A digital twin-based integrated optimization strategy for cascade refrigeration systems to boost energy efficiency

With the expansion of cold store and cold chain scales and the stringent low-temperature requirements, cascade refrigeration systems (CRSs) with evaporative condensers (ECs) have gained unprecedented attention and development, causing substantial carbon emissions and energy waste. Digital twin offers challenging opportunities for the green and intelligent transition of the refrigeration industry, however, simulation and optimization studies of CRSs utilizing this technology are currently limited and remain without a clear data transmission method. To address the aforementioned research gap, this paper establishes a CRS digital twin model and innovatively proposes two bidirectional automated data flow patterns by combining the Internet of Things (IoT) and cloud-edge computing, aiming at broadening the applicability of the energy-consuming components regulation strategy. Taking four typical operating conditions as case studies, thermodynamic performance evaluations indicate that there exist optimum intermediate and condensation temperatures, which can be quickly obtained through an iterative algorithm. Compared to single temperature setpoint optimization strategies, energy-saving potential analyses reveal that the integrated strategy can further boost the coefficient of performance (COP). Based on operating data from three working days, simulation results demonstrate an average increase of 7.2% in the COP after optimization.