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CHAPTER 8

MODELING OF WIRELESS SENSOR NETWORKS FOR

LPG PLANT MONITORING USING CUCKOO SEARCH

8.1 INTRODUCTION

Wireless Sensor Network is a significant field across the globe for

monitoring the proper utilization of resources including water, oil refineries,

petrochemical plants, natural gas, etc (Jawhar et al 2008) . As oil and gas are

highly hazardous, maintaining the economic progress of a country depends on

aiding of these resources and facilities. The increase in demand of energy

efficiency in oil and gas industry paves way for the development and

investment in Wireless Sensor Network technologies. There are numerous

technologies to monitor the resource exploration, production and

transportation. Network services like localization, tracking, data aggregation

and energy-efficient multihop routing are used for monitoring the resources

with appropriate control mechanism. Considering the above characteristics

and prominent features of monitoring, a mesh model is developed to monitor

the effective functioning of Liquefied Petroleum Gas (LPG) Plant.

LPG is an eco-friendly fuel and hazardous chemical which may

result in mass destruction if not handled with care and safety. There are many

safety precautions to be followed in a LPG bottling plant and they are non-

negotiable. The manual effort alone cannot support in practicing the safety

operations, in addition to aid manual work, some sophisticated and automated

systems are essential to preclude accidents. Hence in this research the use of

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WSN technology is investigated to monitor LPG plant. Mesh topology is

considered and the performance of the network is analyzed by ZigBee and

Wireless Hart protocol.

8.2 LIQUIFIED PETROLEUM GAS PLANT OPERATION

LPG bottling plant is a plant where LPG is filled into cylinders for

storage and distribution through various LPG distributors. The schematic

setup of a LPG plant is described in Figure 8.1. The plant consists of various

units as Bullet storage unit, Pump and compressor unit, Bottling unit,

Reposition and Dispatch unit, and Maintenance and Control unit (Chandra

2009). The plant has the facility to receive bulk LPG by Tank trucks of

various capacities e.g. 7MT, 18MT etc (or) pipeline from a reliable source

such as refinery and other LPG Bottling Plant. The LPG is transferred to

bullets laid in the plant by means of hoses and pipelines. LPG will be stored

in bullets at a pressure of 5 7 bar and ambient temperature conditions.

Figure 8.1 Schematic View of LPG Bottling Plant

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Then the LPG is channelized to cylinders by means of pipelines

positioned inside the plant. The primary operation in a bottling plant is

associated with proper filling of LPG cylinders. The sequence of activities for

bottling include receipt of cylinders, visual inspection and segregation,

cylinder filling, tare weight marking, tightness test etc. Once the cylinders are

received, it is thoroughly checked under two classifications namely new

cylinders from manufacturers and old cylinders from customers. All new

cylinders received should be supported by Test Certificate for ISI (Indian

Standards Institute) inspection and approval from CCE (Chief Controller of

Explosives) before they are put into use. Based on the visual inspection the

cylinders are segregated as under filled, leaky/defective, spurious, etc. In-

depth examination of each and every cylinder is performed and appropriate

remarks are put on the relevant transfer documents.

The liquid LPG at a differential pressure of 8 bar is filled into the

cylinders. Then the cylinders are verified for correct weight to prevent under-

filling and over-filling. The corrections are carried out if required. The self-

closing valve is checked for leakage and any other defects. Then the cylinders

are immersed in test bath of water, to identify bung leak or body leak and if

leaky cylinders are identified it is evacuated and scrapped immediately. The

good cylinders are sealed and stored in the repository unit. Then the cylinders

are dispatched on the basis of requirements to the needy areas. The overall

control of the plant is monitored from the control room. Any obstruction in

these activities will shackle the entire operation of the plant.

8.3 NEED FOR PLANT MONITORING

Monitoring resource plants relies first and foremost on sensory data

information about its surroundings as well as about its internal working .

This sensory data represent the next evolutionary development step in plant

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as follows (LPG specifications 2012);

1. The level of LPG stored in the bullets as well as bottled in the

cylinders should not exceed 85% of its capacity to ensure

thermal expansion and safety norm as pressure should not

raise above 11 bar.

2. The gas flow from the outlet of the reservoir bullet should not

exceed 40 Gallons/minute.

3. The temperature of the plant should be ambient and should not

exceed 410 degree Celsius.

4. LPG has an explosive range of 1.8% to 9.5% volume of gas in

air. This is considerably narrower than other common gaseous

fuels. LPG liquid is lighter than water while LPG vapor is

heavier than air. This gives an indication of hazard of LPG

vapor accumulated in low lying area in the eventuality of the

leakage or spillage.

If these constraints are violated, then the infrastructure of the plant

will be ruined. Small amount of gas leakage seems to be a minor issue but

eventually it leads to fire blaze and explosions if unattended. The lack of basic

safety features will ultimately cause loss of life and destruction to property.

