Smart
models
for
cleaner
production
in
Industry
4.0:
A
Scoping
Review
A
b
s
t
r
a
c
t
.
-
A
s
c
o
p
i
n
g
r
e
v
i
e
w
i
s
p
r
e
s
e
n
t
e
d
t
o
k
n
o
w
t
h
e
n
e
w
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
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g
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n
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e
t
r
e
n
d
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i
n
d
e
v
e
l
o
p
i
n
g
e
n
v
i
r
o
n
m
e
n
t
a
l
p
r
o
p
o
s
a
l
s
f
o
r
i
n
d
u
s
t
r
y
4
.
0
.
A
r
e
v
i
e
w
o
f
1
3
a
c
a
d
e
m
i
c
p
a
p
e
r
s
p
u
b
l
i
s
h
e
d
i
n
h
i
g
h
-
i
m
p
a
c
t
j
o
u
r
n
a
l
s
was
carried
out
to
evaluate
the
environmental
proposals
of
expert
researchers
for
the
digitized
industry.
The
main
results
show
that
there
is
a
high
tendency
to
research
in
the
area
of
environmental
process
management,
waste
management,
and
treatments.
In
the
material
analyzed,
there
are
no
contributions
from
engineering
developments
or
software
developments
for
creating
new
and
better
materials
that
contribute
to
the
environment
and
cleaner
production
plans.
K
e
y
w
o
r
d
s
:
S
m
a
r
t
m
o
d
e
l
s
,
c
l
e
a
n
e
r
p
r
o
d
u
c
t
i
o
n
,
i
n
d
u
s
t
r
y
4
.
0
,
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
,
e
n
v
i
r
o
n
m
e
n
t
a
l
d
e
v
e
l
o
p
m
e
n
t
s
.
ISSN-E:
2737-6419
Athenea
Journal,
Vol.
4,
Issue
11,
(pp.
23-31)
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
F
r
a
n
y
e
l
i
t
S
u
á
r
e
z
-
C
a
r
r
e
ñ
o
http://orcid.org/0000-0002-8763-5513
franyelit.suarez@udla.edu.ec
Universidad
de
las
Américas,
Facultad
de
Ingeniería
y
Ciencias
Aplicadas,
Carrera
de
Ingeniería
Industrial
Quito-Ecuador
Resumen:
Se
presenta
una
revisión
del
alcance
para
conocer
las
nuevas
tendencias
en
inteligencia
artificial
en
el
desarrollo
de
propuestas
ambientales
para
la
industria
4.0.
Se
realizó
una
revisión
de
13
documentos
académicos
publicados
en
revistas
de
alto
impacto,
para
evaluar
las
propuestas
ambientales
de
investigadores
expertos
para
la
industria
digitalizada.
Los
principales
resultados
muestran
que
existe
una
alta
tendencia
hacia
la
investigación
en
torno
a
la
gestión
de
procesos
ambientales,
gestión
de
residuos
y
tratamientos.
Se
observó
que
en
el
material
analizado
no
hay
aportes
de
desarrollos
de
ingeniería
o
desarrollos
de
software
para
la
creación
de
nuevos
y
mejores
materiales
que
contribuyan
al
medio
ambiente
y
planes
de
producción
más
limpios.
Palabras
clave:
Modelos
inteligentes,
producción
más
limpia,
industria
4.0,
inteligencia
artificial,
desarrollos
ambientales.
Modelos
inteligentes
para
una
producción
más
limpia
en
la
Industria
4.0:
una
revisión
de
alcance
23
Received
(10/09/2022),
Accepted
(09/01/2023)
M
a
r
c
o
B
r
i
c
e
ñ
o
-
L
e
ó
n
https://orcid.org/0000-0001-6900-7460
marco.briceno@udla.edu.ec
Universidad
de
las
Américas,
Facultad
de
Ingeniería
y
Ciencias
Aplicadas,
Carrera
de
Ingeniería
Ambiental
Quito-Ecuador
https://doi.org/10.47460/athenea.v4i11.51
I. Introdución
Cleaner
production
is
a
form
of
industrial
production
that
reduces
the
use
of
non-renewable
resources
and
waste
and
the
emission
of
harmful
materials
such
as
greenhouse
gases
and
chemicals.
This
is
achieved
by
adopting
clean
technologies,
improving
existing
production
processes,
introducing
new,
cleaner,
and
more
efficient
production
processes,
and
eliminating
hazardous
materials
or
processes
[1].
