Optimization of Crude Oil Distillation

Computers and Chemical Engineering 59 (2013) 178 185

Contents lists available at ScienceDirect

Computers and Chemical Engineering

j ourna l ho me p age : w ww.elsev ier .com/ lo

Operat onarticia

Lluvia MSchool of Chem Manc

a r t i c l

Article history:Received 2 OcReceived in reAccepted 23 MAvailable onlin

Keywords:Heat integratioHeat exchangeProduct yields

at-intcial nmn a

to syrticuly incto acts. A um u

1. Introdu

In recent years, the design and optimization of crude distilla-tion systems has received considerable research interest. Rigorous,simplied and statistical/empirical models have been employedto simulate the complex distillation column. These models maybe incorporconguratiotions) that a

Rigoroustillation colGupta, & Sar& Banjara, 2is optimizedsuch as proUppaluri et2004) or crNevertheleswork (HEN(1998) andcedures to rigorous mgrated apprthe distillat

CorresponE-mail add

robin.smith@m

idelimodels are more accurate than simplied and statistical models.Nevertheless, it is difcult to implement rigorous models into anoptimization algorithm that considers both the column and HEN,due to the large number of variables and non-linear equations thatneed to be solved simultaneously (Hartmann, 2001; Uppaluri et al.,

0098-1354/$ http://dx.doi.oated in an optimization scheme to determine the bestn (owsheet structure and associated operating condi-chieves a dened objective function.

models have been used to optimize the crude oil dis-umn (Basak, Abhilash, Ganguly, & Saraf, 2002; Inamdar,af, 2004; Seo, Oh, & Lee, 2000; Uppaluri, More, Bulasara,010). In these methodologies, the distillation column

in order to increase annual prots considering factorscessing different types of crude oils (Basak et al., 2002;

al., 2010), real plant measurements (Inamdar et al.,ude oil preheat temperature change (Seo et al., 2000).s, interactions with the associated heat exchanger net-) are not considered. Liebmann, Dhole, and Jobson

Bagajewicz and Ji (2001) presented step-by-step pro-design heat-integrated crude distillation units usingodels. Although both procedures employed an inte-oach for grassroots design of crude distillation systems,ion column design strategies require user interaction

ding author.resses: [email protected] (M. Jobson),anchester.ac.uk (R. Smith).

2010).Simplied models (Suphanit, 1999) have been incorporated

and further extended into a systematic approach to design heat-integrated crude oil systems considering HEN structure (Chen,2008; Gadalla, 2003; Rastogi, 2006); this possibility for rigorousand statistical models has not yet been explored. The methodologyemploying simplied models performs simultaneous optimizationof both column and HEN, allowing interactions within the system tobe exploited to produce an improved overall design. However, themain drawback of these simplied models is the need to specify keycomponents and their recoveries, since the models are based on theFenskeUnderwoodGilliland method. Chen (2008) developed anoptimization methodology to calculate key components and theirrecoveries using ow rates and true boiling point (TBP) specica-tions for crude oil distillation products. Currently, the formulationof this optimization problem involves complex and iterative cal-culations, requiring sensible initial guesses. Solutions cannot beguaranteed if the initial guess is not suitable.

Statistical models have been widely used to simulate complexchemical processes (Himmelblau, 2000; Nascimento, Giudici, &Guardani, 2000; Palmer & Realff, 2002). In the eld of oil ren-ing, applications of statistical models include property prediction(Osman, Fahd, & Arabia, 2002; Shirvany, Zahedi, & Bashiri, 2010),

see front matter 2013 Elsevier Ltd. All rights reserved.rg/10.1016/j.compchemeng.2013.05.030ional optimization of crude oil distillatil neural networks

. Ochoa-Estopier, Megan Jobson , Robin Smithical Engineering and Analytical Science, The University of Manchester, Sackville Street,

e i n f o

tober 2012vised form 21 May 2013ay 2013e 24 June 2013

nr networks

a b s t r a c t

A new methodology for optimizing heThe new procedure considers an articolumn. Models of the distillation coluporated in an optimization frameworkthe overall process economics. Of paproducts obtained from the column bless valuable products, while taking inery, energy and equipment constrainoperating conditions based on minimnetwork is designed.

ction and previous work and gucate /compchemeng

systems using

hester M13 9PL, UK

egrated crude oil distillation systems is proposed in this work.eural networks (ANN) model for representing the distillationnd the associated existing heat exchanger network are incor-stematically determine the operating conditions that improvear interest is the problem of optimizing the net value of thereasing the yield of higher-value products at the expense ofcount feasibility of the distillation specications, heat recov-two-stage procedure is applied to rst optimize the columntility requirements. In the second stage the heat exchanger

2013 Elsevier Ltd. All rights reserved.

nes that do not guarantee an optimal design. Rigorous

L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185 179

simulation of individual operations (Alhajree, Zahedi, Manan, &Zadeh, 2011; Bellos, Kallinikos, Gounaris, & Papayannakos, 2005;Liau, Yang, & Tsai, 2004; Lpez & Mahecha, 2009; Motlaghi, Jalali,& Ahmadabadi, 2008; Zahedi, Lohi, & Karami, 2009; Zhao, Chen, &Shangxu, 2000) and renery simulation for optimization purposes(Abilov & Zeybek, 2000).

