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ty
p
e
h
ea
t
ex
ch
an
g
e
r
s
,
r
ep
lacin
g
th
e
cu
r
r
en
t
ce
n
tr
if
u
g
al
p
u
m
p
s
y
s
tem
with
th
r
e
e
n
ew
u
n
its
,
an
d
u
p
g
r
ad
in
g
th
e
two
in
s
tr
u
m
en
tatio
n
an
d
co
n
tr
o
l
u
n
its
to
a
n
ew
s
i
n
g
le
-
u
n
it
in
te
g
r
ated
co
n
tr
o
l
s
y
s
tem
,
also
k
n
o
wn
as
th
e
s
u
p
er
v
is
o
r
y
co
n
tr
o
l
an
d
d
ata
ac
q
u
is
itio
n
(
SC
ADA)
s
y
s
tem
[
4
]
.
I
n
ad
d
itio
n
to
th
e
p
r
im
ar
y
co
o
lin
g
s
y
s
tem
,
a
s
ec
o
n
d
ar
y
co
o
lin
g
s
y
s
tem
is
a
ls
o
n
ec
ess
ar
y
to
m
ain
tain
th
e
co
o
l
n
ess
o
f
th
e
r
ea
ctan
t
p
r
o
ce
s
s
in
a
p
o
wer
p
lan
t
r
ea
cto
r
.
Nev
er
th
eless
,
n
o
ca
s
e
s
tu
d
y
em
p
lo
y
in
g
v
a
r
iab
le
m
o
d
elin
g
h
as
b
ee
n
ca
r
r
ied
o
u
t
u
p
to
th
is
p
o
i
n
t.
Pre
v
io
u
s
r
e
s
ea
r
ch
h
as
id
en
tifie
d
th
r
ee
is
s
u
es
wh
ich
ar
e
th
e
lik
elih
o
o
d
o
f
R
T
P'
s
co
n
tin
u
ed
o
p
er
atio
n
.
T
h
e
d
aily
p
r
o
ce
s
s
es
f
o
r
th
e
d
ata
o
n
R
T
P
ar
e
s
till
m
an
u
al
o
p
er
atio
n
s
an
d
in
s
p
ec
tio
n
ch
ec
k
in
g
n
o
wa
d
ay
s
.
T
h
is
is
b
ec
a
u
s
e
th
er
e
a
r
e
not
as
m
a
n
y
s
taf
f
o
n
d
u
ty
to
o
v
er
s
ee
o
p
er
atio
n
s
ar
o
u
n
d
th
e
clo
c
k
,
wh
ich
in
cr
e
ases
th
e
r
is
k
o
f
an
u
n
in
ten
tio
n
al
m
is
h
ap
.
W
h
ile
er
o
s
io
n
is
a
m
ec
h
an
ical
s
u
r
f
ac
e
d
ef
o
r
m
atio
n
b
y
liq
u
id
at
h
ig
h
f
lo
w
v
elo
cities,
ty
p
ical
m
ater
ial
co
r
r
o
s
io
n
r
eq
u
i
r
es
ch
em
ical
an
d
elec
tr
o
ch
em
ical
r
ea
ctio
n
s
[
5
]
.
T
h
e
p
r
ac
tical
ap
p
licatio
n
o
f
th
is
tech
n
o
lo
g
y
d
ep
e
n
d
s
cr
itically
o
n
n
u
clea
r
r
ea
cto
r
s
af
ety
an
d
ef
f
icien
c
y
.
T
h
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
is
an
ess
en
tial
o
p
er
atio
n
in
t
h
e
co
m
p
le
x
n
etwo
r
k
o
f
a
n
u
clea
r
r
ea
cto
r
s
y
s
tem
b
ec
au
s
e
it
d
ir
ec
tly
af
f
ec
ts
th
e
wate
r
co
o
lan
t
s
y
s
tem
'
s
o
v
er
all
p
er
f
o
r
m
an
ce
a
n
d
th
er
m
al
s
tab
ilit
y
[
6
]
.
Dete
r
m
in
is
tic
m
o
d
els
h
av
e
h
is
to
r
ically
b
ee
n
u
s
ed
t
o
o
p
tim
iz
e
co
o
lin
g
p
r
o
ce
d
u
r
es
in
n
u
clea
r
r
ea
cto
r
s
.
Ho
wev
er
,
th
ese
m
o
d
els
m
ay
f
in
d
it
d
if
f
icu
lt
to
ac
c
o
u
n
t
f
o
r
th
ese
in
tr
icate
s
y
s
tem
s
'
d
y
n
am
ic
an
d
n
o
n
lin
ea
r
ch
ar
ac
ter
is
tics
[
7
]
.
T
h
e
n
ee
d
f
o
r
n
o
v
el
ap
p
r
o
ac
h
es
th
at
ca
n
p
r
ec
is
ely
f
o
r
ec
ast
an
d
o
p
ti
m
ize
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
in
r
ea
l
-
tim
e
is
r
is
in
g
as
th
e
n
ee
d
f
o
r
g
r
ea
ter
ef
f
icien
cy
an
d
s
af
ety
b
ec
o
m
es
m
o
r
e
p
r
ess
in
g
.
T
h
e
s
ig
n
if
ican
ce
o
f
th
e
p
r
o
ject
to
war
d
e
x
ec
u
tio
n
in
ce
r
tai
n
n
u
clea
r
r
esear
c
h
r
ea
ct
o
r
s
e
ctio
n
s
is
d
iv
er
g
i
n
g
ac
co
r
d
in
g
t
o
p
r
ed
ictiv
e
a
n
aly
s
is
d
ata.
T
h
e
in
f
o
r
m
atio
n
in
d
icate
s
th
e
n
ee
d
f
o
r
f
u
tu
r
e
r
ec
o
m
m
en
d
atio
n
s
f
o
r
p
o
ten
tial
ev
en
ts
an
d
s
ce
n
ar
io
s
.
Usi
n
g
v
ar
io
u
s
p
er
tin
e
n
t
tech
n
iq
u
es,
s
o
m
e
p
r
o
jects
h
av
e
b
e
en
im
p
lem
en
te
d
in
p
r
im
ar
y
co
o
la
n
t
f
o
r
m
o
n
ito
r
i
n
g
s
y
s
tem
s
.
Fo
llo
win
g
th
e
r
e
p
lace
m
en
t
o
f
th
e
h
ea
t
ex
ch
a
n
g
er
in
Ap
r
il
2
0
1
0
,
r
esear
ch
er
s
f
r
o
m
Ma
lay
s
ia'
s
n
u
clea
r
r
esear
ch
r
ea
ct
o
r
s
u
g
g
ested
in
cr
ea
s
in
g
th
e
e
f
f
ec
tiv
en
ess
o
f
t
h
e
R
T
P
p
r
im
ar
y
c
o
o
lin
g
s
y
s
tem
r
ec
o
n
s
tr
u
ctio
n
.
T
h
e
in
cr
ea
s
in
g
er
r
o
r
in
s
y
s
tem
s
tab
ilit
y
,
s
y
s
tem
in
d
icatio
n
s
,
n
o
n
-
f
u
n
ctio
n
al
f
u
n
ctio
n
s
,
in
ter
m
i
tten
t
s
ig
n
als
in
th
e
s
y
s
tem
,
in
cr
ea
s
ed
r
ea
cto
r
d
o
wn
ti
m
e,
an
d
in
cr
ea
s
ed
m
ain
ten
an
ce
tim
e
ar
e
th
e
r
ea
s
o
n
s
b
eh
in
d
t
h
e
R
T
P's
p
r
o
p
o
s
al
to
im
p
r
o
v
e
R
T
P
in
s
tr
u
m
en
tati
o
n
an
d
c
o
n
tr
o
l
[
8
]
.
No
wad
ay
s
,
d
ata
an
aly
s
is
is
cr
u
cial,
p
ar
ticu
lar
ly
f
o
r
p
r
o
jectio
n
s
ab
o
u
t
th
e
f
u
tu
r
e.
Nu
m
er
o
u
s
p
latf
o
r
m
s
f
o
r
p
r
e
-
an
aly
s
es
an
d
an
aly
tics
h
av
e
b
ee
n
cr
ea
ted
o
n
o
cc
asio
n
.
T
h
e
s
e
m
eth
o
d
s
ar
e
ap
p
lied
b
y
s
e
v
er
al
to
o
ls
th
at
u
s
e
h
is
to
r
ical
o
r
co
n
tem
p
o
r
ar
y
d
ata
to
ass
ess
th
e
in
f
o
r
m
atio
n
.
T
h
is
in
itial
d
ata
c
h
ar
ac
te
r
izatio
n
will
allo
w
m
o
d
elin
g
,
s
im
u
latin
g
,
an
d
a
n
aly
zin
g
d
y
n
a
m
ical
s
y
s
tem
s
in
a
f
lo
w
s
h
ee
tin
g
co
n
tex
t
.
