Investigation On Solid Phase Drifting Mechanism And Wear Prediction Method In The Multiphase Separation & Cooling Process Of Coal Liquefaction

With the contradiction between supply and demand of oil increasing acute,the development of coal liquefaction project has become the easing oil crisis and the important guarantee for the security of energy strategy.The direct coal liquefaction process,as the core technology of coal deeply processing,frequently exists the failure problem of deposition blockage or material erosion in the downstream separation and cooling equipment.The main reason is that the whole process involves the multiphase transmission containing solid particles(coal,minerals,catalyst,etc.),which can caused other failure problems,and this failure mechanism is extremely complex.Therefore,revealing the solid phase drifting mechanism and establishing the quantitative prediction and evaluation method in multiphase flow process are one of the important ways to evaluate the risking level and improve the safe operation of the relevant important equipment.In this paper,the failure caused by particles drifting and deposition in the separation and cooling system of coal liquefaction process is taken as research object.The relevant mathematical model of particles drifting and material wear is established,the quantitative prediction method is proposed,and the impact wear properties experiment of carbon steel elbows with gas and solid two-phase flow is carried out,and the method of rapid prediction based on artificial neural network is combined.The main contents and conclusions are as follows:(1)Based on the structural characteristics of the hot temperature and high pressure(HTHP)separator and the physical parameters of the multiphase flow,a mathematical model of particle phase drifting is established,and a quantification method of particle drifting is proposed.The VOF(Volume of Fluid)model and the DPM(Discrete Phase Model)model are used to calculate the influence of imported solid phase mass concentration ?,particle size dp on the distribution characteristics of solid phase at gas outlet and liquid outlet.The optimized scheme for reducing the particles drifting rate ? of gas outlet was proposed by the addition of the static separation baffle.Based on the numerical simulation,the distribution regulation of velocity field,mean particle concentration field,and particle drift rate in gas phase was obtained under five inclination angles ? in the HTHP separation.(2)Taken the related cooling equipment-air coolers as research object,the mathematical model of drifting and deposition of solid particles was established based on its structural characteristics,operating conditions,and physical parameters.The Mixture model and the DPM model were used to predict the particle drifting and deposition in air-cooler tubes under the environment of multiphase flow.The liquid fraction,the particle residence time distribution and the deposition rate are proposed as the characterization parameters of the particle drifting and deposition regulation.A good qualitative agreement between simulation results and surface temperature field measured by infrared thermal imager and the tube box opened have been found,which can verify the correctness of this numerical method,and then the prediction method of particle deposition was built based on the particle drifting.(3)The experimental study on the wear behavior of particles under different particle types,sizes and impact velocities was carried out,the parameters of impact angle,relative wear rate and velocity index were determined,and a modified mathematical model of particle erosion was established.Accorfing to the structural characteristics and physical parameters of the elbow,numerical simulation was carried out based on the method of particle drifting built in the simulation of HTHP,to study the law of wear situation in the elbow.This simulation result was consistent with the wall thinning area measured by ultrasonic thickness gauge.Meanwhile,combined with three kinds of artificial neural networks,a neural network model which can achieve a higher prediction accuracy of wear model was obtained,and then the prediction method of particle erosion was built based on the particle drifting.The innovation of this paper is to establish a predictive research system of particle drifting in the coal direct liquefaction separation and cooling process,and to reveal the mechanism of particles motion and erosion failure under the complex process.The mathematical model of particle drifting and deposition is established,the key parameters of particles motion wear characteristics was proposed,and the prediction method of particle deposition and erosion characterization was formed based on the particle drifting.The research results provide the basic theoretical support for the service inspection,risking assessment,optimization design and operation in the coal direct liquefaction system.