Interface Layers Detection in Oil Field Tanks: A Critical Review

201
Hence, for a time delay less than a threshold twater*(th
liquid
/d) (where d is the distance
between the sensor and reflector) the type of liquid being sensed by the actual sensor
corresponds to water. Otherwise, in case the time delay is greater than oil*(t
hliquid
/d), then
the liquid is either emulsion or oil depending on the number of pulses being collected (i.e.
emulsion for less than 3 pulses, oil otherwise). Finally, in case no echo is detected, then the
corresponding phase corresponds to foam or gas. Note that the thresholds, t
water
*(th
liquid
/d)
and t
oil
*(th
liquid
/d) (e.g. according to Section 2.1(a) and Figure for an operation temperature
ranging from 20
0
C to 70
0
C setting twater and toil to 140 μs and toil, = 150 μs, respectively is
reasonable for thliquid = d) were selected in such a way that the classification is independent
of the temperature. The same procedure is done for all sensors of the device to provide the
water-cut profile of the column. This algorithm, which has been coded in assembly and

=
=×
∑
(13) Fig. 23. Neural Network algorithm for water-cut determination

Expert Systems for Human, Materials and Automation

202
Where g
j
is the activation function which is usually selected as non linear to enable the
network to model to some extent some nonlinearities present in the problem. Following
extensive experiments, the Logsig function was found to be the most appropriate in our
case. Thus, for a particular input vector, the output vector of the network is determined by
feedforward calculation. We progress sequentially through the network layers, from inputs to
outputs, calculating the activation of each node using Eq. (7), until we calculate the
activation of the output nodes.
3.4 Electronic design
The overall system is modular and consists of a 1-D array of tens of ultrasonic transducers
which are connected to each other in a daisy chain manner via stainless-steel shielded wires
and an embedded transmitter based on Reduced Instruction Set Computer (RISC) processor
to perform control, data acquisition and real-time pattern recognition tasks. In addition it
delivers the output results (i.e. low and high position of the emulsion layer) either as current
loop 4-20 mA or RS-485 protocol to the remote control room. The temperature of the tanks
which can reach up to 700C in summer season. Furthermore, and following the results
obtained from the experimental setup, each transducer has been equipped with a
temperature sensor. In addition, two pressure sensors were added to sensors 1 and 26


203
3.4.2 Transmitter
The transducers are sequentially enabled by the transmitter in a time multiplexed manner to
sense the surrounding liquid. The corresponding analog echoes signal is then sent to the
transmitter for digitalization at a sampling rate of 100 Msamples/s and for further
processing. This latter task is handled by a RISC ARM-based processor which also transfers
the final results (i.e. tank profile) to the remote control room.
12 wires Amplifier
Address
Transducer-1 (n=1)
Ultrasound
waves
Selector
Transducer-n (n=28)
Address
Selector
Amplifier Fig. 24. Electronic design: Transducer-Transducer connections.
The transmitter also comprises a main processing unit that implements the pattern
recognition algorithm and provides an Input/Output interface to/from the remote
computer (RS485 or 4-20 mA standards which generates three levels corresponding to the

determine the profile of oil tanks for various values of water-cut. Overall, the averaged
relative error for oil and water was always less than +/- 3%. It is defined respectively as:
() ()
( )[%] 100[%]
()
ar
r
QW QW
Error W
QW
−
=× and
() ()
()[%] 100[%]
()
ar
r
QO QO
Error O
QO
−
=×
Where Q
r
(W) and Q
r
(O) are the total quantities of water and oil respectively injected into the
column and Q
a
(W) and Q


Fig. 27. Plot comparing the measured water-cut versus the reference for high water-cut.
Regarding the emulsion layer detection, Figures 18(a) and (b) shows the dynamic behavior
of the emulsion for one of the sensors of the device (sensor #16) in case of water dominated
(e.g. water fraction more than 90%) or oil dominated mixture (e.g. oil fraction more than
90%) respectively. It could be seen that in case of water dominant emulsion, the delay keeps
decreasing since the bubbles of oil tend to disappear. However, in oil dominant emulsion,
the delay keeps increasing since the bubbles of water tend to disappear.
Figure 29 shows the results of tracking the emulsion layer in the column. Initially, the
column was filled with water (of height 285 cm) and oil (of height 75 cm). By filling the
column with water (of height 30 cm), an emulsion layer has been created on the top of the
column. As the water tends to move downward, the thickness of the emulsion layer tends to
increases and reaches its maximum value at time t = 20 s. Next, pure oil starts to appear at
the top of the tank and its thickness tends to increase until it reaches its maximal value at
time = 78 s. Hence, the water thickness increases by 30 cm from its initial value. Figure 30
shows the graphical user interface in the computer of the control room showing a snapshot
of the above experiment in which an emulsion layer was formed between the water and