ANN motions such aization reac2004; Motland uid caoped by Al(2008) andmization apto obtain thand Motlagcrude oil disin optimizametamodel(for crude ooil distillatiregression orates, produfrom rigoromore robus2009) and mization m

Liau et aels to optimeasuremeusing informThe model operationalologies is thANN modelertheless, tMotlaghi etthe crude otion is limtheir commto performmethodologperformanc

In this woptimizatioANN modellems presesmaller numtime than tions are pemodel, so tis incorpormine the coprot.

2. Ann dist

The prodistillation steps. The knowledge the selectiothird step model.

2.1. Crude oil distillation samples

Aspen HYSYS is applied for rigorous simulation of the complexdistillation column; a subset of these results is used to train the net-work. The of freedom

are rse si

les, a. Forraturtputsalityine

(ens strts st

N st

struust

he nuheur

Vanethomha,proay & Mrs anN std batputction011)ddit

dete the irigorlity Ane hns fotermsibilsimuhe did to

ANN to phe ANr the

odel

e thted f

and(weignctiod de.

t-in

his wted dels have been employed to simulate renery opera-s hydrocracking (Alhajree et al., 2011), hydrodesulphur-tors (Bellos et al., 2005), crude oil distillation (Liau et al.,aghi et al., 2008), delayed coking (Zahedi et al., 2009)talytic cracking (Zhao et al., 2000). The models devel-hajree et al. (2011), Liau et al. (2004), Motlaghi et al.

Zhao et al. (2000) were successfully included in opti-proaches, showing good agreement with the data usede models. Lpez and Mahecha (2009), Liau et al. (2004)hi et al. (2008) employed statistical models to simulatetillation columns and these models were implementedtion methodologies. Lpez and Mahecha (2009) useds (for crude oil distillation towers) and rigorous modelsil preheat trains) to perform optimization of three crudeon units. The procedure requires the construction andf several metamodels for each group of variables (owct properties, temperatures, etc.) using data generatedus model simulations. Metamodels are reported to bet and faster than rigorous models (Lpez & Mahecha,are suitable for implementation in a systematic opti-ethodology.l. (2004) and Motlaghi et al. (2008) used ANN mod-

mize crude oil distillation columns using real plantnts. In their methodologies, an ANN model is builtation from the distillation column operating history.

then constitutes the knowledge database to perform optimization. The main advantage of these method-at optimization can be implemented on-line and the

can be updated using new plant measurements. Nev-he approaches developed by Liau et al. (2004) and

al. (2008) do not consider heat integration but onlyil distillation column. In addition, the objective func-ited to maximizing the product yields according toercial importance, ignoring the energy requirements

such separation. As a consequence, the proposedies can fail to design a system with optimal economice.ork, an ANN model is built to facilitate operational

n of heat-integrated crude oil distillation systems. The has the advantages of overcoming convergence prob-nted in rigorous and simplied models, handling aber of variables and performing calculations in less

required by more rigorous models. Rigorous simula-rformed to build the database used to train the ANN

hat accuracy is not compromised. The resulting modelated in a sequential optimization scheme to deter-nditions in the column and HEN that maximize overall

illation model

cedure for obtaining the ANN model of the crude oilcolumn, as presented below, consists of three mainrst step is the construction of samples used as thedatabase for the ANN model. The second step isn of the ANN structure and parameters; nally, theis performing the regression (training) of the ANN

inputsFor thovariabisteredtempeThe ouuct qudetermsystemprocesproduc

2.2. AN

TheANN mmine tuse of Wang,atic mNarasiand apStanleof layeThe ANforwarone oufer funet al., 2

In abuilt totion ofof the feasibiwith ofunctioalso dethe feaorous build tare use

Theis usedbuild tused fo

2.3. M

OncgeneramodelANNs LAB fudetaile(2011)

3. Hea

In tintegraindependent variables, also called inputs, are degrees for optimization of the heat-integrated system. Theandomly varied between their lower and upper values.mulations that converged, the values of the dependentlso called outputs of the ANN, are determined and reg-

this case, the inputs of the system are the duty ande drop of the pump-arounds, and the product ow rates.

