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

2026-03-06

AI Summary

This study addresses key challenges in chemical process fault prediction—namely, limited interpretability, poor robustness, and scarcity of real fault data—by introducing, for the first time, a general-purpose symbolic machine learning system to the ethylene oxide production process. The proposed approach learns context-sensitive probabilistic rule models directly from noisy simulation data, achieving high-accuracy fault prediction. It outperforms baseline models such as random forests and multilayer perceptrons in predictive performance while generating compact, human-interpretable rules. These rules are readily integrable into operator assistance agents, offering a novel paradigm for enhancing safety in chemical process operations through transparent and reliable decision support.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Failure Detection
• Chemical Processes
• Symbolic Machine Learning
• Ethylene Oxidation
• Interpretability

Innovation

• symbolic machine learning
• failure detection
• interpretable AI
• chemical process simulation
• probabilistic rule learning