Predictive analytics allow researchers and laboratory technicians to proactively identify and rectify problems that may lead to ultra-low temperature freezer failures.
©iStock, paulburns1
Ultra-low temperature (ULT) freezers are essential in most research institutions, from basic research to biotechnology and pharmaceutical laboratories. Because of the extremely low temperature ranges that ULT freezers maintain, they are one of the most energy-intensive pieces of equipment in scientific facilities. Their high energy consumption necessitates technological advances that improve and optimize their operation and performance. Additionally, digital technologies that help scientists quickly detect and diagnose ULT freezer faults and malfunctions, such as onboard predictive analytics, are ushering in a new era of storage that ensures sample integrity and quality.1
What Is Onboard Predictive Analytics?
Predictive analytics uses data to identify correlations, patterns, and trends in a dataset to predict what is likely to occur in the future based on those data-informed patterns.2 Such data-driven approaches have gained increasing attention as strategies for optimizing energy system operations.1
In the case of ULT freezer optimization, predictive monitoring systems may help researchers and laboratory technicians proactively identify problems that lead to freezer failures, allowing laboratory personnel to have freezers repaired or replaced prior to a failure occurring.3 Onboard predictive analytics pairs the power of predictive monitoring through data directly measured from the ULT freezer’s control boards, with the simplicity of a built-in user interface that keeps researchers in tune with their equipment’s health.
How Does Predictive Analytics Improve Sample Safety and Freezer Health?
Common external disturbances such as door openings and ambient temperature shifts make ULT freezers particularly vulnerable to system state changes that need close monitoring to ensure sample safety and equipment longevity.1 For instance, temperature changes that force the equipment to work harder and use more energy ultimately place increased stress on the freezer that leads to malfunctions and failures.4 As such, built-in technologies that promote effective ULT freezer management and efficient running play a vital role in maintaining sample integrity and quality.4
Onboard predictive analytics can help scientists identify and resolve potential issues early and preemptively, allowing uninterrupted operations and minimizing costly freezer downtime and compromised or lost samples.1 Integrating data-driven techniques for ULT freezer monitoring also helps ensure operational control, flexibility, and reliability that reduces energy costs, curbs carbon emissions, and mitigates the economic and environmental consequences of unexpected or unrepairable equipment malfunctions.1
Staying Ahead of Freezer Failures
The Stirling VAULT100 freezer was designed to ensure sample integrity with tighter control over the ultra-low storage environment through new onboard predictive analytics. Unlike conventional compressor-based ULT freezers that collect and analyze data from externally added sensors, Stirling VAULT100 freezers tap into the already available data within existing control boards. Using existing data ensures that the analytical process is accurate and realistically reflects actual freezer operating conditions. This unique design helps provide researchers with real-time event monitoring, guidance for preventative maintenance, and proactive recommendations based on each freezer’s current operating conditions and stage of life.
The Stirling VAULT100 also features a new and more robust free-piston Stirling engine design, a first-ever -100 to -20°C temperature range, and a 360-degree sustainability approach that does not sacrifice performance or energy efficiency. Its large user interface displays insights into several engine sensors that are monitored and analyzed through predictive analytics, comparable to the lights and notifications on a car’s dashboard, opening a window into the overall health of each freezer. In addition, a built-in ethernet port helps scientists set alerts for door openings, temperature fluctuations, and engine status to avoid unexpected cooling failures and preserve precious samples with confidence.
- Huang T, et al. Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics. Sci Data. 2023;10:888.
- Cozzoli N, et al. How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. BMC Health Serv Res. 2022;22:809.
- Predictive Monitoring for Ultra-Low Temperature Freezers. NIH Environmental Management System. Accessed February 28, 2025.
- Graham M, et al. Roadmap for low-carbon ultra-low temperature storage in biobanking. J Transl Med. 2024;22(1):747.
