ChemBioEng Reviews,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 15, 2025
Abstract
The
need
of
process
intensification
(PI)
strategies
for
biodiesel
production
focuses
on
enhancing
the
efficiency,
sustainability,
and
economic
viability
process.
PI‐transformative
approach
to
aligns
with
green
chemistry
principles
promotes
higher
yields
cost‐effectiveness.
from
second‐generation
feedstocks,
an
emphasis
selectivity,
yield,
is
extensively
discussed
in
this
abstract.
This
comprehensive
review
methodically
describes
various
classes
PI
technologies,
including
microwave,
ultrasonic,
reactive
distillation,
microreactors
production.
It
also
about
recent
advancements
emphasizing
improving
efficiency
sustainability.
highlights
challenges
possessed
by
these
technologies.
present
supports
selection
a
more
ecologically
friendly
technology
good‐yield
synthesis
biodiesel,
facilitating
scaling
up.
Energy Conversion and Management X,
Journal Year:
2024,
Volume and Issue:
23, P. 100669 - 100669
Published: July 1, 2024
One
of
the
main
limitations
to
economic
sustainability
biodiesel
production
remains
high
feedstock
cost.
Modeling
and
optimization
are
crucial
steps
determine
if
processes
(esterification
transesterification)
involved
in
economically
viable.
Phenomenological
or
mechanistic
models
can
simulate
processes.
These
methods
have
been
used
manage
processes,
but
their
broad
use
has
constrained
by
computational
complexity
numerical
difficulties.
Therefore,
it
is
necessary
quick,
effective,
accurate,
resilient
modeling
methodologies
regulate
such
complex
systems.
Data-driven
machine-learning
(ML)
techniques
offer
a
potential
replacement
for
conventional
deal
with
nonlinear,
unpredictable,
complex,
multivariate
nature
Artificial
neural
networks
(ANN)
adaptive
neuro-fuzzy
inference
systems
(ANFIS)
most
often
utilized
ML
tools
research.
To
effectively
attain
maximum
yield,
suitable
based
on
nature-inspired
algorithms
need
be
integrated
these
obtain
best
possible
combination
various
operating
variables.
Future
research
should
focus
utilizing
approaches
monitoring
managing
increase
effectiveness
promote
commercial
feasibility.
Thus,
review
discusses
optimizing
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 15, 2025
Abstract
Researchers
and
stakeholders
have
shown
interest
in
heterogeneous
composite
biodiesel
(HCB)
due
to
its
enhanced
fuel
properties
environmental
friendliness
(EF).
The
lack
of
high
viscosity
datasets
for
parent
hybrid
oils
has
hindered
their
commercialisation.
Reliable
models
are
lacking
optimise
the
transesterification
parameters
developing
HCB,
scarcity
predictive
affected
climate
researchers
experts.
In
this
study,
basic
were
analysed,
developed
yield
HCB
kinematic
(KV)
biodiesel/neem
castor
seed
oil
methyl
ester
(NCSOME)
using
Artificial
Neural
Network
(ANN)
Adaptive
Neuro
Fuzzy
Inference
System
(ANFIS).
Statistical
indices
such
as
computed
coefficient
determination
(R
2
),
root-mean-square-error
(RMSE),
standard
error
prediction
(SEP),
mean
average
(MAE),
absolute
deviation
(AAD)
used
evaluate
effectiveness
techniques.
Emission
NCSOME-diesel
blends
also
established.
study
investigated
impact
optimised
types/NCSOME-diesel
(10–30
vol%),
ZnO
nanoparticle
dosage
(400–800
ppm),
engine
speed
(1100–1700
rpm),
load
(10–30%)
on
emission
characteristics
(EFI)
carbon
monoxide
(CO),
Oxides
Nitrogen
(NOx),
Unburnt
Hydrocarbon
(UHC)
Response
Surface
Methodology
(RSM).
ANFIS
model
demonstrated
superior
performance
terms
R
,
RMSE,
SEP,
MAE,
AAD
compared
ANN
predicting
KV
HCB.
optimal
levels
CO
(49.26
NO
x
(0.5171
UHC
(2.783)
achieved
with
a
type
23.4%,
685.432
ppm,
1329.2
rpm,
10%
ensure
cleaner
EFI.
can
effectively
predict
fuel-related
improve
process,
while
RSM
be
valuable
tool
accurate
forecasting
promoting
environment.
provide
reliable
information
strategic
planning
automotive
industries.
