Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 24
ANN SOFT SENSOR TO PREDICT QUALITY OF PRODUCT BASED ON TEMPERATURE OR FLOW RATE CORRELATION
Totok R. Biyanto
Engineering Physic Dept. - FTI – ITS Surabaya Kampus ITS Keputih, Sukolilo, Surabaya 60111
Tell: 62 31 5947188 Fax: 62 31 5923626 Email: totokrbep.its.ac.id
ABSTRACT
Analizer has slow respon performance, lack of reliability, and expensive, then inferensial
measurement by using temperature measurement, reflux flow rate and reboiler steam flow rate are
usualy the best way to predict it. This paper will describe Artificial Neural Network ANN soft sensor
capability to predict mole fraction Distillate Xd and mole fraction bottom product Xb at binary
distillation column.
Inferensial measurement could built by using reflux flowrate and reboiler steam flowrate at LV
structure, by using smart positioner feedback signal, or by measuring tray temperature.
Soft sensor which using tray temperature correlation or flow rate correlation have good Root
Mean Square Error RMSE. So, the conclusion is application of ANN soft sensor can build using
temperature or flow rate correlation, depend on control strategy or sensor availability.
Key-words: ANN soft sensor, composition prediction, temperature and flow rate.
1. INTRODUCTION
ANN soft sensor was developed for a batch distillation column, in order to estimate product
compositions using available temperature measurements [10], and a non linear soft sensor was
developed using temperature top tray correlation for ternary batch distillation column using Hysys plant
and Matlab [9]. The others researches are using some flow rate and fuel gas burner pressure to predict
oxygen content at stack of boiler [3].
Composition measurement and control at binary distillation column often use inferential measurement
and control, because analizer has slow respon performance, lack of reliability, and expensive. [4,10]
Mole fraction distillate and bottom product could predicted by using correlation between temperature
and mole fraction at certain trays. Inferential composition measurement by using temperature
correlation usually use single or multi thermocouple at certain place.
Another way to predict mole fraction distillate is using reflux flow rate and to predict mole fraction
bottom product is using stream flow rate at reboiler.[1] Relation between temperature and mole fraction
are non liner and influenced by pressure of distillation column, mole fraction feed, flow feed, flow steam of
re-boiler, condenser level, etc. So, soft sensor must have capability to predict composition product without
influenced by the others, non linier, easy to build, and no need special instrumentations.
This paper will shown that ANN with the same MLP structure, 6 history lengths, 13 hidden nodes and
trained for 200 times computer iteration, applied to predict mole fraction composition distillate and
bottom product at methanol-water binary distillation column by using temperature or flow rate correlation.
2. DISTILATION COLUMN AND ARTIFICIAL NEURAL NETWORK
A single feed stream is feed as saturated liquid onto the feed tray N
F
. Feed flow rate is F molehour and composition X
F
mole fraction more volatile component. The overhead vapor totally condensed in
a condenser and flows into the reflux drum, whose holdup of liquid is M
D
moles. The content of the drum is at its bubble point. Reflux is pumped back to
the top tray N
T
of column at a rate L. Overhead distillate product is removed at a rate D. Figure 1
Fig. 1. Binary Distillation Column
Ann Soft Sensor to Predict Quality of Product Based on Temperature or Flow Rate Correlation – Totok R. Biyanto
ISSN 1858-1633 2005 ICTS 25
At the base of the column, liquid bottoms product is removed at a rate B and with composition X
B
. Vapor boil up is generated in thermosiphon reboiler at
rate V. Liquid circulates from the bottom of the column through the tubes in the vertical tube-in shell
reboiler because of the smaller density of the vapor liquid mixture in the reboiler tubes. We will assume
that the liquids in the reboiler and in the base of the column are perfectly mixed together and have the
same composition X
B
and total holdup M
B
moles. The composition of the vapor leaving the base of the
column and entering tray 1
st
is y
B
. It is equilibrium with the liquid with composition X
B
. The column contains a total of N
T
theoretical trays. The liquid hold up on each tray including the
downcomer is M
N.