Therefore continuous monitoring is pivotal for a crucial infrastructure which

causes financial losses to the industry. There are number of technologies to

monitor and protect the resource plants. Most of the technologies are

specifically designed to detect, locate and report pipeline leakages as pipeline

gement plan.

The solutions rely on the availability of a wired or fiber optic network to

transfer the information to the base station. The wired networks are usually

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connected to regular sensor devices that measure specific attributes such as

flow rate, pressure, temperature, etc.

There are a number of problems using wired networks with regular

sensors for plant monitoring. Mohamed and Jawhar (2008) states that if there

is any damage for any part of the wires of the network, the whole pipeline

monitoring system will be compromised. It is easy for unauthorized people

to disable the monitoring system by cutting the network wires. Different types

of information are reported through the network. Some of this information is

considered more important to be delivered to the control station than others.

For example information reporting a fire is more important than information

about pressure measurement. In addition, Duplicate and unwanted

information can be transferred on the network causing significant delay for

other more important information. This is due to the lack of quality of Service

(QoS) support in these existing networks.

Jawhar et al (2007) explains the advent of technology in computing

and electronics pioneered the wired networks by Wireless Sensor Networks

with its immediate physical environment allows each sensor to provide

localized measurements and detailed information. The ability to

communicate not only allows sensor data and control information to be

communicated across the network of nodes, but nodes to cooperate in

performing more complex tasks such as statistical sampling, data

aggregation, system health and status monitoring. In this research work, the

pipeline monitoring is revised and extended for LPG plant application

considering overall functioning of the plant. A reliable sensor network model

is developed with Cluster based routing methodology for resilient network

communication. Sensor nodes are placed in and around various units of the

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plant. The nodes at each unit are clustered by cuckoo search algorithm. The

sensed information is transmitted to the base station by wireless HART

protocol. Various test conditions are considered and the performance of the

plant is evaluated to meet the real time scenario.

8.4 DESIGN OF CUCKOO BASED HART PROTOCOL

The developed mesh network model aims to implement an energy

efficient plant in leakage detection and monitoring of events. The LPG plant

system comprises of uniform distribution for sensor placement. The sensor

nodes are deployed in and around the five major units in the plant namely,

Tanker Unloading Section; Bullet storage unit; Pump and compressor unit;

Bottling unit; and Reposition and Dispatch unit. Ten numbers of sensors are

placed around each unit. To avoid redundancy and to provide collaborative

data processing, clustered architecture is employed. The cuckoo search

algorithm is applied for cluster formation.

As detailed in chapter six of section 6.3.1, the cluster formation is

carried out by considering node eccentricity, distance constraints and energy

level of the nodes. The energy function (8.1) is related to the minimization of

energy and maximization of lifetime of the nodes.

1

1

*100)(n

i

ididff

(8.1)

The dynamic clustering architecture helps to suppress redundant

data and offers consistency in cluster formation. After the clusters are formed,

the Cluster Heads (CHs) fuse or aggregate the information before forwarding it

to the base station. The transmission of data to the base station or control room

is carried out by HART protocol.

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8.4.1 Wireless HART

Highway Addressable Remote Transducer (HART) communication

protocol is established for process monitoring and control in industrial

automation. The protocol developed in 1989. The wired HART devices have

certain constraints like cost, unreliable data delivery to the end user. So the

wired HART technologies are modified to the next level of implementation.

i.e., Wireless HART. The sophisticated communication protocol was

developed in 2007 to provide simple, cost-effective and reliable way to

deploy new points of measurement and control without the wiring costs. It is

designed for wireless sensing and actuation, where wired sensing is prone to

hazards. Hart Foundation (2011) states that the Wireless HART standard

supports multiple messaging modes including one-way publishing of process

and control values, spontaneous notification by exception, ad-hoc

request/response, and auto-segmented block transfers of large data sets. As

per the requirements, these capabilities allow communications to be tailored

and aids in power reduction and overhead.

Wireless HART operates with IEEE 802.15.4-2006 compliant

radio model and uses Time Division Multiple Access (TDMA) and

channel hopping to communicate between devices. The communication

protocol differs from 802.15.4 MAC in Time-synchronized Mesh structure

employing Direct Sequence Spread Spectrum. The frequency management is

done as per packet basis and the communication protocol operates in 2.4 GHz.

The three main components of HART network are wireless field devices,

network manager and gateways. Each device in the mesh network can serve

as a router for messages from other devices. Thus it helps in inter

communication within the devices to increase the scalability of the network

(Leander et al 2011).

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The main advantage of employing Time-synchronized Mesh

prototype is to avoid Radio Frequency Interference from other wireless

systems such as Wi-Fi networks and cordless phones, RF noise from

machinery, physical obstruction of radio paths between devices, multipath

effects between sources and receivers, and node losses due to depleted battery

supplies and environmentally unfriendly operating conditions (Wagner 2010).