of the ANN model are the variables that describe prod- (5 and 95% TBP points) and also variables needed tothe minimum energy requirements of the integratedthalpy change, supply and target temperatures of alleams involved, such as the condenser, reboilers andreams, etc.).

ructure

cture, transfer functions and number of neurons of thebe dened (Beale, Hagan, & Demuth, 2011). To deter-mber of layers and neurons, some authors suggest the

istic rules (Beale et al., 2011; Heaton, 2005; Sarle, 1995; Gelder, & Vrijling, 2005) or the application of system-dologies such as pruning and growing (Heaton, 2005;

Delashmit, Manry, Li, & Maldonado, 2008; Sarle, 1995)ches based on genetic algorithms (Nol & Parisi, 2002;iikkulainen, 2002). However, for simplicity, the numberd neurons was chosen by trial and error (Heaton, 2005).ructure used to model the distillation column is a feed-ckpropagation network, consisting of one hidden layer,

layer, a hyperbolic tangent function and a linear trans- for the hidden and output layers, respectively (Beale.ion to the distillation column model, another ANN wasrmine the feasibility of the distillation column as a func-nputs. In this case, feasibility refers to the convergenceous model in Aspen HYSYS, given a set of inputs. TheNN is also a feed-forward backpropagation network,

idden layer, one output layer, and hyperbolic transferr both layers. The number of neurons for this ANN isined by trial and error. The information used to trainity network is obtained while generating random rig-lations. Only the converged scenarios are employed tostillation model, while all scenarios (converged or not)

predict the feasibility of the set of inputs. toolbox embedded in MATLAB (The MATH WORKS Inc.)erform the calculations. Function newt is employed toN distillation model structure, while function newpr is

feasibility predictor ANN.

regression (ANN training)

e method for training the network is selected, the datarom rigorous simulations is used to regress the column

feasibility ANN. The values of the parameters of thehts and biases) are calculated during this stage. MAT-n trainlm is employed to regress the created ANNs. Ascription of this algorithm can be found in Beale et al.

tegrated distillation system optimization

ork, a new design methodology for optimizing heat-crude oil distillation systems using ANN models is

180 L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185

, Gran

proposed. Tfocuses on product yiededicated toThe trade-osented by this a simple mand is empduring the ments are though the for represebetter resulconsidered

3.1. Stage 1

Once thporated in Minimum eare calculatcolumn simprocess moin order to ocan be consulations forGCC does n(e.g. miniminto the calthe actual e2, using the

Cost mooperating cpenalty funand out-of-as:

max F(x) =

where x reparound dutF(x) is the

ainedpena

are son. Sd, whto stoe chn f c

od

1

Cpro

Cprots, re

the cstripum rlity c

T5c

T9

T95 Fig. 1. Optimization approach for heat-integrated distillation system. GCC

he procedure is carried out in two stages. The rst stagethe optimization of the distillation column in terms ofld and minimum energy demand. The second stage is

the HEN optimization. Fig. 1 illustrates this procedure.ffs between the distillation column and HEN are repre-e grand composite curve (GCC) (Smith, 2005). The GCCethod for calculating minimum energy requirements,

loyed to assess the energy performance of the systemrst stage of the procedure. The real energy require-determined during the HEN optimization stage. Eventwo-stage methodology employs a simplistic approachnting the interactions between the column and HEN,ts could be obtained if the column and HEN details weresimultaneously.

: Column optimization

e crude oil distillation model is obtained, it is incor-the problem formulation to optimize column prot.nergy requirements (i.e. red heating, cooling water)ed using the GCC and information obtained from theulation. Liebmann et al. (1998) used the GCC to identifydications that change cooling or heating requirements,btain energy savings. With the help of the GCC, the HENidered as a whole without performing detailed HEN sim-

each iteration during the optimization. However, the

constri are Eq. (1)mizativiolaterithm must bfunctio

f =Nprj=

whereproducrate ofof the miniminequa

T5lb,j T95lb.i

T5 and

ot incorporate the HEN structure or system constraintsum temperature approach for every heat exchanger)culation of energy requirements. In order to determinenergy demand, the HEN details are considered in Stage

stream information calculated in Stage 1.dels are employed to determine product income andosts (Eq. (2)) Additionally, constraints in the form ofctions are considered to account for infeasible inputsspecication products. The objective function is dened

f (x) I

i=1i max(0, gi(x))

gi(x) 0(1)

resents the degrees of freedom for optimization (pump-ies and temperature drops; and product ow rates),unconstrained objective function, f(x) is the original

indicate low(3) must be4 Nprod cofeasibility obility ANN. whether thzero, depenvates a verypoint x.

