AI-Driven Refrigeration Optimization: How Rockwell Automation and Actemium Reduce Energy Consumption in Frozen Food Manufacturing
, 4 دقيقة وقت القراءة
Rockwell Automation and Actemium use AI-driven RtCOP technology with PlantPAx DCS to optimize industrial refrigeration systems, achieving 17% energy savings and improving operational efficiency in frozen food manufacturing.
Introduction: Artificial Intelligence Brings New Efficiency to Industrial Refrigeration
Energy consumption has always been a major challenge for frozen food manufacturers because refrigeration systems require continuous operation and represent a significant portion of total plant electricity usage. Rockwell Automation and its PartnerNetwork™ member Actemium have developed an AI-driven optimization solution that improves refrigeration performance through autonomous decision-making.
The solution, based on Rockwell Automation’s PlantPAx® modern distributed control system (DCS), enables industrial refrigeration equipment to automatically select the most energy-efficient operating combinations. For frozen food producers, this approach moves refrigeration management from traditional demand-based control toward intelligent, real-time optimization.
Actemium RtCOP Solution Delivers Measurable Energy Savings
Actemium developed the Real-Time Coefficient of Performance (RtCOP) application for a major frozen french fry manufacturer. The system continuously evaluates refrigeration system conditions and determines the optimal operating strategy for compressors, condensers, and evaporators.
According to operational results, RtCOP has helped achieve approximately 17% energy efficiency improvement, with estimated annual savings of around $130,000 per production site. In addition to reducing electricity costs, the solution decreases unnecessary equipment loading, which can contribute to improved asset operating conditions.
From an engineering perspective, this achievement demonstrates that refrigeration optimization should not rely only on equipment upgrades. Intelligent software-based control strategies can often deliver significant improvements by maximizing the performance of existing assets.
How AI Enables Autonomous Refrigeration Control
RtCOP operates as a virtual refrigeration specialist that continuously analyzes real-time operating data. The application considers system capacity, equipment efficiency, ambient conditions, and cooling requirements before selecting the best combination of refrigeration components.
Unlike traditional control systems that mainly respond to cooling demand, AI-based optimization evaluates multiple operating scenarios simultaneously. This allows the system to balance production requirements with energy consumption targets.
The solution essentially provides operators with continuous performance analysis that would be difficult to achieve manually. Human operators can monitor system performance, while AI technology handles complex optimization calculations in the background.
PlantPAx DCS Provides the Automation Foundation
The PlantPAx modern DCS acts as the core automation platform supporting the RtCOP application. It provides access to real-time process data, control execution capability, and system-wide visibility required for autonomous optimization.
For industrial facilities, a modern DCS is no longer limited to traditional process control functions. It has become an information platform that connects field equipment, operational data, advanced analytics, and intelligent applications.
In this application, PlantPAx enables seamless communication between refrigeration equipment and the AI optimization layer, creating a closed-loop system that continuously improves energy performance.
Why Refrigeration Optimization Matters in Food Manufacturing
Industrial refrigeration can represent up to 70% of a food processing plant’s electricity consumption. Therefore, improving refrigeration efficiency provides one of the largest opportunities for reducing operational costs and carbon emissions.
Many refrigeration systems are traditionally operated to maintain required temperatures without continuously evaluating whether equipment combinations are operating at maximum efficiency. This creates opportunities for AI-based optimization technologies.
As energy prices increase and sustainability requirements become stricter, manufacturers need solutions that improve efficiency without disrupting production. Autonomous optimization provides a practical pathway by using existing automation infrastructure more effectively.
Supporting Workforce Challenges Through Intelligent Automation
Food manufacturers are facing increasing challenges related to specialized technical skills, particularly in refrigeration operation and maintenance. AI-driven applications can help reduce dependence on manual analysis while supporting operators with better information.
The value of autonomous systems is not to replace experienced engineers but to extend their capabilities. By continuously monitoring equipment performance and identifying optimal operating conditions, AI tools allow technical teams to focus on higher-value engineering tasks.
Scaling Performance Across Multiple Manufacturing Sites
Actemium is supporting the expansion of RtCOP across multiple refrigeration facilities. Performance dashboards provide visibility into key performance indicators (KPIs), allowing manufacturers to compare energy efficiency between different sites.
This site-to-site benchmarking capability helps organizations identify improvement opportunities and establish consistent operating standards across their production network.
For global food manufacturers, centralized performance monitoring combined with autonomous optimization can become an important strategy for achieving long-term energy management goals.
Engineering Perspective: The Future of Industrial Energy Management
The collaboration between Rockwell Automation and Actemium highlights an important trend in industrial automation: the integration of AI with existing control platforms.
In the future, more industrial systems will move beyond simple automation toward adaptive operations, where equipment continuously adjusts itself based on performance data, environmental conditions, and production requirements.
For energy-intensive industries, AI optimization represents a practical method to improve efficiency, extend equipment life, and support sustainable manufacturing without requiring complete system replacement.
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