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CHAPTER 4

INDUSTRIAL SYSTEM STUDY

4.1 INTRODUCTION

Industrial case studies used in the research work are described in

this chapter. The operating sequence which specifies the detailed flow of the

critical system selected and different problems considered with specific

reference to the Rotary Pumping System (RPS) are presented. The following

case studies are confined based on the safety considerations for evaluating the

performance of fault detection and diagnosis model developed using

intelligent techniques. The systems selected are

1) Liquefied Petroleum Gas (LPG) transfer system in LPG

bottling plant (Located in southern part of Tamilnadu).

2) Fluid Catalytic Cracking Unit (FCCU) - preheating system

in petroleum refinery (Located in Tamilnadu).

3) Urea Synthesis System in fertilizer industry (Located in

Andrapradesh).

The required data such as historical data, operational data,

maintenance data to develop the fault detection and diagnosis model are

collected from the industries. The hazards present in the system are identified

by using HAZOP. The common failure modes of RPS which are used in the

plant are identified through FMEA.

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The neural networks are able to handle continuous input data, and

the learning must be supervised in order to solve the fault detection and

diagnosis problem. Due to their powerful nonlinear function approximation

and adaptive learning capabilities, neural networks have drawn great attention

in the field of fault diagnosis. The proposed methodology for fault detection

in the above considered system is based on using Artificial Neural Network

(ANN) for detecting the normal and abnormal conditions of the given

parameters, which lead to various faults. The information required for the

development of the neural network model for fault detection was collected

from the field experts, the operational log book, history registers, operating

manual and maintenance records which are maintained by the plant operators.

Fault diagnosis is a classical area for fuzzy logic applications.

Compared to algorithmic approaches, the advantage of fuzzy logic-based

approach is that it gives possibilities to follow human’s way of fault

diagnosing and to handle different information and knowledge in a more

efficient way. The information required for the development of the fuzzy

system was collected from the industrial experts. The collected information

includes the fault-symptom relationship and the ranges of the variables. The

objective here is to take control the implicit knowledge behind the diagnosis

process, which is embedded in the information collected from the experts

through the developed model so that it can be applied for the diagnostic

process when the plant is in operation.

The developed model was tested with a number of test data

collected from the plant. The result produced by the model is compared with

the results obtained by the traditional risk analysis technique namely HAZOP

and is presented in this chapter. The following subsections describe the

description of all case studies with various hazards and risks existing in the

system.

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4.2 CASE STUDIES

This section contains the details of the following three case studies

used in this research.

Liquefied Petroleum Gas (LPG) Transfer System

Fluidized Catalytic- Cracking Unit (FCCU) System

Urea Synthesis System

4.2.1 Liquefied Petroleum Gas Transfer System

Liquefied Petroleum Gas (LPG) is a generic term for propane and

butane or mixtures of the two. LPG is obtained from two distinct sources

from: (i) the processing of crude oil in refineries or as a by-product from

secondary processing plant (ii) the natural gas streams, which largely consist

of methane with smaller quantities of heavier hydrocarbons. LPG is normally

supplied in pressurized condition in the form of pressure cylinders or small

pressure tanks. Hence, before being supplied to the consumers, it is to be

bottled in the cylinders at the bottling plant. The activities involved in LPG

bottling plant are unloading of LPG empty cylinders, checking of cylinder

condition, purging, LPG filling, correction of cylinders weights, cap fitting,

cap sealing, inspection and loading, and transferring of LPG filled cylinders.

The LPG transfer system is an important section in LPG bottling

plant, which contains the critical process sections like pump house, bullet

storage, filling section and tanker lorry decantation. Figure 4.1 shows the

block diagram representation of LPG transfer system. First the LPG vapour

from the storage bullet (SB) is transferred to the lorry tanker through the

compressor. Due to the occurrence of pressure variation in tanker lorry, the

liquid LPG is moving from the tanker to storage bullet. During this operation

85

M P

PV2

TLD

CPV2

CPV1

PV1BIV

G5G4G2

G1

G3

CP

M MotorG1 - G5 Pressure Gauges

CP Compressor

SB Storage Bullet

P Pump

SV

BIV Bullet valve

CPV1 & CPV2 Compressor Valve

PV1 & PV2 Pump ValveTLD Tanker Lorry Decantation

SBU

PH

PH Pump houseSBU Standby Unit

SB

SV Safety Valve

ES Electric Supply

FS Filling Station

FS ES

the initial pressure and the temperature of the bullet and tanker will be

maintained between 4 bar and 10 bar; 25oC to 40oC and 5 bar and 28oC

respectively. Next, the LPG liquid from the bullet is transferred to the filling

station with the help of the centrifugal pumps.

Figure 4.1 LPG Transfer System

The various safety instruments used in the LPG transfer system are

pressure gauge, temperature gauge, butterfly valves, safety relief valves,

excess flow check valves, remote operated valve, pressure differential meters,

etc., The problems involved in LPG handling are asphyxia, cold burns, metal

brittleness due to low temperature, flammability and explosion. The possible

fire scenarios in LPG installations are pool fire, jet fire and flash fire.

