Energy, exergy, exergoeconomic, and environment analyses of a composition adjustment zeotropic Linder-Hampson cycle
Year: 2026
Type: article
Abstract: To enhance the performance of the traditional Linder-Hampson cycle under varying operating conditions, this study proposes a composition-adjustable Linder-Hampson cycle for a −90 °C freezer. By actively regulating the composition of the zeotropic mixture, the cycle maintains favorable temperature matching across a wide range of ambient conditions. Aspen HYSYS is coupled with a MATLAB genetic algorithm to determine the optimal composition under different operating conditions, and R1150/R600 was identified as the most suitable mixture among the three mixtures. With adaptive composition control, the composition adjustment Linder-Hampson cycle demonstrates remarkable performance enhancements: the coefficient of performance is improved by up to 0.422, thus transforming the annual performance trend from an 'M' shape to a 'V' shape. The system maintains a higher actual volumetric refrigeration capacity and achieves an absolute increase in exergy efficiency of 0.42–19.54% by drastically reducing irreversible loss in key heat exchangers. In January, the exergy efficiency increases from 16.1% to 35.6%. Moreover, a climate-based seasonal performance evaluation is conducted by integrating hourly ambient temperature variations across five typical Chinese cities. The results indicate that the composition-adjustable cycle exhibits superior adaptability to seasonal climate variations, with the seasonal energy efficiency ratio increased by up to 50% in Harbin, where the highest value of 0.69 is achieved. Exergoeconomic and environmental analyses further validate its advantages, showing a lower exergy loss cost rate and significantly reduced pollutant emissions.
Source: Energy
Institutions: Qingdao University, City University of Hong Kong
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,260
OpenAlex ID: W7127640585