Expert Systems for Human, Materials and Automation

206
kerosene. The emulsion layer is represented by two windows: In window 1 the plot of the
emulsion layer is represented, whereas in Window 3, the profile of the whole tank is
represented by assigning each sensor with a specific color (e.g. Blue for water, pink for
emulsion, yellow for gas, and brown for crude oil). Fig. 28. Dynamic tracking of sensor 16 in water-dominant (a) and oil dominant (b) emulsion.

Interface Layers Detection in Oil Field Tanks: A Critical Review

2006.
[3] Holler, G.; Thurner, T.; Zangl, H. and Brasseur, G; “A novel capacitance sensor principle
applicable for spatially resolving downhole measurements”, Proceedings
IMTC/2002, Volume 2, pp. 1157 – 1160, Volume 2, May 2002.
[4] Weiss, M and Knochel, R, “A sub-millimeter accurate microwave multilevel gauging
system for liquids in tanks”, Microwave Theory and Techniques, IEEE
Transactions on Volume 49, Issue 2, pp. 381 - 384 Digital Object Identifier
10.1109/22.903101, February 2001.
[5] R.Meador and H. Paap, “Emulsion Composition Monitor”, U.S. Patent No. 4,458,524,
date of Patent: 10 July 1984.
[6] Foden, P.R. Spencer, and R. Vassie, J.M.; “An instrument for-accurate sea level and wave
measurement”, Proceedings in OCEANS '98 Conference, pp. 405 – 408, Volume 1,
28 September-October 1
st
, 1998.
[7] Antonio Pietrosanto, and Antonio Scaglione “Microcontroller-Based Performance
Enhancement of an Optical Fiber Level Transducer”, from Giovanni Betta, Associate
Member, IEEE, IEEE Transactions on Instrumentation and Measurement, Volume
47, No. 2, April 1998.
[8] Lee Robins, “On-line Diagnostics Techniques in the Oil, Gas, and Chemical Industry”, in
Proceedings Third Middle East Non-destructive Testing Conference, 27-30
November, Bahrain, Manama, 2005.
[9] Al-Naamany, A. M.; Meribout, M.; and Al Busaidi, K., “Design and Implementation of a
New Nonradioactive-Based Machine for Detecting Oil–Water Interfaces in Oil
Tanks”, IEEE Transactions on Instrumentation and Measurement, Volume 56, Issue
5, pp. 1532 –1536, Oct. 2007.
[10] Mackenzie and Kenneth V.;“Discussion of sea-water sound-speed determinations".
Journal of the Acoustical Society of America Volume 70, Issue 3, pp. 801-806, 1981.
[11] Urick R. J., “Sound propagation in the sea”; The Journal of the Acoustical Society of
America, Volume 86, Issue 4, October 1989, pp. 1626.

prototype development, knowledge acquisition, knowledge representation and prototype
development. Scheduled waste expert system is developed based on five types of scheduled
waste management which are label requirements, packaging requirements, impact of
scheduled wastes, recycling of scheduled wastes, and recommendations. Besides, it contains
several sub modules by which the user can obtain a comprehensive background of the
domain. The output is to support effective integrated scheduled waste management for KL
and world-wide as well.
2. Scheduled wastes
Even though use of information technology plays a major role in application of technology
nowadays, application of artificial intelligence (AI) is still in its infancy in Kuala Lumpur.
During the last decade AI has grown to be a major of research in computer science. Varieties
of AI-based application programs have been developed to address real life problems and
have been successfully field-tested (L.C. Jayawardhanaa et al, 2003). As Kuala Lumpur still
lacks proper systems of information assimilation, archival and delivery, AI tool can
effectively be employed to solve for the management of scheduled waste.

