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Hybrid Neural Net, Physical Modeling Applied to a Xylene Splitter
HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
14th World Congress of IFAC
N-7a-07-4
Copyright ·f) 1999 IFAC ] 4tb Triennial World Congress, Beijing, P.R. China
HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A XYLENE SPLI'TTER Amish Sabhar,,·al*, w~ y ~ Svrcek*, and D.E. Seborg+
* [Jniversit)/ {)fCalgar..¥~ Depar/ment ofChemical Engineering, 2500 Univer.\·ity Dr. ,-V. W., Calgory, AB, T2lV-l/'tl4 Canada, Te/: (403) 220-5755 Fax (403) 282-3945, 1
1
e-lnail: anlish. sabha,\-llalrZiJhvprotech. COIn, e-mail: [email protected] t University o.fCalifornia. Departlnent olChemical Engineering, lJniversityof Ca1t!orniaSantaBarbara, CA. 93106, USA, rei: (805) 893-3352, Fax (805) R93-4731 e-Inail: seborg@engineering. UL'sh. edu
ABSTRACT There is a considerable current interest in developing effective methods for the integration of physic.al and empirical dynamic models. In this paper, a novel integration strategy is applied to a full scale} distillation column in the fv[izushima Oil Refinery of the Japan Energy Corporation. Artificial neural networks (ANN) models of a xylene splitter were constructed using commercial software, Process Insjghts (Pavilion Technology Inc.~ 1996)~ and two different types of data sets, slep response tests and data sets for nominal operation (without plant tests). A first principles model of the distillation column \vas developed using a conunercial software package, HYSYS (Hyprotech Ltd_, 1996). The models \vere compared based on their accuracy in predicting plant data. In this application, it is shown that the lack of excitation in the nominal data during the training of an AhTN model proved to be significant in the model's inability to predict the plant's transient conditions. Rut v..,'hen the nominal training data \\'as augmented by the simulated response data from the physical illudel, the accuracy of the ANN model was improved significantly. Copyright © 1999 IFAC. Keywords: Neural columns, Hybrid
1.
~em~orks,
Dynamic Modeling;.
INTRODUCTION
Physical (or first principles) models of industrial processes lend to be complex and computationally denlanding. Thus~ simplifying assumptions must often be made that limit their accuracy. However, recent advances in sofh-vare and computational speed make it possible to develop dynamic models using the same kno\vledge base that has been used for steady-state models, the underlying chetnistry~ physics, and them1.odynamics of the process
Simulation_~
Distillation
Dynamic simulation can be used to obtain a better understanding of process dynamic behavior or to provide insight into the interaction between inputs and outputs of a process. Dynamic simulations of distillation columns employ rigorous stage by stage ITIodels that include component, mass and energy balances, liq uid flow dynaulics, and pressure dynamics on each stage (Luyben, 1992). 111ese nl0dels tend to be quite conlplex, consisting of hunilieds of nonlinear ordinary differential equations plus nonlinear algebraic equations (e.g., bubble point calculations) \.\"hich U1USt be solved iteratively. An altenlative approach to physical ll10deling is the use
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HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
of empirical models, which rely on process data to develop a relations.hip benvcen process inputs and OlltputS. Artificial Neural Networks (ANN) have been \.vide1y used as empirical nlodels for many chemical engineering problems (Su and Mc..A .voy, 1997). The fast execution times of ANN models Inakc them ideal for Teal-time process optimization (Sabhar\val et aI., 1997). Their universal approximation capabilities (Homjk et at) 1 989) make AN:\[ good candidates fOT mode ling nonlinear chemical process. The major limitation of these models is their prediction accuracy is only as good as the data used to train the models. Therefore, a model developed ,"",ith process data that does not span a wide operating region may have limited utility. With the increasing number of ANN applications in the process industries, there has been considerab le interest in trying to create a suitable integration of neural net\.vorks and physical models (Psychogios & Ungar~ 1992; Su et aI., 1992; Thompson & Kramer, 1994; Aganval, 1997). But only a fe\v hybrid
The data collection is one of the most important steps in the identification of accurate dynamic models. The excitations present jn the input/output data and the regions of operation are important factol"s in determining if the data is a good representation of process behavior. In typical industrial applications, process data is coIl ccted over a long time period, which may extend from several \veeks to months, in order to collect data that provide infonnation about a wide range of process operations. In this application, only a small amount of process data \vas collected. Data \vas col]cctcd for both closed-loop and open loop conditions for a period of 10 days using a sanlpling period of three minutes.
+--~iII5Io-"":-""",,"-------lM----:"'_--'*F257
modeling case studies have been published (Agarwal, 1997).
I
i
r:MI1la1e Rtt
l~he
focus of this paper is to show that the ANN and first principle modeling techniques are indeed conlplementary. In the distillation application.~ the physical model is used to provide knowledge in the form of simulated data that is used to augment the identification data used to train the ANN model. 1.1 Process Description and Data Collection This study is concerned with developing accurate dynamic models of a full-scale distillation column in the Mizushima refinery of the Japan Energy Corporation. Column TW252 is the middle column 10 the train of five columns for the No. 2 xylene distiHation unit. The column consists of an overhead condenser, cooler, reboiler, and 30 internal trays. The feed enters as a liquid at the design condition of 160 C and 760 mmH~ and contains a rnixtuTc of C8'g (ethyl benzene, mlp-xylene, o-xylenc) and C9's components (1 M2-e-benzene, and traces of 1,3-ebenzene and naphthalene). The column is controlled by several single input,
single output (SISO) temperature and pressure control loops as shown in the schematic diagram in Figure 1. The nominal control strategy is to regulate the temperature at stage 27 (Y255), to infer the distillate composition, by manipulating a slave reflux floVlIate controller, F256. However, due to unsatisfactory performance the loop is no",,' open, slave controller is in manual, and the reflux is used to regulate top purity. 1'he bottoms temperature (Y284) is controlled to infer the bottoms composition by adjusting the steam flO'-" rate to the reboiler (F267). For a more detailed description of the process, see Sabharwal (1998).
Fig.I. Process Flov.,' Diagram and Tag Descriptions During this period, eight experimental "bump tests~' were perfonned on TW252 (Sabbarwal, 1998); step changes up and dovro. in the setpoints of Y255, P252~ L254, F256, F257, Y284, and F267. rfhe "*,, in Figure 1 indicates all the variables measured as a result of the tests. The input test signal used for all the open and closed loop experiments is shov-.'n in Figure 2. However,. for some tests, only a step up or a step down "vas allowed because of possible product specification violations (Swanda, 1999).
9=J---
Llp ___ S:ep---..I
Slep DDwn
Fig. 2. Example of a Test Signal The step response data, \vere merged into onc datasct, and labeled as l.t. In addition to the experimental "bump test" data, nominal datasets were collected for three vel)' different sets of "nonnal" operating conditions. Dataset N.1 consists of six consecutive days of nominal operating conditions recorded during the month of July, 1995. Datasets N.2 and N.3 are indicative of operation during May and June;> 1995,
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respect.ively. These nominal dataset were combined into dataset I.n.