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4.2.2 Fluidized Catalytic Cracking Unit System

The second system selected for the case study is Fluid Catalytic

Cracking Unit (FCCU) - Preheating system of a petroleum refinery. A fluid

catalytic cracking unit (FCCU) usually consists of preheater(s); catalyst

handling equipment; reactor; catalyst stripper; regenerator air blower;

pumping system; catalyst regenerator including flue gas multistage cyclone

separators, downstream regenerator, vent gas control equipment (also any

downstream power or heat recovery equipment), main fractionator’s,

overhead condenser and receiver, downstream fractionation, and any

associated gasoline storage tanks. Figure 4.2 shows the block diagram

representation of FCCU Preheating system. The functional description and its

key parameters in a petroleum refinery process plant are explained below:

Feed for FCCU is Heavy Vacuum Gas Oil (HVGO) from Crude

Vacuum Distillation unit. For increasing the feed temperature, an additional

HVGO stream (referred to as very hot feed) is taken from upstream of HVGO

tempered water cooler. Both these streams combine and flow through a shut-

off valve to feed surge drum. Hot feed flow and very hot feed flow are

measured by Flow Indicator. Excess hot feed flows to cold feed tank through

control valve on the rundown line. Cold feed is pumped to the surge drum

through surge drum level controller. Feed temperature is measured by

temperature indicator located on suction line and the surge drum gets a boot

which separates water that is present in feed.

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Figure 4.2 FCCU Preheating System

The clarified oil from slurry settler top is cooled by tempered water

or cooling water to 60oC. The cooled clarified oil is pumped to the storage

tank by using pumps. In addition to supplying feed, the pump serves the

purpose of supplying raw oil as flushing oil, torch oil and diluents to slurry

settler when the unit is shutdown.

FCCU performs a catalytic process for increasing the distillates and

producing LPG, Gas oil, etc. The feedstock is Vacuum Gas Oil (VGO). The

product is of good quality due to the controlled nature of catalytic reaction.

However, using different types of catalyst can easily change the product

pattern in FCCU. Failures associated with FCCU are component failures such

as gasket leaks, equipment failures, process failures, physical and chemical

hazards and maintenance problems.

PUMPING

FEED PRE HEATING AND

PRODUCT COOLING

CONTAINMENT

CONTROL

CLO To Storage

Pre heated Feed to Furnace

Cooled oil -MCB

Raw oil to Fractination

Cooling Water

Tempered

DCS status

RAW

AC Power Supply

Clarified

HOT Oil from MCB

Instrument

Cooling Water Tempered Water

DCS DCS input signal

88

4.2.3 Urea Synthesis System

The third system selected for this case study is urea synthesis

system of a fertilizer process plant. The fertilizer industry is one of the major

process industries in terms of both employment and contribution to the nation

economy. The fertilizer industry consists of three major sections viz., urea

manufacturing, ammonia manufacturing and water treatment. The focused

area for this study is confined to urea manufacturing plant.

Urea manufacturing plant contains the following systems namely

synthesis, purification, concentration and prilling. The urea synthesis system

is taken for detailed study and is shown in Figure 4.3. The production of urea

requires ammonia and CO2 as the inputs, both of which are available from

ammonia plant. The CO2 from ammonia plant is compressed to about 160 bar

and sent to the Urea Reactor. Liquid ammonia is pumped using high-pressure

reactor feed pump and along with recycle carbamate enters into Urea Reactor.

Urea Reactor operates at about 156 bar and 188oC.

Following reactions take place in the Urea Reactor:

2 NH3 + CO2 ---------NH2COONH4 (Ammonium Carbamate) + heat

NH2COONH4 ---------CO (NH2)2 + H2O – heat (Urea)

The product stream from the Urea Reactor contains in addition to

urea, a large quantity of unconverted ammonia, CO2 and water. The

ammonium carbamate in the product stream is recovered in three stages viz.,

high pressure stage, medium pressure stage and low pressure stage by

decomposing the carbamate into ammonia and CO2, separating the gases from

the liquid product stream and recondensing the gases back to carbamate

solution which is recycled back to the Urea Synthesis Reactor. In this process,

89

the product stream becomes richer and richer in the urea content. In the high-

pressure section, separation of ammonia and CO2 in the falling film of liquid

in the tubes is stripped by ammonia vapour. Medium pressure steam supplies

the required heat.

As the Urea Reactor operates with excess ammonia, the excess

ammonia is recovered in ammonia condenser. The product stream leaving the

low-pressure section contains 70% urea. This is further concentrated in the

vacuum concentrators to get 99.8% urea melt. This molten urea is pumped to

the top of urea prilling tower and fed into a prilling bucket. The prilling tower

of 22m diameter and 75m free fall height operates under natural draft. The

urea prills from the bottom of the prilling tower are transported through

mechanised belt conveyor system into urea storage silo or directly to urea

bagging plant. The bagged urea is dispatched by rail wagons/road trucks. The

urea synthesis stage is the first phase in the urea section, where the urea is

manufactured, concentrated and then prilled down to molecules for easy

usage. The common problems associated with urea synthesis system are

irritation to respiratory tract, gastrointestinal tract, skin and eye.

Figure 4.3 Urea Synthesis System

NH3

S T R I P P E

UREA R E A C T

E12

COMPRESSOR

105

Pump

Pump 101

NH3 CO2

Condensed liquid

OH Gases

Urea

90

4.3 DESCRIPTION OF THE PROBLEM WITH SPECIFIC

REFERENCE TO ROTARY PUMPING SYSTEM

Process industries are the critical industries, which require a

number of safety related issues to be addressed. The various activities

performed in process industries pose lot of risk to the people working in the

plant and the people living in the surrounding area. These industries handle

large quantities of highly hazardous chemicals often at extreme conditions of

temperature and pressure. Generally the systems used in the process

industries are designed to operate safely under normal conditions; however,

improper operation can lead to equipment failure and release of potentially

hazardous materials. In addition to that any wrong operation is prone to be a

source of disaster, causing heavy financial losses as well as casualties. This

necessitates continuous monitoring of various parameters of the plant on real

time basis.

