Computers and Chemical Engineering 66 (2014) 201213

Contents lists available at ScienceDirect

Computers and Chemical Engineering

j ourna l ho me pa g e: www.elsev ier .com/ lo

A systematic methodology for design of tailor-ma

Nor Alaza Yunusa,b,, Krist V. Gernaeya, John M. Woodleya, Ra Department of Chemical and Biochemical Engineering, Technical University of Denmark, Sltofts Plads, 280b Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Ma

a r t i c l

Article history:Received 2 NoReceived in re21 December 2Accepted 24 DAvailable onlin

Keywords:Product designBlended produGasoline blendLubricant base

f tailrodu

mainprop

retrie the aintsinally

andsolin

1. Introdu

A blendous chemicals in the liquid state to obtain a liquid mixture witha particular set of desired characteristics and qualities. Exam-ples of blended liquid products are synthetic fuels and lubricants.Consumer-oriented liquid products, which are also blends of chem-icals, are usually called formulated products, where a solid activeingredient example, thactive ingreproduct, sotives that e2011). The one or moent and perrelease heaup heat in aity of the p

Abbreviatioethyltetracosaacetone; DFE,GLY, propane-MoT, modelinoil.

CorresponTechnical UnivTel.: +45 4525

E-mail add

c basil pries e

mulations that are blended liquid mixtures are considered, andthey will be referred to as tailor-made blended products through-out this article. A tailor-made blended product will refer to amixture of several chemicals tailored to match specic productattributes.

0098-1354/$ http://dx.doi.ois dissolved and blended with other chemicals. Fore formulation of an insect repellent contains a soliddient that is responsible for the main function of thelvents that deliver the active ingredient, and addi-nhance the quality of the product (Conte, Gani, & Ng,blended liquid products, on the other hand, containre liquid chemicals that serve as the main ingredi-form the main function of the product (for example,t when combusted or absorb heat or release and take

cyclic operation) and additives that enhance the qual-roduct. For example, a lubricant blend may contain a

ns: 2BE, 2-butanone; 2MT, 2-methyltricosane; 3ET, 3-ne; 3ME, 3-methyleicosane; 9ODA, cis-9-octadecenoic acid; ACE,

1H-dibenzo[a,i]uorene,eicosahydro-; ETOH, ethanol; G, gasoline;1,2,3-triol; MeTHF, furan,tetrahydro-2-methyl-; MI, main ingredient;g tool; PE, polyethylene; THF, tetrahydrofuran; WCO, waste cooking

ding author at: Department of Chemical and Biochemical Engineering,ersity of Denmark, Sltofts Plads, 2800 Kongens Lyngby, Denmark.2910; fax: +45 45932906.resses: noy@kt.dtu.dk, alaza@gmail.com (N.A. Yunus).

In order to efciently design tailor-made blended products,a systematic methodology is needed. Several efforts have beenreported on the development of systematic methodologies forproduct design. Churi and Achenie (1997) proposed a mathemati-cal programming approach to design refrigerant mixtures. A smallset of individual refrigerants were used as the building blocksin the mixtures design. This approach is practical in obtainingthe best mixture by optimizing a performance criterion but theapproach only implies one type of ingredient in the mixtures. Onthe other hand, Cheng, Lam, Ng, Ko, and Wibowo (2009), Conte,Gani, and Ng (2011), Conte, Gani, Cheng, and Ng (2012) and Teixeira,Rodriguez, Rodrigues, Martins, and Rodrigues (2012) designed con-sumer oriented chemicals based products that involve varioustypes of ingredients. Cheng et al. (2009) proposed an integratedapproach to design a skin care cream, taking into consideration bothtechnical as well as business-related factors. Meanwhile, Conteet al. (2011) developed a model-based computer-aided method-ology to design and verify formulated products (for example, paintand insect repellent lotion). Conte et al. (2012) added an experi-mental component to their model-based approach. That is, the nalvalidation, selection and adjustment of the design is made throughexperiments.

see front matter 2014 Elsevier Ltd. All rights reserved.rg/10.1016/j.compchemeng.2013.12.011 e i n f o

vember 2013vised form013ecember 2013e 3 January 2014

ct

oil

a b s t r a c t

A systematic methodology for design oblended products, one identies the pThe systematic methodology has fourare identied, translated into target Secondly, target property models aredesign algorithm is applied to obtaina set of blends that match the constrthe values of the target properties. Fof rigorous models for the propertiesthrough two case studies involving ga

ction

ed liquid product is dened as a formulation of vari-

specibase oadditivcate /compchemeng

de blended products

aqul Gania

0 Kongens Lyngby, Denmarklaysia, 81310 Skudai, Johor, Malaysia

or-made blended products has been developed. In tailor-madect needs and matches them by blending different chemicals.

tasks. First, the design problem is dened: the product needserties and the bounds for each target property are dened.ved from a property model library. Thirdly, a mixture/blend

mixtures/blends that match the design targets. The result is, the composition of the chemicals present in the blend, and, the mixture target property values are veried by means

the mixtures. In this paper, the methodology is highlightede blends and lubricant base oils.

2014 Elsevier Ltd. All rights reserved.

e oil as the main ingredient and a set of additives. Themarily determines the lubricant performance and thenhance its quality. In this work, only the class of for-

202 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

Nomenclature

Indexesi j B k m

Notation 0B100 a HcGmix

[] BI C HHV log LC50Mw NC PcPsatPP R RON RVP SG TcTfTmTgTrVvapVI VcWtO2xk,m1,LB

xk,m1,UB

ZRA

Design oferent wayingredient tproperties. suitable chethat the endperformancproduct deslikelihood ohow to deablended procient soluti

It is the intention to implement the model-based methodology pro-posed in this paper at the early stage of blend design. The proposedapproach is suitable to perform two of the four product design stepsof (Cussler & Moggridge, 2011): generate ideas and select ideas. The

computer-aided methods allows to quickly identify the moste blend candidates and avoid spending efforts on infeasibles of tces bnd/o

obdoloputed intoandidsuppicalcompound i in blendcompound j in blendblendtarget propertymixture

activity coefcientsolubility parameter (MPa1/2)target propertydynamic viscosity (cP)kinematic viscosity (cSt)

use of suitablregionresourifying a

Themethoa comdividetion, cThree a chemkinematic viscosity of 0 VI oil at 40 C (cSt)

kinematic viscosity of blend oil at 40 C (cSt)kinematic viscosity of 100 VI oil at 40 C (cSt)density (g/cm3)amorphous density (g/cm3)acentric factorheat of combustion (kJ/mol)energy of mixingintrinsic viscosityblending indexcosthigher heating value (MJ/kg)lethal concentration (mol/L)

molecular weight (g/mol)number of compoundscritical pressure (bar)saturated vapor pressure (kPa)pour point (K)gas constantresearch octane numberReid vapor pressure (kPa)specic gravitycritical temperatureash point (K)melting point (K)glass transition temperature (K)reduced temperature (K)vapor loss (wt%)viscosity indexmolar volume at critical pointweight percent of oxygen (%)

the lowest composition of component 1 in mixture,m that satises target property, kthe highest composition of component 1 in mixture,m that satises target property, kconstant of the Modied Rackett equation

f tailor-made blended products is challenging in dif-s. Tailor-made blended products usually have a mainhat is mixed with additives, to obtain the desired end-The challenge in the design of these products is to ndmicals and their compositions within the blend such-properties of the resulting product achieve the desirede. Chemical selection is an important step in blendedign and has the potential to signicantly enhance thef nding truly innovative products. Another challenge isl with the phase behavior issue since by denition, theducts must be stable liquid solutions. Therefore, ef-

on strategies are needed to deal with all the challenges.

