Journal of Environmental Science, Computer Science and Engineering & Technology

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1 JECET; March 214-May 214; Vol.3.No.2, E-ISSN: X Research Article Journal of Environmental Science, Computer Science and Engineering & Technology An International Peer Review E-3 Journal of Sciences and Technology Available online at Section C:Engineering & Technology Improving the Performance of the Naphtha Stabilizer in the Refinery Plant *Ahmed Raheem Hashim and Ala a Abdulrazaq Jassim *Department of Chemical Engineering, Basra University Basrah, Iraq Department of Chemical Engineering, Basra University Basrah, Iraq Received: 18 March 214; Revised: 4 April 214; Accepted: 12 April 214 Abstract: In this study, theoretical investigations have been made concerning naphtha stabilizer column at the Basra refinery. The main objective of the naphtha stabilizer distillation is to separate the light ends and LPG product from the reformate product. The naphtha stabilizer was subjected to simulation and optimization to find the optimum operating conditions by using Aspen HYSYS V7.1 and MATLAB (R28a) program. A steady state simulation model is utilized to study the behavior of multi-component nonideal mixture in the naphtha stabilizer distillation. A procedure has been introduced to build up a mathematical model and simulation for a naphtha stabilizer based on the MESH equations. The set of algebraic equations are solved by using tridiagonal matrix method. The simulation model was subjected to the validity test by comparing the simulation results with the HYSYS simulation results. The validity test shows a great similarity and reliability for simulation model where it was highly accurate and very close to HYSYS simulation results, where the ABS deviation was(<.14). Optimization results shows that, it s possible to increase the recovery of C 5 + 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. HYSYS process modeling software was used to analyze the effect of reflux ratio, feed temperature, column pressure, column efficiency, condenser cooling duty and reboiler heating duty on the recovery of C 5 + in the reformate and the reformate production naphtha stabilizer distillation column. Keywords: Refinery Plant, Naphtha stabilizer, Modeling, Simulation, Optimization, HYSYS. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

2 INTRODUCTION Naphtha is transformed into reformate by catalytic reforming. This process involves the reconstruction of low-octane hydrocarbons in the naphtha into more valuable high-octane gasoline components without changing the boiling point range. Naphtha and reformate are complex mixtures of paraffins, naphthenes, and aromatics in the C 5 C 12 range. The purpose of catalytic reforming is primarily to increase the octane number of the naphtha feedstock to a level that makes the reformate product suitable as a gasoline blend stock. The octane number represents the ability of a gasoline to resist knocking during combustion of the air gasoline mixture in the engine cylinder. Stabilizing the reformates in the naphtha stabilizer lead to increase RON, and this is done by eliminating the impurities in the reformate (impurities like Hydrogen, Sulfur, light gases and LPG). A stabilizer is essentially a distillation column intended to remove what is normally a relatively small amount of "light ends" from a product. The general objective of distillation is the separation of substances that have different vapor pressures at any given temperature. The word distillation as used here refers to the physical separation of a mixture into two or more fractions that have different boiling points 1. Separation technology plays an important role in the process engineering. The typical separation techniques are such as distillation, absorption, extraction and so on. On account of the complexities of feeds, products and by-products as well as various additives, multi-component multi-stage separation is especially important and widely used, so the development of the proper algorithm for simulating such processes is critically important 2. 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 such complex process has been paid much attention and several procedures have been developed since the last decades. The equations that describe the multi-component multi-stage separation process operating at steady state, while appearing to be simple, are in fact highly nonlinear and interdependent. Hence, the solution of these equations is intrinsically iterative, complex and hard to convergence 3. Amundson and Pontinen 4 for the first time proposed a set of nonlinear simultaneous equations for the multi-component and multi-stage separation process resolved by the matrix method, in which the S (Summation) equations and the H (Enthalpy) equations are solved separately for obtaining the tray temperature and the flow rate. It is effective for simulating the distillation separation, but not suitable for absorption or extraction processes because of wide boiling feed. Wang and Henke 5 modified Amundson's algorithm and proposed the tri-diagonal matrix method, which eliminated the inversion of huge matrices and reduced calculation time. Kumar et al 6 developed steady state, multicomponent distillation model for fractionation of crude oil based on equilibrium stage relations. The model equations for an ordinary equilibrium stage of a simple distillation column, commonly known as Mass balance, Equilibrium, Summation and Enthalpy balance (MESH) equations. The set of nonlinear algebraic equations is solved using the Newton Raphson method. Computation of equilibrium constants and enthalpies of different streams as functions of temperature and composition are based on the cubic equation of state proposed by Peng Robinson. The results obtained from this simulation was compared and validated with Aspen Plus simulation software. It is found that the proposed model have good match with the data published in literature as well as given by the commercial simulator. Bhutani et al. 7 subjected a separation distillation column of styrene production plant to the modeling and simulation. Fortran 9 program used for simulation the distillation column. The shortcut method, commonly known as Fenske- Underwood-Gilliland (FUG) method is employed for calculation of actual number of theoretical stages, reflux ratio, condenser and reboiler duty in F9.The simulation results obtained using Hysys JECET; March 214-May 214; Vol.3.No.2, Sec. C;

