Over the last few decades, the optimal design and operation of energy-intensive industries such as cryogenic process has gained considerable attention. Because of their high energy efficiency, compact design, and energy-efficient heat transfer, mixed refrigerant (MR) systems are used in several industrial applications. The optimal refrigerant compositionwhich is difficult to obtainis crucial to the efficiency of MR systems. In this research, we explore the MR cryogenic process optimization using 17 different components in the refrigerant stream with normal boiling points ranging from −268.9 to 36 °C to achieve the lowest specific energy consumption. Here, we developed a discrete-continuous genetic algorithm (DCGA) consisting of five steps to resolve the mathematical difficulties of the many-variable optimization problem. Through conducting two case studies, we proved that DCGA can locate the optimal solution in a reasonable amount of time. Compared to the best optimization practices in the literature, the new approach saved up to 12.5% of the unit specific energy consumption. In addition to MR systems, DCGA can also optimize other extreme problems with many independent variables.

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