compositiocase studieThis paper i(Yunus, Ger

2. Method

The sysapproach (design proare identisystematicasible to gen

The tailoematical prProgrammia specic p(target propthe solutionthe chemicestimated t

Consideeral tailor-m

min or ma

Subject

Mixture con

Target prop

Other const

x {x|x R

y {0, 1

}

fobj is the obthe followimixture (y)product peto the type to the mixtproperties;limits, respwith respecmust be satlated from other constfraction.he search space. The objective here is to save time andy quickly identifying the promising blends and then ver-r improving them through focused experimental work.

jective of this paper is to present a systematicgy for design of tailor-made blended products usingr-aided model-based technique. The methodology is

four main tasks; problem denition, model identica-ate (blends) generation and model-based verication.orting tools are developed for this work, namely,

database, a property model library and a blend-n optimizer. The methodology has been applied to twos involving the design of gasoline and lubricant blends.s an extended version of a short ESCAPE-23 contributionnaey, Woodley, & Gani, 2013).

ology

tematic methodology employs the reverse designGani & Pistikopoulos, 2002), where the targets of theblem are dened and blends that match the targetsed. In blend design, thousands of blend candidates arelly generated and screened. For most problems it is pos-erate and screen all potential feasible blend candidates.r-made blend design problem is formulated as a math-oblem, which results in a Mixed Integer Non-Linearng (MINLP) problem. The objective here is to optimizeerformance, subject to product stability and attributeserties) as well as process specications. The result from

of the problem is information on the blends in term ofals that are present in it, their compositions, and thearget property values.ring the multiple types of constraint equations, the gen-ade blend problem is formulated as follows:

x fobj(x, y, C, E, S, Q ) (1)

to

straints : g1(x, y) 0 (2)

erty constraints : LB g2 (x, y, ) UB (3)

raints : g3(x) = 0 (4)n, 0 x 1

}(5)

(6)

jective function to minimize/maximize one or more ofng parameters: the blend composition (x), the type of, cost (C), environmental impact (E), safety factor (S) orrformance (Q); y is an integer variable, which is linkedof mixtures; x is a continuous variable, which is relatedure compositions; corresponds to a vector of target

subscripts UB and LB represent the upper and lowerectively; g1 corresponds to the mixtures constraintst to blend miscibility and the solubility condition thatised; g2 is a vector of target property constraints trans-product needs, for example, viscosity; g3 is a vector ofraints such as the denition of mole or weight or volume

N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213 203

Tailor-made Blended Product Design

Task 1 Problem definition

STA

BILI

TY to

ol

Fig. 1. Work design.

The abodatabase ofbinatorial pemploying (Karunanithcomplexitysearch spacthe MINLP and relativeconsidered in terms ofear constraUsing the loconstraintsconsidered.bounds but4.1 in Sectiestablishedthe objectivtion step, tand the optwork-ow in Fig. 1.

2.1. Tasks o

This systis to dene tied, transdeterminedels are retrithe blend pthe blends ttarget properties and mdatabase he

2.1.1. Task 1 Problem denition2.1.1.1. Task 1.1 Identify product needs. The needs for blendedproducts are primarily determined from the principal product func-

ty, which is the main reason for a products to be of interestentiaal fu

evenve mnvironal cedged to

. Tascallyt neet ne

moailab

ing

. Tasts ary alanc

Task . Tas. There coee Tnt se

Task . Tasin inith t

. Tasible Task 3 Mixt ure/blend des ign

3.1 Collect input data 3.2 Gen era te and scree n b lend s using the

mixture /blend des ign a lgorith m 3.3 Rank b lend c and ida tes ac cordin g to a

selection criteri on

Task 2 Prope rty model identification

2.1 Retrieve models from library

1.1 Id enti fy needs 1.2 Tran slate need s into ta rget prop erties 1.3 Set the ta rget val ues

Chemical

databases

Blend formulations

Task 4 Mode l-based verifi cation

4.1 Ver ify using rigorou s mode ls

Property m

odels lib

rary

ow in the systematic methodology for tailor-made blended product

ve-mentioned blend design problem, involving a large chemicals and non-linear constraints, creates a com-roblem resulting in a very large search space. Bya systematic decomposition based solution approachi, Achenie, & Gani, 2005), it is possible to manage the

of this design problem efciently and to reduce thee. The decomposition based solution approach dividesproblem into several sub-problems that are simplerly easy to solve. First only the linear constraints areand the compositions of the blends are characterized

lower- and upper-bounds within which all the lin-

tionalifor potprincipand prmay hafrom eadditioknowlare use

2.1.1.2speciproducproducusing aare avperform

2.1.1.3producues maperform

2.1.2. 2.1.2.1libraryand pulem (sdiffere

2.1.3. 2.1.3.1the maicals w

2.1.3.2all feasints are satised (see steps 3.1 and 3.2 in Section 2.2).wer- and upper-bounds of compositions from the linear

problem, the non-linear property constraints are now Solution of this problem gives new lower- and upper-

still within the range of the linear constraints (see stepon 2.2). In the nal step, the compositions within the

bounds from the non-linear constraints that optimizee function (Eq. (1)) are determined. As a nal verica-he solution from the nal step is given as initial stepimization problem dened by Eqs. (1)(6) is solved. Theof the decomposition based methodology is presented

f the methodology

ematic methodology consists of four tasks. The rst taskthe design problem, where the product needs are iden-lated into target properties and the target values are. In the second task, the required target property mod-eved from the property model library. In the third task,roblem is solved using a blend design algorithm to ndhat best match the design targets. Finally, the mixtureerty values are veried by rigorous models for the prop-ixtures that require it. Here, collection of data in the

lps to verify the predicted blend properties.

rithm. FurthOther toolsity test, andto be considthe blend fo

2.1.3.3. Tasture/blend selection crperformanc

2.1.4. Task 2.1.4.1. Tasmixture proerties and mrule is useda good predmay have sther vericvalues are Task 3 is recompositio

The resutargets andlists the mel customers. For example, for an engine lubricant, thenction of the blended product is to reduce the resistancet wear between two moving surfaces. A blended productore than one principal function. Besides, requirementsnmental regulation and safety are also considered asonstraints in the design of these blended products. A

base, a thorough literature search and legislation details determine the product needs in this work.

k 1.2 Translate needs into physico-chemical properties. A developed knowledge base is used to transform theds into desirable target properties. Note that not all the

eds, such as color, odor and shelf life, can be evaluateddel-based approach. However, when validated modelsle, it is easier and faster to test using the model than

experiments on the products.

k 1.3 Set the target values. The target values for similare retrieved from the knowledge base. The target val-so be changed with the aim of improving the producte criteria.

2 Property model identicationk 2.1 Retrieve the required property models from the

model library contains property models for mixturemponent properties that dene the blend design prob-ables 34). Different blend design problems need at of property models.