3 and F9 simulation program are very close and the minor differences are due to differences in physical properties. The predicted results are also comparable to the industrial data. Shan et al. 8 presented a flexible model and a robust algorithm for simulation of multi-stage multi-component separation processes of a twenty-component paraffin mixture from n-c 3 H 8 to n-c 2 H 42. An efficient algorithm was proposed to solve the approximate tri-diagonal matrix derived from the MESH equations. The target variables for the multicomponent distillation column was total flow rate, reflux ratio, the concentration of key components, the draw amount of the key components and the liquid flowrate of the column inter-stage, while the independent variables was the duty of condenser, the duty of reboiler, the vapor draw flowrate of the main column, the liquid draw flowrate of the main column, the draw flowrate of the side stripper column, the recycle flowrate of the inter-stage, the duty of a stage, the side draw flowrate of the main column. The simulation showed that the results are comparable with that of the Inside Out algorithm. The proposed algorithm shows good stability and fast convergence with different initial values. Ramesh et al. 9 modeled the continuous distillation column by using two different models, equilibrium model and rate-based model. A comparison between the two approaches was made, where concluded that the inclusion of liquid and vapor phase non-ideality calculation has increased the accuracy of the equilibrium model. The steady-state and dynamic simulation results have shown that the deviation between these two models is less than 4%. The distillation column was modeled based on MESH equations which are solved to give product distributions, flow rates, temperatures, and so on. UNIFAC method has been chosen for calculating activity coefficient. The virial equation was used to calculate the fugacity coefficients. Mustapha et al 1 modeled a high pressure distillation column by using MESH equations, where the column is designed to separate the load made up of (methane, ethane, propane, iso-butane,n-butane, isopentane, n-pentane) into a distillate rich in propane and a residue fairly rich in n-butane and isobutane. The simulation of the distillation tower was done by using Fortran code. The rigorous method was used based on the bubble point calculation without definition of the light and heavier keys in the column, where the Thomas numerical method used to solve the matrix. The model was validated by comparing the results of simulation with those obtained experimentally, the results obtained showed an extremely low discrepancy (<2%). Behera 11 modeled a steady state distillation column by using equilibrium stage model using MESH equations, in which the equations solved by using Newton- Raphson method. The activity coefficient calculated by using UNIFAC group contribution method. The average percentage of error in determining the activity coefficient by UNIFAC group contribution method varies from 9-15% with respect to the experimental values. So with this much percentage of error the vapor-liquid equilibrium data generated by UNIFAC can be successfully implemented in designing separation processes. Mosorinac and Stevanovic 12 presented an integrated modelling method for continuous multistage multicomponent distillation column using MESH equations. The required specification for the modeling and simulation are: 1) number of plates in each section of the column, 2) quantity, composition and thermal condition of the feed, 3) column pressure, 4) type of overhead condenser, total or partial, 5) reflux ratio. Computation procedure which was used the constant molar overflow is assumed and the material balance and equilibrium relationship equations are solved stage by stage starting at the top or bottom of the column. Nasri and Binous 13 used Mathematica, the famous computer algebra to perform steady-state and dynamic simulations of a multi-component distillation column. Soave Redlich Kwong equation of state (SRK EOS) was used in order to calculate the vapor-liquid equilibrium (VLE) relationships and to compute the vapor- and liquid-phase enthalpies. Rigorous modeling was performed; where both mass and energy balance (MESH) equations are solved. All of steady-state results of the column simulations were agree very well with those obtained using HYSYS. Akpa and Umuze 14 developed a steady state models for a multi-component crude distillation column from the MESH equations, where the equilibrium based JECET; March 214-May 214; Vol.3.No.2, Sec. C;

