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MAY 2014 – JULY 2014, Vol. 4, No. 3; 1851-1865. E- ISSN: 2249 –1929

Journal of Chemical, Biological and Physical Sciences An International Peer Review E-3 Journal of Sciences

Available online atwww.jcbsc.org

Section A: Chemical Sciences

CODEN (USA): JCBPAT Research Article

1851 J. Chem. Bio. Phy. Sci. Sec. A, May 2014 – July 2014; Vol.4, No.3; 1851-1865.

Enhance C5+ Recovery Predicting and Maximizing The Reformate Production in Naphtha Stabilizer Using HYSYS

Ahmed Raheem Hashim* and Ala’a Abdulrazaq Jassim

* 1, 2Department of Chemical Engineering, Basra University, Basrah, Iraq

Received: 19 April 2014; Revised: 28 April 2014; Accepted: 05 May 2014

Abstract: The naphtha stabilizer in Al-Basrah Refinery was subjected to simulation and optimization to find the optimum operating conditions by using Aspen HYSYS V7.1. A steady state simulation model is utilized to study the behaviour of multi-component non-ideal mixture in the naphtha stabilizer distillation. Optimization results showed that, it’s possible to increase the recovery of C5+ in the reformate from 97 % in actual unit to 99.6 %, also the reformate production increases about (2.383%) from the actual reformate production by changing the design variables and operating conditions. A sensitivity analysis has been used to determine which variable can be used in the optimization tool.

Keywords: Refinery, Stabilizer, Optimization, HYSYS, Sensitivity Analysis.

INTRODUCTION

Naphtha is a generic term applied to refined, partly refined or unrefined petroleum products and liquid products of natural gas which distill below1 240oC. Naphtha contains varying amounts of its constituents paraffins, naphthenes, aromatics and olefins in different proportions in addition to potential isomers of paraffin that exist in naphtha boiling range. Naphtha is used as automotive fuel, engine fuel, and jet-B (naphtha type). Broadly, naphtha is classified as Light Naphtha and Heavy Naphtha. Light naphtha is used as rubber solvent, lacquer diluent, while heavy naphtha finds its application as varnish solvent, dyer’s naphtha, and cleaner’s naphtha2. Naphtha is transformed into reformate by catalytic reforming. This process involves the reconstruction of low-octane hydrocarbons in the naphtha into more valuable high-

Comprehensive… Hashim and Jassim.


octane gasoline components without changing the boiling point range. Naphtha and reformate are complex mixtures of paraffins, naphthenes, and aromatics in the C5-C12 range. In the refineries the crude oil feedstock is a complex multi-component mixture which has to be separated into groups of compounds. The complexities of feeds and products made the development of simulating such processes are important. Simulation is a key step in distillation column optimization problems. Accuracy, speed and convergence properties are three important factors in selection of proper simulation method. Accuracy depends mainly on the distillation column modeling assumptions followed by the termination criteria in simulation steps. Simulation of the chemical processes and unit operations such as distillation column are an integral part of optimization. As plant problems to be simulated increased in complexity, like safety problems, feed fluctuations in composition and flow rates temperature fluctuation according to the heat exchanger load and so on, that’s led to further focus on the simulation programs. One example of these simulators is HYSYS software, which is simulation software developed at Hyprotech Ltd which is being used to compare the result with simulation program and the actual data for the distillation column. The simulation program will be the core of the optimization program; it shall be used to simulate the actual distillation column.

In 2000, Vaughan .M3 simulated a steady state multicomponent multistage distillation column by using HYSYS software. The thermodynamic properties calculated using Soave-Redlich-Kwong equation of state. A good working knowledge of HYSYS was concluded and its operation was realized. An understanding of the benefits, as well as, the idiosyncrasies of HYSYS was also obtained through a lot of trial and error and reading the help menus and manuals. In 2003, Okeke and Akofe4 optimized gasoline production in crude oil distillation and naphtha stabilizer unit, where maximizing the yield of gasoline and its intermediates will directly impact positively on total pool gasoline production. Steady state simulation of the fractionator and the stabilizer was done by using HYSYS. The operating conditions were depended for optimizing the fractionator and the stabilizer. The optimization was done by using Hysys software with using Sequential Quadratic Programming. The effect of the gasoline yield was studied with respect to the return temperature of the reboiler, where found that decreasing reboiler return temperature lead to increase the gasoline yield.

