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d
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(M
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we
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u
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ATLAB
to
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istu
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ize
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li
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ey
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C
o
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tr
o
l sy
s
tem
H
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in
ity
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Ma
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p
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S
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itiv
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C
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Kan
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Dep
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lectr
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n
g
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Gr
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Dee
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ac
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i
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1.
I
NT
RO
D
UCT
I
O
N
Su
g
ar
f
ac
to
r
ies
p
lay
an
in
s
tr
u
m
en
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le
as
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in
d
u
s
tr
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p
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tate
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ar
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o
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tr
ac
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g
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d
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s
in
g
s
u
cr
o
s
e
o
u
t
o
f
s
u
g
ar
ca
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e.
T
h
e
cr
u
s
h
in
g
m
ill
is
th
e
k
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u
n
it
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f
th
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g
ar
m
ill
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g
a
s
eq
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en
ce
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ic
ate
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tep
s
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ten
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ed
to
co
n
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t r
aw
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g
ar
ca
n
e
to
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ed
s
u
g
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cr
y
s
tals
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d
th
e
b
y
-
p
r
o
d
u
cts
wh
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is
r
e
p
r
esen
t
ed
th
r
o
u
g
h
Fig
u
r
e
1
.
T
h
e
p
r
o
ce
s
s
s
tar
ts
with
r
ec
eiv
in
g
an
d
p
r
e
p
ar
in
g
th
e
s
u
g
ar
ca
n
e
h
ar
v
ested
an
d
tr
an
s
p
o
r
ted
to
th
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s
u
g
a
r
m
ill.
T
h
en
it
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n
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an
d
s
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tak
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t
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im
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ities
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m
ak
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it
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o
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ex
t
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ac
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n
.
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w
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cr
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s
h
ed
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s
h
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tr
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p
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im
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y
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ice
w
h
ich
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etain
s
v
ar
io
u
s
im
p
u
r
ities
alo
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g
with
th
e
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A
s
eq
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e
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ce
o
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s
tep
s
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iz.
clar
if
icatio
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,
f
ilter
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g
,
e
v
ap
o
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atio
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,
ar
e
n
o
w
ex
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ted
f
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r
r
em
o
v
i
n
g
th
e
im
p
u
r
ities
an
d
co
n
ce
n
tr
atin
g
t
h
e
s
u
cr
o
s
e
co
n
ten
t
.
T
h
e
f
o
r
m
ed
s
y
r
u
p
is
th
en
s
u
b
jecte
d
to
cr
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s
tallizatio
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,
ce
n
tr
if
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g
in
g
,
an
d
d
r
y
i
n
g
f
o
r
y
ield
i
n
g
th
e
h
ig
h
-
q
u
ality
cr
y
s
tals
.
I
n
ad
d
itio
n
t
o
y
ield
in
g
s
u
g
ar
,
th
is
p
r
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ce
s
s
g
iv
es
also
s
ev
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al
b
y
-
p
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d
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lik
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m
o
l
ass
es,
b
ag
ass
e,
an
d
th
e
f
ilter
ca
k
e
h
a
v
in
g
a
p
p
lic
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n
s
in
v
a
r
io
u
s
in
d
u
s
tr
ies
lik
e
b
io
f
u
els,
f
o
o
d
p
r
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ce
s
s
in
g
,
an
d
an
im
al
f
ee
d
.
T
h
er
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o
r
e,
s
u
g
ar
f
ac
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ies
p
la
y
a
v
ital
r
o
le
n
o
t
o
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ly
in
p
r
o
d
u
cin
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th
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s
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g
ar
b
u
t
also
in
p
r
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d
u
ctio
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o
f
v
alu
ab
le
p
r
o
d
u
cts
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tili
zin
g
th
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b
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p
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d
u
cts,
g
iv
in
g
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s
ig
n
if
ican
t
co
n
tr
ib
u
tio
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to
th
e
p
r
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f
itab
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an
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tain
ab
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f
th
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in
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s
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[
1
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,
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2
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
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2
Ma
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a
n
d
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u
to
ma
ted
c
o
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tr
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l str
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teg
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r
s
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g
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cru
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in
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…
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Go
vin
d
S
in
g
h
Je
th
i
)
555
I
n
s
u
g
ar
m
ills
,
th
e
o
p
tim
izatio
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clu
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es
s
ev
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al
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p
r
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ctio
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im
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ca
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ills
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ican
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u
r
e
1
.
Su
g
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f
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s
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T
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p
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s
s
d
y
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am
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f
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ex
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ter
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ties
,
a
n
d
d
e
s
ig
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o
f
ex
tr
ac
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ip
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Fu
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t
h
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s
o
m
e
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h
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p
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y
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ical
f
ac
to
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s
lik
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s
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cr
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d
m
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is
tu
r
e
co
n
ten
t
o
f
s
u
g
ar
ca
n
e,
p
H
an
d
tem
p
er
atu
r
e
o
f
ex
tr
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tio
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m
ed
iu
m
,
ca
n
e
q
u
ality
v
ar
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n
s
,
an
d
wea
th
er
co
n
d
itio
n
s
.
also
af
f
ec
t
th
e
d
y
n
am
ics
o
f
th
is
p
r
o
ce
s
s
.
Mo
r
e
o
v
er
,
th
is
p
r
o
ce
s
s
in
clu
d
es n
o
n
lin
ea
r
itie
s
an
d
tim
e
-
v
ar
y
in
g
b
eh
av
io
r
.
T
r
ad
itio
n
al
c
o
n
tr
o
l
m
eth
o
d
s
i
n
s
u
g
ar
m
ills
lack
r
o
b
u
s
tn
ess
to
d
is
tu
r
b
an
ce
s
an
d
ar
e
in
ef
f
icien
t
in
h
an
d
lin
g
s
y
s
tem
n
o
n
lin
ea
r
ities
an
d
p
a
r
am
eter
u
n
ce
r
tain
ti
es.
I
t
n
ee
d
s
s
o
m
e
ad
v
a
n
ce
d
tech
n
iq
u
es
s
u
ch
as
m
o
d
el
p
r
ed
ictiv
e
co
n
tr
o
l
[
3
]
wh
ich
ca
n
d
ea
l
with
d
y
n
am
ic
an
d
u
n
ce
r
tain
b
eh
a
v
io
r
o
f
th
is
p
r
o
ce
s
s
g
u
ar
an
teein
g
ef
f
icien
t
o
p
er
atio
n
an
d
g
o
o
d
p
r
o
d
u
ct
q
u
ality
.
T
h
e
ju
ice
ex
tr
ac
tio
n
p
r
o
ce
s
s
is
an
im
p
o
r
ta
n
t step
in
th
e
s
u
g
ar
f
ac
t
o
r
y
a
n
d
is
n
ec
es
s
ar
y
f
o
r
s
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g
ar
p
r
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d
u
ctio
n
.
T
h
e
ef
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icien
cy
a
n
d
q
u
ality
co
n
tr
o
l
o
f
th
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p
r
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ce
s
s
n
o
t
o
n
ly
af
f
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ts
th
e
p
r
o
d
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c
ap
ac
ity
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f
th
e
f
ac
to
r
y
b
u
t
als
o
h
as
a
s
ig
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if
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t
im
p
ac
t
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co
s
ts
an
d
en
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g
y
co
n
s
u
m
p
tio
n
.
