Aspen Gamble

8/3/2019 Aspen Gamble

1/12

Don't Gamble WithP yscal PropertiesFor Simula ionsF in din g g oo d va lu es

fo r in a de qu ateo r m issin g p hy sic alp ro p er ty p a rame te rs

is the key to asuccessful

simu la tio n. A n dth is depen ds u ponch oosin g th e righ te stima tion me thods .

E ric C . C a rls on ,Aspen Techno logy .nc .

Chemica len gin eers use pro ces sS imU la .ti o n t o p er fo rm a . varietyo f impo rtan t wo rk. This workran ges from ca lcu la tio n s o fmass- and energy ba lan ces o f la rge flow-sheets to predic tio n o f the perfo rm ance o fp ro ces s a ltern atives tha t c an s av e m illio nso f dollars, An engin eer very quickly candefin e a complex flowsheet and an [hepro cess co nditio n s. Desk top computersnow allow ra ting. s izing. optim izatio n ,an d dyn am ic ca lcula tio ns tha t p revio uslyrequired la rge ma in frame computers . Inthe pa st, these simula tio n s were o ftenbuilt by a group o f experts , in c luding aphys ic al pro perty expert. N ow , s im ula to rssuch a s ASPEN PLUS. C hemCAD Ill,HYSIM , PRO 11, and SPEEDUP are eas i-er to use and mo re powerful. than thestanda lo ne program s o f the pas t. To day, asingleengineer can set up the ba s ic s imu-la tio n s pec ific atio ns , in cludin g th e phys i-c a l properties, in very little time.

M iss ing o r in adequa te physic a l prop-erties , however, can underm ine the accu-racy o f a model o r even preven t you f romperfo rm in g the sim ula tio n .. Tha t so me re-quired in fo rm a tio n is m iss ing is no t ano versight in the s imula to r. A fter a ll, fo rmos t compounds. phys ica l property pa~rameters a re n ot known fo r every thermo -dynam ic mode! o r fo r aU tempera ture o rpres sure ranges. M odels have built-in a s-sum ptio ns an d pra c tic a l lim its tha t sho uldapply.

[0 this a r tic le we will provide prac ti-c a l tips and techn iques to help you accu-

- ra rely desc ribe the physic a l propertiesn eeded in a simula tio n . As an eng in eer.

you alw ays w ill have to make a ssump-tio n s in term s o f physic a l properties ,however. The goa l o f this a rtic le is to out-lin e the appropr ia te as sumptio n s and topro vide techn iques when properties a remissing.The five important tasks

Success fully desc ribing the phys ica lproperties to be used in a s im u la ti o n in -v olv es fiv e ta sk s:1. selec tin g the appropria te phys ica l

p ro pe rty m eth od s;2. v alid atin g th e p hy sic al p ro perties ;3. desc rib in g nondarabank compo -

nen ts (chem ica l spec ies o r compound)a nd m is sin g p ar am eters ;

4. obta in in g and us in g phys ica l prop-er ty data ; an d

5. estim ating an y m issing propertyparameters .

It c an be a rgued tha t these ta sks a reno t sequen tia l and, to some degree, theya re co ncurren t. Durin g sim ula tio n devel-opmen t, however, you will n eed to vis itea ch a rea to be co nfiden t [ha t yo ur sim u-la t ion is a s ac cura te a s po ss ib le - sotha t impo rtan t dec is io n s c an be madeba sed o n the results o f yo ur s im ula tio ns .Se lecti n9 thea.ppropriatephysical property methods

This es sen tia l firs t s tep will affec t a llsubsequen t ta sks in developing accura tephys ica l propen ies in yo ur s imula tio n .In deed. the cho ice o f the physic a l pro per-ty models fo r a simula tio n can be one o fthe mo st impo rtan t dec is io n s fo r an engi-n eer . Severa l fac to rs n eed to be con s id-

C H E M IC A L E N G IN E E R IN G P R O G R .E .S S O C T O B E R 1 9 9 6 35


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SUCCEEDING AT SIMULATION

ered, and no single method can han -dle a ll sys tems. Table I lis ts so methermodynam ic models ava ilable insimula tors .

The four fac to rs that you shouldcon sider when choo sing propertymethods are:

[he n ature o f the properties o finterest;

the co mpos itio n o f the mixture; the pressure a nd tem pera ture

range; and th e a va ila bility o f p ara meters .To ea se the selec tio n o f the right

physica l property methods, we sug-gest usin g the dec is io n trees shown inFigures L-3. These trees a re based onthe fo ur fa c to rs fo r selec ting propertymethods, and can be used when thechemica l componen ts and approxi-m ate tempera ture an d pressure ran gesa re known . W hile these diagrams ares implific a tio n s , they do show theba sic s teps o f the dec is io n -makingpro cess , while the no tes in the s ideba ramplify some o f the key po in ts .The nature of (he properties of in-terest. A question tha t you may askyourself when starting a simula tio n is"Do es the cho ice o f physica l pro pertymethods matter?" The an swer is anempha tic YES. The cho ice canstro ngly a ffec t the predic tio n of thesimula tio n . You should be selec ting acol lec t ion of methods that w ill bestpredict th e properties o r results o f in -terest to you.

Because many chem ica l pro ces ss im ula tio ns in clu de distillation ..strip-ping, o r evapora tio n , one impo rtan tpo ten tia l co nsidera tio n fo r the cho iceof phys ic a l property models isvapo r/liquid equilibrium (V LE ). Thisis the a rea in w hic h the m ost physica lproperty wo rk is fo cused in chemica lengineering. L iq ui d/l iq ui d e qu il ib ri -um (LLE ) a lso becomes impo rtan t inpro cesses such a s so lven t extra c tiona n d e xtr ac ti ve d is ti ll ati o n.

Ano ther critic a l con sidera tio n ispure-C omponen t and mixture en-tha lpy. E nrha lpies an d hea t c apac itiesa re im portan t fo r un it o pera tion s sucha s hea t exchangers , c ondensers , dis -tilla tio n c olu mn s, a nd rea cto rs .

Table 1.Thermodynamic property modelsavailable in a simulator.E qua ti on -o l- Sl al e M il de isBenedic t-Webb-RubinlBWR.I-Lee-Star t ingHayden-O 'Conne l l *H yd ro ge nflu orid e e qu atio n o f s ta te fo rhexarner izat ion"Id ea l g as la w*L ee -K e sle r I LK ]Lee-Kes le r -P lockerPengRobinson IP .R)Per turbed-Herd-ChainP re dic tiv e S R KR ed lic h-K wo ng I R KIR e dlic h Kw on gS oa ve I RK SIRKSor PR with Wo ng "S an dl er m ix in g r ul eR KS o r P R w rth m od in ed -H llro nV id al-Z m ix -i n g r u leS an ch ez -L a c om be fo r p oly m er s* N ot us ed fo r the liq uid p has e

Act iv ity C o ef fi ci en t M o de lsE le c tro ly te N R TLAory-Hugg insNRTLS catchard-H i ldebrandUN IQUACUN IFACV an L aa rWi lsonSpecial ModelsA P I s ou r-w ate r m e th odB ra un K -IOChao -Seade rG re ys o n - S tr a e dKent-EisenbergS te am T ab le s

Ncn-elsctolvte S ee F ig ure 2

Polar

ElectolyteReal?

Electol 'yte N R T Lo r P itz er

A ll Nonpo l a r

Pe ng , Rob i n son ,Be dlich-Kwong-Scave,L e e K e s l e r- P l oc k e r

Polarity

Rea lorP s e u d c c o rn p o n e n ts

Chac-Seade r ,G ravs on -S tra ed o rB rau n K -IO

P?

Vacuum Braun K -IO o r Id ea l

36 O CTO BER 1 99 6. C HEM IC AL E NG INE ERIN G PR OGR ESS

< ! > Elec to ly tes< ! ! > Pressure Figure 1. The first steps for selecting physical property methods.

Sou r ce : (7 )


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Navigating the decision treesH ere a re s om e p oin te rs to he lp yo u n av ig ate the d ec is io n tre es tha t a p-p ea r a s F ig ure s 1-3.

