Flow diagram of methodology
Synthesize pipe in
PIPENET
Is flow
Optimum?
Obtaining new
pipeline sizes
Recommending
changes
Define problem
Obtain real
time data
Calculate heat duties
Calculate optimum
flow rate
Plot piping network
in PIPENET
No
Yes

5. Pipe Network Analysis of a Complex Flow System Using Pipenet – A Case Study
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4. CASE STUDY
Present case study deals with a complex pipe network where, cooling towers provides
cooling water to several heat exchangers of different capacity and specifications of a
petrochemical industry. Pumped CW is delivered to the process equipment through a
system of complex pipe network as shown in Figure 1. Analysed CWN consists of three
boosters pump (Refer Figure 4.) and parallel configuration piping network supplying
CW to 21 exchangers. Total flow rate of 9310 m3/hr is required to satisfy heat duties at
inlet pressure of 4.5 Bar and outlet pressure of 3.2 Bar. Booster pumps supply water to
exchangers which are at higher elevations consuming 167 KWh energy.
Current status of analyzed system with flow and temperature change is shown in
Table 1. Heat duty is calculated using equation 1.
Using this heat duty and optimum temperature difference of 8 °C, optimum flow
rate also called as required flow rate was found out by using heat transfer equation as
shown in Table 2.
Now, setting this required flow rate as the reference flow to be achieved, sizing
was done. Changes are done in virtual prototype to get balanced network.
Table 1 Status of cooling water network.
Heat exchangers
Existing flow rate
(m3
/hr)
Change in temp(
°C)
Heat duty (KW)
E116 324 8 2991
E171 3451 7 27877
E118 319 8 2945
E122 31.5 5 182
E306 444 1 512
E305 1490 7 12036
E404 210 17 4120
C133 450 6 3116
E415A 36 10 415
E415B 80 3 277
E415C 16 19 351
E603 104 15 1800
E610 4.5 43 223
E572 60 8 554
E406 1.2 42 58
E621 43 3 149
E612 98 19 2149
E308 1468 0.5 847
E631 270 8 2493
E1606 350 12 4847
E610X 60 25 1731
Total 9310 - -
4.1. Virtual Prototype
Virtual prototype is a validated computer generated representation of the existing
network. In this case PIPENET is used to create a virtual prototype of the existing

6. Hrishikesh Epte and R. S. Maurya
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cooling water network using data like isometric drawings, P&Id’s, equipment data
sheets, existing flow rate and heat duties.
Table 2 Flow requirement in heat exchangers
Heat Exchanger Heat Duty (KW) Temp change( °C)
Required Flow
Rate (m3
/hr)
E116 2991 8 324
E171 27877 8 3020
E118 2945 8 319
E122 182 8 20
E306 512 8 56
E305 12036 8 1304
E404 4120 8 310
C133 3116 8 338
E415A 415 8 36
E415B 277 8 30
E415C 351 8 36
E603 1800 8 97
E610 223 8 24
E572 554 8 60
E406 58 8 6
E621 149 8 16
E612 2149 8 155
E308 847 8 92
E631 2493 8 143
E1606 4847 8 350
E610X 1731 8 188
Total 6923
Following assumptions are made while performing computer based simulation for
the case: under investigation.
1. Steady state of fluid is considered.
2. Working fluid is water which is incompressible and pressures do not affect density.
3. 100 % accuracy of instruments used for live data collection.
4. Required flow rate is considered as reference flow.
5. Specific heat and density is assumed to be constant.
6. Outlet of CWN is set at pressure of 3.2 Bar.
Now, changes can be performed in the network to get required flow for every
exchanger. Changes like line size changing, introducing new lines at locations where
it is required was carried out. After number of iterations, a balanced network was
obtained producing nearly required results. Layout of PIPENET generated optimum
model showing entire piping network of system under consideration shown in Figure
4.

7. Pipe Network Analysis of a Complex Flow System Using Pipenet – A Case Study
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Figure 3 Modified part network diagram of cooling water
5. RESULTS AND DISCUSSIONS
Improvement in performance can be observed in the results produced in PIPENET
after modifications. Figure 3 shows that line size for E171 which was earlier 24” in
now changed to 20”. A line is bypassed from supply of E171 to E122, to balance flow
and pressure. By doing this we can block supply of E122.
Figure 4 Optimized piping network layout of case study in PIPENET.

