Mixing and separation of liquid-liquid two-phase in a novel cyclone reactor of isobutane alkylation catalyzed by ionic liquid

To improve the existing problems of the traditional isobutane alkylation catalyzed by ionic liquid reactors， a novel liquid-liquid cyclone reactor has been designed for the liquid-liquid heterogeneous reaction. Compared with the traditional hydrocyclone, the novel cyclone reactor consists of two inlets for light phase and heavy phase respectively. The light phase is injected into the reactor through two symmetric tangential slots in the inlet, while the heavy phase inlet is the axial entry with guide vane. The trajectory and residence time distribution (RTD) of the light phase could influence the reaction process and the products quality. In order to study the contact-mixing and separation mechanism of liquid-liquid in the novel cyclone reactor, the trajectory and residence time distribution in the reactor were investigated. The simulation using species transport equation and experiment were performed under oil-water system. The tangential and radial dispersion process of oil was observed in the simulation. The simulation results show that the mean residence time of the oil is between 0.6s~1.0s under different operating parameters. The oil flow in the reactor is not a smooth flow or a complete mixing flow judging from the dimensionless variance. The separation efficiency in simulated method was higher than 99%. The volume fraction of water in the overflow mixture was lower than 5%. And the deviation between the simulated and experimental results was no more than 5%, which indicates that the simulated results are reliable and accurate

Engineering mobility in quasiperiodic lattices with exact mobility edges

We investigate the effect of an additional modulation parameter $\delta$ on the mobility properties of quasiperiodic lattices described by a generalized Ganeshan-Pixley-Das Sarma model with two on site modulation parameters. For the case with bounded quasiperiodic potential, we unveil the existence of self-duality relation, independent of $\delta$. By applying Avila's global theory, we analytically derive Lyapunov exponents in the whole parameter space, which enables us to determine mobility edges or anomalous mobility edges exactly. Our analytical results indicate that the mobility edge equation is described by two curves and their intersection with the spectrum gives the true mobility edge. Tuning the strength parameter $\delta$ can change the spectrum of the quasiperiodic lattice, and thus engineers the mobility of quasi-periodic systems, giving rise to completely extended, partially localized, and completely localized regions. For the case with unbounded quasiperiodic potential, we also obtain the analytical expression of the anomalous mobility edge, which separates localized states from critical states. By increasing the strength parameter $\delta$, we find that the critical states can be destroyed gradually and finally vanishes.Comment: 10 pages,6 figure

MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past. With the current increasing density of seismic data collection, the efficiency of manual picking has been unable to meet the actual needs. Therefore, automatic picking methods have been greatly developed in recent decades, especially those based on deep learning. However, few of the current supervised deep learning-based method can avoid the dependence on labeled samples. Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the generalization problem across worksites and the picking problem in the case of low SNR. In MSSPN, there are four sub-models to simulate the manually picking processing, which is assumed to four stages from coarse to fine. Experiments on seven field datasets with different qualities show that our MSSPN outperforms benchmarks by a large margin.Particularly, our method can achieve more than 90\% accurate picking across worksites in the case of medium and high SNRs, and even fine-tuned model can achieve 88\% accurate picking of the dataset with low SNR