It
can
help
significantly
reduce
production
costs,
improve
product
quality,
increase
productivity,
reduce
energy
consumption
and
operating
costs,
improve
occupational
safety
and
worker
health,
and
reduce
air
and
water
pollution.
These
b
e
n
e
f
i
t
s
c
a
n
b
e
p
a
r
t
i
c
u
l
a
r
l
y
i
m
p
o
r
t
a
n
t
f
o
r
l
o
c
a
l
b
u
s
i
n
e
s
s
e
s
a
n
d
s
m
a
l
l
a
n
d
m
e
d
i
u
m
-
s
i
z
e
d
e
n
t
e
r
p
r
i
s
e
s
(
S
M
E
s
)
that
may
need
more
resources
to
invest
in
cleaner
technologies
[2].
Cleaner
production
is
becoming
a
priority
for
many
companies
as
it
helps
them
improve
their
reputation
and
comply
with
environmental
regulations.
This,
in
turn,
allows
them
to
take
advantage
of
new
business
o
p
p
o
r
t
u
n
i
t
i
e
s
,
s
u
c
h
a
s
p
r
o
d
u
c
i
n
g
p
r
o
d
u
c
t
s
w
i
t
h
g
r
e
e
n
l
a
b
e
l
s
,
w
h
i
c
h
a
t
t
r
a
c
t
c
o
n
s
u
m
e
r
s
i
n
t
e
r
e
s
t
e
d
i
n
environmental
protection.
It
also
contributes
to
sustainable
economic
growth
once
improved
productivity
and
reduced
production
costs
[3].
This,
in
turn,
generates
employment,
increases
competitiveness,
and
improves
the
quality
of
life
of
the
population.
In
short,
cleaner
production
is
a
form
of
industrial
production
based
on
continuous
improvement
and
reduction
of
pollution,
energy
efficiency,
and
the
use
of
resources
to
achieve
sustainable
industrial
production.
Several
countries
are
promoting
cleaner
production
through
initiatives
such
as
the
Kyoto
Protocol,
the
Stockholm
Convention,
and
the
United
Nations
Environment
Programme
[3],
[4].
These
initiatives
set
goals
and
standards
to
reduce
pollution
and
improve
the
energy
efficiency
of
industrial
production
to
achieve
sustainable
development.
In
short,
cleaner
production
is
an
essential
tool
for
sustainability
and
sustainable
economic
development.
This
technique
helps
companies
improve
their
reputation,
reduce
production
costs,
increase
productivity,
and
contribute
to
sustainable
development.
In
addition,
governments
also
promote
cleaner
production
to
achieve
sustainable
development
globally.
This
paper
will
describe
the
elements
that
characterize
the
cleaner
production
process
in
digitized
industries
and
the
participation
of
artificial
intelligence
in
the
formulation
of
new
sustainable
proposals.
In
this
sense,
this
work
aims
to
show
the
contributions
of
artificial
intelligence
in
cleaner
production
processes
in
the
new
business
and
industrial
vision.
Therefore,
it
consists
of
four
main
sections,
the
introduction,
where
the
essential
elements
of
the
study
problem
have
been
described.
A
second
section,
where
the
theoretical
aspects
that
support
this
research
will
be
described,
then
the
methodology
and
the
results
obtained
are
reflected
to
expose
the
conclusions
finally.
II. Industry 4.0 and its participation in environmental improvements
Industry
4.0
focuses
on
increasing
efficiency
and
productivity
using
digital
and
connected
technologies
to
improve
production
processes.
This
is
achieved
by
connecting
production
systems,
automating
processes,
and
collecting
and
analyzing
data
[4].
This
also
allows
production
to
be
more
flexible
and
production
changes
faster.
In
addition,
Industry
4.0
also
cares
about
the
environment.
This
is
achieved
by
reducing
production
costs,
which
reduces
the
energy
and
resources
needed
to
produce
a
product.
It
is
also
achieved
through
using
renewable
energy
to
power
production
systems.
This
helps
reduce
carbon
emissions
and
other
greenhouse
gases,
minimizing
environmental
impact
[5],
[4].
Thus,
Industry
4.0
is
concerned
with
efficiency,
productivity,
and
the
environment.
This
makes
it
an
ideal
solution
for
companies
looking
for
more
sustainable
production.
In
addition,
this
technology
also
helps
to
improve
product
quality,
which
contributes
to
higher
customer
satisfaction.
24
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Athenea
Journal,
Vol.
4,
Issue
11,
(pp.
