(30a) Concept Validation for a Very Large Lng Train | AIChE

(30a) Concept Validation for a Very Large Lng Train

Authors 

Bauer, H. C. - Presenter, Linde Engineering
Fredheim, A. O. - Presenter, SatoilHydro ASA
De Simone, S. - Presenter, GE Energy Oil & Gas


After a stagnation of the LNG train size at about 4 to 5 mtpa several large LNG trains, which are presently under construction in Qatar, changed the situation. New process technologies and high performance main equipment had to be developed and qualified to reach a nameplate capacity of 7.8 mtpa. Statoil and Linde, however, already own the appropriate technology, which is presently in the last phase of commercialisation on Melkøya near Hammerfest/Norway. This Mixed Fluid Cascade (MFC®) process is perfectly suitable for very large LNG trains, as the three underlying mixed refrigerant cycles can be arranged in such a manner that the power consumption of all three cycles is identical. Thus, using the largest available gas turbine three times as driver for the refrigerant compressor trains promises an unbeatable concept to reach a record capacity.

While Statoil and Linde own technologies for the liquefaction process and spiral wound heat exchangers a partner for the main rotating equipment still was required. General Electric was obviously the first choice and agreed to participate in a concept validation programme. The three parties defined a case for the validation work (a hypothetical LNG plant in a warm climate) and succeeded in optimising the concept. Using only qualified and proven equipment like Frame 9 gas turbines, large centrifugal compressors from Nuovo Pignone's portfolio and spiral wound heat exchangers manufactured by Linde AG a LNG capacity of 1,500 tons per hour could be exceeded (equivalent to more than 12 mtpa at 335 stream days). This result is especially remarkable as the number of involved equipment could be minimised, e.g. there is no parallel rotating equipment and the number of refrigeration compressor casings is limited to five. The paper explains in detail findings during the optimisation of the concept.

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