3 Mixture/blend designk 3.1 Collect input data. The input data for this task aregredient properties and composition, and a list of chem-heir associated properties.

k 3.2 Generation and screening. Generate and screen forblend alternatives using the mixture/blend design algo-er explanation on this algorithm is given in Section 2.2.

employed in this task are, a STABILITY tool for miscibil- a chemicals database, for the list of chemicals that areered in the blend design. The results from this task arermulations, compositions, and the target values.

k 3.3 Rank blend candidates. Rank the results of the mix-design algorithm according to a selected criterion. Theiterion can be blend composition, any target property,e criterion or cost, if available.

4 Model-based vericationk 4.1 Verication. Here, the objective is to verify theperty values by means of rigorous models for the prop-ixtures that require it. For example, a linear mixing

to estimate the viscosity of blends. The model givesiction for ideal mixtures. However, the linear models

ignicant errors for non-ideal mixtures. Therefore, fur-ation using rigorous models is necessary. If the targetnot matched with the rigorous property models, thenpeated for the corresponding blends by assigning newns as input until a matched blend formulation is found.lt from this task is a set of blends that satisfy all property

that can now be further veried, if necessary. Table 1thods and tools required in the methodology.

204 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

Table 1List of methods and tools used in the blend design methodology.

Tasks Description Methods/tools

Task1

Task2

Task3

Task4

Three tosolve the balgorithm fthem systembe used as bels library design. Alsoemployed f

2.2. Mixtur

The mixmethod, whand solvedscreening thanalyze theinto accountively.

The mixcase of binmulti-compspecied astures. It canbinary mixtfrom the daplus two coscripts i andany repetitof the subssubscript i.

2.2.1. LevelAt this le

database anNote that, t

Step 1.1chemical(s)and UB for of the binarchemical i; the target perty, k. This in the desigProblem denition1.1 Identify needs1.2 Translate needs into targetproperties1.3 Set the target values

Knowledge base Knowledge base Knowledgebase/legislation/safety

Property models identication2.1 Retrieve models fromlibrary

Property modelslibrary

Mixture/blend design3.1 Collect input data3.2 Generate and screen blendsusing the mixture/blend designalgorithm3.3 Rank blend candidatesaccording to a selectioncriterion

Chemicals databaseMixture/blend designalgorithm, chemicalsdatabase, STABILITYtest

Model-based verication4.1 Verify using rigorousmodels

Property modelslibrary

ols are developed specically for this work in order tolending problem. They are; the mixture/blend designor generating the blend candidates and for screeningatically; the chemicals database to store chemicals to

uilding blocks in blend design; and the property mod-to store the property models that are required in the, the STABILITY tool developed by Conte et al. (2011) isor identifying the miscible blends.

e/blend design algorithm

ture/blend design algorithm employs a decompositionere the problem is decomposed into four sub-problems

accordingly as shown in Fig. 2. The rst level is fore pure component properties, and the second level is to

mixture stability. The third and fourth levels are takingt the linear and non-linear target properties, respec-

ture/blend design algorithm is described below for theary and ternary mixtures. It can also be extended toonent mixtures. The rst compound in mixtures is

the main ingredient (MI), and it must exist in all mix- be a single compound or a mixture of compounds. Aure is a combination of the MI and a compound i (Bi)tabase (MI + Bi), while a ternary mixture consists of MI

mpounds, i and j from the database (MI + Bi + Bj). Sub-

j represent the number of both compounds. To avoidion of formulations of the ternary mixture, the valuecript j must always be greater than the value of the

1: Pure components constraintsvel, the pure component properties of chemicals in thed MI are compared with respect to the target values.his step is applied only for the linear target properties.: Compare the target property, k of the MI and the

in the mixture with the target value boundaries, LBeach target property k. Fig. 3 illustrates the comparisony mixture, where k

irepresents the target property of

kMI is the target property of MI; kLB is the lower bound of

roperty, k; and kUB is the upper bound of the target prop-step is done for all possible mixtures that are consideredn binary, ternary or multi-component mixtures.

Rule 1: Rthe pure comlower thangreater thaFig. 2. The mixture/blend design algorithm.

eject a binary mixture if the property value of MI andponent property value of the chemical i are both either

the lower bound values (kMI < kLB and

ki

< kLB), orn the upper bound values (kMI >

kUB and

ki

> kUB).

N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213 205

Target regi on

Fig. 3. Representation of the property comparison. The binary mixture of MI andchemical i is infeasible.

Rule 2: Reject a ternary mixture if the property value of MI andpure component property values of the chemicals i and j are eitherlower than the lower bound values (kMI <

kLB and

ki

< kLB and kj

kUB and

ki

>

kUB and kj

> kUB).

2.2.2. LevelStep 2.1

data consisRasmussenmixtures, aperformed.

Step 2.2binary mixresult obtapairs indicaimmiscible.

Step 2.3mixtures isbility resullisting all bmixture anand partiallscreening.

Rule 3: immiscible unstable. Rphase split

2.2.3. LevelStep 3.1

property fotarget valueture is illus

The combinary mixt

xk,m1,LB =k,mUBk1

xk,m1,UB =k,mLBk1

where, kUB is the upper bound of the target property, k; kLB is the

lower bound of the target property, k; k1 and LB g4(x, ,

) UB

are the target property values k of the chemicals 1 and 2, respec-tively. The specic composition, xk,m1 for a dened mixture is givenby,

xk,m1 =k,m k2k1 k

(9)

where k,m

Step 3.2for each bintarget prop

and xk,m1,UB cafollows:

max

min

e 4: Re 5: Aasiblble. Fected

of thble.s 3.1oblet only

max

2 (x,

0

x R

g2 is or vpositaints

3.3:regioes is

regmpo

e 6: Rmisc

Level 4.1ain

xm1 kUB is solved as a non-linear optimization problem to min-aximize the blend composition subject to non-linear

as follows:

fobj(x) (16)

206 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

s.t.

LB g4(x, ,

) UB (17)

g5(x) = 0 (18)

x {x|x Rn

}

where g4 iadditional ptemperaturor weight oby lower aare obtainelower than This ensuresatised.

Step 4.2:lished boundened objoriginal optwith the boas the initia

2.3. Databa

A databaof each chsenting eacproducts reties. The lisin blended ated using t(Gani, Nielsated chemithe databasspecied asent tempercorrosive. Othus they sthe databasfuel substitious types, amides and

The lubrhigh viscossection conalcohols, emers. The their paramretrieved frPapaeconomImmergut, pure compotration, solutool (Hukkeare used if tare given in

2.4. Propert

A librarycomponentmixture anThe modelsdeveloped

Table 2Section of the database with numbers of available chemicals. The last column indi-cates the pure chemical property present in the database.