2. SIMULATION MODEL In order to develop a physically based process model of TW252 that realistically predicts the plant dynamics, a dynamic distillation model was developed using a conunercial sofnvare package, HYSYSTM, from Hyprotech Ltd. The development of the HYSYS model was based both on engineering process knowledge and judgement. A steady-state model was first developed that determined the nominal steady-state condition from the unsteadystate mass and energy balances. This steady state is then used to initialize the dynamic model. Next, the dynamic model for TW252 was developed that included the Inass and energy balances for each tray in the column, for the condenser, and reboiler. Dynamic Distillation Tray Model The physical model for the distillation tOV.ler v.,ras developed using the single distillation tray model. The dynamic nlode} for a single tray contains Nc-l differential material balances, where Ne is the number of components in the system, and one overall material and energy balance. Because the tower pressure is not very high, (maximum 760 mmHg), the follo\ving assunlptions were made (Sabharwal,1998): 1. 2.
3. 4.
Single flow pass tray hydraulics The change in specific enthalpy is very small compared to the total tray enthalpy. Vapor mass is less than 30% of the total tray material; hence~ vapor holdup is negligible. Tray pressure drop is constant and the pressure profile is detennined linearly from the condenser pressure.
Luyben (1992) has suggested that the above assumptions are "good enough" to solve 95?-{} of industrial distillation problems. C~ondenser and
Reboiler The condenser model represents the achlaJ condenser as an un-vented (no vapor product) partial condenser ,vith varying pressure. It assumes that the amount of hea t required to condense the overhead vapor to its bubble point temperature plus the sensible heat required for any subcooling is calculated from the utility fluid parameters. The amount of condensation determines the condenser pressure and the condensate temperature of the liquid leaving the condenser, allowing for the amount of subcooling to be specified. For the rcboiler, the heat duty is calculated by means of enthalpy balances around the column. Equip/llent Specifications
14th World Congress ofIFAC
For the purpose of the dynamic HYSYS model, all equiplnent sizes were based on physical dinlensions of the actual equipnlent specifications received from the refinery. The physical dimensions that were calculated for TW252 include the tray sizes (weir heights, weir lengths, and tray volume). The to"ver and condenser cooling volumes were inferred from the column diameter (3.25 m) and from the maximum allowable reflux flow rate (50 m 3 /h) (Sabhanval, 1998). Note, it is very important that reasonable values for the tower volume be specified because this parameter defmes the vapor traffic profile in the column during dynamic simulation. Table 1 summarizes the HYSYS model specification for the tray model. Table I HYSYS model equipment specifications for TW252~s trays Numbe r of Trays
30
Tray Hold up Time (s)
30
Tray Weir Height
Max.
Total
Liquid
To~'er
\'o ]urnc
(m)
(m 3 )/tray
\lolume (m 3 )
0.05
0.83
250
The condenser and reboiler are sized based on a liquid holdup, w'i th the holdup set equal to 10 min for each vessel. The condenser and reboiler volunles were not available from the equipment specification sheets. Again, based on the maximum allo\vabJe reflux and distillate (F257) flo\v rate, the condenser volume and reboiler volumes are determined and shown in Table 2. Table 2 HYSYS model equipment specifications for TW252'5 vessels Vessel Hold up Time (min) 10
Reboiler Volume (mJ ) 30
Condenser Volume (m3 ) 30
Standard PI controllers \vere enlployed for level control of the reboiler, condenser, as well as
temperature, flow, and pressure controL All of the controller settings and transmitter spans required for the simulation \vere obtained from the DeS at the Mizushima Oil Refmery ~ 2.1 Model Validation The key to development of a useful simulation model is "validation)~ with actual plant data. A good model must match the plant at steady-state and accurately track the process during dynamic upset. The model verification includes a check of the overall material balances, steady-state temperature profiles~ product flows as well as a steady-state gain analysis. Furthermore, the model must be able to track the open and closed loop perfonnancc of most of the process variables. Finally, good model accuracy for closed loop operation will guarantee that the model is accurate and useable.
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HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
The HYSYS Inodel developed for TW252 was validated using plant data for open and closed loop step responses and nominal plant data. The steadystate behavior of the column was fust validated using nominal plant operating data. A quantitative assessment of TW252's non-linearity and interactive nature was investigated by calculating steady-state process gains and perfanning a relative gain array (RGA) analysis. Also a qualitative assessment of the physically based dynanlic model ~s ability to predict transient conditions is benclunarked against plant data. The results will be sho'\.vn in Section 4.
3. NEURAL NETWORK MODEL In recent years there has been a tremendous mnount of interest and speculation about chemical process modeling using a11ificial neural nehvorks (Su and McAvoy, 1997). With their siluple structure and fast computational perfonnance~ ANN are an ideal complement to traditional process modeling (Sabhanval et aI., 1997). Using a commercial neural net\.vork sofhVare package, Process Insights™ from Pavilion Technologies, ANN models \vere developed for 7\\'252 and are sununarized in Table 3. Table 3 Classification of the ANN models developed for l'\~l2 52 Tw252 Category IntcTI1al 'Tray Temperature
Distillate
A~7'
Model Name Temp Model
Camp Model
Compositions
Outputs Y282, Y255, Y297~ Y257, Y284
A254, A256, A257
Internal Flo\v Model
Rev Model
F256,F267
Condenser and
Lev Model
L254,L253
Reboiler Levels
To ide,ntify ANN models~ the training signals should be sufficiently rich so that the process response data reflects the desired range of transient and steady-state operating conditions. Ho\vever, no general specification is available in the literature for selecting an optimal training signal. Hence, care must be taken when selecting the type and range of data to be used for model identification and val idarioD. l~hc steadystate behavior of the column was f1.rs[ validated using nominal plant operating data. A quantitative assessment of TW252 ~s non-linearity and interactive nature was investigated by calculating steady-state process gains and perfoffiling a relative gain array (RGA) analysis. Also a qualitative assessment of the physically based dynamic model ~ s ability to predict transient conditions is benchmarked against plant data. 'The results will be shovln in Section 4~ The
A1'JN models listed in Table 3 two datasets: Lt and J.n, which (Temp,t, Camp.t, Rev.t, Lev.t) Comp.n, Rev.n, Lev.n ) models
\vere identified using are labeled as ANN.t and ANN.n (Temp.n~ respectively.
