Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments

arXiv cs.AI / 3/31/2026

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Key Points

The paper proposes “Galactic Bioware’s” life support system for a positive-pressure firefighting suit, using a semi-closed breathing loop with soda lime CO2 scrubbing, silica gel dehumidification, and finite pure O2 replenishment.
It formulates physicochemical foundations from first principles, including thermochemistry consistency, adsorption isotherms, and oxygen-management constraints tied to both fire safety and toxic exposure limits.
An AI control architecture is introduced that fuses three sensor tiers—external environment sensing, internal suit atmosphere sensing with triple-redundant O2 cells and median voting, and firefighter biometrics.
The controller combines receding-horizon model-predictive control with a learned metabolic model and a reinforcement-learning policy advisor, while enforcing safety via a control-barrier-function filter that gates all actuator commands.
In simulation using an 18-state, 3-control nonlinear state-space model (with feasible structural firefighting sensors), the approach reports 18–34% endurance improvement over PID baselines while maintaining tighter physiological and fire-safety margins.

Abstract

This paper introduces Galactic Bioware's Life Support System, a semi-closed-circuit breathing apparatus designed for integration into a positive-pressure firefighting suit and governed by an AI control system. The breathing loop incorporates a soda lime CO2 scrubber, a silica gel dehumidifier, and pure O2 replenishment with finite consumables. One-way exhaust valves maintain positive pressure while creating a semi-closed system in which outward venting gradually depletes the gas inventory. Part I develops the physicochemical foundations from first principles, including state-consistent thermochemistry, stoichiometric capacity limits, adsorption isotherms, and oxygen-management constraints arising from both fire safety and toxicity. Part II introduces an AI control architecture that fuses three sensor tiers, external environmental sensing, internal suit atmosphere sensing (with triple-redundant O2 cells and median voting), and firefighter biometrics. The controller combines receding-horizon model-predictive control (MPC) with a learned metabolic model and a reinforcement learning (RL) policy advisor, with all candidate actuator commands passing through a final control-barrier-function safety filter before reaching the hardware. This architecture is intended to optimize performance under unknown mission duration and exertion profiles. In this paper we introduce an 18-state, 3-control nonlinear state-space formulation using only sensors viable in structural firefighting, with triple-redundant O2 sensing and median voting. Finally, we introduce an MPC framework with a dynamic resource scarcity multiplier, an RL policy advisor for warm-starting, and a final control-barrier-function safety filter through which all actuator commands must pass, demonstrating 18-34% endurance improvement in simulation over PID baselines while maintaining tighter physiological and fire-safety margins.