In 2005, Vasconcelos et al.5 simulated the debutanizer column of a fluid catalytic cracking unit by using Hysys software, where the feed was naphtha mixture. The results and the top and bottom temperatures were analyzed to maintain the appropriate values of the LPG and the gasoline Reid Vapor Pressure. The reboiler duty and the reflux flow rate were used as manipulated variables. The multi-variable steady state HYSYS optimizer was used in the optimization, which the net profit is used as the objective function. It was used the SQP optimization method and the RVP of gasoline as the constraint. In 2009, Saghatoleslami et al.6 simulated the distillation column of 31 trays with a condenser, two side strippers and a reboiler in HYSYS software by using Modified Hysim Inside Out method . The distillation was optimized using Hysys software. To obtain this objective, a nonlinear SQP (Sequential Quadratic Program) model has been adopted for the optimization purposes. The objective function was chosen in a way that it would maximize the annual income. In 2010, Begum et al.7 studied the quality of three products of a fractionation column considering different design conditions of the column using natural gas condensate as column feed. The whole simulation study and analysis was done on ASPEN HYSYS V7.1. Design basis for simulation used are: fluid package Peng-Robinson, method of simulation pseudo-component, generation and plate by plate calculation, solver HYSIM Inside-Out, properties generation HYSYS properties. In 2010, Moghadassi et al.8 carried out the simulation of the atmospheric distillation unit of an existing petroleum refinery by using Hysys software. Optimization was performed on the



prepared simulation through the use of a sequential quadratic programming approach (SQP) by using Hysys optimizer. The objective function consisted of energy minimization and production level maximization. The results obtained from optimization were implemented on the real process and it was demonstrated that the suggested changes increased overhead production levels and maintained product quality. In 2010, Nawaz and Jobson9 simulated a demethanizer by using HYSYS 2006.5 software to determine the process power requirement and its sensitivity to selected variables. The process separates ethane and heavier components, from natural gas (NG). The optimization problem solved by the SQP algorithm is carried out in MATLAB using the function ‘fmincon’. The objective function is to minimize the total power requirement for the process. The results after optimization were the operating pressure is 36 bar and the reflux ratio is 0.6. In 2011, Sharifzadeh and Thornhill10 simulated and optimized a sequence of three distillation towers that separate C5, C6, C7+ and heavy-ends products. The first column, depentanizer, has three products. The required computational effort of simulating the process is relatively high because the pyrolysis gasoline must be estimated by 34 components. The modified Peng- Robinson equation of state is applied for thermodynamic calculation. The simulation is performed using Aspen-HYSYS V7.1 and the optimization algorithm is GA toolbox of MATLAB which is linked to Aspen HYSYS. In 2012, Jibril et al.11 simulated an atmospheric crude distillation tower by using HYSYS software, the column consist of 29 trays plus a partial condenser. The simulation results showed that the column needed to be optimized in order to convert more of the atmospheric residue into other premium products like diesel, kerosene and naphtha.

In 2012, Moghadam et al.12 investigated condensate stabilization using two methods of multistage flash vaporization and distillation (fractionation) by using HYSYS simulation software, where a distillation column with a reboiler and without any condenser (non-refluxed column) was used. The scope of this process is to separate very light hydrocarbon gases, methane and ethane in particular, from the heavier hydrocarbon components (C3+). Peng Robinson equation of state was applied for thermodynamic calculations of both gas and liquid phases. It was found that heat duty in multistage flashing is much higher than distillation while liquid recovery is less than the stabilization unit. The results showed that depending on the properties of feed to be stabilized, the fractionation method is preferred over the multistage flashing. In 2012, Raji et al.13 modeled and simulated a single stage distillation which used as main fractionators for vapor effluents from fluid catalytic cracking (FCC) unit and separation into end products recovery such as the flue gases, gasoline (C5+) and bottom oil.