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r
ad
itio
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ally
,
th
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p
r
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s
s
was
co
n
tr
o
lled
m
an
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ally
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s
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to
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ati
ca
lly
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b
u
t
m
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tech
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lo
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y
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d
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v
a
n
ce
d
m
a
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ag
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en
t h
as h
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th
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s
s
to
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t a
n
d
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f
e
ctiv
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T
h
is
p
ap
er
ad
d
r
ess
es
th
e
o
p
ti
m
izatio
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an
d
au
t
o
m
atio
n
o
f
t
h
e
ju
ice
e
x
tr
ac
tio
n
p
r
o
ce
s
s
,
f
o
cu
s
in
g
o
n
m
ain
tain
in
g
d
esire
d
ca
n
e
lev
e
ls
in
th
e
Do
n
n
elly
ch
u
te
a
n
d
i
m
p
r
o
v
i
n
g
ju
ice
o
u
tp
u
t
q
u
ality
.
T
h
e
r
esear
ch
aim
s
to
d
ev
el
o
p
a
m
ath
em
atica
l
m
o
d
el
o
f
th
e
cr
u
s
h
in
g
u
n
it
an
d
ap
p
ly
a
d
v
an
ce
d
co
n
tr
o
l
s
tr
ateg
ies
an
d
i
n
tr
o
d
u
ce
s
s
ec
o
n
d
-
o
r
d
er
tr
a
n
s
f
er
f
u
n
ctio
n
m
o
d
el
o
f
th
e
ju
ice
e
x
tr
ac
ti
o
n
p
r
o
ce
s
s
an
d
im
p
lem
en
ts
H
-
in
f
in
ity
an
d
m
o
d
el
p
r
ed
ictiv
e
co
n
tr
o
l
(
MPC
)
co
n
tr
o
ller
s
u
s
in
g
MA
T
L
AB
.
T
h
es
e
s
tr
ateg
ies
ar
e
ev
alu
ated
b
ase
d
o
n
s
ettlin
g
tim
e,
o
v
er
s
h
o
o
t,
s
en
s
itiv
ity
to
p
ar
a
m
eter
v
ar
iatio
n
s
,
an
d
en
er
g
y
co
n
s
u
m
p
tio
n
.
T
h
e
m
ain
co
n
tr
i
b
u
tio
n
o
f
t
h
is
p
ap
er
is
g
iv
en
as:
i)
d
ev
elo
p
th
e
m
at
h
em
atica
l
m
o
d
el
o
f
th
e
ju
ice
ex
tr
ac
tio
n
p
r
o
ce
s
s
an
d
g
iv
e
th
e
tr
an
s
f
er
f
u
n
ctio
n
;
ii)
d
esig
n
co
n
tr
o
l
s
y
s
tem
f
o
r
ju
ice
ex
tr
ac
tio
n
p
r
o
ce
s
s
es
u
s
in
g
H
-
I
n
f
i
n
ity
an
d
MPC
tech
n
iq
u
e;
an
d
iii)
c
h
ec
k
an
d
co
m
p
ar
e
th
e
p
e
r
f
o
r
m
an
ce
o
f
H
-
I
n
f
in
ity
an
d
MPC
co
n
tr
o
l
tech
n
iq
u
es.
T
h
e
ef
f
ec
tiv
e
n
ess
o
f
th
is
co
n
tr
o
l
s
y
s
tem
is
m
ea
s
u
r
ed
b
y
lear
n
in
g
th
e
p
ar
am
ete
r
s
s
u
ch
as
s
ettlin
g
tim
e
an
d
p
ea
k
o
v
er
s
h
o
o
t.
T
h
is
p
ap
er
is
o
r
g
an
ized
in
to
f
iv
e
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ec
tio
n
s
i
n
clu
d
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g
th
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in
tr
o
d
u
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.
Se
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2
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o
5
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m
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esp
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tiv
ely
.
2.
RE
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AT
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o
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ca
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lev
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r
ee
in
p
u
ts
was
d
esig
n
ed
b
y
Misra
an
d
Kam
at
h
[
4
]
.
Ho
u
et
a
l.
[
5
]
s
tu
d
ied
au
to
m
atio
n
co
n
tr
o
l f
o
r
s
u
g
ar
ca
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cr
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s
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im
p
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ted
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tr
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ted
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n
tr
o
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tem
(
DC
S),
wh
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im
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r
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v
ed
c
r
u
s
h
in
g
p
er
f
o
r
m
an
ce
s
ig
n
if
ican
tly
.
L
u
ts
k
a
et
a
l.
[
6
]
p
r
o
p
o
s
ed
a
n
eu
r
al
n
etwo
r
k
-
b
ased
o
p
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s
u
p
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t
s
y
s
tem
t
o
p
r
ed
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c
h
an
g
es
in
r
eso
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r
ce
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f
f
icien
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in
s
u
g
ar
p
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d
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ctio
n
p
r
o
ce
s
s
es.
R
o
s
ar
i
et
a
l
.
[
7
]
d
e
v
elo
p
e
d
a
m
ac
h
in
e
lear
n
i
n
g
-
b
ased
f
o
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asti
n
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m
o
d
el
to
esti
m
ate
I
n
d
o
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s
wh
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s
u
g
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s
u
p
p
ly
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d
d
e
m
an
d
f
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p
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tab
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lity
an
d
im
p
r
o
v
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s
u
p
p
ly
ch
ain
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f
icien
cy
.
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[
8
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in
tr
o
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d
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h
o
d
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lo
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y
t
o
m
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p
r
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ts
.
W
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l.
[
9
]
d
esig
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m
ac
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v
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io
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ased
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[
1
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p
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en
ted
a
m
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(
ML
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f
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p
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ased
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p
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AC
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co
n
v
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ter
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a
l
.
[
1
1
]
ap
p
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m
ac
h
in
e
lear
n
in
g
to
f
o
r
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ast
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.
[
1
2
]
u
s
ed
a
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ath
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atica
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ap
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t
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latio
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[
1
3
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p
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ased
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1
4
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s
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co
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atin
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o
n
al
v
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s
.
L
u
ts
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l.
[
1
5
]
f
o
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m
u
late
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a
p
r
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m
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d
el
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ased
o
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1
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Hay
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[
1
7
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wo
r
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cr
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[
1
8
]
p
r
e
d
icted
s
y
r
u
p
c
o
n
ce
n
tr
ati
o
n
an
d
wate
r
e
v
ap
o
r
ate
d
as
o
u
tp
u
t
f
o
r
p
er
f
o
r
m
ac
e
im
p
r
o
v
em
e
n
t
o
f
m
u
ltis
tag
e
e
v
ap
o
r
ato
r
s
tatio
n
s
b
y
c
r
af
tin
g
a
n
e
u
r
al
n
etwo
r
k
.
T
h
e
s
tu
d
y
in
[
1
9
]
s
tu
d
ied
a
v
ar
iety
o
f
co
n
f
ig
u
r
atio
n
s
em
p
l
o
y
ed
in
c
ascad
ed
H
-
b
r
id
g
e
m
u
ltil
ev
el
co
n
v
er
ter
s
in
o
r
d
er
to
u
n
d
er
s
tan
d
d
iv
er
s
e
ap
p
licatio
n
s
o
f
s
lip
p
o
wer
r
ec
o
v
er
y
i
n
s
u
g
ar
m
ills
.
C
h
ar
les
et
a
l
.
