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<p>An AI-powered algae bioreactor scheme that links IoT sensing, reinforcement learning control, and geospatial optimization for a smart conservation regulator is exhibited in this study. Real-time optimization of microalgae growth and oxygen production is made feasible by the proposed Q-learning framework, which adaptively controls CO<sub>2</sub> concentrations, light, pH, and temperature. Integrated design and a grid-built spatial placement model enhance deployment elasticity and environmental impact even additional. The adaptive RL-based system outperforms traditional control methods in terms of efficiency, oxygen output, and energy spending, according to experimental estimation. The model uses a hybrid edge-cloud interaction structure for collaborative predictive conservation, remote monitoring, and scalable strategy. All machines considered, this work presents a unified AI–IoT–biotechnology solution that promotes natural urban air quality development and suggests a workable direction for intelligent and climate-resilient city infrastructure.</p>

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A reinforcement learning based approach for optimised oxygen generation deployed in high pollution zones using smart grid and IoT integration

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • M. Misba,
  • M. Benila,
  • J. Jelba,
  • R. Roselin Kiruba,
  • J. Kavitha,
  • L. Sharmila

摘要

An AI-powered algae bioreactor scheme that links IoT sensing, reinforcement learning control, and geospatial optimization for a smart conservation regulator is exhibited in this study. Real-time optimization of microalgae growth and oxygen production is made feasible by the proposed Q-learning framework, which adaptively controls CO2 concentrations, light, pH, and temperature. Integrated design and a grid-built spatial placement model enhance deployment elasticity and environmental impact even additional. The adaptive RL-based system outperforms traditional control methods in terms of efficiency, oxygen output, and energy spending, according to experimental estimation. The model uses a hybrid edge-cloud interaction structure for collaborative predictive conservation, remote monitoring, and scalable strategy. All machines considered, this work presents a unified AI–IoT–biotechnology solution that promotes natural urban air quality development and suggests a workable direction for intelligent and climate-resilient city infrastructure.