Wha t a r e pseuaocomoonen ts ? In m an y a pp lic atio ns w he re o nly n on -p ola r m ole cu le s a re p re se nt (s uc h a s in hyd ro ca rb on p roc es sin g a nd re -fin in g) . the m ix tu re is s o c om p le x tha t in ste ad o f re pre se ntin g it b y a ll thek no wn c on stitu en ts , it is e as ie r to g ro up the c on stitu en ts b y s om e u se fu lp ro pe rty s uc h a s b oilin g p oin t. In this w ay , a m ix tu re o f h un dre ds o f c on -s titu en ts c an b e re du ce d to 30 o r fe we r. T he p ro pe rtie s a t t he se g ro up edc on stitu en ts , c alle d p se ud oc om p on en ts , a re re pre se nte d b y a n a ve ra gebo ilin g p o in t, s p ec ific g ra v rty , a n d m ole c u la r w eight I f yo u do n ot us ep se ud o-c om p on en ts , the c on stitu en ts s ho uld b e d es crib ed b y a m o le cu -la r fo rm ula a nd a re re fe rre d to a s re al c om p on en ts .

Wh y a re e le ctr oly te m ix tu re s d iffe re nt? E le c tr oly te m i xtu re s in c lu d ec om pon en ts tha t a re c ha rg ed m ole cu le s (io ns l o r tha t fo rm s alts . S om es im ula to rs a llo w c alc ula tio n o f e le ctro ly te re ac tion e qu ilib rium w ithp has e e qu ilib r iu m. T his is a ve ry p ow erfu l m ethod a nd its us ag e c ove rsmany a pp lic atio ns s uc h a s c au stic s cru bb in g, n eu tra liz atio n. a cid p ro -d uc tio n, a nd s alt p re cip ita tio n. T he n on id ea lity o f e le ctro ly te s olu tio ns ,u su ally c on ta in in g w ate r, c an b e o bs erv ed in b oil in g p oin t e le va tio n, s alt-in g o ut o f g as es ltha t is , a dd in g s alts to the s olu tio n to c ha ng e the s olu bil-ity o f g as es ), a nd s alt p re c i p ita tio n, T he m os t c om m on e le ctro ly te m ath-o ds a re the P itze r m od el, a nd the m od ifie dN RT L a ctiv ity c oe ffic ie ntm od el o f C he n a nd c ow orke rs . S om e e le ctro ly te s, like fo rm ic ac id a nda ce tic a cid , a re v ery w ea k a nd a n e le ctro ly te m e tho d is n ot re qu ire d.

W hich type of me/hod should be chosen for mouses conta iningpo la r c omponen ts tm l no e lect ro ly tes? T he re a re tw o g ro up s o f m e tho ds- b as ed o n a ctiv ity c oe ffic ie nts o r e qu atio ns o f s ta te . U s e a ctiv ity -c oe f-fic ie ntb as ed m e tho ds w he n p re ss ure s a re lo w to m e diu m [ty pic ally le sstha n 1 0 b ar o r 150 p sia ) a nd if n o c om p on en ts a re n ea r c ritic al p oin t. A c-tiv ity c oe ffic ie nt m od els a ls o o fte n a re u se d to a cc ura te ly p re dic t n on -id ea l liq uid b eha vio r s uc h a s fo r V lE a nd fo r L LE . I n c on tras t. e qua tio n-o f-s ta te m e tho ds e xc el in the ir a bility to re pre se nt d ata a nd e xtra po la tew ith te mp era tu re a n d p re ss u re u p to an d a hove the m ix tu re c ritic a lp oin t. N ow , how eve r, m ethod s re ly in g on c ub ic e qua tio ns o f s ta te w ithp re dic tive m ix in g ru le s e ffe c tive ly c om bin e the s tre ng ths o f the tw om e tho ds . (S ee T ab le 2.) F or hig he r p re ss ure s la nd te m pe ra tu re s), the ses pe cia l e qu atio ns o f s ta te a re b ette r a s the y w ere d ev elo pe d 10 app l y toa w id er ra ng e o f te m pe ra tu re s. T he se m e tho ds in co rp ora te a ctiv ity c oe f-fic ie nts in the c alc ula tio n o f c om p on en t in te ra ctio ns re pre se nte d b y e x-c es s G ib bs fre e e ne rg y. M os t o f the la tte r u se a U NIF AC ba se d a ctiv ityc oe ffic ie nt m od el a s the d efa ult. b ut y ou c an u se a ny a ctiv ity c oe ffic ie nt

A t s im ula tion p re s s u re s le s s tha n 1 0 a rm an d whe re the re a re n on ea r c ritic al c om pon en ts , fo r the b es t re su lts us e the W ils on , N RT I_ , orU N IQ U A C b in ary p ara me te rs tha t m ay b e a va ila ble in b uilt- in d ata ba nk s,o r fit b in ary p ara me te rs to e xp erim e nta l d ata [if a va ila ble ) u sin g a ctiv ityc oe ffic ie nt m o de ls . T he se p ara me te rs m ay ha ve b ee n d ete rm in ed a t d if-fe re n t te m p era tu re s, p re s su re s , a nd c om p os itio ns th an y ou a re s im u la tin q ,tho ug h, s o y ou m ay n ot o bta in the b es t p os sib le a cc ura cy . I f In te ra ctio np ara m ete rs a re n ot a va ila ble , ho we ve r, y ou c an u se the U N IF A C m e tho d.

W he n s ho uld U NIF AC b e u se d?U N IF A C a nd o the r U N IF A Cb as ed a c-tiv ity c oe ffic ie nt m od els a re p re dic tiv e a pp ro ac he s tha t u se s tru ctu ra lg ro up s to e stim a te c om p on e nt in te ra ctio ns . F ro m s tr uc tu ra l in fo rm a tio nab ou t o rg an ic c em pc ne nts u su ally ava ilab le in the b uilt-in d atab an k,U NIF AC is a ble to p re dic t the a ctiv ity c oe ffic ie nts a s a fu nc tio n o f c om -p os itio n a nd te mp era tu re . You c an m ak e lis e o f U NIF AC w he n you do n otha ve e xp erim en ta l da ta o r b in ary p ara me te rs o r w he n a n a pp rox im atev alue is ac ce ptab le [fo r in stan ce , fo r a c om po ne nt w ith low p rio rity ). In

re ce nt ye ars , the re ha ve be en im pro ve me nts to U NIF AC (s e e Tab le 3)tha t c an b ette r p re dic t V LE , he at o f m ix in g, a nd L LE o ve r a w id e; te m pe r-a tu re ra ng e. R ec en t e xte ns io ns to U N IF A C p ro po se d fo r m ole cu le s s uc ha s re frig e ra n ts a n d s ugars m ay be u s e fu l, an d you c an add the g rou p sa n d p a ra m e te rs to y ou r s lrru rla tio n. S im ula to rs m ay ha ve the a bility tog en era te b in ary in te rac tion p ara me te rs fo r W ils on , U NiO UA C, o r N RT lfro m U N IF A C .

N ot a ll c om po ne nts c a n b e d es cribe d u sin g U N IF AC , ho we ve r, an dn ot a ll g ro up in te ra ctio ns a re a va ila ble . ha mp le s o f c om p on en ts tha t d on ot ha ve U N IF A C g ro up s in c lu de m e ta ls , o rg an om e ta ls . a n d p ho sp ha te s .So , w e highly re com men d a lw ays do in g a s e a rc h fo r a va ila b le da ta onb in ar y o r te rn a ry s ys te m s o f in te re s t.

How should rhe vapor phase be treated? The cho ic e o f the V lEm ethod u s in g an a c tiv ity c oe ffic ie nt m ode l a ls o re qu ire s a cho ic e o fm od el fo r the va por p ha se p ro pe rtie s, I f va po r p ha se a ss oc ia tio n is o b-s e rve d la s in the c as e o f ac e tic a c id ), the n the vap or p ha s e m ode ls ho uld b e H ay de n-O 'C on ne ll o r N othn ag el. A s ys te m c on ta in in g hy dro -ge n flu orid e m ay re qu ire a s pe cia l m od e! to re pre se nt the hig h d eg re e o fas soc ia tio n du e to hyd ro ge n b on din g. A ss oc ia tion in the va po r p ha sec an ha ve a s tro ng e ffe ct o n p ha se e qu ilib ria a nd e ntha lp y.