Due to mization alcompared tequations (mulated inGelatt, 1983variables x eters. The Gemployed td Composite Curve; SA, Simulated Annealing.

objective function, gi(x) are the inequality constraints,lty factors that ensure that all the terms included incaled and become of same importance during the opti-mall penalty factor values may cause constraints to beereas very large values can cause the optimization algo-p prematurely. These values are problem-specic and

osen very carefully based on experience. The objectivean be expressed mathematically as:

d,jFprod,j (

CcrudeFcrude + CstFst +Nutilk=1

Cutil,kUmin,k

)

(2)

d and Fprod are the prices and ow rates of the Nprodspectively; Ccrude and Fcrude are the price and the owrude oil feed; Cst and Fst are the price and the ow rateping steam; Cutil is the price of utility k; and U is theequirement of utility k, calculated using the GCC. Theonstraints for product quality can be written as:

alc,j T5ub,j j = 1, 2, , Nprod5calc,i T95ub,j

(3)

represent the 5 and 95% TBP point, subscripts lb and ub

er and upper bounds specied for these properties. Eq.

adapted to the form of g(x) in Eq. (1); this constitutesnstraints. The last penalty function, associated with thef the distillation column, is calculated using the feasi-Given the set of inputs x, the feasibility ANN evaluatese column design is feasible, assigning a value of one ording on the case. A value of zero for the feasibility acti-

large penalty function, causing the optimizer to reject

its stochastic nature, Simulated Annealing (SA) opti-gorithm has better chances of nding global optimumo deterministic models, especially for highly non-linearChen, 2008). For this reason, the NLP problem for-

Eq. (1) is solved using a SA algorithm (Kirkpatrick &). Note that the lower and upper bounds of the decision

are not included in Eq. (1) but are specied as SA param-lobal Optimization Toolbox developed by MATLAB waso perform the optimization.


3.2. Stage 2: HEN optimization

Once the column optimization is carried out, the process streaminformation is passed to the HEN, represented by the modeldeveloped used to repnamely proers, bypasseunique temciated nodethree (one fer coefciestream.

Optimizmodicatioand area mtural modithe HEN areaccording tno additionmented durnew columheat transfetransfer enhmentation 3). Changinof modicaperformingoptimizatiosplit fractio

The optifunctions amodicatiois to minim

Total annua

= AnnualIn this casecosts, nameering the HInteger Nonemploying Process Inteform this ofound in th(2010).

4. Case stu

The objeand operatikeeping prosiders the atcolumn proand 300 kPa(HN), light The producthe TBP curSteam at 45column andtively. The cand the maiPA3) and th

The crud(Watkins, 1

Fig.

ling p

illed (by volume) TBP (C)

3.063.5

101.7221.8336.9462.9680.4787.2894.0

Density: 865.4 kg/m3

say is cut in 25 pseudocomponents using the oil charac-ion technique embedded in Aspen HYSYS with the defaultterization parameters.

N model

distillation unit specied above is simulated in Aspen. The independent variables are randomly varied along theirand lower bounds to ensure that the search space is fullyed. Table 2 shows the upper and lower bounds of the inde-nt variables. Eight hundred scenarios were generated; ofscenarios, 619 were feasible (i.e. led to converged simula-and are used to build the ANN distillation column model. Allenarios are employed to train the ANN feasibility predictor.

ANNs are created in the ANN Toolbox in MATLAB. For thetion column model, a network consisting of 50 neuronsgressed using the 619 converged samples from rigoroustions. Likewise, a network of 35 neurons was specied forsibility ANN and was trained using the 800 scenarios ini-roposed. For both ANNs, seventy per cent of the samplesployed to train the network; fteen per cent are used forion, and the rest for testing the performance of the neuralrks.

3 compares the ANN distillation model predictions againsts simulations for some regressed variables. The results ofby Rodrguez (2005). In this model, unique nodes areresent the link between each component of the HEN,cess-to-process heat exchangers, utility heat exchang-s, mixers and splitters. Each node is associated with aperature: for example, a heat exchanger has four asso-s (two inlet and two outlet streams); a splitter hasinlet and two outlet streams); and so on. Heat trans-nts and heat capacities are assumed constant for every

ation can be performed achieving different levels ofns: (1) operational variables, (2) operational variablesodications, (3) operational variables, area and struc-cations. For Level 1, only heat loads and split fractions of

adjusted, and the utility requirements are determinedo the new values. This is the most desirable option, asal area is required and the modications can be imple-ing operation. If the HEN is not able to accommodate then operating conditions (from Stage 1 of the procedure),r enhancement is proposed (Level 2). In practice, heatancement requires less capital investment and imple-