Expert Systems for Human, Materials and Automation

210
Scheduled wastes are defined as wastes or combination of wastes that pose a significant
present or potential hazard to human health or living organisms. This definition specifically
excludes municipal solid waste and municipal sewage. Scheduled wastes are broadly
classified into the categories of chemical wastes, biological wastes, explosives and
radioactive wastes (Chapter 5 Waste Disposal). Scheduled waste management has long been
a problem area for local authorities in Kuala Lumpur. Continued illegal dumping by waste
generators is being practiced at large scale due to lack of proper guidance and awareness. In
2007, the Department of Environment Malaysia (DOE) was notified that 1 698.118 metric
tones were generated. In addition, Kuala Lumpur has enjoyed tremendous growth in its
economy. This has brought about a population growth along with a great influx of foreign
workforce to cities. It resulted in an increase in the amount of waste generated. The main

which 1.29 million tons (78%) were generated by the nonindustrial (community) sector. As
well as the industrial and nonindustrial sectors, a main source of hazardous waste
generation is the transport of hazardous wastes from foreign countries into Thailand. More
than 70% of the hazardous waste generated in Thailand is in the form of heavy metal sludge

Integrated Scheduled Waste Management System in Kuala Lumpur Using Expert System

211
and solids. Other important groups of hazardous waste are oils, acid wastes, infectious
wastes, solvents, and alkaline wastes. It has also been reported that petroleum refineries and
the electroplating, textile, paper, and pharmaceutical industries are the primary producers
of hazardous wastes in Thailand. Besides, for the nonindustrial hazardous waste is
generated from everyday activities in nonindustrial or community sources, such as
automotive repair shops, gas stations, hospitals, farm and households. Hazardous waste
from community sources consist primarily of used oils, lead acid and dry-cell batteries,
cleaning chemicals, pesticides, medical wastes, solvents, and fuels (Hiroaki et.al, 2003).
Amounts of wastes generated from industries in Dar es Salaam are estimated at 76 326 tonne
per year (about 203.6 tonne per day or 58 kg per capita per year). The hazardous waste
generation from industries in Dar es Salaam as estimated was a total of 46 340 tonne per
year (about 127 tonne per day or 29 kg per capita per year). Assuming a negligible annual
increase, the hazardous wastes production is about 40% of the total waste production in Dar
es Salaam industries. The hazardous waste production levels in Dar es Salaam (Tanzania)
can be estimated at 95 000 tonne per year or 3.8 kg per capita per year. The per capita waste
generation rate is about 60% of that of Japan, 17% of Denmark and 3.8% of the Netherlands
(Mato et. al, 1999).
In India, the HWs (Management and Handling) Rules, 1989, as amended in 2003 defined 36
industrial processes, which generate HW (HWM Rules, 2003). In order to encourage the
effective implementation of the HW (M&H) Rules 1989 as amended in 2003. The key issues
in India for HW management are the environmental health implications of uncontrolled
waste generation, improper waste separation and storage prior to collection, multiple waste

membership functions in various types of solid waste management alternatives (Ni-Bin et.
al, 1997).
Another system that had been used was Analytic Network Process (ANP) and Decision
Making Trial and Evolution Laboratory (DEMATEL) to evaluate the decision-making of
municipal solid waste management in Metro Manila. ANP has a systematic approach to set
priorities and trade-offs among goals and criteria, and also can measure all tangible and
intangible criteria in the model while DEMATEL convert the relations between cause and
effect of criteria into a visual structural model (Ming-Lang, 2008).
5. Methodology
Expert system (ES) has been chosen to organize part of the knowledge domain in scheduled
waste management from all data collected to non-expert users (Nassereldeen, 1998). This
knowledge should support them in term of label and packaging requirements, impact and
recycling of scheduled wastes, recommendations, besides predicting the scheduled waste
generated and population in Kuala Lumpur.
5.1 Visual Basic Expert System (VBES) development
Figure 1 below shows the flow diagram of this project, problem identification, problem
statement, literature review and identifications of domain experts are done. For other phases

Problem Statement &
Literature Review
Identify the
domain experts
Prototype
development
Knowledge
acquisition
Knowledge
representation
Prototype
validation