A computer based model was Presented that can simulate and optimize the performance of an existing industrial (FCC) unit in steady state. Computer simulation was carried out using Hysys V3.2, where Peng Robinson was used as equation of state in the modeling. The simulation studies to optimize the unit was performed by manipulating various process variables such as the molar flow rate of recycle bottom oil and reflux ratio, which were subject to constraint Reid Vapor Pressure (RVP) of gasoline. The objective function was to maximize net profit of the desired products. The effect of the reflux ratio was studied on the objective function which is the net profit, where found that as the reflux ratio decreases from 6.76 to 4 the gasoline product flow decreases as well but the net profit percentage showed the net profit change of 0.0027 and 0.0047 respectively. In 2013, Binous and Bellagi14 investigated the fractionation of various industrially relevant hydrocarbon mixtures, where the mixture is light hydrocarbons (ethane, propane, butane, pentane, hexane and heptanes. The overhead product of this column is propane rich and is condensed in the condenser. The simulation has been done by using Mathematica software. Simulation results were compared to those obtained using Aspen-HYSYS software. In calculations with Mathematica and Aspen HYSYS, the Peng-Robinson equation of state has been used.



Simulation of Naphtha Stabilizer: In this simulation, the unstabilized naphtha feed stream containing hydrogen, methane, ethane, Propene, Propane through n-Tetradecane is processed in a distillation column to remove Hydrogen and the lighter hydrocarbons. A vapor (off gas) and liquid (LPG) draw are taken off the overhead receiver, while the reformate off from the bottom yielding a liquid product with a high octane number. A flow sheet for this process is shown below in Figure 1.

Figure 1: Process flow sheet for naphtha stabilizer.

The molar flow rate of the feed stream was set at 390.34 kgmol/hr, with a temperature of 176 oC and pressure of 14 kg/cm2. The distillation column was set up with 30 trays and the feed stream entering at tray 16. In the simulation of naphtha stabilizer the Peng-Robinson Package was selected. The main reason behind this, it is widely used for refinery simulation as it can handle the hypothetical pseudo-components. From properties generation, two databases can be used for ASPEN HYSYS 7.1 – HYSYS properties and ASPEN properties.

Sensitivity Analysis: The objective of this process is to evaluate the influence of main variables on the recovery of C5+ in order to decide which variables should be optimized. The sensitivity analysis is done using Aspen HYSYS as a tool to evaluate different variables over the reformate production, as well as the condenser and reboiler duty in the distillation column. The variables listed below are the ones tested.

1. Number of Stages (NS)

2. Reflux Mole Ratio (RR)

3. Reboiler heating duty

4. Condenser heating duty

5. Feed Stage (FS)

6. Feed Temperature (TF)

7. Feed flow rate (F)



As can be seen in Figure 2. The number of stages and the reflux ratio were analyzed, where more reflux ratio gives the highest C5+ recovery. Moreover, columns with the number of stages greater than 32, give the same C5+ recovery in the reformate, for all the cases studied. This means that increasing the number of stages above 32 would not affect the separation.

Figure 2: Influence of the number of stages and reflux ratio in the C5+ recovery in the reformate.

Figure 3: Influence of the number of stages and reflux ratio on the condenser duty.

0200400600800

100012001400160018002000

26 28 30 32 34 36

Con

dens

er D

uty

in K

W

Number Of Stages

Condenser Duty RR 0.5

RR 1

RR 1.5

RR 2

RR 2.5

RR 3

RR 3.5

RR 4

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

N=26 N=28 N=30 N=32 N=34 N=36

C5+

rec

over

y

Number of Stages

Recovery of C5+ in the Reformate RR 0.5

RR 1

RR 1.5

RR 2

RR 2.5

RR 3

RR 3.5

RR 4



Figure 4: Influence of the number of stages and reflux ratio on the reboiler duty.

On Figures 3 and 4 the energy (condenser and reboiler) duty is analyzed using the same variables. As can be seen the condenser and reboiler duty do not have changes on the recovery of C5+ for different stage number column, while more reflex ratio require more energy consumption, where at higher reflux ratios, greater condenser and reboiler duty needed. In Figures 5 to 7 the feed stage location influence is studied. A similar behavior with the number of stages is observed: columns with more than 30 stages show similar curves.

Figure 5: Influence of the number of stages and the feed stage in C5+ recovery in the reformate.

0

500

1000

1500

2000

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3000

26 28 30 32 34 36

Reb

oile

r D

uty

in K

W

Number of Stages

Reboiler Duty RR 0.5

RR 1

RR 1.5

RR 2

RR 2.5

RR 3

RR 3.5

RR 4

0.970.9710.9720.9730.9740.9750.9760.9770.9780.979

0.98

14 15 16 17 18 19

C5+

reco

very

Feed Stage

C5+ recovery in reformate

N=26

N=28

N=30

N=32

N=34



Figure 6: Influence of the number of stages and the feed stage in the reboiler duty.