C
o
n
tin
u
o
u
s
tim
e,
s
am
p
lin
g
tim
e,
o
r
a
co
m
b
in
atio
n
o
f
th
e
two
ca
n
d
escr
ib
e
lin
ea
r
an
d
n
o
n
lin
ea
r
s
y
s
tem
s
[
9
]
.
Pre
d
ictiv
e
m
ain
ten
an
ce
esti
m
atio
n
was
d
o
n
e
u
s
in
g
s
tatis
tically
b
ased
m
o
d
els
b
u
ilt
f
r
o
m
p
r
o
ce
s
s
d
ata
b
y
f
itti
n
g
a
p
r
o
b
a
b
ilis
tic
m
o
d
el
to
th
e
d
at
a
with
o
u
t
d
ep
en
d
in
g
o
n
en
g
i
n
ee
r
in
g
o
r
p
h
y
s
ics
p
r
in
cip
les
[
1
0
]
.
Gen
er
al
p
at
h
m
o
d
els (
GPMs)
an
d
s
to
ch
asti
c
p
r
o
ce
s
s
m
o
d
els (
SP
Ms)
ar
e
t
wo
m
ajo
r
s
tatis
tically
-
b
ased
m
o
d
el
ty
p
es
[
1
1
]
.
T
h
e
p
r
ed
ictiv
e
an
aly
s
is
s
tr
o
n
g
ly
s
u
g
g
ests
th
e
i
s
s
u
e
'
s
lik
ely
r
em
ed
y
.
Mo
d
el
p
ar
a
m
eter
d
ete
r
m
in
atio
n
m
ay
b
en
ef
it
f
r
o
m
th
e
s
am
e
p
r
o
ce
s
s
es
th
at
h
elp
s
tan
d
ar
d
ize
q
u
ality
p
ar
am
eter
s
[
1
2
]
.
Pre
d
ictiv
e
an
aly
s
is
m
eth
o
d
o
l
o
g
ies
f
r
eq
u
en
tly
em
p
lo
y
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
f
u
zz
y
l
o
g
ic,
d
ec
i
s
io
n
tr
ee
s
,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
,
a
n
d
e
v
o
lu
tio
n
ar
y
p
r
o
g
r
am
m
in
g
.
T
h
u
s
,
u
tili
zin
g
th
e
c
o
m
b
in
atio
n
o
f
two
o
r
m
o
r
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
ca
lled
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
th
e
p
r
ed
ictio
n
d
ata
in
t
h
is
s
tu
d
y
will
b
e
p
r
esen
ted
u
s
in
g
tem
p
er
atu
r
e
,
f
lo
w,
an
d
p
r
ess
u
r
e
p
ar
am
eter
s
f
r
o
m
d
ata
g
en
er
ated
in
th
e
s
ec
o
n
d
a
r
y
co
o
lin
g
s
y
s
tem
.
An
aly
s
ts
u
s
e
p
atter
n
s
in
h
is
to
r
ical
d
ata
a
n
d
cu
r
r
e
n
t
ev
e
n
t
s
to
p
r
e
d
ict
f
u
tu
r
e
e
v
en
ts
.
E
v
en
th
o
u
g
h
ac
cu
r
ate
p
r
ed
ictio
n
d
o
es n
o
t e
x
is
t,
an
aly
s
ts
s
tan
d
a
b
etter
ch
an
ce
o
f
s
u
cc
ess
if
th
ey
p
o
s
s
ess
th
o
r
o
u
g
h
k
n
o
wled
g
e
.
T
h
r
ee
p
r
im
ar
y
d
ata
m
in
i
n
g
tech
n
iq
u
es,
clu
s
ter
in
g
,
class
if
icatio
n
,
an
d
r
eg
r
ess
io
n
ar
e
ap
p
lied
in
v
ar
io
u
s
s
ettin
g
s
.
C
lu
s
ter
in
g
is
an
u
n
s
u
p
er
v
is
ed
lear
n
in
g
tech
n
i
q
u
e
in
wh
ic
h
th
e
alg
o
r
ith
m
p
r
o
ce
s
s
es
th
e
d
ata
s
im
u
ltan
eo
u
s
ly
.
T
h
e
in
f
o
r
m
atio
n
m
u
s
t
b
e
d
i
v
id
ed
in
t
o
d
ata
clu
s
ter
s
b
y
t
h
e
alg
o
r
ith
m
.
M
o
s
t
well
-
clu
s
ter
in
g
m
eth
o
d
s
ar
e
d
en
s
ity
-
b
ased
s
p
atial
clu
s
ter
in
g
o
f
ap
p
licatio
n
s
with
n
o
is
e
(
DB
S
C
AN)
,
h
ier
ar
ch
ical
clu
s
ter
in
g
,
k
-
m
ea
n
s
clu
s
ter
in
g
,
an
d
k
-
m
e
d
o
id
s
cl
u
s
ter
in
g
.
C
lass
if
icatio
n
,
o
r
s
u
p
er
v
is
ed
lear
n
i
n
g
,
r
e
q
u
ir
es
a
s
u
b
s
et
o
f
r
ec
o
r
d
ed
s
am
p
les
ca
lled
tr
ain
in
g
d
ata.
A
class
if
icatio
n
m
o
d
el
is
co
n
s
tr
u
cted
a
n
d
f
itted
to
th
e
an
aly
s
is
to
an
ticip
ate
th
e
p
r
o
s
p
ec
t.
T
h
is
m
o
d
el
is
th
en
u
s
ed
f
o
r
a
s
ep
ar
ate
s
et
o
f
s
to
r
ed
r
ec
o
r
d
s
d
esig
n
ated
test
in
g
d
at
a
[
1
3
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ata
[
1
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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SS
N:
2088
-
8
7
0
8
Op
tima
l e
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clea
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…
(
I
b
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u
Ha
ja
r
)
6289
T
h
e
ef
f
icien
cy
o
f
n
u
clea
r
r
ea
c
to
r
wate
r
co
o
lan
t
s
y
s
tem
s
i
s
c
r
itical
f
o
r
m
ain
tain
in
g
s
af
e
an
d
o
p
tim
al
o
p
er
atio
n
with
in
n
u
clea
r
p
o
wer
p
lan
ts
.
T
h
is
s
tu
d
y
aim
s
t
o
e
n
h
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ce
th
e
u
n
d
er
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tan
d
in
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a
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ce
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r
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u
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p
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s
iv
e
r
ev
iew
an
d
ad
v
an
ce
d
an
aly
tical
m
et
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o
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s
.
Fo
cu
s
in
g
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n
th
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ar
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co
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lin
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s
y
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tem
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,
p
ar
ticu
lar
ly
th
e
p
ar
am
eter
s
in
f
lu
en
cin
g
th
eir
ef
f
icien
cy
,
we
s
ee
k
to
id
en
tify
o
p
tim
al
o
p
e
r
atio
n
al
p
r
ac
tices
an
d
p
r
ed
ictiv
e
m
o
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els.
E
m
p
lo
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in
g
a
co
m
b
in
atio
n
o
f
tr
ad
itio
n
al
s
tatis
tical
ap
p
r
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ac
h
es
an
d
m
o
d
er
n
m
ac
h
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e
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h
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el
o
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t
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o
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els
th
at
ca
n
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ict
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im
p
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th
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f
f
icien
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f
th
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co
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y
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tem
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T
h
e
m
a
n
u
s
cr
ip
t
is
o
r
g
an
ized
as
f
o
llo
ws:
t
h
e
m
eth
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d
s
ec
tio
n
d
etails th
e
r
ev
iew
o
f
th
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ec
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d
ar
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s
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ata
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ar
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eter
s
,
th
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ap
p
licatio
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o
f
a
m
ax
im
u
m
lik
elih
o
o
d
esti
m
ato
r
f
o
r
o
p
ti
m
al
ef
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icien
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an
d
d
ep
lo
y
in
g
v
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io
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s
m
ac
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lear
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m
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els,
in
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h
an
d
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m
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ce
m
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T
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lts
an
d
d
is
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s
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io
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ec
tio
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p
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in
d
in
g
s
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elate
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to
th
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co
o
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t
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y
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tem
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m
ac
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m
o
d
el,
tim
e
s
er
ies
an
aly
s
is
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p
r
ed
ictiv
e
m
o
d
els,
an
d
cr
itical
p
ar
am
eter
s
,
alo
n
g
s
id
e
ex
p
lo
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c
h
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a
p
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an
d
s
ec
o
n
d
ar
y
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ata
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aly
s
is
.
Fin
ally
,
th
e
co
n
clu
s
io
n
s
u
m
m
ar
izes th
e
in
s
ig
h
ts
an
d
i
m
p
licatio
n
s
o
f
o
u
r
s
tu
d
y
.