[
2
0
]
ap
p
lied
o
p
t
im
ized
PID
an
d
MPC
tech
n
iq
u
es
f
o
r
lo
ad
f
r
eq
u
en
cy
c
o
n
tr
o
l
(
L
FC
)
in
p
o
wer
p
la
n
ts
.
T
h
e
r
esear
ch
em
p
h
asized
th
e
b
en
e
f
its
o
f
t
u
n
in
g
co
n
tr
o
ller
s
with
g
e
n
etic
alg
o
r
ith
m
s
,
with
p
o
ten
tial
ap
p
licatio
n
s
in
s
u
g
ar
in
d
u
s
tr
y
p
r
o
ce
s
s
co
n
tr
o
l.
Su
n
o
r
i
et
a
l.
[
2
1
]
co
n
d
u
ct
ed
s
ev
er
al
s
tu
d
ies:
m
ath
em
atica
l
m
o
d
elin
g
o
f
ca
n
e
ca
r
r
ier
d
y
n
am
ics
u
s
in
g
tim
e
-
d
o
m
ain
d
ata,
o
p
tim
izatio
n
o
f
s
u
g
ar
c
r
y
s
tallizer
co
n
tr
o
l
v
ia
p
ar
ticle
s
war
m
o
p
t
im
izatio
n
[
2
2
]
,
d
e
v
elo
p
m
e
n
t
o
f
Z
N,
I
MC,
an
d
L
QG
-
b
ased
c
o
n
tr
o
l
s
y
s
tem
s
f
o
r
th
e
cr
y
s
tallizatio
n
h
ea
t
e
x
ch
a
n
g
er
[
2
3
]
,
an
d
co
n
tr
o
l
d
esig
n
f
o
r
ca
n
e
lev
el
r
e
g
u
latio
n
u
s
in
g
PID
,
f
u
zz
y
lo
g
ic,
an
d
MPC
tech
n
iq
u
es [
2
4
]
.
Fu
r
th
er
,
Su
n
o
r
i
et
a
l.
[
2
5
]
r
e
d
u
ce
d
a
FOPDT
m
o
d
el
o
f
p
H
n
eu
tr
aliza
tio
n
u
s
in
g
Pad
é
ap
p
r
o
x
im
atio
n
an
d
s
q
u
ar
e
r
o
o
t
b
ala
n
ce
tr
u
n
c
atio
n
,
f
o
llo
wed
b
y
co
n
tr
o
l
s
y
s
tem
d
esig
n
u
s
in
g
m
u
ltip
le
tec
h
n
iq
u
es.
Ven
k
ates
h
et
a
l
.
[
2
6
]
p
e
r
f
o
r
m
ed
a
n
u
m
e
r
ical
s
tu
d
y
with
C
ap
u
to
f
r
ac
t
io
n
al
d
er
iv
ativ
es,
aim
ed
at
M
p
o
x
d
y
n
am
ics
b
u
t
ad
ap
tab
le
to
s
u
g
ar
p
r
o
ce
s
s
co
n
tr
o
l.
Fin
ally
,
Ma
n
iv
el
et
a
l
.
[
2
7
]
p
r
o
p
o
s
ed
a
d
if
f
er
e
n
tial
eq
u
atio
n
-
b
ased
m
o
d
el
f
o
r
an
aly
zin
g
s
m
allp
o
x
s
p
r
e
ad
,
wh
ich
ca
n
b
e
co
n
ce
p
tu
a
lly
ex
ten
d
ed
to
h
ea
lth
r
is
k
m
o
d
elin
g
in
s
u
g
ar
p
r
o
d
u
ctio
n
in
d
u
s
tr
ies.
A
s
u
m
m
ar
y
o
f
s
o
m
e
o
f
th
e
im
p
o
r
tan
t
m
o
d
els an
d
tec
h
n
iq
u
es is
g
iv
e
n
in
T
ab
le
1
.
T
ab
le
1
.
Su
m
m
a
r
y
o
f
m
o
d
els an
d
tech
n
i
q
u
es in
s
u
g
ar
in
d
u
s
t
r
y
S
.
N
o
.
M
o
d
e
l
s
a
n
d
t
e
c
h
n
i
q
u
e
s
R
e
f
No
K
e
y
p
o
i
n
t
s
1.
F
u
z
z
y
s
y
s
t
e
m
[
4
]
To
m
a
i
n
t
a
i
n
c
a
n
e
l
e
v
e
l
3
i
n
p
u
t
s
r
e
q
u
i
r
e
d
.
2.
C
o
n
t
r
o
l
sy
s
t
e
m
[
2
0
]
O
p
t
i
mi
z
e
d
P
I
D
a
n
d
M
P
C
u
s
i
n
g
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
ms
:
A
l
g
o
r
i
t
h
ms c
a
n
a
d
j
u
s
t
c
o
n
t
r
o
l
p
a
r
a
m
e
t
e
r
s t
o
i
m
p
r
o
v
e
st
a
b
i
l
i
t
y
[
9
]
n
o
v
e
l
m
a
c
h
i
n
e
v
i
si
o
n
-
b
a
se
d
q
u
a
l
i
t
y
c
o
n
t
r
o
l
s
y
st
e
m,
2
-
s
t
a
g
e
d
e
t
e
c
t
i
o
n
s
y
s
t
e
m.
G
r
e
a
t
e
r
t
h
a
n
9
0
p
e
r
c
e
n
t
a
c
c
u
r
a
c
y
i
n
r
e
a
l
-
t
i
me
d
e
t
e
c
t
i
o
n
3.
A
n
o
v
e
l
mu
l
t
i
l
e
v
e
l
i
n
v
e
r
t
e
r
[
1
0
]
D
r
i
v
e
f
o
r
s
u
g
a
r
c
a
n
e
s
h
r
e
d
d
i
n
g
i
n
t
h
e
s
u
g
a
r
i
n
d
u
s
t
r
y
4.
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
[
7
]
P
r
o
v
i
d
i
n
g
p
r
i
c
e
s
t
a
b
i
l
i
t
y
a
n
d
p
o
t
e
n
t
i
a
l
su
p
p
l
y
c
h
a
i
n
e
f
f
i
c
i
e
n
c
y
[
1
1
]
F
o
r
e
c
a
st
e
d
e
l
e
c
t
r
i
c
m
o
t
o
r
s
b
r
e
a
k
d
o
w
n
s b
y
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
met
h
o
d
s
[
1
5
]
Th
e
f
o
r
e
c
a
s
t
a
c
c
u
r
a
c
y
w
i
t
h
e
r
r
o
r
b
e
l
o
w
1
%
h
a
s
b
e
e
n
p
r
o
v
i
d
e
d
b
y
t
h
i
s
mo
d
e
l
5.
N
e
u
r
a
l
n
e
t
w
o
r
k
s
[
1
7
]
Th
i
s
me
t
h
o
d
h
a
s
a
n
a
b
i
l
i
t
y
o
f
l
a
b
e
l
l
i
n
g
t
h
e
c
r
y
st
a
l
s
w
i
t
h
h
i
g
h
a
c
c
u
r
a
c
y
a
n
d
r
e
mo
v
i
n
g
e
x
t
r
a
p
a
r
t
s
[
1
8
]
P
r
e
d
i
c
t
s
ma
i
n
p
e
r
f
o
r
ma
n
c
e
i
n
d
i
c
a
t
o
r
s,
su
c
h
a
s s
y
r
u
p
c
o
n
c
e
n
t
r
a
t
i
o
n
a
t
e
v
a
p
o
r
a
t
o
r
o
u
t
p
u
t
,
a
n
d
t
h
e
e
v
a
p
o
r
a
t
e
d
w
a
t
e
r
a
mo
u
n
t
a
t
t
h
e
st
a
t
i
o
n
[
6
]
P
r
e
d
i
c
t
i
n
g
c
h
a
n
g
e
s i
n
t
h
e
r
e
so
u
r
c
e
e
f
f
i
c
i
e
n
c
y
e
s
t
i
m
a
t
e
s
6.
