Numerical study of short-circuiting flow and particles in a gas cyclone

Short-circuiting flow is an important secondary flow in gas cyclones, which has a negative impact on the separation performance. To improve the understanding of the short-circuiting flow and guide the optimization of gas cyclones, this paper presents a numerical study of a cyclone using computational fluid dynamics. Based on the steady flow field, three methods were adopted to investigate the formation mechanism and characteristics of the short-circuiting flow and particles. The temporal variation of the tracer species concentration distribution reveals that the formation mechanism of the short-circuiting flow is the squeeze between the airflows entering the annular space of the gas cyclone at different times. The short-circuiting flow region, distinguished through the spatial distribution of the moments of age, is characterized by a small mean age and a large coefficient of variation. The proportion of the short-circuiting particles increases with the increase of the inlet velocity only for small particles. But with the increase of particle size, the proportion of the short-circuiting particles decreases faster at higher inlet velocities, resulting in significant differences in collection efficiency curves.

Gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

Performance of Inner-core Supersonic Gas Separation Device with Droplet Enlargement Method

To improve the separation performance of a supersonic gas separation device for the treatment of gas mixture with a single heavy component, a novel structure with shorter settlement distance was constructed and a method of droplet enlargement was applied. A series of experiments were carried out in the improved separation device under various conditions, using air-ethanol vapor as the medium and micro water droplets as nucleation cen- ters. The effects of the inlet pressure, temperature and relative humidity, the swirling intensity, and mass flow rate of water on the separation performance were investigated. The separation was improved by increasing the inlet pressure and relative humidity. With the decrease of swirling intensity and mass flow rate of water, the separation efficiency increased first and then decreased. The inlet temperature had a slight effect on the separation. The results showed that the separation performance was effectively improved using the proposed structure and method, and the best separation in this study was obtained with the ethanol removal rate about 55% and dew point depression 27 K. The addition of water had little pollution to the air-ethanol vapor system since the water carry-over rate was within the range of -2 %-0 in most cases.

Performance of Dual-throat Supersonic Separation Device with Porous Wall Structure

In this paper, a dual-throat supersonic separation device with porous wall has been proposed to solve the starting problem of supersonic separator, and the feasibility of the proposed device has been tested numerically and experimentally. Its flow characteristics have been investigated and the effect of some important parameters includ-ing nozzle pressure ratio(RNP), inlet temperature and swirl intensity were examined. In the device, the supersonic flow state and strong centrifugal acceleration of 240000g can be obtained, which are necessary for the condensation and separation of water vapor. The supersonic region in the device enlarged and the shock wave shifted downstream along with the increasing RNP. The separation performance was improved with the increasing RNP and the inlet temperature. The best separation performance in this study was obtained with ΔTd? 28 K.

Separation-and-Recovery Technology for Organic Waste Liquid with a High Concentration of Inorganic Particles

The environmentally friendly and resourceful utilization of organic waste liquid is one of the frontiers of environmental engineering. With the increasing demand for chemicals, the problem of organic waste liq- uid with a high concentration of inorganic pollutants in the processing of petroleum, coal, and natural gas is becoming more serious. In this study, the high-speed self-rotation and flipping of particles in a three- dimensional cyclonic turbulent field was examined using a synchronous high-speed camera technique; the self-rotation speed was found to reach 2000-6000 rad.s 1. Based on these findings, a cyclonic gas- stripping method for the removal of organic matter from the pores of particles was invented. A techno- logical process was developed to recover organic matter from waste liquid by cyclonic gas stripping and classifying inorganic particles by means of airflow acceleration classification. A demonstration device was built in Sinopec＇s first ebullated-bed hydro-treatment unit for residual oil. Compared with the T-STAR fixed-bed gas-stripping technology designed in the United States, the maximum liquid-removal effi- ciency of the catalyst particles in this new process is 44.9% greater at the same temperature, and the time required to realize 95% liquid-removal efficiency is decreased from 1956.5 to 8.4 s. In addition, we achieved the classification and reuse of the catalyst particles contained in waste liquid according to their activity. A proposal to use this new technology was put forward regarding the control of organic waste liquid and the classification recovery of inorganic particles in an ebullated-bed hydro-treatment process for residual oil with a processing capacity of 2×106 t.a^1. It is estimated that the use of this new tech- nology will lead to the recovery of 3100 t.a 1 of diesel fuel and 647 t.a^1 of high-activity catalyst; in addi- tion, it will reduce the consumption of fresh catalyst by 518 t.a^1. The direct economic benefits of this process will be as high as 37.28 million CNY per year.