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

• URL: http://arxiv.org/abs/2603.26697v1
• Date: Mon, 16 Mar 2026 20:29:26 GMT
• Title: Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments
• Abstract summary: Galactic Bioware's Life Support System is a breathing apparatus designed for integration into a positive-pressure firefighting suit.<n>One-way exhaust valves maintain positive pressure while creating a semi-closed system in which outward venting gradually depletes the gas inventory.<n>Part I develops the physicochemical foundations from first principles, including state-consistent thermochemistry.<n>Part II introduces an AI control architecture that fuses three sensor tiers, external environmental sensing, internal suit atmosphere sensing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• DREAM Technical Report [53.37525197047151]
  We present DREAM, an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them.<n> DREAM has two core components. First, a three-tier Intent Engine fuses on-device signals into structured L0/L1/L2 intent representations; its edge-cloud trigger chain reduces reporting volume to approximately 8.7%.<n>Second, a Meta Engine uses a MetaModel for layered M1-to-M2-to-M3 reasoning: intent summarization, strategy planning informed by Strategy Memory, and parameter translation. It dispatches the resulting parameters through a unified outlet with
  arXiv  Detail & Related papers  (2026-08-10T10:35:32Z)
• Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations [42.47287159687486]
  This paper investigates the performance of three recurrent network architectures (LSTM, Gated Recurrent Unit (GRU), Selective State Space Model (Mamba)) and two training algorithms (Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC)) to identify the best setup for tuning ICCBF class-K functions via meta-RL.<n>Results indicate that state space models such as Mamba when used with PPO achieve superior task completion, safety, and fuel-savings compared to other architectures.
  arXiv  Detail & Related papers  (2026-06-16T01:53:54Z)
• A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation [0.0]
  This paper presents a new approach to optimizing HVAC systems through the use of Deep Reinforcement Learning (DRL) algorithms.<n>The DRL system uses a second order-capacitor thermal model and an integrated dynamic mass balance of CO2 to replicate the complex physics associated with buildings.<n>An enthalpy-based economiser is used to create free cooling from the outdoor environment.
  arXiv  Detail & Related papers  (2026-05-23T05:31:09Z)
• From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark [67.29937933325849]
  Operando IR Prediction aims to forecast the time-resolved evolution of spectral fingerprints'' from a single static spectrum.<n>OpIRSpec-7K comprises 7,118 high-quality samples across 10 distinct battery systems.<n>ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines.
  arXiv  Detail & Related papers  (2026-02-20T18:58:43Z)
• Fuzzy expert system for the process of collecting and purifying acidic water: a digital twin approach [0.0]
  Purifying sour water is essential for reducing emissions, minimizing corrosion risks, and enabling the reuse of treated water in industrial or domestic applications.<n>This paper presents a fuzzy expert system, combined with a custom-generated digital twin, developed from a documented industrial process.<n>Although demonstrated here for sour water treatment, the proposed fuzzy expert system is general-purpose.
  arXiv  Detail & Related papers  (2026-01-27T12:08:11Z)
• Adaptive Reinforcement and Model Predictive Control Switching for Safe Human-Robot Cooperative Navigation [10.614812216110673]
  This paper addresses the challenge of human-guided navigation for mobile collaborative robots under simultaneous proximity regulation and safety constraints.<n>We introduce Adaptive Reinforcement and Model Predictive Control Switching (ARMS), a hybrid learning-control framework.<n>We show that ARMS achieves an 82.5 percent success rate in highly cluttered environments.
  arXiv  Detail & Related papers  (2026-01-23T12:02:18Z)
• Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control [61.155940786140455]
  Reinforcement learning (RL) has shown promising results in active flow control (AFC)<n>Current AFC benchmarks rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support.<n>We introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC.
  arXiv  Detail & Related papers  (2026-01-21T14:13:44Z)
• Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids [50.64533198075622]
  Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty.<n>In this paper, we introduce Shielded Controller Units (SCUs), a systematic and interpretable approach that leverages prior knowledge of system dynamics.<n>We demonstrate the effectiveness of SCUs on a remote microgrid optimization task with strict operational requirements.
  arXiv  Detail & Related papers  (2025-11-30T19:28:34Z)
• Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL [56.085103402298905]
  We propose a trajectory entropy-constrained reinforcement learning (TECRL) framework to address these two challenges.<n>Within this framework, we first separately learn two Q-functions, one associated with reward and the other with entropy, ensuring clean and stable value targets unaffected by temperature updates.<n>We develop a practical off-policy algorithm, DSAC-E, by extending the state-of-the-art distributional soft actor-critic with three refinements.
  arXiv  Detail & Related papers  (2025-10-25T09:17:47Z)
• STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks [58.13757830013997]
  Wireless networks based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications.<n>They face severe signal attenuation in dense environments due to obstructions.<n>To address this critical issue, we consider introducing collaborative beam of UAVs and omni-directional re-altitude beamforming.
  arXiv  Detail & Related papers  (2025-10-25T01:28:37Z)
• Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants [0.6533091401094101]
  We present data driven deep learning models for forecasting and monitoring amine emissions and key performance parameters in amine-based post-combustion carbon capture systems.<n>For emission prediction, models were designed for 2-amino-2-methyl-1-propanol (AMP) and Piperazine emissions measured via FTIR and IMR-MS methods.<n>These models achieved high predictive accuracy exceeding 99% and effectively tracked both steady trends and abrupt fluctuations.
  arXiv  Detail & Related papers  (2025-09-05T16:57:54Z)
• A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Control [2.499907423888049]
  Digital twins bridge real-world systems with digital tools to reduce costs, enhance decision-making, and boost operational efficiency.<n>In this work, a digital twin framework is designed to operate the Gen-IV Fluoride-salt-cooled High-temperature Reactor.
  arXiv  Detail & Related papers  (2025-06-09T02:23:34Z)
• Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning [0.3562485774739681]
  We introduce the use of reinforcement learning (RL) algorithms for intelligent control in nuclear microreactors. RL agent is trained using proximal policy optimization (PPO) and advantage actor-critic (A2C) Results demonstrate the excellent performance of PPO in identifying optimal drum positions.
  arXiv  Detail & Related papers  (2024-06-22T20:14:56Z)
• Neural optimal feedback control with local learning rules [67.5926699124528]
  A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. We introduce a novel online algorithm which combines adaptive Kalman filtering with a model free control approach.
  arXiv  Detail & Related papers  (2021-11-12T20:02:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.