Other
Digital
Wastes
Ink
Catridge
Handphone
/ iPhone
Battery
Human
Health
Socio-
economy
Disaster/
Tragedy
Environment
Impact of
Scheduled
Waste
Packaging
Requirements
Label
Requirements
Recycling of
Scheduled
Waste
Recommendations
Expert System
Development
Law
Cleanliness

• Processor

Expert Systems for Human, Materials and Automation

214
According to Microsoft, a processor speed of 600 MHz (megahertz) is the minimum and
1 GHz (gigahertz) is recommended. Because upgrading a processor by replacing the
motherboard is not so inexpensive or easy, another alternative is boosting your system
RAM, discussed next if the user is on the borderline.
• RAM
According to Microsoft, 128MB (megabytes) is the minimum, and 256MB is
recommended.
5.4 Knowledge acquisition
Knowledge acquisition is the lengthiest process in building of an expert system. However, it is
the single most important process of the knowledge engineer upon which quality of the expert
system depends on. The central core of the knowledge base was acquired from the published
text books, journals, magazines, experts, meeting authorities and pamphlet. This knowledge
consists of well established facts, rules, theory and guidelines that had been practiced over
many years. Annual Report of Department of Environment (DOE) related to statistics of
scheduled waste generated have provided very valuable sources of information. This source of
information provided a means to build a unique knowledge base for Scheduled Waste Expert
System (SWES). All the sources are come from Department of Environment, Kuala Lumpur
(DOE), Kuala Lumpur City Hall (DBKL), and Alam Flora Sdn. Bhd (AFSB).
Knowledge acquisition has now become relatively easy than two decades ago, due to the
advancement of Internet facilities. Much valued information about management of
scheduled waste of Kualiti Alam and Radicare, organization, companies, recycling
procedure and so on, were acquired through the Internet. These were helpful in building the
sub modules of the Scheduled Waste Expert System (SWES).
6. Results and discussion
6.1 User interface

6.3 Rules for impact of Scheduled Wastes
The information is converted into ES rules in a simple language as in figure 5.
The rule will be in a form of radio button and the meaning of the rule is:
If the selection is RadioButton1, then Example SW 110 E-Waste <> (1) Toxic ingredients in E-
Waste such as lead, beryllium, mercury, cadmium and bromibated flame retardants can pose
both occupational and envitonmental health threats. (2) E-Waste that are lanfilled produce
highly contaminated leachate which eventually pollutes the environment especially surface
water and grounwater. (3) Acid and sludge obtained from melting computer chips if disposed
into the ground will cause acidification of soil and subsequently contamination of
groundwater. (4) Brominated flame retardant plastic or cadmium containing plastics are
landilled, both polybrominated diphenyl ethers (PBDE) and cadmium may leach into the soil
and groundwater. (5) Combustion of E-Waste will emit toxic fumes and gases that pollute the
surrounding air. When E-Wastes are exposed to fire, metals and other chemical substances,
extremely toxic dioxins and furans will be emitted. The toxic fall-out from open burning affects
both the local environment and broader global air quality, depositing highly toxic byproducts
in many places throughout the world. (6) If E-Wastes are discarded together with other
household wastes, the toxic compnents will pose a threat to both health and the vital
components of the ecosystem; if the selection is RadioButton2, then Example SW 311 Oil <> (1)

Expert Systems for Human, Materials and Automation

216

IF selection is RadioButton1
THEN Example SW 110 E-Waste <> (1) Toxic ingredients in E-Waste such as lead, beryllium, mercury, cadmium and
bromibated flame retardants can pose both occupational and envitonmental health threats. (2) E-Waste that are lanfilled
produce highly contaminated leachate which eventually pollutes the environment especially surface water and grounwater. (3)
Acid and sludge obtained from melting computer chips if disposed into the ground will cause acidification of soil and
subsequently contamination of groundwater. (4) Brominated flame retardant plastic or cadmium containing plastics are
landilled, both polybrominated diphenyl ethers (PBDE) and cadmium may leach into the soil and groundwater. (5) Combustion

health and financial implications. (2) The health impacts of direct and indirect exposure to
oil include carcinogenic effects, reproductive system damage, respiratory effects, central
nervous system effects and many more. The selection is continuously until RadioButton5.