The liquid on each tray is assumed to be perfectly mixed with composition X
N
. [3]
2.1 Rigorous Modeling of Distillation Column
Condensor and reflux drum
Mass balance:
D L
V dt
dM
NT NT
D
− −
=
+1
……………….1 Component mass balance:
D NT
NT NT
D D
x D
L y
V dt
x M
d
1
+ −
=
+
….2 Energy balance:
D NT
NT NT
NT D
D
Q Dh
H L
H V
dt h
M d
+ −
− =
+ +
1 1
...3
Reboiler and base column
Mass balance:
B V
L dt
dM
RB n
− −
=
1 ……………………4
Component mass balance: b
B RB
B B
Bx y
V x
L dt
x M
d −
− =
1 1
…….5 Energy balance:
b b
B RB
B B
Q Bh
H V
h L
dt h
M d
+ −
− =
1 1
..........6 Feed tray n = N
F
Mass balance:
NF NF
NF NF
NF
V V
F L
L dt
dM −
+ +
− =
− +
1 1
..7 Component mass balance:
z NF
NF NF
NF NF
NF NF
NF NF
NF
F y
V y
V x
L x
L dt
x M
d +
− +
− =
− −
+ +
1 1
1 1
.8 Energy balance:
F NF
NF NF
NF NF
NF NF
NF NF
NF
Fh H
V H
V h
L h
L dt
h M
d +
− +
− =
− −
+ +
1 1
1 1
.9
N
th
tray
Mass balance:
B V
L dt
dM
RB n
− −
=
1 ….…..…………….10
Component mass balance:
n n
n n
n n
n n
n n
y V
y V
x L
x L
dt x
M d
− +
− =
− −
+ +
1 1
1 1
.11 Energy balance:
n n
n n
n n
n n
n n
H V
H V
h L
h L
dt h
M d
− +
− =
− −
+ +
1 1
1 1
.12
2.2 Artificial Neural Network
There are many reasons to apply artificial neural network as followed :
• Self-learning ability • Non-linear mapping
• Massively parallel distributed processing
∑
Fig. 2. Neuron
Levenberg Marquard Learning Algorithm
Levenberg Marquardt algorithm can be described as followed : [6]
1. Choose initial weight vector w and initial value
of λ
. Whereas w is matrix weight and
λ search direction.
2. Find out the right direction
] [
i i
i i
w G
f I
w R
− =
+ λ
……13 then obtain f and substitute it to:
, min
arg
N N
w
Z w
V w
=
if V
N
w
i
+ f
i
,Z
N
V
N
w
i
,Z
N
then fulfill w
i+1
= w
i
+ f
i
as new iteration, so, λ
i+1
= λ
i
. If w
i+1
= w
i
+ f
i
not fulfill then find out new r ,
, ,
i i
i N
i N
N i
i N
N i
N i
f w
L Z
w V
Z f
w V
Z w
V r
+ −
+ −
= ..14
If r
i
0,75 then λ
i
= λ
i
2 If r
i
0,25 then λ
i
= 2 λ
i
Whereas: G
f f
f f
w L
T i
i T
i i
i i
i
− =
+ λ
..15 3. If criteria is reached, calculation will stop. If
criteria is not reached, back to step 2.
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 26
Main TS - Stage Temperature 1__Main TS 70
75 80
85 90
95 100
105 110
1 245
489 733
977 1221 1465 1709 1953 2197 2441 2685 2929
waktu menit te
m p
[C ]
Main TS - Stage Temperature 14__Main TS 64.5
64.6 64.7
64.8 64.9
65 65.1
65.2 1
248 495
742 989 1236 1483 1730 1977 2224 2471 2718 2965
waktu menit te
m p
[ C
]
B - Comp Mole Frac Methanol 0.02
0.04 0.06
0.08 0.1
0.12 0.14
0.16 0.18
1 248
495 742
989 1236 1483 1730 1977 2224 2471 2718 2965
waktu menit me
th an
o l
D - Comp Mole Frac Methanol 0.979
0.981 0.983
0.985 0.987
0.989 0.991
0.993 0.995
0.997 1
252 503
754 1005 1256 1507 1758 2009 2260 2511 2762 3013
waktu menit m
e th
a n
o l
3. RESULTS 3.1 Soft sensor