4 models are developed for the distillation with an improved computational algorithm. In these models, the non-linear equation sets are solved by using Newton-Raphson method. These equations were transformed into a matrix and solve by matrix inversion using the Matlab solver. The model results of the concentrations and temperatures for five components compared favorably with output values from the distillation unit with maximum deviations of 8.33% and 6.62% respectively. In this work the equilibrium stage model was used and the set of non-linear equations governing the column (MESH) equations used for the mathematical modeling of the multicomponent distillation to model the naphtha stabilizer from the catalytic reforming unit in the Basra refinery and the solution procedure is the tridiagonal matrix method. The algorithm used for the model solution is Wang-Henke algorithm, the program used to solve the set of equations is MATLAB software. Commercial Simulation Software (HYSYS): HYSYS is an advance process simulation environment for the process industries developed by Hyprotech. HYSYS is one of the software which is widely accepted and used for refinery simulation. It performs the oil distillation calculation through detail plate by plate calculation. It is a market leading process modeling tool for conceptual design, optimization, business planning, asset management and performance monitoring for oil and gas processing, petroleum refining, and air separation industries. Process Description: Naphtha stabilizer column located in catalytic reforming unit in Basra refinery/iraq, the unstabilized naphtha consists of the fuel gas, LPG and naphtha reformate after being reconstructed in the catalytic reformers is pumped to naphtha stabilizer at 176 C o and 14 Kg/cm 2 after preheating in the stabilizer feed/bottom exchanger as shown below in the figure 1. Figure 1: Schematic diagram for a catalytic reformer unit. Stabilizer column has 3 trays with feed entering on the 16 th tray. Fuel gas, fuel oil and H.P steam are utilized as reboiling medium for the stabilizer. Stabilizer reboiler is a furnace reboiler. The overhead products are partially condensed in the stabilizer overhead condenser. The LPG and Off Gas are cooled down to 45 C o before routing to stabilizer reflux drum. Stabilizer reflux is pumped by stabilizer reflux pumps. Reflux is fed on tray no. 1 of stabilizer column. Stabilized naphtha product from stabilizer bottom exits with 223 C o and exchanges heat with the feed in the heat exchanger before being routed under its own pressure to gasoline pool. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

5 Naphtha Stabilizer Modeling: Mathematical model of any process is a set of equations including the necessary input data to solve the equations, whose solution gives a specified data representative of the process to a corresponding set input that allows us to predict the behavior of chemical process system 15. 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 the proper algorithm for simulating such processes is important. Modeling methods for distillation columns are divided into three methods according to the assumptions made in the development of the model as: approximate, equilibrium and rate based methods. The equilibrium based methods are the most commonly used method and is based on the equilibrium assumption between leaving vapor and liquid flows for each stage 9 as shown below in the figure 2. A complete separation process of naphtha stabilizer is modeled as a sequence of N of these equilibrium stages. In this method the model equations of the column called MESH (Material balance equation, Equilibrium phase equations, Summation equations and Heat balance equations) are solved. The set of non-linear model (MESH) equations that describe multi-component separation processes are solved iteratively by using matrix method. Figure 2: Schematic diagram of an equilibrium stage. Model assumptions: The complexity of Model depends on the types of assumption considered the mathematical and computational difficulties occurring in solving the model equations (computing time, store location) are reasons for the model simplifications required in the majority of cases. It also leads to appropriate type of modeling techniques. Together with the assumptions, the specific column simulated will be assumed to have the conditions and configurations listed in the following: 1. Steady state. 2. Liquid and vapour phases are perfectly mixed for the equilibrium stages including the condenser and reboiler. 3. Partial condensation in the condenser and partial evaporation in the reboiler. 4. Liquid and vapors phases leave each stage at thermal equilibrium (same temperature and pressure). 5. Neglect vapor and liquid holdup in all stages including the condenser and reboiler. 6. Physical properties can be determined by using different thermodynamic packages. 7. No heat is lost from any tray (adiabatic). 8. Column pressure is constant. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

