Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of t...Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.展开更多
We propose a nonlinear ultrasonic technique by using the mixed-frequency signals excited Lamb waves to conduct micro-crack detection in thin plate structures.Simulation models of three-dimensional(3D)aluminum plates a...We propose a nonlinear ultrasonic technique by using the mixed-frequency signals excited Lamb waves to conduct micro-crack detection in thin plate structures.Simulation models of three-dimensional(3D)aluminum plates and composite laminates are established by ABAQUS software,where the aluminum plate contains buried crack and composite laminates comprises cohesive element whose thickness is zero to simulate delamination damage.The interactions between the S0 mode Lamb wave and the buried micro-cracks of various dimensions are simulated by using the finite element method.Fourier frequency spectrum analysis is applied to the received time domain signal and fundamental frequency amplitudes,and sum and difference frequencies are extracted and simulated.Simulation results indicate that nonlinear Lamb waves have different sensitivities to various crack sizes.There is a positive correlation among crack length,height,and sum and difference frequency amplitudes for an aluminum plate,with both amplitudes decreasing as crack thickness increased,i.e.,nonlinear effect weakens as the micro-crack becomes thicker.The amplitudes of sum and difference frequency are positively correlated with the length and width of the zero-thickness cohesive element in the composite laminates.Furthermore,amplitude ratio change is investigated and it can be used as an effective tool to detect inner defects in thin 3D plates.展开更多
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, quan...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.展开更多
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(22021004)Sinopec Major Science and Technology Projects(321123-1).
文摘Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61571222,61602235,and 11474160)the Six Talent Peaks Project of Jiangsu Province,China
文摘We propose a nonlinear ultrasonic technique by using the mixed-frequency signals excited Lamb waves to conduct micro-crack detection in thin plate structures.Simulation models of three-dimensional(3D)aluminum plates and composite laminates are established by ABAQUS software,where the aluminum plate contains buried crack and composite laminates comprises cohesive element whose thickness is zero to simulate delamination damage.The interactions between the S0 mode Lamb wave and the buried micro-cracks of various dimensions are simulated by using the finite element method.Fourier frequency spectrum analysis is applied to the received time domain signal and fundamental frequency amplitudes,and sum and difference frequencies are extracted and simulated.Simulation results indicate that nonlinear Lamb waves have different sensitivities to various crack sizes.There is a positive correlation among crack length,height,and sum and difference frequency amplitudes for an aluminum plate,with both amplitudes decreasing as crack thickness increased,i.e.,nonlinear effect weakens as the micro-crack becomes thicker.The amplitudes of sum and difference frequency are positively correlated with the length and width of the zero-thickness cohesive element in the composite laminates.Furthermore,amplitude ratio change is investigated and it can be used as an effective tool to detect inner defects in thin 3D plates.
基金financially supported by the Innovative Research Group Project of the National Natural Science Foundation of China (22021004)Sinopec Major Science and Technology Projects (321123-1)
文摘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.