2.
M
E
T
H
O
D
T
h
e
p
r
o
m
in
en
t
p
u
b
lis
h
in
g
d
at
ab
ases
'
o
n
lin
e
s
ea
r
ch
to
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ls
ar
e
th
e
m
o
s
t
p
r
ef
er
r
ed
m
eth
o
d
o
f
f
in
d
i
n
g
r
ev
iew
s
tr
u
ctu
r
es.
T
h
e
r
ev
iew
s
tr
u
ctu
r
e
f
o
r
th
e
o
p
tim
al
ef
f
i
cien
cy
o
f
t
h
e
s
ec
o
n
d
a
r
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co
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s
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m
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is
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ep
icted
in
Fig
u
r
e
1
.
Mo
s
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jo
u
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ch
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OPUS
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Go
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r
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asis
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o
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itical
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m
p
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o
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o
llo
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o
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an
d
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b
r
i
d
m
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el.
E
v
e
r
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ap
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elate
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el.
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e
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a
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2
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1
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2
3
.
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o
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1
9
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3
,
m
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th
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u
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als
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ce
ed
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n
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e
s
e
lecte
d
.
Pap
er
s
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b
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h
ed
b
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o
r
e
y
ea
r
s
o
f
2
0
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9
,
a
f
ew
jo
u
r
n
als
an
d
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in
g
s
wer
e
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ted
.
T
h
e
r
e
ar
e
r
o
u
g
h
ly
6
3
r
e
v
iews,
in
clu
d
i
n
g
5
0
f
r
o
m
jo
u
r
n
als
a
n
d
1
2
f
r
o
m
p
r
o
ce
ed
i
n
g
s
.
Fig
u
r
e
1
.
T
h
e
r
ev
iew
s
tr
u
ctu
r
e
o
f
o
p
tim
al
ef
f
icien
cy
o
n
a
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
f
o
r
n
u
clea
r
r
ea
cto
r
wate
r
p
lan
t c
o
o
lan
t sy
s
tem
2
.
1
.
Nuclea
r
re
a
ct
o
r
wa
t
er
c
o
o
la
nt
pla
nt
s
ec
o
nd
a
ry
co
o
lin
g
pro
ce
s
s
re
v
iew
Pre
co
n
d
itio
n
s
a
r
e
a
p
p
lied
to
s
p
ec
if
ic
p
h
ases
o
f
th
e
d
ec
is
io
n
tr
ee
tec
h
n
iq
u
e
,
th
e
p
r
im
ar
y
e
x
p
er
im
en
t
u
s
ed
in
p
r
ed
ictiv
e
an
aly
s
is
.
T
h
is
en
s
u
r
es
th
at
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
s
y
s
tem
o
p
er
ates
s
m
o
o
th
ly
an
d
p
r
o
v
id
es
p
r
ec
is
e
d
ata.
As
a
r
esu
lt,
th
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p
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im
ar
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co
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an
d
s
ec
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n
d
ar
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c
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m
p
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T
h
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r
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o
r
e
,
th
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s
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ar
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o
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ly
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eter
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g
t
h
e
an
aly
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is
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th
e
p
r
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a
r
y
g
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al.
T
h
e
s
y
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p
r
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ce
s
s
o
p
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atin
g
in
th
e
R
T
P
s
ec
o
n
d
ar
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c
o
o
lin
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y
s
tem
is
d
ep
icte
d
in
Fig
u
r
e
2
.
T
h
e
s
ec
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n
d
a
r
y
wate
r
co
o
la
n
t
p
lan
t
is
th
e
r
ed
-
c
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lo
r
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s
u
b
s
tan
ce
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at
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u
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s
f
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m
th
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.
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a
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eter
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ar
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r
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lan
t
p
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s
s
ar
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f
u
n
d
a
m
en
tal
f
o
r
ef
f
ec
tiv
ely
m
o
n
ito
r
in
g
an
d
co
n
tr
o
llin
g
cr
itical
o
p
er
atio
n
a
l
p
ar
am
eter
s
.
T
h
ese
to
o
ls
in
clu
d
e
tem
p
er
atu
r
e
s
en
s
o
r
s
f
o
r
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
o
f
co
o
lan
t te
m
p
er
at
u
r
es,
p
r
ess
u
r
e
g
au
g
es to
en
s
u
r
e
s
af
e
o
p
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atin
g
co
n
d
itio
n
s
,
an
d
f
lo
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m
eter
s
f
o
r
ac
c
u
r
ate
m
ea
s
u
r
em
en
t o
f
c
o
o
lan
t f
lo
w
r
ates
[
1
5
]
.
T
h
e
in
te
g
r
atio
n
o
f
t
h
ese
p
ar
am
eter
to
o
ls
en
ab
les
co
m
p
r
eh
e
n
s
iv
e
m
o
n
i
to
r
in
g
an
d
co
n
tr
o
l
o
f
th
e
s
ec
o
n
d
ar
y
wate
r
co
o
lan
t
p
lan
t
p
r
o
ce
s
s
,
en
s
u
r
in
g
o
p
tim
al
p
er
f
o
r
m
an
ce
,
r
eliab
ilit
y
,
an
d
s
af
ety
in
p
o
wer
g
en
er
atio
n
o
p
er
atio
n
s
.
T
h
e
wate
r
co
o
lan
t
p
r
o
ce
s
s
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
W
ater
f
r
o
m
th
e
r
ea
cto
r
f
l
o
ws
in
to
th
e
h
ea
t
ex
ch
a
n
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er
in
th
e
p
r
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m
ar
y
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o
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p
o
f
th
e
s
y
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tem
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wh
ile
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r
f
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m
th
e
co
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g
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lo
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to
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t
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ch
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er
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I
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2
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I
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,
Vo
l.
14
,
No
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6
,
Dec
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e
r
20
24
:
6
2
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6290
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c
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ter
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ter
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ter
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p
o
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t
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e
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em
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ter
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ater
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HE
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[
1
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]
.
Fig
u
r
e
2
.
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h
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s
ec
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d
a
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t
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s
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at
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2
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g
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y
s
t
e
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er
a
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a
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h
e
p
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s
s
o
f
co
llectin
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d
a
ta
f
o
r
r
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l
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tim
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o
r
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f
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m
a
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ter
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ical
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ee
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p
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s
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r
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Fig
u
r
e
3
illu
s
tr
ates
th
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s
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etail,
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ig
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ata
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o
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d
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r
ess
u
r
e,
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o
n
lin
e
o
r
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f
f
lin
e
o
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er
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le
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y
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tem
s
.
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en
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o
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d
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u
an
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itativ
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ata
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r
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r
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t.
An
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ter
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ar
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m
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o
f
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es
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llected
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m
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ile
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ata.
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llectio
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d
p
r
o
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s
in
g
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r
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ex
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r
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m
o
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el
cr
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tio
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t
ically
p
r
e
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ain
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ce
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h
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co
m
p
lex
ity
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d
n
o
n
lin
ea
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ity
o
f
s
en
s
o
r
s
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g
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als
n
ec
ess
itate
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ilter
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g
th
e
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ata.
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h
u
s
,
th
e
m
ain
o
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jectiv
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o
f
f
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t
u
r
e
e
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u
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e
th
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atter
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s
an
d
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en
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s
in
th
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en
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o
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tp
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ts
to
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s
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ata
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ath
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g
o
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m
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.
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th
en
ab
s
tr
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ted
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im
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p
er
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ce
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elin
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h
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llected
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ata
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tatis
t
ically
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aly
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,
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d
cr
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p
ar
a
m
eter
s
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b
e
f
o
u
n
d
.
T
h
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b
est
m
o
m
en
t
t
o
is
s
u
e
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aler
t
f
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is
io
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ain
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eter
m
in
ed
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ee
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tr
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o
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m
atio
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in
to
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p
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m
o
d
el
th
a
t
h
as b
ee
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n
s
tr
u
cted
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h
e
co
ld
-
wate
r
cy
cles
th
r
o
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h
th
e
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ir
s
t
th
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s
o
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s
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n
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A
(
HE
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r
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(
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)
is
u
s
ed
th
r
o
u
g
h
a
v
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e
to
c
o
n
tr
o
l
tem
p
er
atu
r
e,
p
r
ess
u
r
e,
an
d
f
lo
w
f
r
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m
th
e
co
o
lin
g
t
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ex
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g
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v
er
y
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ay
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h
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Hea
t
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x
ch
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g
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r
v
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ies
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d
if
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.
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s
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ab
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1
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th
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p
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itio
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p
lace
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o
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tag
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o
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s
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lib
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d
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r
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s
s
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r
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d
f
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ate
m
o
n
ito
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g
s
y
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tem
s
.
