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

TL;DR

This paper explores using symbolic machine learning to predict failures in chemical processes, specifically ethylene oxidation, emphasizing interpretability and feasibility with simulated data, outperforming traditional methods.

Contribution

It introduces a symbolic machine learning approach for failure prediction in chemical processes, demonstrating its effectiveness and interpretability using simulated data.

Findings

Symbolic machine learning outperforms baseline methods like random forest and neural networks.

The approach produces compact, interpretable rule-based models.

Feasibility is demonstrated using simulated chemical process data.

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.