Proceedings of the 1st Annual Gas Processing Symposium. 10–12 January 2009, Doha, Qatar

446 Pages·2008·7.479 MB·English

by  Hassan E. Alfadala, G.V. Rex Reklaitis and Mahmoud M. El-Halwagi (Eds.)

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Preface
Natural gas is one of the cleanest sources of fossil energy and is gaining a growing share
in the global energy market. It is estimated that about 23% of the World’s energy usage
is provided by natural gas. Driven by environmental, economic, and supply issues, this
share is expected to increase over the next two decades. Natural gas is also becoming a
key feedstock to many industrial processes. As such, it is important to develop efficient
and effective technologies for processing the gas into various usable forms. There are
numerous processing pathways and associated technologies that are being developed
and refined to enhance the efficiency and added value of processing and utilization of
natural gas. This book provides state-of-the-art advances in the area of gas processing.
The chapters in this book were selected from contributions presented at the Gas
Processing Center First Annual Symposium held in Doha, Qatar from January 10 to 12,
2009. A key theme in the Symposium was Liquefied Natural Gas (LNG) because of its
growing importance of the global energy market. The Symposium also covered the
following gas processing applications in parallel sessions
(cid:120) Natural Gas Processing and treatment
(cid:120) Gas To Liquid (GTL)
(cid:120) Gas To Petrochemicals, including olefins, ammonia and methanol
Several engineering areas pertaining to gas processing are covered. These include:
(cid:120) Design, including debottlenecking and retrofitting
(cid:120) Operation, including plantwide control; process, supply-chain, reliability and
enterprise-wide optimization
(cid:120) Process Safety
(cid:120) Environmental Sustainability, including clean production and efficient use of
Natural resources and energy
The book contains nine chapters clustered into the following categories:
- Liquefied Energy Chain
- Natural Gas Process Equipement Design
-Process Design
-Process Synthesis and Optimization
-Process Control
- Acid Gas Removal
-Sustainability, Safety and Asset Management in LNG Industry
- Gas-to-Liquids
-Gas to Petrochemicals
Preface xi
Several individuals and organizations have been instrumental in supporting the
Symposium and the book. Grateful acknowledgement is given to His Highness Sheikh
Tamim Bin Hamad Al-Thani, the Heir Apparent of the State of Qatar under whose
patronage this Symposium was held. Her Excellency Professor Sheikha Al-Misnad,
President of Qatar University and the many university departments are gratefully
acknowledged for extending unwavering administrative and financial support. All
employees of Qatar University Gas Processing Center are also acknowledged. We
would also like to acknowledge the support provided by sponsors and co-sponsors
including Qatar Petroleum, Qatargas, RasGas, ExxonMobil, the American Institute of
Chemical Engineers (AIChE), the European Federation of Chemical Engineering
(EFCE), and the Gas Processors Association – Gulf Countries Chapter (GPA – GCC
Chapter). Special thanks are due to Engineer Omnia Abdel-Gawad, the Symposium
Manager for her outstanding efforts in smoothly organizing the various aspects of the
Symposium and the book. We would also like to thank the members of the International
Technical Committee and the many reviewers from around the world who provided
much advice to the direction and content of the book. Finally, our deep appreciation to
the authors of the various chapters for sharing their knowledge and expertise and for
their cooperation during the editing of the book.
Hassan Alfadala, Qatar University, Qatar
G. V. Rex Reklaitis, Purdue University, USA
Mahmoud El-Halwagi, Texas A&M University, USA
Annual Gas Processing Symposium Editors
International Technical Committee
Symposium Chairman
Hassan Alfadala, Gas Processing Center, Qatar
Technical Committee Members
Ahmed Al-Thani, QatarGas, Qatar
Andrzej Kraslawski, Lappeenranta University of Technology, Finland
Dennis Spriggs, Matrix Process Integration, U.S.A
Dominic Foo, University of Nottingham, Malaysia Campus
Ernest Du Toit, Sasol, Qatar
Fadwa Eljack, Qatar University, Qatar
Farid Benyahia, Qatar University, Qatar
Fotis Rigas, National Technical University of Athens, Greece
G. V. Rex Reklaitis, Purdue University, USA
Ghanim H. Al-Ibrahim, Qatar Fuel Additives Company Limited, Qatar
Hamad Al Mohanadi, RasGas, Qatar
Hasan Al-Hammadi, University of Bahrain, Kingdom of Bahrain
Iftikar Karimi, National University of Singapore, Singapore
Kenneth Hall, Texas A&M, U.S.A
Mahmoud El-Halwagi, Texas A&M, U.S.A
Mark R. Pillarella, Air Products, U.S.A
Martin Picon Nunez, University of Guanajuato, Mexico
Mert Atilhan, Qatar University, Qatar
Nafez Bsesio, RasGas, Qatar
Pedro Medellín-Milán, Universidad Autónoma de San Luis Potosí, Mexico
Per Gerhard Grini, StatOilHydro, Norway
Rafiqul Gani, Technical University of Denmark, Denmark
Rakesh Agrawal, Purdue University, USA
International Technical Committee xiii
Robin Smith, The University of Manchester, UK
Saad Al Kaabi, Qatar Petroleum, Qatar
Simon Perry, The University of Manchester, UK
Sigurd Skogestad, Norwegian University of Science & Technology, Norway
Truls Gundersen, Norwegian University of Science & Technology, Norway
Proceedings of the 1st Annual Gas Processing Symposium
H.E. Alfadala, G.V. Rex Reklaitis and M.M. El-Halwagi (Editors)
© 2009 Elsevier B.V. All rights reserved.
