Energy-intensive industrial processes are becoming an important target for artificial intelligence because even small improvements in operating efficiency can produce substantial savings when equipment runs continuously. A new deployment involving Rockwell Automation and Actemium demonstrates this approach in the food manufacturing sector, where an AI-powered application is being used to optimize industrial refrigeration.

The application, known as Real-Time Coefficient of Performance, or RtCOP, operates with Rockwell Automation's PlantPAx modern distributed control system. According to the companies, the autonomous application helped a large frozen French fry producer reduce refrigeration energy consumption by 17% while also reducing equipment strain.

The development provides an interesting example of how AI can be introduced into an existing industrial control environment without replacing the underlying automation architecture.

Refrigeration Is a Major Automation Challenge

Industrial refrigeration is essential in frozen food manufacturing.

Production facilities must maintain controlled temperatures across processing, freezing, storage, and distribution operations. Refrigeration equipment can include compressors, condensers, evaporators, pumps, fans, valves, sensors, and associated electrical systems.

Because these systems operate continuously and consume significant amounts of energy, optimization has a direct impact on operating costs.

The challenge is that refrigeration efficiency is not constant.

The most efficient combination of equipment can change according to production demand, ambient conditions, equipment availability, temperature requirements, and other operating variables.

A fixed control strategy may therefore leave efficiency opportunities unused.

This is where advanced optimization can provide additional value.

From Conventional Control to Continuous Optimization

Traditional PLC and DCS systems are extremely effective at executing deterministic control strategies.

A control engineer can define temperature setpoints, pressure limits, compressor sequences, interlocks, alarm thresholds, and operating modes. The control system then executes these instructions consistently.

AI introduces a different capability.

Instead of simply following a predefined sequence, an optimization application can evaluate operating conditions and select configurations intended to improve a defined performance objective.

In the Rockwell and Actemium application, that objective is connected to refrigeration efficiency.

The system continuously evaluates operating conditions and selects energy-efficient configurations for refrigeration equipment.

This does not replace the fundamental control system. Instead, the AI application works above the existing automation layer and uses the control system as the operational foundation.

That distinction is important for industrial facilities.

Manufacturers generally do not want to replace proven PLC or DCS infrastructure simply to introduce AI.

They want to add intelligence while maintaining the reliability of existing control systems.

PlantPAx Provides the Control Infrastructure

PlantPAx is Rockwell Automation's process automation system and is designed to provide distributed control capabilities for industrial processes.

In the refrigeration application, PlantPAx provides the control environment supporting the AI optimization application.

This illustrates a growing architecture for industrial AI:

Field devices → PLC/DCS → industrial data → optimization software → control recommendations or automated adjustments

The control layer remains responsible for deterministic operation, sequencing, alarms, interlocks, and equipment control.

The optimization layer focuses on finding better operating conditions.

This separation can make AI adoption more practical because manufacturers can introduce optimization without redesigning every control loop.

Why the 17% Energy Reduction Matters

A 17% reduction in refrigeration energy consumption is particularly meaningful because refrigeration systems can operate around the clock.

Consider a facility with large refrigeration loads running continuously throughout the year.

A percentage reduction applied across thousands of operating hours can produce a significant reduction in electricity consumption.

The financial benefit can also extend beyond electricity.

More efficient operating conditions can reduce unnecessary compressor loading, cycling, and thermal stress. Over time, this may contribute to improved equipment reliability and maintenance performance.

The project therefore demonstrates that industrial AI does not necessarily need to involve futuristic autonomous factories.

Some of the most commercially valuable applications may be much simpler: optimizing equipment that already exists.

AI Works Best When It Understands the Process

Industrial AI differs from many consumer AI applications because the physical process matters.

A refrigeration system cannot simply optimize for minimum electricity consumption.

Temperature requirements must be maintained.

Product quality must be protected.

Pressure and equipment operating limits must be respected.

Compressors cannot be operated outside their engineering constraints.

Safety systems must remain active.

This means industrial AI must operate within the boundaries established by process engineering.

The control system therefore remains critical.

An AI algorithm may identify a theoretically efficient operating point, but the automation system must ensure that the selected configuration remains within safe and valid operating conditions.

This is one reason why integrating AI with established industrial control platforms can be valuable.