23-31)
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
This,
in
turn,
improves
the
image
of
the
company
and
its
financial
results.
In
summary,
Industry
4.0
is
a
solution
that
brings
benefits
both
in
terms
of
productivity
and
sustainability.
This
makes
it
an
ideal
solution
for
any
company
looking
to
improve
production
[6],
[7].
In
addition,
this
technology
also
helps
to
improve
the
quality
of
the
product,
which
contributes
to
greater
customer
satisfaction
and
improves
the
image
of
the
company
and
its
financial
results.
For
these
reasons,
Industry
4.0
is
an
ideal
solution
for
any
company
looking
to
improve
its
production.
In
conclusion,
Industry
4.0
offers
an
ideal
solution
for
companies
looking
to
improve
their
products
and
care
about
the
environment
[6],
[8].
This
technology
offers
benefits
in
terms
of
productivity
and
sustainability,
as
well
as
contributing
to
i
m
p
r
o
v
i
n
g
p
r
o
d
u
c
t
q
u
a
l
i
t
y
,
c
u
s
t
o
m
e
r
s
a
t
i
s
f
a
c
t
i
o
n
,
a
n
d
t
h
e
c
o
m
p
a
n
y
'
s
f
i
n
a
n
c
i
a
l
r
e
s
u
l
t
s
.
F
o
r
t
h
i
s
r
e
a
s
o
n
,
I
n
d
u
s
t
r
y
4.0
is
an
ideal
solution
for
all
those
companies
that
want
to
improve
their
products
responsibly.
A.
Industry
4.0
and
cleaner
production
Industry
4.0
is
a
new
industrial
revolution
that
combines
information
and
communication
technologies
(ICT)
and
automation
to
improve
productivity,
efficiency,
and
quality
[9].
This
is
achieved
by
optimizing
production
processes,
reducing
errors,
improving
customer
service,
and
reducing
costs.
This
industrial
revolution
also
allows
the
production
of
higher-quality
products
with
fewer
resources.
Cleaner
production
is
a
concept
related
to
Industry
4.0.
It
is
a
systemic
approach
to
industrial
production
that
improves
productivity
and
efficiency
by
reducing
waste,
risks,
and
environmental
costs
[5],
[10].
This
involves
a
design
approach
focusing
on
reducing
pollution
and
energy
use,
improving
production
processes,
and
using
more
efficient
materials
to
reduce
environmental
impact.
This
contributes
to
the
sustainability
of
industrial
production
[11].
F
i
n
a
l
l
y
,
I
n
d
u
s
t
r
y
4
.
0
a
n
d
c
l
e
a
n
e
r
p
r
o
d
u
c
t
i
o
n
a
r
e
d
i
r
e
c
t
l
y
r
e
l
a
t
e
d
.
I
n
d
u
s
t
r
y
4
.
0
e
n
a
b
l
e
s
g
r
e
a
t
e
r
e
f
f
i
c
i
e
n
c
y
a
n
d
p
r
o
d
u
c
t
i
v
i
t
y
b
y
a
u
t
o
m
a
t
i
n
g
p
r
o
d
u
c
t
i
o
n
p
r
o
c
e
s
s
e
s
,
w
h
i
l
e
c
l
e
a
n
e
r
p
r
o
d
u
c
t
i
o
n
f
o
c
u
s
e
s
o
n
r
e
d
u
c
i
n
g
w
a
s
t
e
,
r
i
s
k
s
,
and
environmental
costs
[12].
This
contributes
to
the
sustainability
of
industrial
production.
These
concepts
promote
industrial
innovation
and
produce
higher-quality
products
with
fewer
resources
[13].
This
helps
to
improve
the
competitiveness
of
companies,
reduce
costs
and
improve
the
efficiency
of
production
processes.
B.
Artificial
intelligence
in
the
industry
Artificial
intelligence
is
projected
to
play
an
essential
role
in
cleaner
production
through
process
automation
and
resource
optimization.
For
example,
AI
is
expected
to
help
reduce
energy
consumption
and
carbon
emissions
by
optimizing
energy
efficiency
in
factories
and
implementing
green
technologies
[14].
AI
is
also
expected
to
help
improve
waste
management
and
material
recovery.
In
addition,
AI
is
expected
to
assist
the
industry
in
decision-making
and
sustainable
planning.
Artificial
intelligence
[15]
plays
an
essential
role
in
generating
eco-sustainable
materials,
as
it
can
help
identify
new
ways
to
produce
materials
with
less
environmental
impact.