3.1 ]Veural Jv~enf,/ork Topology Before ANN.t and ANN.n models can be identified~ the topology of the models must be defined. Hence, the ANN topologies must minimize the number inputs and optimally utilize the correct number of hidden nodes to predict the outputs of the models. In turn reducing the number 0 f inputs will decrease the COlllputatiollal load of the ANN by minimizing the number of weights needed betv.leen the inputs and the outputs. The optimum ANN models \vere determined using an iterative procedure that involved initially specifying a generic nenvork topology with all possible inputs that might affect the outputs (Sabhar\¥al~ 1998). Using a sensitivity analysis (Process Insights Reference Manual 1996), for each output, the inputs were ranked in order of highest average absolute sensitivity (average sum of the absolute values of the partial derivatives of the input-output pairs). A three layer ~~extemal recurrent backpropagation'~ approach (Hecht-Neilson, 1997) was used identify the models. "}'his algorithm is useful in nlodeling dynamic systenlS because it recursively presents past values of the outputs of the network as inputs to the ANN. Moreover, the number of past values of both the Input (m) and output (n) are very important because they include the transient behavior in the A~~ topology. Furthermore, the tim.e delay (8) associated between a particular input and output was also included. From these input/output configurations, the Process Insights software detennined the appropriate number of hidden nodes (Nh). A summary of the selected topologies for the nlodels listed in Table 3 are ShO\Vll in Table 4.
Table 4 The selected topulogies for the other TV"i"252 ANN models M:deI
~
m
n
~
l~
CtJq:Us
Tc:p:jo;:ry
TetTP
DjraTlc
3
3
17
Pl52
0
Yl8'.2
35-17-5
Y200
0
Y255
F256
0
'm7
F~
0
Y257
Co'T1J
RrN
Leve
C¥aric
Djrnric
~
2
3-
3
2
3
3
13
10
10
F2l34
0
Y284
P252
20
A254
F256
20
A256
1J2.57
F267
20
A264
40
A2.65
40
A.266
40
P252
0
F256
Y255
a
Fa37
Y284
0
F257
0
L254
F27'\
a
l253
F284
Q
24-13-3
18-10-2
18-10-2
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Copyright 1999 IFAC
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HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
Note that for the Camp Model in ]'able 4, the apparent time delays between the inputs and the three Ol1tputs \vere approximately 60 minutes or 20 sampling intervals for P252, F256, F267 and 40 sampling intervals for the feed compositions
The large apparent tinle delay is due to the sampling location of the composition analyzer. The same analyzer is used to measure the feed and distillate compositions, hence the total time delay associated ben:vcen the inputs and outputs incorporates both inherent and processing time delays~
14th World Congress of IFAC
"[he prediction results of the physical model and the models are also summarized In Table 5 for other process variables using I.sim and I.ann respective1y, In general, Table 5 sho"\vs that the lIYSYS model captured the dynamic response of T'\\T252 for dataset t.4 quite effectively. lIo",-ever, the . .- \NN.t model proved to be the most accurate model ",-,hile the ANN.n model "vas the least ac.curate. The shaded regions in the table indicate that the model prediction \vas grossly unreasonable. A~
4 .. COMPARISON OF ANN AND HYSYS MODEL PREDICTIONS The ANN and IIYSYS model predictions are conlpared with plant data using the follo,ving performance index.
where: y"k,i
=
Sk,i
=
actual jth output at kth sample pattern predicted i th output at k th sample pattern
mean of the in the dataset
jth
output for all the patterns (P)
There is no general nununurn threshold used to determine if the performance index (I) is satisfactory. I was only used as a relative comparison to gauge the predictive ability of an A1\~ relative to other ANN's and in particular, relative to the HYSYS model. However, some generalizations for I are made based on this 'Vvork. l'he perfonnance of the HYSYS model is donated by I.sim~ vlhereas, the performance of the ANN.t and ANN.n nlodels will be donated by I.ann. 4.1 C o rnparis on \A/ith P fan! Data Figure 3 shows the ANN.t models (Temp.t) Camp,t and Lev.t) and HYSYS model predictions of a snapshot of the 480 available samples of plant dataset, L4, Dataset t.4 has a series of manipulated variable setpoint changes in F256 (+5.0 m 3 /h and -2.5 3 m 3 /h) from its noJIIinal value of 41 ~2 m /h (SabharwaI,1998). Both the Y255 and Y284 control loops were opened during these bump tests. Figure 3 (a) and (b) provides comparisons of the plant response data to the HYSYS and ANN. t predictions of Y257 (stage 1 temperature) and A254 (C9+ wt.% in the distillate) for a F256 (Reflux flow rate) step change. Figure 3 (c) is a comparison of the plant data to the HYSll"S and ANN.t responses of L254 (Condenser liquid level) to a F257 (Distillate flow rate) step change.
Fig. 3. Conlparison of the HYSYS and ANN.t prediction of plant open loop behavior for selected T\V252 variables. In contrast, the ANN.t n10dels ,,"'ere identified using five sets of step response plant data, ,"vhich included Dataset t.4. Therefore based on I.ann values? the ANN.t models should be able to predict dataset t.4 much more accurately than the HYSYS model. Based just on the performance indices (I.sim and Lann), "more accurately'~ or ~\better~" sometimes does not guarantee that the prediction is right. From Figure 3 (b) the Camp.t model accurately predicted, A254 with an Lann = 1.07 '70 in comparison to the HYSYS
n1.odel prediction of I.sim 33 .83t}~ at the composition analyzer points~ The accuracy of the ANN model to predict the plant data cannot be denied; hOVv'ever, the DeS recordable span for A254 was limited to an upper limit of 2 wt.%. Hence, from the recorded process data there was no accurate indication of A254 above 2 "Wt.%. The HYSYS model predicted response of A254 \vas indeed ,,"vc]] above the DeS limlt of 2 wt. 0/0. Hence, there is no reasonable indication of A254 above this value. The ANN model is only as good as the data used to train it, therefore its liITlited knowledge of the process (Le. limitations of the DeS span) restricts its ability to extrapolate. The on-line analyzer's low frequency sampling and the DeS limitation provided no apparent information about the dynanlic behavior of A254 in dataset t.4, On the other hand, the HYSYS model apparently captured the true dynamic behavior of A254 between the on-line analyzer samples and above the DeS upper limit.
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ISBN: 0 08 043248 4
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A similar trend was observed for the other datascts. In general, based on the performance indices, the AN>J.t model predicted the plant data more accurately than the physical model, as was expected because the ANN.t models \vere directly identified from the compared data. IdentifIcation ufAl\lN using Nominal Plant Data The A~ lTIodels in Table 4 are also identified using dataset Lll. The tDotivation behind identifying the models using nominal data is that plant data stored in a data historian largely consists of data for quasisteady state operation. Typically, step response data from plalllled plant tests are considered a luxury. Hence, it is imperative to evaluate the prediction capabilities of ANN models identified on nominal data CANN.n models). Using the same datascts as before (i.e. dataset t.4 for the Temp, Comp and Rev models and t.5 for the Lev tnodel) the 1. ann values are tabulated in Table 5. As indicated by the many highlighted values in Table 5~ the ANN.n nl0dels are not able to capture the dynanlic open loop nature of datasets t.4 and t.5 very effectively. It is concluded that the ANN.n models trained on dataset 1.n are not able to accurately predict open step response data.
