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FCCU Reactor - Regenerator Advanced Control
FCCU REACTOR - REGENERATOR ADVANCED CONTROL
Yan~ l\1ayinl.t
Rong Gang++ Wang Shuqing++" Lan Hongscn +H Chcn Qinghai~~+ Kang Biao +++
... fllll/lul£: (Jfflljimllali(J17 Engil1C'ering, Zhl'jial1g Ul1Iversilyof Technology, ffal1g::holl 3l001./, PR China Il1slilllll' for Indu slriall'rocess Conlrol, Zhejiang Unil'ersi(v, l[al1g::holl 3100::7, FRChil1a ++ .... FUjian [(cfinery, Huian36::100, PRChina
Abstr:lct: A multivariable coordinated predictive control is designed for a refinery's FCCU reactor-regenerator system. Computer simulation and on-line experiment show its efficiency ;lIld \\ill make confidence re:ll-time closed-loop adnnccd control of the reactorregenerator system Key\\ords: Chemical industry, predictive control, coordination, multivariable control systems, coupled mode analysis
I. INTRODUCTION
2. PROCESS DESCRlPTION
The nuid catal~1ic cracking unit CFCCU) is the workhorse of the modern refinery Its function is to convert he:l\} hydrocarbon petroleum fractions into :l state of more usable products such as gasoline, middle distillates and light olcfins. Computer ad\'anced control and on-line optimization can mo\'e an FCCU process to the most profitable operating condition \\ hen feed ;llld catalyst propel1ies are continuously changing As the kernel of the FCCU, reactor-regenerator sho\\s its ch:lllcnge to the control engineer stemming from the complex and highly non-linear process and from the fact that its controlled \ariables ;lre kept not so lIluch at targets but against constraints. In this p:lper wc concentrate on the applic1tion of ad\;lnced control in the reactorregenerator cont rol. Computer si mulation and industri:ll application experiment sho\\' its efficiency.
The FCCU at Fujian refinery processes mixtures of heavy distillate oil and recycle oil. A simplified flow diagram is shown in Fig. l , which includes onc reactor and two regenerators, complimented with a heat exchanger and a deaerator.
*Corresponing
author.
Tel.
Recycle oil and distillate oil arc mixed together, preheated, atomized and subsequently injected at the bottom of the riser, where it mixes with fresh catalyst from the second regenerator vessel. The mixture travels upwards through the riser where most of the catalytic reaction takes place. The conversion yields hydrocarbon vapors as well as coke which deposits on the catalyst and suppresses its reactivity. Reaction products and spent catalyst discharge in the reactor whose main function is to disengage the catalyst particles from the vapor product through a batter)' of cyclones. The spent catalyst is held up in a small fluidized bed in the stripping section of the reactor before being returned to the first regenerator. Catalyst regeneration is :tchieved by burning off the coke deposit in fluidized bed inside the two cascade regenerators . Steam turbine dri\'en air blowers
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Fig.2) , so it is possible not to use the glob:11 ki~etics model but the identified model ne
supply thc o.xygcn to burn the cokc deposit. Yapor products from thc reactor arc scnt to the 1l1:11n fraction:1lor \\ hcre \arious boiling point fractions are withdrawn .
Pressure
Fig.2 Operator's Comfort Zone On the basis of the stripper steam-recycled cat
I :dcacrator 2:2nd rcgcncr:1tor 3: 1st rcgcncr:1tor G:riscr rcactor 4:rccantcr 5:hc:ll cxch:lnger Sist regenerator 7:2nd regcncrator fluc gas flu c gas l) :product IO :fccd II :air
percentage of the second regener
bed temperature of the first regenerator and dense bed temperature of the second regenerator are treated as controlled variables (denoted as cv" cv 2 ' cv) and C"4 ) . Feed preheat temperature , recycled oil flowrate , vacuum residue flowrate, paraffine flowr
Fig. 1 FCCU RC:lctor-regencr:1tor Flow Shcct
J. COORDINATED PREDICTIVE CONTROL OF
RE ACTOR -REGENERATOR Thc FCCU is a co mpl cx and diffi cult process to understand . Complexity ariscs from the reaction kinctics, catalyst hydrodynamics, coke combustion on catalysts, proccss economics, opcrating constraints and proccss vari:1ble interactions that are domin:1ted by thc hcat , m:1SS and pressure balances bctwecn thc reactor
It can be seen from Tab. I that lots of the paths have
long lags and some have non-minimum phase characteristics. The paths are strongly coupled. From process control points of view, some controlled variables have their setpoints and others have their zone limits. The manipulated variables should be manipulated within high and low limits, and given the freedom, they should be manipulated toward their ide
Thc objecti\c of the rcactor-regencrator control is, to
The coordin:1ted monitoring level task C:1n be dcscribcd in brief as to select the CYs and MYs to be
692
used in the b;lSic predictive control algorithm, that is, at every sample time. determining the CVs to be controlled and the MVs to be adjusted currentl~'
considering the operating status and the priority order of the variables
Table I:: Reactor-regenerator Identification Model Cl '
l
0.728e- lls
00576c -c7 , ()I 02(1 + s)e -
1 :,
-0.1025(1-155)e-
()
- 10714(' - ~'
o
1+ 4.1
0.1 I 14(1+6s)e ()
nn'G
13
59.17s"+8s+1
1+ 95
-0.2273e -' 18.9s" + 9.ls + 1
14 .8s" + 8.85 + I
15.85 2 + 7.85 + I
decrease of the priorities respectively. include more than one MV too .
loy",
r-=L =~--~--~"r ~I V : I ~IV ! hi\'
! ~
' '-.,
\i;'
!