Section Mainingredient

Additives: numberof compounds

Pure chemical property

Gasoline Conventionalgasoline

Bio-based: 22Others: 660

Non temperaturedependence;Mw, Tc, Pc, , ZRA, LC50, Tf ,Hc , ON, WtO2Temperature dependence: (15 C), (15 C), Psat(38 C),

ant Base oil- Mineral oil- Glycerol- WCO- Waste PE

Hydrocarbons: 913Bio-based: 25Polymers: 150Others: 624

Non temperaturedependence;Mw, Tf , Tm , PP, , Tg , [], aTemperature dependence: (100 C), (40 C),

(100 C), (40 C), Psat(25 C)

roperties of blend and models used in this work.

property Model Function

ic viscosity, Linear mixing ruleGC(UNIFAC)-based method(Cao, Knudsen,Fredenslund, & Rasmussen,1993)

f (i , xi)f (i , i , xi)

atic viscosity, Denition, = f (, )ity Index, VI Correlation (Rizvi, 2009) f ()r Heating Value, Linear mixing rules f (HHVi , xi)

y, Linear mixing rule (onmolar volume basis)Modied Rackett equation(Spencer & Danner, 1973)

f (i , xi)f (ZRAi , Vci , T, xi)

ch Octaneber, RON

Linear mixing rules f (RONi , xi)

apor Pressure, GC(UNIFAC)-based method f (Pvap,i , i , xi)

n content, WtO2 Linear mixing rules f (O2,i , xi)cup ash point, Tf GC(UNIFAC)-based method

(Liaw, Lee, Tang, Hsu, & Liu,2002; Liaw, Tang, & Lai,2004)

f (Tf,i , i , xi)

Linear mixing rules f (Ci , xi)ty parameter, Linear mixing rules f (LC50,i , xi)

of mixing,ix

UNIFAC (Magnussen et al.,1981)

f (GC, xi)

oint, PP Linear mixing rules (usingblending index) (Fahim,Alsahhaf, & Elkilani, 2010)

f (BIPPi , xi)

model to estimate the pure component property.

rty of purepound

Model Function

r heating value,i

GC method (developed) f (GC)

ic viscosity, i M&G GC method (Conte,Martinho, Matos, & Gani,2008)

f (GC)

atic viscosity, i Denition, (i = i/i) f (i , i) pressure, Pvap,i Correlation (Yaws, 2003) f (T)y, i Modied Racket Equation

(Spencer & Danner, 1972)f (Tc,i , Pc,i , T)

cup ash point, C&G GC method(Constantinou & Gani,1994)

f (GC, Tfp,i)

i Correlation f (i) concentration,,i

M&G GC based method(Hukkerikar, Kalakul, et al.,2012)

f (GC), xLB x xUB (19)

s a vector of non-linear constraints whereas is thearameter required for non-linear constraints, such ase and activity coefcient, and g5 is a vector of moler volume fractions. The composition, x, is restricted

nd upper limits. As a result, new composition rangesd, where the lower-bound value is not allowed to bethe specied bound and vice versa for the upper-bound.s that all linear and non-linear property constraints are

In this step, the mixture compositions within the estab-ds from step 4.1 that minimizes (or maximizes) the

ective function Eq. (1) are determined. As a nal test, theimization problem with all the constraints are solvedunds from step 4.1 and the optimal solution from abovel estimate.

se

se of chemicals stores the physico-chemical propertiesemical. The database is divided into sections repre-h specic type of blended product because differentquire different types of chemicals and sets of proper-t of chemicals includes those that are commonly foundproducts. Furthermore, additional chemicals are gener-he computer-aided molecular design (CAMD) techniqueen, & Fredenslund, 1991) to ll in the gaps. The gener-cals that satisfy a set of target properties are stored ine. For the gasoline database, the target properties are

follows: chemicals must be in liquid form at ambi-ature (Tm < 293 K), and must not be unstable, toxic orlens are easily oxidized to form gums during storage,hould not be used in gasoline blends. This section ofe stores chemicals that have the potential to be used asutes. The gasoline database holds 660 chemicals of var-for example ethers, esters, aldehydes, ketones, amines,

furans.icant section of the database contains chemicals withity that makes them suitable as base oil. The lubricanttains; 913 alkanes, 624 organic chemicals includingthers, esters, acids and derivatives, and 150 poly-information on the physico-chemical properties andeters are also provided in the database. They areom the CAPEC database (Nielsen, Abildskov, Harper,ou, & Gani, 2001) and from handbooks (Brandrup,

Grulke, Abe, & Bloch, 1999; Yaws, 2003). The missingnent properties, for instance, ash point, lethal concen-bility, etc., are predicted using the property predictionrikar, Sarup, et al., 2012). Note that experimental datahey are available. The details on the chemicals database

Table 2.

y model library

of property models used to estimate mixture and pure properties has been developed. Tables 3 and 4 list thed pure component properties covered by this library.

are collected from the literature, and some of them areusing the group contribution approach. Linear mixing

Lubric

Table 3Target p

Target

Dynam

KinemViscosHighe

HHVDensit

Researnum

Reid VRVP

OxygeOpen

Cost, CToxici

LC50Energy

Gm

Pour p

Table 4Property

Propecom

HigheHHV

Dynam

KinemVaporDensit

Open Tf,i

Cost, CLethal

LC50

N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213 207

rules are used to estimate those mixture properties for which it canbe assumed that the given property is additive. Additive propertiesinclude oxygen content, standard specic gravity and normal boil-ing point. However, properties like viscosity, density, ash point,pour point, and Reid vapor pressure (RVP) are not additive. Nev-ertheless, the linear mixing rule is employed to some of them as asimplication in the blend design methodology. The models havebeen veried using experimental data and found to be of accept-able accuracy. The estimated values of viscosity and density arecompared with the experimental data obtained from the literature(Chen, Shen, Ko, & Chang, 2011; Kermanpour, Jahani, & Iloukhani,2009; Teja & Rice, 1981; Totchasov, Nikiforov, & Alper, 2002).Table 5 shows the average absolute error (AAE) of the estimatedproperties for several mixtures at various temperatures.

3. Applications

The systematic methodology and its implementation are high-lighted through two case studies: tailor-made gasoline blends andbase oil (lubricant) blends. The optimization problems (steps 3.1,4.1 and 4.2, in Section 2.2) are solved using MATLAB, where the lin-ear problem is solved using the linprog solver, while, the functionfmincon is unumber of i

3.1. Case st

For gasoamong otheof crude oitoxic constias well as toderived frotional gasoloil consumnew formulmance, sho

The objeare suitablehot climateis chosen aderived frobio-based ccarbon numThe blends mgasoline chthe base ga

3.1.1. Task 1: Problem denition3.1.1.1. Task 1.1 Identify product needs. Based on the knowledgebase the gasoline blend must have the following characteristics: canbe burnt and run the engine efciently; can ow continuously fromthe fuel tank to the combustion chamber; have a suitable amma-bility limit; and have low toxicity. In addition, the gasoline blendsmust be stable, meaning that the blends do not evaporate easily; donot oxidize to form unwanted by-products, such as gums, sludgeand deposits during storage; and must not split into two liquidphases.