In process industries, occurrence of faults in the functional devices

of the system is inevitable that lead to undesired or intolerable performance

(failure) of the system. Fault detection and diagnosis are important tasks in

process industry. It deals with the timely detection, diagnosis and correction

of abnormal condition of faults in the system. Typical examples of such faults

are:

• Defective constructions in the system, such as cracks,

ruptures, fractures, leaks, loose parts, etc.

• Faults in the drives, such as damage in the bearings,

deficiencies in force or momentum, defects in the gears,

ageing effects, etc.

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• Faults in sensors, such as scaling errors, hysteretic, drift,

dead zones, shortcuts, contact failures, etc.

• Abnormal parameter variations.

Traditionally, safety in the design of any system employed in the

process industry relied upon the application of codes of practice, design codes

and checklists based on the wide experience and knowledge of professional

experts and specialists in the industry. However, such approaches can only

cope-up with problems that have arisen earlier. With the increasing

complexity in all aspects, these traditional approaches are likely to miss other

issues, which need to be considered during any stage of functioning.

A hazard has the potential to cause harm and result in injury to

people or damage to property, plant, products or the environment and

production losses. In the process industries such hazards fall into particular

categories: chemical, thermodynamic, electrical and electromagnetic, and

mechanical. The system that belongs to the above mentioned case studies

could potentially give rise to hazards in all these categories. The major rotary

equipments used in the above mentioned systems are centrifugal pumps,

compressors, turbines, chillers, fans, drum filters, etc. The detailed analysis of

past historical data for the period of eight years (1995-2003) from a petroleum

refinery located in Tamilnadu is shown in Figure 4.4.

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0 10 20 30 40 50 60 70 80

Pump faults

Compressor faults

Chillers

Colling tower fans

FinFan coolers

Turbine

Drum filters

mixers

others

Nam

e of

the

rota

ry s

yste

m

Percentage

Figure 4.4 Fault occurrence in various rotary systems

Based on the analysis, the in-depth study was conducted for the

rotary system i.e pumps which are used in all the above mentioned system.

The pump is a mechanical device by means of which liquid may be

transferred from one place to the other. The nature of the liquids that are

pumped varies from the most volatile fluid to the thick mud and sludge, from

water to the most corrosive acids and alkalis, from fluids at low temperature

to many types of molten metals. Pumping means addition of energy to a liquid

to move from one place to the other and this is done by means of piston,

plunger, impeller, propeller, gears, screws, etc. The pump may be classified

broadly, according to the principle of operations into two general classes

namely centrifugal pumps and reciprocating pumps. The rotary pump system

selected for this research study is centrifugal pumping system.

The Hazard and Operability (HAZOP) studies were used to identify

the various potential hazards and operability problems that existed in the case

studies. Failure Mode Effect Analysis (FMEA) was used to identify the

various faults present in the rotary pumping system with its causes and

93

consequences. The outcome of a HAZOP and FMEA analysis are the

findings, which include identification of hazards and operating problems; the

partial list of failure modes (causes and problems); recommendation for

changes in design, procedures, etc., to improve the system. The result of both

HAZOP and FMEA of the selected system are reported in the Appendix 1 and

2 respectively. The common failure causes and its problems (derived from the

source data presented in the Appendix 3) with its cause code of centrifugal

pumping system are presented in the Table 4.1 and

Table 4.2 respectively. The outcome results have given important leads in

improving safety at different stages of operation and maintenance of the plant

and also used at the different stages of the model development for fault

diagnosis.

Table 4.1 Common failure causes in centrifugal pumping system

Causes Code Causes Code Bent Shaft Cp1 Mechanical defects Cp17 Casing distorted Cp2 Misalignment Cp18 Cavitations Cp3 Misalignment (Pump and driver) Cp19 Clogged impeller Cp4 Mismatched pumps in series Cp20 Driver imbalance Cp5 Non condensable liquid Cp21 Electrical problems Cp6 Obstructions in lines Cp22 Entrained air Cp7 Rotor imbalance Cp23 Hydraulic instability Cp8 Specific gravity too high Cp24 Impeller installed backward Cp9 Speed too high Cp25 Impeller mechanical seal Cp10 Speed too low Cp26 Inlet strainer partially clogged Cp11 Total system head higher than

design Cp27

Insufficient flow through pump Cp12 Total system head lower than design

Cp28

Insufficient suction pressure Cp13 Unsuitable pumps in parallel operation

Cp29

Insufficient suction volume Cp14 Viscosity too high Cp30 Internal wear Cp15 Wrong rotation Cp31 Leakage in piping, valves Cp16

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Table 4.2 Common failure problems with causes in centrifugal

pumping system

S.No. Problems Causes (Code)

1 Insufficient discharge pressure

Cp3, Cp4, Cp7, Cp9, Cp11, Cp13, Cp14, Cp15, Cp16, Cp20, Cp21, Cp22, Cp24, Cp26, Cp27, Cp29, Cp30, Cp31

