Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial pro...Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.展开更多
The characteristics of the packing material under an alternating electric field are an important factor in the removal of FCCS particles.In this study,the electric field distribution of a separation unit consisting of...The characteristics of the packing material under an alternating electric field are an important factor in the removal of FCCS particles.In this study,the electric field distribution of a separation unit consisting of packed spheres under an alternating electric field is simulated,and the movement mechanism of catalyst particles is analysed.An"effective contact point"model is derived to predict the adsorption of filler contact points on catalyst particles under the alternating electric field,and the model is validated by simulations and experiments.The numerical calculation and experimental results indicate that the electrical properties of the filler spheres,the filler angleθ,and the frequency f of the alternating electric field affect the adsorption of catalyst particles.As the frequency of the electric field increases,the particle removal efficiency of the high-conductivity filler(silicon carbide)increases and then settles,and the separation efficiency of the low-conductivity filler(glass,zirconia)is not sensitive to the change in electric field frequency.展开更多
High-temperature treatment is key to the preparation of zeolite catalysts.Herein,the effects of hightemperature treatment on the property and performance of HZSM-5 zeolites were studied in this work.X-Ray diffraction,...High-temperature treatment is key to the preparation of zeolite catalysts.Herein,the effects of hightemperature treatment on the property and performance of HZSM-5 zeolites were studied in this work.X-Ray diffraction,N2physisorption,27Al magic angle spinning nuclear magnetic resonance(MAS NMR),and temperature-programmed desorption of ammonia results indicated that the hightemperature treatment at 650℃ hardly affected the inherent crystal and texture of HZSM-5zeolites but facilitated the conversion of framework Al to extra-framework Al,reducing the acid site and enhancing the acid strength.Moreover,the high-temperature treatment improved the performance of HZSM-5 zeolites in n-heptane catalytic cracking,promoting the conversion and light olefins yield while inhibiting coke formation.Based on the kinetic and mechanism analysis,the improvement of HZSM-5 performance caused by high-temperature treatment has been attributed to the formation of extra-framework Al,which enhanced the acid strength,facilitated the bimolecular reaction,and promoted the entropy change to overcome a higher energy barrier in n-heptane catalytic cracking.展开更多
文摘Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.
基金supported by the Natural Scienceof Shandong Province,China(ZR2019MEE033)。
文摘The characteristics of the packing material under an alternating electric field are an important factor in the removal of FCCS particles.In this study,the electric field distribution of a separation unit consisting of packed spheres under an alternating electric field is simulated,and the movement mechanism of catalyst particles is analysed.An"effective contact point"model is derived to predict the adsorption of filler contact points on catalyst particles under the alternating electric field,and the model is validated by simulations and experiments.The numerical calculation and experimental results indicate that the electrical properties of the filler spheres,the filler angleθ,and the frequency f of the alternating electric field affect the adsorption of catalyst particles.As the frequency of the electric field increases,the particle removal efficiency of the high-conductivity filler(silicon carbide)increases and then settles,and the separation efficiency of the low-conductivity filler(glass,zirconia)is not sensitive to the change in electric field frequency.
基金the financial support from the National Natural Science Foundation of China(21908010)Jilin Provincial Department of Science and Technology(20220101089JC)the Education Department of Jilin Province(JJKH20220694KJ)。
文摘High-temperature treatment is key to the preparation of zeolite catalysts.Herein,the effects of hightemperature treatment on the property and performance of HZSM-5 zeolites were studied in this work.X-Ray diffraction,N2physisorption,27Al magic angle spinning nuclear magnetic resonance(MAS NMR),and temperature-programmed desorption of ammonia results indicated that the hightemperature treatment at 650℃ hardly affected the inherent crystal and texture of HZSM-5zeolites but facilitated the conversion of framework Al to extra-framework Al,reducing the acid site and enhancing the acid strength.Moreover,the high-temperature treatment improved the performance of HZSM-5 zeolites in n-heptane catalytic cracking,promoting the conversion and light olefins yield while inhibiting coke formation.Based on the kinetic and mechanism analysis,the improvement of HZSM-5 performance caused by high-temperature treatment has been attributed to the formation of extra-framework Al,which enhanced the acid strength,facilitated the bimolecular reaction,and promoted the entropy change to overcome a higher energy barrier in n-heptane catalytic cracking.
