Numerical Simulation of Enhanced Oil-Water Separation in a Three-Stage Double-Stirring Extraction Tank

Numerical simulation of enhanced fluid flow characteristics in a three-stage double-stirring extraction tank was conducted with the coupling of an Eulerian multiphase flow model and a Morsi-Alexander interphase drag force model. Results show that the addition of a stirring device into the settler can efficiently reduce the volume fraction of out-of-phase impurity in the outlet, and accelerate the settling separation of oil-water mixture. Such addition can also effectively break down the oil-water-wrapped liquid droplets coming from the mixer, inhibit reflux from the outlet, and improve the oil-water separation. The addition of a stirring device induces ignorable power consumption compared with that by the mixer, and can thus facilitate the commercialized promotion of this novel equipment.

Design aspect of a novel L-shaped pulsed column for liquid–liquid extraction applications: Energy consumption and the characteristics velocity concept

This article deals with the evaluation of the consumption of energy for a steady state solvent extraction in a novel L-shaped pulsed sieve-plate column, which is highly required for design and optimization of the periodic flow processes for industrial applications. In this regard, a comprehensive evaluation on the energy consumption in case of a pulsed flow for three different chemical systems is conducted and besides the influence of pulsation intensity, the effect of geometrical parameters including the plate spacing and the plate free area is investigated as well. Moreover, the concept of characteristic velocity models at flooding points is evaluated with respect to the variation of pressure drop along the column at different operational conditions.

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.In this paper,we propose a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network(CNN).According to the different characteristics of the attack flow and the normal flow in the IP protocol,the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary.Based on the network flow grayscale matrix feature(GMF),the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation,global features and local features of the network flow are extracted.A DDoS attack classifier based on multi-scale convolution neural network is constructed.Experiments show that compared with correlation methods,this method can improve the robustness of the classifier,reduce the false alarm rate and the missing alarm rate.

TLC Identification of Schisandra Chinensis Liquid Extract and Antler Essence in Qianglinaoqingsu Tablets

[Objectives]To study the quality standard of Chinese drug in compound Qianglinaogingsu Tablets(Fructus Schisandrae Chinensis and Coru Cervi Pantotrichum).[Methods]The thin layer chromatography(TLC)differentiation method was established for Fructus Schisandrae Chinensis and Cornu Cervi Pantotrichum.[Results]TLC spots developed were fairly clear,and the blank test showed no interference.[Conclusions]The method is fast,simple and can be used for quality control of Qianglinaoqingsu Tablets.