W hen should defaults be overridden for a ther physica l propertymethods? P re dic tio n o f d en sity , e nrha lp y, a nd v is co sity a ls o a re irnpor-tan t in s im ula to rs , a nd yo u s hou ld n't au to ma tic ally a cc ep t the d efau ltm e th od s. C he C K t he s im u la to r d oc um e n ta tio n fo r th e d e fa ult m e tho d a ndm i xi ng r ul es .

V ap or d en sity is c alc ula te d by a n e qua tio n o f s ta te o r the id aa l g asla w. M ix tu re liq uid d en sitie s c an b e c alc ula te d b y a n e qu atio n o f s ta te , ate m pe ra tu re -d ep en de nt m od el s uc h a s tha t o f R ac ke tt, o r b y a te m pe ra -tu re - a nd p re ss ure -d ep en de nt m od el s uc h a s the C DS TA LD . F o r p su ed o-c om p on en ts , a n A m er ic an P etro le um In stitu te (A P I) m e tho d ty pic ally ise m plo ye d. T he R ac ke tt m od el is re co mm e nd ed fo r g en era l u se ,

V ap or e ntha lp y u su ally is c alc ula te d v ia a n id ea l ga s a ss um ptio n D ra n e qu atio n o f s ta te . T he e qu atio n-o f-s ta te m e tho ds c alc ula te a d ap ar-tu re fro m id ea Hty c alle d the v ap or e ntha lp y d ep artu re . F or c om p on en tss uc h a s a ce tic a cid , the H av de n-O 'E cn na ll m od el is b es t. a nd w ill c atc u-la te a la rg e r-th an -n or ma l v ap or e n tha lp y d ep a rtu re .

L iq uid e ntha lp ie s a re c alc ula te d b y a v arie ty o f m e tho ds . I f the s im u-la to r u se s the id ea l g as a s [he re fe re nc e s ta te , the n the p urs -c om p un en tliq uid e ntha lp y is c alc ula te d fro m the id ea l g as e ntha lp y a nd a liq uid e n-tha lp y d ep artu re . T his c an b e w ritte n a s

H",I -= H".f9 + {W' . W.'o} (1 1whe r e W.lis the p ure -c om p on e nt liq uid e n tha lp y, W J9 is the id ea l g as e n-th al py , a n d (W -'- H 'w ) is the liq uid e n th alp y d e pa rtu re . T his d e pa rtu re in -c lud es the he at o f va po nza tic n. the v ap or e ntha lp y d ep artu re from theid ea l p re ss ure to the s atu ra tion p re ss ure , a nd the liq uid p re ss ure c or-re ctio n fro m the s atu ra tio n p re ss ure to the re al p re ss ure . S im ula to rsa ls o a llow s ep ara te c alc ula tio ns to r a liq uid e ntha lp y d ire ctly fro m theliq uid -ha at-c ap ec itv p oly no mia l. F or s om e c om p on en ts . the m e tho d inE q. I w il l n ot b e a cc ura te e no ug h fo r liq uid -he at-c ap ac ity p re dic tio ns .T his c an b e v ery im p orta nt if y ou a re e xp ortin g y ou r p ro pe rty in fo rm atio nto a no the r p ro gra m s uc h a s o ne fo r rig oro us he at-e xc ha ng er d es ig n. Y ouc an u se the la tte r liq uid -he a t-c ap ac ity [C pL I m e tho d to im p ro ve the a c-c ur ac y o f l iq uid he a t c a p s c itie s .

V is co sity is a no the r im p orta nt p ro pe rty fo r s iz in g 01 p ip in g, p u mp s ,he at e xc ha ng ers , a nd d is ti lla tio n c olu mn s. T he re a re v ario us v ap or a ndliq uid m e th od s fo r c alc ula tin g v is co sity a n d, g en e ra lly . th e p a ra m ete r r e-q uire m e nts fo r th es e m e th od s a re s ub sta ntia l.

CHEM ICAL ENGINEERING PROGRESS OCTOBER 1996 3 7


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Ye s.------ NRTL. U N IOUAC .a n d T he ir V a ri an c es

P < 10 ba r W IL SO N . N R TL . U N IQ U A C,L __ _N .. :. .:o ~~ a n d T he ir V aria nc es.-----Y-e -s --- U NIF AC LLES ee a ls oF ig ure 3 )

Wi lsonNRTL

UN IQUACUN IFAC

U NIF AC a nd its' -- _N_o :____ . . .. E x t e n s ion s,--------- S chwarte n trube r-H e n on ,PR o r A KS w ith W S.P R o r R KS w ith M HV 2P> IOba r

PSRK .L-_.:...:N .:_o PR or RKS with M HV2

~ Liqu id /liqu idressure

I nte ra cti on P a ra m e te rsAva i lab le

Figure 2. Proceeding for polar and nonelectrolyte components.Source : 1 7 )

Hexame rs W ils on , NRTL, UN IQ UAC ,r-----~r U NIFA C w ith spe c ia l EO Sf or h ex am e rs

Ye s

O im e rs W ils on . NATL. U N IQ UAC ,'------ .. UN IFAC w ith H ayden O 'Conne llor N othn ag e l E OS

W ils on , N R TL , U N IQ U A C,'------------ o r UN IFAC" w ith Idea l G a sor RK EO S

No

~ Vapor Phase A ssoc ia tion

~ Degree s of Po lym e rization

* UN I F A C a nd its E x1 e ns io ns

Figure 3. Options for vapor-phase calculations with activity-coefficient models.- Sourc e : 1 7 )

38 O C T O B E R 1996. C H E M IC A L E NG IN E E R IN G P RO G R E S S

T ab le 2. E xamp le s o f s pe ciale qua tio n s o f s ta te .P re dic tiv e S R K (P SR K)P R w ith m od ifie d H uro n-V id al-2 m ixin g ru leP R w ith P an ag io to po lo us m ixin g ru leP R w ith W on g-S an dle r m ix in g ru leR KS w ith m od ifie d H uro n- V id al-2

m ix in g r uleR KS w ith P an aq io to pn lo us m ix in g ru leRK S with W on g-S an dle r m ix in g ru le

T ab le 3 . U NIFAC re vis io nsand ex tensions .Model PredictsDor tmund-modi f ied V L E, L LE , H e , r:UN IFAC ( 19 93 ) ( 8/K le ibe r e x te n s ion VLE of fluorin ate d( 19 94 ) ( 11 / hydrocarbonsLyng bv-rnndi f iad V lE , H e ( Ex ce ssUN IFAC ( 19 86 ) ( 13 / Entha lpy)UN IFAC , L L E LL E(1 98 0 ) ( 12 /UN1FAC . r e vi si on 5 VL E(1991) (9 )"lnfinite-ditutien a c ti vi ty c o e ff ic i en t

In a dditio n, den sity. vis co sity. pH ,an d therma l co nduc tivity may be e -sen ria l fo r o ther pro cess ca lc ula tio ns ,T ran spo rt properties are impo rtan tw hen do in g equipm en t s izin g ca lc ula -t ions . Also, p ro c es ses s uc h a s m eta llu r-gy a nd m in in g w ill req uire c alc ula tio nsfo r p ha se eq uilib ria in clu din g s olid s.

The composition of {he mixture,C om po sitio n w ill in fluen ce a ll pro per-ties , due to the w ay m ixture pro pertiesa re c al cu la ted . It will affec t phaseequilibria greatly beca use o f the in ter-a c tio n o f the componen ts in the mix-lure. Usually, the in tera c tio n in theliquid phase is the mo re im po rtan t be-c ause o f the c lo se proxim ity o f themo lecules in that phase. The na ture o fthe va po r pha se a lso c an be sign ifica nti f the componen ts fo rm complexes.The im po rtan t in term olecular fo rcesa re e lec tr os ta ti c, i nd uc ti on . a ttr ac ti on ,an d repuls io n between n on po la r co m-ponen ts , and chemica l fo rces such a shydrogen bonding. A good overviewo f these fo rces is given in Ref. I.


