Modeling Of Agglomeration In A Fluidized Bed

Author : Aditi Khadilkar
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (959 download)

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Download or read book Development of a Fluidized-Bed Agglomeration Modeling Methodology to Include Particle-level Heterogeneities in Ash Chemistry and Granular Physics written by Aditi Khadilkar and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The utility of fluidized bed reactors for combustion and gasification can be enhanced if operational issues such as agglomeration are mitigated. The monetary and efficiency losses could be avoided through a mechanistic understanding of the agglomeration process and prediction of operational conditions that promote agglomeration. Pilot-scale experimentation prior to operation for each specific condition can be cumbersome and expensive. So the development of a mathematical model would aid predictions. The heterogeneity in ash chemical composition, gaseous atmosphere and distributive granular physics properties affect agglomeration. As the particle size distribution changes with agglomeration, the hydrodynamics such as particle collision frequencies also change continuously. This progression makes it challenging to predict the particle growth kinetics, since the chemistry- and physics-based parameters are interdependent. Existing models consider only one of these two aspects. The present work aimed to account for the heterogeneous conditions and develop a modeling methodology that integrates ash chemistry and granular physics.With this motivation, the study comprised of the following model development stages-1) development of an agglomeration modeling methodology based on binary particle collisions, 2) study of heterogeneities in ash chemical composition and gaseous atmosphere, 3) computation of a distribution of particle collision frequencies based on granular physics for a poly-disperse particle size distribution, 4) combining the ash chemistry and granular physics inputs to obtain agglomerate growth probabilities and 5) validation of the modeling methodology. The modeling methodology comprised of testing every binary particle collision in the system for sticking, based on the extent of dissipation of the particles kinetic energy through viscous dissipation by slag-liquid (molten ash) covering the particles. In the modeling methodology developed in this study, thermodynamic equilibrium calculations are used to estimate the amount of slag-liquid in the system, and the changes in particle collision frequencies are accounted for by continuously tracking the number density of the various particle sizes. The particle number density is also affected by the ash chemistry as solid particles melt to form slag. Computational fluid dynamics modeling is used in conjunction to obtain the initial granular physics inputs to the model. In this study, the heterogeneities in chemical composition of fuel ash were studied by separating the bulk fuel into particle classes that are rich in specific minerals. FactSage simulations were performed on two bituminous coals and an anthracite to understand the effect of particle-level heterogeneities on agglomeration. The mineral matter behavior of these constituent classes was studied. Each particle class undergoes distinct transformations of mineral matter at fluidized bed operating temperatures, as determined by using high temperature X-ray diffraction, thermo-mechanical analysis and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). The heaviest density fraction (>2.6 g/cc) indicated the occurrence of ternary and multi-phase eutectics involving CaO, Fe2O3, SiO2 and Al2O3 phases and a low slag formation onset temperature of 850 C. Thus, up to 10 % slag-liquid levels were observed to form around certain particles even at fluidized bed operating temperatures, under oxidizing conditions. Initiation of ash agglomeration occurs around such particles that tend to become sticky, and then propagates in the bed. It was also found that slag-liquid formation began at lower temperatures under the studied reducing environment than under oxidizing conditions and the amount of slag formed was at least 4 times greater for the mineral rich density fractions. Thus, presence of local reducing conditions in the reactor could enhance the propagation of agglomeration.For the incorporation of a particle size distribution, bottom ash from an operating plant was divided into four size intervals and the system granular temperatures and dynamic bed height were computed using MFIX, a CFD simulation software. The kinetic theory of granular flow was used to obtain a distribution of binary collision frequencies for the entire particle size distribution. With this distribution of collision frequencies, which is computed based on hydrodynamics and granular physics of the poly-disperse system, as the particles grow, defluidize and decrease in number, the collision frequency also decreases. Under the conditions studied, the growth rate in the latter half of the run decreased to almost 1/5th the initial rate, with this decrease in collision frequency. Additionally, the ash chemistry influenced the melting and the number density of solid particles in the system, which in turn affected the collision frequency.This interdependent effect of chemistry and physics-based parameters, at the particle-level, was used to predict the agglomerate growth probabilities of Pittsburgh No. 8, Illinois No. 6 and Skidmore anthracite coals in this study, to illustrate the utility of the modeling methodology. The study also showed that agglomerate growth probability significantly increased above 15 to 20 wt. % slag. It was limited by ash chemistry at levels below this amount. Ash agglomerates were generated in a laboratory-scale fluidized bed combustor at Penn State to support the proposed agglomerate growth mechanism. Ash agglomerates were produced by operating the reactor at a superficial gas velocity close to the minimum fluidization velocity of the particles, using rejects from Pittsburgh No. 8 coal, with about 82 % ash content, under oxidizing conditions. Agglomerate samples were also obtained from another fluidized bed combustion facility in Canada. Polished cross-sections of these agglomerates were studied using SEM-EDX to relate the particle-level slag formation and sticking to the chemical composition. FactSage simulations of the slag-forming components were used to estimate the agglomeration temperatures.This study also attempted to gain a mechanistic understanding of agglomerate growth with particle-level initiation occurring at the relatively low operating temperatures of about 950 C, found in some fluidized beds. The results of this study indicated that, for the materials examined, agglomerate growth in fluidized bed combustors and gasifiers is initiated at the particle-level by low-melting components rich in iron- and calcium-based minerals. Although the bulk ash chemical composition does not indicate potential for agglomeration, study of particle-level heterogeneities revealed that agglomeration can begin at lower temperatures than the fluidized bed operating temperatures of 850 C. After initiation at the particle-level, more slag is observed to form from alumino-silicate components at about 50 to 100 C higher temperatures caused by changes in the system, and agglomerate growth propagates in the bed. A post-mortem study of ash agglomerates using SEM-EDX helped to identify stages of agglomerate growth. Additionally, the modeling methodology developed was used to simulate agglomerate growth in a laboratory-scale fluidized bed combustor firing palm shells (biomass), reported in the literature. A comparison of the defluidization time obtained by simulations to the experimental values reported in the case-study was made for the different operating conditions studied. This indicated that although the simulation results were comparable to those reported in the case study, modifications such as inclusion of heat transfer calculations to determine particle temperature resulting from carbon conversion would improve the predictive capabilities.