3.1.1.2. Task 1.2 Translate needs into physico-chemical properties.Using the knowledge base, the product needs are translated totarget properties. The ability to start up a combustion engine isdepends on the amount of vapor generated initially. Gasoline issprayed to form fuel droplets, and then vaporized to form the fuelvapor. The vapor pressure of gasoline is measured at a tempera-ture of 308 K in a chamber with a vapor/liquid volume ratio of 4:1,which is called the Reid vapor pressure (RVP) (Anderson, Kramer,Mueller, & Wallington, 2010). The potential power of a fuel is mea-sured from its heating value. The heating value of a fuel is dened asthe amount of heat released during a complete combustion of a unit

(Feliramoss ctane tane-ignier, reck meede

are tro be bfcienncy obility:: : dationmenta

. Tasgisla

Forsys.

to be

efc

Table 5Average absol

Mixtures

Viscosity

n-Hexane + bn-Hexane + tn-Hexane + e

Methanol + o

Ethanol + oct

Density

Heptane + oc

2-Methyl-1-EGMBE + 2-e2-Ethyl-1-bused to solve the non-linear problem with the maximumnterations set to 1000.

udy 1: design of gasoline blends

line, the following two issues need to be consideredrs; the rst is related to the security (or availability)

l supply and the second is related to the presence oftuents in gasoline that are harmful to the environment

humans. To address these issues, potential chemicalsm renewable sources are being blended with conven-ine. Adding bio-based chemicals can reduce the crudeption and the amount of released toxic chemicals. Theation of gasoline blends should have a good fuel perfor-uld be safe and have low environmental impacts.ctive of this case study is to design gasoline blends that

for a car (spark-ignition type) engine and used in a with average ambient temperature of 27 C. Gasolines the main ingredient and is blended with chemicalsm renewable sources called bio-based chemicals. Thehemicals selected in the database are alcohols with lowber (C2C5), ethers, ketones, acid and furan derivatives.ay consist of two or more chemicals (in addition to the

emicals) to form either binary or ternary mixtures withsoline.

of fuelRayo Jation, grthe ocLow ocby prechambto knoing is nneeds Ability tEngine eConsisteFlammaToxicityStabilityLow oxiEnviron

3.1.1.3ucts, le2004; follow

Ability

Engine

ute errors (AAE) of estimated viscosity and density at different temperature (K).

AAE

298.15 308.15 enzene 1.09E02 1.65E02 oluene 0.90E02 0.71E02 thylbenzene 1.73E02 4.59E02

293.15 298.15 ctane 0.70E02 0.65E02

263.15 268.15 ane 9.15E02 6.87E02

333.15 353.15tane 0.16E02 0.15E02

293.15 298.15 butanol + dibutyl ether 8.36E04 8.08E04 thyl-1-butanol 4.25E04 4.10E04 tanol + dibutyl ether 8.66E04 8.69E04 pe Ramirez-Verduzco, Esteban Rodriguez-Rodriguez, &illo-Jacob, 2012). It is also called as the heat of combus-aloric value or total heating value. On the other hand,rating is widely used to measure the gasoline quality.

gasoline might cause engine knocking. Knock is causedtion or unwanted chemical reactions in the combustionsulting in loud noise inside the engine. Long exposureay cause engine damage. Therefore, higher octane rat-d in order to increase the engine efciency. The otheranslated as follows:urned: Reid vapour pressure (RVP)cy: Octane rating (RON) and heating value (HHV)

f fuel ow: Dynamic viscosity () and density () Flash point (Tf)

Lethal concentration (LC50)Gibbs energy of mixing (Gmix)

: Choice of chemicalsl aspect: Oxygen content (WtO2 )

k 1.3 Set target values. Referring to the existing prod-tion and literature study (van Basshuysen & Schfer,the, 2003) the target values for each property are set as

burned : 45 RVP (kPa) 60 (20)

iency : RON 92 (21)

323.150.76E020.57E021.34E02303.15 313.150.59E02 0.51E02273.15 283.15 293.155.97E02 5.28E02 3.92E02

303.15 308.15 313.1530.1E04 3.92E04 3.89E04 3.69E048.74E04 8.60E04 8.55E04

208 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

Table 6Pseudo-component of gasoline to represent the MI.

Chemicals Composition, wt%

Butane 6.58Heptane 12.60Iso-octane 53.991-Pentene 3.63Methylcyclopentane 8.47Toluene 14.73

HHV (MJ/kg

Consistency

0.720 (g

Flammabili

Toxicity :

Stability :

Environmen

Low oxid

3.1.2. Task 3.1.2.1. Taserty modelslinear mixinThe linear mRVP and Tf a

k =NCi

xi

NCi=1

xiiPsati

RV

NCi=1

xiiPsati

Psati

(Tf

In Eq. (3(31), given its ash poi

3.1.3. Task 3.1.3.1. Tasselected as chemicals athese chemline blend d

3.1.3.2. Tasdesign algordesign algo

Level 1: PStep 1.1

ing value,and oxygThe combout of th

lethal concentration values of gasoline and 2-methylpropanalare 3.33 mol/L and 3.94 mol/L, respectively. The target value tobe achieved is less than 3.08 mol/L. Applying rule 1, the blendof gasoline and 2-methylpropanal is rejected as both the targetproperty values are greater than the upper limit.

Level 2: Stability analysisStep 2.1: The UNIFAC-LLE group representation is obtained for

28 chemicals (including the MI components), and the tempera-is setp 2.2(Conetriep 2.3. 8 oe mil 3: Lp 3.1d coms: heentrained p 3.poneures r-bops 3tion lendh rept giv

max

NC

i

xi

1 =

< 1

solu (notor bihile

p 3.3 in themov.

l 4: Np 4.1on-lput istep) 35 (22)

of fuel ow : 0.30 (cP) 0.60 (23)

/cm3) 0.775 (24)

ty : Tf (K) 300 (25)

log LC50(mol/L) < 3.08 (26)

Gmix < 0 (27)

tal aspect : 2 WtO2 (wt%) 20 (28)

ation: Chemicals choice (no target values).

2 Property model identicationk 2.1 Retrieve the required property models from the prop-

library. Six of the target properties are estimated usingg rules, which are , , RON, HHV, log LC50, and WtO2 .ixing model is represented by Eq. (29). The propertiesre predicted using non-linear models, Eqs. (30)(31).

ki (29)

(308 K)

PB= 1 (30)

(Tf )

,i)= 1 (31)

0), for given x and T = 308 K, RVPB is calculated. In Eq.x and saturated vapor pressure of component i, Psat

iat

nt, Tf,i, Tf is calculated.

3 Mixture/blend designk 3.1 Collect input data. Conventional gasoline isMI and its composition is given in Table 6. 22 bio-basedre selected from the gasoline database section, whereicals are used as building blocks in the tailor-made gaso-esign.

k 3.2 Generate and screen blends using the mixture/blendithm. The problem is solved using the mixture/blendrithm.

ture Ste

tool are r

Stelyzedall th

LeveSte

blenertieconcobta

Stecommixtuppe

Stemizathe bwhictarge

min or

s.t.

kLB

NCi=1

xi

0 < xi

Theblendsplus 1 fstep, w

Steblendswere rand 3.2

LeveSte

the nas inThis ure component constraints: Screen all the possible blends by comparing the heat-

viscosity, density, octane number, lethal concentrationen content of pure chemicals with the target values.inations of mixtures that have pure property valuese target value ranges are rejected. For instance, the

the objecsubject togiven by

min or max at the ambient temperature.: The stability test is performed using the STABILITYte et al., 2011) and the results for 378 binary mixturesved.: The results for binary and ternary mixtures are ana-f the binary mixtures are partially miscible. However,xtures are considered for Level 3.inear constraints: Using the list of mixtures resulting from Step 2.1, theposition ranges are calculated for all linear target prop-ating value, viscosity, density, octane number, lethaltion and oxygen content. The composition ranges arefor binary mixtures.2: Identify the overall composition range for multi-nt blends. The results of this step gives the feasiblewith their composition ranges dened by lower- andunds..1 and 3.2 are combined and solved as a linear opti-problem with the objective to minimize and maximize

composition subject to the linear constraints, Eq. (33)resents RON, HHV, , , log LC50 and WtO2 to match theen by Eqs. (21)(24), (26) and (28), respectively.

fobj(x) (32)

ki kUB (33)

0 (34)

(35)

tion of the problem leads to 8 blends and 112 ternarye that this means the compounds of the model gasolinenary and 2 for ternary blends) being selected for the next8 binary blends and 109 ternary blends are rejected.: Re-check the stability of the partially miscibility of thee region of interest. All the partially miscible mixturesed after considering the linear constraints in Steps 3.1

on-linear constraints: The non-linear constraints RVP is now considered asinear constraint. The compositions from level 3 are usedn this step, and new composition ranges are obtained.

is solved as a non-linear optimization problem wheretive functions are to be minimized and/or maximized

the non-linear constraint, Eq. (37), to match the targetEq. (20).

fobj(x) (36)

N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213 209

22 Level 1

Binary mixtur e Ternary mixture

Fig. 5. Numbeture/blend des

s.t.