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" " "o " ,Vapor

Molefraction '" L i q u i dMolefraction

"o. "

Figure 4 (above). VU:: of acetonitrilelwater system at 1 atm. Figure 5 (right). VL of totuenetphenol system (It J atm.

T he m agn itude o f the elec tro sta ticand inductio n fo rces i rela ted to thepo la rity o f the componen ts . C ompo-nen ts uch a s water, aceto ne.fo rm aldehyde, and methyl chlo rideare strong dipo les . M any po la r com -pounds a re a sso c ia tive, and fo rmcomplexes o r disso c ia te in to io n s .C omponen ts like ethane and II-hep-tune U J " e nonpo lar . You C an use your

simula to r to repo rt the dipo le mo -m en ts o f databa nk c om po nen ts a s onemeasurement o f po la rity. In genera l,mixtures of n onpo lar co mpo nen tsw ill exhibit less n on idea l beha vio r.

Figures 4-7 il lustrate the effec t o fpo la rity on bin a ry vapor/liquid equi-lib ria . F igu re 4 sho ws the predic teda n d e xp er im en ta l VLE of two highLypolar co mpo nen ts. a ceto nitr ile an d

\ 0 o - - - V a p o r M o l er ra c ti on C 6 H 6 - - L i q u i d Molefract ion C S H 6 119.5 \' oe/./79


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0-- Liquid2Molefraction C S H 1 2 0 - - - Liquid! Molefraction C 6 H 1 2 0

(the non random two -liqu id acuvirycoeffic ien t model (NRTL) andRedlich-Kwong equa tio n o f sta te fo rthe va po r p has e). F igure 6 depic ts theVLE o f a m ixture o f cyc lohexane andbenzene a t I atm. H ere, the in tera c-tio n o f seem ingly sim ila r mo leculeswith a differen ce in bo iling po in t o fles s than 1C cau es an azeo trope at aco mpo sitio n o f abo ut 0.54 m ole fra c-tio n o f ben zen e. A mixture such a sethane a nd pro pylen e (F igu re 7) is ana lmos t ideal o ne. an d do es n ot devia temuch from Raoult's law .

M ixtures o f nonpo la r an d po la rcompo unds, such as wa ter and hydro -ca rbon s, o ften w ill fo rm two liquidpha ses tha t a re very imm isc ible. F ig-ures 8 and 9 show examples o f misci-ble an d imm isc ible sys tem s o f liq-uid/liquid eq uilib ria , res pec tiv ely , a t Ia tm . In Figure 8, cyc lohexano l is im -m isc ible in the wa ter pha se but theo rgan ic pha se con ta in s up to 0.50mole frac tio n water (0 .10 mass frac -tio n wa ter). Figure 9 shows the highdegree o f imm isc ibility in bo th theo rgan ic and wa ter pha ses fo r a m ix-ture o f benzene and wa ter wherethere is less than 0 .06% by mo le ben -zene (03% by mass). Because o f thisbehavio r, so me sim ula to rs have a spe-c ia l property method to trea t thewa ter pha se as o rgan ic -free (a lsoc a ll ed F r ee -Wa t er ).

'M ost s im ula to rs o ffer co llec tio nso f property methods in predefin ed

T em pe ra lu re . 'C

Figure 8 (left). LLE of cyclohexanollwaser system a . t 1 atm. Figure 9. (above). LLE of benzene/water system at 1 atm.

sets ba sed UpOD methods tha t fre-quen tly a re used fo r certa in types o fm ixtures . U sua lly the sets a re iden ti-fied by the method used fo r pha seequilibria , W hen these sets use anequatio n -o f-sta te model, the samemode l is used fo r m an y properties, in -c ludin g tho se fo r pha se equilibria .

The pressure and temperaturerange. Th is is espec ia lly im po rtan t incho o sing the method to perfo rmphase equilibria c a lcula tio n s . M eth-ods that a re ba sed o n Raoult's law o rtha t use ac tiv ity coeffic ien ts a re no ra c cura te a t high pressure o r when thetempera ture is abo ve the c ritic a l tem -pera tu re o f a compo nen t. You can useH en ry 's law when you have ligbtga ses in ubcritical so lven ts , but itgenera lly is n o t recommended fo rcon cen tra tio n s o f so lute grea ter than5%. In genera l, equa tio n s o f sta te a rebetter suited to predic t VLE over aw id e tem pe ra tu re or pres sure ra nge,espec ia lly a t high tempera ture andpressure .

The availability of parameters.W itho ut suffic ien t pure-co mpo nen tand bin a ry parameters , you w ill beun able to c a lcula te pure-co mpo nen t o rm ixture properties . Y ou mus t choo seamong obta in ing an d us ing experi-m en ta l o r litera ture da ta , estima tin gpa rameters , o r choo s in g a less rigo r-o us m etho d. This sho uld be invest iga t-ed fo r a ll phys ica l pro perty m etho ds in -c ludin g tho se s ho wn in F igures 1-3.

40 O CT OBE R 1 99 6. C HEM IC AL E NG IN EE RIN G P RO GR ES S

Validatingthe physical propertiesA n eces sa ry step in a ny s im ula tio n

pro jec t is va lida tio n of th e physica lproper t ies . This i nv ol ve s r ep o rti ng , ta b-u la tin g, o r p lo ttin g p ur e-c om po nen t a ndm ix ture pro perties an d c om par in g th eresults to kn own da ta o r expected be -havio r. This is an im po rtan t step in an ys im uJ atio n a nd s ho uld be p er fo rm ed f ordatabank as well a s n o nd ar ab an k c om -ponents . Simula tors ca n pro vide thesec alc ula ted p ro perties in ta bu la r a nd p lo tfo rm at This is a useful to o l fo r under-stan din g ho w pure-c om po nen t an d m ix-ture properties, such as d en si ty , h ea t c a-pac i ty, and ex ces s p ro perties , v ary w ithte mp er atu re, p re ss ur e, a nd c omp os iti on .an d ho w they behave when extrapo la t-ed . S im il ar ly , s uc h r es ults c an be u se d togen era te p lo ts o f V LE and LLE to com -pare to diagrams in th e l ite ra tu re a n d a c-tua l field data . Some simula to rs havet he c a p a bi li ty to g en er ate r es id ue c ur vefo r d is tilla tio n o f tern ary m ix tu res . T heres id ue p lo t c ap ab ility a ls o is a p ow erfu lto o l fo r d is ti ll ati on a n al ys is .

U se the tabula tio n and plo ttingtoo ls to determ ine the cause o f dis -c rep an cies in p ro perties . If a m ix tu repro per ty is in co rrec t, in vestiga te if asingle componen t is the cause by re-po rtin g pure-co mpo nen t pro perties .Ano ther useful techn ique is to com -pa re the same flow sheet o r p roperty-table results while us ing differen tp hy sic al p ro perty m eth od s.


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By default, most phase equilibriacalculations are performed assumingvapor and liquid phases. If your pro-cess involves two liquid phases(VLLE), be sure to specify three-phase calculations. If not, you willget incorrect results. As a part of thevalidation, you also should check thatyour property methods do not falselypredict two liquid phases.Simulators let you specify that onlyone phase is present in a stream or aunit operation. If vapor and liquidphases are possible, however, youshould use the two-phase specification.Nondatabank componentsand missing parameters

When you want to simulate non-databank components or have compo-nents for which parameters are miss-ing, ask yourself the fol-lowing:

Is this a major compo-nent in the mixture? If it isminor, can I take it out ofthe simulation?

Does the componenttake part in VLE? Is the component non-volatile?

create a list of parameters that aremissing. You should detail this infor-mation when communicating the as-sumptions of the simulation to otherusers or your management.

Certain property parameters alwaysare required for a simulation. Thesecan include molecular weight, vaporpressure, and ideal-gas heat capacityconstants. The need for other parame-ters depends upon your choice ofphysical property methods. The simu-lator manuals should include the infor-mation about the parameter require-ments (7). There also are parametersthat will be required for calculating theheat of reactions or the reaction equi-librium constants. This includes theheat of formation and the Gibbs freeenergy of formation of all componentsthat participate in the reactions.

check for different ordering of atoms.For instance, ammonia can be de-scribed as H3N instead of NH3. Ref. 2contains a formula index of organiccompounds and is a good resource foralternative names.

