Failure Detection in Chemical Processes using Symbolic Machine Learning: A Case Study on Ethylene Oxidation

ArXi:2603.06767v1 Announce Type: new Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in this domain.