Intelligent Filtration Systems: Leveraging Neural Networks and Integrated SCADA/MES Data for Particle Control and Product Purity in Industrial Environments

Abstract

This article analyzes existing studies on particle filtration systems in industrial environments, focusing on filter efficiency and contaminant isolation methods to ensure final product purity. It explores the application of neural networks, both unsupervised for data segmentation and behavioral profiling, and supervised for scenario detection and prediction, utilizing data from sensors, SCADA and MES systems. The objective is to lay the groundwork for future practical model development, highlighting the potential of artificial intelligence to optimize product purity and operational efficiency in industrial processes.