Once you have determined the pa-rameter requirements that are not sat-isfied, the next stage should be ob-taining and using physical propertydata.Obtaining and usingphysical property data

Sources of data. To provide pa-rameters for nondatabank compo-nents or to do regression for pure-component and binary parameters,you will need to search for availabledata. Such data may be found in a va-riety of sources, including data-com-

pilation references, hand-books, journals, and inter-nal data collections.While most streams insimulations contain mix-tures, accurate property cal-

culations are not possiblewithout accurate pure-com-ponent properties. The im-portance of pure compo-nent data should not be underestimat-

- ed as they are the basis for both pure-component and mixture properties.For instance, pure component proper-ties such as vapor pressure will beused in phase equilibria calculations.Table 4 contains common sources forpure component properties, whileTable 5 lists common sources formixture properties.

The recommended order of datasearch is:1. critically evaluated data sources;2. nonevaluated sources;3. experimental measurements; and4. estimation techniques.Binary parameters for phase equi-libria. Because of the large numberof binary pairs in even a simulationof only ten components, we recom-

mend ranking the components so asto prioritize the pairs and focus theliterature search and measurement ef-forts on the most important parame-

Is it polar or nonpolar? Will reaction (including decom-position) cause this component to be

depleted? What properties need to be accu-rate for the chosen property methods?These questions will help you toidentify the parameters that are need-ed based on your choice of physical

property methods. If these parametersare not available or cannot be deter-mined through literature search, re-gression, or estimation, then you willhave to reevaluate your choice ofphysical property methods or obtaindata by measurement.

You should determine what the pa-rameters will default to if the simula-tor does not find any available. It isdangerous to assume that the physicalproperty parameters wer.e availablejust because the simulator did notgive you an error message. Use thesimulator manuals and on-line help to

T ech niq ues to rem o ve or m in im izeth e im p act o f sp ec ific p aram eters

shou ld be used w ith cau tion .You can use your judgment about

the importance of a parameter to setnominal values for unimportant prop-erties. For example, if you know thata component is very nonvolatile andare using Antoine's equation forvapor pressure (In P = A + B/(T+C,you can set the value of parameters A,Band C to -100, 0, and 0, respective-ly. (T is ternperature.) This will assignthe vapor pressure used in Raoult'sLaw a very small value, almost zero(3.7 x 10.441). This and similar tech-niques to remove or minimize the im-pact of specific parameters should beused with caution, however.

If you can't find a component inthe simulator's databanks, make sureyou check for synonyms. For exam-ple, methoxybenzene may be listed asmethyl phenyl ether or anisole. Agood approach is to search for thecomponent using its formula. Whenselecting the component by formula,

C H E M IC A L E N G IN E E R IN G P R O G R E S S O C T O B E R 1 9 9 6 41


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T ab le 4 . E xa mp les an d re lia bility o f so urceso f p ure c om p on en t d ata .Source Evaluated Generally Relieb le?C ritic al D ata o f P ure S ub sta nc es , D EC H EM A * Crit ical ly . Ye sC RC H an dbo ok of C he mis try a nd P hys ic s (B eils te in )* N on critic allyD I PP R Da ta C om p il ati on * Crit ical lyE n c yc lope d ia o f C hem ica ls , D rugs , and B io log ic a ls N on c ritic a lly YesE ncyc lop e d ia o f Po lym e r Sc ie n ce an d En g in e e rin g N on c ritic a lly YesESDU V alidate d E n g in e e rin g Data In dex N on c ritic a lly YesH andbook o f T he rm op hys ic a l P rop e rtie s o f G ase s N on c ritic a lly Yes' an d L iq u id s

JA NA F T he rm oc hem ica l Tab le s C ritic a lly Ye sLan ge 's H an dbook o f C hem is try N on c ritic a lly Ye sPe rry 's C hem ica l E n g in e e r's H andbook Non c ritic a lly Ye sP rope rtie s o f Gase s an d L iqu id s N on c ritic a lly Ye sSe le c te d V alue s o f P rop e rtie s o f C hem ica l C ritic a lly Ye sC om p ou nd s (T R C)*

V ap o r P re s su re o f P ure S ubs tan ce s N on c ritic a lly Ye s

* P arts o f the se s ourc es a re a va ilab le on -lin e from D IA LO G In fo rm ation S erv ic es , S TN In te rn a-tio na l, o r T ec hn ic al D ata ba se s S erv ic es , In c. (T DS )

ters. First, divide the componentsinto three groups: high, medium, andlow priority. Base the priority on cri-teria such as composition, and thepurity specifications of the process- if a component purity is specified,that component is important even if itappears only in low concentrations.Second, pair the components intohigh/high, high/medium. high/low,medium/medium, medium/low, andlow/low groups. Search the availablesources, including in-house ones, forany data for all groups. If certaincomponent pairs are known to be-have ideally, they can be excludedfrom the search. Next, use the UNI-FAC method for the missing pairs inthe medium/medium, medium/low,and low/low categories. UNIFAC isnot recommended, however, for anypairs that include the components ofhigh priority. A secondary literaturesearch can be used to find binary datafor similar compounds and those pa-rameters then substituted. Proposeexperimental work if any binary pa-rameter data are still missing or ifdata regression exposes data as inad-equate (3).

Regressing dataData regression is a powerful tool

for engineers not just to make thebest of available data, but also to ana-lyze the goodness of fit of a physicalproperty model to the data. Most sim-ulators include a data regression fea-ture. Examples of commonly re-gressed data include binary VLE andLLE, vapor pressure, heat of vapor-ization, density, and heat capacity.

Data regression finds the best fit ofparameter estimates to the experimen-tal data. The best fit is represented byfinding the lowest value of an objec-tive function while matching thephase equilibrium or other constraints.One common regression technique iscalled Maximum Likelihood Estima-tion. The objective function for thismethod is:

wherejis a data group, C r and C ;, aremeasured and estimated variables, re-spectively, such as temperature, pres-sure, composition, or heat capacity, c,is the standard deviation or the error inthe measurement of the variable, andWj is the weighting of the data group.

42 OCTOBER 1996. CHEM IC A L E NG IN E ER ING PROGRESS

When fitting phase equilibria data, theregression algorithm attempts to re-duce the objective function while thephysical property method is being usedto check that the components meet theconstraints of phase equilibria.

The work of a successful regres-sion involves selecting the right phys-ical property model and parameters,representing the data properly, choos-ing appropriate standard deviations ofthe data, and starting with suitableinitial estimates of the parameters.The following are general guidelinesfor data regression.

Make sure that you are regress-ing the right parameters. Use thesame physical property method andbuilt-in databank that you will beusing in the simulation. Choose pa-rameters that have impact on the databeing used. For example, when usingan equation-of-state method such asPeng-Robinson or Redlich-Kwong-Soave, you should determine theacentric factor, co. But, if you areusing an activity coefficient method,you should determine two or moreconstants for the Antoine model.

Estimate as few parameters aspossible. There is a tendency to use alarge number of parameters when fit-. ring a model to data such as tempera-ture-dependent properties or binaryphase equilibria. Try to regress thedata with as few parameters as possi-ble. If the regression results reportthat the standard deviation of the esti-mated parameters is of the same orderof magnitude as the values of the pa-rameters, you may be estimating toomany parameters for your given data.The larger the temperature range ofyour data, the more parameters thatyou can estimate. Watch out for incomplete data. Aregression may yield poor results ifthere are missing data points, particu-larly composition data. For example,some authors do not report all com-positions in VLLE or immiscibleLLE. You may need to estimate themissing compositions so that phaseequilibrium can be calculated for allcomponents. Find out how your sim-


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ula ro r handles m issing data to bestdea l w itb incom plete data .

