Multi-Agent Workflow for Fugitive Emissions Detection with Drone Fleets

This agent is the central scheduler, responsible for generating optimal patrol routes for the drone fleet based on facility maps, historical leak data, wind conditions, and regulatory inspection frequencies. It dynamically reprioritizes missions in response to ad-hoc requests or sensor alerts from other areas, ensuring maximum coverage with minimal flight time and energy use. It integrates with airspace management systems and weather APIs to ensure safe, compliant operations.

Deployed on the drone's edge compute module, this agent manages real-time navigation, stabilizes the optical gas imaging (OGI) camera, and triggers the capture of geo-tagged imagery and video when a potential plume is detected. It handles low-level flight controls, obstacle avoidance, and communication back to the orchestration layer, ensuring the drone operates autonomously within its defined mission parameters even in GPS-denied or high-interference environments common in industrial facilities.

This agent processes the raw OGI footage, using specialized vision models to distinguish methane/VOC plumes from background steam, dust, or thermal artifacts. It quantifies leak size and confidence score, and extracts precise geospatial coordinates. To reduce false positives, it can cross-reference with process data (e.g., is that valve supposed to be venting?) from the plant historian via an integration layer. Findings are packaged into structured alerts for the triage agent.

Acting as the workflow's decision router, this agent receives validated leak alerts. It enriches them with asset metadata (e.g., valve ID, maintenance history, criticality) from the CMMS/EAM and applies business rules to prioritize severity. For high-confidence, non-critical leaks, it automatically generates a detailed work order in the CMMS (like SAP PM or Maximo), assigning it to the appropriate maintenance crew with SOPs and location data. High-criticality or ambiguous alerts are routed to a human operator dashboard for review.

This agent ensures the entire workflow produces an audit-ready record for regulatory bodies like the EPA. It compiles a immutable log of all flights, detections, actions taken, and repair verifications. It can auto-generate reports demonstrating leak detection and repair (LDAR) program compliance, calculate estimated emission volumes for reporting, and prepare data packages for voluntary methane reduction initiatives. This transforms a manual, quarterly reporting burden into a continuous, automated byproduct of operations.

Building this workflow requires a LangGraph or Temporal-based orchestration layer to manage agent state and handoffs. Drones communicate via a MQTT/ROS2 bridge to a central control plane. The vision agent typically runs on Azure AI Vision or AWS SageMaker for model hosting. Critical integrations are with the CMMS (e.g., SAP, IBM Maximo) for work orders, the Plant Historian (e.g., OSIsoft PI) for process context, and GIS systems for spatial data. Deployment must include robust offline fallback protocols for drone control and a human-in-the-loop dashboard (built with Streamlit or Grafana) for exception handling and oversight.

The workflow integrates with DJI Enterprise or Skydio fleet management APIs to schedule patrols, manage battery cycles, and enforce geofences. A central orchestrator ingests live telemetry (GPS, altitude, battery) and weather data to dynamically adjust flight paths, ensuring complete facility coverage and safe operation. This replaces manual pilot dispatch and logkeeping, enabling 24/7 inspection cadences.

Drones are equipped with FLIR or Teledyne OGI cameras. Raw video is processed at the edge using lightweight YOLO or Segment Anything models to detect methane/VOC plumes in real-time. Only positive detections (with geo-tags, timestamps, confidence scores) are transmitted, minimizing bandwidth use. This edge filtering is critical for scaling across large, remote facilities with limited connectivity.

Detected plumes are fused with GIS asset maps (ArcGIS, QGIS) and P&ID diagrams to pinpoint the exact valve, flange, or tank. A prioritization agent scores each leak based on emission rate (estimated via plume analysis), asset criticality, and proximity to ignition sources. This creates a ranked worklist, directing crews to the highest-risk, highest-volume leaks first to maximize emissions reduction ROI.

The prioritized leak list triggers automatic work order creation in IBM Maximo, SAP PM, or Fiix. The orchestrator enriches tickets with OGI imagery, GPS coordinates, asset history, and recommended SOPs. It checks crew certifications and availability, then dispatches via integrated Microsoft Teams or Slack. This closes the loop from detection to repair, slashing mean-time-to-repair (MTTR) from weeks to days.

All detection data, repair actions, and follow-up verification scans are streamed to a centralized Snowflake or AWS S3 data lake. A reporting agent maps this data to EPA OOOOa/b or OGMP 2.0 frameworks, auto-generating audit-ready reports and calculating leak detection rates. This creates a single source of truth for regulators and ESG reporting, eliminating manual data aggregation and reducing compliance audit prep from months to days.

Critical actions require human approval via a custom dashboard or ServiceNow integration. Before dispatching high-cost repairs or submitting regulatory reports, the workflow routes tasks to environmental engineers for validation. All AI decisions, overrides, and approvals are logged with full explainability (e.g., 'Why was this leak prioritized?'). This governance layer is non-negotiable for operational safety and regulatory defensibility.