6 9. Stage efficiency is 1%. 1. Heat of mixing is zero. Model Equations: The equations that model equilibrium stages are termed the MESH equations, MESH being an acronym referring to the different types of equations that form the mathematical model. The M equations are the Material balance equations: Total material balance is: L +V +F L V U W = (1) Mass balance for component i: L x, +V y, +F z, (L +U )x, (V +W )y, = (2) The E equations are the phase equilibrium relations for each component: E, =y, K, x, = (3) Where K, is the phase equilibrium ratio for species i on stage j. The estimation of the K-values from fugacity and activity coefficients. The S equations are summation equations: (S ) = y, 1.= (4) (S ) = x, 1.= (5) The liquid flow rate at each stage can be calculated as following: L =V + (F U W ) V (6) And the H equations are the heat balance equations: L H +V H +F H L +U H V +W H Q = (7) Where H, H H are the feed, vapor and liquid enthalpy respectively at the tray j. y i.j and x i,j are the mole fractions of vapor and liquid streams. Solution procedure: To solve the above equations a tridiagonal matrix algorithm used, which results from the equation (2). Proceeding by elimination of L and y,, by substituting equation (6) and equation (3) into equation (2). Thus, equations for calculating y and L are partitioned from the other equations. The result for each component and each stage is as follows where the i subscripts have been deleted from the B, C and D terms. The mathematical model equations for a column of N trays can be reduced according to Thomas algorithm to: A x, +B x, +C x, =D (8) Where: JECET; March 214-May 214; Vol.3.No.2, Sec. C;

7 A =V + (F W U ) V 2 j Ν (9) B =-V + (F W U ) V +U +(V +W )K, 2 j Ν (1) C =V K, 1 j Ν 1 (11) D = F z, 1 j Ν (12) With m refers to number of the trays. Equation (8) can be solved for N x C equations, where the unknowns are the compositionsx,. For each component, the Thomas numerical method can be used to solve the matrix form Equation (13) in order to determine the composition profiles as function of the tray position ,,..., , = ,..,, (13) [ABC] [, ] = [ ] for 1 i C (14) Constants B and C for each component depend only on tear variables T and V provided that K-values are composition independent. If not, compositions from the previous iteration may be used to estimate the K-values. A new set of V tear variables is computed by applying the following modified heat balance equations, which is obtained by substitution of equation (6) in to equation (7) twice to eliminate L and L. The equations can be written after rearrangement as follows: α V +β V =γ (15) Where: α =H H (16) β =H H (17) γ = (F W U ) V H H +F H H +W H H +Q (18) Q = F H U H W H Q V H L H (19) Q =V H +F H (L U )H (V +W )H (2) JECET; March 214-May 214; Vol.3.No.2, Sec. C;

8 The vapor and liquid enthalpies are evaluated at the stage temperatures last computed rather than at those used to initiate the iteration. Written in diagonal matrix form (15) applied over stages 2 to N-1 is: β α β V V α β V. α β V α β V α β V = γ α V γ γ γ γ γ (21) Matrix equation (21) is readily solved one equation at a time by starting at the top where V known and working down recursively is. Thus V = (22) And so on. Corresponding liquid flow rates are obtained from equation (6). K-values calculation: To solve equations by the Thomas method, Ki,j values are required. When they are composition dependent, initial assumptions for all x, and y, values are needed. K-values are computed from: K = (23) Where φ, φ vapor and liquid fugacity coefficients which can be calculated by using equation of state 16. φ =exp (Z 1). ln(z B). ln (24) Constants A, B, A and B depend on T, P and ω. for pure species A = a (25) B =.8664 (26) ai=1+mi(1 T. ) (27) mi= ωi+.176 ωi (28) Where ω acentric factor, Z compressibility factor, P r reduced pressure and T r reduced temperature. For vapour mixtures: A= y y A (29) A =A A. (3) JECET; March 214-May 214; Vol.3.No.2, Sec. C;