Integrated Scheduled Waste Management System in Kuala Lumpur Using Expert System

217
Figure 6 shows the translation of the rule into impact of scheduled waste using VB while
figure 7 shows the output after the user click on any radio buttons.
6.4 Scheduled Waste Expert System (SWES) Fig. 7. Interface for Scheduled Waste Expert System
Once the user clicks on the SWES button at the main user interface, they will be five
categories listed as in figure 7. Then, user can choose any categories and the system will give
user the best solutions. The system will produce the answer through texts, graphs and
pictures within a single form. Scheduled Waste Management module has been designed for
the use of the novices to the field. It has been divided into premises and companies handling
scheduled waste in Kuala Lumpur, labeling and packaging requirement, transportation, and
process flow. For process flow, it divided into two which are Kualiti Alam’s process flow
and Radicare’s process flow as in figure 8. Fig. 8. Interface for Scheduled Waste Management Sub Module
6.5 System validation
In validating the scheduled waste expert system, it should be remembered that the purposes
of the study are to develop on integrated scheduled waste management system in KL by

Expert Systems for Human, Materials and Automation


the outputs are corrected and validated. Fig. 11. Area in the circle shows scheduled waste generated in 2002 is 1 560.420 tonne metric
7. Conclusion
The purpose of the study includes understanding scheduled waste generated in Kuala
Lumpur and service provided for scheduled waste management by the authority which is
Department of Environment (DOE). In addition, scheduled waste management system in
Kuala Lumpur will be developed by using Visual Basic Expert System (SWES). Finally, a
new approach for integration of scheduled waste management system in Kuala Lumpur is
recommended.
From the result obtained, the project can be considered as successful as the integrated
program for scheduled waste management system had been developed. Scheduled waste
expert system is developed based on five types of scheduled waste management which are
label requirements, packaging requirements, impact of scheduled wastes, recycling of
scheduled wastes, and recommendations. The knowledge base of this system is based on
ruled-base expert system which is IF THEN rule and the acquisition knowledge that is
gathered for this study is organized into this rules. The development of scheduled waste
expert system consists of six main forms or interfaces which are photo gallery, scheduled
waste management, literature, legislations, training tool, and scheduled waste expert system
itself. It has been incorporated with several user interfaces in order to make the system user
friendly as much as possible. SWES can also be used as a stand-alone learning tool in
environmental studies and by others. Thus a system of much versatility has been developed.

Expert Systems for Human, Materials and Automation

220
This is use of tools of information technology to help in solve local problems in managing
scheduled waste in an informative manner.
8. References

12
Expert System Development for Acoustic
Analysis in Concrete Harbor NDT
Mohammad Reza Hedayati
1
, Ali Asghar Amidian
2
and S. Ataolah Sadr
3

1,2
University of Applied Science and Technology Faculty of Telecommunication,
1
Information Technology Mechatronic Offshore (ITOM) &
3
Port and Maritime Organization (PMO)
,

I. R. of Iran
1. Introduction
Port and Maritime Organization of Iran (PMO), in connection with a research project at
Information Technology Mechatronic Offshore research and development cooperative
society (ITMO), has added another dimension to its subsea inspection activities by
introducing new methods of NDT and expert system for condition monitoring and
assessment of concrete structures. ITOM provided a wide range of special and advanced
techniques for most aspects of subsea and underwater. The repair of concrete structures
under water presents many complex problems.
The harsh environmental conditions and specific problems associated with working
underwater or in the splash zone area causes many differences. Proper evaluation of the
present condition of the structure is the first essential step for designing long-term repairs.