Some
examples
of
how
AI
is
used
in
the
generation
of
eco-sustainable
materials
include:
Material
design:
AI
can
help
design
new
materials
with
specific
properties,
such
as
higher
strength
or
lower
environmental
impact
[16].
Production
processes:
AI
can
help
optimize
production
processes
to
reduce
energy
consumption
and
waste
[2].
Recycling:
AI
can
help
improve
material
recycling
by
automated
material
identification
and
optimization
of
separation
processes
[15].
25
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6
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1
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Athenea
Journal,
Vol.
4,
Issue
11,
(pp.
23-31)
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
AI
is
expected
to
create
a
circular
economy
where
waste
is
turned
into
valuable
resources
through
advanced
technologies
[17].
A
circular
economy
process
with
AI
could
include
the
following
stages:
W
a
s
t
e
c
o
l
l
e
c
t
i
o
n
a
n
d
s
o
r
t
i
n
g
:
A
I
c
o
u
l
d
u
s
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
i
m
a
g
e
p
r
o
c
e
s
s
i
n
g
t
e
c
h
n
o
l
o
g
i
e
s
t
o
automatically
sort
waste
and
separate
it
by
type.
This
could
help
reduce
the
time
and
costs
associated
with
m
a
n
u
a
l
s
o
r
t
i
n
g
[
1
4
]
.
Optimization
of
recycling
processes:
AI
could
use
algorithms
to
optimize
recycling
processes
and
maximize
the
recovery
of
valuable
materials
[18].
For
example,
you
could
use
machine
learning
techniques
to
predict
the
best
method
for
each
type
of
waste
and
adjust
the
processing
parameters
accordingly.
Material
design:
AI
could
use
machine
learning
techniques
to
design
new
materials
from
recycled
waste
[11].
This
could
help
reduce
dependence
on
natural
resources
and
create
new
sustainable
products
and
solutions.
D
e
m
a
n
d
p
r
e
d
i
c
t
i
o
n
:
A
I
c
o
u
l
d
u
s
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
t
o
p
r
e
d
i
c
t
f
u
t
u
r
e
d
e
m
a
n
d
f
o
r
p
r
o
d
u
c
t
s
a
n
d
materials,
helping
the
industry
plan
production
and
resource
use
more
efficiently.
Monitoring
and
evaluation:
AI
could
use
data
analysis
techniques
to
monitor
and
evaluate
the
performance
of
circular
economy
processes
and
determine
areas
for
continuous
improvement.
Overall,
the
use
of
AI
in
the
circular
economy
could
help
improve
efficiency
and
sustainability
at
all
stages
of
the
product
lifecycle,
from
production
to
recycling
and
the
design
of
new
materials
[15],
[17],
[14],
[19].
III. Methodology
In
this
work,
a
non-in-depth
literature
review
was
carried
out
to
know
what
contributions
artificial
intelligence
offers
to
the
best
environment
within
industry
4.0
to
initiate
new
research.
Scientific
articles
from
primary
sources
were
evaluated,
showing
interest
in
formulating
new
proposals
that
help
the
best
climate
in
the
digital
sector.
Figure
1
presents
the
characteristics
of
the
references
made,
taking
into
account
the
sources
and
the
contributions
they
offer.
The
research
carried
out
is
simplified,
with
the
fundamental
purpose
of
evaluating
the
conceptual
knowledge,
theories,
or
characteristic
elements
of
artificial
intelligence
as
a
tool
for
the
generation
of
sustainable
environmental
proposals
in
industry
4.0.
To
this
end,
the
methodology
proposed
by
Kirtchenham
and
Okoli,
and
Schabram
on
desk
review,
which
in
practice
is
similar
to
the
PRISMA
[11]
(Preferred
Reporting
Items
for
Systematic
reviews
and
Meta-Anayses)
review
model,
was
considered.
The
proposed
method
consists
of
three
phases:
planning,
development,
and
reporting
of
the
systematic
review,
which
are
carried
out
following
eight
steps
for
its
execution:
determine
the
purpose
of
the
evaluation;
define
the
protocol
and
training;
Perform
literature
search;
screening
for
inclusion;
quality
assessment;
data
extraction;
study
synthesis
and
review
writing.
26
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Athenea
Journal,
V
o
l
.
2
7
,
N
ú
m
.
1
1
8
,
(
p
p
.
2
3
-
3
1
)
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
27
ISSN-E:
2737-6419
Athenea
Journal,
Vol.
