Eco-Friendly Production Forecasting in Industrial Pollution Control with IoT and Logistic Regression
S. Sriram, D. Chandrakala, K. Kokulavani, N. Mohankumar, N. Suthanthira Vanitha, S. Sakthivel Murugan
Abstract
Modern industrial pollution requires proactive measures to mitigate its environmental effects. This paper advances eco-friendly production forecasting using IoT and Logistic Regression (LR). Optimizing output, reducing pollution, and minimizing industrial activities' environmental imprint are goals. Industrial IoT sensors keep track of energy consumption, raw material use, and pollution in real-time. Processing and analyzing this data identifies production patterns and trends. LR is used to predict production factors and pollution levels. The model's use has several advantages. First, it provides real-time production environmental effect data. This lets firms alter production on the go, optimizing resource use and reducing emissions. The system can estimate future pollution levels based on projected production scenarios, enabling proactive pollution management and environmental compliance. LR and IoT enable sustainable industrial production. Company eco-friendly policies, waste reduction, and manufacturing efficiency may be improved by recognizing pollution drivers. This method supports global sustainability and environmental preservation. This technology is shown to revolutionize industrial pollution management, decrease ecological damage, and encourage environmentally aware production forecasting. It's a promising move toward eco-friendly production and meets the rising need for a responsible and sustainable industry.