Abstract:
Abstract
The study aimed at designing and developing a sophisticated factory exhaust emission analyzer equipped with remote data monitoring capabilities, addressing the urgent need for real-time environmental surveillance and compliance with increasingly stringent environmental regulations.This system integrates cutting-edge sensor technology for the detection and quantification of key pollutants, such as carbon dioxide (CO2), sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter, emanating from factory exhausts. Through the deployment of Internet of Things (IoT) technology, the collected data are transmitted in real-time to a centralized cloud platform, enabling continuous monitoring and analysis of emission levels from remote locations. The development process involved rigorous testing under varied operational conditions to ensure the system’s accuracy, reliability, and durability. Machine learning algorithms were employed to analyze emission data, predict potential exceedances of pollutant levels, and identify trends indicative of equipment malfunctions or inefficiencies in production processes. The system's user interface was designed for accessibility, allowing factory managers and regulatory bodies to easily interpret data and make informed decisions. Preliminary results from the system testing revealed significant improvements in the ability to detect and respond to emission anomalies, leading to more efficient operational adjustments and compliance with environmental standards. This study not only demonstrates the feasibility and
effectiveness of the developed emission analyzer in enhancing environmental monitoring and compliance but also highlights its potential to serve as a model for future advancements in the field of industrial emission control. The integration of remote monitoring capabilities presents a promising avenue for scaling up environmental surveillance and fostering more sustainable industrial practices on a global scale.
