Bridging the Gap in Chemical Process Monitoring: Beyond Algorithm-Centric Research Toward Industrial Deployment

Over the past few decades, industrial process monitoring has been greatly advanced by machine learning and data analytics, with sophisticated models continuing to be developed and validated on benchmark processes such as the Tennessee Eastman simulation process. Yet, these models face significant barriers in real-world industrial deployment, as their increasing complexity leads to poor generalization, limited transparency, and difficulty in simultaneously achieving fault detection and root cause diagnosis, creating a persistent academia–industry gap. To address this challenge, this review systematically synthesizes three core categories of data-driven process monitoring methods (statistical, probabilistic, and deep learning-based approaches), delves into cutting-edge techniques tailored to mitigate the aforementioned practical constraints, and conducts an in-depth analysis of current obstacles hindering industrial application. Our analysis identifies a predominant algorithm-centric bias in current research, marked by overreliance on simulated benchmarks, insufficient model interpretability, and a misalignment with industrial requirements for lightweight and operable solutions. By integrating theoretical progress with industrial priorities, this study offers a cohesive roadmap for future research, establishing a foundation for developing more efficient, reliable, and secure chemical processes that bring practical utility to both academic and industrial practice.