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With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.

Production of liquefied natural gas (LNG) at a small scale typically relies on single mixed refrigerant (SMR) cycles to provide the necessary refrigeration. The shaft power required for refrigerant compression is by far the largest contributor to the operating costs of the entire LNG plant. The highly nonlinear interactions between operating variables mean that the optimization of these SMR cycles is very challenging. Current design methodologies for SMR cycles mostly rely on stochastic search algorithms to search the solution space. However, the quality of the results obtained from these methodologies is difficult to assess. Exergy analysis can be performed, but it provides limited information. This work provides a thermodynamic-based analysis to minimize the shaft power demand. Four different SMR cycle configurations are studied in one case study. The analysis brings insights regarding their optimized conditions, including the refrigerant composition. These insights are demonstrated to be useful with a second case study, which is used to develop effective design methodologies for SMR cycles. Overall, the analysis of structural modifications to SMR cycles in this work enables a more fundamental understanding of their optimal design for high energy efficiency.

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