The study of the effect of gas-phase fluctuation on slag flow and refractory brick corrosion in the slag tapping hole of an entrained-flow gasifier

2021 ◽  
Author(s):  
Kuo Lin ◽  
Zhongjie Shen ◽  
Qinfeng Liang ◽  
Jianliang Xu ◽  
Haifeng Liu
Keyword(s):  
Gas Phase ◽  
Phase Fluctuation ◽  
Refractory Brick ◽  
Entrained Flow ◽  
Entrained Flow Gasifier

Numerical Modeling of Coal Gasification in an Entrained-Flow Gasifier

2012 ◽  
Author(s):  
Cheng Zhang
Keyword(s):  
Experimental Data ◽  
Numerical Modeling ◽  
Gas Phase ◽  
Combined Cycle ◽  
Carbon Conversion ◽  
Particle Phase ◽  
Entrained Flow ◽  
Gasification Process ◽  
Entrained Flow Gasifier ◽  
Gasification Technology

The gasification technology has been applied in Integrated Gasification Combined Cycle (IGCC) plants for the production of power, and polygeneration plants for the production of industrial chemicals, fuels, hydrogen, and power. The major advantages of the gasification technology are its potential for feed-stock flexibility, product flexibility, and relative simple removal of harmful emissions of nitrogen oxides (NOx), sulfur oxides (SOx), and CO2. Entrained-flow gasifiers are the preferred gasifier design for future deployment due to their high carbon conversion, high efficiency and high syngas purity. Current entrained-flow gasifier designs still have serious problems such as injector failure, refractory failure, slag blockages, downstream fouling and poisoning, poor space efficiency, and lack of dynamic feedstock flexibility. To better understand the entrained-flow gasification process, we performed steady Reynolds-averaged Navier-Stokes (RANS) modeling of the laboratory-scale gasifier developed at Brigham Young University (BYU) using ANSYS Fluent. An Eulerian approach is used to describe the gas phase, and a Lagrangian approach is used to describe the particle phase. The interactions between the gas phase and particle phase is modeled using the particle-source-in-cell approach. Turbulence is modeled using the shear-stress transport (SST) k–ω model. Turbulent particle dispersion is taken into account by using the discrete random walk model. Devolatilization is modeled using a version of the chemical percolation devolatilization (CPD) model, and char consumption is described with a shrinking core model. Turbulent combustion in the gas phase is modeled using a finite-rate/eddy-dissipation model. Radiation is considered by solving the radiative transport equation with the discrete ordinates model. Second-order upwind scheme is used to solve all gas phase equations. First, to validate the flow solver, we performed numerical modeling of a non-reacting particle-laden bluff-body flow. For the non-reacting flow, the predicted mean velocities of the gas phase and the particle phase are in good agreement with the experimental data. Next, we performed numerical modeling of the gasification process in the BYU gasifier. The predicted profiles of the mole fractions of the major species (i.e. CO, CO2, H2, and H2O) along the centerline are in reasonable agreement with the experimental data. The predicted carbon conversion at the gasifier exit agrees with the experimental data. The predicted temperature at the gasifier exit agrees with the estimated value based on water-gas shift equilibrium considerations. The numerical model was further applied to study the effects of the equivalence ratio, particle size, and swirl on the gasification process.

scholarly journals Numerical simulation of lignocellulosic biomass gasification in concentric tube entrained flow gasifier through computational fluid dynamics

2019 ◽  
Vol 37 (3) ◽  
pp. 1073-1097 ◽  
Author(s):  
Ghulamullah Maitlo ◽  
Imran Nazir Unar ◽  
Rasool Bux Mahar ◽  
Khan Mohammad Brohi
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Gas Phase ◽  
Mole Fraction ◽  
Sugarcane Bagasse ◽  
Rice Husk ◽  
Reaction Rates ◽  
Entrained Flow ◽  
Cotton Stalks ◽  
Entrained Flow Gasifier

Thermochemical conversion of biomass is an encouraging way for the production of syngas. In the present research, four different biomass materials were used for gasification which includes rice husk, cotton stalks, sugarcane bagasse, and sawdust. These biomass sources were selected because they are common Pakistani feedstocks. Gasification of selected biomasses was performed using concentric tube entrained flow gasifier. Three-dimensional computational fluid dynamics model was used to investigate the impacts of kinetic rate and diffusion rate on the gasification performance. The Euler–Lagrange method was used for the development of entrained flow biomass gasifier using commercial computational fluid dynamics code ANSYS FLUENT®14. Discrete phase model was used to predict the movement of particles, whereas the gas phase was treated as the continuous phase with a standard k–ε turbulent model to predict the behavior of gas phase flow. Finite rate/Eddy dissipation model was applied for the calculation of homogenous and heterogeneous reaction rates. Oxygen was used as a gasifying agent. Cotton stalks and sugarcane bagasse produced higher mole fractions of hydrogen (H2) and carbon monoxide (CO) than sawdust and rice husk. Regarding carbon conversion efficiency, cold gas efficiency, and higher heating value cotton stalks and sugarcane bagasse produced better syngas quality as compared to sawdust and rice husk. The oxygen/fuel (O/F) ratio is a key operating parameter in the field of gasification and combustion. The O/F ratio above 0.42 favored combustion reactions and increased mole fraction of water vapor (H2O) and carbon dioxide (CO2) in syngas composition, whereas gasification reactions dominated below 0.42 O/F ratio, resulting increased mole fraction of H2 and CO in syngas composition.

Co-gasification of coal and biomass wastes in an entrained flow gasifier: Modelling, simulation and integration opportunities

2017 ◽  
Vol 37 ◽  
pp. 126-137 ◽  
Author(s):  
Dalia A. Ali ◽  
Mamdouh A. Gadalla ◽  
Omar Y. Abdelaziz ◽  
Christian P. Hulteberg ◽  
Fatma H. Ashour
Keyword(s):  
Entrained Flow ◽  
Entrained Flow Gasifier ◽  
Modelling Simulation
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