9 B= y B (31) Enthalpy Calculation: The ideal vapor enthalpy can be calculated: H = (y H ) (32) Liquid enthalpy for ideal solution is obtained from the following equation 16: H = x H λ (33) Where λ is molal heat of vaporization, which can be obtained from 16: λ= ( ) (34) Where T in F (T(F o )=1.8T(C o )+32) and T is absolute temperature, A 2 and A 3 are Antoine equation constants. For nonideal behavior: For vapor phase 16: H = (y H )+RT Z 1 ln1+ (35) For liquid phase 16 : H = (x H )+RT Z 1 ln1+ (36) Where H is function of temperature and can be calculated from H = C dt= a +a T+a T +a T dt, where a 1,a 2,a 3,a 4 are constants. R is the universal gas constant (8.314 Kj/kmol.K) Validation of the column model: In order to analyse the impact of the various operating parameters on the Naphtha stabilizer, the model was subjected first to validity test to verify the model validity and its reliability to demonstrate the actual naphtha stabilizer tower. The column is designed to separate the load made up of H 2 / C 1 / C 2 / and traces of C 3 and C 4 as Off gas and iso-c 4 /n-c 4 /iso-c 5 /n-c 5 as LPG distillate and a residue (Reformate) rich in C 5 +. Comparing the results obtained from MATLAB program with that obtained from HYSYS simulator, the results obtained show an extremely low absolute deviation (<.14). Optimization of a Distillation Column with HYSYS: At the end of model development and simulation, the model was thereafter optimized using the optimization section of model analysis tools of Aspen HYSYS by incorporating an optimizer into the flowsheet as depicted in figure 3. The manipulated variables of the optimization were the reflux ratio and the reboiler and the condenser temperatures of the column while the objective function was the maximization of the reformate production in the bottom product of the column. The ranges of the manipulated variables used for the optimizations are as shown in Table 1. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

10 Figure 3: Aspen HYSYS distillation optimization flowsheet Table-1: The limits of the manipulated variables of the process. Parameter Low bound High bound Reflux ratio 1 4 Condenser temperature (C o ) Reboiler temperature (C o ) 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. In this work, for the optimization purposes an objective function was adopted in such a way that it would maximize the productivity of the reformate. RESULTS AND DISCUSSION Simulation Results: The model equations utilized for the operation type of a distillation column were applied to a multicomponent mixture, which were referred to as components shown in the appendix(3). The model equations were solved using MATLAB program. An algorithm for solving the operation type of multicomponent distillation problem is shown in appendix (1). The process flow sheet used in the simulation of the naphtha stabilizer is shown below in Figure 4. The input data are shown in appendix (2). In the application of the proposed method, a feed of kg mol/h containing the composition as shown in appendix (3) is to be fractionated in a distillation column(naphtha stabilizer) operating at a pressure of 14 kg/cm2,feed stage is 16, the distillate off gas rate is kg mol/h, while LPG rate is kg mole/h and the reflux ratio is 2.5. The total number of theoretical stages in the column is 3. The parameters used in obtaining simulated results are as follows: the top and bottom temperatures and the reflux ratio. In Figure 5 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 vapours are partially condensed and that temperature is the bubble point temperature. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

11 Appendix 1: Algorithm for the computational procedure for Distillation Column. Appendix-2: Normal operating conditions for the naphtha stabilizer feed stock. No. Discretion Value Units 1 Reflux ration Reboiler heat duty KW 3 Condenser heat duty KW 4 Feed temperature 176 Cº 5 Feed pressure 14 Kg/cm 2 6 Feed flow rate Kmole/hr JECET; March 214-May 214; Vol.3.No.2, Sec. C;

12 Appendix-3: Feed composition for the naphtha stabilizer feed stock. S/N Component composition% S/N Component composition% 1 hydrogen Methyl-t-pentene methane Methylpentane ethane Methyl-2-pentene ethylene Methylpentane Propene Hexene Propane c-hexene i-butane t-hexene Butene c-hexene Isobutene t-hexene n-butane n-hexane t-butene Methyl-c-pentene c-butene ,2-Dimethylpentane Methylbutene Methylcyclopentane i-pentane(2methylbutane) ,4-Dimethylpentane Pentene ,2,3-Trimethylbutane Methylbutene Benzene n-pentane ,3-Dimethylpentane t-pentene Cyclohexane c-pentene Methylhexane Methylbutene ,3-Dimethylpentane ,2-Dimethylbutane ,1-Dimethylcyclopentane Cyclopentene Methylhexane Cyclopentane Ethylpentane ,3-Dimethylbutane t,2-Dimethylcyclopentane ,2,4-Trimethylpentane Ethylcyclohexane n-heptane Ethylbenzene c,2-Dimethylcyclopentane o-xylene Methylcyclohexane m-xylene Ethylcyclopentane p-xylene ,5-Dimethylhexane Nonene ,4-Dimethylhexane n-nonane ,3-Dimethylhexane.79 8 i-propylbenzene ,3,4-Trimethylpentane n-propylbenzene Toluene Methyl-3-ethylbenzene ,3-Dimethylhexane Methyl-4-ethylbenzene Methyl-3-ethylpentane ,3,5-Trimethylbenzene Methylheptane Methyl-2-ethylbenzene Methylheptane ,2,4-Trimethylbenzene Methyl-3-ethylpentane sec-butylbenzene ,4-Dimethylhexane n-decane c,3-Dimethylcyclohexane ,2,3-Trimethylbenzene Methylheptane Methyl-3-i-propylbenzene Ethylhexane n-undecane t,4-Dimethylcyclohexane ,2,4,5-Tetramethylbenzene ,1-Dimethylcyclohexane Methylnaphthalene.9 7 2,2,5-Trimethylhexane Methylnaphthalene n-octane n-tetradecane c,2-Dimethylcyclohexane.247 JECET; March 214-May 214; Vol.3.No.2, Sec. C;