27,
Núm.
118,
(pp.
23-31)
Fig. 1.
Methodology proposed by Kirtchenham and Okoli and Schabram [11]
Smart
AND
modelsAND
for
AND
cleaner
AND
production
AND
inAND
industry
4.0
(6
documents).
Artificial
AND
intelligence
AND
inAND
environmental
AND
proposals
(153
documents).
Smart
AND
models
AND
environment
(190
documents).
Phase
1:
In
this
phase,
the
research
questions
have
been
defined,
considering
the
relevance
and
timeliness
of
the
topic
of
study,
in
this
sense
the
questions
posed
are:
Q1:
How
does
artificial
intelligence
participate
in
cleaner
production
processes
in
Industry
4?
0?
Q2:
How
do
smart
models
look
in
environmental
proposals
for
Industry
4.
0?
Q3:
What
variables
have
been
considered
in
the
new
proposals
for
intelligent
models
for
clean
production?
The
search
process
consists
of
conducting
research
of
scientific
documents
that
allow
finding
studies
related
to
the
subject
of
study,
specifically
in
the
environmental
area
for
industry
4.0
and
the
contributions
of
artificial
intelligence
in
this
regard.
In
addition,
the
search
is
limited
to
the
most
recent
years,
from
2020
to
2023,
as
it
is
a
current
topic,
it
is
intended
to
analyze
the
new
proposals
for
intelligent
models
for
cleaner
production
in
the
digitized
industry.
The
Scopus
database
and
the
publications
of
the
Elsevier
publishing
house
that
were
open
access
were
used.
A
first
search
chain
was
defined
based
on
the
title
and
central
field
of
the
subject
studied,
with
these
elements
the
search
chain
is
redefined
considering
the
titles
found,
the
keywords,
the
referenced
studies,
to
finally
achieve
the
following
search
chains:
In
Table
1,
the
first
results
found
in
different
Scopus
journals
are
sampled,
only
in
the
year
2023.
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
28
ISSN-E:
2737-6419
Athenea
Journal,
V
o
l
.
2
7
,
N
ú
m
.
1
1
8
,
(
p
p
.
2
3
-
3
1
)
The
manuscripts
analyzed
were
classified
according
to
the
year
of
publication,
in
addition
to
the
journal
where
it
was
published,
the
corresponding
database,
the
number
of
citations,
the
methodology
used,
where
experimental
research,
industrial
case
studies,
and
bibliographic
reviews
had
priority.
The
primary
research
was
obtained
through
a
chain
of
queries
from
the
research
questions.
To
know
the
findings
of
the
articles
and
the
quality
of
the
topics,
four
criteria
were
applied:
population,
intervention,
comparison,
and
outcome
(PICO).
In
this
sense,
the
population
refers
to
published
studies.
The
intervention
is
related
to
artificial
intelligence
and
cleaner
production
in
the
new
proposals
of
industry
4.0.
The
comparison
refers
to
carefully
selected
studies
with
artificial
intelligence
in
environmental
proposals
and
the
type
of
research.
The
result
includes
published
studies
on
artificial
intelligence
in
new
environmental
developments
and
its
participation
in
Industry
4.0;
based
on
PICO,
five
new
questions
were
asked
to
ensure
the
quality
of
the
extracted
articles,
as
shown
in
Table
2.
Table 1
. Length as a function of time, (a) theoretical valúes, (b) experimental values.
Table 2.
Evaluation of the quality of the documents analyzed.
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
29
The
inclusion
and
exclusion
criteria
aim
to
find
significant
primary
documents
to
answer
the
research
questions
posed.
The
agreement
between
the
evaluators
was
resolved
by
applying
Cohen's
Kappa
coefficient
=
0.5
with
a
percentage
of
agreement
of
87.1%,
which
implies
a
moderate
agreement
among
the
evaluators.
The
inclusion
criteria
were
that
the
preliminary
research
is
associated
with
publications
in
journals
on
the
c
o
n
t
r
i
b
u
t
i
o
n
s
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
t
h
e
n
e
w
e
n
v
i
r
o
n
m
e
n
t
a
l
p
r
o
p
o
s
a
l
s
f
o
r
i
n
d
u
s
t
r
y
4
.
0
,
t
h
a
t
t
h
e
y
e
a
r
o
f
publication
is
recent,
between
2019
and
2022,
that
the
document
is
presented
in
a
high-impact
journal,
preferably
in
English.
