Towards energy-efficient LNG terminals: Modeling and simulation of reciprocating compressors

•Systematic procedure to develop physics-based empirical models for predicting gas flow and power for reciprocating compressors with suction valve unloaders.•Embedding proposed models within commercial process simulators like Aspen HYSYS or Unisim.•Demonstrating utility of our empirical models for s...

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Bibliographic Details
Published in:Computers & chemical engineering 2019-09, Vol.128, p.312-321
Main Authors: Reddy, Harsha V, Bisen, Vikas S, Rao, Harsha N, Dutta, Arnab, Garud, Sushant S, Karimi, Iftekhar A, Farooq, Shamsuzzaman
Format: Article
Language:English
Subjects:
Dynamic simulation
LNG
Metamodel
Reciprocating compressor
Suction valve unloader
Volumetric efficiency
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Summary:•Systematic procedure to develop physics-based empirical models for predicting gas flow and power for reciprocating compressors with suction valve unloaders.•Embedding proposed models within commercial process simulators like Aspen HYSYS or Unisim.•Demonstrating utility of our empirical models for simulating steady state and dynamic operations of reciprocating compressors.•Illustrating the full range application of our contribution using real data from a BOG compression train in an LNG regasification terminal.•Usefulness of our empirical models for real life applications is elucidated by comparing them with popular machine learning approaches. Major countries are installing Liquefied Natural Gas (LNG) terminals worldwide, as they transition towards carbon-free economies. Compressors are energy-intensive equipment in LNG import/export terminals. While reciprocating compressors are in wide use, models to estimate volumetric and energetic efficiencies do not exist, especially for those with suction valve unloaders. Furthermore, commercial process simulators such as Aspen HYSYS or Unisim are not equipped to simulate them rigorously. This paper presents a procedure to develop empirical models for predicting flow and power based on process insights and real operational data. It also demonstrates how these models can be embedded inside simulators to simulate compressor operations in both steady and dynamic modes. Real data from a BOG compressor train in an LNG regasification terminal are used to illustrate the full range of their applications. Finally, the suitability and efficacy of data-driven machine learning approaches are evaluated to show the superiority of proposed empirical models.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2019.06.013