time than performing structural modications (Levelg the HEN structure is the least desirable of all levelstions and is omitted in this work, since the purpose is

operational optimization. The degrees of freedom forn are the utility consumption, heat loads of each unit,ns and adding new heat transfer area.mization methodology employs the HEN model, penaltynd cost models to calculate the cost of performing HENns and to determine utility costs. The objective functionize total annualized costs, dened as:

lized cost

ized capital investment+Operating costs

, the operating costs only include energy consumptionly red heating and cooling water, calculated consid-EN details. The optimization is formulated as a Mixed-linear Programming (MINLP) problem, which is solveda SA algorithm. The software SPRINT of the Centre forgration, University of Manchester, is employed to per-ptimization. More details about this procedure can bee work of Chen (2008) and Smith, Jobson, and Chen

dy

ctive of this case study is to optimize product yieldsng conditions in order to improve overall prot whileduct quality within specied limits. The case study con-mospheric crude oil distillation unit shown in Fig. 2. Thecesses 4161.2 bbl/h (661.6 m3/h) of crude oil at 25 C

into ve products: light naphtha (LN), heavy naphthadistillate (LD), heavy distillate (HD), and residue (RES).t specications, expressed in terms of T5 and T95 onve, are reported together with the results, in Table 4.0 kPa and 260 C is used as a stripping agent in the main

HD stripper, consuming 1200 and 250 kmol/h, respec-rude oil distillation system comprises the preheat trainn tower, one condenser, three pump-arounds (PA1, PA2,ree side strippers, as shown in Fig. 2.e oil to be processed is Venezuela Tia Juana Light crude979); its true boiling point curve is shown in Table 1.

Table 1True boi

% Dist

0 5

10 30 50 70 90 95

100

The asterizatcharac

4.1. AN

TheHYSYSupper explorpendethose tions) 800 sc

Thedistillawas resimulathe featially pare emvalidatnetwo

Fig.rigorou 2. Crude oil distillation unit considered in the case study.

oint data.


Table 2Optimization results.

Item Lower bound Upper bound Base case Optimized case Units

Optimization variablesLN ow rate 517 533 bbl/hHN ow rate 508 492 bbl/hLD ow rate 932 952 bbl/hHD ow rate 267 296 bbl/hPA1 duty 12.8 16.0 MWPA2 duty 17.9 16.9 MWPA3 duty 11.2 9.3 MWPA1 tempera 72.0 30.0 CPA2 tempera 71.0 49.0 CPA3 tempera 65.0 50.0 C

Calculated cCondenser 47.5 47.1 MWHN-Reb 3.8 3.7 MWLD-Reb Hot utility (GCold utility (

Hot and cold u

the distillatthose from predictor arst stage obelow.

4.2. Stage 1

The SA ain Eq. (1). Talation of prThe formeryear and thcost per unity, calculat(2008).340 720 316 740

555 1350 155 400

0 19.0 0 24.0 0 21.0

ture drop 30.0 100.0ture drop 30.0 100.0 ture drop 30.0 100.0

ondenser and reboiler duties, and minimum utility requirements

CC) GCC)

tilities are calculated using the GCC (stage one of the optimization procedure).Fig. 3. Neural networks model test results for some outputs. First row, reboilers

ion model may be seen to be in good agreement withrigorous models in Aspen HYSYS. The ANN feasibilitynd the ANN distillation model are incorporated in thef the optimization procedure; the results are presented

: Column optimization

lgorithm is employed to solve the NLP problem statedble 3 provides the necessary information for the calcu-oduct revenue and operating cost described in Eq. (2).

is calculated assuming an operating time of 8600 h pere latter is calculated employing unit cost data (annualit of energy, $ kW1 y1) and the demand for each util-ed using the GCC. The cost of utilities is taken from Chen

Table 2 variables. Ralgorithm wANN model

Table 3Feed, product

Item

Crude oil Light naphthHeavy naphLight distillaHeavy distilResidue Fired heatinCooling watStripping ste7.5 7.7 MW 32.7 MW 37.5 MWand condenser duties. Second row, TBP points.

summarizes the optimized values of the independentesults found by the distillation column optimizationere simulated in Aspen HYSYS for nal validation of the, showing good agreement. Note that the optimization

and utility prices.

Price Units

80.5 $/bbla 103.7 $/bbl

tha 92.7 $/bblte 99.0 $/bbllate 96.6 $/bbl

61.3 $/bblg 150.00 $/(kW y)er (1040 C) 5.25 $/(kW y)am (260 C, 450 kPa) 0.14 $/kmol


Table 4Product quality results.