8. Hrishikesh Epte and R. S. Maurya
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Similar modifications as shown are carried out throughout the network. Obtained
results, though do not match exactly but efforts were taken to match required flow as
far as possible. Results show that we can eliminate use of one out of three booster
pump. Revised network results shows only two booster pumps consuming 122 KWh
of energy are required to pump water for elevated exchangers. Savings in both energy
and water was achieved. Temperature change in exchangers E404, E415C, E610,
E406, and E612 was brought close to 8 °C by increasing flow through Heat
exchanger. Temperature change for exchangers E306 and E308 was initially very less
because cooling water flow was more, but after modifications flow is reduced, thus
temperature change will increase saving flow of cooling water in the network.
Cooling water supply and return pressure of plant inlet and outlet remained same as
earlier i.e. 4.5 bar inlet pressure and 3.2 bar outlet pressure. Comparison of existing
and revised results obtained for every exchanger from modified model is shown in
Table 3.
Observing the results for E610X, cooling water temperature increase is shown for
modified network from 25 °C to 28.9 °C. In reality this temperature will fall below 25
°C. Reason behind temperature decrease is, E1606 and E610X lie on common CW
line also E610 and E610X have process fluid on same line, obtained results shows
that heat duty for process fluid will be satisfied by E610, thus need of CW in E610X
will be eliminated. This CW will be diverted entirely to E1606 thus decreasing
temperature change in heat exchanger.
Table 3 Comparison of existing and revised model
Heat
exchanger
CW Flow Rate (m3
/hr) CW pressure(Bar)
Temperature Change(
°C)
Existing Revised Existing Revised Existing Revised
E116 324 315 3.94 4.17 8 8.2
E171 3451 3090 3.83 4.03 7 7.8
E118 319 309 3.88 4.11 8 8.2
E122 31.5 29 4.15 4.33 5 5.4
E306 444 75 3.20 3.93 1 5.9
E305 1490 1367 3.50 3.65 7 7.6
E404 210 303 3.10 3.82 17 11.8
C133 450 443 4.12 4.39 6 6.1
E415A 36 49 3.89 4.45 10 7.3
E415B 80 42 4.17 4.43 3 5.7
E415C 16 35 3.91 4.45 19 8.6
E603 104 90 4.14 4.41 15 17.3
E610 4.5 58 3.97 4.65 43 3.3
E572 60 64 2.96 3.22 8 7.4
E406 1.2 12.6 3.95 2.62 42 4
E621 43 45 4.11 4.38 3 2.9
E612 98 208 5.29 5.86 19 9
E308 1468 101 3.06 3.29 0.5 7.2
E631 270 213 3.28 3.38 8 10.1
E1606 350 306 3.92 4.11 12 13.7
E610X 60 52 2.93 3.1 25 28.9
Total 9310 7206 - - - -
Increase in pressure at inlet of exchangers was observed which will result in
increasing velocity of CW in tube bundles, thus reducing scaling and fouling of

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exchangers. A clearance of 10% in CW is required to absorb all the fluctuations
occurring in plant.
5.1. Savings
Total savings in CW = Existing flow rate - (Revised flow rate + clearance in
Network)
= 9310-(7206+720) m3
/hr.
CW savings = 1384 m3
/hr.
Savings of 14.86 % in cooling water is achieved.
Energy saved = (Existing energy requirement – Revised network requirement)
= (167 – 122) KWh
Energy saved = 45 KWh.
Savings of 26.95% in energy is achieved.
6. CONCLUSION
Optimization of water consumption is one of the challenging issues faced by
industries. This can be tackled using the above mentioned methodology along with
PIPENET as a mathematical tool. This paper shows the benefit of using numerical
method for analysing and optimizing of existing network. Changes performed on
virtual network can be given as recommendations to obtain optimum piping network
in the field. Although, there will be some cost incurred to execute changes in the
network, results obtained shows savings in CW and energy along with increased
performance and efficiency of network. Problems of flow mal-distribution, pipe flow
velocity, and increased pressure drop in network can be reduced. Retro-fit solution
can be obtained using this methodology for expanding piping network. Companies
which cannot afford to change entire piping network will find this methodology more
economical and feasible. This method can be applicable to wide range of industry
which requires water optimization. To show applicability of methodology, a case
study has been carried out, savings in CW of almost 15% and energy savings of about
27% is seen along with network balance and increased efficiency.
7. NOMENCLATURE
Qm - Mass flow rate
C - Specific heat capacity of water
T1, T2 - Inlet and Outlet temperature respectively
8. ABBREVATIONS
HE – Heat Exchanger
CW– Cooling Water
CWN – Cooling Water Network
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