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<p>Poor indoor air quality (IAQ) due to invisible pollutants in closed environments is a major threat to human health. The current monitoring systems are unable to provide precise information in real time, which is why there is a need for a smarter system of predictive IAQ management. This study presents a novel predictive system based on artificial intelligence called SLIF (Swarm-Liquid Intelligence Framework), for Smart IAQ Management. SLIF is a combination of Hybrid LSTM-GRU and Liquid Neural Networks (LiquidAdaptedNet) to predict IAQ with better accuracy, taking into consideration the dynamic environmental variations. The system integrates intelligent IAQ prediction, adaptive control, and HVAC energy-efficient optimization. It is integrated with HVAC to control air quality in real-time, and swarm intelligence methods are applied to streamline the functionality. Using evolutionary refinement techniques, such as swarm intelligence, the optimized LiquidAdaptedNet architecture achieves RMSE, MAE, R2, and MSPE values of 6.52, 6.27, 97.63%, and 1.41, respectively. New learning paradigms, including single-task, multitask, and refinement-based learning, have demonstrated significant improvements in model generalization and predictive performance. In addition to considering external parameters of the environment, the SLIF framework presents accurate forecasts for three important indoor pollutants, namely nitrogen dioxide (NO<sub>2</sub>), particulate matter (PM2.5), and carbon monoxide (CO). Depending on operating conditions, SLIF is used to achieve as much as 99.97%, 98.67%, and 94.47% of pollutant concentrations. The simulation results showed that up to 91.45% of energy savings can be achieved in the HVAC system, while maintaining good indoor air quality. Using environmental monitoring and HVAC control together with artificial intelligence, SLIF provides a scalable and dependable approach to managing the indoor air quality with the help of intelligent indoor air quality in residential, commercial, and industrial settings. This assists in making the indoor environment healthier and more sustainable.</p>

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SLIF: Swarm-Liquid Intelligence Framework for Smart IAQ Management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

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  • Mona A. S. Ali,
  • Walaa N. Ismail,
  • Fathimathul Rajeena P. P.,
  • Mohamed A. Moustafa,
  • Tamer Hamdy Ayad

摘要

Poor indoor air quality (IAQ) due to invisible pollutants in closed environments is a major threat to human health. The current monitoring systems are unable to provide precise information in real time, which is why there is a need for a smarter system of predictive IAQ management. This study presents a novel predictive system based on artificial intelligence called SLIF (Swarm-Liquid Intelligence Framework), for Smart IAQ Management. SLIF is a combination of Hybrid LSTM-GRU and Liquid Neural Networks (LiquidAdaptedNet) to predict IAQ with better accuracy, taking into consideration the dynamic environmental variations. The system integrates intelligent IAQ prediction, adaptive control, and HVAC energy-efficient optimization. It is integrated with HVAC to control air quality in real-time, and swarm intelligence methods are applied to streamline the functionality. Using evolutionary refinement techniques, such as swarm intelligence, the optimized LiquidAdaptedNet architecture achieves RMSE, MAE, R2, and MSPE values of 6.52, 6.27, 97.63%, and 1.41, respectively. New learning paradigms, including single-task, multitask, and refinement-based learning, have demonstrated significant improvements in model generalization and predictive performance. In addition to considering external parameters of the environment, the SLIF framework presents accurate forecasts for three important indoor pollutants, namely nitrogen dioxide (NO2), particulate matter (PM2.5), and carbon monoxide (CO). Depending on operating conditions, SLIF is used to achieve as much as 99.97%, 98.67%, and 94.47% of pollutant concentrations. The simulation results showed that up to 91.45% of energy savings can be achieved in the HVAC system, while maintaining good indoor air quality. Using environmental monitoring and HVAC control together with artificial intelligence, SLIF provides a scalable and dependable approach to managing the indoor air quality with the help of intelligent indoor air quality in residential, commercial, and industrial settings. This assists in making the indoor environment healthier and more sustainable.