Honeywell Launches Experion Cognition Platform

Honeywell has introduced Experion Cognition, an artificial intelligence (AI) enabled control system platform engineered to advance autonomous industrial operations through predictive recommendations and automated decision-making. The platform aims to enhance facility safety and optimize production outputs by identifying and resolving abnormal operational conditions.

Mitigating Workforce Gaps and Control Anomalies
Modern industrial facilities face a significant demographic transition as highly experienced panel operators and process engineers reach retirement age. This shift creates an operational knowledge gap and industrial workforce shortage across complex process environments. Experion Cognition addresses this skills gap by converting legacy, experiential plant data into algorithmic models. By delegating complex cognitive tasks and real-time situation management to autonomous software agents, the platform allows less experienced operators to manage plants with the same level of precision and procedural awareness as senior personnel.

According to Jim Masso, President and CEO of Honeywell Process Automation, the platform transitions the industry from theoretical discussions to the practical execution of autonomous control rooms, supporting enhanced safety and performance metrics within highly complex operating environments. The platform embeds advanced AI models into the automation framework to act proactively on behalf of human operators during control room process anomalies.

Field Validation and Integration Infrastructure
A live proof-of-concept for the platform was demonstrated at Borouge Group International AG’s ("Borouge International") Ruwais petrochemical complex located in Abu Dhabi. Dr. Hasan Karam, Chief Operating Officer of Borouge International, stated that the initiative marks an industry first for AI autonomous operations within the petrochemical sector, directly supporting workforce upskilling, operational competitiveness, and asset efficiency under the company's Digitalization and Technology framework.

Architecturally, Experion Cognition functions as an embedded extension of Honeywell's Experion Process Knowledge System (PKS) distributed control network. This structural compatibility allows the platform to integrate into existing control room infrastructures, leveraging historical, site-specific data loops while maintaining established hardware and software standards. The platform incorporates multiple AI features, including the Operations Assistant module. Across multiple industrial pilot deployments, this predictive tool anticipated process abnormalities and alarm incidents an average of 5 to 10 minutes before hardware thresholds were breached, granting personnel a sufficient operational window to implement corrective measures.

Additional Context
This section details technical specifications not included in the original news release.

Distributed Control Systems (DCS) utilize deterministic, closed-loop feedback algorithms—primarily Proportional-Integral-Derivative (PID) control loops—to maintain process variables such as pressure, temperature, and volumetric flow rates at specified setpoints. In standard industrial plants, these loops operate independently, lacking the contextual capacity to evaluate the holistic thermodynamic or chemical state of interconnected units. During a process upset, this localized logic can result in alarm flooding, where a single equipment failure triggers a cascade of sequential alerts across downstream subsystems. This phenomenon challenges the cognitive limits of human operators, who must manually analyze variable interactions to isolate the root cause of the anomaly.

Autonomous control room extensions alter this operational environment by introducing probabilistic reasoning models alongside deterministic base-layer logic. These platforms employ machine learning algorithms, such as recurrent neural networks or transformer-based sequence models, to ingest hundreds of high-frequency data tags directly from the DCS data highway. The system generates real-time predictive simulations of the plant's operational state, identifying minor cross-variable deviations—referred to as pre-alarm signatures—that indicate a developing process anomaly.

When a deviation is detected, the reasoning engines compare the live data matrix against historical steady-state parameters and digitized Standard Operating Procedures (SOPs). Instead of presenting an isolated sensor breach, the system groups the variables into a unified situational analysis. It calculates the statistical probability of specific failure modes and delivers actionable, step-by-step instructions directly to the operator interface. In advanced autonomous configurations, the platform executes corrective setpoint adjustments through the DCS controllers, mitigating risks before the plant reaches an emergency trip threshold.

Edited by Romila DSilva, Induportals Editor, with AI assistance.