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 NH3, CO, NO2, CH4, CO2, SO2, O3, 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 99â¯% 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 84â¯% 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.
ABSTRACT
BACKGROUND:: The current method of prescribing composite running-specific energy-storing-and-returning feet is subjective and is based only on the amputee's static body weight/mass. OBJECTIVES:: The aim was to investigate their dynamic characteristics and create a relationship between these dynamic data and the prescription of foot. STUDY DESIGN:: Experimental Assessment. METHODS:: This article presents the modal analysis results of the full range of Össur Flex-Run™ running feet that are commercially available (1LO-9LO) using experimental modal analysis technique under a constant mass at 53 kg and boundary condition. RESULTS:: It was shown that both the undamped natural frequency and stiffness increase linearly from the lowest to the highest stiffness category of foot which allows for a more informed prescription of foot when tuning to a matched natural frequency. The low damping characteristics determined experimentally that ranged between 1.5% and 2.0% indicates that the feet require less input energy to maintain the steady-state cyclic motion before take-off from the ground. An analysis of the mode shapes also showed a unique design feature of these feet that is hypothesised to enhance their performance. CONCLUSION:: A better understanding of dynamic characteristics of the feet can help tune the feet to the user's requirements in promoting a better gait performance. CLINICAL RELEVANCE: The dynamic data determined from this study are needed to better inform the amputees in predicting the natural frequency of the foot prescribed. The amputees can intuitively tune the cyclic body rhythm during walking or running to match with the natural frequency. This could eventually promote a better gait performance.