Specify [he right number of phas-es. A regressio n w ill yield in co rrec tresults if the number of phases is notspec ified co rrec tly. This is a com mo nproblem in VLLE systems. Fo r somelitera ture data , the number o f phasesis ha rd to in terpret because o f the pre-sen ta tio n o f the da ta o r la ck o f de-scriptio n. O ften in VLLE data . o n ly ato ta l liquid compositio n is repo rtedeven though two liquid phases werepresen t. The autho r m ay be reportin ga heterogeneous azeo trope - anazeo trope where the vapor compo si-tio n equa ls the to ta l liquid compo si-tio n but two liquid pha ses are presen t.W hen do ing the regression o f a het-erogeneous azeo tro pe, divide the datain to two groups. the VLE data and theVLLE da ta . This w ill ensure that thec orrec t ph as e eq ui lib ria is c on sid ered .In regressions such as this, it is im-por tant [0 us e the pro perly ta bula tio nand plo tting fea tures of the s imula to rto check that the pa rameter es timatesc orrec tly repro duce the o rigin al da ta .

Use a model's full functionality. Aphysica l property model may be usedto ca lc ula te severa l pro perties . Fo r ex-ample, you can use bin ary excess-en -thalpy (H[) da ta and bin ary VLE orLLE data to determine b in a ry p a ra n le -te l'S fo r a c ti vi ty c o eff ic ie nt 1 11 0 de Js .F o req ua tio n-o f-s ta te m od els , yo u s im ulta -neously can use liquid- an d vapo r hea tcapac ity, vapor pressure, and hea t o fvapo riza tio n da ta . If data are availablefo r these pro perties . use these da ta to -g eth er to stimate th e p ara meters . D atagroups o f different types c an be usedtogether in th e s am e reg res sio n.

If necessary. regress parameterseven if values are available in {hedatabank. The physic a l property pa-ra rn eters found in the built-in pure-component and bin ary databanks gen-era lly are very reliable. You may find,however, tha t you need to determ inenew pa rameters to replace the data-bank va lues fo r your applica tion .C heck the built-in param eters to en -sure tha t the reco mm en ded ternpera-,rure, pressure, an d com positio n ran ge

T ab le 5 . E xam ples o f s ou rces o f m ixtu re da ta .SourcesA ctiv ity C oe ff ic ie nts a t In fin ite D ilu tio n, D EC H E M A C he mis try S erie sB in ary V LE D ata fite4, DIPPRD ortm un d Da taba nk (s up ers et o f DE CH EM A data collectionl"H ea ts o f M ixin g D ata C olle ctio n, D EC HE M A C he mis try S erie sliq uid -L iq uid E qu ilib riu m D ata C olle ctio n, D EC HE M A C he mis try S erie sP ha se E qu ilib ria a nd E ntha lp ie s o f E le ctro ly te S olu tio ns , D EC HE M A C he mis try S erie sV a p o r - L i q u i d E qu ilib riu m D ata C olle ctio n, D EC HE MA C he mis try S erie sV ap orL iq uid E qu ilib riu m D ata fo r E le Gtro ly te S olu tio ns , D EC HE MA C he mis try S erie sV ap or-liqu id E qu ilib r ium fo r M i~tu re s o f L ow B oilin g S ubs ta n cas , D EC HE MA C he mis try S erie sS ele cte d V alu es 0 1 C he mic al C om po un ds , T ex as A &M U niv ers ityS olid -liq uid E qu ilib riu m D ata C olle ctio n, D EC HE M A C he mis try S erie s O n -lin e d ata ba nk s

is n o t outs ide the range o f your simu-la tio n . For example, vapo r pressurepa rameters may not have been deter-m ined a t tempera tures below the no r-ma l bo ilin g po in t. M ost physica l pro p-erty m odels extrapo la te outside th etem pera tu re bo un ds reasonably well- but a t some compromise in a ccura -cy. The pa rameter va lues a lso mayapply to a very wide ran ge o f tempera-ture and thus no t provide as good a fi tif you on ly need a na rrow range in thesim ula tio n. Fo r phas e equilibria c alcu-la tio ns . to improve the accura cy ofVLE o r LLE predic tio ns , you maywan t to use terna ry o r quatern ary datato fin e-rune bin ary parameters tha tm ay be availa ble in the sim ula to r.

Check that 1 1 m parameters repro-duce the data. The simula to r w ill re-por t qualitative results o f the regres-s i on , in cludin g the r es id ua ls ( ex pe ri -m en ta l m in us es tim ated v aria bles ). U sethe pro perty ta bula tio n o r plo ttin g fea-tures to repro duce the data at the speci-fied co nditio ns . T his ca n be performedin the sam e regression run . C heck thatthe co rrec t number o f pha ses is pre-dic ted by a llo win g tw o-liquid-phasecalculat ions fo r the property table o rplot . In additio n, yo ur s imula to r mayhave an optio n where yo u can eva lua tethe fit using the existing pa rametersand model with experim en ta l dataw ith out d oin g a reg res sio n.

Remove components not in phaseequilibria. Lf componen ts that a reso lids o r io n s do no t appear in a

pha se, you can remove them from thephase equi libria con stra in ts . This isu : e fu l in VLE.

Generate equilibrium dow. Ifyou have bi n ary parameters fo r an ac -ti vi ty c oeffic ie nt o r eq ua tio n -o f-s ta temodel. your simula to r may be able togenerate VLE o r LL E da ta fo r reg res -s ion using these parame te rs . You C ::U 1regress these "da ta " w i rh anotherphysic a l pro perTY mo del. Thi. a llo wco n olidaiion of known parametersin to a ingle property method.

Fit other data. Y our s im ula to rmay have a data tiltin g feature that ca nbe used fo r plan t da ta . This metho dmay not be as useful for predictives imula tio n , though, if the data are notfrom a w ide variety o f c on ditio ns .Estimating missingproperty parameters

Pro perty estim atio n usually is do neafter a data search is perfo rmed tosupply mi sing property pa rameter.Y ou can use built-in estim atio n meth-ods to f i l l in some gaps in your physi-cal-properly-parameter requirements.Simula to rs in c lude one or mo re es ti-m atio n methods fo r each o f the mostcommon parameters . There a re twotypes o f estima tio n methods fo r purecomponen t parameters: s truc tura lgro up, an d c orrespo ndin g s ta tes .

Structura l group methods a re basedon the idea that con tributio ns o f theparts 01' s tr uc tu ra l g rOL IpSo f th e c ompo -nen t are additive Fo r p ro perties such a s

C HE MtC AL ENG tNEER IN G PR OG R.ESS O CTO BER 199 6 43


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T ab le 6 . C om pa ris on o f e stim ate d a nd e xp erim en ta l p arame te rs fo r p he neto l (C BH 100) .P ro pe rty N a me U n its Da ta E s tim a te d V alue "t o Error E s ti m ati on M e th odC ritic a l tem p e ra tu re K 647,15 6 5 7 - 1 2 6 5 1.54 Jobac~C ritic a l tem p e ra tu re K 647,15 653.138 1.02 Lvde r sB .n ,C ritic a l tem p e ra tu re K 647.15 652.1763 0.87 AmbroseC ri ti ca l p r es s u re N fm l 3,420,000 3.577 ,070 4.59 JobackCrit ic al p r e s su r e N /m2 3.420,000 3,509,780 2.63 Lyde r s enCrit ics I p r e s su r e Nfml 3.420.000 3.474,970 1.61 AmbroseCrit ics I vo l ume ml fkmole 0.39 0.3935 0.90 JobackC r iti ca l v o lu m e m' /kmole 0.39 0.391 0.26 Lyde r sBnC r iti ca l v o lu m e ml lkmole 0,39 0.389603 0.10 FadersS ta nd ar d h ea t 0 1 fo rm ation - J /km o le 101,600.000 105,400,000 3.74 BensonS tanda rd he a t o f f o rm ation " J /km o le 101,600,000 '104,140,000 2 .50 Jobock at 1 e tm , 25'C lo r ide al ga s,

T ab le 7 . E stim ate d p ro pe rtie s fo r p ro py l p hen yl e th er (C,H,zO).P ro p er ty N a me Un i ts Es tim a te d V alue E s tim a tio n M e thodM o le c ula r w e ig ht 136.1937 Fo rmu laC ritic al tem p e rature K 568,6672 AmbroseC r iti ca l p re s s ur e N / m l 3,085,350 AmbroseC r iti ca l v o lu m e mJ /Kmo l e 0 .442373 Fedo r sStandard heat of f o rm atio n ' J /Km ole -125,33!l,OOO Benson at 1 at rn , 2 5 " C fo r id ea l g as .