Daily
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d
m
o
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th
ly
ch
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ts
will
in
v
esti
g
ate
s
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ety
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d
o
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r
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an
d
f
lo
w
r
ate
p
a
r
am
eter
s
.
T
ab
le
1
p
r
esen
ts
th
e
o
p
tim
al
r
an
g
e
f
o
r
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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C
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u
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ated
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r
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tify
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ar
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en
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ic
r
o
u
te
m
o
d
el
ca
p
tu
r
es
th
e
u
n
it
-
wis
e
v
o
latilit
y
i
n
d
eg
r
ad
atio
n
d
ata
,
wh
ich
also
p
er
m
its
th
e
d
ir
ec
t
u
s
e
o
f
d
eg
r
ad
atio
n
d
ata.
T
h
e
d
eter
io
r
atio
n
p
ath
o
f
u
n
it
is
s
p
ec
if
ied
f
o
r
ev
er
y
g
iv
en
tim
e
as
(
1
)
,
(
)
=
(
,
,
)
+
(
1
)
T
h
e
v
ec
to
r
r
ep
r
esen
ts
th
e
f
ix
ed
(
p
o
p
u
latio
n
)
ef
f
ec
ts
f
o
r
e
v
er
y
u
n
it,
r
ep
r
esen
ts
th
e
r
an
d
o
m
(
in
d
i
v
id
u
al)
ef
f
ec
ts
f
o
r
th
e
th
u
n
it,
an
d
th
e
n
o
r
m
ally
d
is
tr
ib
u
ted
m
ea
s
u
r
e
m
en
t
er
r
o
r
s
ar
e
r
ep
r
esen
ted
b
y
(
0
,
2
)
.
T
h
r
e
e
p
r
im
ar
y
p
r
esu
m
p
tio
n
s
u
n
d
er
p
i
n
th
is
ap
p
r
o
ac
h
.
I
n
itially
,
it
is
n
ec
ess
ar
y
to
tr
an
s
f
er
th
e
d
eter
io
r
atio
n
d
ata
to
a
(
∙
)
f
u
n
ctio
n
b
y
ca
p
tu
r
in
g
it
u
s
in
g
v
alid
f
ailu
r
e
m
o
d
els.
Fu
r
th
er
m
o
r
e,
t
h
e
h
is
to
r
ical
d
ata
m
u
s
t
b
e
g
ath
er
e
d
u
n
d
er
co
m
p
ar
ab
le
cir
c
u
m
s
tan
ce
s
,
co
n
s
id
er
in
g
a
f
ai
r
v
a
r
ian
c
e
o
f
e
v
er
y
elem
en
t.
W
h
en
a
c
o
m
p
o
n
e
n
t
o
r
p
r
o
ce
s
s
f
ails
,
a
f
in
al,
p
r
e
d
eter
m
in
e
d
cr
itical
d
eg
r
ee
o
f
d
eter
io
r
atio
n
i
s
k
n
o
wn
as
a
"so
f
t
f
ailu
r
e"
.
V
ar
io
u
s
d
is
tr
ib
u
tio
n
s
ca
n
b
e
c
o
n
s
id
er
ed
f
o
r
t
h
e
(
∙
)
p
ar
am
eter
s
,
lik
e
W
eib
u
ll,
n
o
r
m
al,
lo
g
-
n
o
r
m
al,
an
d
o
th
er
s
.
E
r
r
o
r
s
in
d
ata
co
llectio
n
in
tim
e
ca
n
b
e
id
en
tifie
d
with
m
o
d
el
p
ar
am
eter
s
.
Pre
d
ictio
n
m
ain
ten
an
ce
ca
n
b
e
d
o
n
e
wh
er
e
th
e
p
ar
am
eter
s
in
th
e
m
o
d
el
n
ee
d
to
b
e
esti
m
ated
at
th
e
o
f
f
lin
e
s
tag
e.
T
h
is
r
esear
ch
is
liv
e
d
ata
co
llected
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
0
8
8
-
8
7
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I
n
t J E
lec
&
C
o
m
p
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n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
2
8
7
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6
2
9
9
6292
u
p
d
ated
at
th
e
o
n
lin
e
s
tag
e,
tak
en
f
r
o
m
tim
e
to
tim
e.
Ma
x
im
u
m
lik
elih
o
o
d
esti
m
atio
n
(
ML
E
)
m
ay
b
e
u
s
ed
f
o
r
liv
e
d
ata
p
ar
a
m
eter
esti
m
atio
n
,
an
d
th
e
e
x
p
ec
tatio
n
-
m
ax
i
m
izatio
n
(
E
M)
a
p
p
r
o
ac
h
y
ield
s
ac
cu
r
ate
r
esu
lts
.
Acc
o
r
d
in
g
to
th
e
r
ev
iew,
th
e
o
n
lin
e
p
ar
am
eter
s
a
r
e
b
est
d
et
er
m
in
ed
u
s
in
g
t
h
e
B
ay
esian
f
r
am
ewo
r
k
,
in
w
h
ich
p
o
s
ter
io
r
d
is
tr
ib
u
tio
n
s
o
f
th
e
m
o
d
el
p
ar
a
m
eter
s
ar
e
p
r
o
d
u
ce
d
u
s
in
g
r
ec
e
n
tly
g
ath
e
r
ed
d
ata
.
2
.
4
.
M
a
chine le
a
rning
deplo
y
m
ent
s
A
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
d
e
p
lo
y
m
en
t
is
a
c
r
u
cial
p
h
ase
in
th
e
m
ac
h
in
e
lear
n
in
g
life
cy
cl
e
wh
er
e
a
tr
ain
ed
m
o
d
el
is
av
ailab
le
in
a
p
r
o
d
u
ctio
n
en
v
ir
o
n
m
e
n
t
[
1
7
]
.
Dep
lo
y
i
n
g
a
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
in
v
o
lv
es
tr
an
s
itio
n
in
g
f
r
o
m
a
d
ev
elo
p
m
en
t
o
r
r
esear
ch
s
ettin
g
to
a
p
r
ac
tical,
r
ea
l
-
wo
r
ld
ap
p
licatio
n
[
1
8
]
.
Dep
lo
y
in
g
a
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
f
o
r
o
p
tim
izin
g
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
in
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
lan
t
p
lan
t
is
a
co
m
p
lex
task
th
at
r
eq
u
ir
es
ca
r
ef
u
l
p
lan
n
i
n
g
an
d
c
o
n
s
id
er
atio
n
o
f
s
af
ety
,
ac
cu
r
ac
y
,
an
d
r
ea
l
-
tim
e
p
er
f
o
r
m
an
ce
.
Dep
lo
y
in
g
a
m
a
ch
in
e
lear
n
in
g
m
o
d
el
f
o
r
o
p
ti
m
izin
g
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
in
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
la
n
t
p
lan
t
is
a
m
eticu
lo
u
s
p
r
o
ce
s
s
th
at
in
v
o
lv
es
ad
d
r
ess
in
g
th
e
s
p
ec
if
ic
ch
allen
g
es
an
d
s
af
ety
co
n
s
id
er
atio
n
s
in
h
er
e
n
t
to
n
u
clea
r
f
ac
ilit
ies.
T
h
e
in
itial
s
t
ep
s
in
clu
d
e
d
ef
in
in
g
th
e
o
p
ti
m
izatio
n
g
o
als
an
d
co
llectin
g
h
is
to
r
ical
d
ata
r
elate
d
to
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
,
wh
ich
ty
p
ically
co
m
p
r
is
es
tem
p
er
atu
r
e
,
p
r
ess
u
r
e,
an
d
f
lo
w
r
ate
v
a
r
iab
l
es.
Featu
r
e
en
g
in
ee
r
in
g
b
ec
o
m
es
cr
u
cial
to
e
n
h
an
ce
th
e
m
o
d
el'
s
u
n
d
er
s
tan
d
in
g
,
an
d
ch
o
o
s
in
g
a
n
ap
p
r
o
p
r
iate
m
ac
h
in
e
lear
n
in
g
m
o
d
el,
s
u
c
h
as
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NN
s
)
o
r
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
,
is
v
ital
f
o
r
ca
p
tu
r
in
g
tem
p
o
r
al
p
atter
n
s
in
th
e
d
ata.
T
r
ain
in
g
th
e
m
o
d
el
in
v
o
lv
es
s
p
litt
in
g
th
e
d
ataset
in
to
tr
ain
in
g
a
n
d
v
alid
atio
n
s
ets,
em
p
h
asizin
g
f
in
e
-
tu
n
in
g
h
y
p
er
p
ar
am
eter
s
f
o
r
im
p
r
o
v
e
d
ac
cu
r
ac
y
.
E
v
alu
atin
g
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
wit
h
m
etr
ics
lik
e
en
er
g
y
ef
f
icien
cy
an
d
tem
p
er
at
u
r
e
s
tab
ilit
y
is
p
ar
am
o
u
n
t.
