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

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.

Figures (3)

• Figure 1: Control-oriented architecture of the semi-closed positive-pressure breathing loop. Solid lines illustrate gas flow and dashed lines sensor inputs / control links. Moist exhaled gas is drawn through the soda-lime scrubber first, then the downstream silica-gel dehumidifier, before O$_2$ replenishment and return to the breathing zone. Outward venting through one-way exhaust valves makes the suit semi-closed; the controller uses external, in-suit, and biometric sensing to regulate O$_2$ injection, fan speed, and scrubber bypass.
• Figure 2: AI control architecture with explicit safety gating. External, in-suit, and biometric sensors feed the EKF-based sensor-fusion and state-estimation layer. The learned metabolic model provides physiological estimates to the MPC, while the RL policy advisor supplies a warm-start policy hint during nominal operation and a fallback candidate action if the MPC fails or times out. All candidate actuator commands pass through the control-barrier-function safety filter before reaching the actuators and, through them, the physical life-support plant and firefighter.
• Figure 3: Oxygen tank depletion curves for Scenario A. The MPC controller's dynamic conservation extends endurance by modulating O2 delivery rate as the tank depletes.

Theorems & Definitions (6)

• Remark 1
• Remark 2: Why not pure NaOH?
• Remark 3: Implications for system architecture
• Remark 4: Common error in molar-sink analyses
• Remark 5: On the absence of SpO2 and $P_{\mathrm{tc}}\mathrm{CO}_2$ sensing
• Remark 6: On core temperature estimation