Addition of'Simulated Response Dala to the .4/vN Training Set The predictive abilities of AJ\..TJ\T models can be improved by using the HYSYS model to generate simulated response data for conditions not present in the nominal dataseL In particular, the lIYSYS simulation of the open-loop step response data in datasets 1.4 and 1.5 are added to the three previous sets of nominal operating data. This augmented dataset will be referred to as dataset I.n+s and the corresponding ANN models are given the following extension "n+s"(i\1\TN,n+s). The perfonnance indices, I. ann, for ANN .n+s model prediction of dataset t.4 are also shown in 1'able 5. Table 5 Comparison of HYSYS and ANN models (identified on Lt, Ln and I."+s) predictions of open loop plant data
14th World Congress of IFAC
The ANN.n+s models had a much lower number of Lann violations of the upper limit values~ as indicated by the smaller amount of highlighted values,. than the ANN.n models. The Temp_n+s (a subclass of ANN.n+s) model predicted aB the internal tray temperatures with relatively better l.ann values than Tcmp.n but not quite as accurate as the Temp.t model. The Temp.n+s had I.ann violations for outputs Y282 and Y255~ ho\vever for Y257 the ·Temp.n-r-s had a slightly better Lann value than the Temp.t model. The prediction of Y257 is plotted in Figure 4.
l Turprr I;;rn: 19F/o TfTJ1Jn+s Jan =2Jel/o 11S~~:::;;;;;::::::.;;;;::::;;;;;;;;~::::::;;;;;;;:;;;;;;::::;:::=:::::::::::~::::::::;:::::::=~;;:::;:;;;;;;;:::;;:~
145........,~T'I"'I"I~'TT'r'lrTT"TlI"Tt"T"l'TTT'rnTl"T'IT"'"'"'"TTT"I"TTfTTTIT'I'TT'I'TTTI'"'"TTT'f'""TT'l
1
Te-ITlp
Camp Rev
Y2&2 Y255 Y297 Y257 Y284 A254 A256
A257 F256 F267
ANN.t
ANN.n
ANN.n+
l.sim
{unn
s I. ann (%)
(~/;)
(~-o)
1. ann (o/a)
32.16
6.49
2:24.10
17.11
51.27
3.32 2.13
94~94
17.78 2.47 2.76 1.53 0.60 1.58 l.53
13.48 33.83 29.30 6.06
5.06
3.51
1928
0.84
2.70
1.07
5253 4.70 6167
O.8~
1.10 1.43 1.1'
lO4~92
48.42
5.43 357.42
70.05
79.5 35.33
10.73
0.74
Datasct t.5
LEV
~
~
~
~
$
~
~
~
- - T91l'lO'S
m
$ .
~
~
~
~
-x· • TerTpn!
Fig. 4. COlnparisoll of Tenlp.n+s and Tenlp.n models prediction of plant data, dataset t.4
Note that the Tenlp.n luodel predictions v\"ere inaccurate for Y257 at t>45. However, w:ith the addition of the simulation data to the training set~ the Tenlp.n+s nlodel clearly eliminated the overshoot. ~o\.lso~ the Comp.n+s model outperformed the Comp.n model and "vas more accurate in predicting A254 than the Comp.t model. A simiJar comparison between Lev.t, LeV.ll and Lev.n+s models prediction of open loop dataset t.5 is shown in Table 5. The Lev.n+s model outperfonned both Lev.n considerably for the predictionL254 and moderately achieved better Lann values than the Lev.! model. These and other comparisons (Sabharwal, 1998) clearly indicate the advantage of augmenting the ANN training set with simulated response data for operating conditions that are not represented in the training data.
5. CONCLUSIONS
HYSYS
22.60 5.04
fl
: --+- AatOta
Dataset 1.4 Output
6
L254
7.91
1.55
L253
101.24
14.03
0.50
This investigation has provided a unique and detailed comparison of a physically-based model and a set of neural network dynamic models with industrial data fOT a xylene splittcr, d.istillation column. This application produced a number of interesting results. 1. Recurrent ANN models identified using step response plant data models predicted the open loop
step response data nlore accurately than the physically-based, HYSYS model. But ANN models identified using nominal plant data (over a narrow range of conditions) proved to be fatal for prediction of step response data because the nominal data lacked sufficient excitation.
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Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress of IFAC
HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
2. Consequently, a hybrid approach was devised in v/hich the nominal training data was augmented with simulated step response data generated from the I-IYSYS model. The ANN models trained with the augnlented training data provided much more accurate predictions of the step response data. In fact, some of these predictions were as accurate as the ones from ANN models that were trained on step response data. 3. This industrial application demonstrates that a physically- based model and an artificial neural netvlork are indeed complimentary and can be used together to good advantage. A companion Shldy (Cben & Seborg, 1997) produced similar conc.lusions when a physically-based model was used to enhance the predictive ability of a multivariate statistical model for an industrial paraffin column.
6.
ACKNOWLEDGEMENTS
Financial support from the Japan Energy Corporation and the assistance of the technical staff at the IV1izushima re finery are gratefull y acknowledged. Anthony Swanda (UCSB) supervised the collection of sonle of the step test data.
1. REFERENCES
Agarwal, M., "Combining Neural Networks and Conventional Paradigms for Modeling, Prediction~ and Control'\ Int~ J. Systems Sci. \Io1. 28, pg. 65 (1997). Baratti~ R., A. Servida and G. Vacca; (~Neural Network Modeling Of Distillation Columns~" lIydrocarbon Processing, VaL 74, No. 6 pg.6 ( 1995). Chen, C. & D. E. Seborg, HProcess Monitoring of Two Industrial Distillation Columns Using A Physical Model and Principal Component Analysis", Paper presented at the 1997 Annual AIChE Meeting, Los Angeles. Hccht-Nielson, R. and J. Lambert, "Application of feedforv..'ard and recurrent neural networks to chetnical predictive nlodeling," I&EC Research, \'01. 34, No. 4, pg. 14 (1997). HimmelbIau, D.M., and I.C. MacMurray, "Modeling and Control of Distillation Column using Artificial ~eural ~etviorks," Computers Chem. Eng~ Vo1.19," No.l0, pg.I07? (1995). Homik, K., M. Stinchcombe, and H., VVhite "MuItilayer Feedforward Networks are Universal Approximators~\ Neural Networks, Vol.l, pg. 359 (1989). HYSYS (v1.1) Reference Manual, Hyprotech Ltd.) Calgary, Canada (1995) Luyben, W. L., Practical Distillation Control, 1st Edition; vran Nosrrand Reinhold~ Ne\v York
Physical l\.1odels can Benefit Customers, n Hyprotech User Conference~ San Antonio, TX~ (1996). Psichogios, D. C., and L. H. IJllgar, "A Hybrid Neural Nenvork - First Principles Approach to Process Mode1ing~~, AIChE 1., '\-'01. 38, pg. 1499 (1992). Process Insights (v.3.2) Reference !\.1anual, Pavilion Technologies Inc., Austin, TX (1996). Ramchandran, S. and R. R. Rhinehart, HAn Introduction to Computing v,,'ith Neural Nets," Journal of Process Control~ Vat 5, pg. 1] 5 (1995). Sabhan:val i\., \Vada, ·r.~ and Bhat, N. "Benefits of Integrating Empirical Modeling and Physical in an Oil Refinery Application," Hydrocarbon Processing, Vol 76, No. 10, pg. 105 (1997). Sabhanval, A., HA Hybrid Approach Applied to an Industrial Distillation Column that Compares Physical and Neural Netv.rork Modeling 'Techniques," :N1.S. Thesis~ LTniversity of Calgary ( 1998). Seborg, D. E., T.F. Edgar and D.A. Melhchamp~ Process Dynamics and Control, Wiley~ N. Y., (1989). Su H .T., N. Bhat, P. A. I\1inderrnan" and T . . J. McAvoy, ~'Intcgrating Neural Neh\'orks and First Principles Models for Dynamic Modelingn~ Proc. IFAC DYCOPS Sympos.~ pg. 77~ College Park, MD, USA (1992)~ Su H. T. and T. .J. McAvoy~ "Artificial Neural Nei\,vorks for Konlinear Process Identification and Control''", in Nonlincar Process Control, M.A. Henson and D.E. SeboTg (cd.), PrenticcHan~ NJ (1997). S\\-randa" A., Ph.D. thesis, IJCSB (in preparation, 1999). Thompson, M. And M. A. Kramer, "Modeling Chemical Processes Using Prior Knowledge and Neural Net"vorks", AIChE J., Vol. 40, pg. 1328 ( 1994).