Saf
ety
an
d
r
eg
u
lato
r
y
c
o
m
p
lian
ce
ar
e
in
teg
r
al
co
m
p
o
n
en
ts
,
en
s
u
r
i
n
g
th
e
m
o
d
el
alig
n
s
with
s
tr
in
g
en
t
s
tan
d
ar
d
s
ap
p
licab
le
to
n
u
clea
r
f
ac
ilit
ies.
W
h
eth
er
o
n
-
p
r
e
m
is
es,
clo
u
d
,
o
r
ed
g
e
d
e
v
ices,
th
e
d
ep
lo
y
m
e
n
t
en
v
ir
o
n
m
en
t
m
u
s
t
s
u
p
p
o
r
t
r
ea
l
-
tim
e
p
r
ed
ictio
n
s
an
d
in
teg
r
ate
s
ea
m
less
ly
with
th
e
p
lan
t'
s
co
n
tr
o
l
s
y
s
tem
s
.
T
h
e
cr
u
cial
asp
ec
ts
in
clu
d
e
s
ca
lab
ilit
y
co
n
s
id
er
atio
n
s
,
m
o
n
ito
r
i
n
g
m
ec
h
an
is
m
s
f
o
r
r
ea
l
-
tim
e
p
er
f
o
r
m
an
ce
tr
ac
k
in
g
,
s
ec
u
r
ity
m
ea
s
u
r
es,
an
d
co
n
tin
u
o
u
s
im
p
r
o
v
em
en
t
th
r
o
u
g
h
a
f
ee
d
b
ac
k
lo
o
p
.
C
o
llab
o
r
atio
n
b
etwe
en
d
ata
s
c
ien
tis
ts
,
en
g
in
ee
r
s
,
an
d
d
o
m
ai
n
ex
p
er
ts
is
ess
en
tial
f
o
r
s
u
cc
ess
f
u
l
d
ep
lo
y
m
en
t,
an
d
r
e
g
u
lar
m
ain
ten
a
n
ce
c
h
e
ck
s
,
u
s
er
tr
ai
n
in
g
,
an
d
an
e
m
er
g
en
cy
r
esp
o
n
s
e
p
lan
co
n
tr
ib
u
te
to
th
e
o
v
er
all
r
o
b
u
s
tn
ess
o
f
th
e
d
ep
lo
y
ed
m
a
ch
in
e
lear
n
in
g
m
o
d
el.
2
.
5
.
H
y
brid m
a
chine le
a
rning
I
n
teg
r
atin
g
a
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
m
o
d
el
f
o
r
o
p
tim
izin
g
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
in
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
lan
t
p
lan
t
in
v
o
l
v
es
a
n
u
an
ce
d
an
d
s
o
p
h
is
ticated
ap
p
r
o
ac
h
.
T
h
e
d
ep
lo
y
m
en
t
s
tr
ateg
y
b
eg
in
s
with
a
m
eticu
lo
u
s
d
ef
i
n
itio
n
o
f
t
h
e
o
p
tim
izatio
n
o
b
j
ec
tiv
es
an
d
th
e
ac
q
u
is
itio
n
o
f
p
er
tin
en
t
h
is
to
r
ical
d
ata
en
co
m
p
ass
in
g
v
ar
ia
b
les
lik
e
tem
p
er
atu
r
e,
p
r
ess
u
r
e,
an
d
f
lo
w
r
ates.
T
h
e
h
y
b
r
id
m
o
d
el
wh
ich
co
m
b
in
es
elem
en
ts
o
f
n
eu
r
al
n
etwo
r
k
s
a
n
d
tr
ad
itio
n
al
s
tatis
tical
ap
p
r
o
ac
h
es
is
ad
ep
t
at
ca
p
tu
r
in
g
th
e
in
tr
icate
d
y
n
am
ics
o
f
th
e
s
ec
o
n
d
a
r
y
co
o
lin
g
p
r
o
ce
s
s
.
Featu
r
e
en
g
in
ee
r
in
g
b
ec
o
m
es
p
iv
o
tal,
allo
win
g
th
e
m
o
d
el
to
ex
tr
ac
t
m
ea
n
in
g
f
u
l
p
atter
n
s
an
d
r
ela
tio
n
s
h
ip
s
f
r
o
m
th
e
d
ata.
T
h
e
m
o
d
el'
s
s
e
lectio
n
in
v
o
lv
es
an
astu
te
f
u
s
io
n
o
f
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
tec
h
n
iq
u
es
to
h
a
r
n
ess
p
r
ed
ictiv
e
ca
p
ab
ilit
ies
an
d
e
x
p
lo
r
ato
r
y
in
s
ig
h
ts
.
T
r
ain
in
g
a
n
d
v
alid
atio
n
p
h
ases
ar
e
d
ed
icate
d
t
o
r
ef
in
i
n
g
t
h
e
m
o
d
el'
s
p
ar
am
eter
s
f
o
r
o
p
tim
al
ac
cu
r
ac
y
a
n
d
ef
f
icien
cy
.
Key
ev
al
u
atio
n
m
etr
ics,
s
u
ch
as
en
er
g
y
ef
f
icien
cy
an
d
tem
p
er
at
u
r
e
s
tab
ilit
y
,
g
u
id
e
th
e
th
o
r
o
u
g
h
ass
es
s
m
en
t
o
f
th
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
ce
.
Dep
lo
y
m
en
t
co
n
s
id
er
atio
n
s
in
clu
d
e
s
ea
m
less
in
teg
r
atio
n
in
to
th
e
p
lan
t'
s
in
f
r
astru
ctu
r
e,
wh
eth
er
o
n
-
p
r
e
m
is
es
o
r
in
th
e
clo
u
d
,
e
m
p
h
asizin
g
r
ea
l
-
tim
e
ca
p
ab
ilit
ies
an
d
s
ca
lab
ilit
y
.
C
o
n
tin
u
o
u
s
m
o
n
ito
r
in
g
,
r
o
b
u
s
t
s
ec
u
r
ity
m
ea
s
u
r
es,
s
u
s
tain
ed
co
llab
o
r
atio
n
ac
r
o
s
s
d
iv
er
s
e
ex
p
er
tis
e,
an
d
a
co
m
p
r
eh
e
n
s
iv
e
m
ain
ten
an
ce
s
tr
ateg
y
co
n
tr
i
b
u
te
to
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
m
o
d
el,
en
s
u
r
in
g
o
p
tim
al
ef
f
icien
c
y
in
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
o
f
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
l
an
t p
lan
t.
2
.
6
.
M
a
chine le
a
rning
perf
o
rm
a
nce
m
e
a
s
ure
Desig
n
in
g
ef
f
ec
tiv
e
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
es
f
o
r
m
ac
h
in
e
le
ar
n
in
g
m
o
d
els
d
ep
lo
y
ed
to
o
p
tim
ize
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
in
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
l
an
t
p
lan
t
is
cr
u
cial
f
o
r
en
s
u
r
in
g
ef
f
icien
cy
an
d
s
af
ety
in
n
u
clea
r
o
p
e
r
atio
n
s
.
On
e
o
f
th
e
k
ey
m
etr
ics
is
e
n
er
g
y
ef
f
icien
cy
,
r
e
f
lectin
g
th
e
m
o
d
el'
s
ab
ilit
y
to
s
tr
ik
e
a
b
alan
ce
b
etwe
en
ef
f
ec
tiv
e
co
o
lin
g
a
n
d
en
e
r
g
y
c
o
n
s
er
v
atio
n
.
I
n
a
n
u
clea
r
r
e
ac
to
r
,
m
ain
tain
in
g
tem
p
er
atu
r
e
s
tab
ilit
y
is
cr
itical
to
p
r
ev
en
t
f
lu
ctu
atio
n
s
t
h
at
co
u
l
d
co
m
p
r
o
m
is
e
th
e
r
ea
cto
r
'
s
s
af
ety
an
d
s
tab
ilit
y
.
T
h
e
m
o
d
el'
s
ca
p
ac
ity
to
ac
c
u
r
ately
p
r
ed
ict
an
d
c
o
n
tr
o
l
tem
p
er
atu
r
e
v
a
r
iatio
n
s
is
in
teg
r
al
to
th
e
o
v
er
all
s
u
cc
ess
o
f
th
e
co
o
lin
g
s
y
s
tem
.
Ad
d
itio
n
ally
,
th
e
s
y
s
tem
'
s
r
esp
o
n
s
iv
en
ess
to
d
y
n
am
ic
c
h
an
g
es
i
n
o
p
er
atin
g
co
n
d
itio
n
s
is
a
cr
u
c
ial
p
er
f
o
r
m
an
ce
m
ea
s
u
r
e,
en
s
u
r
in
g
t
h
at
th
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
ca
n
ad
ap
t
s
wif
tly
to
v
ar
y
in
g
d
em
an
d
s
.
