High-fidelity model development of CO2 booster refrigeration systems in supermarkets using field measurements.

• A high-fidelity model of CO 2 booster refrigeration systems is proposed. • Critical but normally unavailable in practice variables are inferred. • Model has a minimum requirement on real measurements as inputs. • Onsite data from three UK supermarkets are used for demonstration. • Data integrity and thermodynamic laws are secured in the model. Supermarket refrigeration systems adopting traditional refrigerants with high global warming potential (GWP) have impacts on global warming for indirect and direct greenhouse gases (GHG) emissions. CO 2 is a popular low-GWP alternative. The transcritical operation of CO 2 systems worsens their energy performance, but provides recoverable heat as a heat source to reduce gas consumption. To evaluate operation performance, data-driven models, trained by historical data, are weak in implementation with datasets outside the scope of training data; in contrast, theoretical models have better extrapolation ability to calculate all operation conditions of CO 2 systems at supermarket. Existing theoretical modeling approaches often lack validation against the limited public-access data, which reduces model reliability for further applications, and adopt oversimplified inference methods for unmeasured variables, which increases the risks of breaking thermodynamic laws and lowering model accuracy. This study therefore develops a steady-state theoretical model for CO 2 booster refrigeration systems validated against field measurements from three UK supermarkets. The available measurements are utilized to the best level to ensure model accuracy and physical interpretability. Proposed methods to infer missing variables in CO 2 systems include condenser outlet temperature, evaporating temperature, compressor isentropic efficiency and compressor mass flow rate. Results show that proposed inference methods enhance the abilities of the proposed modeling approach to ensure data integrity, avoid breaking thermodynamic laws, and improve model accuracy by reflecting real-time actual values of unmeasured variables rather than rough assumptions. The proposed modeling approach provides satisfactory accuracy validated using high-resolution measurements across the whole year from three real supermarkets. [ABSTRACT FROM AUTHOR]