The time synchronization among the nodes helps to maintain the information

among the nodes by self-made acknowledgement and reception of packets in

respective time slots. Clear channel assess tests are available and blacklisting

avoids the frequently used channels.

8.4.2 Algorithm for Plant Monitoring

The proposed protocol design is detailed as follows,

1. Initialize the number of nodes, cuckoo nests, eggs in nests,

number of cuckoo, step size, location of base station, location

of nodes, and energy of nodes.

2. Establish the network, by deploying the nodes around the

units.

3. Perform cuckoo search by choosing a node at random from

every unit.

4. Check whether the energy function of node is greater than the

threshold?

5. If yes, rank the solution as cluster head; else update the

solution.

6. Cluster head broadcasts advertisement to abandon nests in the

unit.

7. The Worse nodes join the cluster head as cluster members.

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8. Nodes sense information.

9. CH creates TDMA schedule for each node to transmit the

sensed data.

10. CH receives data from members.

a) Normal Functioning Yes /No

b) Area of Detection pipelines/ Bullet/ storage/filling/

Tanker unloading

c) Type of Fault Leak/ Increase in flow/ thermal

expansion/ Fire

d) Level of Leakage

11. CH aggregates the data.

12. CH sends signal to actuates the alarm if a) =2.

13. Transmit the data to the base station using Wireless HART

mechanism.

14. Switch on fire water engine if c) =4.

15. Trip the equipments and halt the plant within 3 minutes.

8.5 EXPERIMENTAL RESULTS AND DISCUSSION

The proposed Cuckoo clustering with HART protocol is analyzed

by experimental results in order to indicate the quality of decisions the cuckoo

search makes on clustering of nodes and efficient data communication to the

base station by HART mechanism. The network is developed by analyzing

the graph routing algorithm proposed by Liao and Ge (2010) for wireless

mesh networks. The network model is simulated using MATLAB . The

schematic view of LPG Plant of SHV energy private limited is given in

Figure 8. 2. The simulated view of the plant is given in Figure 8.3.

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Figure 8.2 Topographical view of LPG Plant

The transmission energy of the nodes is set as 0.036 watt. The

receiving energy of nodes is set as 0.014 watt. The energy spent in active state

of the nodes is set as 0.014 watt. The number of nodes is chosen as 50.The

energy spent in the sleep state of the nodes is set as 0.15 micro watt. The

initial energy of the nodes is set as 1000 Joules. The sensor nodes are allowed

to sense the data every 5 seconds and the cluster heads are allowed to transmit

the data to the sink every 1 minute. The long interval helps in reduction in

energy consumption. In Table 8.1, the energy consumption of the plant for the

two different architectures is analyzed. It is found that the energy

consumption of the Cuckoo Based Hart Protocol exhibits nearly ten times less

when compared to ZigBee Protocol.

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0 50 100 1500

50

100

150

x position (metre)

y positio

n (m

etre)

Figure 8.3 Simulated view of LPG Plant

Table 8.1 Energy consumption

Simulation

time(s)

ZigBee Architecture

Energy consumption

(J)

Wireless HART

Architecture Energy

consumption (J)

10 0.23276 0.2116

20 0.36535 0.38155

30 0.73624 0.55711

50 1.10554 0.75188

70 1.48815 0.92267

100 1.886 1.21997

175 3.905 2.973

300 5.9828 5.348

600 12.370 10.585

800 14.609 16.034

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0 100 200 300 400 500 600 700 800 9000

2

4

6

8

10

12

14

16

18

simulation time 9seconds)

Energ

y c

onsum

ption (

joule

s)

HART

ZigBee

Figure 8.4 Energy consumption of the network

The energy consumption of 50 nodes for 1000 seconds is depicted

in Figure 8.4. It is inferred that the ZigBee and HART architecture has almost

similar energy consumption. Due to the variation in the timeslot in the MAC,

the energy consumption in HART is lesser compared to ZigBee Protocol. The

HART protocol can be varied as per the applications to achieve desirable

results.

8.6 CONCLUSION

The Cuckoo Based HART Protocol is developed to achieve energy

efficient Wireless Sensor Network by utilizing multimodal objective

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functions. Cuckoo search is applied for cluster head selection and formation

of clusters among the Sensor nodes. The proposed protocol is compared with

the standard ZigBee protocol. The simulation results exhibits that the

proposed protocol produces comparable results mainly due to optimal search

process in cluster formation and allocation of appropriate paths in

transmission of sensed data. The developed optimal algorithm reduces

complexity in chain formation and the test case conditions are verified. The

results are obtained by running more number of simulations. The plant

modeled with 50 nodes deployed at various units around the plant and the

different stages of leakage are analyzed.