T
h
is
ad
ap
tab
ilit
y
is
p
a
r
ticu
lar
ly
s
ig
n
if
ican
t
in
a
n
u
clea
r
r
ea
ct
o
r
,
wh
e
r
e
r
ea
l
-
tim
e
r
esp
o
n
s
es
to
o
p
er
atio
n
al
c
h
an
g
es
ar
e
ess
en
tial
f
o
r
m
a
in
tain
in
g
o
p
tim
al
e
f
f
icien
cy
an
d
s
af
ety
.
T
h
e
p
er
f
o
r
m
an
ce
e
v
alu
atio
n
p
r
o
ce
s
s
in
v
o
lv
es
a
co
m
p
r
eh
e
n
s
iv
e
an
aly
s
is
o
f
th
ese
m
etr
ics,
co
n
s
id
er
in
g
im
m
ed
iate
an
d
lo
n
g
-
ter
m
im
p
ac
ts
o
n
th
e
n
u
clea
r
r
ea
cto
r
'
s
o
p
er
atio
n
al
s
tab
ilit
y
.
Fin
e
-
tu
n
in
g
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J E
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C
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p
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g
I
SS
N:
2088
-
8
7
0
8
Op
tima
l e
fficien
cy
o
n
n
u
clea
r
r
ea
cto
r
s
ec
o
n
d
a
r
y
co
o
lin
g
p
r
o
ce
s
s
u
s
in
g
ma
ch
in
e
…
(
I
b
n
u
Ha
ja
r
)
6293
b
ased
o
n
th
ese
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es
b
ec
o
m
es
a
n
iter
ativ
e
p
r
o
ce
s
s
,
aim
in
g
to
ac
h
iev
e
an
o
p
tim
al
eq
u
ilib
r
i
u
m
b
etwe
en
ef
f
icien
c
y
an
d
s
af
ety
.
C
o
n
tin
u
o
u
s
m
o
n
ito
r
in
g
an
d
a
d
ap
tatio
n
to
ev
o
lv
in
g
co
n
d
itio
n
s
co
n
tr
ib
u
te
to
t
h
e
m
o
d
el'
s
r
o
b
u
s
tn
ess
in
n
a
v
ig
ati
n
g
th
e
co
m
p
lex
ities
o
f
th
e
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
c
ess
in
a
n
u
clea
r
r
ea
cto
r
wate
r
co
o
lan
t
p
lan
t.
I
n
ess
en
ce
,
estab
lis
h
in
g
an
d
r
ef
in
i
n
g
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es
ar
e
f
u
n
d
a
m
en
tal
to
m
ac
h
in
e
lear
n
in
g
m
o
d
els'
s
u
cc
ess
f
u
l
d
ep
lo
y
m
e
n
t
an
d
s
u
s
tain
ed
ef
f
ec
tiv
en
ess
in
en
h
an
cin
g
th
e
ef
f
icien
cy
o
f
th
e
s
ec
o
n
d
ar
y
c
o
o
lin
g
p
r
o
ce
s
s
in
n
u
clea
r
f
ac
ilit
ies.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
o
v
id
es a
d
etailed
d
is
cu
s
s
io
n
o
f
th
e
n
u
clea
r
co
o
lan
t sy
s
tem
p
ar
am
eter
s
.
T
h
e
d
etails an
d
co
m
p
ar
is
o
n
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el
wer
e
d
ef
in
e
d
an
d
co
m
p
ar
e
d
.
Pre
d
ictio
n
s
u
s
in
g
th
e
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
wer
e
e
x
am
in
ed
u
s
in
g
v
a
r
io
u
s
p
ar
a
m
eter
s
.
L
astl
y
,
r
esear
ch
g
ap
s
wer
e
p
r
esen
ted
in
T
ab
les
in
th
e
d
esig
n
,
a
n
d
th
e
n
ex
t
p
r
e
d
ictio
n
s
y
s
tem
was
d
ep
lo
y
e
d
to
id
en
tif
y
o
p
tim
al
ef
f
icien
cy
f
o
r
th
e
n
u
clea
r
r
ea
cto
r
'
s
s
ec
o
n
d
ar
y
co
o
lin
g
p
r
o
ce
s
s
.
3
.
1
.
Co
o
la
nt
s
y
s
t
em
a
t
t
he
n
uclea
r
re
a
ct
o
r
T
h
e
p
r
im
a
r
y
c
o
o
lin
g
s
y
s
tem
h
as
a
p
iv
o
tal
r
o
le
in
n
u
clea
r
r
e
ac
to
r
s
.
I
t
allo
ws
r
ea
cto
r
co
o
la
n
t
to
cir
c
u
late
in
a
lo
o
p
f
r
o
m
th
e
r
ea
cto
r
c
o
r
e
to
a
s
ec
o
n
d
a
r
y
c
o
o
lin
g
s
y
s
tem
[
1
9
]
.
I
t
also
e
n
s
u
r
es
th
at
th
e
r
ea
cto
r
c
o
r
e
is
k
ep
t
co
o
l
en
o
u
g
h
to
av
o
id
d
am
a
g
e
d
u
r
in
g
o
p
e
r
atio
n
.
I
n
a
PW
R
,
th
e
p
r
im
ar
y
co
o
lin
g
s
y
s
tem
cir
cu
lates
p
r
ess
u
r
ized
wate
r
h
ea
ted
f
r
o
m
th
e
r
ea
cto
r
co
r
e
an
d
tr
an
s
f
er
s
th
e
h
ea
t
to
a
s
ec
o
n
d
ar
y
s
y
s
tem
in
th
e
s
team
g
en
er
ato
r
[
2
0
]
.
T
h
e
wate
r
in
th
e
s
ec
o
n
d
ar
y
s
y
s
tem
is
h
ea
ted
an
d
co
n
v
er
ted
to
s
team
,
wh
ich
d
r
iv
es
th
e
tu
r
b
in
e.
T
h
en
,
th
e
s
team
co
o
ls
d
o
wn
an
d
r
etu
r
n
s
to
wate
r
in
th
e
co
n
d
en
s
er
[
2
1
]
.
Dif
f
er
e
n
t
ty
p
es
o
f
r
e
ac
to
r
s
u
s
e
d
if
f
er
en
t
co
o
lan
ts
in
th
eir
c
o
o
lin
g
s
y
s
te
m
s
.
T
ab
le
2
s
h
o
ws th
e
k
i
n
d
o
f
r
ea
cto
r
with
its
r
ea
cto
r
co
o
lan
t
.
T
ab
le
2
.
T
y
p
e
o
f
r
ea
cto
r
co
o
la
n
ts
R
e
a
c
t
o
r
T
y
p
e
C
o
o
l
a
n
t
R
e
f
e
r
e
n
c
e
s
P
r
e
ssu
r
i
z
e
d
w
a
t
e
r
r
e
a
c
t
o
r
Li
g
h
t
w
a
t
e
r
[
2
2
]
G
a
s
-
c
o
o
l
e
d
r
e
a
c
t
o
r
H
e
l
i
u
m
g
a
s
[
2
3
]
–
[
2
5
]
Li
q
u
i
d
me
t
a
l
-
c
o
o
l
e
d
r
e
a
c
t
o
r
Le
a
d
-
b
i
sm
u
t
h
e
u
t
e
c
t
i
c
[
2
6
]
,
[
2
7
]
P
o
o
l
-
t
y
p
e
r
e
a
c
t
o
r
W
a
t
e
r
[
2
8
]
3
.
2
.
M
a
chine le
a
rning
m
o
de
l
Ma
ch
in
e
lear
n
in
g
(
ML
)
is
a
s
u
b
s
et
o
f
ar
tific
ial
in
tellig
en
ce
(
AI
)
th
at
s
im
u
lates
th
e
lear
n
in
g
p
r
o
ce
s
s
b
y
estab
lis
h
in
g
a
p
r
e
d
ictiv
e
m
o
d
el,
an
aly
zin
g
its
o
u
tp
u
t
g
iv
en
s
p
ec
if
ied
p
a
r
am
eter
s
,
an
d
p
r
o
g
r
ess
iv
ely
u
p
d
atin
g
t
h
e
m
o
d
el
t
o
in
cr
ea
s
e
its
ac
cu
r
ac
y
[
2
9
]
.
A
m
ac
h
in
e
lear
n
in
g
ap
p
licatio
n
c
o
m
p
r
is
es
th
r
ee
m
ajo
r
co
m
p
o
n
en
ts
:
m
o
d
el,
p
a
r
am
et
er
s
,
an
d
lear
n
e
r
.
