Industrial AI Analytics for Energy Operations

Seeq Corporation and Applied Computing have established a commercial agreement to connect physics-grounded operational diagnostics with advanced time-series analytics across energy processing facilities. The technical collaboration addresses operational inefficiencies in continuous process industries, including petrochemicals, ammonia synthesis, liquefied natural gas production, and oil and gas refining.

Operational Context and Engineering Challenges
Operating continuous processing assets involves thousands of concurrent time-series variables spanning temperature, pressure, hydraulic flow, and thermodynamic gradients. Conventional process monitoring depends either on static statistical thresholds, which trigger frequent false-positive alarms, or manual data extraction, which limits root-cause troubleshooting.

The cooperation combines automated anomaly detection with advanced engineering investigation tools. Applied Computing provides the analytical detection engine, while Seeq provides the contextualization, visualization, and validation platform used by process engineers to analyze deviations.

Technical Architecture and System Integration
Applied Computing developed Orbital as an operations-focused foundation model that merges chemical engineering principles, physical constraints, time-series forecasting, and machine learning architectures. Orbital continuously processes telemetry to flag anomalous system behaviors and diagnose equipment degradation prior to critical thresholds.

Seeq serves as the operational intelligence layer, querying plant historians and distributed control systems. When Orbital flags process anomalies, engineering teams use Seeq's interface to isolate time windows, calculate mechanical tolerances, run empirical regressions, and identify root causes across historical performance profiles. The engineering roadmap includes bidirectional API integration between Orbital and Seeq to automate two-way parameter exchanges and contextual feedback loops.

Deployment at Scale
Initial deployment of the integrated solution supports the INSITE digital platform operated by KBR. Diagnostic telemetry from downstream ammonia, petrochemical, and gas processing plants is evaluated using Orbital's model.

Engineering analysts at the KBR Innovation Studio Bengaluru use Seeq to interrogate the model's flags, model plant physics against operational history, and formulate corrective maintenance protocols and client-facing engineering advisories. This architecture establishes a continuous verification workflow where automated statistical inferences are validated against empirical operating envelopes.

"By bringing together physics-based AI and advanced industrial analytics, Applied Computing and Seeq can help operations teams turn complex data into faster, more robust decisions," said Dr. Lisa Graham, CEO of Seeq.

"Orbital was built to detect and diagnose issues in the world's most complex energy environments, and Seeq gives operations teams a powerful way to investigate, contextualize, and act on what it finds," said Dan Jeavons, President of Applied Computing.

Plant Reliability and Process Impact
Coupling foundation-level physical modeling with operational data visualization eliminates diagnostic delay in continuous energy assets. Process units gain earlier detection of fouling, catalyst attrition, valve throttling instability, and thermal inefficiency. By establishing deterministic root causes instead of probabilistic guesses, facilities protect equipment life cycles, lower fugitive emissions through prompt heat-exchanger and seal fault isolation, and maintain process stability across changing feedstocks.

Edited by Sucithra Mani, Induportals editor – adapted by AI.

www.seeq.com