M
a
t
h
e
ma
t
i
c
a
l
m
o
d
e
l
s
[
2
1
]
M
o
d
e
l
s
o
f
t
h
e
c
a
n
e
c
a
r
r
i
e
r
p
r
o
c
e
ss
o
f
a
su
g
a
r
mi
l
l
u
s
i
n
g
M
A
T
LA
B
o
n
b
a
s
i
s
o
f
t
i
m
e
d
o
m
a
i
n
d
a
t
a
[
2
4
]
D
e
v
e
l
o
p
e
d
c
o
n
t
r
o
l
s
y
st
e
ms,
b
a
se
d
o
n
t
h
e
P
I
D
,
f
u
z
z
y
l
o
g
i
c
,
a
n
d
M
P
C
,
f
o
r
r
e
g
u
l
a
t
i
n
g
t
h
e
c
a
n
e
l
e
v
e
l
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Ma
th
ema
tica
l m
o
d
ellin
g
a
n
d
a
u
to
ma
ted
c
o
n
tr
o
l str
a
teg
ies fo
r
s
u
g
a
r
ca
n
e
cru
s
h
in
g
…
(
Go
vin
d
S
in
g
h
Je
th
i
)
557
T
h
e
p
r
o
ce
s
s
o
f
ex
tr
ac
tin
g
s
u
g
ar
ca
n
e
ju
ice
is
a
co
m
p
lex
,
m
u
lti
-
v
ar
iab
le
s
y
s
tem
wh
er
e
s
ev
er
al
p
r
o
ce
s
s
es
ar
e
in
ter
co
n
n
ec
ted
.
Alth
o
u
g
h
tr
ad
itio
n
al
co
n
tr
o
l
t
ec
h
n
iq
u
es
h
av
e
b
ee
n
u
s
ed
in
th
is
p
r
o
ce
s
s
,
s
o
m
e
r
esear
ch
g
ap
s
s
till
ex
is
t,
h
ig
h
li
g
h
tin
g
th
e
n
ee
d
f
o
r
f
u
r
th
e
r
r
es
ea
r
ch
in
th
is
f
ield
.
T
h
ese
g
ap
s
ar
e
lis
ted
b
elo
w:
i)
Use
o
f
tr
ad
itio
n
al
co
n
tr
o
l
eq
u
i
p
m
en
t
:
Ma
n
y
s
u
g
ar
f
ac
to
r
ies
s
till
u
s
e
tr
ad
itio
n
al
co
n
tr
o
l
eq
u
i
p
m
en
t
s
u
ch
as
PID
co
n
tr
o
l.
Alth
o
u
g
h
th
ese
t
ec
h
n
iq
u
es
a
r
e
s
im
p
le
a
n
d
ea
s
y
to
u
s
e,
th
e
y
ar
e
n
o
t
p
o
wer
f
u
l
an
d
wo
r
k
well
in
co
m
p
lex
p
r
o
ce
s
s
es
s
u
ch
as
ju
ice
ex
tr
ac
tio
n
.
T
h
is
is
a
b
ig
g
ap
th
at
r
eq
u
ir
es
ad
v
a
n
ce
d
te
ch
n
o
lo
g
y
to
b
e
u
s
ed
.
ii)
Dis
tu
r
b
an
ce
s
:
I
n
ter
n
al
d
is
tu
r
b
an
ce
s
s
u
ch
as
ch
an
g
es
in
s
u
g
ar
q
u
ality
o
r
e
x
ter
n
al
d
is
tu
r
b
an
ce
s
s
u
ch
as
tem
p
er
atu
r
e
ch
a
n
g
es
d
u
r
in
g
th
e
ju
ice
ex
tr
ac
tio
n
p
r
o
ce
s
s
m
ay
af
f
ec
t
th
e
p
r
o
ce
d
u
r
es.
T
r
a
d
itio
n
al
m
eth
o
d
s
ca
n
n
o
t
ef
f
ec
tiv
ely
a
d
d
r
ess
th
ese
ef
f
ec
ts
,
in
d
icatin
g
a
g
ap
in
r
esear
ch
.
T
h
is
is
wh
y
it
is
n
ec
ess
ar
y
t
o
in
v
esti
g
ate
a
tech
n
o
lo
g
y
s
u
ch
as H
-
I
n
f
in
ity
th
at
ca
n
m
ain
tain
g
o
o
d
p
er
f
o
r
m
a
n
ce
d
esp
ite
s
u
c
h
o
b
s
tacle
s
.
iii)
Simu
ltan
eo
u
s
co
n
tr
o
l
r
e
q
u
ir
ed
:
J
u
ice
ex
tr
ac
tio
n
is
a
d
y
n
am
ic
p
r
o
ce
s
s
th
at
r
eq
u
ir
es
s
im
u
ltan
eo
u
s
co
n
tr
o
l
o
f
m
an
y
d
if
f
er
e
n
t
f
ac
to
r
s
.
So
,
a
m
o
d
el
n
ee
d
s
b
e
d
e
v
elo
p
ed
to
u
n
d
er
s
tan
d
th
e
f
u
tu
r
e
b
e
h
av
io
r
o
f
th
is
p
r
o
ce
s
s
.
T
o
b
r
id
g
e
th
is
g
ap
,
h
er
e
a
m
o
d
el
h
as b
ee
n
d
e
v
elo
p
ed
th
at
ca
n
p
r
ed
ict
th
e
f
u
tu
r
e
b
eh
av
i
o
r
o
f
th
e
s
y
s
tem
.
iv
)
Op
tim
izatio
n
n
ee
d
ed
:
Alth
o
u
g
h
th
e
tr
ad
itio
n
al
co
n
tr
o
l
m
eth
o
d
is
b
ased
o
n
f
ee
d
b
ac
k
,
s
u
g
ar
f
ac
to
r
ies
h
ar
d
ly
u
s
e
tech
n
o
lo
g
ies s
u
ch
as M
P
C
.
T
h
is
m
eth
o
d
ca
n
p
r
ed
ict
f
u
tu
r
e
d
eter
io
r
atio
n
an
d
co
n
tr
o
l th
e
s
y
s
tem
ac
co
r
d
in
g
t
o
f
u
tu
r
e
p
r
o
b
ab
ilit
y
.
T
o
ad
d
r
ess
th
is
g
ap
in
r
esear
ch
,
it
is
aim
ed
to
o
p
tim
ize
t
h
e
s
y
s
tem
u
s
in
g
MPC
tech
n
iq
u
e.
v)
C
o
s
t
o
p
tim
izatio
n
:
Op
tim
izatio
n
o
f
en
er
g
y
c
o
n
s
u
m
p
tio
n
a
n
d
co
s
ts
is
im
p
o
r
tan
t
i
n
th
e
ju
ice
ex
tr
ac
tio
n
p
r
o
ce
s
s
,
b
u
t
r
esear
ch
in
th
is
ar
ea
is
o
n
ly
lim
ited
.
