Attention-based early warning framework for abnormal operating conditions in fluid catalytic cracking units

Abstract

Fluid Catalytic Cracking Unit (FCCU) is a critical processing technology in the oil refining industry, playing a vital role in energy efficiency and environmental protection. However, FCCU often encounters various abnormal operating conditions, leading to safety hazards, downtime, and reduced production efficiency. Early warning of these abnormal conditions is crucial but challenging due to high noise, strong hysteresis, and class imbalance problems. To tackle these challenges, a novel and universal attention-based framework called AEW-AOC (Attention-based Early Warning for Abnormal Operating Conditions) is specifically designed for FCCU applications. The proposed AEW-AOC framework incorporates three key components: (1) a Self-Correlation Denoiser (SCD) module is proposed to exploit spatiotemporal data correlation to effectively reduce noise; (2) a Convolutional Long Short-Term Memory (Conv-LSTM) module is employed to address the issue of strong hysteresis by capturing temporal variation features of process parameters; (3) an Anomaly Pattern Attention (APA) module is proposed to enhance the distinguishability of abnormal operating conditions based on clustering results from historical abnormal instances. Extensive experiments demonstrate the effectiveness and superiority of the proposed AEW-AOC framework, particularly in practical applications. Specifically, the AEW-AOC framework obtains an impressive fβ score of 91.00% on LIC201, 90.45% on LIC202, and 90.64% on LIC801. The proposed AEW-AOC framework shows great potential in enhancing safety, reducing downtime, optimizing efficiency, promoting sustainability, and expanding its applicability beyond FCCU. Its proactive and versatile nature makes it a valuable tool for improving industrial processes and driving advancements in the field of abnormal operating condition detection and prevention.