NCi=1

xiiPsati

RV

NCi=1

xi 1 =

xLB < xi < x

The solutiothat satisfy is calculateRVP. All theEq. (25). Aft75 of binarwere remothey were generated abinary and

Step 4.2:line compoknown from

3.1.3.3. Tasterion. The according togives the regasoline co

3.1.4. Task This tas

the propertdata. What engine cond

3.1.5. ProduThe qua

Gasoline blHHV valuesventional gmeasure thnot be predfor pure com

further. Among the proposed gasoline blends, MeTHF becomes oneof the favorable chemicals because it is present in most blend can-didates. MeTHF has good fuel properties, which are high-energycontent, low vapor pressure, moderate oxygen level and consid-

toxi vali

se st

moscomse oied f

ginatum tic oing

ed, bman

conal prr buradaIn tehydrtly idsimily comomatigh m. Nevucedhemtheirchem.

bases to bf aront peve ofd orgw ened sed cil ma16 16

5

5

Level 2

Level 3

Level 4

231

221

221

112

67

r of gasoline blend candidates reduced after screening using the mix-ign algorithm.

(308 K)

PB= 1 (37)

0 (38)

UB (39)

n of the above problem gives new composition rangesthe linear and the non-linear constraints. The ash pointd using Eq. (31) and the composition that satised the

blend candidates are satised the ash point constraint,er this step, 45 ternary blends are removed, while a totaly plus ternary blends are retained. None of the blendsved due to the calculated ash-point temperatures asall within the specied bound. The number of blendsnd screened at each level is shown in Fig. 5 for bothternary blends.

The objective here is to obtain the minimum gaso-sition in the blend formulations. Since value is already

Step 4.1, this step is not necessary.

k 3.3 Rank blend candidates according to a selection cri-blends that satised all the constraints are ranked

erablyfurther

3.2. Ca

Thewhich The bais derivi.e. oripetrolesyntheperformdegradlow decan bechemiccheapebiodegerties. many is mosinto a oil maand arwith hcationsbe redbasic cing to These design

Theenginetures odiffereobjectioils anwith lomentiobio-babase o the minimum amount of gasoline in the blends. Table 7sults, where the blends are listed in terms of decreasingmposition.

4 Model-based vericationk was not necessary for the nal gasoline blends asy models used are already validated with experimentalcould be useful, however, to check these blends underitions, which is outside the scope of this work.

ct analysislity of fuel is measured from its energy content (HHV).ends proposed in Table 4 have a high HHV value. Their

are actually quite close to the heating value of a con-asoline (4447 MJ/kg). The RON is commonly used toe performance of fuel. Nevertheless, the RON value can-icted for some blends due to missing octane numberponents, so these blends could not be considered any

3.2.1. Task 3.2.1.1. Tasdened usinmain functibetween twat high temIt should hshould be nlosses to thstable, meain the syste

3.2.1.2. TasUsing the knerties. The related neecity content. These blend candidates, however, needdation through experimental work.

udy 2: design of base oil blends for lubrication

t important component in a lubricant is the base oil,prises 6598% of the total composition of the lubricant.l properties inuence the lubricant attributes. Base oilrom three sources: petroleum, synthetic and biological,ing from plants or animals. Mineral oils derived fromare the most widely used base oils. On the other hand,ils are man-made oils with superior properties, thereby

well in extreme conditions. Vegetable oils are easilyut have poor properties, making them suitable only ford applications. Comparing various types of base oils, itcluded that synthetic oils have excellent physical andoperties, but they are expensive, while mineral oils aret less environmentally friendly, and vegetable oils areble but have poor oxidative stability and cold ow prop-rm of composition, mineral oil contains a mixture ofocarbons. As a consequence, the base oil compositionentied by groups or lumps of compounds that fallar chemical classication. The components of mineralprise different percentages of parafns, naphthenes

ics. A high content of parafns makes mineral oil waxy,elting point and suitable for high-temperature appli-ertheless, aromatics and unsaturated chemicals should

in order to avoid excessive lubricant oxidation. Theicals present in lube feedstocks are classied accord-

content of parafns, napthenes and aromatic groups.icals are used as building block in the base oil mixture

oil mixture is designed as single grade oil for gasolinee used in a hot climate with average ambient tempera-und 27 C. The components of mineral oil may comprisercentages of parafns, naphthenes and aromatics. The

this work is to design mixtures of base oils from mineralanic chemicals that have good lubrication propertiesnvironmental impact. In order to achieve the above-objective, a set of chemicals from lube feedstocks andhemicals are used as building blocks. The mixtures ofy form binary, ternary or multi-component mixtures.

1: Problem denitionk 1.1 Identify product needs. The needs for base oil wereg both a literature survey and the knowledge base. Theon of lubricant base oil is to lubricate and prevent wearo moving surfaces. In addition, it must be able to resistperature and ow continuously at low temperature.ave a suitable specic gravity for handling purposes,on-ammable and with low vaporization rate to avoide environment. Besides, the base oil mixtures must bening that the mixtures do not oxidize to form depositsm to be lubricated, and must be a single-phase.

k 1.2 Translate needs into physico-chemical properties.owledge base the product needs are translated to prop-following target properties are used to measure theds:

210 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

Table 7Gasoline blended candidates with their composition and properties.

No. Composition (vol%) Properties

HHV WtO2 RVP RON log LC501 G (69) THF (11) MeTHF(20) 41 7.2 46 0.48 2.72 G (67) ACE(13) MeTHF(20) 41 7.8 46 0.47 2.73 G (72) ACE(10) 2BE(18) 40 7.3 4 G (75) 2BE (13) MeTHF(12) 43 5.5 5 G (77) ETOH(12) (12) MeTHF(11) 42 6.7

Note: Property abbreviations are given in the nomenclature list.