T
r
ad
itio
n
al
co
n
tr
o
l
s
y
s
tem
s
ca
n
n
o
t
o
p
tim
ize
en
er
g
y
co
n
s
u
m
p
tio
n
,
s
o
ad
v
a
n
ce
d
c
o
n
tr
o
l sy
s
tem
s
ar
e
n
ee
d
ed
to
im
p
r
o
v
e
en
e
r
g
y
ef
f
icien
cy
.
3.
M
E
T
H
O
D
I
n
th
is
p
ap
e
r
,
we
aim
t
o
g
iv
e
a
co
n
cise
v
iew
o
f
av
ailab
le
m
o
d
els
an
d
tech
n
iq
u
es
u
s
ed
in
th
e
s
u
g
ar
in
d
u
s
tr
y
.
Usi
n
g
id
en
tifie
d
k
ey
wo
r
d
s
s
ea
r
ch
ed
th
r
o
u
g
h
v
ar
io
u
s
d
atab
ases
to
f
in
d
r
elev
an
t
p
ap
er
s
an
d
ar
ticles
f
o
r
liter
atu
r
e
s
u
r
v
ey
.
T
h
en
f
o
ll
o
wed
th
e
f
lo
wc
h
ar
t g
iv
e
n
in
F
ig
u
r
e
2
.
Fig
u
r
e
2
.
W
o
r
k
f
lo
w
o
f
m
eth
o
d
o
lo
g
y
3
.
1
.
M
a
t
hema
t
ica
l
m
o
dellin
g
T
h
e
s
u
g
a
r
ca
n
e
cr
u
s
h
in
g
u
n
it
o
f
a
s
u
g
ar
m
ill
is
th
e
s
u
b
ject
o
f
p
r
esen
t
r
esear
ch
wo
r
k
.
I
t
is
u
s
ed
to
ex
tr
ac
t
th
e
ju
ice
f
r
o
m
s
u
g
ar
ca
n
e
f
ib
er
.
T
h
e
d
u
m
p
ed
ca
n
e
f
i
b
er
o
n
to
th
e
Do
n
n
ely
ch
u
te
is
s
en
t
to
th
e
r
o
ller
s
wh
ich
cr
u
s
h
it.
T
h
e
ca
n
e
le
v
el
in
th
e
Do
n
n
ely
ch
u
te
m
u
s
t
b
e
m
ain
tain
ed
at
a
p
r
e
-
d
ef
in
ed
v
alu
e
.
I
t
s
h
o
u
l
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
15
,
No
.
2
,
J
u
n
e
20
26
:
554
-
564
558
n
eith
er
g
o
u
p
n
o
r
d
o
wn
o
f
th
is
v
alu
e
[
4
]
.
T
h
e
am
o
u
n
t
o
f
ex
t
r
ac
ted
ju
ice
in
a
g
iv
en
tim
e
p
r
i
m
ar
ily
d
ep
en
d
s
o
n
two
f
ac
to
r
s
,
th
e
ca
n
e
lev
el
in
t
h
e
Do
n
n
ely
ch
u
te
a
n
d
th
e
an
g
u
lar
s
p
ee
d
o
f
r
o
ller
s
.
T
h
e
r
ate
o
f
c
h
an
g
e
i
n
ex
tr
ac
te
d
ju
ice
q
u
a
n
tity
,
is
r
ep
r
esen
te
d
b
y
(
1
)
.
Her
e
(
)
is
th
e
ca
n
e
lev
el
an
d
(
)
is
th
e
an
g
u
lar
v
elo
city
o
f
r
o
ller
s
an
d
is
p
r
o
p
o
r
tio
n
ality
c
o
n
s
tan
t.
T
h
e
s
u
p
p
lied
en
er
g
y
to
r
o
ller
s
is
d
ir
ec
tly
p
r
o
p
o
r
tio
n
al
to
s
q
u
a
r
e
o
f
an
g
u
lar
v
elo
city
o
f
r
o
ller
s
i
.
e.
,
r
ep
r
esen
ted
b
y
(
2
)
.
=
(
)
(
)
(
1
)
E
(
t
)
α
2
(
)
(
2
)
No
w
tak
in
g
th
e
i
n
er
tia
an
d
d
a
m
p
in
g
ef
f
ec
ts
in
to
co
n
s
id
er
ati
o
n
,
th
e
s
y
s
tem
d
y
n
am
ics ca
n
b
e
ex
p
r
ess
ed
as
(
3
)
.
2
(
)
2
+
(
)
=
(
)
(
3
)
Her
e
an
d
ar
e
m
o
m
e
n
t
o
f
in
er
tia
an
d
d
am
p
in
g
c
o
ef
f
icien
t
r
esp
ec
tiv
ely
.
T
ak
in
g
L
ap
lace
tr
an
s
f
o
r
m
,
t
h
e
s
ec
o
n
d
o
r
d
er
tr
a
n
s
f
er
f
u
n
ctio
n
f
o
r
m
o
f
ju
ice
ex
t
r
ac
tio
n
p
r
o
ce
s
s
is
o
b
tain
ed
as p
r
esen
ted
in
(
1
)
.
(
)
=
2
+
+
(
4
)
Her
e
,
=
2
√
.
L
et’
s
tak
e
M
=
0
.
5
k
g
.
m
2
,
=
0
.
25
,
K
=
0
.
0
8
m
/s
.
r
ad
,
b
ased
o
n
av
ailab
le
s
ec
o
n
d
ar
y
d
ata
,
th
ese
v
alu
es h
av
e
b
ee
n
ta
k
en
.
T
h
is
will g
iv
e
as in
(
5
)
.
(
)
=
0
.
08
0
.
5
2
+
0
.
1
+
0
.
08
(
5
)
I
t
r
ep
r
esen
ts
a
SISO
m
o
d
el
o
f
s
u
g
ar
ca
n
e
cr
u
s
h
in
g
p
r
o
ce
s
s
in
a
s
u
g
ar
m
ill
with
ca
n
e
lev
el
as
in
p
u
t
an
d
th
e
ex
tr
ac
ted
ju
ice
q
u
an
tity
as th
e
o
u
tp
u
t.
3
.
2
.
Co
ntr
o
l sy
s
t
em
des
ig
n
T
h
e
H∞
(
H
-
in
f
in
ity
)
co
n
tr
o
l
s
tr
ateg
y
is
a
r
o
b
u
s
t
m
eth
o
d
wh
ich
is
lar
g
ely
ad
o
p
ted
to
d
ev
elo
p
co
n
tr
o
ller
s
h
av
i
n
g
ca
p
ab
ilit
y
to
d
ea
l
ef
f
e
ctiv
ely
with
d
i
s
tu
r
b
an
ce
s
,
u
n
ce
r
tain
ties
,
an
d
o
th
er
ch
allen
g
es
in
h
er
en
t
i
n
th
e
d
y
n
am
ic
s
y
s
tem
s
.
I
t
ca
n
well
h
a
n
d
le
t
h
e
m
u
l
ti
-
v
ar
iab
les
in
v
o
lv
ed
in
MI
M
O
s
y
s
tem
s
m
ak
in
g
it
well
s
u
ited
to
d
esig
n
co
n
tr
o
l
s
y
s
tem
s
with
co
o
r
d
in
ated
co
n
tr
o
l
s
tr
ateg
y
f
o
r
MI
MO
p
r
o
ce
s
s
es.
