Development of an Energy-Efficient Single Mixed Refrigerant Cycle for Small-Scale LNG Production

Abstract

Energy-intensive single mixed refrigerant (SMR) cycles are employed to produce liquefied natural gas (LNG) at small scale. The energy required for refrigerant compression (shaft work) dominates the overall operating costs of the LNG plant. While current methods to minimize shaft work demand (e.g., exergy analysis) focus on the PRICO cycle, this paper shows that structurally modifying the configuration of SMR cycles can yield significant savings in shaft work demand with low added complexity. The novel SMR cycle developed in this work is based on the CryoMan SMR cycle; its benefits, in terms of energy savings, are demonstrated through a case study for natural gas liquefaction at small scale. The shaft work demand of the PRICO, CryoMan, and novel SMR cycles are minimized using a genetic algorithm and nonlinear optimization. The structural modifications applied tailor the refrigerant composition and exploit complex trade-offs between the operating variables to enhance the energy efficiency of the SMR cycles. The results from the case study demonstrate that the novel SMR cycle achieves 10% savings in shaft work demand in comparison with the PRICO SMR cycle.

Structural Modifications on Single Mixed Refrigerant Cycles for Performance Improvements in Small-Scale LNG Processes

“…Note that only the MultiStream SMR cycle is addressed in detail here, that is, the step-by-step description of its methodology. A more detailed description of the remaining methodologies can be found in the thesis of Almeida-Trasvina …”

“…The SMR cycle configurations studied are taken from the work of Zheng and that of Almeida-Trasvina et al . and are presented in Figure .…”

“…The SMR cycle configurations studied are taken from the work of Zheng 31 and that of Almeida-Trasvina et al 7 and are presented in Figure 7. These SMR cycles are the PRICO cycle (previously shown in Figure 1), the MultiStream SMR cycle (Figure 7a), the PreFlash SMR cycle (Figure 7b), the CryoMan SMR cycle (Figure 7c), and the Bypass SMR cycle (Figure 7d).…”

“…This case study has been used by Almeida-Trasvina et al 7 for the optimization of the PRICO, CryoMan, and Bypass cycles. Hence, this work expands this study to evaluate the performances of the MultiStream cycle and the PreFlash cycle.…”

“…9 Metaheuristic (stochastic search) methods add randomness to search the solution space to avoid local optima and help find a better solution; for example, genetic algorithms create a population of random initial guesses. 10 Alternatively, the performance of the CryoMan SMR cycle 11 and the work of Almeida-Trasvina et al, 7 which developed the so-called Bypass SMR cycle, indicate that making changes in the configuration of an SMR cycle can bring significant improvements in terms of energy efficiency: up to 10% in shaft power savings compared to the PRICO cycle under optimized conditions. Both configurations were shown to benefit from creating multiple refrigerant streams and adjusting their composition to reduce their overall shaft power demand.…”