13 Figure 4: Process flowsheet for the Naphtha stabilizer. Temperature in C Temperature profile Stage Number Temperature profile Figure 5: Distillation Column Temperature Profile. Molar Flow kmol/h Molar Flow Profile Liquid Vapor Stage number Figure 6: Column Molar Flow Profile. There is a temperature augmentation on the second stage, because of the reflux introduction to the column. The temperature continue increasing as the stages go down till the feed stage where the feed is entering in high temperature in 176 C o. 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. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

14 In Figure 6 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 vapour 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 vapours which result in evaporating the liquid that comes down to the reboiler. The Effect of Decision Variables: There are several of decision variables that can effect on the operation of naphtha stabilizer specifically on the recovery of C 5 + in the reformate and on the heating and cooling duty. Reflux ratio effect: Figure 7.(a,b,c,d) below shows the effect of various values of reflux ratio on the recovery of C 5 + in the reformate, reboiler and condenser duty, reformate flow rate and off gas flow rate. As seen from figure (7) below after reflux ratio of 3 there are no effect on the recovery of C5+, reformate and off gas flow rates, while the condenser and reboiler duty keep rising up with increasing in reflux ratio. C5+ recovery in reformate a 3 25 b C5+ recovery C5+ recovery in reformate Heating Duty Condenser Duty (KW) Reboiler Duty (KW) Reflux Ratio Reflux Ratio Reformate flow rate in Kmole/h c Off gas flow rate in kmole/h d Reflux ratio Reformate flow rate in Kmole/h Reflux ratio Off gas flow rate in kmole/h Figure 7: Influence of the reflux ratio on the various parameters. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

15 Condenser heat duty effect: The effect of the condenser cooling duty on the recovery of C 5 +, reboiler duty, off gas and reformate flow rates for naphtha stabilizer is shown in the figure 8(a,b,c,d) below. From the figure (8a) below it is being noticed that increasing in the condenser duty will increase the amount of the off gas product, the reformate production slightly increases with the increasing in condenser duty as shown in figure (8b). The increasing in the condenser duty increases the reboiler duty and vice versa as shown in figure (8c). Finally the effect of the condenser duty on the recovery of C 5 + in the reformate is shown in figure (8d), which is as expected it will increases when the condenser duty increases Off gas rate in Kmole/h a Off gas rate in Kmole/h Reformate rate in Kmole/h b Reformate rate in Kmole/h Condenser duty in KW Condenser duty in KW Reboiler duty in KW c Reboiler duty in KW C5+ recovery in reformate d C5+ recovery in reformate Condenser duty in KW Condenser duty in KW Figure 8: Influence of the condenser duty on the various parameters. Reboiler heat duty effect: The effect of reboiler duty on the reformate production and the condenser duty of the naphtha stabilizer was studied as shown in figures 9(a,b,c,d) below. In Figure (9a) the reformate production decreases with the increasing of the reboiler duty. With the temperature increasing in the bottom of the tower, the heat transfer to the distillation tower increases which made the liquid boiled up and increasing in vapours over the liquid inside the tower where in this case the recovery of the C 5 + in the reformate decreases as well due to the huge amount of the heavy component which in results will be evaporated from the bottom as shown in Figure (9b). Reboiler duty has a major effect on the top of the tower, where the condenser duty increases JECET; March 214-May 214; Vol.3.No.2, Sec. C;