T
h
e
p
r
o
ce
s
s
o
f
cr
ea
tin
g
a
n
aly
tical
m
o
d
el
s
is
au
to
m
ated
b
y
m
ac
h
in
e
lear
n
in
g
.
T
h
e
elem
e
n
t
o
f
th
e
s
y
s
tem
th
at
r
ec
eiv
es
in
p
u
ts
an
d
p
r
o
d
u
ce
s
id
en
tific
atio
n
s
o
r
p
r
e
d
ictio
n
s
is
ca
lled
th
e
m
o
d
el.
Par
am
eter
s
in
m
ac
h
in
e
lear
n
in
g
ar
e
d
ata
in
p
u
ts
f
o
r
an
aly
tical
m
o
d
els
u
s
ed
to
p
r
o
ce
s
s
u
s
in
g
th
e
m
o
d
el.
An
y
s
ig
n
al,
co
m
p
o
n
en
t,
o
r
d
ata
item
th
e
m
o
d
el
m
ay
em
p
lo
y
f
o
r
d
ec
is
io
n
-
m
ak
i
n
g
is
ca
lled
a
p
ar
am
eter
.
T
h
e
lear
n
e
r
is
th
e
co
m
p
o
n
en
t
o
f
th
e
s
y
s
tem
th
at
ev
alu
ates
ac
tu
al
r
esu
lts
ag
ain
s
t
p
r
ed
ictio
n
s
m
ad
e
b
y
th
e
m
o
d
el
a
n
d
m
o
d
if
ies
t
h
e
m
o
d
el'
s
p
ar
am
eter
s
a
n
d
,
co
n
s
eq
u
en
tly
,
its
o
v
er
all
s
tr
u
ct
u
r
e
in
r
esp
o
n
s
e
to
th
e
f
in
d
in
g
s
.
Fo
r
alg
o
r
ith
m
s
,
a
v
a
r
iety
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
el
ty
p
es
ar
e
f
r
eq
u
en
tly
u
s
ed
.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
d
if
f
e
r
in
th
e
is
s
u
es
th
ey
ca
n
s
o
lv
e
,
h
o
w
th
ey
a
p
p
r
o
ac
h
p
r
o
b
lem
s
d
if
f
er
en
tly
,
an
d
wh
at
k
in
d
s
o
f
d
ata
th
ey
n
ee
d
to
wo
r
k
at
th
e
ir
b
est.
T
h
e
th
r
ee
m
o
s
t
co
m
m
o
n
alg
o
r
ith
m
s
ar
e
s
u
p
er
v
is
ed
,
u
n
s
u
p
er
v
is
ed
,
an
d
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
,
as
in
s
tu
d
y
[
3
0
]
.
Su
p
er
v
is
ed
lear
n
i
n
g
alg
o
r
ith
m
s
a
r
e
ch
a
r
ac
ter
ized
u
s
in
g
tr
ain
in
g
d
ata,
a
s
et
o
f
tr
ain
in
g
ex
am
p
les
co
n
tain
in
g
s
ev
er
al
in
p
u
ts
an
d
th
e
d
esire
d
o
u
tp
u
t.
As
th
e
tr
ain
in
g
d
ata
ar
e
p
r
o
ce
s
s
ed
b
y
th
e
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
i
th
m
,
a
f
u
n
ctio
n
o
r
m
o
d
el
is
o
p
tim
ized
th
at
ca
n
b
e
u
s
ed
to
p
r
ed
ict
th
e
o
u
tp
u
t
f
o
r
in
p
u
ts
th
at
wer
e
n
o
t
p
r
esen
t
in
th
e
in
itial
tr
ain
in
g
d
ata
[
3
1
]
.
C
las
s
if
icatio
n
,
r
eg
r
ess
io
n
,
an
d
s
im
ilar
ity
lear
n
i
n
g
ar
e
th
r
ee
ty
p
es
o
f
s
u
p
er
v
is
e
d
lear
n
in
g
alg
o
r
ith
m
s
[
3
2
]
,
wh
ile
s
u
p
er
v
is
ed
lear
n
in
g
al
g
o
r
ith
m
s
r
ef
in
e
a
p
r
ed
ictiv
e
m
o
d
el
b
ased
o
n
d
a
ta
with
in
p
u
ts
an
d
o
u
tp
u
ts
,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
ar
e
u
s
ed
to
f
i
n
d
s
tr
u
ctu
r
es,
p
atter
n
s
,
a
n
d
r
ela
tio
n
s
h
ip
s
in
ca
s
es
with
o
u
t
t
r
ain
in
g
d
ata.
Un
s
u
p
e
r
v
is
ed
alg
o
r
ith
m
s
s
u
ch
as
g
r
o
u
p
in
g
o
r
cl
u
s
ter
in
g
o
f
d
ata
p
o
in
ts
ar
e
h
elp
f
u
l
i
n
an
al
y
zin
g
d
ata
th
at
h
as
n
o
t y
et
b
ee
n
ca
t
eg
o
r
ized
o
r
la
b
eled
[
3
3
]
.
T
h
e
alg
o
r
ith
m
s
u
s
ed
i
n
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
d
i
f
f
er
f
r
o
m
th
o
s
e
in
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
e
r
v
is
ed
lear
n
in
g
.
T
h
ey
d
esig
n
ate
a
s
ettin
g
wh
e
r
e
th
e
s
o
f
twar
e
ag
en
t
wo
r
k
s
to
war
d
a
s
p
ec
if
ic
aim
,
an
d
th
e
y
o
p
tim
ize
a
m
o
d
el
o
r
f
u
n
ctio
n
th
o
s
e
aid
s
i
n
th
e
s
o
f
twar
e
ag
e
n
t'
s
ac
h
iev
em
en
t
o
f
th
e
g
o
al.
I
n
th
e
c
o
n
te
x
t
o
f
an
ap
p
licatio
n
lik
e
Go
o
g
le'
s
A
lp
h
a
Z
er
o
,
th
e
en
v
ir
o
n
m
e
n
t
co
u
ld
b
e
a
ch
es
s
g
am
e,
an
d
th
e
aim
co
u
ld
b
e
win
n
in
g
th
e
g
am
e
[
3
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
2
8
7
-
6
2
9
9
6294
3
.
3
.
T
im
e
s
er
ies m
a
chine le
a
rning
m
o
del
A
tim
e
s
er
ies
m
ac
h
in
e
lear
n
in
g
m
o
d
el
is
d
esig
n
ed
to
m
ak
e
p
r
ed
ictio
n
s
o
r
class
if
icatio
n
s
b
ased
o
n
d
ata
th
at
v
ar
ies
with
tim
e.
T
i
m
e
s
er
ies
d
ata
co
n
s
i
s
ts
o
f
o
b
s
er
v
atio
n
s
o
r
m
ea
s
u
r
em
en
ts
co
llected
an
d
r
ec
o
r
d
e
d
o
v
er
r
e
g
u
lar
in
ter
v
als,
s
u
ch
a
s
h
o
u
r
s
,
d
ay
s
,
a
n
d
m
o
n
th
s
.
T
h
e
m
ain
g
o
al
o
f
a
tim
e
s
er
ie
s
m
ac
h
in
e
lear
n
in
g
m
o
d
el
is
to
ca
p
tu
r
e
p
atter
n
s
,
tr
en
d
s
,
an
d
d
e
p
en
d
e
n
cies
with
in
h
is
to
r
ical
tim
e
-
o
r
d
er
ed
d
ata
to
m
ak
e
in
f
o
r
m
e
d
p
r
ed
ictio
n
s
ab
o
u
t
f
u
tu
r
e
v
al
u
es
[
3
5
]
.
T
im
e
s
er
ies
m
o
d
els
ex
p
licitly
co
n
s
id
er
th
e
tem
p
o
r
al
o
r
d
er
o
f
o
b
s
er
v
atio
n
s
[
3
6
]
.
T
h
e
r
elati
o
n
s
h
ip
b
etwe
en
cu
r
r
en
t
an
d
p
ast
o
b
s
er
v
atio
n
s
is
c
r
u
cial
f
o
r
m
a
k
in
g
ac
cu
r
ate
p
r
ed
ictio
n
s
.
T
im
e
s
er
ies
d
at
a
is
in
h
er
en
tly
s
eq
u
en
tial,
a
n
d
th
e
s
eq
u
en
ce
o
f
o
b
s
er
v
at
io
n
s
m
atter
s
.
T
h
is
s
eq
u
en
tial
n
atu
r
e
d
is
tin
g
u
is
h
e
s
tim
e
s
er
ies
m
o
d
els f
r
o
m
tr
ad
itio
n
al
m
ac
h
in
e
lear
n
i
n
g
m
o
d
e
ls
th
at
ass
u
m
e
ea
c
h
o
b
s
er
v
atio
n
is
in
d
ep
e
n
d
en
t
[
3
6
]
.