While
the
exclusion
criteria
were
the
preliminary
study
is
limited,
literature
review
articles
and
similar
articles
from
different
sources.
I
V
.
R
e
s
u
l
t
s
T
h
e
d
o
c
u
m
e
n
t
s
a
n
a
l
y
z
e
d
t
o
s
h
o
w
t
h
a
t
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
o
f
f
e
r
s
a
n
e
s
s
e
n
t
i
a
l
c
o
n
t
r
i
b
u
t
i
o
n
t
o
t
h
e
g
e
n
e
r
a
t
i
o
n
of
new
environmental
proposals
for
industry
4.0.
In
this
sense,
the
review
showed
that
many
investigations
are
being
carried
out
around
the
opportunities
offered
by
artificial
intelligence
in
environmental
applications.
It
was
mainly
found
that
the
proposals
are
framed
in
process
and
quality
management,
noting
that
many
challenges
for
new
materials
and
engineering
developments
have
yet
to
be
substantially
defined,
just
as
no
proposals
for
software
developments
with
artificial
intelligence
that
contribute
to
new
research.
The
research
is
only
an
outline
to
open
future
work
since
the
development
of
new
materials
that
could
be
designed
using
artificial
intelligence
will
have
to
be
considered.
Some
examples
might
include:
Bioplastics:
AI
could
help
design
bioplastics
from
organic
waste,
such
as
agricultural
or
food
waste.
These
bioplastics
could
be
used
in
various
applications,
such
as
packaging
and
single-use
products.
That
the
study
of
the
following
materials
can
also
be
included
within
the
category
of
bioplastics:
Polyathide
polyester
(PLA):
This
bioplastic
is
produced
from
organic
substrates
such
as
sugar
cane
or
beets
and
is
one
of
the
most
common
bioplastics.
It
is
biodegradable
and
used
in
various
applications,
including
packaging
and
single-use
products.
Aliphatic
polyester
(PBAT):
This
bioplastic
is
produced
from
a
mixture
of
polylactic
acid
and
aliphatic
polyester
polyesters.
It
is
biodegradable
and
is
mainly
used
in
packaging
applications.
Polyactidic
acid
(PHA)
polyester:
This
bioplastic
is
biodegradable
from
microorganisms
metabolizing
carbohydrates.
It
is
used
in
various
applications,
including
packaging,
single-use
products,
and
toys.
Cellulose
polyester
(Cellulose):
This
bioplastic
is
biodegradable
from
wood
pulp
or
plant
cellulose.
It
is
used
in
stationery
and
packaging
applications.
Starch-based:
This
bioplastic
is
biodegradable
from
cereal
or
tuber
starch.
It
is
used
in
various
applications,
including
packaging,
single-use
products,
and
toys.
Composite
materials:
AI
could
help
design
new
composite
materials
from
recycled
and
natural
waste.
These
materials
could
have
improved
properties,
such
as
increased
strength
and
lower
environmental
impact.
Building
materials:
AI
could
help
design
new
building
materials
from
recycled
waste,
such
as
glass,
plastic,
and
metal.
These
materials
could
be
used
in
various
applications,
such
as
ceilings
and
flooring.
Hybrid
materials:
AI
could
help
design
new
hybrid
materials
that
combine
the
properties
of
different
existing
materials,
improving
their
characteristics
and
performance.
Superconducting
materials:
AI
could
help
design
new
superconducting
materials
with
improved
characteristics,
such
as
increased
energy
efficiency
and
transmission
capacity.
ISSN-E:
2737-6419
A
t
h
e
n
e
a
J
o
u
r
n
a
l
,
Vol.
27,
Núm.
118,
(pp.
23-31)
.
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
30
ISSN-E:
2737-6419
Athenea
Journal,
Vol.
27,
Núm.
118,
(pp.
23-31)
Overall,
using
AI
to
design
new
materials
could
help
create
more
sustainable
and
efficient
solutions,
reducing
environmental
impact
and
increasing
the
efficiency
of
production
processes.
T
h
e
a
n
s
w
e
r
s
t
o
t
h
e
r
e
s
e
a
r
c
h
q
u
e
s
t
i
o
n
s
r
e
s
o
l
v
e
d
f
r
o
m
t
h
e
a
n
a
l
y
s
i
s
t
o
t
h
e
s
t
u
d
i
e
s
c
o
l
l
e
c
t
e
d
i
n
t
h
e
l
i
t
e
r
a
t
u
r
e
review
are
presented
below.
