Industrial Process

Industrial process optimization is a major focus of current research, aiming to improve efficiency, safety, and product quality through data-driven methods. This involves developing advanced soft sensors for real-time monitoring, employing machine learning models (including deep learning architectures like neural networks and graph convolutional networks) for anomaly detection and root cause analysis, and leveraging techniques like knowledge graph learning to extract meaningful relationships from complex time-series data. These advancements enable more accurate predictions, improved process control, and enhanced interpretability of complex industrial systems, ultimately leading to significant economic and safety benefits.