Ability to lubricate and prevent wear: kinematic viscosity ()Resist at high temperature: viscosity Index (VI)Ability to ow at the ambient temperature: pour point (PP)Handling purpose: density ()Non-ammabLow vaporizat

3.2.1.3. Tas(Anderson ethe knowlefollows.

Ability to lu

Resist at hig

Ability to o

Handling pu

Non-amm

Low vaporiz

3.2.2. Task 3.2.2.1. Taserty modelsusing liner respect to tblending inmated using(48)) and astraints. Th(Smith, Van

BIB =n

i=1xv

BIPPi = PP1/i

o Bo 100

1

3.2.3. Task 3.2.3.1. Tasbase oil feelubricant dablock in the

3.2.3.2. Taslem is solveof the algor

sideear c

at Leity in

l 1: Pp 1.1sity,ry mie binted dl 2: Sp 2.1emi

andp 2.2the rep 2.3ures

ared to

they+ n-terateforel 3: Lp 3.1lendropeined p 3.poneare mps 3tion maxint, Eqarget

maxle: ash point (Tf)ion rate: volatility (Vvap)

k 1.3 Set target values. Referring to the existing productst al., 2010; Kramer et al., 1999) as benchmark and usingdge base, the target values for each property are set as

bricate and prevent wear : (cSt) 4.119 (40)

h temperature : VI 80 (41)

w at ambient temperature : PP (K) 293 (42)

rpose : 0.80 (g/cm3) 0.9 (43)

able : Tf (K) 490 (44)

ation rate : Vvap (wt%) 15 (45)

2: Property model identicationk 2.1 Retrieve the required property models from the prop-

library. Kinematic viscosity and density are estimatedmixing rules Eq. (29). Density is blended linearly withhe molar volume. Pour point is blended linearly usingdex, Eq. (46) and the pour point of the blend is esti-

Eq. (47). On the other hand, the viscosity index (see Eq.sh point (Eq. (31)) are estimated using non-linear con-

e vaporization rate is estimated using ash calculation Ness, & Abbott, 2005).

iBIPPi (46)

0.08 (47)

00 VILB (48)

are conthe lin3; and(viscosused.

LeveSte

viscobinais threjec

LeveSte

44 chature

Steand

Stemixtturesfounthustriol tempTher

LeveSte

the bget pobta

Stecomstep

Stemizaand straithe t

min or

s.t.3: Mixture/blend designk 3.1 Collect input data. 44 basic chemicals, serving asdstocks, and organic chemicals are selected from thetabase section, and these chemicals are used as building

blend design. In this case, MI is not specied.

kLB NCi

xi

NCi=1

xi 1 =49 0.48 2.745 0.50 2.945 96 0.57 2.8

k 3.2 Perform mixture/blend design algorithm. The prob-d using the mixture/blend design algorithm. At Level 1ithm, the properties of viscosity, density and pour pointred. Then the blend stability is checked at Level 2. Allonstraints, viscosity and density are considered at Levelvel 4 the blends that satisfy the non-linear constraints,dex, pour point, ash point and vaporization loss) are

ure component constraints: This step is applied to all chemicals by comparing their

density and melting points with their target values. 946xtures are screened and 901 are rejected. One exampleary mixture of n-hexacosane + 1,1-Biphenyl, which isue to the pure component viscosities.tability analysis: The UNIFAC-LLE group representation is obtained forcals, and the temperature is set at the ambient temper-

at the operating condition, 100 C.: The stability test is performed using the STABILITY toolsults for 946 binary mixtures are extracted.: The results for binary, ternary and multi-componentare analyzed for both temperatures. 58 binary mix-

partially miscible and 34 of the binary mixtures aretally immiscible at one or both temperatures, and

are rejected. For example, mixture of propane-1,2,3-etradecanoic acid is partially miscible at the ambienture. However, it is completely immiscible at 100 C., this mixture is not considered in the blend design.inear constraints: Using the list of mixtures resulting from Step 2.1,

composition ranges are calculated for all linear tar-rties: Viscosity and density. The composition ranges arefor binary mixtures.2: Identify the overall composition range for multi-nt (ternary and quaternary) blends. The results of thisixtures with their composition ranges.

.1 and 3.2 are combined and solved as a linear opti-problem while the objective functions are to minimizemize the blend composition subject to the linear con-. (50), which represents viscosity and density to match

values given by Eqs. (40) and (43).

fobj(x) (49)ki kUB (50)

0 (51)

N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213 211

Table 8The reduced number of blend candidates using mixture/blend design algorithm.

Level Number of candidates

Binary Ternary Quaternary

N 946 13,244 135,751N1 60 1847 30,645N2 45 1400 17,427N3 45 1400 17,427N4 14 966 5483

x {x|x Rn, 0 x 1

}(52)

Step 3.3: Re-check the stability of the partially miscible mixturesin the region of interest. Two partially miscible mixtures are iden-tied and their stability regions are compared with the region ofinterest. One of the mixtures is rejected because it was found unsta-ble at the region of interest, while the other is accepted because itwas found to be totally miscible in the region of interest.

Level 4: Non-linear constraintsStep 4.1: Calculate the non-linear constraints: Viscosity index,

pour point, ash point and vaporization loss have been estimatedand the nfrom the

This stepthe objectivcompositioto satisfy thEq. (44) anblends that

min or max

s.t.

BIB =n

i=1xv

BIPPi = PP1/io B

o 100 1

NCi=1

xi 1 =

xLB < xi < x

Table 10Comparison of the linear and rigorous viscosity values.

Mixture Viscosity

-linear -rigorous

Binary 4.12 4.4559Ternary 4.12 4.4547Quaternary 4.12 5.0110

About 51%, 7% and 68% of the binary, ternary and quaternaryblends are removed at this level, respectively. A total number of14, 966 and 5483 of the binary, ternary and quaternary blendsare found to satisfy all the constraints, respectively. The numberof blends generated and screened at each level is listed in Table 8for three types of blends.

Step 4.2: The objective function, Eq. (59) is optimized to obtain alow-cost base oil blend. The new composition ranges obtained fromStep 4.2 are used as the upper and lower boundary. Then, the targetproperty values for all feasible mixtures are recalculated using newcompositions obtained from the optimization.

fobj = minNC

. TasThe any

ked ectede are

Task . Tas

neees. Tmodmods va

xtureorou

alsosible

Produixtun Tabent it in b

Table 9The base oil bl

Blend

I

Binary2MT +GLY

95

Ternary3ME +3ET +GLY

01

Quaternary2MT +DFE +GLY +9ODA

Note: Propertyew composition ranges are obtained. The compositionsprevious task are used as input in this step.

is solved as a non-linear optimization problem wheree functions are to minimize and maximize the blend

ns subject to the non-linear constraints, Eqs. (54)(56)e target property values, Eqs. (41)(42). The ash point,d vaporization loss, Eq. (45) were calculated for the

satised the non-linear constraints.

fobj(x) (53)

iBIPPi (54)

0.08 (55)

00 VILB (56)

0 (57)

UB (58)

3.2.3.3terion. Since mare ranthe selmixtur

3.2.4. 3.2.4.1Table 8mixturorous linear rigorouthe mithe rigshouldpermis

3.2.5. A m

listed iis prespresen

ends at minimum price with their estimated property values.

xi Cost ($/L) Properties

V

0.560.44

7.80 4.12

0.0787 6.88 4.12 1

0.54470.3765

0.58200.05190.35610.0100

6.63 4.12 108

abbreviations are given in the notations list.i

xiCi (59)

k 3.3 Rank blend candidates according to a selection cri-price of the blend is used as the selection criterion.

blend formulations satised all the constraints, theyaccording to the minimum price that is achievable for

purpose. The blends with lowest price for each type of given in Table 9.