Figure 7: Influence of the number of stages and the feed stage in the condenser duty.

In Figure 5. It is shown that if the feed stage location changed there is no much effect on the C5+ recovery in the reformate. A similar behavior is exposed on Figure 6 and Figure 7, where the influence of the feed stage and the number of stages is analyzed over the condenser and reboiler duties. In the Figures 8, 9 and 10 below it shows that there are small effect or almost absent for changing the stages from 26 to 34 by changing the feed temperature on the recovery of C5+ in the reformate, while the cooling duty will increases with increasing the feed temperature and heating duty decreases but still the effect of changing the number of stages absent for the stages from 26 to 34.

2085

2090

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14 15 16 17 18 19

Qr i

n K

W

Feed Stage

Reboiler Heat Duty

N=26

N=28

N=30

N=32

N=34

1350135513601365137013751380138513901395

14 15 16 17 18 19

Qc i

n K

W

Feed Stage

Condenser Heat Duty

N=26

N=28

N=30

N=32

N=34



Figure 8: Influence of the number of stages and the feed temperature on C5+ recovery in the reformate.

Figure 9: Influence of the number of stages and the feed temperature

on Reboiler duty.

Figure 10: Influence of the number of stages and the feed temperature on Condenser duty.

0.970.9710.9720.9730.9740.9750.9760.9770.978

125 135 145 155 165 175 185 195Feed temperature in oC

C5+ recovery

N=26

N=28

N=30

N=32

N=34

0500

1000150020002500300035004000

125 135 145 155 165 175 185 195Feed temperature in oC

Reboiler duty in KW

N=26N=28N=30N=32

1250

1300

1350

1400

1450

1500

125 135 145 155 165 175 185 195Feed Temperature in oC

Condenser Duty in KW

N=26

N=28

N=32

N=34



As shown in the Figures 11to 13 the effect of changing the feed flow by changing the number of stages on the recovery of C5+ in the reformate is slightly changed and can be ignored while in case change the feed flow with changing the number of stages the heating flow will increase dramatically.

Figure 11: Influence of the number of stages and the feed flow on C5+ recovery in the reformate.

Figure 12: Influence of the number of stages and the feed flow on Condenser duty.

0.9736

0.9738

0.974

0.9742

0.9744

0.9746

0.9748

0.975

0.9752

225 250 275 300 325 350 375 400 425Feed flow in kmole/h

C5+ recovery in reformate

N=26

N=28

N=30

N=32

N=34

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1200

1400

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225 250 275 300 325 350 375 400 425Feed Flow in Kmole/h

Condenser Duty in KW

N=26

N=28

N=30

N=32

N=34



Figure 13: Influence of the number of stages and the feed flow on Reboiler duty.

From above study the more variables that can effect on the recovery of C5+ and the reformate production are the reflux ratio, the cooling and the heating duties.

Optimization of A Distillation Column: Distillation is the most common means for separation of chemical components and lies at the heart of petroleum refining; it has no moving parts and scales easily and economical to all production levels. However, distillation is highly energy intensive and can consume a lot of the total energy in a typical chemical or petrochemical process. As a result, optimization of distillation columns is essential for the productivity and profitability of these processes. Moreover, because distillation feeds, product demands and even ambient conditions change over time, optimization in response to these changes is also a key contributor to successful operation.

Optimization in the chemical and petroleum industry is gaining increased interest because of its importance in maintaining product quality and enhancing production levels while improving profit margins. The optimization of the distillation is different from one process to another, due to the difference in the objective function of the distillation column. The objective function was chosen in the present study in a way that it would maximize the productivity of the reformate by considering the variables that have been studied in the sensitivity analysis. Hysys contains a multi-variable steady-state optimizer. Once the flowsheet has been built and a converged solution has been obtained, the optimizer could be utilized to find the operating conditions which minimize (or maximize) an objective function.

The following terminology is used in describing the HYSYS optimizer:

Primary variables which are imported from the flowsheet whose values are manipulated in order to minimize (or maximize) the objective function.

Objective function is the function which should be minimized or maximized.

Constraint functions are defined in three ways. Inequality and equality constraint functions which may be defined in the optimizer spreadsheet.

