It is estimated that 17% of the global electricity production is used by a broad array of industrial refrigeration systems, collectively known as the Cold Chain.  This global refrigeration industry encompasses a wide range of disciplines, including the healthcare industry, where refrigeration preserves medicines and pharmaceuticals, including vaccines, and the food sector, where temperature-controlled warehouses, trucks and shipping containers maintain food safety. The need for industrial refrigeration is expected to grow in the coming years due to global warming. 

The pharmaceutical industry, in particular, has very stringent temperature storage requirements, and some of the required storage temperatures can be extremely low, leading to significant refrigeration system power use.  Performance enhancements, reduction in energy consumption and greenhouse gas emission, and improved equipment maintenance intervals can be achieved by using physics-based thermodynamic modeling methods [2-5] to develop a digital twin for a range of industrial refrigeration systems. Implementations have been demonstrated for stand-alone, single-loop commercial vapor compression refrigeration systems (refrigerators or commercial cooling units, e.g., vaccine storage units) and for multi-loop, multi-compressor industrial refrigeration systems used in temperature-controlled warehouses up to several hundred thousand square feet in size.  Such digital twins enable real-time performance monitoring by computing mass- and energy-balances using measured data, and the calculated results can be trended and used by machine learning algorithms to identify common equipment failures and alert personnel to operational problems.  

Examples are presented illustrating how the trended calculated results enable root-cause identification of operational inefficiencies as well as reduction in system performance due to equipment degradation and improper hardware selection.