4: Model-based vericationk 4.1 Validation. The viscosities of blends listed ind further validation because all of them are non-idealhe viscosities of the blends are estimated using a rig-el and compared with the values estimated using ael. The comparison results are given in Table 10. Thelues are slightly higher than the linear values. Even so,s are still acceptable because the values obtained withs model are within the target range of 4.1212.5 cSt. It

be noted that, in general, viscosity has a wider range of values.

ct analysisre with low cost is selected for each type of mixture asle 9. The bio-based chemical, propane-1,2,3-triol (GLY)n all blends, while cis-9-Octadecenoic acid (9ODA) islend 3. It indicates that the consumption of mineral base

PP SG Tf Vvap

263 0.9790 494 0

273 0.9781 565 0283 0.9778 566 0

212 N.A. Yunus et al. / Computers and Chemical Engineering 66 (2014) 201213

oil can be reduced by replacing it with base oil derived from renew-able sources. As the price of bio-based chemicals is currently higherthan mineral based oils, their blends also have higher prices. How-ever, high viscosity indicates good quality of the lubricant, whichis achieved

4. Conclus

A systemproducts haies on desigblends. A dthe blendinout a large at each hieever, need production

The knoproduct neevalues in Tation on exiavailabilityuct design. database fostill mostlytechnical uuct is requiof target predge, whichbe an openresearchersto have a prequired betask. A compis under de

Tools suplay imporin the datathe chemicchemicals, icals in thecomplexityselection caucts. The lifor the ideato be veridata are mpoint (CPt)bon compoSmolenskii,Gaganis, & FVlasova, & Lto be suitabpounds (kemodels nee

The mixalternativesthe undesirnumber altblend candset accordinthe producttarget propcompositioreduce the

also can be used to rank the blend selection since it determinesthe energy content of the fuel.

The methodology can be used to design or tailor many liquidblended products, where the scope and size depend on the chem-

vailaty m

usinerim

wled

na Maogi al Re

nces

T. A. (2ber of576n, J. E.hanolgy & Fp, J., Ibook

Knudosity p

& Eng. K., Sities aene +toluenneers,. S., Laoach neerin., & Aco-evaS35tinou,erties., Martatom viscos., Ganhodolo., Ganrimen

E. L., &bridg. A., Aamenamireb, A. (er hea, 91, 1, W. E

, Nielsputer

& Pisprocekar, Aation

able pels an823kar, Ap-con

roved 2543ithi, Aputersolven785pour, ved th2-eth15 K. J in the blends listed in Table 9.

ions and future work

atic methodology for design of tailor-made blendeds been developed, and was highlighted with case stud-n of tailor-made gasoline blends, and base oil lubricantecomposition-based method has been applied to solveg problem, where the objective was to quickly screen-number of alternatives and to reduce the search spacerarchical step. The shortlisted blend candidates, how-further experimental verication before moving in to.wledge base method has been used for identication ofds, translation to properties and the setting of the targetsk 1. Information found in the literature and informa-sting products has formed the basis of this work. The

of a proper knowledge base is very critical in prod-Currently, there is no established knowledge base orrmat for chemical product design. Product design is

relying on the expertise of the designer. For example,nderstanding of the functionality of a designed prod-red for translation of this functionality into a numberoperties. This understanding is considered as knowl-

should be transferred into a proper database. It should database, where laboratories, chemists, engineers and

can share their knowledge and experiences. In orderowerful database, input from various stakeholders iscause the product design itself is a multi-disciplinaryrehensive database for chemicals based product design

velopment.ch as chemical databases and a property models librarytant roles in the mixture/blend design as chemicalsbase act as the starting building blocks. The size ofals database can be further increased by adding moreespecially bio-based chemicals. The choice of chem-

database is a key control issue with respect to the of the mixture/blend problems. The property modeln also inuence the performance of the blended prod-near mixing rule applied in this work is only suitablel mixtures. Thus, for the non-ideal mixtures, they needed using rigorous models. Some of the pure propertyissing, for example, octane number (ON) and cloud. Models to predict the octane number for hydrocar-unds are available in literature (Albahri, 2003; Lapidus,

Bavykin, Myshenkova, & Kondratev, 2008; Pasadakis,oteinopoulos, 2006; Perdih & Perdih, 2006; Smolenskii,apidus, 2004). Nevertheless, none of them were foundle for the estimation of the ON for oxygenated com-tone, alcohol, acid, ester and ether). Therefore, newd to be developed.ture/blend design algorithm guided the screening of

wisely. At each level, rules are applied to eliminateed chemicals or blend candidates, thus reducing theernatives. Selection criteria are introduced to rank theidates that satisfy all the constraints. The criteria areg to the product design objectives, which can be cost,

performance, the chemical composition, as well as theerties. For instance, gasoline is ranked according to then of the gasoline in blends because the objective is tofossil-fuel consumption. Besides, their heating value

icals aproperdesignan exp

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Kermanderiand 313.ble in the databases and the models available in theodel library. Current work has considered the productg a model-based approach. A logical next step is to addental component to verify the designed products.

gements

ncial support for this PhD project, which was fundedlaysian Ministry of Education (MoE) and UniversitiMalaysia (UTM), and partially funded by the Qatarsearch Fund (QNRF), is gratefully acknowledged.

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A systematic methodology for design of tailor-made blended products1 Introduction2 Methodology2.1 Tasks of the methodology2.1.1 Task 1 Problem definition2.1.1.1 Task 1.1 Identify product needs2.1.1.2 Task 1.2 Translate needs into physico-chemical properties2.1.1.3 Task 1.3 Set the target values

2.1.2 Task 2 Property model identification2.1.2.1 Task 2.1 Retrieve the required property models from the library

2.1.3 Task 3 Mixture/blend design2.1.3.1 Task 3.1 Collect input data2.1.3.2 Task 3.2 Generation and screening2.1.3.3 Task 3.3 Rank blend candidates

2.1.4 Task 4 Model-based verification2.1.4.1 Task 4.1 Verification

2.2 Mixture/blend design algorithm2.2.1 Level 1: Pure components constraints2.2.2 Level 2: Stability analysis2.2.3 Level 3: Linear constraints2.2.4 Level 4: Non-linear constraints

2.3 Database2.4 Property model library

3 Applications3.1 Case study 1: design of gasoline blends3.1.1 Task 1: Problem definition3.1.1.1 Task 1.1 Identify product needs3.1.1.2 Task 1.2 Translate needs into physico-chemical properties3.1.1.3 Task 1.3 Set target values

3.1.2 Task 2 Property model identification3.1.2.1 Task 2.1 Retrieve the required property models from the property model's library

3.1.3 Task 3 Mixture/blend design3.1.3.1 Task 3.1 Collect input data3.1.3.2 Task 3.2 Generate and screen blends using the mixture/blend design algorithm3.1.3.3 Task 3.3 Rank blend candidates according to a selection criterion

3.1.4 Task 4 Model-based verification3.1.5 Product analysis

3.2 Case study 2: design of base oil blends for lubrication3.2.1 Task 1: Problem definition3.2.1.1 Task 1.1 Identify product needs3.2.1.2 Task 1.2 Translate needs into physico-chemical properties3.2.1.3 Task 1.3 Set target values

3.2.2 Task 2: Property model identification3.2.2.1 Task 2.1 Retrieve the required property models from the property model's library

3.2.3 Task 3: Mixture/blend design3.2.3.1 Task 3.1 Collect input data3.2.3.2 Task 3.2 Perform mixture/blend design algorithm3.2.3.3 Task 3.3 Rank blend candidates according to a selection criterion

3.2.4 Task 4: Model-based verification3.2.4.1 Task 4.1 Validation

3.2.5 Product analysis

4 Conclusions and future workAcknowledgementsReferences