2 Intermittent operations Cp3, Cp7, Cp13, Cp14, Cp21, Cp27

3 Insufficient capacity Cp3, Cp4, Cp7, Cp9, Cp11, Cp13, Cp14, Cp15, Cp16, Cp20, Cp21, Cp22, Cp26, Cp27, Cp29, Cp30

4 No liquid delivery Cp3, Cp4, Cp13, Cp14, Cp16, Cp22, Cp26, Cp27, Cp29, Cp31

5 High bearing temperature Cp1, Cp2, Cp3, Cp8, Cp14, Cp17, Cp18, Cp27, Cp28, Cp29

6 Short bearing life Cp1, Cp2, Cp5, Cp6, Cp8, Cp18, Cp20, Cp23

7 Short mechanical seal life Cp1, Cp2, Cp3, Cp5, Cp6, Cp8, Cp10, Cp17, Cp18, Cp23, Cp27, Cp28

8 High vibration Cp1, Cp2, Cp3, Cp4, Cp5, Cp6, Cp7, Cp8, Cp11, Cp13, Cp14, Cp15, Cp18, Cp19, Cp20, Cp21, Cp22, Cp23, Cp28, Cp29

9 High noise levels Cp3, Cp7, Cp8, Cp11, Cp13, Cp14, Cp21, Cp28, Cp29

10 Power demand excessive Cp1, Cp2, Cp4, Cp6, Cp9, Cp15, Cp17, Cp18, Cp19, Cp20, Cp24, Cp25, Cp28, Cp30, Cp31

11 Motor trips Cp6, Cp19, Cp25, Cp28, Cp29

12 Elevated motor temperature

Cp2, Cp6, Cp7, Cp18, Cp21, Cp22, Cp24, Cp26, Cp27, Cp30, Cp31

13 Elevated liquid temperature

Cp3, Cp11, Cp12, Cp14, Cp19, Cp22, Cp27, Cp28, Cp29

95

4.4 FAULT DETECTION USING NEURAL NETWORKS

In this section the details of the development of fault detection

models using neural networks for the three case study systems: Case 1

represents LPG transfer system, Case 2 represents FCCU preheating system

and Case 3 represents urea synthesis system are presented. The various issues

to be addressed during the development of neural network model are

discussed and presented.

4.4.1 Introduction

The proposed methodology for fault detection in the system is

based on using Artificial Neural Network (ANN) for detecting the normal and

abnormal conditions of the given parameters, which lead to various faults.

The normal condition represents no fault situation and abnormal condition

represents fault occurrence. The main purpose of selecting ANN as a tool is

inability otherwise to form a mathematical relationship due to the nonlinearity

between the inputs and the outputs, good generalization ability, fast real time

operation, simple online control and to perform the complicated mapping

without functional relationship.

The neural network approach for this purpose has two phases;

training and testing. During the training phase, neural network is trained to

capture the underlying relationship between the chosen inputs and outputs.

After training, the networks are tested with a test data set, which are not used

for training. Once the networks are trained and tested, they are ready for

detecting the fault at different operating conditions.

96

The following issues are to be addressed while developing the

model for fault detection: a) Selection of variables, b) Data generation, c)

Data normalization and d) Selection of network structure.

4.4.1.1 Selection of variables For the application machine learning approaches, it is important to

properly select the input variables, as ANN are supposed to learn the

relationships between input and output variables on the basis of input-output

pairs provided during training. In neural network based fault detection model,

the input variables represent the operating state of the pumping system, and

the output is the condition of normal or abnormal which may cause in turn the

faults.

4.4.1.2 Data Generation

The generation of training data is an important step in the

development of ANN models. To achieve a good performance of the neural

network, the training data should represent the complete range of operating

conditions of the system which contains all possible fault occurrences.

4.4.1.3 Data Normalization

If the generated data is directly fed to the network as training

patterns, higher valued input variables may tend to suppress the influence of

smaller ones. Also, if the raw data is directly applied to the network, there is a

risk of the simulated neurons reaching the saturated conditions. If the neurons

get saturated, then the changes in the input value will produce a very small

change or no change in the output value. This affects the network training to a

greater extent. So the data is normalized before being presented to the neural

97

network such that ANN will give equal priority to all the inputs. One way to

normalize the data x is by using the expression:

minmax

min

xxrangexxxn starting value (4.1)

where, nx is the normalized value and andxmin maxx are the minimum and

maximum values of the variable x .

4.4.1.4 Selection of Network Structure In this work, back propagation network is selected for the cases

namely LPG transfer system and FCCU preheating system; auto associative

network is selected for the case of urea synthesis system. To make a neural

network to perform some specific task, one must choose how the units are

connected to one another. This includes the selection of the number of hidden

nodes and type of the transfer function used. The number of hidden-units is

directly related to the capabilities of the network. For the best network

performance, an optimal number of hidden-units must be properly determined

using the trial and error procedure.

4.4.2 Model Development

Two different ANN models were developed for fault detection:

Model I: Neural network model for LPG Transfer System and FCCU

Preheating system using back propagation network and Model II: Neural

network model for Urea synthesis system using auto associative neural

network.

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Model I: Neural Network Model for LPG Transfer System and

FCCU Preheating System using BPN

This section presents the details of the development and testing of

ANN models for fault detection on LPG transfer system and FCCU

preheating system. The proposed methodology for fault detection in LPG

transfer system and FCCU preheating system is based on using BPN. The

neural network model is developed using MATLAB 6.5 Neural Network

Toolbox in Pentium IV with 2.40 GHz processor with 512 MB of RAM. The

ANN model used here has two hidden layers of tansigmoidal neurons, which

receive the inputs, then broadcast their outputs to an output layer of linear

neurons, which compute the corresponding values. The back propagation

training algorithm, which propagates the error from the output layer to the

hidden layer to update the weight matrix, is most commonly used for feed

forward neural networks.