Q1
How
does
artificial
intelligence
participate
in
cleaner
production
processes
in
Industry
4.
0?
The
documents
analyzed
show
that
the
most
significant
participation
is
being
presented
in
the
management
of
processes
and
products,
improvements
in
waste
treatment,
and
process
management
that
optimize
productivity.
Q2:
How
do
intelligent
models
look
in
environmental
proposals
for
Industry
4.
0?
The
documents
analyzed
show
that
intelligent
models
have
a
long
way
to
go,
and
their
development
and
participation
in
Industry
4.0
as
an
alternative
for
cleaner
production
is
still
incipient.
Q3:
What
variables
have
been
considered
in
the
new
proposals
for
intelligent
models
for
clean
production?
The
review
showed
that
the
main
variables
analyzed
are
occupational
health
and
safety
and
the
human-machine
relationship
for
production
improvement.
A
h
i
g
h
p
e
r
c
e
n
t
a
g
e
o
f
t
h
e
w
o
r
k
s
a
n
a
l
y
z
e
d
(
6
4
%
)
s
h
o
w
s
t
h
a
t
p
r
o
c
e
s
s
m
a
n
a
g
e
m
e
n
t
f
o
r
i
m
p
r
o
v
e
m
e
n
t
s
i
n
production,
occupational
safety,
and
health
within
environmental
contexts
are
the
most
studied
aspects
c
o
n
c
e
r
n
i
n
g
c
l
e
a
n
e
r
p
r
o
d
u
c
t
i
o
n
i
n
i
n
d
u
s
t
r
y
4
.
0
.
I
t
i
s
e
s
s
e
n
t
i
a
l
t
o
c
o
n
t
i
n
u
e
w
i
t
h
e
x
t
e
n
s
i
v
e
s
t
u
d
i
e
s
o
n
a
r
t
i
f
i
c
i
a
l
intelligence
and
its
contribution
to
environmental
improvements.
Hence
it
is
also
necessary
to
include
engineering
developments
that
directly
influence
products
and
services
for
cleaner
production.
Conclusions
The
review
is
fundamental
and
needs
to
include
an
in-depth
analysis
of
the
selected
articles.
However,
it
allowed
the
yielding
of
relevant
results
that
characterize
the
contributions
of
cleaner
production
in
industry
4.0.
The
analyzed
documents
reveal
a
more
significant
trend
of
studies
that
focus
on
managing
environmental
processes,
waste
treatment,
and
production
management
using
intelligent
tools
rather
than
developing
engineering
proposals
that
affect
the
composition
of
materials
and
promote
other
treatment
alternatives.
The
limitations
of
this
work
lie
in
the
fact
that
only
contributions
in
English
and
open
access
were
analyzed,
ruling
out
possible
contributions
from
other
countries,
which
could
include
the
development
of
intelligent
software
that
offers
new
materials
and
waste
reduction
models.
References
[1]
Y.
Aleman,
P.
Alarcon,
G.
Monzon,
and
K.
Pastor.,
«Education
priorities
in
the
wake
of
the
COVID-19
Pandemic,»
Minerva
Journal,
vol.
2,
nº
5,
pp.
5-12,
2021.
[2]
C.
A.
Ávila-Samaniego
and
M.
F.
Granda-Juca,
«Adopción
de
Tics
y
sus
Efectos
sobre
los
Procesos
en
las
Pymes.
Una
Revision
de
Literatura,»
Revista
Polo
del
Conocimiento,
pp.
1287-1303,
2022.
[3]
Banco
Mundial,
«Banco
Mundial,»
17
agosto
2022.
[En
línea].
Available:
https://www.bancomundial.org/es/home.
[4]
M.
Dini
and
G.
Stumpo,
Mipymes
en
América
Latina:
un
frágil
desempeño
y
nuevos
desafíos
para
las
políticas
de
fomento,
Santiago
de
Chile:
CEPAL,
2021.
[5]
J.
González-Mendoza,
J.
Sánchez-Molina
and
M.
Cárdenas-García,
«Pensamiento
estratégico
y
restructuración
industrial.,»
Desarrollo
Gerencial,
pp.
1-20,
2022.
[6]
O.
Flor,
H.
Tillerías,
B.
Mejía,
C.
Proaño,
M.
Rodriguez,
F.
Suarez
and
C.
Chimbo,
«Impact
of
Industrial
Automation
in
Employability,»
IEEE
Xplore,
pp.
157-162,
2022.
[7]
V.
Tripathi,
S.
Chattopadhyaya,
A.
