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February 20, 2026Journal of Thermal Science and Engineering Applications0 citations

Multi-objective optimization of novel cryogenic cold energy recovery power generation system using response surface methodology

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LPLalatendu PattanayakTMTaraprasad MohapatraBPBiranchi Narayana Padhi

Key Points

  • This research aims to enhance power generation efficiency by utilizing LNG cold and pressure energy through a new system design.
  • Proposed a cryogenic power production system based on direct expansion cycle (DEC) utilizing LNG cold energy.
  • Conducted sensitivity analysis on LNG flow rate, temperature, and pressure at the expander inlet.
  • Employed response surface methodology (RSM) for multi-objective optimization of thermal and exergy efficiencies.
  • Used ANOVA analysis to verify model adequacy.
  • Achieved an exergy efficiency increase up to 15.43%.
  • Attained thermal efficiency of 16.2%.
  • Demonstrated effective elimination of seawater as a heat source.

Abstract

Abstract Liquefied natural gas (LNG) regasification releases a significant amount of cold energy. As a result, the use of LNG for cold energy has gained attention in both academic and engineering study. In the case of high-pressure LNG (meeting the demands of the gas supply networks following re-gasification), applying organic Rankine cycle (ORC) and seawater as heat source results in a significant exergy loss and relatively poor power generation. The purpose of this work was to propose a cryogenic power production system based on direct expansion cycle (DEC) for the effective use of LNG cold and pressure energy. The proposed system utilizes the low-temperature condensate from heat recovery steam generator (HRSG) served as a low-grade heat source to reheat the re-gasified LNG to eliminate the use of seawater as a heat source. The LNG flow rate m˙LNG, temperature T4 and pressure P4 at the expander inlet selected for sensitivity analysis. Then a multi-objective optimization technique using response surface methodology (RSM) is employed to maximize the thermal efficiency ηth and exergy efficiency ηex and minimizing the exergy destruction. ANOVA analysis is used to verify the model adequacy, and the constructed model capacity to accurately predict the output responses is examined. Sensitivity analysis is used to recognize and rank different key parameters in order of relevance. The proposed system design demonstrated ηex increased up to 15.43% and ηth of 16.2% at m˙LNG/P4 of 100 kg s−1/100 bar.

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Cite This Study

Pattanayak et al. (2026) studied this question.

synapsesocial.com/papers/6997fa49ad1d9b11b3453578https://doi.org/10.1115/1.4071149
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