Artificial intelligence is moving from experimental applications into real industrial control environments. A recent project involving Rockwell Automation and Actemium demonstrates how AI can work together with a distributed control system to improve energy efficiency in food manufacturing.

Rockwell Automation’s PlantPAx modern distributed control system is being used to support an autonomous AI application developed by Actemium for industrial refrigeration. The solution was developed for a large frozen food producer, where refrigeration represents a major part of overall energy consumption.

The application, known as Real-Time Coefficient of Performance, or RtCOP, continuously evaluates refrigeration operating conditions and selects more energy-efficient configurations. The project has demonstrated a significant reduction in refrigeration energy consumption.

The development is significant because refrigeration systems in food manufacturing are not simple standalone machines. They typically involve compressors, pumps, condensers, evaporators, temperature sensors, pressure sensors, valves, drives, and other field devices. Coordinating these assets requires reliable industrial control and continuous access to operating data.

PlantPAx provides the DCS control environment, while the AI application analyzes operational information and identifies more efficient operating configurations. This combination illustrates an emerging industrial automation model in which conventional control systems remain responsible for dependable process control while AI provides additional optimization capabilities.

For PLC and DCS engineers, this approach is particularly interesting. AI does not necessarily need to replace the existing control architecture. Instead, it can operate as an optimization layer connected to established automation systems.

This can be important for brownfield factories where completely replacing PLCs, DCS controllers, sensors, and industrial networks would be expensive and disruptive. By using existing automation infrastructure as the foundation, manufacturers can gradually introduce advanced analytics and AI without rebuilding the entire production system.

Energy efficiency is also becoming a major consideration for industrial automation projects. Rising electricity costs and sustainability targets are encouraging manufacturers to monitor equipment performance more closely. Industrial AI can help identify operating patterns that may not be obvious through traditional HMI monitoring or manual engineering analysis.

The Rockwell Automation project therefore represents more than an energy-saving application. It shows how DCS technology, industrial data, and AI can work together to optimize real production processes.

As factories become increasingly connected, future automation architectures are likely to combine PLCs, DCS platforms, edge computing, industrial networks, machine learning, and AI-based optimization. The goal is not simply to automate production, but to make industrial processes more intelligent, efficient, and adaptable.