A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks
Mohsen Shayanmehr,
Sepehr Aarabi,
Ahad Ghaemi
и другие.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 24, 2025
This
study
employed
some
machine
learning
(ML)
techniques
with
Python
programming
to
forecast
the
adsorption
capacity
of
MOF
adsorbents
for
thiophenic
compounds
namely
benzothiophene
(BT),
dibenzothiophene
(DBT),
and
4,6-dimethyl
(4,6-DMDBT).
Five
ML
models
were
developed
help
a
dataset
containing
676
rows
correlate
adsorbent
features,
conditions,
adsorbate
characteristics
sample's
sulfur
capability.
Among
approaches,
MLP
model
achieved
best
performance
low
mean
squared
error
(MSE)
0.0032
on
test
set
0.0021
training
relative
(MRE)
15.26%
set.
Also,
Random
Forest
yielded
higher
MSE
0.0045
MRE
17.83%.
Feature
importance
analysis
was
performed
by
utilizing
shapely
additive
plan
(SHAP)
method,
findings
revealed
that
"initial
concentration
sulfur"
(SHAP
value
0.51)
"contact
time"
0.37)
crucial
factors
influenced
desulfurization
process
efficiency.
Additionally,
comparative
features
network
classified
into
three
primary
categories:
characteristics,
characteristics.
Consequently,
condition
identified
as
most
significant
group
compared
others.
Finally,
optimization
indicated
maximum
DBT
161.6
mg/g
Zr-based
could
be
when
including
BET,
TPV,
pore
size,
oil/adsorbent
ration,
temperature
tuned
around
756
m2/g,
0.955
cm3/g,
5.96
nm,
449.85
g/g,
20.1
°C,
respectively.
Язык: Английский
A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent
Journal of Environmental Management,
Год журнала:
2024,
Номер
370, С. 122614 - 122614
Опубликована: Окт. 8, 2024
Язык: Английский
Optimizing Photocatalytic Dye Degradation: A Machine Learning and Metaheuristic Approach for Predicting Methylene Blue in Contaminated Water
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103538 - 103538
Опубликована: Дек. 1, 2024
Язык: Английский
Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies
Chem & Bio Engineering,
Год журнала:
2025,
Номер
2(4), С. 210 - 228
Опубликована: Март 5, 2025
As
the
chemical
industry
shifts
toward
sustainable
practices,
there
is
a
growing
initiative
to
replace
conventional
fossil-derived
solvents
with
environmentally
friendly
alternatives
such
as
ionic
liquids
(ILs)
and
deep
eutectic
(DESs).
Artificial
intelligence
(AI)
plays
key
role
in
discovery
design
of
novel
development
green
processes.
This
review
explores
latest
advancements
AI-assisted
solvent
screening
specific
focus
on
machine
learning
(ML)
models
for
physicochemical
property
prediction
separation
process
design.
Additionally,
this
paper
highlights
recent
progress
automated
high-throughput
(HT)
platforms
screening.
Finally,
discusses
challenges
prospects
ML-driven
HT
strategies
optimization.
To
end,
provides
insights
advance
future
Язык: Английский
Advanced Ciprofloxacin Quantification: A Machine Learning and Metaheuristic Approach Using Ultrasensitive Chitosan-Gold Nanoparticle Based Electrochemical Sensor
Journal of environmental chemical engineering,
Год журнала:
2024,
Номер
unknown, С. 115094 - 115094
Опубликована: Дек. 1, 2024
Язык: Английский
Efficient Machine-Learning-Based New Tools to Design Eutectic Mixtures and Predict Their Viscosity
ACS Sustainable Chemistry & Engineering,
Год журнала:
2024,
Номер
12(52), С. 18537 - 18554
Опубликована: Дек. 17, 2024
The
development
of
models
that
accurately
predict
the
formation
eutectic
mixtures
(EMs,
including
well-known
deep
solvents)
and
their
viscosity
is
imperative
to
save
time
in
synthesizing
new
solvents.
We
developed
reliable
machine-learning-based
classifiers
able
discern
between
noneutectic
(non-EM)
regressors
an
EM.
A
experimental
data
set
219
EMs,
384
non-EMs,
1450
points
at
different
temperatures
water
contents
provided
used
challenge
several
models,
defined
both
by
algorithm
descriptors.
top-performing
EM/non-EM
classifier
yields
accuracy
92%,
best
regressor
achieves
predictions
with
a
mean
absolute
error
2.2
mPa·s;
extrapolation
capabilities
latter
were
assessed
on
additional
measurements
outside
range
training
set,
revealing
good
low
viscosities.
SHapley
Additive
exPlanations
(SHAP)
was
employed
as
eXplainable
Artificial
Intelligence
(XAI)
technique
quantify
input
feature
contributions
model
output.
These
results
represent
significant
step
forward
developing
robust
highly
accurate
for
determining
viscosity.
Язык: Английский