0

500

1000

1500

2000

2500

225 250 275 300 325 350 375 400 425Feed Flow in kmole/h

Reboiler Duty in KW

N=26

N=28

N=30

N=32

N=34



0100200300400500600700

1 4 7 10 13 16 19 22 25 28 31

Mol

ar F

low

km

ol/h

Stage number

Molar Flow Profile

Liquid

Vapor

b

In order to maximize the productivity and the recovery of C5+ to the maximum , the objective function should be a function of the top, bottom temperature and reflux ratio parameters. To obtain this objective, it was essential to manipulate the variables and allow some of the variables to be adjusted in way such as to achieve the necessary limits. Furthermore, to enhance the results and to accomplish the required limits for a particular variable, it was essential to apply some constraints on the objective function. The constraints of the manipulated variables used for the optimizations are as shown in Table 1.

Table-1: The constraints of the manipulated variables of the process.

Parameter Low bound High bound Reflux ratio 1 4 Condenser temperature (oC) 35 55 Reboiler temperature (oC) 220 230

RESULTS AND DISCUSSION

The naphtha stabilizer distillation column used in the production of reformate has been simulated using Aspen HYSYS 7.1 simulator. There are many variables should be determined first before the simulation of this process such as feed temperature and flow rate , condenser temperature, reboiler temperature, column pressure and molar reflux. HYSYS V7.1 optimizer was used to choose the optimum manipulated variables (reflux ratio, reboiler temperature and condenser temperature).

In Figure 14a. The temperature profile shows how the temperature changes in the column. In the first stage where the temperature is about 45 °C, where the lowest temperature of the condenser is the temperature where the vapors are partially condensed and that temperature is the bubble point temperature. There is a temperature augmentation on the second stage, because of the reflux introduction to the column. The temperatures continue increasing as the stages go down till the feed stage where the feed is entering in high temperature in 176 °C. Near to the reboiler the temperature increases dramatically because of the reboiler’s heat duty where high temperature needed to vaporize all the components in the reboiler and that temperature is the dew point temperature.

Figure 14: Distillation Column Temperature and Flow rate Profiles.

0

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100

150

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250

1 4 7 10 13 16 19 22 25 28 31

Tem

pera

ture

in o C

Stage Number

Temperature profile

Temperature profile

a



In Figure 14b. The molar liquid and vapour flows are exposed. As feed is introduced in the liquid phase, there is an increase on the liquid molar flow on stage 16; a decrease of the flow rate is noticed due to the high temperature which reigns at the bottom of columns. The vapour flow tends to be constant in the column, except on stages 2 and 16, this may happen because the reflux temperature is low and condensates some of heavy components in the vapor phase, while in the stage 16 the feed temperature is very high and vaporizes some of the heavy components and traces of light gases in the liquid phase. For trays( >17) the vapour slowly increases that is due to the more volatile components that are existed in the feed as well as the amount of the vapor which result in evaporating the liquid that comes down to the reboiler.

Optimization Results: The initial focus of the optimization work was to arrive at operational changes that could be implemented without significant plant interruptions or modification. Many different variables were methodically tested to see the effects on throughput. A theoretical approach to optimization was also adopted, to find ways to increase throughput without sacrificing product quality. The HYSYS software was employed to see if the changes resulted in improvements. A large number of solutions were found at the end of this process.

Optimization of the naphtha stabilizer distillation column was achieved by using the manipulated variables (temperature of condenser, temperature of reboiler and the reflux ratio) , while the objective function was the maximization of the production of the reformate in the bottom product of the column. The key for good and reliable optimization is the simulating model, and specifications on the variables and the objective function. Aspen HYSYS provide multi-objectives in the same optimization runs. In Aspen HYSYS optimization the decision variables must be defined and the step change in each manipulated variables. At each iteration Aspen HYSYS assumed a value for the decision variables (temperature of condenser, temperature of reboiler and the reflux ratio) and send the assumption to the simulating model in the HYSYS to calculate Off gas rate, LPG rate and the reformate production. The estimated result from simulating model will be used in optimization to calculate and evaluate the reformate production rate.

Table-2: Aspen HYSYS optimizer iterations results.

Iter.NO Reflux Ratio TC (°C) TR (°C) Objective function

1 2.5 45 223 325.36

2 2.5 46 220 331.20

3 2.5 46 220 331.20

4 3.05 45.44 220 332.15

5 3.05 45.44 220 332.15

6 3.05 45.44 220 332.15

7 3.03 46.79 220 332.11

8 3.93 46.78 220 332.70

9* 4 46.77 220 332.71



Table-3: Aspen HYSYS optimization results.