The algorithm used for the training of ANN model is given below:

Step 1: Load the data in a file.

Step 2: Separate the input and output data.

Step 3: Separate the training and test data.

Step 4: Normalize all the input and output values.

Step 5: Define the network structure.

Step 6: Initialize the weight matrix and biases.

Step 7: Specify the number of epochs.

Step 6: Train the network with the train data.

Step 7: Test the network with the test data.

Step 8: Re-normalize the results.

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The training data required for this purpose is generated through off

line simulation and collected from the real time operational data of the

concerned industries. The data contains three input features namely pressure,

flow and temperature and one output that is labeled as either normal or as a

fault, with exactly one specific fault. All the input features are continuous

variables. The generated training data is normalized and applied to the neural

network with corresponding output to learn the input-output relationship. The

neural network model was trained using the back propagation algorithm with

the help of MATLAB neural network toolbox. At the end of the training

process, the model obtained consists of the optimal weight and the bias

vector. After training, the generalization performance of the network is

evaluated with the help of the test data and it shows that the trained ANN is

able to produce the correct output even for the new input. After training the

network with least error rate, the testing data is fed as input to the network.

The testing data comprise of both normal and abnormal data. The output of

the testing data is corrected based on the minimization of an energy function

representing the instantaneous error.

For the cases, LPG transfer system and FCCU preheating system,

the output performance results from the network is given in the Table 4.3.

Figure 4.5 shows the training performance of LPG transfer system using

BPN. In this figure Y axis refers to Training Performance Index against Level

of Training at X axis. The drooping curve (learning curve) represents the

minimization of the error over the training period.

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Table 4.3 Performance of the neural network model using BPN of

LPG transfer system and FCCU preheating system

Name of the system LPG transfer

system FCCU preheating

system

Number of Training Data 300 300

Number of Testing Data 160 160

Transfer Function used Tansigmoidal Tansigmoidal

Training Time 3.6250 seconds 3.0156 sec

Maximum no. of epochs 500 500

Mean Square Error during Training 9.7849e-004 0.0012

Mean Square Error during Testing 0.010 0.0013

Percentage of Classification 100% 100%

0 50 100 150 20010

-5

10-4

10-3

10-2

10-1

100

234 Epochs

Trai

ning

-Blu

e G

oal-B

lack

Performance is 9.94597e-005, Goal is 0.0001

Figure 4.5 ANN Training performance – LPG Transfer System

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Model II: Neural Network Model for Urea Synthesis System using

AANN

This section presents the details of the development and testing of

ANN models for fault detection on urea synthesis system. The proposed

methodology for fault detection in urea synthesis system is based on using

AANN. The AANN is developed to model the normal state of the system and

during the actual use; the data from abnormal condition will be reflected as

deviation from the normal state. The neural network model is developed

using MATLAB Neural Network Toolbox in Pentium IV with 2.40 GHz

processor with 512 MB of RAM.

The architecture used in this work consists of two halves, the

mapping layer and the demapping layer. These halves are interconnected

through the bottle-neck layer. The mapping layer compresses the data into a

reduced order representation, eliminating the redundancies and extracting the

key features in the data. The demapping layer recovers the encoded

information from the principal components.

The input layer and the output layer of network have same number

of processing units. One of the hidden layers known as bottleneck layer or

dimension compression layer has smaller dimension than the input layer.

These networks can be trained using learning algorithms such as

backpropagation to reconstruct the input data at the output layer. The units of

the dimension compression hidden layer represent the significant features of

the input data like in the case of principal component analysis. This

characteristic of AANN model is exploited extensively for linear and

nonlinear dimension compression of the input data.

102

The information and data required for the development of the

neural network system was collected from the fertilizer industry. The

collected information includes the performance data of the equipment used in

urea synthesis system, real time data for the critical equipments namely

pumps, compressors and reduction gear box, list of various faults occurred in

the system, critical parameters of the system and their operating ranges. The

following variables were selected as the input features of the AANN such as

Speed (RPM) of pump, Inlet Pressure (bar), Exhaust Pressure (bar), Oil

Pressure (bar), Temperature (0C) [Front & Back], Head locations and

Vibration. The experiments are conducted with operational data in the system.

Steps involved in conducting the experiments are as follows:

Training Phase:

Urea synthesis data collected from the fertilizer industry

used in the training of neural network.

Fault detection models are trained in auto associative mode

with the feature vectors derived only from the normal data.

Stopping criteria used for network training is a negligible

change in the training error.

Testing Phase:

Urea synthesis data collected from the fertilizer industry is

used in the testing of neural network.

Feature vectors derived from the data set that are as both

normal and abnormal data are used for testing.

During the testing, actual outputs from the network are

compared with the desired outputs using Euclidian distance

measure to arrive at the testing error.

103

The collected training data is normalized and applied to the neural

network for learning. The neural network model was trained using the

MATLAB neural network toolbox. In order to have good performance using

this simple decision logic: I) Find out a suitable threshold for the error values,

II) Accept the data if error is less than the threshold and III) Reject the data if

error is more than the threshold. After training, the generalization

performance of the network is evaluated with the help of the test data. This

shows that the trained AANN is able to produce the correct output even for

the new input.

