Energy Consumption Optimization Analysis of LNG Receiving Station Based on Genetic Algorithm


  • Weihua Lu
  • Tingxia Ma
  • Longyao Zhang



LNG receiving station; energy saving optimization; genetic algorithm.


In response to the problem of high energy consumption in LNG receiving stations, based on literature research, numerical simulation combined with genetic algorithm optimization methods were used to establish a calculation model and ASPEN HYSYS steady-state simulation model for the LNG receiving station equipment, each unit process and the overall receiving station process. The various parameters in the LNG receiving station will have a certain impact on the overall energy consumption of the LNG receiving station. Combined with the operating requirements of the LNG receiving station site under different working conditions, and considering the coupling effect of multiple factors, the technology of optimizing the operating parameters of the LNG receiving station to reduce energy consumption is selected, and the adjustable decision-making parameters and their range of changes are selected, and the constraints are set according to the actual situation and Establish the energy optimization objective function of the LNG receiving station process system, and evaluate and analyze the energy consumption indicators of the optimized process system.


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KIM Y, BLANK S. US shale revolution and Russia: shifting geopolitics of energy in Europe and Asia [J]. Asia Europe Journal, 2014, 13(1): 95-112.

MOZAKKA M, SALIMI M, HOSSEINPOUR M, et al. Why LNG Can Be a First Step in East Asia’s Energy Transition to a Low Carbon Economy: Evaluation of Challenges Using Game Theory [J]. Energies, 2022, 15(17).

SOLDO B. Forecasting natural gas consumption [J]. Applied Energy,, 2012, 92: 26-37.

SZOPLIK J. Forecasting of natural gas consumption with artificial neural networks [J]. Energy Policy, 2015, 85: 208-20.

AZADEH A, ZARRIN M, RAHDAR BEIK H, et al. A neuro-fuzzy algorithm for improved gas consumption forecasting with economic, environmental and IT/IS indicators [J]. Journal of Petroleum Science and Engineering, 2015, 133: 716-39.

RODGER J A. A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings [J]. Expert Systems with Applications, 2014, 41(4): 1813-29.

F. G F G P G. Artificial neural network modeling for forecasting gas consumption [J]. Energy Sources, 2004, Vol.26(NO.3): 299-307.

AZADEH A, ASADZADEH S M, GHANBARI A. An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments [J]. Energy Policy, 2010, 38(3): 1529-36.

EYNARD J, GRIEU S, POLIT M. Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption [J]. Engineering Applications of Artificial Intelligence, 2011, 24(3): 501-16.

KARLIK R K B. Comparison neural networks models for short term forecasting of natural gas consumption in Istanbul [Z]. 2008 First International Conference on the Applications of Digital Information and Web Technologies Ostrava, Czech Republic. 2008

IZADYAR N, ONG H C, SHAMSHIRBAND S, et al. Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption [J]. Energy and Buildings, 2015, 104: 208-14.

ASKARI S, MONTAZERIN N, ZARANDI M H F. A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables [J]. Applied Soft Computing, 2015, 35: 151-60.

THOMAS NG S, SKITMORE M, WONG K F. Using genetic algorithms and linear regression analysis for private housing demand forecast [J]. Building and Environment, 2008, 43(6): 1171-84.

MAY G, STAHL B, TAISCH M, et al. Multi-objective genetic algorithm for energy-efficient job shop scheduling [J]. International Journal of Production Research, 2015, 53(23): 7071-89.

LEE S. Multi-parameter optimization of cold energy recovery in cascade Rankine cycle for LNG regasification using genetic algorithm [J]. Energy, 2017, 118: 776-82.

JEONG M, CHO E-B, BYUN H-S, et al. Maximization of the power production in LNG cold energy recovery plant via genetic algorithm [J]. Korean Journal of Chemical Engineering, 2021, 38(2): 380-5.







How to Cite

Energy Consumption Optimization Analysis of LNG Receiving Station Based on Genetic Algorithm . (2024). Academic Journal of Science and Technology, 10(1), 136-140.

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