(448am) Steam-Blown Biomass Gasification in Fluidized Beds:Gas-Flow Distribution for Advanced Reactor Network Models
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Particle Technology Forum
Poster Session: Particle Technology Forum
Tuesday, November 15, 2016 - 6:00pm to 8:00pm
In this study, reactive 3D CFD simulations are conducted for steam-blown biomass gasification in bubbling fluidized beds of varying diameters and pressures. The hydrodynamics are coupled with a global devolatalization mechanism for biomass conversion [1], while the steady state char concentration is predicted using a detailed char particle gasification and combustion model [2]. The physical model and numerical tool were developed and validated in previous studies [3], while 3D Bubble statistics are computed using MS3DATA (Multiphase Statistics using 3D Detection and Tracking Algorithm) [4]. Simulation data is analyzed and the distribution of both the oxidant and devolatalized gases, and their residence times, are computed. Accurate description of the gas-flow is critical for large-scale reactor design since quantifying the gas distribution can determine the fuel rich zones (in the emulsion) as well as the oxidant bypass through the bubbles (through-flow) leading to inefficient performance, and the formation of recalcitrant tar compounds. Additionally, insights from this study will be valuable for reactor network modeling of fuel conversion systems [1].
REFERENCES
[1] A.K. Stark, C. Altantzis, R.B. Bates and A.F. Ghoniem, Towards an advanced reactor network modeling framework for fluidized bed biomass gasification: Incorporating information from detailed CFD simulations. Chemical Engineering Journal. 303: 409-424, 2016.
[2] R. Bates, C. Altantzis, and A.F. Ghoniem, Modeling of Biomass Char Gasification, Combustion, and Attrition Kinetics in Fluidized Beds. Energy Fuels 30(1):360â??376, 2016
[3] A. Bakshi, C. Altantzis, R.B. Bates and A.F. Ghoniem, Study of the effect of reactor scale on fluidization hydrodynamics using fine-grid CFD simulations based on the two-fluid model, Powder Technology, 299: 185-198, 2016
[4] A. Bakshi, C. Altantzis, R.B. Bates and A.F. Ghoniem, Multiphase-flow Statistics using 3D Detection and Tracking Algorithm (MS3DATA): Methodology and application to large-scale fluidized beds. Chemical Engineering Journal, 293: 355-364, 2016