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

• URL: http://arxiv.org/abs/2603.06767v1
• Date: Fri, 06 Mar 2026 15:25:47 GMT
• Title: Failure Detection in Chemical Processes using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
• Abstract summary: In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning.<n>Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules.<n> Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron.
• Score: 40.201013672886454
• License: http://creativecommons.org/licenses/by/4.0/
• 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-world failure data, we conduct our feasibility study leveraging data generated from a chemical process simulator. Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron, while preserving interpretability through the generation of compact, rule-based predictive models. Finally, we explain how such learned rule-based models could be integrated into agents to assist chemical plant operators in decision-making during potential failures.

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