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Process AI Agent

Autonomous Process Optimization

A physics-grounded AI agent platform that detects operational faults hours before traditional alarms and advises panel operators on lowest-energy steady states.

95% - 100% Diagnostic Accuracy
~35 Min Median Detection (From Event Start)
The Industry Challenge

The Industrial Data Tomb

The Status Quo

  • Data Underutilization: Plants gather tens of thousands of sensor readings, but 99% is buried in historians and only accessed retroactively after an incident.
  • Cognitive Strain: Live operations are highly complex. While skilled panel operators can monitor their units, continuously synthesizing dozens of parallel data streams in real time strains human cognitive limits.
  • Delayed Interventions: Traditional DCS systems trigger alarms only after operational limits are crossed, leaving operators minimal time to prevent critical escalation and costly shutdowns.
Unplanned Downtime Cost: Average of $1.2M - $20M+ per plant annually.

The AI Solution

  • Continuous Synthesis: Machine learning monitors, cleans, and evaluates the entire sensor dataset concurrently in real time.
  • Physics-Grounded Detection: Blends mathematical modeling of thermodynamics/hydraulics with AI to foresee failure states before alarms occur.
  • Proactive Adjustments: Unlocks a critical warning window—detecting anomalies at a median of ~35 minutes from event initiation—allowing operators to adjust parameters and avoid equipment degradation or safety trips.
Target Improvement: Prevent up to 85% of minor process upset escalations.
Modular Architecture

System Components

The Host Framework

The core orchestration framework that coordinates all downstream segments. It handles data ingestion schedules, manages system permissions, triggers simulation runs, and feeds cleansed sensor logs into the diagnostics layer.

Unified Process Orchestration
Client Unit Standardization
On-Premises Data Ingestion
Permission & Security Boundary
Physics-First Simulator

Reads user-defined Design Basis spreadsheets to construct a thermodynamic and hydraulic simulator of the process. It outputs synthetic training datasets containing hundreds of randomized fault transients, modeling true physical constraints.

High-Fidelity Hydraulics
Thermal Heat-Loss Model
DCS Telemetry Synthesis
Transient Fault Injection
AI Diagnostic Engine

A multi-layered diagnostic system. Maps arbitrary DCS tags to canonical roles. Resolves local client units to standard SI units. Evaluates process health (8 faults) alongside rotating equipment health (bearing temperature/vibration trends).

Automatic DCS Tag Mapping
Vibration & Temp Degradation
Multi-Pump Duty Isolation
Energy Optimization Layer
Reporter & Advisor

Translates algorithmic classifications into human-readable operations briefings. Generates PDF reports detailing fault timelines, Excel spreadsheets mapped back to client tag formats, and sends live advisory alerts to operators.

Automated PDF Summaries
Excel Log in Client Units
32-Minute Alert Window
Lowest-Energy Advisories
System Architecture

How Data Flows Through the Agent

Client DCS Raw CSV Export Canonical Map Tag Standardizer AI Agent Core Physics-First Model Advisor Panel Live Operations Reporter Engine PDF & Excel Log

End-to-End Integrity

  • Zero Tag Leakage: Client tags (e.g. `LIC-xyz`) remain strictly standard internally but map automatically to physical inputs (`Vessel Level`).
  • Secure Validation: The core maps incoming data against DCS configurations, prompting engineering validation *before* execution.
  • Energy Advice: When steady state is identified, the optimization layer compares design boundaries to suggest the lowest power duty configuration.
Independent Cross-Examination

Double-Blind Validation Protocol

01

Decoupled Data Generator

A separate simulation generates 30 days of physically sound process data with injected randomness, missing sensor gaps, and randomized tag naming.

02

The Blind Experiment

The AI Agent is fed the design basis and DCS data. It runs without any prior knowledge of faults, mapping tags dynamically and identifying anomalies blind.

03

Independent Scorer Algorithm

A third software compares the Agent's reports against the secure "ground truth" ledger, calculating exact diagnostic accuracy and temporal responsiveness.