( 1992). Martin G., and N. Bhat, "How Hybrid Modeling Approaches Behveen Neural Net\Vorks and
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N-7a-07-4
Copyright ·f) 1999 IFAC ] 4tb Triennial World Congress, Beijing, P.R. China
HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A XYLENE SPLI'TTER Amish Sabhar,,·al*, w~ y ~ Svrcek*, and D.E. Seborg+
* [Jniversit)/ {)fCalgar..¥~ Depar/ment ofChemical Engineering, 2500 Univer.\·ity Dr. ,-V. W., Calgory, AB, T2lV-l/'tl4 Canada, Te/: (403) 220-5755 Fax (403) 282-3945, 1
1
e-lnail: anlish. sabha,\-llalrZiJhvprotech. COIn, e-mail: [email protected] t University o.fCalifornia. Departlnent olChemical Engineering, lJniversityof Ca1t!orniaSantaBarbara, CA. 93106, USA, rei: (805) 893-3352, Fax (805) R93-4731 e-Inail: seborg@engineering. UL'sh. edu
ABSTRACT There is a considerable current interest in developing effective methods for the integration of physic.al and empirical dynamic models. In this paper, a novel integration strategy is applied to a full scale} distillation column in the fv[izushima Oil Refinery of the Japan Energy Corporation. Artificial neural networks (ANN) models of a xylene splitter were constructed using commercial software, Process Insjghts (Pavilion Technology Inc.~ 1996)~ and two different types of data sets, slep response tests and data sets for nominal operation (without plant tests). A first principles model of the distillation column \vas developed using a conunercial software package, HYSYS (Hyprotech Ltd_, 1996). The models \vere compared based on their accuracy in predicting plant data. In this application, it is shown that the lack of excitation in the nominal data during the training of an AhTN model proved to be significant in the model's inability to predict the plant's transient conditions. Rut v..,'hen the nominal training data \\'as augmented by the simulated response data from the physical illudel, the accuracy of the ANN model was improved significantly. Copyright © 1999 IFAC. Keywords: Neural columns, Hybrid
1.
~em~orks,
Dynamic Modeling;.
INTRODUCTION
Physical (or first principles) models of industrial processes lend to be complex and computationally denlanding. Thus~ simplifying assumptions must often be made that limit their accuracy. However, recent advances in sofh-vare and computational speed make it possible to develop dynamic models using the same kno\vledge base that has been used for steady-state models, the underlying chetnistry~ physics, and them1.odynamics of the process
Simulation_~
Distillation
Dynamic simulation can be used to obtain a better understanding of process dynamic behavior or to provide insight into the interaction between inputs and outputs of a process. Dynamic simulations of distillation columns employ rigorous stage by stage ITIodels that include component, mass and energy balances, liq uid flow dynaulics, and pressure dynamics on each stage (Luyben, 1992). 111ese nl0dels tend to be quite conlplex, consisting of hunilieds of nonlinear ordinary differential equations plus nonlinear algebraic equations (e.g., bubble point calculations) \.\"hich U1USt be solved iteratively. An altenlative approach to physical ll10deling is the use
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of empirical models, which rely on process data to develop a relations.hip benvcen process inputs and OlltputS. Artificial Neural Networks (ANN) have been \.vide1y used as empirical nlodels for many chemical engineering problems (Su and Mc..A .voy, 1997). The fast execution times of ANN models Inakc them ideal for Teal-time process optimization (Sabhar\val et aI., 1997). Their universal approximation capabilities (Homjk et at) 1 989) make AN:\[ good candidates fOT mode ling nonlinear chemical process. The major limitation of these models is their prediction accuracy is only as good as the data used to train the models. Therefore, a model developed ,"",ith process data that does not span a wide operating region may have limited utility. With the increasing number of ANN applications in the process industries, there has been considerab le interest in trying to create a suitable integration of neural net\.vorks and physical models (Psychogios & Ungar~ 1992; Su et aI., 1992; Thompson & Kramer, 1994; Aganval, 1997). But only a fe\v hybrid
The data collection is one of the most important steps in the identification of accurate dynamic models. The excitations present jn the input/output data and the regions of operation are important factol"s in determining if the data is a good representation of process behavior. In typical industrial applications, process data is coIl ccted over a long time period, which may extend from several \veeks to months, in order to collect data that provide infonnation about a wide range of process operations. In this application, only a small amount of process data \vas collected. Data \vas col]cctcd for both closed-loop and open loop conditions for a period of 10 days using a sanlpling period of three minutes.
+--~iII5Io-"":-""",,"-------lM----:"'_--'*F257
modeling case studies have been published (Agarwal, 1997).