T
h
e
s
tate
-
s
p
ac
e
m
o
d
el
o
f
H∞ c
o
n
tr
o
ller
,
d
ev
el
o
p
ed
in
MA
T
L
AB
,
f
o
r
th
e
p
lan
t r
ep
r
es
en
ted
b
y
(
5
)
.
=
[
−
0
.
2
−
0
.
32
0
.
5
−
1
.
42
∗
10
−
16
]
;
=
[
−
8
.
778
∗
10
−
33
2
.
225
∗
10
−
16
]
=
[
−
6
.
39
∗
10
−
32
−
4
.
004
∗
10
−
49
]
;
=
[
0
]
An
o
th
er
ap
p
r
o
ac
h
o
f
co
n
tr
o
lle
r
d
esig
n
th
at
will
b
e
im
p
lem
e
n
ted
n
o
w
is
th
e
MPC
.
I
t
is
a
n
ad
v
a
n
ce
d
d
ig
ital
co
n
tr
o
l
tech
n
iq
u
e
wh
ich
u
s
es
s
y
s
tem
’
s
d
y
n
am
ic
m
o
d
el
f
o
r
an
ticip
atin
g
its
f
u
tu
r
e
b
eh
a
v
io
r
an
d
o
p
tim
izin
g
co
n
tr
o
l
ac
tio
n
s
f
o
r
a
f
in
e
tim
e
s
p
an
as
s
h
o
wn
in
Fig
u
r
e
3
[
1
7
]
.
T
h
is
tech
n
iq
u
e
is
v
er
y
p
o
p
u
lar
in
s
ev
er
al
in
d
u
s
tr
ial
a
p
p
licatio
n
s
b
ec
au
s
e
o
f
its
ab
ilit
y
o
f
h
a
n
d
lin
g
co
n
s
tr
ain
ts
,
u
n
ce
r
tai
n
ties
,
an
d
m
u
ltiv
ar
ia
b
le
in
ter
ac
tio
n
s
ef
f
ec
tiv
ely
.
MPC
im
p
lem
en
tatio
n
b
e
g
in
s
with
f
o
r
m
u
latio
n
o
f
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
d
eter
m
in
i
n
g
th
e
co
n
tr
o
l
in
p
u
ts
m
in
im
izin
g
th
is
o
b
jectiv
e
f
u
n
ctio
n
tak
in
g
v
ar
io
u
s
p
r
o
ce
s
s
co
n
s
tr
ain
ts
in
to
co
n
s
id
er
atio
n
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
u
s
u
all
y
in
v
o
lv
es
ter
m
s
r
elate
d
to
c
o
n
tr
o
l
ef
f
o
r
t,
co
n
s
tr
ain
t
v
io
latio
n
s
,
an
d
s
et
-
p
o
in
t
tr
ac
k
in
g
.
MPC
s
o
lv
es
th
is
o
p
t
im
izatio
n
p
r
o
b
lem
to
c
o
m
p
u
t
e
o
p
tim
al
c
o
n
tr
o
l
ac
tio
n
at
ev
er
y
tim
e
s
tep
in
t
h
e
lig
h
t o
f
co
n
s
tr
ain
ts
,
g
u
ar
a
n
teein
g
r
eliab
le
an
d
ef
f
icie
n
t o
p
er
at
io
n
o
f
t
h
e
p
r
o
ce
s
s
.
No
w
th
e
lin
ea
r
d
y
n
a
m
ic
m
o
d
e
l u
s
ed
b
y
th
e
MPC
is
as
(
6
)
.
Her
e,
Δ
+
1
is
th
e
ch
an
g
e
in
th
e
e
x
tr
a
cted
ju
ice
q
u
an
tity
at
tim
e
s
tep
(
n
+
1
)
,
Δ
is
th
e
ch
an
g
e
in
t
h
e
lev
el
o
f
ca
n
e
at
t
h
e
tim
e
s
tep
n
,
an
d
Δ
is
th
e
ch
an
g
e
in
co
n
tr
o
l e
f
f
o
r
t a
t tim
e
s
tep
n
an
d
,
,
ar
e
th
e
m
o
d
el
p
a
r
am
eter
s
.
Δ
+
1
=
Δ
+
Δ
−
1
+
Δ
(
6
)
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
to
b
e
m
in
im
ized
b
y
th
e
MPC
is
as
(
7
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Ma
th
ema
tica
l m
o
d
ellin
g
a
n
d
a
u
to
ma
ted
c
o
n
tr
o
l str
a
teg
ies fo
r
s
u
g
a
r
ca
n
e
cru
s
h
in
g
…
(
Go
vin
d
S
in
g
h
Je
th
i
)
559
=
∑
{
(
Δ
−
Δ
L
)
2
+
(
Δ
)
2
−
1
=
0
}
(
7
)
Her
e,
th
e
f
ir
s
t
ter
m
is
p
en
alizi
n
g
th
e
d
e
v
iatio
n
f
r
o
m
r
ef
e
r
en
ce
ca
n
e
le
v
el.
T
h
e
s
ec
o
n
d
ter
m
is
p
en
alizin
g
th
e
co
n
tr
o
l e
f
f
o
r
t.
Fig
u
r
e
3
.
Stru
ctu
r
e
o
f
MPC
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
H∞
co
n
tr
o
ller
an
d
p
r
o
p
o
s
ed
co
n
tr
o
ller
a
r
e
d
esig
n
e
d
in
MA
T
L
AB
/Si
m
u
lin
k
s
o
f
twar
e
u
s
in
g
co
n
tr
o
l
p
ar
a
m
eter
s
wh
ich
ar
e
tab
u
lated
in
T
ab
le
2
.
Fig
u
r
e
s
4
an
d
5
s
h
o
ws
th
e
s
et
-
p
o
in
t
r
esp
o
n
s
e
o
f
H∞
co
n
tr
o
ller
a
n
d
MPC
co
n
tr
o
ller
an
d
it
is
o
b
s
er
v
e
d
th
at
s
etp
o
in
t
r
esp
o
n
s
e
o
f
MPC
co
n
tr
o
ller
i
s
m
u
ch
b
etter
th
a
n
H∞
co
n
tr
o
ller
as
its
s
ettlin
g
t
im
e
is
m
u
ch
s
m
aller
,
an
d
th
e
r
e
is
a
co
n
s
id
er
a
b
le
r
e
d
u
ctio
n
in
p
ea
k
o
v
er
s
h
o
o
t
also
.
T
h
e
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
o
f
b
o
th
c
o
n
tr
o
l
s
y
s
tem
s
is
p
r
esen
ted
in
T
ab
le
3
.
Fig
u
r
e
6
s
h
o
ws
th
e
co
n
tr
o
ller
ef
f
o
r
t
o
f
MPC
co
n
tr
o
ller
.
I
t
en
ab
les
th
e
co
n
tr
o
ller
to
r
ec
o
n
cile
th
e
in
ten
d
e
d
co
n
tr
o
l
aim
with
th
e
ac
tu
atio
n
co
s
t,
s
o
av
er
tin
g
ex
ce
s
s
iv
e
co
n
tr
o
l
ac
tio
n
s
th
at
m
ay
r
esu
lt
in
wea
r
,
en
er
g
y
in
ef
f
icien
c
y
,
o
r
in
s
tab
ilit
y
.
T
ab
le
2
.