Property Lower specication Upper specication Base case Optimized case Units

LN T5 4 16 6 7 CHN T5 114 116 CLD T5 190 190 CHD T5 294 293 CRES T5 364 370 CLN T95 125 134 CHN T95 205 206 CLD T95 319 328 CHD T95 369 378 CRES T95 851 852 C

algorithm vincreasing tand HD), wsome heaviing the temto travel toand the rebtion. Modiconsumptio

Table 2 increases bdecreases tcolumn to bing at this To compenduties of PArial ows inat lower tem

Table 4 pT5 and T95tions to thencounterestream pro104 124 180 200 284 304 354 374 115 135 195 215 309 329 359 379 841 851 Fig. 4. Heat exchanger network for the crude oil d

aries the product ow rates according to their prices,he ow rates of the three most valuable products (LN, LDhile reducing HN and RES owrates. As a consequence,er components are recovered to each product, increas-perature of each product. Since more vapor is required

the top of the column, duties of intermediate coolersoilers must be modied to achieve the desired separa-cations must be performed without increasing utilityn.shows that cooling at the highest temperature (PA1)y approximately 3.2 MW, while the temperature dropo 30 C; this change allows more useful heat from thee transferred to the preheat train. However, more cool-level reduces the ow of vapor to the stages above.sate for the reduced reux above pump-around 1, the2, PA3 and the condenser are reduced to increase mate-

the top stages, with the result that less heat is rejectedperature.resents the results for the product quality parameters,

of each product. As a consequence of the modica-e operating conditions, more heavy components ared in the products located above the RES stream, andperties, such as density, change. As expected, the T95

specicatiohowever, thset.

4.3. Stage 2

After oplation columare used toobjective fuannualizedand the annications pare the uti(i.e. heat ex

Table 5Heat exchange

Item

Installed areInstalled useAdditional aistillation unit.

n for products LN, LD, and HD are the most affected;ey remained within acceptable limits, i.e. the bounds

: HEN optimization

timizing the operating conditions of the complex distil-n, all process stream data predicted by the ANN model

optimize the HEN associated with the column. Thenction for the HEN optimization is to minimize total

costs, namely, the cost of red heating, cooling waterualized capital investment to perform the HEN mod-

roposed by the optimizer. The optimization variableslity consumption and the possible HEN modicationschanger duties, split fractions, additional area). Table 3

r network results.

Base case Optimized case

a (m2) 11,725 11,725d area (m2) 11,263 10,928rea (m2) 144


Table 6Summary of results for the crude oil distillation unit optimization.

Item Base case Optimized case Change Units

Hot utility (HEN) 90.6 89.0 1.6 (2%) MWCold utility (Additional aCapital invesCrude oil cosSteam cost Utility costs Operating coProduct reveTotal prot

Hot and cold u dure)

shows the uEq. (4) provused to calc

Cost of addi

= 1530 An operatinterion withConstraintsa minimumoptimizatio

The exisHEN requirdate the nefor the basethe overall optimizatioences betw(Table 2) anences suggerepresentatguarantee ations. BetteHEN were o

Note thaan increasebe deducedthe annualithat considchanging thMoreover, ttillation sysoperating cproducts anthe HEN.

5. Conclus

A new omization opresented icolumn waThe resultinthe distillatcalculationsframeworkrst stage oconditions consumptiosent the intminimum e

erature, eratied s.2 1.8 1ing cansfeuch mnd tptimsideraneo

wled

authciencbertod for

nces

., & Zess inProces, I., Zah

indueptemicz, Mospheistry

., Abhcrudeneerin. H., Hck, MA. D., Ke peroach.

5055(2008anche

M. (2UniveHEN) 95.1 93.8rea 144 tment 0.04 t 2883.1 2883.1

1.7 1.7 14.1 13.8

sts 2898.9 2898.6 nue 2903.1 2919.9

4.1 21.2

tilities are calculated considering HEN details (stage two of the optimization proce

tility prices used to compute operating costs of the HEN.ides the heat exchanger modication costs (Chen, 2008)ulate capital investment.

tional heat transfer area ($)

(additional area [m2])0.63

(4)

g time of 8600 h per year and a two-year payback cri- 5% interest rate were assumed in these calculations.

