Predictive Maintenance in Chemical Industries Using Machine Learning: A Novel Approach

This study details the creation and execution of an economical, real-time safety monitoring system for industrial chemical processes, employing sensor data and machine learning algorithms to forecast dangerous situations, including gas leaks and temperature variations. The system amalgamates diverse sensors with IoT connections, facilitating real-time data acquisition and processing. Machine learning algorithms, such as linear regression, multiple regression, and k-nearest neighbors (KNN), were utilized to forecast system efficiency and identify potential hazards. The system reliably predicts hazardous conditions, with linear regression demonstrating the highest accuracy in gas burner stability. Its cost-effective design makes it suitable for industrial use The system’s efficiency, particularly in delivering early alerts and enabling proactive measures, provides a sense of security. Future efforts will optimize the system to improve resilience in dynamic industrial settings. The proposed approach enhances safety protocols in the chemical industry.