A Multi-Paradigm Energy Model for Liquid
Natural Gas Analysis
Bri-Mathias S. Hodgea, Joseph F. Peknya,b and Gintaras V. Reklaitisa
aSchool of Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West
Lafayette, IN 47907, USA
be-Enterprise Center, Discovery Park, Purdue University, 203 S. Martin Jischke Drive,
West Lafayette, IN 47907, USA
Abstract
The current complex world energy system dictates that energy policy decisions can have
far reaching and often unintended consequences. Therefore, sophisticated modeling
techniques which allow possible future scenarios to be simulated and analyzed in
advance are necessary in order to improve the decision making process. Multi-
paradigm modeling allows different parts of the system under consideration to be
represented using the modeling technique most appropriate. This approach has been
applied to the United States natural gas system and the future prospects of liquid natural
gas imports over a medium term time frame have been examined.
Keywords: Energy Systems, Multi-Paradigm Simulation, Agent-based Modeling,
Systems Dynamics
1. Introduction
The current global energy system is both highly complex and increasingly coupled.
Changes in the energy supply or demand in one region of the world, or one sector of the
economy, can have far reaching and often unforeseen consequences. Energy policy
decisions at both the government and corporate level play a critical role in the
determination of energy usage and the availability of suitable forms of energy where
necessary. These policy decisions may only benefit from a deeper understanding of the
interactions of entities within the system as well as the system capabilities and
limitations. As the size and importance of the system prevents direct experimentation,
insights must be gleaned through the modelling and simulation of energy systems.
The most prominent large scale energy system models already in use come from
international and national organizations such as the International Energy Agency (IEA,
2007) and the United States Energy Information Administration (EIA, 2007). Both of
these models are highly detailed mathematical programming models which produce
single solutions to the given problem instead of the spectrum of possible future
scenarios which the large uncertainty involved should dictate. System Dynamics, which
uses highly aggregated data with defined feedback loops to forecast system behaviour,
has also been widely used in energy modelling. Typical system dynamics models for
energy systems analysis are the Energy 2020 (Backus et al., 1995) and Fossil2 (Naill,
1992) models. Agent-based models work from a bottom-up approach, as opposed to the
2 B-M. Hodge, et al.
top-down approach of system dynamics, and specify the behaviour and the means of
interaction between individuals within the system. With the individual’s behaviour
specified it is then the combination of the interactions between system components that
is studied in order to better understand the system. A framework for an agent-based
overall energy system model has been developed and applied at the sub-national level
(Hodge et al., 2008), while the agent-based system concept has also been applied to
electricity systems (Koritarov, 2004). A more comprehensive review of past work in
energy systems modelling can be found in (Wei et al., 2006) which contrasts the
approaches which have been examined and gives examples of applications at varying
geographical scale.
All of the modelling paradigms mentioned above have some inherent limitation. Agent-
based methods with their disaggregated approach can suffer from scaling problems
when there are a very large number of individual entities which must be modelled
separately. Mathematical programming produces only individual solutions instead of
looking at multiple possible future scenarios and is often limited to linear model
components. In many cases the formal mathematical relationship necessary for a
system dynamics model may not be readily apparent. The aggregation that is common
in system dynamics models may miss key differences between system components
which help to explain the system behaviour. It is thought that by combining these, and
other, techniques in a multi-paradigm modelling system each modelling standard may
be allowed to work on only those segments of the problem for which it is most suited.
