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Development of slurry mixing models using resistance tomography
Richard A Williams
, X. Jia
, S. L. McKee
School of Engineering & Physical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
67
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Citations (Scopus)
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INIS
mixing
100%
design
100%
tomography
100%
slurries
100%
electrical resistance
75%
mixers
75%
data
50%
experimental data
50%
solids
50%
correlations
25%
applications
25%
information
25%
particles
25%
concentration
25%
modeling
25%
simulation
25%
liquids
25%
speed
25%
simulators
25%
equipment
25%
processing
25%
hydrodynamics
25%
artificial intelligence
25%
optimization
25%
testing
25%
verification
25%
particulates
25%
computational fluid dynamics
25%
particle size
25%
libraries
25%
flow models
25%
on-line control systems
25%
Engineering
Mixers (Machinery)
100%
Electric Resistance
100%
Electrical Resistance
100%
Impeller
33%
Process Variable
33%
Control Strategy
33%
Key Process
33%
Hydrodynamics
33%
Computational Fluid Dynamics
33%
Dynamic Models
33%
Cellular Model
33%
Artificial Intelligence
33%
Chemical Engineering
Artificial Intelligence
100%
Process Simulator
100%
Agitation Speed
100%
Material Science
Tomography
100%
Electrical Resistance
75%
Hydrodynamics
25%
Computational Fluid Dynamics
25%