no rm al bo ilin g po in t. critica l tem pera -t ur e. c r it ic a l pressure, ideal-gas heal ca-pacity, and standard hea t o f formation.Some methods, such as tha: o f Benson .c ontain additio na l c orrectio ns fo r next-n ea rest-n eighbo r a to ms or fo r rin gs.Structural gr oup c on tr ibu tio ns a re d ete r-m in ed by ta kin g a n a ver age c on tr ibutio nbased on known physical con stan ts o fmany o rgan ic compounds. Because theBenson. Joback (10). n n d o th er s tr uc -tu ra l- gr ou p m et ho ds ar e ba sed m ain lyon data fo r o rga nic compounds, theycanno t be used fo r i n o r gan i c s , includingmetals. o r ions . In addition . structuralgro up m etho ds do n ot a cc ura tely repre-sen t very la rge o rgan ic molecules (thatis . o nes with a m olec ular w eight> 2(0)such a s pro tein s, N ew gro up-c on tribu-tio n m etho ds like that o f C on sta nrin ouand Gan i (4 ) po ten tia lly may providebetter estimation s fo r organ ic s. O therpo ss ibly useful m etho ds a re pro po sed inthe litera ture but may apply to on ly cer-la in familie s o r c om po ne nts .

C o rresponding sta tes methods a rebased on empi tical mathematica l re-la tionships among properties . Fo r ex-ample. the Letsou-Stiel method re-lates liquid visco sity to cri tic al tem -perature. cr itical pressure, and acen -tric fac to r. These methods most likelywi II be inaccu rate whe n used forcompounds un like tho se upon whichthe co rrela tion was based:

A good approach fo r bo th groupcontribution and co rresponding sta tesmethods is to check the accuracy of:IS many methods as poss ible fo rcompou nels fo r which properties a reknown and which a re s truc turallysimi la r to the compound you are es ti-mating. The fo llowing exampleshows the LIse of th is c o nc ep t

[Slimming the properties of propylphenyl ether. Let's say tha t you aremodeling a process con ta in ] ng propylphenyl ether (PPE ). a lso ca lledpropyloxy benzene. The on ly datayou have are its bo iling po in t

44 O CT OB ER 1 99 6 C HE MI CA L E NG IN EE .R IN G P RO GR ES S

(189.9C ). density at 2 5C (0 .947 4g/crn"). a nd m olec ula r s tr uc tu re :

V-0CHZCH2CH3

You wan t to es timate the proper-ties o f PP E usin g the m ost a ppro pri-a te m eth od s.

Step I.D eterm in e the best estim a-tion methods fo r a sim ila r phenyle th er . S elec t o th er corrrpoundis) chem-ica lly sim ila r to PPE fo r which youhave experimenta l property da ta . (O fcourse. the more sim ila r compoundsyou can Lise. the grea ter your con fi-dence tha t you are selecting the mostappropriate r ne rhods. ) In this case, fo rsim plic ity. let's c ho os e o nly phenetol:

V-0CH1CH ,

Data fo r phene ro l is a va ila ble fro mthe DIPPR data collection (5),

U se the s im ula to r'S b uilt-in m eth od sto estimate properties fo r phene t o l ,Then . c om pa re the results o f th e va rio usm etho ds w ith the exper im en ta lly deter-m in ed va lues to iden tify w hic h m etho dsgive the best estimates fo r this c la ss o fc om po un ds . T ab le 6 lis ts the results fo rthe differen t m etho ds fo r phenetol

You can see tha t the Ambrosemethod gives the best overa ll predic -tio ns fo r cr itic al tem pera ture an d pres-sure. the Fedo rs m etho d fo r c ritic al vo l-ume. and the Joback method for s tan -da rd hea t of fo rm atio n fo r phene t o l , So ,


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we will use these methods [0 predictthe c orresp on din g p ro per ties fo r PPE .Step 2. En ter the ava ilable da taand s tructure fo r PPE . E n ter n ormalbo ilin g po in t a nd m olec ula r structureo f PPE , and specify the methods tha tgave the best predic tio n s fo r phenetol,Step 3. E xa min e the estim atio n re-sults fo r PPE . These appea r in Table 7 .

One area of p ro pe rty e stim a tio ntha t is m ore d iffic ult is d ifferen tia tin gthe properties o f s tereo isom ers. Som egroup-con tributio n m ethods have C Or-rec tio n , fo r onho, meta, an d paraconfigurat ions , bu t few ha ve built-incor rec t ions fo r optica l isomers. Thes parauon o f these isom ers in a chem -ica l process is based on their s lightlydifferen t pro perties - rela tive vo la til-ity in dis tilla tio n is o ne exa mple.Employing simpler methods

In adeli t i n to . trucrural group an dc orrespo ndin g s ta tes m ethods , a no theru s e fu l e s rir na rio n approach is providedby ser ies and fam ily plo ts . Ser ies plo tslo ok a t the values o f a p ro pe rty s uc h asnormal bo ilin g po in t w ith increasingmo lec ula r w eig ht Of carbon num ber fo rcompounds in a series that differ byo n e s ubs ti tu e nt group.: uch a s the CH:!-u nit in Il-a lk an es . F ig ure 10 is a seriesplot fo r the normal bo ilin g po in t o f /1-II I ky lbenzenes , F am ily p lo ts are simi-la r. but the number o f groups is la rger .For ex am ple, F ig ur e II s hows a familyplo t o f the critic a l p r e ssu r o fmethyhhydrogenjchlorosilane . YouC an u se th ese p lo ts to p redic t p ro pertiesby extending the curve or to checkyour data fo r erro rs (6). To creme auseful series o r fam ily pia l, however .you m ust be c areful abo ut the co mpo -n en ts i nc lu de d.

W hen a cc ura cy is n ot c ritic al. c on sid -er the s im ple but pow erful techn ique o fc ompon e nt s ubs ti tu ti o n. In this , yo u useth e pr operti s o f a no th er. s im ila r c om po -n en t fo r a ll p ro perties o f the c om po nen to f in terest that you do not know. A s im i-la r componen t is o ne tha t ha s a compa-r ab le v ol ati li ty ( va p or p re ss ur e) , d en si ty .and hea l c apacity. This is use f u l if thecomponent is n onvo la tile o r is n o t in -v olv ed in ph ase eq uilib ria . F or ex am ple,

you have a small amount o f a non-vo la tile com ponen t in a s tream tha t is a tI O O c C and I a rm . You can access theproperties o f a n o nv o la ti le c omp o ne nt,sa y ~H42 (m o lec ula r w eig ht = 282 .55 ,an d boiling poin t = 3 43 .7 8C ), in stea do f estimating properties, 111i s me tho d isve ry e f fi c ie n t if you do nOI need accurateproperties o f the com ponen t Take ca re,tho ugh, if yo u lise this a ppro ac h an d o neo f the UNTFAC activity-coefficientmethods . a s you m ay c han ge the a s-s um ptio ns m ad e a bo ut the liq uid p ha se.

Ano ther techn ique to s implify amixture o f s im ilar componen ts is torepresen t them with a single compo -nen t. This is a useful techn ique whencomponen ts a re no t known exac tly.Fo r instance. Component 5+ couldrepresen t hydro carbon s o f 5 carbonatoms and greater.

Estimating binary parametersYou can es timate bin a ry parame-

ters fo r W ilson , NRTL, and UNl-QUA ac tivity-coeffic ien t modelsusing two approaches : UN lFAC andin fin ite -d i lu tio n a ctiv ity c oeff ic ie nts .U NlF AC -estim ated b in ary p ara metersusual ly do no t provide enough accu-ra cy and, so , o n ly are recommendedfo r ear ly stages o f physic a l propertyda ta investiga tio n and to "fill in theblanks ' fo r com ponents with m ediumo r lo w p ri or ities .

