Self-Healing Diagnostics of Carbon Capture Plant Instrumentation Workflow

Instrumentation drift in carbon capture plants corrupts process data, leading to inefficient solvent cycles, compliance miscalculations, and unplanned shutdowns. A self-healing diagnostic workflow automates this by deploying specialized agents that continuously cross-reference sensor readings against physics-based digital twin simulations and historical performance envelopes. When discrepancies exceed statistical thresholds, the orchestrator initiates a diagnostic sequence, isolating the fault to a specific transmitter, control valve, or data bus. This real-time integrity check eliminates the manual calibration rounds and reactive repairs that cost plants hundreds of hours in lost capture capacity annually.

Implementation integrates with the plant's Distributed Control System (DCS), historian (e.g., OSIsoft PI), and Computerized Maintenance Management System (CMMS) like SAP PM or IBM Maximo. The orchestrator, built on frameworks like LangGraph, manages the diagnostic logic and exception routing. Critical actions, such as switching primary instruments, require human-in-the-loop approval via a connected workbench. The workflow includes robust observability for audit trails, tracking every diagnosis, action, and energy impact. This architecture turns instrumentation from a reliability liability into a self-correcting asset, ensuring data integrity for optimization and compliance.

This workflow automates the detection and initial remediation of instrumentation faults, a critical operational bottleneck that causes false readings, process instability, and unplanned downtime. The business value comes from eliminating manual diagnostic labor, preventing capture efficiency losses due to bad data, and reducing maintenance costs through precise, predictive work orders. Implementation integrates with plant historians (OSIsoft PI, Aveva), distributed control systems (DCS), and computerized maintenance management systems (CMMS) like SAP PM or IBM Maximo, using a mesh of specialized AI agents for signal validation, model-based reasoning, and action orchestration.

The architecture requires robust controls, including confidence scoring for autonomous actions, mandatory human review for low-confidence diagnoses, and an immutable audit trail linking signals, diagnoses, and actions for compliance. Observability is built in via a central dashboard showing agent health, diagnostic accuracy, and mean-time-to-repair metrics. Rollout is sequenced by instrument criticality, starting with non-safety-critical sensors, with each agent containerized for deployment in on-premise or hybrid cloud environments adjacent to operational technology networks.

Phase 1 establishes the core diagnostic engine, ingesting real-time telemetry from pressure transmitters, flow meters, and control valves via OPC-UA or Modbus. Anomaly detection agents, trained on historical patterns and first-principle models, flag deviations. Diagnoses are logged in a central event store (e.g., TimescaleDB) and routed to a human-in-the-loop dashboard for validation, building operator trust and a labeled dataset for refining the AI models before any autonomous action is permitted.

Phase 2 introduces closed-loop remediation, where high-confidence diagnoses automatically trigger actions like instrument recalibration or failover to spares via integrated DCS/SCADA APIs, all gated through a supervisory console. Phase 3 expands the system's scope, integrating with the plant's CMMS (e.g., SAP PM, Maximo) to auto-generate precise maintenance tickets, order parts, and schedule crews, completing the self-healing loop. Each phase is governed by approval workflows, audit trails, and rollback procedures to de-risk adoption.

Deploying self-healing diagnostics requires a governance layer that enforces safety and data integrity before any autonomous action. The workflow must integrate with the plant's Distributed Control System (DCS) and Computerized Maintenance Management System (CMMS) via secure APIs, with every diagnostic inference and proposed action logged to an immutable audit trail. Approval gates are mandatory for actions affecting safety-critical instruments, routing high-confidence, low-risk calibrations to auto-execution while escalating ambiguous diagnoses for human engineer review within the control room HMI.

A phased rollout mitigates operational risk. Phase 1 deploys monitoring and alerting only, building trust in the diagnostic accuracy without autonomous action. Phase 2 enables auto-recovery for a pre-approved list of non-critical sensors, with a mandatory cool-off period and rollback capability. Phase 3 expands to control valves and critical instruments, contingent on proven performance in prior phases. Each phase requires updated SOPs, operator training, and defined KPIs for Mean Time To Recover (MTTR) and reduction in false positive rates to measure ROI.