Par
am
eter
s
o
f
MPC
P
a
r
a
me
t
e
r
V
a
l
u
e
S
a
mp
l
e
t
i
m
e
(s
)
0
.
4
P
r
e
d
i
c
t
i
o
n
h
o
r
i
z
o
n
10
C
o
n
t
r
o
l
h
o
r
i
z
o
n
1
Fig
u
r
e
4
.
Set
-
p
o
in
t r
esp
o
n
s
e
o
f
H
-
in
f
in
ity
c
o
n
tr
o
ller
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
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t J Ap
p
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E
n
g
,
Vo
l.
15
,
No
.
2
,
J
u
n
e
20
26
:
554
-
564
560
Fig
u
r
e
5
.
Set
-
p
o
in
t r
esp
o
n
s
e
o
f
MPC
Fig
u
r
e
6
.
C
o
n
tr
o
ller
ef
f
o
r
t o
f
MPC
T
ab
le
3
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
C
o
n
t
r
o
l
l
e
r
S
e
t
t
l
i
n
g
t
i
me
(
S
e
c
.
)
P
e
a
k
o
v
e
r
s
h
o
o
t
(
%)
M
P
C
1
1
.
3
7
7
.
4
%
H
-
I
n
f
i
n
i
t
y
63
8
8
%
4
.
1
.
Sens
t
iv
it
y
a
na
ly
s
is
T
h
e
MPC
an
d
H
-
in
f
in
ity
b
ased
co
n
tr
o
ller
s
wer
e
ev
al
u
ated
b
y
ex
am
in
i
n
g
h
o
w
p
e
r
ce
n
tag
e
ch
an
g
es
in
v
ar
io
u
s
k
e
y
p
ar
am
eter
s
s
u
ch
a
s
ca
n
e
lev
el,
d
r
u
m
s
p
ee
d
,
d
am
p
in
g
c
o
ef
f
icien
t (
in
d
icatin
g
en
er
g
y
d
is
tr
ib
u
tio
n
i
n
th
e
s
y
s
tem
)
a
n
d
m
o
m
e
n
t
o
f
i
n
er
tia
s
en
s
itiv
ity
af
f
ec
ted
v
ar
i
o
u
s
p
er
f
o
r
m
a
n
ce
m
etr
ics.
Valu
es
s
u
ch
as
s
ettlin
g
tim
e,
r
is
e
tim
e,
an
d
m
ax
im
u
m
ar
e
f
ix
e
d
an
d
er
r
o
r
is
f
ix
ed
.
T
h
e
r
esu
lts
ar
e
s
h
o
w
n
in
Fig
u
r
e
7
.
As
ca
n
b
e
s
ee
n
f
r
o
m
th
e
f
ig
u
r
e,
th
e
lar
g
er
th
e
v
ar
iatio
n
b
etwe
en
th
e
two
co
n
tr
o
ller
s
,
th
e
lo
n
g
er
th
e
p
la
n
n
i
n
g
tim
e.
Ho
wev
er
,
th
e
MPC
co
n
tr
o
ller
co
n
s
is
ten
tly
s
h
o
ws
f
aster
ac
ce
ler
atio
n
c
o
m
p
ar
ed
to
th
e
H
-
in
f
in
ity
co
n
tr
o
ller
.
T
h
is
s
h
o
ws
th
at
th
e
MPC
is
b
etter
at
s
tab
ilizin
g
q
u
ick
l
y
e
v
en
wh
en
th
e
r
e
is
ch
a
n
g
e.
T
h
e
o
v
e
r
s
h
o
o
t
is
g
en
er
ally
s
lig
h
tly
h
ig
h
er
f
o
r
th
e
H
-
in
f
in
ity
c
o
n
tr
o
ller
co
m
p
a
r
ed
t
o
th
e
MPC
,
in
d
icatin
g
th
at
th
e
MPC
h
as
a
b
etter
b
eh
av
i
o
r
.
T
h
e
r
is
e
tim
e
r
em
ain
e
d
c
o
n
s
tan
t
d
u
r
in
g
th
e
tr
an
s
itio
n
p
er
io
d
,
b
u
t
th
e
MPC
s
aw
a
s
lig
h
tly
f
ast
er
r
is
e
tim
e
in
m
o
s
t
ca
s
es,
in
d
icatin
g
th
at
it
ca
n
r
e
s
p
o
n
d
f
aster
to
ch
a
n
g
es.
B
o
th
co
n
tr
o
ller
s
s
h
o
w
a
s
ig
n
if
ican
t
s
tate
d
ev
iatio
n
in
all
v
ar
iab
les,
with
MPC
p
er
f
o
r
m
in
g
s
lig
h
tly
b
etter
in
ch
ec
k
i
n
g
p
o
s
t
-
p
r
o
ce
s
s
in
g
ac
cu
r
a
cy
.
T
h
e
an
aly
s
is
r
esu
lts
ar
e
s
h
o
wn
in
T
a
b
le
4
.
4
.
2
.
E
nerg
y
c
o
ns
um
ptio
n
Fig
u
r
e
8
an
d
T
ab
le
5
s
h
o
w
th
e
p
o
wer
co
n
s
u
m
p
tio
n
co
m
p
a
r
is
o
n
.
Un
d
er
n
o
m
in
al
co
n
d
itio
n
s
,
th
e
MP
C
co
n
tr
o
ller
co
n
s
u
m
es
1
.
8
k
W
h
/to
n
,
wh
ile
th
e
H
-
in
f
i
n
ity
co
n
tr
o
ller
co
n
s
u
m
es
2
.
0
k
W
h
/to
n
.
As
th
e
p
ar
am
eter
ch
an
g
es,
th
e
p
o
wer
co
n
s
u
m
p
ti
o
n
o
f
b
o
t
h
co
n
tr
o
ller
s
in
cr
ea
s
e
s
p
r
o
p
o
r
tio
n
ally
.
Ho
wev
er
,
th
e
MPC
a
lway
s
u
s
e
s
less
p
o
wer
with
ea
ch
p
ass
.
As
s
ee
n
in
th
e
en
er
g
y
s
av
in
g
g
r
a
p
h
,
th
e
e
n
er
g
y
s
av
in
g
r
ate
o
f
MPC
co
m
p
ar
ed
to
H
-
in
f
in
ity
v
ar
ies b
etwe
en
8
%
an
d
1
2
%.
As
an
o
u
tp
u
t
,
MPC
is
m
o
r
e
p
o
te
n
t in
d
if
f
er
en
t p
r
o
c
ess
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Ma
th
ema
tica
l m
o
d
ellin
g
a
n
d
a
u
to
ma
ted
c
o
n
tr
o
l str
a
teg
ies fo
r
s
u
g
a
r
ca
n
e
cru
s
h
in
g
…
(
Go
vin
d
S
in
g
h
Je
th
i
)
561
Fig
u
r
e
7
.
Settli
n
g
tim
e,
p
ea
k
o
v
er
s
h
o
o
t,
r
is
etim
e,
s
tead
y
-
s
tate
er
r
o
r
v
s
p
ar
am
eter
v
ar
iatio
n
p
lo
t
T
ab
le
4
.
I
m
p
ac
t o
f
p
ar
am
ete
r
v
ar
iatio
n
o
n
p
er
f
o
r
m
a
n
ce
m
atr
ices o
f
co
n
tr
o
ller
V
a
r
i
a
t
i
o
n
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S
T_
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i
n
f
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n
f
(
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R
T_
H
i
n
f
(S)
S
S
E_
H
i
n
f
S
T_
M
P
C
(S)
O
S
_
M
P
C
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T
ab
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5
.
