The Escalating Energy Challenge in Industrial Refrigeration

Industrial refrigeration represents one of the most energy-intensive operational areas within food manufacturing plants. In typical frozen food production facilities, refrigeration systems consume up to 70% of total electrical power.

Historically, plant engineers operated compressor racks and chillers based solely on cooling demand rather than maximum energy efficiency. Human operators face significant limitations when managing dynamic load fluctuations, ambient temperature shifts, and complex variable-frequency drives (VFDs) in real time.

Autonomous AI Optimization Powered by Modern DCS Architecture

To address these inefficiencies, Rockwell Automation collaborated with system integration partner Actemium to develop an autonomous optimization solution. Known as Real-Time Coefficient of Performance (RtCOP), this advanced application operates directly on Rockwell's PlantPAx® Distributed Control System (DCS).

RtCOP functions as a continuous virtual operator across the plant network. The system evaluates operating capacities, equipment health, thermal efficiency, and ambient conditions simultaneously. Furthermore, it dynamically selects and commands the most energy-efficient operating setpoints for compressors, evaporators, and condenser fans.

Key Technical Highlight: PlantPAx DCS provides high-speed controller-to-controller deterministic communication and seamless industrial IoT connectivity, ensuring low latency between AI model inference and real-time control loop execution.

Quantifiable Energy Savings and Operational Benefits

Initial field implementations at a major frozen french fry manufacturing facility yielded immediate, measurable results:

Moreover, fleet-wide KPI dashboards allow engineers to benchmark coefficient of performance (COP) metrics across multiple geographic sites in real time.

Expert Insight: The Shift toward Autonomous Factory Automation

From a senior industrial automation perspective, this project highlights a critical trend: the convergence of classical process control and modern artificial intelligence at the edge.

Traditional programmable logic controllers (PLCs) and DCS platforms excel at deterministic safety interlocks and proportional-integral-derivative (PID) loop regulation. However, linear PID control struggles to optimize non-linear, multi-variable thermal systems efficiently.

By running AI-driven real-time optimization models alongside deterministic control layers, engineers achieve closed-loop energy optimization without compromising process stability or functional safety standards (such as IEC 61508 / IEC 61511).

Application Scenario: Retrofitting a Multi-Stage Industrial Ammonia Chiller

To illustrate how this system functions in a practical facility environment, consider a typical large-scale food freezing operation:

1. Baseline Assessment: A multi-stage industrial ammonia refrigeration plant utilizes five 500 HP screw compressors and four evaporative condensers.
2. DCS Integration: Engineers install the RtCOP software layer onto the existing PlantPAx DCS, connecting process instrumentation via EtherNet/IP networks.
3. Data Ingestion: The AI model processes real-time suction pressure, discharge pressure, wet-bulb temperature, power consumption meters, and ambient humidity.
4. Autonomous Setpoint Control: Instead of running three compressors at fixed 80% loads, RtCOP autonomously adjusts VFD speeds to run four compressors at an optimized 58% sweet spot, drastically reducing total power draw (kW/Ton) while maintaining strict process freezing temperatures.

About the Author

Zhang Wei is a Principal Industrial Automation & Control Systems Architect with over 15 years of hands-on field experience. He specializes in large-scale Distributed Control Systems (DCS), Programmable Logic Controllers (PLCs), Turbomachinery Instrumentation & Control (TSI), and industrial power grid protection across process industries worldwide.