The results obtained by changing the structure of the AANN model

with different network configuration are as shown in Table.4.4. The five layer

network with network configuration 11L-30N-9N-30N-11L is selected

because the training error value is low with compared to other network

configuration. Table 4.5 shows the performance of fault detection in urea

synthesis system using the AANN model with the five layer network

configuration. It refers to different possible configurations tested for the

performance in 5 Layer network. The training error noted in each case is

recorded in the table. Among the various combinations of five layer network

configuration, the least value of training error falls on the 11L-30N-9N-30N-

11L network configuration. So this network configuration is selected for

further training and testing of the case study system. The training performance

of the neural network developed using MATLAB is as shown in Figure 4.6.

In Figure 4.6, Y axis refers to Training Performance Index against Level of

Training at X axis. The drooping curve (learning curve) represents the

minimization of the error over the training period.

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Table 4.4 Performance of the Auto associative neural network model

Network Layer

Network configuration Training

error

5 11L-30N-9N-30N-11L 0.0014

6 11L-30N-9N-30N-30N-11L 0.0014

7 11L-30N-30N-9N-30N-30N-11L 0.0015

8 11L-30N-30N-30N-8N-30N-30N-11L 0.0021

9 11L-30N-30N-30N-9N-30N-30N-30N-11L 0.0017

Table 4.5 Performance of the AANN model in five layer configurations

Network configuration Training error

11L-30N-5N-30N-11L 0.004

11L-30N-7N-30N-11L 0.007

11L-30N-9N-30N-11L 0.0014

11L-30N-10N-30N-11L 0.0027

11L-20N-9N-20N-11L 0.0022

11L-25N-9N-25N-11L 0.0018

11L-35N-9N-35N-11L 0.0053

After training the network with least error rate, the testing data was

fed as input to the network. The testing data comprises of both normal and

abnormal data. The performance result obtained for this case is given below

in the Table 4.6. This shows that the trained neural network model is able to

produce the correct output even for a new input.

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Table 4.6 Performance of the AANN model of Urea Synthesis system

Number of Training Data 1500

Number of Testing Data 300

Training Time 155.34 sec

No. of Epochs to train 874

Mean Square Error during Training 0.00099777

Mean Square Error during Testing 0.010

Percentage of Classification 100%

Figure 4.6 AANN Training performance – Urea synthesis system

4.4.3 Conclusions

Model I has presented a neural network based approach for fault

detection in LPG transfer system and FCCU Preheating system. In the model

II, the problems of fault detection in a section of a fertilizer plant have been

addressed. The data required for the development of neural network model

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have been obtained through the real time operational and maintenance data of

the system considered. For the ANN Model the testing data is fed to the

designed model to check the accuracy. The testing samples are different from

the training samples and they are new to the trained network. Simulation

results show that this neural network approach is very much effective in

detecting the various faults in the system. To further improve the performance

of the model the input features of the network can be selected through

dimensionality reduction techniques.

The model has been validated with respect to the performances of

the real plant, and simulations have been performed for different significant

operating conditions. This work has shown that neural diagnostic tools

perform very well, especially in terms of generalization capabilities, i.e., the

ability to decide correctly, even under operating conditions in the plant that

are different from those used in the training phase. Simulation results confirm

that such neural diagnostic tools may provide adequate solutions to diagnostic

problems in complex processes.

4.5 FAULT DIAGNOSIS USING FUZZY LOGIC

This section presents the details of the fuzzy logic based diagnostic

system developed for rotary pumping system which is used in the three case

study systems namely LPG transfer system, FCCU preheating system and

urea synthesis system.

4.5.1 Introduction

Fault diagnosis is a classical area for fuzzy logic applications.

Compared to algorithmic approaches, the advantage of fuzzy logic-based

approach is that it gives possibilities to follow human’s way of fault

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diagnosing and to handle different information and knowledge in a more

efficient way. Building a model for fault diagnosis involves embedding the

heuristic knowledge inherent in the decision-making abilities of the human

experts. Human beings acquire heuristic knowledge by experience and

observations over a period of time. This knowledge has inherent fuzziness

because it comes from uncertain and imprecise nature of expressing the

abstract thoughts. Fuzzy logic can afford the computers, the capability of

manipulating abstract concepts commonly used by the humans in decision-

making.

The advantage of fuzzy logic-based approach is that it gives

possibilities to follow human’s way of fault diagnosing and to handle

different information and knowledge in a more efficient way. One of the most

important considerations in designing any fuzzy systems is the generation of

the fuzzy rules and the membership functions for each fuzzy set. In most

existing applications, the fuzzy rules are generated by experts in the area,

especially for fault diagnosis problems with only a few inputs.

4.5.2 Model Development

The information required for the development of the fuzzy system

was collected from the literature reference, discussion with field experts and

using safety management technique i.e HAZOP and FMEA. The collected

information includes the fault-symptom relationship for centrifugal pumps

and the ranges of the variables. The objective here is to capture the implicit

knowledge behind the diagnosis process, which is embedded in the

information collected from the experts through the developed model so that it

can be applied for the diagnostic process when the system is in operation.

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To design a fuzzy model based on available expert knowledge, the

following steps can be followed:

o Select the input and output variables, the structure of the

rules, and the inference and defuzzification methods.

o Decide on the number of linguistic terms for each variable

and define the corresponding membership functions.

o Formulate the available knowledge in terms of fuzzy if-then

rules.

o Validate the model (typically by using data). If the model

does not meet the expected performance, iterate on the

above design steps.