C
o
m
p
a
r
ativ
e
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alu
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o
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y
s
av
in
g
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er
f
o
r
m
a
n
ce
o
f
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d
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in
f
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ity
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n
tr
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ller
s
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r
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v
a
r
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a
t
i
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n
(
%)
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e
r
g
y
M
P
C
(
k
W
h
/
t
o
n
)
En
e
r
g
y
H
∞
(
k
W
h
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t
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e
r
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ased
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DATA AV
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RE
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NC
E
S
[
1
]
O
.
Z.
El
-
A
b
d
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.
M
.
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k
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[
2
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En
c
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.
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].
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4
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5
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.
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h
:
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[
6
]
N
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a
,
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L.
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n
2
0
2
3
I
EEE
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[
7
]
B
.
R
.
I
.
R
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sar
i
,
R
.
S
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r
n
o
,
M
.
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.
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I
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w
h
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st
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si
n
g
m
a
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n
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l
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n
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n
g
,
”
i
n
2
0
2
3
1
st
I
n
t
e
rn
a
t
i
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n
a
l
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v
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n
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5
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8
5
4
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0
2
3
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6
7
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6
2
.
[
8
]
P
.
S
u
n
d
h
a
r
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s
a
l
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n
g
a
m,
M
.
M
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,
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.
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.
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a
,
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.
D
.
K
u
mar,
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n
d
R
.
A
a
r
t
h
i
,
“
Le
a
n
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e
a
d
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n
e
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a
ss
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e
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t
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,
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n
2
0
2
3
I
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t
e
r
n
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7
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[
9
]
L.
W
e
i
,
B
.
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t
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d
,
C
.
D
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y
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X
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,
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A
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n
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n
2
0
2
3
I
n
t
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[
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0
]
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.
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u
mar
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n
d
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.
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,
“
A
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,
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I
EEE
T
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a
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s
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2
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2
3
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3
2
9
4
3
0
1
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[
1
1
]
N
.
Z
a
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e
t
s,
N
.
L
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s
k
a
,
L.
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so
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n
2
0
2
3
I
EE
E
4
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h
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3
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3
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2
9
7
4
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[
1
2
]
V
.
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i
j
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y
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,
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p
H
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s
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s
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n
2
0
1
5
I
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n
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.
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6
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.
[
1
3
]
T.
K
.
B
h
u
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y
a
,
N
.
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n
c
h
a
l
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a
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d
R
.
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a
r
sh
,
“
I
n
st
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n
t
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n
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s
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t
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/
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t
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sy
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s
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s
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n
d
u
st
r
y
,
”
i
n
2
0
2
3
I
E
EE
Mi
c
ro
w
a
v
e
s
,
An
t
e
n
n
a
s,
a
n
d
Pro
p
a
g
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t
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e
(
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N
)
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3
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p
.
1
–
6
,
d
o
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:
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0
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1
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9
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A
P
C
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N
5
8
6
7
8
.
2
0
2
3
.
1
0
4
6
4
1
5
7
.
[
1
4
]
M
.
R
.
A
h
me
d
,
A
.
K
.
K
a
r
ma
k
e
r
,
a
n
d
M
.
R
.
I
sl
a
m,
“
A
sse
ssi
n
g
t
h
e
t
e
c
h
n
o
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e
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o
n
o
m
i
c
f
e
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si
b
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t
y
o
f
c
o
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e
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e
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t
i
o
n
sy
s
t
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ms
i
n
B
a
n
g
l
a
d
e
s
h
’
s
s
u
g
a
r
mi
l
l
s
,
”
i
n
2
0
2
3
1
0
t
h
I
EEE
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
Po
w
e
r
S
y
s
t
e
m
s
(
I
C
PS
)
,
D
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c
.
2
0
2
3
,
p
p
.
1
–
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,
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:
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1
1
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C
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S
6
0
3
9
3
.
2
0
2
3
.
1
0
4
2
8
9
0
7
.
[
1
5
]
N
.
Lu
t
sk
a
,
L
.
V
l
a
s
e
n
k
o
,
N
.
Z
a
i
e
t
s
,
a
n
d
V
.
L
y
se
n
k
o
,
“
M
o
d
e
l
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n
g
t
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e
p
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d
u
c
t
i
v
i
t
y
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f
a
su
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r
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s
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n
g
mac
h
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n
e
l
e
a
r
n
i
n
g
met
h
o
d
s,
”
i
n
2
0
2
2
I
EE
E
1
7
t
h
I
n
t
e
r
n
a
t
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o
n
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l
C
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f
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c
e
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C
o
m
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S
c
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n
c
e
s
a
n
d
I
n
f
o
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t
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o
n
T
e
c
h
n
o
l
o
g
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e
s
(
C
S
I
T
)
,
N
o
v
.
2
0
2
2
,
p
p
.
3
5
3
–
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,
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o
i
:
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1
1
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9
/
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T5
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9
0
2
.
2
0
2
2
.
1
0
0
0
0
5
7
1
.
[
1
6
]
Y
.
S
mi
t
y
u
h
,
V
.
K
y
s
h
e
n
k
o
,
O
.
R
o
m
a
s
h
c
h
u
k
,
a
n
d
A
.
G
o
r
p
i
n
c
h
e
n
k
o
,
“
I
n
t
e
l
l
i
g
e
n
t
s
y
n
e
r
g
i
s
t
i
c
c
o
n
t
r
o
l
o
f
t
h
e
t
e
c
h
n
o
l
o
g
i
c
a
l
c
o
mp
l
e
x
f
o
r
t
h
e
su
g
a
r
f
a
c
t
o
r
y
,
”
i
n
2
0
2
2
I
EEE
1
1
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
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r
e
n
c
e
o
n
I
n
t
e
l
l
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g
e
n
t
S
y
s
t
e
m
s
(
I
S
)
,
O
c
t
.
2
0
2
2
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
S
5
7
1
1
8
.
2
0
2
2
.
1
0
0
1
9
7
2
8
.
[
1
7
]
Z.
H
a
y
a
l
i
a
n
d
G
.
A
k
b
a
r
i
z
a
d
e
h
,
“
Tr
a
n
sf
e
r
l
e
a
r
n
i
n
g
o
n
sem
a
n
t
i
c
se
g
me
n
t
a
t
i
o
n
f
o
r
su
g
a
r
c
r
y
s
t
a
l
a
n
a
l
y
s
i
s,”
i
n
2
0
2
2
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
M
a
c
h
i
n
e
V
i
si
o
n
a
n
d
I
m
a
g
e
Pr
o
c
e
ssi
n
g
(
M
VI
P)
,
F
e
b
.
2
0
2
2
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
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V
I
P
5
3
6
4
7
.
2
0
2
2
.
9
7
3
8
7
7
8
.
[
1
8
]
N
.
Za
i
e
t
s
,
N
.
L
u
t
s
k
a
,
a
n
d
L
.
V
l
a
se
n
k
o
,
“
I
mp
r
o
v
i
n
g
t
h
e
e
f
f
i
c
i
e
n
c
y
o
f
a
mu
l
t
i
s
t
a
g
e
e
v
a
p
o
r
a
t
o
r
st
a
t
i
o
n
f
o
r
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