Ma
n
y
tim
e
s
er
ies
m
o
d
els
u
s
e
lag
f
ea
tu
r
es,
wh
ich
in
v
o
l
v
e
u
s
in
g
p
ast
v
alu
es
o
f
th
e
tim
e
s
er
ies
as
in
p
u
t
f
ea
tu
r
es
f
o
r
p
r
ed
ictin
g
f
u
tu
r
e
v
al
u
es.
T
h
e
id
ea
is
to
ca
p
tu
r
e
te
m
p
o
r
al
p
atter
n
s
an
d
tr
en
d
s
.
T
im
e
s
er
ies
o
f
ten
e
x
h
ib
it
tr
en
d
s
(
l
o
n
g
-
te
r
m
ch
a
n
g
e
s
)
an
d
s
ea
s
o
n
ality
(
r
ep
ea
tin
g
p
atter
n
s
)
.
E
f
f
ec
tiv
e
m
o
d
els
s
h
o
u
ld
b
e
ab
le
to
ca
p
t
u
r
e
an
d
ac
co
u
n
t
f
o
r
th
ese
co
m
p
o
n
e
n
ts
.
T
im
e
s
er
ies
m
o
d
el
s
ar
e
u
s
ed
in
v
ar
io
u
s
d
o
m
ain
s
wh
er
e
o
b
s
er
v
atio
n
s
ar
e
co
llected
o
v
er
r
e
g
u
lar
i
n
ter
v
als,
s
u
ch
as
s
to
ck
p
r
ices
[
3
7
]
,
[
3
8
]
,
wea
th
er
d
ata
[
3
9
]
,
an
d
en
er
g
y
co
n
s
u
m
p
tio
n
[
4
0
]
,
[
4
1
]
.
W
h
en
wo
r
k
in
g
with
tim
e
s
er
ies
d
ata,
it
i
s
es
s
en
tial
to
co
n
s
id
er
f
ac
to
r
s
s
u
ch
as
s
ea
s
o
n
ality
,
t
r
en
d
s
,
s
tatio
n
ar
ity
,
a
n
d
th
e
s
p
ec
if
ic
ch
ar
ac
ter
is
tics
o
f
th
e
d
ata
[
4
2
]
.
M
o
d
el
s
elec
tio
n
s
h
o
u
ld
b
e
b
ased
o
n
th
e
p
r
o
b
lem
'
s
n
atu
r
e
an
d
th
e
f
o
r
ec
asti
n
g
task
'
s
r
eq
u
ir
em
en
ts
.
E
x
p
er
im
en
tin
g
with
d
if
f
er
en
t
m
o
d
els
an
d
tu
n
in
g
h
y
p
e
r
p
ar
am
ete
r
s
is
o
f
ten
n
ec
ess
ar
y
to
ac
h
iev
e
th
e
b
est
p
er
f
o
r
m
an
ce
f
o
r
a
p
ar
ticu
lar
tim
e
s
er
ies d
ataset
[
4
3
]
–
[
4
5
]
.
3
.
4
.
P
re
dict
iv
e
m
a
chine le
a
r
nin
g
m
o
del f
o
r
s
ec
o
nd
a
ry
co
o
lin
g
s
y
s
t
em
s
Pre
d
ictiv
e
m
ain
ten
an
ce
ca
n
b
e
d
ef
in
ed
as
m
ain
ten
an
ce
s
tr
ateg
ies
th
at
u
s
e
th
e
ac
tu
al
o
p
er
atin
g
co
n
d
itio
n
o
f
an
eq
u
ip
m
en
t
to
p
r
ed
ict
its
f
u
tu
r
e
s
tates
b
ased
o
n
its
p
ast
o
p
e
r
atio
n
.
T
h
is
m
ax
im
izes
th
e
tim
e
in
ter
v
al
b
etwe
en
m
ai
n
ten
an
ce
task
s
an
d
p
r
e
v
en
ts
u
n
n
ec
ess
ar
y
m
ain
ten
a
n
ce
[
4
6
]
.
Pre
d
ictiv
e
m
ain
ten
an
ce
in
th
e
p
r
im
ar
y
co
o
lin
g
s
y
s
tem
h
as
b
ee
n
s
tu
d
ied
in
s
ev
er
al
liter
a
tu
r
e.
Data
-
d
r
iv
en
p
r
ed
ictiv
e
m
ain
ten
an
ce
m
o
d
els
ty
p
ically
ap
p
ly
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
to
estab
lis
h
a
m
o
d
el
th
at
ca
n
tak
e
o
p
er
atio
n
d
ata.
T
h
is
m
o
d
el
r
eq
u
ir
es
a
lar
g
e
am
o
u
n
t
o
f
d
a
ta
f
o
r
th
e
p
r
e
d
ictio
n
to
b
e
ac
cu
r
ate
[
4
7
]
.
T
ab
le
3
s
h
o
ws
s
o
m
e
o
f
t
h
e
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
ap
p
lied
in
p
r
ev
io
u
s
s
tu
d
ies
an
d
th
e
f
o
c
u
s
o
f
th
e
s
tu
d
ies.
T
h
e
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
is
a
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
b
ased
o
n
th
e
h
u
m
an
n
er
v
o
u
s
s
y
s
tem
t
h
at
ca
n
lin
k
a
s
y
s
tem
'
s
in
p
u
t
an
d
o
u
tp
u
t.
Patter
n
r
ec
o
g
n
itio
n
,
d
ata
r
elatio
n
s
h
ip
s
,
an
d
lear
n
i
n
g
th
r
o
u
g
h
e
x
p
er
ien
ce
a
r
e
all
way
s
in
wh
ich
an
ANN
o
b
tain
s
k
n
o
wled
g
e.
I
t
i
s
a
co
llectio
n
o
f
co
m
p
u
tatio
n
al
n
o
d
es
o
r
g
a
n
ized
in
t
o
v
a
r
io
u
s
lay
er
s
.
T
h
e
b
etter
th
e
n
o
d
es
in
a
n
etwo
r
k
ar
e
c
o
n
n
ec
ted
,
th
e
b
etter
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4
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5
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[
5
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[
5
5
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T
ab
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4
s
h
o
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m
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f
t
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v
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tag
es
a
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lim
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th
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liter
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r
k
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Mo
s
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tag
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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I
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N:
2088
-
8
7
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8
Op
tima
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clea
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(
I
b
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Ha
ja
r
)
6295
T
ab
le
4
.
Sev
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al
a
d
v
an
ta
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es a
n
d
lim
itatio
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o
f
m
ac
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[
5
6
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S
V
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[
5
0
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ANN
En
a
b
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p
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[
5
2
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ANN
D
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.
[
5
1
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ANN
A
b
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3
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5
.
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T
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ar
am
eter
s
to
m
ain
tain
th
e
s
af
ety
o
f
its
o
p
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atio
n
[
1
5
]
.
Sev
e
r
al
p
ar
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f
o
u
n
d
in
m
an
y
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v
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s
s
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T
ab
le
5
s
h
o
ws
s
ev
er
al
p
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liter
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r
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s
tu
d
ies
an
d
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ed
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th
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T
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m
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s
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s
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s
ed
in
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liter
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r
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ar
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f
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T
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is
s
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th
at
th
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p
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s
ar
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ess
en
tial
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u
clea
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m
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ito
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.
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h
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p
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th
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p
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s
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o
f
th
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c
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o
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t
s
h
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th
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t
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is
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v
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lo
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y
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tem
.
T
h
u
s
,
th
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e
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a
lack
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h
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id
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m
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g
,
esp
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co
m
b
in
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f
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s
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to
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eg
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ess
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in
teg
r
ate
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m
o
v
in
g
av
er
a
g
e
(
SAR
I
MA
)
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
m
o
d
elin
g
u
s
ed
to
p
r
ed
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th
e
s
ec
o
n
d
ar
y
c
o
o
lin
g
p
r
o
ce
s
s
at
a
n
u
clea
r
r
ea
ct
o
r
.
I
m
p
lem
en
tin
g
s
u
c
h
a
h
y
b
r
id
m
o
d
el
co
u
l
d
s
ig
n
if
ican
tly
en
h
a
n
ce
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
p
r
ed
icti
o
n
s
.
T
h
is
ap
p
r
o
ac
h
f
o
c
u
s
es
o
n
id
en
tify
in
g
f
au
lty
p
r
o
ce
s
s
es
an
d
o
p
t
im
iz
in
g
e
f
f
i
cien
cy
p
er
f
o
r
m
an
c
e
in
a
n
u
clea
r
r
ea
cto
r
.
T
ab
le
5
.
Sev
er
al
p
ast liter
atu
r
e
s
tu
d
ies an
d
th
e
k
e
y
p
ar
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Evaluation Warning : The document was created with Spire.PDF for Python.
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.
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