Department of Software Engineering, Khorramabad branch, Islamic Azad University, Khorramabad, Iran
Abstract
A factory pollutant monitoring system using the Internet of Things (IoT), machine learning, and plasma technology has been designed and implemented. The goal is to effectively reduce pollution and absorb hazardous particles. So, an ESP32 electronic board has been used along with temperature and humidity (DHT22), carbon dioxide (MG812-CO2), and sulfur dioxide (MQ136-SO2) sensors to collect data and transmit it to the server via the IoT. The data is stored in Google Sheets and prepared for modeling after preprocessing. Then, an Autoencoder network has been used, due to its high ability to reduce dimensions and eliminate noise, reconstructs the data with minimal error. The Autoencoder extracts key features of the data to optimize the plasma level and pollution reduction. The results show that this system performs better in reducing pollution and energy consumption than traditional methods such as electro-filters. Also, high efficiency and cost reduction are advantages of this approach.
Khodadadi nezhad, M., & Joudaki, S. (2025). Design and Implementation of a Factory Pollutant Monitoring System using Plasma by Self-Encoding Neural Network. , 2(8), 43-54.
MLA
Mohammad Khodadadi nezhad; Saba Joudaki. "Design and Implementation of a Factory Pollutant Monitoring System using Plasma by Self-Encoding Neural Network", , 2, 8, 2025, 43-54.
HARVARD
Khodadadi nezhad, M., Joudaki, S. (2025). 'Design and Implementation of a Factory Pollutant Monitoring System using Plasma by Self-Encoding Neural Network', , 2(8), pp. 43-54.
VANCOUVER
Khodadadi nezhad, M., Joudaki, S. Design and Implementation of a Factory Pollutant Monitoring System using Plasma by Self-Encoding Neural Network. , 2025; 2(8): 43-54.