Iter.NO Off gas

(kmole/h)

LPG

(kmole/h)

Reformate

(kmole/h)

Qc

(KW)

Qr

(KW) Recovery of C5+

1 31.99 33.00 325.36 1189.89 1914.56 0.97346

2 37.43 21.71 331.20 981.07 1705.82 0.98567

3 37.43 21.71 331.20 981.07 1705.82 0.98567

4 39.27 18.93 332.15 1093.93 1836.68 0.99188

5 39.27 18.93 332.15 1093.93 1836.68 0.99188

6 39.27 18.93 332.15 1093.93 1836.68 0.99188

7 40.65 17.57 332.11 1079.38 1829.84 0.99166

8 42.56 15.09 332.70 1306.59 2071.93 0.99557

9* 42.61 15.01 332.71 1326.34 2092.11 0.99569

As Table 2 shows the objective function is increasing at each iteration and the maximum reformate production was found at iteration (9) at which the production rate equal to (332.71) kmole/h. The percentage increasing in the production based on the production at actual operating conditions (324.78kmole/h) is equal to (2.388%). The local optimums for the naphtha stabilizer from Table 3 are:

The maximum recovery of C5+ in the reformate regarding for other decision variables can be found at iteration (9), where the percentage of the increasing from the normal operation (0.9747) is (2.09%).

The maximum reformate production regarding for other decision variables can be found at iteration (9), the reformate production increases about (2.383%) from the actual reformate production (324.78) kmole/h.

The Off gas production from iteration (9) was increased (26.8%) from the actual off gas production which is (31.19) kmole/h.

The LPG production from iteration (9) was decreased (56.32%) from the actual LPG production which is (34.37) kmole/h.

The energy consumption for the condenser that can be found from iteration (9) is (1326.34KW), the percent of increasing in the energy consumption was (21.23%) from the actual energy consumption (1044.77 KW).

The energy consumption for the reboiler that can be found from iteration (9) is (2092.11) KW, the percent of increasing in the energy consumption at iteration (8) is (15.9%) from the actual energy consumption (1759.19 KW).

The relation between the numbers of iterations with the reformate production is shown in Figure 15. The reformate rate increases and decreases in different iteration.



Figure 15: Reformate production with the Iteration Number.

So, the previous results show, the maximum reformate production can be compared with normal production at different operation conditions as listed in the Table 4.

Table-4: Comparison between operating and optimum values.

S/N Operation condition Normal operation Optimum values 1 Reflux ratio 2.05 4 2 Condenser temperature (°C) 45 46.77 3 Reboiler temperature (°C) 223 220 4 Reformate production(kmole/h) 324.78 332.71

CONCLUSION

Naphtha stabilizer in Al-Basra refinery is successfully simulated using HYSYS software version 7.1. A powerful equation of state Peng-Robinson package can be applied to predict the required pure component properties, such as the fugacity, K-values, compressibility factor and enthalpies for the different phases at given conditions (temperature, pressure, composition). A sensitivity analysis by using HYSYS was used to estimate the effect of the process parameters on the reformate product quantity, the recovery of C5+, top products and energy duties of the naphtha stabilizer. The optimization strategy is incorporated to solve an optimization problem formulating with an objective function: to maximize the reformate product for a given decision variable, reboiler temperature, and condenser temperature and reflux ratio. 99.6% purity of the reformate product can be produced by an optimal reformate production. With changing the operation condition of reflux ratio of 4, temperature of the reboiler and the condenser of 220 oC and 46.77 oC respectively. The percentage of the increasing in the production based on the production at actual operating conditions (324.78kmole/h) is equal to (2.388%). The optimization method is easy and efficient to implement using HYSYS optimizer tool. The profile for the flow rates and temperature were obtained. The temperature profile ranged from 45°C at the condenser to 223°C at the reboiler.

320.00

322.00

324.00

326.00

328.00

330.00

332.00

334.00

1 2 3 4 5 6 7 8 9Ref

orm

ate

prod

uctio

n (k

gmol

e/h)

Iteration number

optimum



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* Corresponding author: Ahmed Raheem Hashim; Department of Chemical Engineering, Basra University, Basrah, Iraq