Influence of filler characteristics on particle removal in fluid catalytic cracking slurry under an alternating electric field

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.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.