According to the observations of industrial experts’, whenever

some fault occurs on some part of the system, this is reflected in the form of

changes in the values of temperature, pressure or flow of the operating liquid.

Hence these variables were taken as the input of the developed fuzzy model

and then the membership functions are formed for all the input variables

based on their values during the normal and abnormal conditions. In all the

cases, triangular and trapezoidal functions are used.

The various faults of the pumping system which are employed in

LPG transfer system, FCCU preheating system and Urea synthesis system

considered for the development of fuzzy model are given in Table 4.7 and

Table 4.8.

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Table 4.7 List of Faults considered in LPG Transfer System (Case-1)

Name of the Fault Fault Code

Name of the Fault Fault Code

Pump cavitation FM1 Rupture and breakage of pipe line FM7

Pump heating FM2 Motor overload FM8

Damage to impeller FM3 Pump vibration FM9

Operability problem FM4 Damage to pump internal mechanism FM10

Fire hazards FM5 Damage to pump impeller casing FM11

Damage to pump FM6 Normal condition FN

Table 4.8 List of Faults considered in FCCU Refinery Preheating

system (Case-2) and Urea Synthesis system (Case-3)

Name of the Fault Fault Code

Name of the Fault Fault Code

Gasket Leak FM1 Terminal flashover FM14

Tube Leak FM2 Oil leak FM15

Floating head gasket leak FM3 Coupling problems FM16

Stuck in position FM4 Erratic flow indication FM17

Pipeline rupture FM5 Partial choking FM18

Passing stream straps FM6 Flange leak FM19

Channel gasket leak FM7 Controller poor response FM20

Tube side fouling FM8 Pump internal mechanism failure FM21

Sluggish operation FM9 Operability problems FM22

Block valve partial choke FM10 Bearing problems FM23

Impeller problems FM11 Pump heating FM24

Pump cavitations FM12 Pump vibration FM25

Strainer failures FM13 Normal Condition FN

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The fault-symptom relationships are formulated in the form of

fuzzy if-then rules. A set of such rules constitutes the rule base of the Fuzzy

Inference System. This form of knowledge representation is appropriate

because it is very close to the way experts themselves think about the

diagnosis and decision process. The operating range of the input variable is

given in the Table 4.9 to Table 4.11. Table 4.12 to Table 4.14 show the fuzzy

rule matrix for all the systems.

Table 4.9 Input variables with operating range (LPG Transfer System)

Name of the variables Minimum Value Maximum Value

Pressure 4 bar 40 bar

Flow 24 m3/hr 48 m3/hr

Table 4.10 Input variables with operating range (FCCU Preheating

System)

Name of the variables Minimum Value Maximum Value

Feed Flow 118 m3/hr 122 m3/hr

Temperature 230˚C 235˚C

Table 4.11 Input variables with operating range (Urea Synthesis System)

Name of the variables Minimum Value Maximum Value

Pressure 23 bar 40 bar

Temperature 130˚C 135˚C

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Table 4.12 Fuzzy rule matrix (LPG Transfer System)

Flow

Pressure

More

Normal Low

More FM15, FM16, FM17, FM18,

FM12, FM14, FM16, FM17

FM11, FM12, FM13, FM14, FM15, FM17,

Normal FM14, FM15, FM18,

FM1N FM13, FM14, FM16,

Low FM14, FM15, FM17, FM16,

FM19

FM12, FM14, FM11, FM14, FM19, FM110, FM111,

Table 4.13 Fuzzy rule matrix (FCCU Preheating System)

Flow

Temp

Very Low Low Normal

More

Low FM1,FM2, FM3, FM4, FM5, FM6

FM1,FM2, FM3,FM4,FM5,

FM6

FM1,FM2, FM3, FM5, FM6, FM7

FM8, FM18, FM19

Normal

FM9, FM10, FM11, FM12, FM13, FM14, FM15, FM16, FM17, FM21

FM9, FM10, FM11, FM12

FM26 FM20

More FM23 FM24, FM23 FM22, FM23,

FM24 FM25, FM22

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Table 4.14 Fuzzy rule matrix (Urea Synthesis System)

Pressure

Temp

Very Low Low Normal

More

Low FM1,FM2, FM3, FM4, FM5,FM6

FM1,FM2,

FM3, FM4, FM5,FM6

FM9,FM18, FM19

FM18, FM19

Normal FM10, FM11, FM12, FM17, FM21

FM24 FM26 FM20

More FM23 FM24, FM23 FM16, FM17,

FM19 FM25, FM22

For illustration, the if-then rules formulated for pumping system in

the LPG transfer system is given below in the form of a matrix

The description of the first rule in the table is given below:

IF flow is more and pressure is more THEN the fault is FM15,

FM16, FM17, and FM18.

The fuzzy rule matrix along with the membership functions will

help to identify the potential faults present in the system. In order to obtain

the global information concerning each fault represented by the

interconnection of its causal chains via AND connectives, is used as an

aggregation support. By placing the AND connectives with their isomorphical

fuzzy operators (T-norms), the confidence level is extracted which combines

the distinct pieces of information concerning fault’s existence from each

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column into a global fault possibility. As fault class with particular threshold

is considered, for the setting of threshold, compromises have to be made

between the detection of fault class and unnecessary false alarms because of

normal fluctuations of the variables. In order to validate the diagnostic

hypothesis according to which fault class may explain the system abnormal

behavior the global possibility of the fault class has been projected within an

implicit act of backward reasoning on the respective column of the fuzzy

diagnostic model.

