In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and o...In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and operation of FCC unit. Consequently, it is significant to predict the coke yield accurately. The coke formation and burning reactions are affected by many parameters which influence each other, so it is difficult to establish a prediction model using traditional models. This paper combines the industrial production data and establishes a generalized regression neural network(GRNN) model and a back propagation(BP) neural network model to predict the coke yield respectively. The comparison and analysis results show that the accuracy and stability of the BP neural network prediction results are better than that of the GRNN. Then, the particle swarm optimization to optimize BP neural network(PSO-BP) and genetic algorithm to optimize the BP neural network(GA-BP) were further used to improve the prediction precision. The comparison of these models shows that they can improve the prediction precision. However, considering the accuracy and stability of the prediction results, the GA-BP model is better than PSO-BP model.展开更多
A three-dimensional model for gas-solid flow in a circulating fluidized bed（CFB） riser was developed based on computational particle fluid dynamics（CPFD）.The model was used to simulate the gas-solid flow behavior ...A three-dimensional model for gas-solid flow in a circulating fluidized bed（CFB） riser was developed based on computational particle fluid dynamics（CPFD）.The model was used to simulate the gas-solid flow behavior inside a circulating fluidized bed riser operating at various superficial gas velocities and solids mass fluxes in two fluidization regimes,a dilute phase transport（DPT） regime and a fast fluidization（FF） regime.The simulation results were evaluated based on comparison with experimental data of solids velocity and holdup,obtained from non-invasive automated radioactive particle tracking and gamma-ray tomography techniques,respectively.The agreement of the predicted solids velocity and holdup with experimental data validated the CPFD model for the CFB riser.The model predicted the main features of the gas-solid flows in the two regimes;the uniform dilute phase in the DPT regime,and the coexistence of the dilute phase in the upper region and the dense phase in the lower region in the FF regime.The clustering and solids back mixing in the FF regime were stronger than those in the DPT regime.展开更多
Gasification technology is suggested to utilize asphalt particles, which are produced in the heavy oil deep separation process of using coupled low temperature separation of solvent and post extraction residue. In thi...Gasification technology is suggested to utilize asphalt particles, which are produced in the heavy oil deep separation process of using coupled low temperature separation of solvent and post extraction residue. In this work, the asphalt particles were first slurried with water and then gasified to produce synthesis gas. The gasification process of asphalt water slurry in an entrained flow gasifier was simulated using a three-dimensional computational fluid dynamics （CFD） model based on an Eulerian- Lagrangian method. The trajectories and residence time of asphalt particles, and the reaction rates, gas species distribution, temperature field and carbon conversion in the entrained flow gasifier were obtained. The predicted results indicated that the asphalt water slurry was a good feedstock for gasification. Moreover, the effects of particle size, oxygen equivalence ratio, and mass content of asphalt particles on the gasification performance of asphalt water slurry were investigated. These results are helpful for industrial application of asphalt water slurry gasification technology.展开更多
文摘In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and operation of FCC unit. Consequently, it is significant to predict the coke yield accurately. The coke formation and burning reactions are affected by many parameters which influence each other, so it is difficult to establish a prediction model using traditional models. This paper combines the industrial production data and establishes a generalized regression neural network(GRNN) model and a back propagation(BP) neural network model to predict the coke yield respectively. The comparison and analysis results show that the accuracy and stability of the BP neural network prediction results are better than that of the GRNN. Then, the particle swarm optimization to optimize BP neural network(PSO-BP) and genetic algorithm to optimize the BP neural network(GA-BP) were further used to improve the prediction precision. The comparison of these models shows that they can improve the prediction precision. However, considering the accuracy and stability of the prediction results, the GA-BP model is better than PSO-BP model.
基金support by the National Basic Research Program (Grant No. 2010CB226906,and 2012CB215000)
文摘A three-dimensional model for gas-solid flow in a circulating fluidized bed（CFB） riser was developed based on computational particle fluid dynamics（CPFD）.The model was used to simulate the gas-solid flow behavior inside a circulating fluidized bed riser operating at various superficial gas velocities and solids mass fluxes in two fluidization regimes,a dilute phase transport（DPT） regime and a fast fluidization（FF） regime.The simulation results were evaluated based on comparison with experimental data of solids velocity and holdup,obtained from non-invasive automated radioactive particle tracking and gamma-ray tomography techniques,respectively.The agreement of the predicted solids velocity and holdup with experimental data validated the CPFD model for the CFB riser.The model predicted the main features of the gas-solid flows in the two regimes;the uniform dilute phase in the DPT regime,and the coexistence of the dilute phase in the upper region and the dense phase in the lower region in the FF regime.The clustering and solids back mixing in the FF regime were stronger than those in the DPT regime.
基金support by the National Basic Research Program (Grant No. 2010CB226906)the Science Foundation of China University of Petroleum, Beijing (No. KYJJ2012-03-01)
文摘Gasification technology is suggested to utilize asphalt particles, which are produced in the heavy oil deep separation process of using coupled low temperature separation of solvent and post extraction residue. In this work, the asphalt particles were first slurried with water and then gasified to produce synthesis gas. The gasification process of asphalt water slurry in an entrained flow gasifier was simulated using a three-dimensional computational fluid dynamics （CFD） model based on an Eulerian- Lagrangian method. The trajectories and residence time of asphalt particles, and the reaction rates, gas species distribution, temperature field and carbon conversion in the entrained flow gasifier were obtained. The predicted results indicated that the asphalt water slurry was a good feedstock for gasification. Moreover, the effects of particle size, oxygen equivalence ratio, and mass content of asphalt particles on the gasification performance of asphalt water slurry were investigated. These results are helpful for industrial application of asphalt water slurry gasification technology.