The Importance of Real-Time Data

Optimization depends heavily on data quality.

A refrigeration system can generate information from temperature sensors, pressure transmitters, flow instruments, compressor status signals, motor measurements, valve positions, fan speeds, and other devices.

The more accurately the system represents actual plant conditions, the better the optimization process can operate.

However, raw data alone is not enough.

Industrial data must be synchronized, contextualized, and interpreted according to the process.

For example, a compressor operating at a certain load may be efficient under one production condition but inefficient under another.

AI-based optimization needs to understand these relationships.

This is where the combination of industrial automation expertise and data analytics becomes important.

Why Existing PLC and DCS Systems Still Matter

The Rockwell deployment also demonstrates why the rise of industrial AI does not mean PLCs and DCS systems are becoming obsolete.

Quite the opposite.

AI applications require reliable data sources and dependable control infrastructure.

Sensors still need to collect physical measurements.

I/O systems still need to transfer signals.

PLCs and DCS controllers still need to execute control logic.

Drives still need to regulate motors.

Valves and actuators still need to respond.

Industrial networks still need to deliver information reliably.

AI sits on top of this physical infrastructure.

This means the future industrial architecture is likely to contain both conventional automation and advanced intelligence.

AI Can Be Applied Beyond Food Manufacturing

Although the reported project focuses on frozen food production, the concept has broader potential.

Industrial refrigeration exists in cold storage warehouses, beverage manufacturing, pharmaceutical production, chemical processing, supermarkets, logistics facilities, and other temperature-controlled environments.

Similar optimization techniques could also be applied to other energy-intensive systems.

Examples include compressed-air networks, chilled-water systems, pumping stations, HVAC systems, boiler plants, and large motor-driven processes.

The general principle is the same:

1. Collect real-time operational data.
2. Understand equipment relationships.
3. Identify inefficient operating conditions.
4. Select improved operating configurations.
5. Maintain process constraints.
6. Continuously evaluate results.

This approach is different from simply installing more sensors.

The objective is to turn existing automation data into operational decisions.

A New Role for Process Control Engineers

The growth of AI will not eliminate process control engineering.

Instead, it is likely to change the role of automation engineers.

Engineers will increasingly need to understand how AI applications interact with PLCs, DCS platforms, industrial networks, historian systems, and field devices.

Control engineers may also become involved in defining the boundaries within which optimization algorithms can operate.

For example, engineers can define:

• Equipment operating limits
• Process constraints
• Alarm conditions
• Safe fallback modes
• Minimum and maximum setpoints
• Manual override functions
• Data-quality requirements

This creates a partnership between traditional control engineering and AI-based optimization.

Energy Efficiency Is Becoming an Automation KPI

Historically, automation projects were often justified through productivity, uptime, labor reduction, and quality improvements.

Energy efficiency is becoming another major KPI.

This is especially important as electricity prices, sustainability targets, and carbon-reduction requirements influence manufacturing decisions.

A control system that can reduce energy consumption without sacrificing production performance can therefore create measurable business value.

The Rockwell Automation and Actemium project provides a practical example of this trend.

Instead of presenting AI as a standalone technology, the project connects AI directly to an industrial process and a measurable operational result.

What This Means for Future Industrial Control Systems

The broader message is that industrial AI is moving from demonstrations toward operational optimization.

The most successful applications are likely to be those that solve specific engineering problems.

Refrigeration efficiency is one such problem.

Rather than asking AI to control an entire factory immediately, manufacturers can start with a clearly defined system, measurable energy consumption, existing automation infrastructure, and well-understood process constraints.

That approach lowers implementation risk while providing a clear way to evaluate results.

For PLC, DCS, SCADA, drive, sensor, and industrial networking suppliers, this trend also creates new opportunities.

As factories become more intelligent, the value of reliable industrial data and control hardware increases.

The future factory will not be built by AI alone.

It will depend on the combination of sensors, PLCs, DCS systems, industrial networks, drives, actuators, process engineering, data infrastructure, and AI optimization.

The 17% refrigeration energy reduction reported by Rockwell Automation and Actemium is therefore more than a food-industry efficiency story. It is an example of how established automation platforms can become the foundation for the next generation of AI-assisted industrial operations.