16 proportionally with reboiler duty as shown in figure (9c) due to the vapours increasing. Increasing the reboiler duty led to the increasing in the vapours evaporated which is rich in the C 5 + components within the distillation, that s lead to the increasing in the production of LPG product due to the heavy components increasing while slightly decreasing in the Off gas product, as shown in the figure(9d) below Reformate Flow rate in Kmole/h a Reformate Flow rate in Kmole/h C5+ recovery in the reformate b C5+ recovery in the reformate Reboiler Duty in MW Reboiler Duty in MW Condenser Duty in MW c Condenser Duty in KW d Off gas rate in Kmole/h LPG rate Kmole/h Reboiler Duty in MW Reboiler Duty in MW Figure 9: Influence of the reboiler duty on the various parameters. Feed temperature effect: Feed temperature contributes to the overall column temperature and tray stage temperatures. Figures 1 (a, b) depicts the response of C 5 + recovery in the reformate, reboiler and condenser heat duty to feed temperatures respectively. This variable is not dramatically influent in the reformate C 5 + recovery for temperatures below the operation, while it decreases with increasing in feed temperature above the normal operation temperature as shown in Figure (1a). As shown in Figure (1b) below if the feed is fed at a higher temperature, the reboiler duty decreases significantly, while the condenser duty slightly increases. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

17 If the feed is fed at low temperatures, more energy is needed in the reboiler to evaporate part of the liquid in the column. The condenser duty is higher when the feed is introduced to the column at high temperatures C5+ recovery C5+ recovery in reformate a C5+ recovery in reformate Heating Duty b Condenser Duty (KW) Reboiler Duty (KW) Feed Temperature in C Feed Temperature in C Figure 1: Influence of the feed temperature on the various parameters. Feed flow rate effect: The flow rate was varied from 225 to 425 kmole/hr. The curve relating the flow rate, condenser and reboiler duty indicated an increase of both the dependent and independent variables. The increase in the feed stream gives more work (load) to the reboiler and the condenser duty, as shown in Figure (11a) below. With the increase in flow rate; reboiler and condenser duty increase but there is no effect on the product purity as seen in Figure (11b) below. Heating flow in KW a Condenser duty (KW) Reboiler duty (KW) C5+ recovery in reformate b C5+ recovery in reformate Feed flow in kmole/h Feed flow in kmole/h Figure 11: influence of the feed flow rate on the various parameters. JECET; March 214-May 214; Vol.3.No.2, Sec. C;

18 Operation pressure effect: Pressure is a critical factor in normal distillation. As the pressure is increased, the temperatures throughout the column will also increase and vice versa. Maintaining proper pressure can play a crucial role in the stable operation of a distillation column. Pressure fluctuations make control more difficult and reduce unit performance. Pressure variations alter column vapours loads and temperature profiles, as shown in figures(12a,12b) below Vapor profile V profile at P=1 Stage number a V profile at P=11 V profile at P=12 V profile at P=13 V profile at P=14 V profile at P=15 V profile at P=16 V=(kmole/h) P= (kg/cm2) Temperature profile Stage number b T profile at P=1 T profile at P=11 T profile at P=12 T profile at P=13 T profile at P=14 T profile at P=15 T= (C ) P=(kg/cm2) Figure 12: Influence of the operating pressure on the vapor and temperature profiles. The pressure in a distillation column has an effect on the vapours flow within the distillation column, as the pressure increases the vapours flow decreases, as shown in the figure (13a) below where the effect of the operation pressure versus top and bottom products studied. While the condenser and the reboiler temperature increase as pressure increases, where the latent heat decreases with increasing pressure; this would have the effect of decreasing the reboiler and the condenser duty as pressure increases, as shown in the figure(13b) below Off gas rate in kmole/h Column operating pressure in kg/cm2 a LPG rate in kmole/h reformate rate in kmole/h Column operating pressure in kg/cm2 b CondenserDuty in KW Reboiler Duty in KW Figure 13: Effect of operation pressure on Top and Bottom products and the energy duty JECET; March 214-May 214; Vol.3.No.2, Sec. C;