Instead of allowing the choice of modelling technique to dictate the structure of the
problem we can allow the structure of the problem to drive the decision of which
modelling paradigms are used.
The multi-paradigm approach allows the development of models with multiple
objectives, multiple levels of aggregation and multiple perspectives (Zeigler & Oren,
1986). Hybrid systems, which mix continuous and discrete time, may be considered an
important subset of multi-paradigm models that has received much attention. A good
review of hybrid systems theory may be found in (Barton & Lee, 2002). This approach
has been applied to physical systems (Mosterman & Biswas, 2002) such as supply
chains (Pathak et al., 2003), as well as software systems (de Lara & Vangheluwe, 2002).
The combination of multiple modelling paradigms allows the level of abstraction and
the formalism used in sub-models to differ from that of the meta-model with the
intended goal of a more realistic representation of the system under study. An overview
of the concepts behind multi-paradigm modelling, abstractions and transformations
between formalisms can be found in (Vangheluwe et al., 2002).
2. A Multi-Paradigm Energy Model Framework
A decomposition of the energy system must be undertaken in order to allow the use of
multiple modeling paradigms within the same model. Subsystems must be characterized
so that the modeling style which best fits each component of the system may be
determined. The system has been broken down into three critical components: markets,
supply and demand. An agent based modeling structure is used in order to represent
each of these modules of the energy system at the highest level of abstraction. This
paradigm was chosen for the ease with which communication between different
modules may be facilitated. Messages may be sent between subsystems through
standardized ports which can recognize relevant information and ignore non-relevant
A Multi-Paradigm Energy Model for Liquid Natural Gas Analysis 3
noise. The agent based methods of communication are the most natural fit for
integrating multiple modeling styles of the methods considered. At the meta-model
level agents act as communication wrappers which encompass modules of the system,
allowing sections of the model generated through varied modeling paradigms to
effectively share the information which is needed by diverse subsystems.
Figure 1: Model Framework Overview
2.1.Market
The market modules are at the center of the energy system framework. Markets serve
as a meeting place for supply and demand and are thus the main means by which
information is exchanged between the two. The market facilitates communication
between diverse supply and demand modules through the use of a common language:
the bid. Bids to buy or sell an energy product may be submitted and contain all the
information relevant to the prospective trading partner. Bids consist of six important
pieces of information: the bidder’s name, the market to which the bid is submitted, the
product type for which a bid is placed, whether the bidder is buying or selling as well as
the offered or requested amount and price. The market module operates as a double
blind auction in which each participant receives the equilibrium price and bids may be
partially fulfilled. The fulfillment of a bid can be seen as a contract to supply or receive
an amount of the product during the next time frame. The auction mechanism is not
dynamic but operates once per each discrete time step, or for each tick in the meta-
model agent-based framework. Once supply has been matched with demand and a
market price has been established the market must then communicate the results of the
auction to the participants. This is again easily accomplished by sending bid messages
to those participants whose bid has been accepted notifying them directly of the amount
of their bid which has been fulfilled and the market price that they will pay or receive
per unit of commodity.
2.2.Supply
The producers who supply the product within an energy system have been modeled
using an agent-based approach. The agent-based approach represents suppliers as
autonomous entities which make their production decisions based upon rules
established to govern their behavior and interactions with the other players within the
system. Since the supply side consists of a small number of large producers as opposed
to the large number of small users on the consumption side, individual behavior is more
easily generalized into rules such as profit maximization. In addition the utility of the
product to the suppliers, the money received from the sale, is much simpler than the
4 B-M. Hodge, et al.
utility of the myriad possible uses for the product on the consumption side. This
reduces the complexity of strategic decisions and helps make the case for using agent
based modeling to represent the supply side.
2.3.Demand
The system dynamics modeling approach has been chosen as the most applicable
paradigm for the demand side of the energy system under study. System dynamics
works with aggregated groups of entities instead of representing individuals. Stocks and
flows are individual units, from which feedback loops are built, and form the basis for
system dynamic models. Stocks represent physical entities, such as people or
machinery that participate in the energy system while flows account for changes
between stocks. All stocks in a system dynamics model are homogenous; there is no
differentiating between members of a stock set. In addition, certain parameters are
needed in order to help determine the flow values. For a system with as many
individual users as an energy system this approach is preferable due to the nature of the
data used in the system. The collection of individual user consumption data and
behavior patterns is extremely difficult at best and thus the data available is already
highly aggregated. Due to the large number of users and their varied behavior and
consumption patterns, construction of agents for each individual user type was judged to
be excessively time consuming for the possible gains in consumption accuracy.