I
i
r:MI1la1e Rtt
l~he
focus of this paper is to show that the ANN and first principle modeling techniques are indeed conlplementary. In the distillation application.~ the physical model is used to provide knowledge in the form of simulated data that is used to augment the identification data used to train the ANN model. 1.1 Process Description and Data Collection This study is concerned with developing accurate dynamic models of a full-scale distillation column in the Mizushima refinery of the Japan Energy Corporation. Column TW252 is the middle column 10 the train of five columns for the No. 2 xylene distiHation unit. The column consists of an overhead condenser, cooler, reboiler, and 30 internal trays. The feed enters as a liquid at the design condition of 160 C and 760 mmH~ and contains a rnixtuTc of C8'g (ethyl benzene, mlp-xylene, o-xylenc) and C9's components (1 M2-e-benzene, and traces of 1,3-ebenzene and naphthalene). The column is controlled by several single input,
single output (SISO) temperature and pressure control loops as shown in the schematic diagram in Figure 1. The nominal control strategy is to regulate the temperature at stage 27 (Y255), to infer the distillate composition, by manipulating a slave reflux floVlIate controller, F256. However, due to unsatisfactory performance the loop is no",,' open, slave controller is in manual, and the reflux is used to regulate top purity. 1'he bottoms temperature (Y284) is controlled to infer the bottoms composition by adjusting the steam flO'-" rate to the reboiler (F267). For a more detailed description of the process, see Sabharwal (1998).
Fig.I. Process Flov.,' Diagram and Tag Descriptions During this period, eight experimental "bump tests~' were perfonned on TW252 (Sabbarwal, 1998); step changes up and dovro. in the setpoints of Y255, P252~ L254, F256, F257, Y284, and F267. rfhe "*,, in Figure 1 indicates all the variables measured as a result of the tests. The input test signal used for all the open and closed loop experiments is shov-.'n in Figure 2. However,. for some tests, only a step up or a step down "vas allowed because of possible product specification violations (Swanda, 1999).
9=J---
Llp ___ S:ep---..I
Slep DDwn
Fig. 2. Example of a Test Signal The step response data, \vere merged into onc datasct, and labeled as l.t. In addition to the experimental "bump test" data, nominal datasets were collected for three vel)' different sets of "nonnal" operating conditions. Dataset N.1 consists of six consecutive days of nominal operating conditions recorded during the month of July, 1995. Datasets N.2 and N.3 are indicative of operation during May and June;> 1995,
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respect.ively. These nominal dataset were combined into dataset I.n.
2. SIMULATION MODEL In order to develop a physically based process model of TW252 that realistically predicts the plant dynamics, a dynamic distillation model was developed using a conunercial sofnvare package, HYSYSTM, from Hyprotech Ltd. The development of the HYSYS model was based both on engineering process knowledge and judgement. A steady-state model was first developed that determined the nominal steady-state condition from the unsteadystate mass and energy balances. This steady state is then used to initialize the dynamic model. Next, the dynamic model for TW252 was developed that included the Inass and energy balances for each tray in the column, for the condenser, and reboiler. Dynamic Distillation Tray Model The physical model for the distillation tOV.ler v.,ras developed using the single distillation tray model. The dynamic nlode} for a single tray contains Nc-l differential material balances, where Ne is the number of components in the system, and one overall material and energy balance. Because the tower pressure is not very high, (maximum 760 mmHg), the follo\ving assunlptions were made (Sabharwal,1998): 1. 2.
3. 4.
Single flow pass tray hydraulics The change in specific enthalpy is very small compared to the total tray enthalpy. Vapor mass is less than 30% of the total tray material; hence~ vapor holdup is negligible. Tray pressure drop is constant and the pressure profile is detennined linearly from the condenser pressure.
Luyben (1992) has suggested that the above assumptions are "good enough" to solve 95?-{} of industrial distillation problems. C~ondenser and
Reboiler The condenser model represents the achlaJ condenser as an un-vented (no vapor product) partial condenser ,vith varying pressure. It assumes that the amount of hea t required to condense the overhead vapor to its bubble point temperature plus the sensible heat required for any subcooling is calculated from the utility fluid parameters. The amount of condensation determines the condenser pressure and the condensate temperature of the liquid leaving the condenser, allowing for the amount of subcooling to be specified. For the rcboiler, the heat duty is calculated by means of enthalpy balances around the column. Equip/llent Specifications
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For the purpose of the dynamic HYSYS model, all equiplnent sizes were based on physical dinlensions of the actual equipnlent specifications received from the refinery. The physical dimensions that were calculated for TW252 include the tray sizes (weir heights, weir lengths, and tray volume). The to"ver and condenser cooling volumes were inferred from the column diameter (3.25 m) and from the maximum allowable reflux flow rate (50 m 3 /h) (Sabhanval, 1998). Note, it is very important that reasonable values for the tower volume be specified because this parameter defmes the vapor traffic profile in the column during dynamic simulation. Table 1 summarizes the HYSYS model specification for the tray model. Table I HYSYS model equipment specifications for TW252~s trays Numbe r of Trays
30
Tray Hold up Time (s)
30
Tray Weir Height
Max.
Total
Liquid
To~'er
\'o ]urnc
(m)
(m 3 )/tray
\lolume (m 3 )
0.05
0.83
250
The condenser and reboiler are sized based on a liquid holdup, w'i th the holdup set equal to 10 min for each vessel. The condenser and reboiler volunles were not available from the equipment specification sheets. Again, based on the maximum allo\vabJe reflux and distillate (F257) flo\v rate, the condenser volume and reboiler volumes are determined and shown in Table 2. Table 2 HYSYS model equipment specifications for TW252'5 vessels Vessel Hold up Time (min) 10
Reboiler Volume (mJ ) 30
Condenser Volume (m3 ) 30
Standard PI controllers \vere enlployed for level control of the reboiler, condenser, as well as
temperature, flow, and pressure controL All of the controller settings and transmitter spans required for the simulation \vere obtained from the DeS at the Mizushima Oil Refmery ~ 2.1 Model Validation The key to development of a useful simulation model is "validation)~ with actual plant data. A good model must match the plant at steady-state and accurately track the process during dynamic upset. The model verification includes a check of the overall material balances, steady-state temperature profiles~ product flows as well as a steady-state gain analysis. Furthermore, the model must be able to track the open and closed loop perfonnancc of most of the process variables. Finally, good model accuracy for closed loop operation will guarantee that the model is accurate and useable.
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The HYSYS Inodel developed for TW252 was validated using plant data for open and closed loop step responses and nominal plant data. The steadystate behavior of the column was fust validated using nominal plant operating data. A quantitative assessment of TW252's non-linearity and interactive nature was investigated by calculating steady-state process gains and perfanning a relative gain array (RGA) analysis. Also a qualitative assessment of the physically based dynanlic model ~s ability to predict transient conditions is benclunarked against plant data. The results will be sho'\.vn in Section 4.