19 CONCLUSION Naphtha stabilizer in Al-Basra refinery is successfully modelled using a simple unit model structure based on the MESH equations. The model equations are solved using 19 iterations by using MATLAB (R28a) software. A powerful equation of state (SRK) 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). The predicted equilibrium stage model agrees very well with HYSYS software results obtained by simulating the data that has been taken from Al-Basra refinery/ naphtha reforming unit/ naphtha stabilizer, where it was highly accurate and very close to HYSYS simulation results, where the ABS deviation was(<.14). An efficient and reliable algorithm is proposed to calculate the multi-component multi-stage separation process as seen in appendix (1). A simulation analysis by using HYSYS was used to estimate the effect of the process parameters on the reformate product quantity, the recovery of C 5 +, top products and energy duties of the naphtha stabilizer. The profiles for the flow rates and temperature were obtained. The temperature profile ranged from 45C at the condenser to 223C at the reboiler. The optimization was carried out under the condition of constant pressure across the column. The optimum conditions for the purity of the reformate production and the recovery of C 5 + were achieved using Aspen HYSYS software. At the optimum values of the process parameters, the bottom product is 99.6% pure. The influence of column feed was investigated. An increase was seen when fresh feed flow rate was varied between 225 and 425 K mole/hr. Variation of reflux ratio depicted multiplicity effects between.5 to 3. The reformate production and C 5 + concentration began to increase at increasing in the reflux ratio. It is possible to get high purity of reformate for reflux ratio 3. Feed temperature was varied from 125C to 195C showed a variation of reboiler duty from 3.59MW to 1.36MW (62.11% difference). REFERENCES 1. C.D. Holand, fundamentals and modeling of separation processes prentice-hall, inc Englewood cliffs, new jersey, L.N. Sridhar and A. Lucia Tearing Algorithms for Separation Process Simulation [J]. Comput. Chem. Eng. 199, 14(8): J. Jaroslav, V. Hlavacek, and M. Kubicek, Calculation of Multistage Countercurrent Separation Processes-I. Multi-component Multi-stage Rectification by Differentiation with Respect to an Actual Parameter (J), Chem. Eng. Sci. 1973, 28 (1), N. R. Amundson and A. J. Pontinen Multicomponent Distillation Calculations on a Large Scale Computer [J]. Ind. Eng. Chem., 1958, 5: J.C. Wang and G. E. Henke Tridiagonal Matrix for the Distillation [J]. Hydrocarbon Process, 1966, 45(8): V. Kumar, A. Sharma, I. R. Chowdhury, S. Ganguly and D.N. Saraf A crude distillation unit model suitable for online applications, Fuel Processing Technology, 21, 73, N. Bhutani, A. Tarafder, A. K. Ray and G. P. Rangaiah, Multi-objective Optimization of Industrial Styrene Production Using A Process Simulator and A Genetic Algorithm, AIChE, Prepared for Poster Presentation at the 24 Annual Meeting, Austin, TX, Nov L. Shan, Z. X. Ping, Z. S. Jiang, T.X. Shun and X.S. Guang Simulation of Multi-component Multi-stage Separation Process-An Improved Algorithm and Application, The Chinese Journal of Process Engineering, 26, 6, 2, JECET; March 214-May 214; Vol.3.No.2, Sec. C;

20 9. K. Ramesh, N. Aziz, SR Abd Shukor and M. Ramasamy" Dynamic Rate-Based and Equilibrium Model Approaches for Continuous Tray Distillation Column", Journal of Applied Sciences Research, 27, 3(12): D. Mustapha, O. Fatima and T.Sabria Distillation of a Complex Mixture. Part I: High Pressure Distillation Column Analysis: Modeling and Simulation", Entropy, 27, 9, M. Behera Vapour Liquid Equillibrium Modeling Using UNIFAC Group Contribution Method and Its Application in Distillation Column Design and Steady State Simulation, Bsc thesis, Department of Chemical Engineering National Institute of Technology, Rourkela, T. Mosorinac and J. S. Stevanovic A multistage, multiphase and multicomponent process system modeling, International Journal of Mathematical Models and Methods in Applied Sciences, 212, 1, Z. Nasri and H. Binous, Rigorous distillation dynamics simulations using a computer algebra Computer Applications in Engineering Education, 212, 2, 2, J.AKPA and O. UMUZE Simulation of a Multi-component Crude Distillation Column, American Journal of Scientific and Industrial Research, 213, 4(4): B. Wayne, "Process Dynamics Modeling, Analysis and Simulation", Prentic- Hall PTR, New Jersey, E. J. Henley & J. D. Seader, "Equilibrium-stage separation operations in chemical engineering", New York NY, Wiley, *Corresponding author: Ahmed Raheem Hashim; Department of Chemical Engineering, Basra University Basrah, Iraq JECET; March 214-May 214; Vol.3.No.2, Sec. C;