vibration sensed by related transducers of the testing probe.
It is a common observation that, when there were voids, mix separation or crack the
reflected waves detected by the receiving sensor were different than those from the perfect
areas. The results showed that the analysis of surface wave testing has the ability to detect
changes in the constructed structures. The vibration signals which appear on the perfect part
of structure, give a characteristic vibration signature. This signature provides a base line
against which future measurements can be compared.
It is important to note that similar concrete structure in good condition will have similar
vibration signature differing only in respect of their constructional and structural conditions
tolerances.
2. Development of expert system
Knowledge built in to an expert system may originate from different sources. The prime
source of knowledge for developing an expert system should be the domain expert. To
design and develop knowledge based expert system, the specific knowledge domain or the
subject domain must be acquired. The knowledge domain is to be organized so that the
information can be structured in the computer program for effective use. In this respect, a
knowledge engineer usually obtains knowledge through direct interaction with the expert.
Fig.1 illustrates the process of data procurement for generating the knowledge base.
The domain of reinforced concrete diagnosis serves as a good example in the application
area for:
1. Examining the different means currently used to store and transfer information,
2. The knowledge acquisition and knowledge engineering processes required for
extracting that information and capturing it in a knowledge based expert system, and
3. Showing how the resulting knowledge based expert system provides an integrated
framework for combining specifications, data, and models (Graham-Jones &Mellor
1995). Fig. 1. Experts appropriate evaluations, assessment, data logging and generating the
information for knowledge base in the Shid-Rajaee harbor


224
corrosion where appropriate steps may be taken to slow down the corrosion process. Such
inspection procedures, however, are quite costly as they require experts to conduct the tests
and interpret the results. To wait for the appearance of visible signs of corrosion in a
structure such as rust stains and/or cracks before repair will be conducted is not cost
effective. The presence of such visible signs is indicative of an advanced stage of corrosion
which may require a thorough investigation of the entire structure in order to properly
assess the type of repair or rehabilitation needed for the corroded structure.The use of
prediction models, specifically, the time to initiate corrosion can provide useful information
regarding the early onset of corrosion which allows one to appropriately schedule the
required maintenance.
The subject of diagnosis of deterioration and other problems in reinforced concrete
structures is indeed huge and enormously wide and of great interest to civil engineers.
There are standards for the use of reinforced concrete (British Standards Institution, 1985
&1991). For the purposes of this research work specific domain knowledge relating to
common symptoms of cracking, spalling and delamination is needed.
Vibration condition monitoring of harbor concrete structures makes use of vibration
analysis for the following purposes:
1. Periodic routine vibration measurement to check their structural condition.
2. Trouble shooting for suspected constructional problems.
3. Check to ascertain that the concrete structure has returned to good operating condition
after implementing the reconstruction or repair.
4. Check to enable planning of repair of the harbor concrete structures prior to harbor
service shut- down.
Different defects cause the vibration signatures to change in different ways. A changed
vibration signature provides a means to determine the source of problem as well as prior
warning of the problem itself (Skala & Chobola 2005). This research work is limited to
implementing the acoustic signal processing and condition monitoring of concrete
structures in the splash zone and underwater portions of structures located in the lakes,

Undoubtedly, the most dynamic growth in a particular underwater platform has been
exhibited by Remotely Operated Vehicles (ROVs). ROVs look much like an unmanned
version of a submarine. Fig.4 displays the application of the proposed model of ROV,
especially equipped for NDT of quay wall in Shahid-Rajaee harbor. Thay are compact
devices that are controlled by a remote crew. The operating crew and the vehicle
communicate through an umbilical cord attached to the ROV. The crew operates the ROV
with information provided by transponders attached to the frame of the ROV. Generally
the pilot will maneuver the vehicle as closely as prudent to a point adjacent to the
platform and over the work site. ROVs may be launched directly from the surface or from
a submarine mother ship. Most ROVs are equipped with video and still photography
devices. The vehicle is positioned by ballast tanks and thrusters mounted on the frame.
Some ROVs are also equipped with robotic arms that are used to perform tasks that do
not need a high degree of dexterity. Vehicles owned by industrial users range in depth
capability from 200m to 2400 m; the average is 1300m. Structural investigations of
underwater facilities are usually conducted as part of a routine preventive maintenance
program, an initial construction inspection, a special examination prompted by an
accident or catastrophic event, or a method for determining needed repairs. The purpose
of the investigation usually influences the inspection procedures and testing equipment
used. Underwater inspections are usually hampered by adverse conditions such as poor
visibility, strong currents, cold water, marine growth, and debris build-up. Horizontal
and vertical control for accurately locating the observation is difficult. A diving inspector
must wear cumbersome life-support systems and equipment, which also hampers the
inspection mission.