[2603.06767] Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation

Computer Science > Machine Learning arXiv:2603.06767 (cs) [Submitted on 6 Mar 2026 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation Authors:Julien Amblard, Niklas Groll, Matthew Tait, Mark Law, Gürkan Sin, Alessandra Russo View a PDF of the paper titled Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation, by Julien Amblard and 5 other authors View PDF HTML (experimental) Abstract: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. In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning and conduct a feasibility study in the context of an ethylene oxidation process. Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules from context-dependent noisy examples. This system is a general-purpose symbolic learner, which makes our approach independent of any specific chemical process. To address the lack of real-wo...