JOIV International Journal on Informatics Visualization,
Год журнала:
2024,
Номер
8(1), С. 55 - 55
Опубликована: Март 16, 2024
Integrating
machine
learning
(ML)
and
artificial
intelligence
(AI)
with
renewable
energy
sources,
including
biomass,
biofuels,
engines,
solar
power,
can
revolutionize
the
industry.
Biomass
biofuels
have
benefited
significantly
from
implementing
AI
ML
algorithms
that
optimize
feedstock,
enhance
resource
management,
facilitate
biofuel
production.
By
applying
insight
derived
data
analysis,
stakeholders
improve
entire
supply
chain
-
biomass
conversion,
fuel
synthesis,
agricultural
growth,
harvesting
to
mitigate
environmental
impacts
accelerate
transition
a
low-carbon
economy.
Furthermore,
in
combustion
systems
engines
has
yielded
substantial
improvements
efficiency,
emissions
reduction,
overall
performance.
Enhancing
engine
design
control
techniques
produces
cleaner,
more
efficient
minimal
impact.
This
contributes
sustainability
of
power
generation
transportation.
are
employed
analyze
vast
quantities
photovoltaic
systems'
design,
operation,
maintenance.
The
ultimate
goal
is
increase
output
system
efficiency.
Collaboration
among
academia,
industry,
policymakers
imperative
expedite
sustainable
future
harness
potential
energy.
these
technologies,
it
possible
establish
ecosystem,
which
would
benefit
generations.
Biotechnology Advances,
Год журнала:
2023,
Номер
67, С. 108181 - 108181
Опубликована: Июнь 1, 2023
The
sustainable
utilization
of
biochar
produced
from
biomass
waste
could
substantially
promote
the
development
carbon
neutrality
and
a
circular
economy.
Due
to
their
cost-effectiveness,
multiple
functionalities,
tailorable
porous
structure,
thermal
stability,
biochar-based
catalysts
play
vital
role
in
biorefineries
environmental
protection,
contributing
positive,
planet-level
impact.
This
review
provides
an
overview
emerging
synthesis
routes
for
multifunctional
catalysts.
It
discusses
recent
advances
biorefinery
pollutant
degradation
air,
soil,
water,
providing
deeper
more
comprehensive
information
catalysts,
such
as
physicochemical
properties
surface
chemistry.
catalytic
performance
deactivation
mechanisms
under
different
systems
were
critically
reviewed,
new
insights
into
developing
efficient
practical
large-scale
use
various
applications.
Machine
learning
(ML)-based
predictions
inverse
design
have
addressed
innovation
with
high-performance
applications,
ML
efficiently
predicts
biochar,
interprets
underlying
complicated
relationships,
guides
synthesis.
Finally,
benefit
economic
feasibility
assessments
are
proposed
science-based
guidelines
industries
policymakers.
With
concerted
effort,
upgrading
protection
reduce
pollution,
increase
energy
safety,
achieve
management,
all
which
beneficial
attaining
several
United
Nations
Sustainable
Development
Goals
(UN
SDGs)
Environmental,
Social
Governance
(ESG).
Biofuels Bioproducts and Biorefining,
Год журнала:
2024,
Номер
18(2), С. 567 - 593
Опубликована: Фев. 5, 2024
Abstract
Biochar
is
emerging
as
a
potential
solution
for
biomass
conversion
to
meet
the
ever
increasing
demand
sustainable
energy.
Efficient
management
systems
are
needed
in
order
exploit
fully
of
biochar.
Modern
machine
learning
(ML)
techniques,
and
particular
ensemble
approaches
explainable
AI
methods,
valuable
forecasting
properties
efficiency
biochar
properly.
Machine‐learning‐based
forecasts,
optimization,
feature
selection
critical
improving
techniques.
In
this
research,
we
explore
influences
these
techniques
on
accurate
yield
range
sources.
We
emphasize
importance
interpretability
model,
improves
human
comprehension
trust
ML
predictions.
Sensitivity
analysis
shown
be
an
effective
technique
finding
crucial
characteristics
that
influence
synthesis
Precision
prognostics
have
far‐reaching
ramifications,
influencing
industries
such
logistics,
technologies,
successful
use
renewable
These
advances
can
make
substantial
contribution
greener
future
encourage
development
circular
biobased
economy.
