Self-Sustaining Space Habitat Using AI-Optimised Bioengineered Ecosystems
Аннотация
A primary question about creating long-term human colonies in non-terrestrial settings, such as Martian or lunar bases, is how to construct closed-loop systems capable of repeatedly recycling water, air, and food with resilience to resource depletion, biological instability, and high-energy cosmic radiation. The current mechanical life support systems, including those on board the International Space Station, are heavily reliant on energy-intensive operations and regular maintenance; therefore, they are not suitable for autonomous operation in remote space habitats, where resupply missions are not feasible. As a solution to this problem, we propose a new model that combines AI-powered predictive control with bioengineered ecosystems to create a truly self-sustaining habitat. The solution utilises a high density of electrochemical biosensors to detect nutrients, an optical gas sensor to monitor $\mathrm{CO2} / \mathrm{O2}$, and a microfluidic toxin detector, all of which are linked to neuromorphic edgecomputing chips that can make adaptive decisions in realtime. The control backbone integrates transformer-based predictive models that predict changes in the environment with reinforcement learning agents to optimise key variables, as well as light cycles, microbial balances, and nutrient dosing. On the biological platform, CRISPR-edited strains of algae with radiation-resistant genes, engineered crops with enhanced photosynthesis rates, and synthetic microbial consortia capable of fixing nitrogen and trapping methane provide a solid foundation for regenerative practices. A loop of synergy interconnects all these systems, as AI constantly tracks the imbalance and fills it in, and engineered biology is the most efficient in terms of resource production. Vertical farming, urban wastewater recycling, and climate-resilient agriculture are viable preliminary validation opportunities on Earth, reflecting the broader terrestrial applicability of the framework. The authors of this work provide a roadmap for autonomous, adaptive, and sustainable living environments that cannot only sustain settlement on other planets but also contribute to the overall ecological stability of the Earth. Thus, the proposed solution represents a breakthrough in the life support problem for the future world.