Abstract: The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.

Key words: Petroleum, Mixed-frequency data, Coking, Risk index, Neural networks, Predictive maintenance

摘要： The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.

关键词: Petroleum, Mixed-frequency data, Coking, Risk index, Neural networks, Predictive maintenance