Decision support system for maintenance planning of vortex electrostatic precipitators based on IoT and AI techniques

Abstract

This research addresses the issue of introducing an intelligent decision support system (DSS) for efficient maintenance planning of vortex electrostatic precipitators (VEPs) in industrial settings. Leveraging the integration of Internet of Things (IoT) and artificial intelligence (AI) techniques, the proposed DSS aims to significantly reduce equipment downtime through the optimization of cleaning modes and schedules. In light of the increasing importance of production efficiency and continuous improvement of intelligent technologies, this study becomes particularly relevant as it offers a comprehensive solution for optimizing VEP performance using AI. The intelligent DSS, created based on a hierarchical approach and utilizing two fuzzy subsystems, proves to be a valuable tool, providing operators with essential recommendations based on real-time VEP conditions. By dynamically adjusting cleaning parameters, including intensity, duration, and time until mandatory cleaning, the system demonstrates adaptability and efficiency in maintenance planning, contributing to sustained reliability and effectiveness of VEPs in diverse industrial environments.