The fuzzy model was developed using MATLAB programming.

While developing the fuzzy model min was used for T-norm, max was used

for T- conorm and Mamdani inference was used. The developed model was

tested with a number of test data collected from the system.

Table 4.15 refers to outputs of the Fuzzy model for the inputs of

Flow: 26.23 m3/hr and Pressure: 17.91 bar. For the given set of input data

after the fuzzy reasoning, the final decision regarding the type of fault in the

particular critical node can be determined by taking the fault class that has the

maximum confidence level. For the above case, the inference is based on the

output, i.e. the fault classes FM12, FM14, FM16 and FM17, have the maximum

confidence levels of 0.846. By taking such a decision one can easily detect the

presence of the particular classes of faults at a time, with exact combinations

of input variables of the particular node.

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Table 4.15 Output produced for the sample input LPG transfer system

Fault Confidence Level Fault Confidence Level

FM11 0.2567 FM15 0.2567

FM12 0.8460 FM16 0.8460

FM13 0.2567 FM17 0.8460

FM14 0.8460 FM113 0.2567

Table 4.16 indicates outputs of the same fuzzy model for different

combination of inputs. For the given set of input data after the fuzzy

reasoning, the final decision regarding the type of fault can be determined by

taking the fault classes, having the highest confidence level. For the above

case, the inference based on this procedure is given in Table 4.16. From the

table, it is evident that the fuzzy model has identified multiple faults and also

developing faults. The result produced by fuzzy model is compared with the

traditional risk analysis technique namely HAZOP.

Table 4.16 Output produced by fuzzy model of LPG Transfer system

for the given input values

Input Values FM11 FM1 FM1 FM1 FM1 FM1 FM1 FM1 FM1 FM1 FM1

Model output

0.5000

0

0

0.5000

0

0

0.1282

0.6222

0.1667

0

0.7000

0

0.1282

0.3778

0

0

0.5000

0

0.8333

0.6222

0.2500

0.6111

0.7000

0

0.1282

0

0.2500

0.6111

0.5000

1

0.1282

0.3778

0.1667

0.3889

0.7000

1

0.8333

0

0.1667

0.3889

0.7000

1

0.1282

0

0.2500

0.6111

0

1

0

0.5000

0.1667

0.3889

0

0

0

0.5000

0

0

0

0

0

0.5000

0

0

0

0

0

7.4 & 25.5

11.5 & 28.5

9.5 & 33.5

18.5 & 25.5

18.5 & 29.5

25.9 & 34.8

13.5 & 24.5

2 3 4 5 6 7 8 9 10 11

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Table 4.17 Comparison of fuzzy results with HAZOP for LPG System

Case

No. Input values

Potential Faults

Fuzzy HAZOP

1 7.4 and 25.5 FM12, FM14 FM11, FM14, FM19, FM110, FM111

2 11.5 and 28.5 FM14, FM15, FM18 FM12, FM14

3 9.5 and 33.5 FM14, FM15, FM18, FM19 FM14, FM15, FM16, FM17,

FM19

4 18.5 and 25.5 FM12, FM14, FM16, FM17 FM11, FM12, FM13, FM14, FM15,FM17

5 18.5 and 29.5 FM12, FM14, FM16, FM17 FM12, FM14, FM16, FM17

6 25.9 and 34.8 FM15, FM16, FM17, FM18 FM15, FM16, FM17, FM18

7 13.5 and 24.5 FM13, FM16 FM13, FM14, FM16,

The results produced using HAZOP for the same input are

produced in Table 4.17. Like the fuzzy models, HAZOP has also identified

the multiple faults, but it has produced some false alarms, for instance in

cases 1, 2, 4 and 6. Further, it was not able to identify the developing faults.

This is evident from the results for cases 1 and 2. Hence it is observed that

the Fuzzy model has reduced the false alarm rate and also identified a few

faults which are not noticed by the conventional methods. From this

comparison, it is found that the fuzzy system model has produced more

accurate results than the conventional approaches.

4.5.3 Conclusions

This section has presented a fuzzy logic-based approach for fault

diagnosis in LPG transfer system, FCCU Preheating system and Urea

Synthesis system. Totally 11 faults from the LPG transfer system and 25

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faults from both FCCU system and Urea synthesis system were considered in

the developed model. In the developed model, the fault-symptom

relationships were expressed in the form of fuzzy if-then rules. Simulation

results from the model produced accurate results for the same input. The

output produced by the model was compared with the conventional HAZOP

model. The comparison showed that fuzzy logic approach is more suited for

the fault diagnosis in considered systems compared to the conventional

approaches. To further improve the performance of the model, the numerical

data collected from the system have to be used to fine-tune the membership

functions and the fuzzy rule base.

4.6 SUMMARY

Details of the systems selected for this research work from process

industries with its generalized description of the problem were explained in

this chapter. The focus of this Chapter is on the development of database

component, model base component and knowledge base component derived

from the Industrial system. The database component derived for the industrial

system comprising of all real time industrial data which should represent all

possible fault occurrences, HAZOP data, FMEA data. The model base

component derived from the industrial system is neural network types of BPN

and AANN for fault detection. The knowledge base component of the

industrial system is fuzzy logic. In the following chapters, details of

experimental study carried out and the development of model base and

knowledge base by applying the various intelligent techniques will be

discussed.