Decoupled Simulator Generates Blind Data AI Agent (Blind) Analyzes Telemetry Scorer Algorithm Calculates Metrics Ground Truth DCS Logs ?
Proof of Concept Validation

Validated Performance Metrics

95%+
Diagnostic Accuracy
Correctly identified & categorized fault families across 27 randomized design runs.
~35m
Median Detection Delta
Detected anomalies at a median of 0.58 hours (~35 minutes) from the start of the fault event.
100% Detection
Rotating Equipment Bearing Degradation
Correctly isolated and characterized progressive pump bearing wear trends (vibration/temperature) across all multi-pump duty scenarios.
Performance Comparison
Diagnostic Accuracy 95% - 100%
Median Detection Time (From Event Start) ~35 Minutes
False-Alarm Free Rate 96.3%
*Tested on 27 distinct design scenarios, representing 30 days of continuous 60s DCS inputs.
Diagnostic Coverage

Target Fault Classifications

Feed Loss Process

Upstream feed failure leading to steady liquid inventory decline and pump cavitation risk.

Details

Tank Overfill Process

Inlet valve stuck open. Inventory rises toward emergency high-high trips or atmospheric release.

Details

Pad Valve Stuck Open Process

Nitrogen supply valve stuck in open position, continuously pressurizing vapor space.

Details

PVRV Stuck Open Process

Vapor pressure safety valve venting gas continuously to atmosphere, causing utility loss.

Details

Padding Gas Loss Process

Upstream N₂ supply failure while drawing liquid, creating structural vessel vacuum risk.

Details

Heater De-energized Process

Electrical immersion heater failure. Fluid cools, risking line freezing orSpec failures.

Details

Level Transmitter (LIC) Process

Telemetry level signal freezes. Loop continues static output while inventory drifts blindly.

Details

Pressure Transmitter (PIC) Process

Pneumatic vapor pressure reading freezes. Automated system drifts unchecked.

Details

Pump Bearing Wear Equipment

Progressive bearing degradation across up to 4 pumps. Vibration/temperature trends.

Details
Development Path

Commercialization Roadmap

Phase 1
Multi-Unit Scaling
Expand physics-first model and AI layers to cover complex, interconnected plant sections (e.g. exchanger trains).
Phase 2
Live Shadow Mode
Deploy Agent on-premises to ingest live data streams, validating performance without affecting real control loops.
Phase 3
Operator Advisory
Continuous background monitoring with visual pop-up recommendations, enabling manual corrective actions.
Phase 4
Autonomous Control
Fully certified write-access to the DCS, allowing the Agent to adjust non-safety parameters autonomously.
The Investment Proposition

Why Partner With Us

Air-Gapped Cybersecurity

Designed for on-premises edge server deployments. Zero proprietary plant telemetry leaves the client's internal network, bypassing standard cloud compliance hurdles.

Massive Operational ROI

Early detection of a level drop, heat exchanger fouling, or a bearing degradation will help your asset save time and money in unplanned shutdowns and/or emergency maintenance.

Scalable Physics-First Foundation

By defining physical and thermodynamic bounds dynamically through Design Basis templates, the platform scales efficiently without requiring costly customized rebuilds.

Supreme Cortex Technologies

Ready to lead the next generation of industrial intelligence. Partner with us as we expand our physics-first diagnostics platform across global refining and processing plants.

Dual-Parameter Trend: Pump P_101A Deterioration

Tags: P_101A:VI_BEARING.VIB (Vibe) & P_101A:TI_BEARING.TEMP (Temp)

Vibe Current 4.88 mm/s
Temp Current 64.90 °C
5.0 4.0 2.0 0.0 VIB (mm/s) 80°C 60°C 40°C 20°C TEMP (°C) 02:00:00 02:00:10 02:00:20 02:00:30 02:00:40 02:01:00 API 670 VIB LIMIT (4.0 mm/s) Vib Alert (4.88 mm/s) Temp Warning (64.90 °C)
Vibration Amplitude (mm/s) Bearing Temperature (°C)
Anomaly Correlation: High (98.4%)
Critical Warning Asset ID: LP_T101 / P_101A
Detected: 2026-07-02T02:01:00Z

Pump Bearing Degradation (P_101A)

The physics core has detected concurrent rises in both vibration and bearing temperature on transfer pump P_101A. The vibration amplitude has risen from 2.14 mm/s to 4.88 mm/s in a 60-second window, while bearing temperature rose to 64.9°C under static speed commands (48.5 Hz).

Vibe Amplitude
4.88 mm/s
Bearing Temp
64.9 °C
Confidence
98.4 %
Recommended Operator Actions:
  • Transition transfer flow target from Pump P_101A to standby Pump P_101B.
  • Initiate emergency work order for pump shaft inspection and bearing lubrication check.
  • Verify local vibration transmitter calibration checks to rule out telemetry errors.