Better bin a ry pa rameters can beestima ted us ing in fin ite-dilutio n ac -tivity co effic ien t data . (So me s im ula-to rs may in c lude this feature' undertheir regression to o ls .) This m ethod isbetter because it is based on the com-ponen t s of in teres t. un like the groupc n tr ib ulio n method, whic h a ver ag es

F igure 10(leJI). Series ploloj normal boilingp oin t [ or II -a ll cy / .benzenes.

700~--------------------------------'4 0 0 ;

F igure II(beloK!). Familyplot oj criticalpretsure ojmetllyl(hydrQgerdchlorosilanes.

60 0 I-

3 0 0 , _ ~ I I l _ _ . .6 10 14 t8 22

N um be r of C arb on A to ms

4,700,000. .L.e 4.200,000!Q : 3,700,0003.200.000

2 6

Mo l ec u la r W e ig h l

C H E M IC A L E N G t N E E R IN G P R O G R E S S C H :T O B E R 1 9 9 6 45


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literature Cited1. Reid R. " J. M. Prausnltz, and B.E,

Po ling, "The Properties o f Gases andL iqu id s:' 4 th ed., McGmw Hill, Ne wYork (1987).

2 . "eRC Handbook o r 'hem istry andP hy si cs .v D . Lide , ed . . C RC Press , B ocaRaton, FL (1994).

3. Dewan , A. K ., and M.A. Moore,"Methodology 10 Develop an ASPENPLU S M odel in She.ll D evelo pm en t C om -p an y ." p re se n te d a t ASPEN WORLD ,.Cambridge, MA (1994). (A va il ab le fr omA sp en T ce hn o.l og y. In c.)

4. onstantlnou, L., and R. Gum, "NewGroup C o n tr ib uti on M e th od fo r Estimat-ing Properties o f Pure Compounds : 'MCIlI? J.. 40 (10), p. 1,697 (1994).S. Daubert , T. E., R. P. Danner, n, M.Sibul, and C . Stebbin s , "Physica l andT hc nn cdyn am ic Pro pen les o f Pure hem -ic als : D ata o rn piln rio n," D esign Institutefo r Physica l Pro perly Dum (DIPPR).A I It ,New York (1989 nwsrds).

6. Smith, A . t., " Fa m il y P IO L Sfo r Evaluat-ing Physica l Proper ties o f Organo sil icon

ompounds," AI h J . 40 (2). p. 37(1994).

7. "ASPEN PLUS User G uide." V ol. I. Re -lease 9. Aspen Techn ology. Inc .. C am -br idge. M A (1 995 ).

8 .. Gme .h ll n g. ,. J. , .I . Ll, and M . Schiller, "AMod i f i ed UNIFAC Mode l . 2. Present Pa -rameter M atrix an d Result fo r DifferentT he nn od yu nr nic P ro pe rtie s," I&.EC Res..32 . p 17 8 ( 1 993).

the effect of group interactions fromdifferent comp nents.

E stimation o f physical propertiescan get you started in a simulationproblem - bu t you hould do an ex-haustive litera ture search to findrnissi n g pu r e - c o r nponen t and bi naryparameters.

[Lis important L O enter any knownpa rame t e r bet' re doing property e s -timation. Firs t. ex peri mental datagenerally are 11.10reccurate than esti-mated values. Second, corresponding-s ta tes es tl m ario n methods requ ir eother phy ical constants as input.U ing an experim nta l va lue will im-prove th e prediction of these propertyparameters. Oth rwise, the error inestimating parameters uch as normalboiling point. critical temperature,

9. Hansen H. K., P. Rasmussen, A. Fre-denshmd, M. S c hi ll er , and J. Gmehling,"Vapo r-Liquid Equilibria by UNIFACGroup C on tribution . 5 . Revis ion and E x-t en . i o n ," T&Ee Res" 30 (10 ), p, 2,352(1991).

10. Joback K. G., and R. C. Reid , "Bsuma-Lion o f Pure-C omponen t Properties fromGrcup -Con t r i bu t i cns . " C he ",. E ng . C UIII-,TIl",., 57 , p . 2 3 3 ( 19 87 ).

11. Kldber, M., "A n Extension 10 the UNI-FAC Group Assignment fo r Predic tio n o fV opo r-liquid E quilibr ia o f M ixtures C on -ta in ing Refrigerants," Fluid P/' I lSIl Equi-libria. 107 . p , 161 (1995).

12. Magnussen, I'. , P. Rasmussen an d A.Fredenslood , "UNfFAC Param eter Pre-diction Tabte fo r Prediction o f Liquid-Liquid Equilibria." I&_EC Proc. Dev.. 20.p. 33 1 (1980).

L3 . Larsen , 8., P. Rasmus en , and A. Fre-de n lund, "A Modif ied Group-Contribu-t io n Me th o d fo r Prediction o f P ha se Equi-libr ia and Hems o f M ixing:' I&EC Res. .26. p. 2.274 (1987).

.14. Dortmund D ata ba nk. V LE D atu , S ys te mNo. 980. Univ. of Dortmund, Germany(1995).

1 5. D o rtm un d Dm nb un k, VL E Data, SystemNo. 4,785. Univ. of Do rtmu n d. G e rm a n y( 1995 ) .

16. Do rtmund Databank, VLE Data, SystemNo. 7 52 . Un iv. or Do rtmund, Germany(199.5).

an d critical pressure propagates toother property parameter .Documentingwhat you've done

Simula tion pro jec ts o ften have along life at a company. New usersmay come along an d be unfamiliarwith the assumptions and recom-mended use of the imulati n. Youmay find lhal you need La revisit asimulation a ye ar o r more later . Doc-umenting tbe data urces, the rangeo f app li cab il it y, and physical propertyas sum ptio ns is extrem ely important.This can be incorporated using thecomment or descriptions fields in thes i r n u f a t r. Include a ratemeru about: ' l 1 'Y properties that were not well de-fined or components that should not

46 O CTOBER 1996 . CHEM ICA L EN GINEERING PRO GRESS

AcknowledgmentThe tec hn iques an d guidelin es presen ted int h. i s a r ti c le a r e t he r e su l ts of expe r ie n c e h e lp -i n g s im u la ti o n u se rs s o lv e e n gi n ee ri n g p r o b -lems. r would like to thank: the fol lowingco llea gues a t Aspen T cc hn olo gy fo r a dvic ean d informat ion: Vnlcmijn DeleeuwM arcelo M nrchetti. B ill Mock . AndreaT a kv o ri an , a n d S u ph a t Wn . la n asm .

be added given the phy ical propertymethod employed - for instance,electrolytes when an equation-of-slate method is being used. Keeptrack of the references for data andlist them in the simulation, if possi-hie. Include comment ab ut proper-t ies, such as densities or heats of mix-ing, that were not of interest o r notvalidated in the simulation. Keep th eestimation. regression, and simulation~Ies together , [f p os ib le . create a filecontaining all pure-component andbinary parameters including th os e a c-ce sed in the built-in databanks. Thisway you will b able to reproduceyour results in the future with upcom-ing i rnulation-software relea e .Keeping the right perspectiveThe phy ical pr perry system of

the simulator is no! a black box, but awell developed set of rule and rela-tionships thai can execute very com-plex calculations very quickly. It doesnot replace rhar most ueful of alltool o f a chemical engineer - co r n -m an sense. Always us c your j udg -ment to evaluate im ula rio n er ro r orsu picious re ults to find their source.Tha t way, you'll make the b e s t u s e o fyour simulator, and avoid unnece -sary mistakes. 1mE . C . C A J IL SON is 8 SID!! ong ineor a t A sp en

Tochno logy . I n c . . Cambridgo, Mil .1617{5770100: Fa . 617/5710303:ema i l : ca r l sonCespen te ch . coml . H e isre sp on sib le lo r p ro vid in g le ch nic al g uld am ;ean d treinlng In tho areas 01physica l proport iosa nd re ac to r m od Dlin g to p ro ce ss s im U la tio nUS"". H o r oc oived a B S I ro m Ih e U n!v ,01Rocha.tor and an MS f ro m N or th C ar ol in aSlate U n iv . . b O lM In chemi ca l e n gi ne .n o O. H e I sa membe r 01A IChE.

Aspen Gamble

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