Evaluation of LNG Production Technologies* Oluwaseun Harris**, Ayema Aduku**, Valerie Rivera**, Debora Faria, and Miguel J.

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Transcript Evaluation of LNG Production Technologies* Oluwaseun Harris**, Ayema Aduku**, Valerie Rivera**, Debora Faria, and Miguel J.

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Evaluation of LNG Production Technologies*
Oluwaseun Harris**, Ayema Aduku**, Valerie Rivera**, Debora Faria, and Miguel J. Bagajewicz,
We made an analysis of twelve natural gas liquefaction processes and determined fixed costs and operating efficiency as a function of
capacity. Eight of the eleven processes are currently established in various parts of the world. The four remaining processes are in
developmental stages .
T-Q Diagram

Simulation Method
•Conditions after each stage of refrigeration was
noted

•Processes were translated into simple simulations
•After making simple simulations mimic real
process, variables were transferred to real process
simulation
•Optimization- Refrigerant composition
•Optimization- Compressor work
•Restriction- Heat transfer area
oAll cells in LNG HX must have equal area
•Restriction- Second law of thermodynamics
oCheck temperature of streams
•Utilities- Acquire water flow rate needed

Natural Gas
Cooling Curve

Temperature

Black and Veatch’s PRICO Process

Heat

ConocoPhillips Simple Cascade
Cascade Processes

Single Refrigeration cycle

A series of heat exchangers with

•One refrigeration loop that
cools the natural gas to its
required temperature range.
•Usually requires fewer
equipment and can only handle
small base loads.
•Lower capital costs and a higher
operating efficiency

each stage using a different
refrigerant.
Tailored to take advantage of
different thermodynamic
properties of the refrigerants to be
used.
Usually have high capital costs
and can handle very large base
loads.

Objective of each design: getting the curves closer . It reduces
the amount of work needed

BP Self Refrigerated Process

Processes
Simulation Techniques

Mixed Refrigerant
Linde Process

Axens Liquefin Process

Technip-TEALARC

Black and Veatch Prico
Process

Dual Mixed Refrigerant

ExxonMobil

Technip- Snamprogetti Dual Multi-component

Pure Refrigerant

Inlet conditions
Pressure: 750 psia
Temperature: 1000F

Conoco Philips Simple Cascade

Outlet conditions
Pressure: 14.7 psia
Temperature: -260oF

Both Mixed and Pure Refrigerants

Air Products and Controls inc. C3MR Process

Natural Gas composition
Methane: 0.98
Ethane: 0.01
Propane: 0.01

APCI. APX Process

Enhanced Linde Process

Other
BP Self Refrigerated Process

Williams Field Services co.

ABB Randall Turbo-Expander

Mustang Group

Cost and Capacity Relationship
Economic Life of 20 years
New train required at the documented
maximum capacity of each specific process.
Average cost of electricity and cooling
water throughout the US used in analysis.
Energy cost evaluated at a minimum
capacity of 1.2 MTPA

(*) This work was done as part of the capstone Chemical Engineering class at OU
(**)Capstone Undergraduate Students

Beihai City, China

Capacity: Common min. to max. capacity of process
Common min. Capacity: 200,000 lbs/hr

Self Refrigerated Cycles
• Contains two or more refrigeration
cycles. Refrigerants involved could be
a combination of mixed or pure
component refrigerants.
• Some cycles are setup primarily to
supplement cooling of the other
refrigerants before cooling the natural
gas.
• More equipment usually involved to
handle larger base loads.

APCI . C3MR Process
Multiple Refrigeration cycles
•Takes advantage of the cooling

ability of hydrocarbons available
in the natural gas to help in the
liquefaction process.
•Numerous expansion stages are
required to achieve desired
temperatures.
•Considered as a safer method
because there are no external
refrigerants needing storage.

• Each liquefaction process was successfully

simulated using SIMSCI Pro II software
• Capital and Energy costs were determined using
simulated values.
• Ranking systems were created based on cost,
efficiency and capacity.
• Connections with existing market trends were
identified, but not all results coincide with those
trends
• Because information on operating conditions is scarce
and therefore the process may not be at their global
optimum, but rather at a local one , better
identification of these optimums is required.