Cloud-Integrated Logistic Regression for Optimizing Waste Recycling Plant Operations

The efficient functioning of waste recycling facilities is essential for optimizing resource recovery and minimizing environmental damage. Traditional process control techniques frequently lack the quick insights required for anticipatory decision-making. This research presents a cloud-based predictive modelling approach using Logistic Regression (LR) to enhance recycling facility operations. The model identifies essential operational states, including equipment health and process efficiency, by analyzing real-time and historical sensor data for facilitating the early detection of probable failures and quality discrepancies. Integration with cloud systems facilitates scalable data storage, continuous model updates, and real-time accessibility for plant operators. The LR model was evaluated using plant data, achieving an accuracy 98.75% in forecasting operational results. Significant outcomes comprise a 20% decrease in downtime, a 15% enhancement in material recovery rates, and optimized maintenance scheduling. These results illustrate the efficacy of integrating LR with cloud computing for improved process monitoring and decision-making assistance. This method provides a scalable and pragmatic solution for waste recycling operations aiming to enhance operational efficiency and sustainability with data-driven insights.