3. NEURAL NETWORK MODEL In recent years there has been a tremendous mnount of interest and speculation about chemical process modeling using a11ificial neural nehvorks (Su and McAvoy, 1997). With their siluple structure and fast computational perfonnance~ ANN are an ideal complement to traditional process modeling (Sabhanval et aI., 1997). Using a commercial neural net\.vork sofhVare package, Process Insights™ from Pavilion Technologies, ANN models \vere developed for 7\\'252 and are sununarized in Table 3. Table 3 Classification of the ANN models developed for l'\~l2 52 Tw252 Category IntcTI1al 'Tray Temperature
Distillate
A~7'
Model Name Temp Model
Camp Model
Compositions
Outputs Y282, Y255, Y297~ Y257, Y284
A254, A256, A257
Internal Flo\v Model
Rev Model
F256,F267
Condenser and
Lev Model
L254,L253
Reboiler Levels
To ide,ntify ANN models~ the training signals should be sufficiently rich so that the process response data reflects the desired range of transient and steady-state operating conditions. Ho\vever, no general specification is available in the literature for selecting an optimal training signal. Hence, care must be taken when selecting the type and range of data to be used for model identification and val idarioD. l~hc steadystate behavior of the column was f1.rs[ validated using nominal plant operating data. A quantitative assessment of TW252 ~s non-linearity and interactive nature was investigated by calculating steady-state process gains and perfoffiling a relative gain array (RGA) analysis. Also a qualitative assessment of the physically based dynamic model ~ s ability to predict transient conditions is benchmarked against plant data. 'The results will be shovln in Section 4~ The
A1'JN models listed in Table 3 two datasets: Lt and J.n, which (Temp,t, Camp.t, Rev.t, Lev.t) Comp.n, Rev.n, Lev.n ) models
\vere identified using are labeled as ANN.t and ANN.n (Temp.n~ respectively.
3.1 ]Veural Jv~enf,/ork Topology Before ANN.t and ANN.n models can be identified~ the topology of the models must be defined. Hence, the ANN topologies must minimize the number inputs and optimally utilize the correct number of hidden nodes to predict the outputs of the models. In turn reducing the number 0 f inputs will decrease the COlllputatiollal load of the ANN by minimizing the number of weights needed betv.leen the inputs and the outputs. The optimum ANN models \vere determined using an iterative procedure that involved initially specifying a generic nenvork topology with all possible inputs that might affect the outputs (Sabhar\¥al~ 1998). Using a sensitivity analysis (Process Insights Reference Manual 1996), for each output, the inputs were ranked in order of highest average absolute sensitivity (average sum of the absolute values of the partial derivatives of the input-output pairs). A three layer ~~extemal recurrent backpropagation'~ approach (Hecht-Neilson, 1997) was used identify the models. "}'his algorithm is useful in nlodeling dynamic systenlS because it recursively presents past values of the outputs of the network as inputs to the ANN. Moreover, the number of past values of both the Input (m) and output (n) are very important because they include the transient behavior in the A~~ topology. Furthermore, the tim.e delay (8) associated between a particular input and output was also included. From these input/output configurations, the Process Insights software detennined the appropriate number of hidden nodes (Nh). A summary of the selected topologies for the nlodels listed in Table 3 are ShO\Vll in Table 4.
Table 4 The selected topulogies for the other TV"i"252 ANN models M:deI
~
m
n
~
l~
CtJq:Us
Tc:p:jo;:ry
TetTP
DjraTlc
3
3
17
Pl52
0
Yl8'.2
35-17-5
Y200
0
Y255
F256
0
'm7
F~
0
Y257
Co'T1J
RrN
Leve
C¥aric
Djrnric
~
2
3-
3
2
3
3
13
10
10
F2l34
0
Y284
P252
20
A254
F256
20
A256
1J2.57
F267
20
A264
40
A2.65
40
A.266
40
P252
0
F256
Y255
a
Fa37
Y284
0
F257
0
L254
F27'\
a
l253
F284
Q
24-13-3
18-10-2
18-10-2
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Note that for the Camp Model in ]'able 4, the apparent time delays between the inputs and the three Ol1tputs \vere approximately 60 minutes or 20 sampling intervals for P252, F256, F267 and 40 sampling intervals for the feed compositions
The large apparent tinle delay is due to the sampling location of the composition analyzer. The same analyzer is used to measure the feed and distillate compositions, hence the total time delay associated ben:vcen the inputs and outputs incorporates both inherent and processing time delays~
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"[he prediction results of the physical model and the models are also summarized In Table 5 for other process variables using I.sim and I.ann respective1y, In general, Table 5 sho"\vs that the lIYSYS model captured the dynamic response of T'\\T252 for dataset t.4 quite effectively. lIo",-ever, the . .- \NN.t model proved to be the most accurate model ",-,hile the ANN.n model "vas the least ac.curate. The shaded regions in the table indicate that the model prediction \vas grossly unreasonable. A~
4 .. COMPARISON OF ANN AND HYSYS MODEL PREDICTIONS The ANN and IIYSYS model predictions are conlpared with plant data using the follo,ving performance index.
where: y"k,i
=
Sk,i
=
actual jth output at kth sample pattern predicted i th output at k th sample pattern
mean of the in the dataset
jth
output for all the patterns (P)
There is no general nununurn threshold used to determine if the performance index (I) is satisfactory. I was only used as a relative comparison to gauge the predictive ability of an A1\~ relative to other ANN's and in particular, relative to the HYSYS model. However, some generalizations for I are made based on this 'Vvork. l'he perfonnance of the HYSYS model is donated by I.sim~ vlhereas, the performance of the ANN.t and ANN.n nlodels will be donated by I.ann. 4.1 C o rnparis on \A/ith P fan! Data Figure 3 shows the ANN.t models (Temp.t) Camp,t and Lev.t) and HYSYS model predictions of a snapshot of the 480 available samples of plant dataset, L4, Dataset t.4 has a series of manipulated variable setpoint changes in F256 (+5.0 m 3 /h and -2.5 3 m 3 /h) from its noJIIinal value of 41 ~2 m /h (SabharwaI,1998). Both the Y255 and Y284 control loops were opened during these bump tests. Figure 3 (a) and (b) provides comparisons of the plant response data to the HYSYS and ANN. t predictions of Y257 (stage 1 temperature) and A254 (C9+ wt.% in the distillate) for a F256 (Reflux flow rate) step change. Figure 3 (c) is a comparison of the plant data to the HYSll"S and ANN.t responses of L254 (Condenser liquid level) to a F257 (Distillate flow rate) step change.
Fig. 3. Conlparison of the HYSYS and ANN.t prediction of plant open loop behavior for selected T\V252 variables. In contrast, the ANN.t n10dels ,,"'ere identified using five sets of step response plant data, ,"vhich included Dataset t.4. Therefore based on I.ann values? the ANN.t models should be able to predict dataset t.4 much more accurately than the HYSYS model. Based just on the performance indices (I.sim and Lann), "more accurately'~ or ~\better~" sometimes does not guarantee that the prediction is right. From Figure 3 (b) the Camp.t model accurately predicted, A254 with an Lann = 1.07 '70 in comparison to the HYSYS
n1.odel prediction of I.sim 33 .83t}~ at the composition analyzer points~ The accuracy of the ANN model to predict the plant data cannot be denied; hOVv'ever, the DeS recordable span for A254 was limited to an upper limit of 2 wt.%. Hence, from the recorded process data there was no accurate indication of A254 above 2 "Wt.%. The HYSYS model predicted response of A254 \vas indeed ,,"vc]] above the DeS limlt of 2 wt. 0/0. Hence, there is no reasonable indication of A254 above this value. The ANN model is only as good as the data used to train it, therefore its liITlited knowledge of the process (Le. limitations of the DeS span) restricts its ability to extrapolate. The on-line analyzer's low frequency sampling and the DeS limitation provided no apparent information about the dynanlic behavior of A254 in dataset t.4, On the other hand, the HYSYS model apparently captured the true dynamic behavior of A254 between the on-line analyzer samples and above the DeS upper limit.