restrict the number of allowed HEN modications and temperature approach (Tmin) of 30 C is specied. Then results provide the new HEN conguration.ting HEN structure is illustrated in Fig. 4. The optimizedes 144 m2 of additional heat transfer area to accommo-w column operating conditions. The heat transfer areas

case and the optimized case are shown in Table 5, whileprocess economic results from both column and HENns are shown in Table 6. There are considerable differ-een the utility requirements calculated with the GCCd the actual requirements from Table 6. These differ-st that even if the GCC is useful to provide a simplisticion of the HEN in Stage 1, it is not accurate enough ton optimal solution for both column and HEN optimiza-r results could be achieved if the distillation column andptimized simultaneously.t total prot is increased by $17.1 106/y, mainly due to

in product income of $16.8 106/y. From Table 6, it can that the cost of crude oil dominates the utility costs andzed capital cost. From these results, it is demonstratederable economic benets can be achieved withoute distillation column or HEN structural conguration.he economic performance of the heat-integrated dis-tem was optimized by manipulating only the columnonditions to increase the yields of the most valuabled by a relatively small capital investment enhancing

ions

ptimization approach for performing operational opti-f heat-integrated crude oil distillation systems was

new opprocedand opproposof $21of $16operatheat trform sfail to HEN oto consimult

Ackno

Thecil of Sthe Rogrante

Refere

Abilov, Aprocand

Alhajreeof an78(S

BagajewatmChem

Basak, Kof a Engi

Beale, MNati

Bellos, Gof thappr(5)),

Chen, L. of M

Gadalla,ter: n this work. An ANN model for the crude oil distillations constructed based on results of rigorous simulations.g model shows to be able to represent the behavior ofion column without the expense of performing complex

or requiring initial guesses. A two-stage optimization for the heat-integrated system was proposed. In thef the procedure, a SA optimizer calculates the operatingthat maximize product income and minimize energyn based on targets. The GCC was employed to repre-eractions between the column and HEN, by calculatingnergy requirements. All stream data resulting from the

Hartmann, J. CHydrocarb

Heaton, J. (200Heaton Re

Himmelblau, Dneering. K

Inamdar, S. V.,industrial algorithm

Kirkpatrick, S.220(4598)

Liau, L. C.-K., Ylation unitApplication1.3 (1%) MW m2

106 $ 106 $/y 106 $/y

0.2 (2%) 106 $/y0.3 (0%) 106 $/y

+16.8 (1%) 106 $/y+17.1 106 $/y

.

ing conditions are employed in the second stage of thewhere the HEN is optimized with respect to capitalng costs. Results from the case study showed that thetrategy produced a design with improved total prot06/y, mainly due to an increase in product revenue06/y. The optimized HEN can accommodate the newonditions of the column, requiring 144 m2 of additionalr area and $0.04 106/y of capital investment to per-odications. However, the proposed methodology can

he optimal operating conditions, since the column andizations are performed sequentially. Future work aims

the column operating conditions and HEN constraintsusly.

gements

ors would like to thank the Mexican National Coun-e and Technology (CONACYT, no. 204362/308621) and

Rocca Education Program (RREP) for nancial support the development of this work.

eybek, Z. (2000). Use of neural network for modeling of non-lineartegration technology in chemical engineering. Chemical Engineeringsing, 39(5), 449458.edi, G., Manan, Z. A., & Zadeh, S. M. (2011). Modeling and optimization

strial hydrocracker plant. Journal of Petroleum Science and Engineering,ber (34)), 627636.., & Ji, S. (2001). Rigorous procedure for the design of conventionalric crude fractionation units. Part I: Targeting. Industrial & EngineeringResearch, 40(January (2)), 617626.ilash, K. S., Ganguly, S., & Saraf, D. N. (2002). On-line optimization

distillation unit with constraints on product properties. Industrial &g Chemistry Research, 41(Mar (6)), 15571568.agan, M. T., & Demuth, H. B. (2011). Neural network toolbox users guide.: The MathWorks, Inc.

allinikos, L. E., Gounaris, C. E., & Papayannakos, N. G. (2005). Modellingformance of industrial HDS reactors using a hybrid neural network

Chemical Engineering and Processing: Process Intensication, 44(May15.). Heat-integrated crude oil distillation design. Manchester: Universityster (PhD Thesis).003). Retrot of heat integrated crude oil distillation systems. Manches-rsity of Manchester Institute of Science and Technology (PhD Thesis).

. M. (2001). Determine the optimum crude intake level a case history.on Processing, 80(6).5). Introduction to Neural Networks with Java (2nd ed.). Chestereld:search, Inc.. (2000). Applications of articial neural networks in chemical engi-

orean Journal of Chemical Engineering, 17(4), 373392. Gupta, S. K., & Saraf, D. N. (2004). Multi-objective optimization of ancrude distillation unit using the elitist non-dominated sorting genetic. Chemical Engineering Research and Design, 82(A5), 611623., & Gelatt, C. (1983). Optimization by simulated annealing. Science,, 671680.ang, T. C.-K., & Tsai, M.-T. (2004). Expert system of a crude oil distil-

for process optimization using neural networks. Expert Systems withs, 26(February (2)), 247255.