This
work
emphasizes
using
sophisticated
data‐driven
methodologies
synthesis,
usher
ecologically
friendly
energy
solutions.
breakthroughs
hold
key
more
environmentally
future.
ChemEngineering,
Год журнала:
2023,
Номер
7(3), С. 50 - 50
Опубликована: Май 28, 2023
Biochar
has
gained
attention
as
an
alternative
source
of
solid
energy
and
for
the
proper
disposal
agricultural
biomass
waste
(ABW).
Microwave-assisted
pyrolysis
(MAP)
is
a
promising
approach
production
biochar.
This
review
article
presents
beneficial
use
biochar
soil
fertilization,
machine
learning
(ML),
circular
bioeconomy,
technology
readiness
level.
The
techniques
helps
to
design,
predict,
optimize
process.
It
can
also
improve
accuracy
efficacy
process,
thereby
reducing
costs.
Furthermore,
amendment
be
attractive
option
farmers.
incorporation
into
been
shown
fertility,
water
retention,
crop
productivity.
lead
reduced
dependence
on
synthetic
fertilizers
increased
yields.
development
economy
potential
create
new
job
opportunities
increase
national
gross
domestic
product
(GDP).
Small-scale
enterprises
play
significant
role
in
distribution
biochar,
providing
value-added
products
helping
promote
sustainable
agriculture.
Environmental Chemistry Letters,
Год журнала:
2023,
Номер
21(6), С. 3159 - 3244
Опубликована: Авг. 17, 2023
Abstract
Traditional
fertilizers
are
highly
inefficient,
with
a
major
loss
of
nutrients
and
associated
pollution.
Alternatively,
biochar
loaded
phosphorous
is
sustainable
fertilizer
that
improves
soil
structure,
stores
carbon
in
soils,
provides
plant
the
long
run,
yet
most
biochars
not
optimal
because
mechanisms
ruling
properties
poorly
known.
This
issue
can
be
solved
by
recent
developments
machine
learning
computational
chemistry.
Here
we
review
phosphorus-loaded
emphasis
on
chemistry,
learning,
organic
acids,
drawbacks
classical
fertilizers,
production,
phosphorus
loading,
release.
Modeling
techniques
allow
for
deciphering
influence
individual
variables
biochar,
employing
various
supervised
models
tailored
to
different
types.
Computational
chemistry
knowledge
factors
control
binding,
e.g.,
type
compound,
constituents,
mineral
surfaces,
binding
motifs,
water,
solution
pH,
redox
potential.
Phosphorus
release
from
controlled
coexisting
anions,
adsorbent
dosage,
initial
concentration,
temperature.
Pyrolysis
temperatures
below
600
°C
enhance
functional
group
retention,
while
450
increase
plant-available
phosphorus.
Lower
pH
values
promote
release,
higher
hinder
it.
Physical
modifications,
such
as
increasing
surface
area
pore
volume,
maximize
adsorption
capacity
biochar.
Furthermore,
acid
affects
low
molecular
weight
acids
being
advantageous
utilization.
Lastly,
biochar-based
2–4
times
slower
than
conventional
fertilizers.
Abstract
The
utilization
of
biochar
derived
from
biomass
residue
to
enhance
anaerobic
digestion
(AD)
for
bioenergy
recovery
offers
a
sustainable
approach
advance
energy
and
mitigate
climate
change.
However,
conducting
comprehensive
research
on
the
optimal
conditions
AD
experiments
with
addition
poses
challenge
due
diverse
experimental
objectives.
Machine
learning
(ML)
has
demonstrated
its
effectiveness
in
addressing
this
issue.
Therefore,
it
is
essential
provide
an
overview
current
ML-optimized
processes
biochar-enhanced
order
facilitate
more
systematic
ML
tools.
This
review
comprehensively
examines
material
flow
preparation
impact
comprehension
reviewed
optimize
production
process
perspective.
Specifically,
summarizes
application
techniques,
based
artificial
intelligence,
predicting
yield
properties
residues,
as
well
their
AD.
Overall,
analysis
address
challenges
recovery.
In
future
research,
crucial
tackle
that
hinder
implementation
pilot-scale
reactors.
It
recommended
further
investigate
correlation
between
physicochemical
process.
Additionally,
enhancing
role
throughout
entire
holds
promise
achieving
economically
environmentally
optimized
efficiency.
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