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Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment. | LitMetric

AI Article Synopsis

  • Air pollution, especially during chrome plating, poses serious health risks to workers due to exposure to harmful pollutants like hexavalent chromium and VOCs, which can cause respiratory issues and lung cancer.
  • Traditional air quality monitoring methods are limited in their ability to provide real-time analysis and predictive capabilities, hindering effective pollution management.
  • A new real-time monitoring system using IoT sensors and AI algorithms has been developed for the chrome plating industry, which detects various pollutants and activates exhaust fans to improve air quality proactively based on predictive data.

Article Abstract

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH, CO, NO, CH, CO, SO, O, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

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Source
http://dx.doi.org/10.1016/j.ecoenv.2024.116856DOI Listing

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