Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives
The Science of The Total Environment,
Journal Year:
2024,
Volume and Issue:
944, P. 173999 - 173999
Published: June 13, 2024
Membrane
technologies
have
become
proficient
alternatives
for
advanced
wastewater
treatment,
ensuring
high
contaminant
removal
and
sustainable
resource
recovery.
Despite
significant
progress,
ongoing
research
efforts
aim
to
further
optimize
treatment
performance.
Among
the
challenges
faced,
membrane
fouling
persists
as
a
relevant
obstacle
in
technologies,
necessitating
development
of
more
effective
mitigation
strategies.
Mathematical
models,
widely
employed
predicting
performance,
generally
exhibit
low
accuracy
suffer
from
uncertainties
due
complex
variable
nature
wastewater.
To
overcome
these
limitations,
numerous
studies
proposed
artificial
intelligence
(AI)
modeling
accurately
predict
technologies'
performance
mechanisms.
This
approach
aims
provide
simulations
predictions,
thereby
enhancing
process
control,
optimization,
intensification.
literature
review
explores
recent
advancements
membrane-based
processes
through
AI
models.
The
analysis
highlights
enormous
potential
this
field
efficiency
technologies.
role
defining
optimal
operating
conditions,
developing
strategies
mitigation,
novel
improving
fabrication
techniques
is
discussed.
These
enhanced
optimization
control
driven
by
ensure
improved
effluent
quality,
optimized
consumption,
minimized
costs.
contribution
cutting-edge
paradigm
shift
toward
examined.
Finally,
outlines
future
perspectives,
emphasizing
that
require
attention
current
limitations
hindering
integration
plants.
Language: Английский
Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries
Desalination,
Journal Year:
2024,
Volume and Issue:
592, P. 118092 - 118092
Published: Sept. 7, 2024
Language: Английский
A data-driven segmented model based on variance information for centrifugal pump efficiency prediction
Engineering Applications of Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
136, P. 108992 - 108992
Published: July 19, 2024
Language: Английский
A temporal case-based reasoning approach for performance improvement in intelligent environmental decision support systems
Engineering Applications of Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
136, P. 108833 - 108833
Published: June 27, 2024
Language: Английский
Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal
Processes,
Journal Year:
2024,
Volume and Issue:
12(8), P. 1667 - 1667
Published: Aug. 8, 2024
A
new
landfill-gas-to-biomethane
process
prescribing
decarbonation/desulfurization
via
gas–liquid
membrane
contactors
and
siloxane
absorption
using
Selexol
are
presented
in
this
study.
Firstly,
an
extension
for
HYSYS
simulator
was
developed
as
a
steady-state
contactor
model
featuring:
(a)
hollow-fiber
countercurrent/parallel
contacts;
(b)
liquid/vapor
mass/energy/momentum
balances;
(c)
CO2/H2S/CH4/water
fugacity-driven
bidirectional
transmembrane
transfers;
(d)
temperature
changes
from
heat/mass
transfers,
phase
change,
compressibility
effects;
(e)
external
heat
transfer.
Secondly,
batteries
countercurrent
contact
parallel
were
simulated
selective
landfill-gas
with
water.
Several
separation
methods
applied
the
process:
water
solvent
battery
adiabatic
decarbonation/desulfurization;
regeneration
high-pressure
strippers,
reducing
compression
power
CO2
exportation;
Selexol.
The
results
show
that
usual
isothermal/isobaric
simplification
is
unrealistic
at
industrial
scales.
converts
water-saturated
(CH4
=
55.7%mol,
40%mol,
H2S
150
ppm-mol,
Siloxanes
2.14
ppm-mol)
to
biomethane
specifications
of
CH4MIN
85%mol,
CO2MAX
3%mol,
H2SMAX
10
mg/Nm3,
SiloxanesMAX
0.03
mg/Nm3.
This
work
demonstrates
can
be
validated
bench-scale
literature
data
used
industrial-scale
same
hydrodynamics.
Once
calibrated,
becomes
economically
valuable
since
it
can:
(i)
predict
performance
under
scale-up/scale-down
conditions;
(ii)
detect
faults,
leakages,
wetting;
(iii)
troubleshooting.
Language: Английский