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A similar trend was observed for the other datascts. In general, based on the performance indices, the AN>J.t model predicted the plant data more accurately than the physical model, as was expected because the ANN.t models \vere directly identified from the compared data. IdentifIcation ufAl\lN using Nominal Plant Data The A~ lTIodels in Table 4 are also identified using dataset Lll. The tDotivation behind identifying the models using nominal data is that plant data stored in a data historian largely consists of data for quasisteady state operation. Typically, step response data from plalllled plant tests are considered a luxury. Hence, it is imperative to evaluate the prediction capabilities of ANN models identified on nominal data CANN.n models). Using the same datascts as before (i.e. dataset t.4 for the Temp, Comp and Rev models and t.5 for the Lev tnodel) the 1. ann values are tabulated in Table 5. As indicated by the many highlighted values in Table 5~ the ANN.n nl0dels are not able to capture the dynanlic open loop nature of datasets t.4 and t.5 very effectively. It is concluded that the ANN.n models trained on dataset 1.n are not able to accurately predict open step response data.
Addition of'Simulated Response Dala to the .4/vN Training Set The predictive abilities of AJ\..TJ\T models can be improved by using the HYSYS model to generate simulated response data for conditions not present in the nominal dataseL In particular, the lIYSYS simulation of the open-loop step response data in datasets 1.4 and 1.5 are added to the three previous sets of nominal operating data. This augmented dataset will be referred to as dataset I.n+s and the corresponding ANN models are given the following extension "n+s"(i\1\TN,n+s). The perfonnance indices, I. ann, for ANN .n+s model prediction of dataset t.4 are also shown in 1'able 5. Table 5 Comparison of HYSYS and ANN models (identified on Lt, Ln and I."+s) predictions of open loop plant data
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The ANN.n+s models had a much lower number of Lann violations of the upper limit values~ as indicated by the smaller amount of highlighted values,. than the ANN.n models. The Temp_n+s (a subclass of ANN.n+s) model predicted aB the internal tray temperatures with relatively better l.ann values than Tcmp.n but not quite as accurate as the Temp.t model. The Temp.n+s had I.ann violations for outputs Y282 and Y255~ ho\vever for Y257 the ·Temp.n-r-s had a slightly better Lann value than the Temp.t model. The prediction of Y257 is plotted in Figure 4.
l Turprr I;;rn: 19F/o TfTJ1Jn+s Jan =2Jel/o 11S~~:::;;;;;::::::.;;;;::::;;;;;;;;~::::::;;;;;;;:;;;;;;::::;:::=:::::::::::~::::::::;:::::::=~;;:::;:;;;;;;;:::;;:~
145........,~T'I"'I"I~'TT'r'lrTT"TlI"Tt"T"l'TTT'rnTl"T'IT"'"'"'"TTT"I"TTfTTTIT'I'TT'I'TTTI'"'"TTT'f'""TT'l
1
Te-ITlp
Camp Rev
Y2&2 Y255 Y297 Y257 Y284 A254 A256
A257 F256 F267
ANN.t
ANN.n
ANN.n+
l.sim
{unn
s I. ann (%)
(~/;)
(~-o)
1. ann (o/a)
32.16
6.49
2:24.10
17.11
51.27
3.32 2.13
94~94
17.78 2.47 2.76 1.53 0.60 1.58 l.53
13.48 33.83 29.30 6.06
5.06
3.51
1928
0.84
2.70
1.07
5253 4.70 6167
O.8~
1.10 1.43 1.1'
lO4~92
48.42
5.43 357.42
70.05
79.5 35.33
10.73
0.74
Datasct t.5
LEV
~
~
~
~
$
~
~
~
- - T91l'lO'S
m
$ .
~
~
~
~
-x· • TerTpn!
Fig. 4. COlnparisoll of Tenlp.n+s and Tenlp.n models prediction of plant data, dataset t.4
Note that the Tenlp.n luodel predictions v\"ere inaccurate for Y257 at t>45. However, w:ith the addition of the simulation data to the training set~ the Tenlp.n+s nlodel clearly eliminated the overshoot. ~o\.lso~ the Comp.n+s model outperformed the Comp.n model and "vas more accurate in predicting A254 than the Comp.t model. A simiJar comparison between Lev.t, LeV.ll and Lev.n+s models prediction of open loop dataset t.5 is shown in Table 5. The Lev.n+s model outperfonned both Lev.n considerably for the predictionL254 and moderately achieved better Lann values than the Lev.! model. These and other comparisons (Sabharwal, 1998) clearly indicate the advantage of augmenting the ANN training set with simulated response data for operating conditions that are not represented in the training data.
5. CONCLUSIONS
HYSYS
22.60 5.04
fl
: --+- AatOta
Dataset 1.4 Output
6
L254
7.91
1.55
L253
101.24
14.03
0.50
This investigation has provided a unique and detailed comparison of a physically-based model and a set of neural network dynamic models with industrial data fOT a xylene splittcr, d.istillation column. This application produced a number of interesting results. 1. Recurrent ANN models identified using step response plant data models predicted the open loop
step response data nlore accurately than the physically-based, HYSYS model. But ANN models identified using nominal plant data (over a narrow range of conditions) proved to be fatal for prediction of step response data because the nominal data lacked sufficient excitation.
6804
Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress of IFAC
HYBRID NEURAL NET, PHYSICAL MODELING APPLIED TO A ...
2. Consequently, a hybrid approach was devised in v/hich the nominal training data was augmented with simulated step response data generated from the I-IYSYS model. The ANN models trained with the augnlented training data provided much more accurate predictions of the step response data. In fact, some of these predictions were as accurate as the ones from ANN models that were trained on step response data. 3. This industrial application demonstrates that a physically- based model and an artificial neural netvlork are indeed complimentary and can be used together to good advantage. A companion Shldy (Cben & Seborg, 1997) produced similar conc.lusions when a physically-based model was used to enhance the predictive ability of a multivariate statistical model for an industrial paraffin column.
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ACKNOWLEDGEMENTS
Financial support from the Japan Energy Corporation and the assistance of the technical staff at the IV1izushima re finery are gratefull y acknowledged. Anthony Swanda (UCSB) supervised the collection of sonle of the step test data.
1. REFERENCES
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( 1992). Martin G., and N. Bhat, "How Hybrid Modeling Approaches Behveen Neural Net\Vorks and
6805
Copyright 1999 IFAC
ISBN: 0 08 043248 4