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Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for a fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model pa...

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Detalles Bibliográficos
Autores principales: Abboud, Alexander W., Guillen, Donna P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197788/
https://www.ncbi.nlm.nih.gov/pubmed/30364667
http://dx.doi.org/10.1016/j.dib.2018.09.105
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author Abboud, Alexander W.
Guillen, Donna P.
author_facet Abboud, Alexander W.
Guillen, Donna P.
author_sort Abboud, Alexander W.
collection PubMed
description An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for a fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model parameters – bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate. Data from the simulations were collected on three quantities of interest – bed differential temperature, low solids velocity, and bed void fraction. The data presented here is the full set of response surfaces generated using the process Gaussian response surface model in the Dakota toolkit, as well as the table of data for coefficients of the fitted model. The fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing, respectively.
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spelling pubmed-61977882018-10-25 Response surface data for sensitivity study of industrial spray injected fluidized bed reactor Abboud, Alexander W. Guillen, Donna P. Data Brief Chemical Engineering An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for a fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model parameters – bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate. Data from the simulations were collected on three quantities of interest – bed differential temperature, low solids velocity, and bed void fraction. The data presented here is the full set of response surfaces generated using the process Gaussian response surface model in the Dakota toolkit, as well as the table of data for coefficients of the fitted model. The fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing, respectively. Elsevier 2018-10-03 /pmc/articles/PMC6197788/ /pubmed/30364667 http://dx.doi.org/10.1016/j.dib.2018.09.105 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Chemical Engineering
Abboud, Alexander W.
Guillen, Donna P.
Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title_full Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title_fullStr Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title_full_unstemmed Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title_short Response surface data for sensitivity study of industrial spray injected fluidized bed reactor
title_sort response surface data for sensitivity study of industrial spray injected fluidized bed reactor
topic Chemical Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197788/
https://www.ncbi.nlm.nih.gov/pubmed/30364667
http://dx.doi.org/10.1016/j.dib.2018.09.105
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