Optimization of Crude Oil Distillation
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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
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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.
-
L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185 181
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.
-
182 L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185
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
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L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185 183
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
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184 L.M. Ochoa-Estopier et al. / Computers and Chemical Engineering 59 (2013) 178 185
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
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Abilov, Aprocand
Alhajreeof an78(S
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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.
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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