Propellant Budget Forecasting and Depletion Alerts Automation

This workflow automates the critical, repetitive task of tracking propellant usage—the sole determinant of a satellite's operational lifespan. By continuously ingesting executed maneuver telemetry from systems like AGI STK or FreeFlyer, it models consumption against a calibrated budget. The automation eliminates manual spreadsheet tracking, reduces the risk of human error in lifetime projections, and provides the lead time necessary for compliant, fuel-optimal deorbit planning. The operational upside is direct: extending service life through optimized maneuver planning and avoiding regulatory penalties or collision risk from derelict assets.

Implementation requires integrating with the flight dynamics system of record (e.g., COMET, ODTK) for maneuver data and a rules engine encoding regulatory thresholds like the 25-year deorbit rule. The core orchestrator, built with LangGraph or a custom agent framework, runs the forecasting model, triggers alerts, and routes draft deorbit plans—with fuel-optimal burn sequences—to human operators in tools like ServiceNow for approval. Controls are essential: all depletion forecasts and recommended actions must be logged with full audit trails, and any automated command generation requires multi-stage validation gates before uplink to the satellite.

This workflow automates the critical, calculation-intensive task of tracking propellant mass against executed maneuvers and planned station-keeping. It replaces manual spreadsheet tracking and periodic reviews with a continuous, API-driven control loop. The operational upside is direct: extending asset life through optimized maneuver planning, preventing uncontrolled decommissioning that creates regulatory risk and debris, and enabling precise capital planning for replacement launches. Savings come from averting premature asset loss and reducing the flight dynamics team's manual analysis burden.

Implementation integrates with flight dynamics software (e.g., AGI STK, FreeFlyer), telemetry databases, and mission planning systems. The Propellant Budget Agent uses physics-based models and ML on historical consumption data. Alerts are routed via severity: critical thresholds auto-trigger a deorbit planning sub-workflow in tools like NASA's GMAT, while lower-severity alerts create tickets for human review. Governance requires digital twin validation of all forecast models, mandatory human-in-the-loop approval for final deorbit commands, and immutable audit logs for regulatory compliance with ITU and FCC end-of-life rules.

Phase 1 establishes the foundational data pipeline, ingesting executed maneuver telemetry from flight dynamics systems (like AGI STK or custom propagators) and environmental data into a time-series database. A baseline consumption model is calibrated, providing initial forecasts. This phase delivers immediate visibility into fleet-wide propellant status, replacing manual spreadsheet tracking and establishing the data quality and validation controls necessary for trustworthy automation.

Phase 2 introduces the intelligent orchestration layer, built on LangGraph, which sequences ML forecasting, threshold evaluation, and alert generation. High-confidence depletion alerts are routed to case management systems like Jira or ServiceNow with prescribed review timelines. Phase 3 automates deorbit planning, integrating with regulatory databases to identify compliant windows and generating maneuver sequences for engineering approval, creating a closed-loop system for responsible end-of-life management.

This automation requires strict governance due to its direct impact on asset lifespan and regulatory compliance. A multi-tiered approval framework is essential, where the forecasting model's consumption coefficients are validated by propulsion engineers before deployment, and any automated depletion alert that triggers end-of-life planning must route through a formal review gate. The system must integrate with the satellite's digital twin and command history logs in systems like AGI's STK or custom flight dynamics software to ensure forecast accuracy, with all model assumptions and alert rationales logged to an immutable audit trail for regulatory scrutiny.

Phased rollout mitigates risk. Phase one deploys the forecasting engine in a read-only shadow mode, comparing its predictions against manual analyst calculations for a subset of satellites. Phase two enables alert generation but routes all notifications to a human-in-the-loop review queue within a platform like ServiceNow, preventing autonomous action. The final phase, after validation of accuracy and control procedures, activates automated workflow triggers for deorbit planning, beginning with the lowest-value assets. Each phase requires a formal operational readiness review signed by engineering, safety, and compliance leads.