In refrigerated spaces large and small, from warehouses to refrigerated transportation, physics-based thermodynamic modeling can be used to inform performance improvements, significantly reduce energy consumption and improve maintenance intervals. By leveraging data from a facility’s control system to compute previously unavailable information, these thermodynamic digital twins enable such real-time performance improvements and can be used to identify common equipment failures using machine learning algorithms to alert personnel to take action.

The cold chain faces unique operational challenges not experienced by the rest of the industrial sphere. Sudden spikes in power consumption, equipment degradation and many other issues like frost buildup on evaporator coils can all impact system efficiency, leading to downtime, product spoilage and higher costs. Identifying the root cause of these problems and taking corrective action is critical, but it's often difficult without detailed, real-time visibility into system performance.

Moreover, while the cold chain plays a critical role in preserving food and pharmaceuticals around the world, it is also a major consumer of energy, accounting for an estimated 17% of global electricity production. As the need for industrial refrigeration continues to grow due to global climate change and change in consumer tastes, finding ways to enhance the efficiency and sustainability of cold chain operations has become an urgent priority.

One promising approach is the use of physics-based digital twin modeling. By developing high-fidelity thermodynamic simulations of industrial refrigeration systems, operators can gain unprecedented visibility into system performance, identify opportunities for optimization and reduce energy consumption and greenhouse gas emissions.

The Power of Digital Twins

The key to addressing these challenges lies in the use of physics-based thermodynamic modeling, which can create a "digital twin" of an industrial refrigeration system. By modeling the complex interactions between major components (e.g., compressors, evaporators and condensers), these digital twins can provide a wealth of insights that go far beyond what can be gleaned from typical sensor data.

For example, this digital twin model of a 233-ton industrial ammonia cooling system (below) is able to calculate not just the total cooling capacity and power consumption, but also the detailed performance of individual components. This allows operators to identify anomalies like sudden power spikes, which can result in significant demand charges and greenhouse gas emissions from the power grid.

The digital twin also provides a novel way to evaluate system performance, using a standardized “score" that compares the actual coefficient of performance (COP) to an ideal, practical COP based on the system's operating conditions. This allowed the researchers to benchmark the system's efficiency and track changes over time, highlighting opportunities for optimization.

Going even further, the model can be used to generate synthetic data sets that train machine learning algorithms to accurately identify common failure modes in smaller refrigeration units for vaccines, such as compressor failures, evaporator frosting and expansion valve issues. With this kind of predictive maintenance capability, cold chain operators can take proactive steps to address problems before they lead to product spoilage or service disruptions.

Unlocking Efficiency and Sustainability

The insights provided by digital twin modeling have the potential to unlock significant efficiency and sustainability gains across the cold chain. By monitoring system performance in real-time, operators can identify and address issues like equipment degradation, improper maintenance, and inefficient control strategies. This not only reduces energy consumption and greenhouse gas emissions, but also helps to minimize product spoilage and maintain the integrity of temperature-sensitive goods.

Moreover, the ability to benchmark performance and compare systems using the score methodology opens up new opportunities for continuous improvement. Cold chain facilities can learn from each other, adopting best practices and targeting areas for optimization. Over time, this could drive industry-wide improvements in energy efficiency and sustainability.

As the demand for industrial refrigeration continues to grow, the need for innovative solutions like digital twin modeling will only become more pressing. The current area of focus for digital twin monitoring is the development of more advanced machine learning algorithms that can not only identify failures, but also provide predictive maintenance recommendations. By integrating these capabilities with the detailed performance data from the digital twin, cold chain operators are able to anticipate and prevent issues before they occur, further enhancing efficiency and reliability.

Ultimately, the work being done in this field represents a significant step forward in addressing one of the most pressing challenges facing the global economy and environment. By leveraging the power of physics-based modeling, machine learning, and emerging digital technologies, the cold chain industry can become an example of how innovation and technology can drive meaningful progress towards a more sustainable future.