IN Brief:
- Rockwell Automation and Actemium have deployed an autonomous AI refrigeration optimisation system for frozen food production.
- The RtCOP application uses PlantPAx to select efficient compressor, condenser, and evaporator configurations in real time.
- Industrial refrigeration is becoming an early proving ground for practical AI in food manufacturing energy management.
Rockwell Automation and Actemium have deployed an autonomous AI application designed to reduce energy use and mechanical strain in frozen food refrigeration systems.
Known as Real-Time Coefficient of Performance, or RtCOP, the system was developed by Actemium and runs on Rockwell Automation’s PlantPAx distributed control system. It has been used by a large frozen French fry producer, where it increased refrigeration energy efficiency by 17% and is expected to deliver annual savings of about $130,000 per site.
Rather than operating refrigeration assets solely to meet cooling demand, RtCOP analyses system capacity, efficiency, and environmental conditions in real time. It then selects operating configurations for compressors, condensers, and evaporators, continuously ranking available equipment combinations by energy performance.
That shift is significant for frozen food production, where refrigeration is one of the largest electricity loads on site. Industrial refrigeration can account for up to 70% of a plant’s energy consumption, and efficiency changes constantly with ambient temperature, load variation, defrost cycles, equipment availability, and product scheduling.
Many refrigeration systems still rely on conventional lead-lag control, fixed operating assumptions, and operator judgement. In plants with tight production windows and limited specialist refrigeration cover on every shift, optimisation can become inconsistent, especially when energy performance has to be balanced against product temperature, throughput, and asset reliability.
RtCOP changes the role of the control system from monitoring to continuous adjustment. By applying optimisation logic directly to the refrigeration plant, the application can reduce unnecessary equipment run time while helping avoid operating patterns that increase wear on compressors and associated assets.
Actemium is supporting wider deployment across the customer’s refrigeration fleet, with KPI dashboards designed to provide visibility between sites. That fleet-wide element could be as important as the individual saving, because refrigeration performance can vary widely across plants with similar equipment but different control strategies, maintenance practices, and production profiles.
The development follows growing investment in processing machinery and automation across the food sector. PMMI and FPSA’s analysis of the US food and beverage processing machinery market placed automation, sanitation, AI, and operational efficiency among the forces shaping equipment demand. Refrigeration optimisation gives that trend a practical factory-floor example, where AI is being tied directly to compressors, condensers, evaporators, dashboards, and energy bills.
The same direction was visible around interpack 2026, where connected production, cost control, labour pressure, traceability, and regulation were central themes in FMCG equipment discussions. Utilities are part of that conversation because they influence plant resilience as much as production assets do.
Refrigeration is also a useful test case for industrial AI because the operating boundaries are clear and the outcomes are measurable. Coefficient of performance, energy use, maintenance load, uptime, product temperature, and system response can all be tracked. That gives manufacturers a more tangible route to AI adoption than broad digital-transformation programmes with loose operational endpoints.
Integration will still require careful engineering. Food plants need high-quality data, transparent control logic, secure architecture, and enough operator confidence to allow autonomous adjustment of critical utilities. Optimisation also has to work around hygiene routines, defrost schedules, maintenance windows, batch changes, and food safety requirements.
The early direction is clear enough. AI is entering food factories through applications that solve defined operational problems, and refrigeration is one of the most attractive starting points. It is expensive, technical, energy-intensive, and often under-optimised. A system that reduces energy use while easing mechanical strain gives frozen food producers a concrete reason to move beyond dashboards and into autonomous process optimisation.