Mukhopadhyay
and
S.
Sharma,
«A
Sustainable
Methodology
Using
Lean
and
Smart
Manufacturing
for
the
Cleaner
Production
of
Shop
Floor
Management
in
Industry
4.0,»
Mathematics,
vol.
10,
nº
347,
2022.
[8]
A.
Garzón-Posada,
M.
Jiménez-Ramírez
and
L.
Gómez-Campos,
«Redes
de
colaboración
empresarial
para
pymes,»
Revista
Facultad
de
CIencias
Económicas,
2022.
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review
31
ISSN-E:
2737-6419
Athenea
Journal,
Vol.
27,
Núm.
118,
(pp.
23-31)
[9]
J.
Lemos,
P.
Gaspar,
and
T.
Lima,
«Environmental
Risk
Assessment
and
Management
in
Industry,»
Machines,
vol.
10,
nº
702,
p.
103390,
2022.
[
1
0
]
J
.
R
a
m
í
r
e
z
,
V
.
L
ó
p
e
z
,
S
.
H
e
r
n
á
n
d
e
z
a
n
d
M
.
M
o
r
e
j
ó
n
,
«
L
E
A
N
S
I
X
S
I
G
M
A
E
I
N
D
U
S
T
R
I
A
4
.
0
,
U
N
A
R
E
V
I
S
I
Ó
N
DESDE
LA
ADMINISTRACIÓN
DE
OPERACIONES
PARA
LA
MEJORA
CONTINUA
DE
LAS
ORGANIZACIONES,»
UNESUM
Ciencias,
pp.
151-168,
2021.
[11]
G.
Tebes,
D.
Peppino,
P.
Becker
y
L.
Olsina,
«Especificación
del
Modelo
de
Proceso
para
una
Revisión
Sistemática
de
Literatura
.
Specifying
the
Process
Model
for
a
Systematic
Literature
Review,»
Universidad
Nacional
de
la
Pampa,
La
Pampa,
2019.
[
1
2
]
M
.
R
a
m
i
r
e
z
,
F
.
S
u
á
r
e
z
y
E
.
A
s
c
e
n
c
i
o
,
«
E
n
s
e
ñ
a
n
z
a
d
e
d
i
s
e
ñ
o
d
e
p
r
o
c
e
s
o
s
p
a
r
a
l
a
i
n
d
u
s
t
r
i
a
4
.
0
,
»
R
e
v
.
Univer.Cienc.Tecnologia,
pp.
129-136,
2021.
[13]
D.
Mendoza,
Y.
Rojano
y
E.
Salas,
«El
pensamiento
estratégico
como
herramienta
de
innovación
tecnológica
en
las
PYMES,»
Sotavento
MBA,
pp.
50-65,
2018.
[14]
J.
Mora,
«La
Evolución
de
la
Inteligencia
Artificial,»
2016.
[Online].
Available:
https://mundocontact.com/la-
evolucion-de-la-inteligencia-artificial/.
[15]
B.
Martín
del
Brío,
«Redes
Neuronales
y
Sistemas
Borrosos,»
Eds
Ra-Ma,
pp.
43-47,
2001.
[16]
A.
Álvarez,
G.
Rego,
J.
Leira,
A.
Gomis,
R.
Caramés
y
M.
Andrade,
«Innovación
Turística:
Perspectivas
Teóricas
y
Objetos
de
Estudio,»
ROTUR,
pp.
19-50,
2018.
[17]
D.
Montaño,
«Evaluation
digital
of
tolos
for
the
management
the
portafolio
educational,»
Minerva
Journal,
vol.
2,
nº
4,
pp.
55-61,
2021.
[18]
M.
Pérez,
«¿Podremos
crear
máquinas
verdaderamente
inteligentes?,»
2018.
[Online].
Available:
https://elpais.com/tecnologia/2018/03/27/actualidad/1522139782_730950.html..
[19]
J.
Sanz,
«Qué
es
la
Lingüística
Computacional,»
2018.
[Online].
Available:
http://www.full-on-
n
e
t
.
c
o
m
/
b
l
o
g
/
i
n
t
e
l
i
g
e
n
c
i
a
-
a
r
t
i
f
i
c
i
a
l
/
q
u
-
e
s
-
l
a
-
l
i
n
g
s
t
i
c
a
-
c
o
m
p
u
t
a
c
i
o
n
a
l
.
Suárez-Carreño
et al.
Smart models for cleaner production in Industry 4.0: A Scoping Review