Abstract
The convergence of artificial intelligence (AI) and chemical process systems engineering is creating unprecedented opportunities to transform current refineries from conventionally operated plants into intelligent, automated, and resilient systems. However, the practical deployment of AI in these complex industrial environments faces several critical challenges. First, most existing process datasets contain minimal labeled data, making it difficult to apply supervised learning techniques that require extensive annotations to generate meaningful insights. Furthermore, refinery data typically consists of high-dimensional, multivariate time series, which pose additional complexities in capturing temporal dynamics and system interactions. Traditional Fault Detection and Diagnosis (FDD) frameworks often struggle to address these complexities, lacking adaptability to evolving process conditions, scalability to large plant networks, and explainability in their diagnostic reasoning. To address these gaps, we propose an integrated framework for unsupervised knowledge discovery, adaptive process monitoring, and AI-assisted fault diagnosis. The approach combines Dynamic Time Warping (DTW) with graph-based layout algorithms to extract and visualize temporal structures and similarities within complex process datasets. The resulting graphs function as an unsupervised model, enabling the identification of operational regimes through process clustering. These models can be deployed online to detect anomalies using adaptive fault thresholding, ensuring robustness to changing process dynamics. When faults are detected, the framework leverages large language models (LLMs) as AI assistants to support fault diagnosis. By bridging domain knowledge-such as piping and instrumentation diagrams (P&IDs) and signal flow diagrams-with data-driven insights, including machine learning metrics and feature contribution scores, the system delivers explainable and accurate diagnostic outputs. This hybrid methodology enhances interpretability and decision-making, offering a scalable and adaptive solution for next-generation AI-enabled refinery operations.