Additionally, the sheer number of individual agent instances necessary for such a large
demand system would be computationally prohibitive.
3. Example: The United States Natural Gas System
Natural gas is an important energy commodity within the United States because of its
use in residential and industrial heating, power generation and as a feedstock for
industrial chemical production. The United States is the largest consumer of natural gas
in the world (EIA, 2007). The United States is also the second largest natural gas
producer in the world; however, the total production does not completely fulfill the
domestic demand. The United States contains the sixth largest proven reserves of
natural gas in the world, though this amounts to only three percent of world reserves.
The high levels of production of natural gas in the United States along with the
enormous domestic demand make the United States market an important part of the
world natural gas system. The difference between domestic demand and production
means that imports are required to make up the shortfall. Imports by pipeline from
Canada are insufficient to cover the deficit and therefore liquid natural gas imports are
required.
3.1.Market
For the purposes of this initial model the United States natural gas system has been
treated as one large national market. In reality this assumption is flawed. The difference
between the Henry Hub price and any local city gate price should differ, if only due to
transportation and distribution costs. However, differences in transportation costs are
accounted for within the supply module, as explained below, and the single national
market assumption can be modified if simulation results indicate that further granularity
could be useful.
3.2.Supply
Following the principle that aggregation should be used until it is shown that further
detail would be beneficial, the supply to the United States natural gas system has been
divided into three sectors: domestic supply, near international supply and far
A Multi-Paradigm Energy Model for Liquid Natural Gas Analysis 5
international supply. Near international and far international supply are differentiated
by the transportation method necessary for supplying the product. Near international
supply arrives via pipeline, and thus includes Canada and Mexico as potential suppliers,
while far international supply must be imported via liquid natural gas. Each supply
sector is represented by a supply region agent.
The primary decision to be made for each supply region is the quantity of natural gas
that it should produce in the next time frame. This is accomplished by providing the
cost at which sections of capacity may provide the product. Each lot of capacity is
assigned a series of costs, the sum of which is the total cost of one unit of natural gas
delivered to the consumer from this capacity. The unit of natural gas used within the
model is 1000 cubic feet. There are five costs which pertain to all of the supply regions
and three which are particular to the international region. The lifting cost is the cost of
extracting a unit of natural gas from the ground. The tax cost, which can vary greatly
by region, combined with the lifting cost together form the well known wellhead cost.
In addition there are transmission and distribution costs which account for the costs of
delivering the product to the end use customers. The assumption is also made that for
the medium term time frame of the model, ten years, reserves are held at a constant
level for each region. This assumption necessitates the inclusion of a replacement cost
for each unit of natural gas produced. These finding costs can be a large fraction of the
total cost, but vary greatly from region to region. In addition, the costs of liquid natural
gas must be included for the far international region. These additional costs are
represented by the liquefaction, shipping and gasification costs associated only with
liquid natural gas. For each lot of capacity the applicable costs are randomly generated
using a beta distribution with appropriate upper and lower bounds as well as shape
parameters which mimic the behavior of a bounded normal distribution. Once all of the
above cost factors have been determined, overhead and profit factors are used to
determine the final total production cost. Each lot of capacity then has an associated
total cost which may be bid to the market. The international supply region has an
additional constraint in that there is a finite capacity for liquid natural gas to be
imported into the United States through existing terminals. Therefore only the most
competitive bids up to the point of full import capacity for liquid natural gas are actually
submitted to the market. As the supply only provides a cost at which an amount of the
product may be delivered, it is the market module that ultimately decides the capacity
each region uses through the matching of supply and demand. Once this determination
has been made a bid message is passed to the supply region agent informing it of its
contractual obligations for the next time frame.
3.3.Demand
In the United States natural gas is an important energy source for heating and electricity
generation as well as a feedstock used in chemical production. While the use of natural
gas as a feedstock has fallen due to a shift toward off-shore chemical production, the
generating capacity of power plants which use natural gas as the primary energy source
has risen dramatically. In addition natural gas is used in many parts of the country as
the main energy source for winter heating, as well as other household uses. For the
purposes of the model the United States natural gas demand has been divided into four
groups of users: residential, commercial, industrial and electric.
Each of these user groups are represented in the demand system dynamics model as
stocks of natural gas customers. The stocks of residential, commercial and industrial