Liebmann, K., Dhole, V. R., & Jobson, M. (1998). Integrated design of a conventionalcrude oil distillation tower using pinch analysis. Chemical Engineering Researchand Design, 76, 335347.

Lpez, D. C., & Mahecha, C. (2009). Optimization model of a system of crude oildistillation units with heat integration and metamodeling. Ciencia, Tecnologa yFuturo, 3, 159174.

Motlaghi, S., Jalali, F., & Ahmadabadi, M. (2008). An expert system design for a crudeoil distillation column with the neural networks model and the process opti-mization using genetic algorithm framework. Expert Systems with Applications,35(November (4)), 15401545.

Narasimha, P. L., Delashmit, W. H., Manry, M. T., Li, J., & Maldonado, F. (2008). Anintegrated growing-pruning method for feedforward network training. Neuro-computing, 71(August (1315)), 28312847.

Nascimento, C. A. O., Giudici, R., & Guardani, R. (2000). Neural network basedapproach for optimization of industrial chemical processes. Computers & Chem-ical Engineering, 24(October (910)), 23032314.

Nol, S., & Parisi, D. (2002). Evolution of articial neural networks. In M. Arbib (Ed.),The handbook of brain theory and neural networks (pp. 418421). London: TheMIT Press.

Osman, E. A., Fahd, K., & Arabia, S. (2002). Using articial neural networks to developnew PVT correlations for Saudi crude oils. In Abu Dhabi international petroleumexhibition and conference (pp. 115).

Palmer, K., & Realff, M. (2002). Metamodeling approach to optimization ofsteady-state owsheet simulations. Chemical Engineering Research and Design,80(October (7)), 760772.

Rastogi, V. (2006). Heat-integrated crude oil distillation system design. Manchester:University of Manchester (PhD Thesis).

Rodrguez, C. A. (2005). Fouling mitigation strategies for heat exchanger networks.Manchester: University of Manchester (PhD Thesis).

Sarle, W. S. (1995). Stopped training and other remedies for overtting. Proceedingsof the 27th symposium on the interface of computer science and statistics(pp. 352360).

Seo, J. W., Oh, M., & Lee, T. H. (2000). Design optimization of a crude oildistillation process. Chemical Engineering & Technology, 23(February (2)),157164.

Shirvany, Y., Zahedi, G., & Bashiri, M. (2010). Estimation of sour natural gaswater content. Journal of Petroleum Science and Engineering, 73(August (12)),156160.

Smith, R. (2005). Chemical process design and integration. Chichester: John Wiley &Sons, Ltd.

Smith, R., Jobson, M., & Chen, L. (2010). Recent development in the retrot ofheat exchanger networks. Applied Thermal Engineering, 30(November (16)),22812289.

Stanley, K., & Miikkulainen, R. (2002). Efcient evolution of neural network topolo-gies. In CEC02: Proceedings of the 2002 congress of evolutionary computationPiscataway, (pp. 17571762).

Suphanit, B. (1999). Design of complex distillation systems. Manchester: University ofManchester Institute of Science and Technology (PhD Thesis).

Uppaluri, R., More, R. K., Bulasara, V. K., & Banjara, V. R. (2010). Optimization ofcrude distillation system using Aspen Plus: Effect of binary feed selection ongrass-root design. Chemical Engineering Research and Design, 88(February (2)),121134.

Wang, W., Van Gelder, P. H. A. J. M., & Vrijling, J. K. (2005). Some issues aboutthe generalization of neural networks for time series prediction. In ICANN05:Proceedings of the 15th international conference on articial neural networks: For-mal models and their applications. Berlin: Springer-Verlag.

Watkins, R. N. (1979). Petroleum renery distillation (2nd ed.). Texas: Gulf PublishingCompany.

Zahedi, G., Lohi, A., & Karami, Z. (2009). A neural network approach for identicationand modeling of delayed coking plant. International Journal of Chemical ReactorEngineering, 7.

Zhao, W., Chen, D., & Shangxu, H. (2000). Optimizing operating conditionsbased on ANN and modied gas. Computers & Chemical Engineering, 24,6165.

Operational optimization of crude oil distillation systems using artificial neural networks1 Introduction and previous work2 Ann distillation model2.1 Crude oil distillation samples2.2 ANN structure2.3 Model regression (ANN training)

3 Heat-integrated distillation system optimization3.1 Stage 1: Column optimization3.2 Stage 2: HEN optimization

4 Case study4.1 ANN model4.2 Stage 1: Column optimization4.3 Stage 2: HEN optimization

5 ConclusionsAcknowledgementsReferences

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Optimization of Crude Oil Distillation