...
J.
1\! \ III
-
re
P"i='
CI
,\1 ,'
M!
else if Cl should be controlled ( I)
then if
"I
j
MII
for Cl
else adjust MV towards IRV The priority matrix (I) forms the knowledge base of the coordinated monitoring level, and the rule reasoning is based on the knowledge base.
The series after the second clement at each row
, ... , j = J, ... , I
Mi has freedom to be adjusted
then adjust else ...
where elements of the first column Cl "" Cl denote for the groups of CVs ordered along with the decrease of the priorities respecti\'ely. I
A1i ,1\4 i
for Cl
then adjust M ~ for Cl else .. .
The variable priorities can be expressed as :
lI
Mi
else if M ~ can be adjusted
Fig.3 The architecture of coordinated predictive control
M~
may
then if A1i has freedom to be adjusted then adjust
Af i
M/
The work to select MVs and CVs can be described in the form of rule reasoning: if Cl should be controlled
( b Id
-- --~-- -, ~ .j pr()c~ :,s a rl~ 1 ~ 1I 1!!1..: ";,' H1r ll! IU,lPS 1 C\- d" ta L - _ _ __ ______ _ _ _ I r----'--- - f -
o 0,06167e - 2 s
! ~.• ~;~~~.~g~'~ ~'~ 1---- _ J 1
2
1 U5 + 3.7 s + 1
-0357Ie - 3,
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o 00829(1 + 3s) I I.\.)2 + 4s+ I
1+ 7s
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20.7s 2 +4.5.1'+ 1 0,714e - 2s
'
O.3e -0,
156.35' + IIJ5+ 1 -0 ,03 56e - 26 s
25s" +6s+ I Olg(I-IOs)e -4S
20.7s" +2 .9s + 1
()
02764(1-155}e -:'o,
694s + 8Js + 1 -0.0851(1 + 35)e -34 '
- ().16()X( I + 2.1')(' -, j,
25.1 ' +7.1' + 1
30.95 2 + 7.8s + 1
2
30.l)s' +4 .4.1+ I
.'
-0.048e -24s
()1887e -:)'
-()J 714( 1+ 7.1)e -,', ()
/1/1' ,
2 0S
100/ + 9.1'+ I
8.16s :+4s+1
592 .\: + 7.7.1-1
59,25 2 + 835 + 1
15s 2 + 5s + I
13.7 s' + 7As + I n.145 7 e-2:' s
30.9s: + 7.8s + I
0.07(1 + 25)e -13S
denote the groups of MV
to control CV, Ilhich arc also ordered along with the
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CV's priority is determined through process an:1lysis and experience. \\hile MY's priority C:1n be obt:1ined through inter:1ction analysis. including empiric:1l1) analysis and model-ba sed int era ction analysis.
1
S.t.
:'::,.U noon
::;
denotes for process outputs (i .e. C), set points of y over predict horizon P,
~ =
l
...
if; 111 1
I
cP mm
J
= YA.'
) I)
I)
Beca use of the economic objective of the real process, the controller takes MVs towards their IR V only when all CVs are at their setpoints or their zone limits. Otherwise, an on-line QP calculation is ca rried out to search for an optimal /).U AI (k) satisfying Eq .(4) to Eq .(6),
4. SIMULATION AND INDUSTRIAL APPLICATION EXPERHvfENT more affective The trends of process simulation results for the closed-loop performance of the predictive control law are shown in FigA . Each curve contains 80 points of 5 minutes sampled data, response horizon N =40, predict horizon P = I 0, control horizon M =4, cv's target value is 0.45, and the zone limit is 0.4 through 0.5. mv can be adjusted within 0.1 to 0.9. Setpoint disturbances on cV I to cV4 are changed at the 5th
!v{V, to CV) , the less affective other MVs to CV] . If A.. IJ < 0 .7 or A.. IJ > 1.5 then the coupling of several CVs to MVs should be considered. Thus the 1\1/ series M / , M i , ... along with the priority dec rease arc obtained according to the relative stati c gain analysis of C) .
minute. the 100, 200 and 300th minute respectively, where the target value is moved to 0.5 5 and the zone limit to 0.5 till 0.6. The step response parameter mismatch is 0.02 5 for every path , Simulation results show that the reactor-regenerator's coordinated predictive control has good setpoint tracking performance and robustness to model errors at certain extent.
It is diffi cult to apply Bristol method directly to systems with unequal CV and MV numbers. In this situation a pre-:Inalysi s betwee n CV and MV should be carried out first to eliminate the unimportant MVs and CVs resulting in rank ~ = min(m,p) . then relative static gain method or singular value decomposition mcth od ca n be applied .
