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

Julien Amblard; Niklas Groll; Matthew Tait; Mark Law; Gürkan Sin; Alessandra Russo

cs.LGMar 6, 2026

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

Organizations: Department of Computing, Imperial College London, London, United Kingdom · Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark · ILASP Limited, London, United Kingdom

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.

Figures & tables

Figure 1: Simulation flowsheet of the EO process, with a legend to identify components

Panel of Table 1 — Table 1: The main process variables of the EO process we focus on in this paper — entries in the first column correspond to the steps from Figure 1 , and the units of the process variables are respectively bar, ºC and kg/s for pressure, temperature and flow rate

param	unit	default	min	max	instr
SRCR1.P	bar	2	1.6	1.98	uniform
SRCE1.P	bar	2	1.95	2.05	uniform
SRCR1.M[C2H4]	kmol	4464	4240	4360	uniform

Figure 2: A perturbation file for “low pressure at source”

	Learning parameters	Accuracy		Interpretability		Confidence
	Non-default assignments	$min(auc)$	$avg(auc)$	$avg(n_{body})$	$avg(n_{r}\|cl)$	$avg(prob)$
(1)	$exp:=trivial$	1	1	2.14	1.4	0.74
(2)	$exp:=nontrivial6$	0.82	0.92	3	1.33	0.55
(3)	$exp:=nontrivial10$	0.75	0.88	3	1.7	0.75
(4)	$exp:=nontrivial10;~n\_runs:=125$	0.7	0.85	3.11	1.8	0.73
(5)	$n\_runs:=10$	0.83	0.93	2.5	1	0.7

Table 2: A look at the impacts of adjusting the learning parameters for failure detection with task

T_{dynamic}

, using emphasized text to highlight the results that are preferred under each metric

Config.	SVM	MLP	RF	HGB	AB	Ours
(1)	0.95	0.95	0.97	0.97	0.97	1
(2)	0.85	0.83	0.87	0.89	0.87	0.92
(3)	0.84	0.77	0.85	0.84	0.85	0.88
(14)	0.87	0.86	0.97	0.97	0.97	0.94
(15)	0.85	0.83	0.87	0.89	0.87	0.92
(16)	0.83	0.82	0.89	0.89	0.87	0.9

Table 3: The AUC averaged over the selected experiments for different learning parameter configurations (as given in Table 2 ), comparing different baselines to our approach

Panel of Figure 3 — Figure 3: ROC curves generated from the default learning parameter configuration, for our approach and the baselines

Panel of Figure 4 — Figure 4: Rules generated by DisPLAS for $T_{static}$ ( $\texttt{low2}<\texttt{low1}<\texttt{normal}<\texttt{high1}<\texttt{high2}$ ) when including all nontrivial experiments and process variable measurements over 125 runs of the simulation

Panel of Figure 5 — Figure 5: Rules generated by DisPLAS for $T_{dynamic}$ , modifying only the learning parameter $t_{short\_term}$ — the predicate unchanged indicates process variables with equal initial and short-term values, while remaining predicates in rule bodies refer to measurements taken at $t_{short\_term}$ and (absolute) changes in measurement since $t=0$ (increase: $\nearrow$ , decrease: $\searrow$ )

Fault Detection

Fault detection identifies abnormal behavior in machines, sensors, infrastructure, software, and autonomous systems before it causes harm or disruption.

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Georgios Gravanis, Dimitrios Kyriakou, Spyros Voutetakis +2

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Industrial Fault Diagnosis Fault Diagnosis Gradient-Based Attribution SHAP Feature Attribution

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Oct 1, 2026

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.

Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou +17

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Mar 27, 2026

Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach

Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accuracy compared to baseline approaches across all compliance-aware experiments.

Fabrizio De Santis, Gyunam Park, Wil M. P. van der Aalst +1

University of Parma, Parma, Italy · Eindhoven University of Technology, Eindhoven, Netherlands · RWTH Aachen University, Aachen, Germany

Logic Tensor Networks Predictive Process Monitoring Neurosymbolic Learning

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Abstract

Fault Detection

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