In order to further investi ga te the control system propert}. a multivariable predictive control's industrial applications e.\periment is performed at the reactor-regenerator equipment. The results are shown in FigS
Thc ta sk of tradition:ll precl ictin.! control algorithm le\e1 is to sol\e Eq . (-l ) to «(» \\here the subsystem is formed by C
)
w( k) is the /).U AI (k) is
(2)
ql 'J .
The closer /, IJ approaches I. the
Y
predict horizon P, Q and R are weighting matrices. C and b are constant matrices.
(3)
\\h ere ~ IJ is the mi nor of
),
the control increments of U over control horizon M , Yp.lf (k) is the output predict value of y over
Then the relati\e g:lin of th e jth CV to the ith MV is
/ , I)
(6)
where U denotes process inputs (i .e. M/
Assuming the number of f\1V is equal to CV. m=p. the static gain matrix is
~ ,,,
(5)
c, U Af(k)::; !:,U noaA
C:'::,.U Af(k)??b
Gi"en the process's steady-state gain matrix. the interaction analysis :lmong variables m:ly be implemented referring to the method proposed by Bristol E.H( 1%6)
cP 11
(4)
min J(k) = Ilw(k)- Ynf(k)11le) +116UM (kt ,, (I)
~[
and A1 ]. It can be seen that dlle to 1
the coordinated monitoring level . the MV and CV numbers for on-line calclllation are redllced. thus online calculation reqllirement has been reduced. Mean\\hile. such coordination meets the control requirements. The bas ic tv1PC algo rithm we used here QDMC.The QP problem at e\'ery sample time is :
The dotted lines and real lines shown at Fig.5 are process data cun'es before and at the experiment respectively. A conclusion may be drawn that the multivariable predictive control obviously improves the fluctuation of the process cun'cs, makes the operation smooth and steady, effectively rcstrains the affection of non-tested disturbance.
IS
694
Regenerator Control , Computers Chem. Engng., 17(2), 165-179 RichalcU (1993). Industrial Applications of Model Based Predictive Control, Automatica, 29(5), 1251127-l .
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:; 06'<-i-~-_-=---_ -_-
()
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oA-=--" o 50
100
150
200
250
300
350
400
time (min) 0 . 6i--_~_ __ _ _.---,---:-_,
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I"
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~5o--101j'1 50200
t:
250
300
350
400
l0~ O·I~·~~~ 1 ' . . ._ . ._ . ... ~~->r'-~~
time (min)
o
I'
_
0~--5~O-71~ OO--~ 15=O--~ 20=O--~25=0--~30=0'-~35=0~400
tlme (mm) "Ofj _~_.__ . ,
G
time(min)
_ __ .__ . ~
!
-;
____i 300 350 400
04 .-..:..:::.:..:.:..::....:~: :~~:~:_ . ~_=.:~~-=-=-.:-_·
o
50
100
150
200
250
tlme (min)
.. setpoints ---real valucs Fig.-l Process Coordinatcd Predictive Control Sillllli;ltion Results time(min) N
5. CONCLUSION
o
h~~..~ ....:.· -•. ,,~ ,. ,•.. . •;.,;. •. . I ~,:... ,
Closed-loop simulnlion and industrial application e.\perimenl show it appropriale to apply advanced conI rol to renctor-regeneralor conlrol. The coordinated control str:llegy proposed in this paper shows its efficiency on the tradeoff between system performances and m:lIlipulated vnrinbles ideal resling values , reduce the number of MV and CV lhrough inler(lction analysis. \\hich shO\\s a \\ay to decrease compulation requirements. In order to ensure the conlrol system longtime reliability and effectiveness, 1\\'0 more problems should be considered: (I) ho\\ 10 simplify the on-line optimi zalion calcublion in order to sa\'e memory and illlpro\'e renl lime efficiency. (2) lhe process idenlificnlion sequence should be t(lken after certain period 10 m:lintain control precision and e1illlin(lte the change of property and p:n:lllleters.
E
0
50
100
.
150
. ,. .. . - .:. :... /.}" .':/. : <':.. ...
200
250
300
350
-
400
Cl)
f-
time(min)
0:'"
u
'" 0:\,
~
..
.' .... ........... .. ... .. ............ .
,~"'---./'~ 0,
0 ~~5~0-~ 10 ~~~~~~~~~~~~-74·00
time(min)
in QDMC control no QDMC control
Fig.5 Industrial Application E.\perimcnt Results
REFERENCES
Bristol,E.H. (I %6).On a Ne\\' Measure of Interaction for Mullivariable Process Conlrol, IEEE Trnns. Auto. Contr, II , I]]-I]-l. Garcin ,C.E ., A.Mrv10rshedi (I986) .Quadralic Programming Solulion of Dynamic Mntri.\ Control, Chem Eng Commtlll. . -le). 073 -087. Grosdidier.P. A.Mason. A.Aitolahti, PHeinonen and V.Vanhamaki t1'J'J2) . FCC Unit Rcnctor
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