Process Simulation and Optimization of Crude Oil ... · Process Simulation and Optimization of Crude ... 1,2,3 Department of Chemical ... process modeling, simulation and optimization

DOI: 10.23883/IJRTER.2017.3230.MIIUW 324

Process Simulation and Optimization of Crude Oil Stabilization

Scheme Using Aspen-HYSYS Software

M. Edwin1, S. Abdulsalam

2 and I. M. Muhammad

3

1,2,3Department of Chemical Engineering, Abubakar Tafawa Balewa University, Bauchi-Nigeria,

P.M.B. 0248, Bauchi-Nigeria

Abstract In this time of energy crises, low production rate against the increasing demand of oil

and gas production regularly hampers both domestic and industrial operations. In addition, safety

hazards arising from explosion and increase in the cost of production due to pumping cavitation has

pose a great challenge on offshore Floating Production Storage and Off-loading (FPSO) terminals. In

view of the above, the design of a computer base model for the simulation and optimization of crude

oil stabilization scheme is highly desirable. Aspen HYSYS software (version 8.4) was used for the

simulation and optimization processes and the fluid package employed was Peng-Robinson.

Operational data used for the simulation was obtained from the aforementioned terminal. The model

was based on gas product and liquid product flows. The simulation was performed to optimize the

stabilization unit by manipulating various process variables, which was subject to a Reid vapor

pressure (RVP) of gasoline as the constraint. The process flow diagram of the system was

successfully developed. Furthermore, the optimizer tool of Aspen HYSYS was used to obtain the

optimum operating conditions of the process and Sequential Quadratic Programming (SQP)

algorithm was employed. The results of the base and optimized cases were compared and analyzed.

Economic consideration showed an improvement of 31.11% (i.e. N 42 billion) in net profit for the

optimized case over the base case at optimal operating conditions. In addition, results showed that

the effects of optimum feed conditions (flow rate, temperature and pressure) were between the

ranges of of 6.0 e+03 to 8.0e+03 m3/h, 26 to 29

oC, and 1.65 to 1.8 MPa respectively Therefore, it has

been shown that the developed Aspen HYSYS model of this research work can be used to represent,

simulate and optimize a crude oil stabilization system successfully.

Keywords Stabilization, Simulation, Optimization, RVP, Aspen HYSYS.

I. INTRODUCTION

The economical challenge of modern technologies and customers’ satisfaction claims for a

continuous optimization in every field of life. In chemical industry, products with precise quality

values have to be produced while specific costs have to be on a minimal level. To fulfill these

expectations, chemical process industries are renewed, redesigned, and rebuilt, i.e. modernized

continuously to have the ability to operate complex, highly interconnected plants that are profitable

and that meet quality, safety, environmental and other standards. Towards this goal, process

modeling, simulation and optimization tools are increasingly being used industrially besides of the

design process at every level of subsequent plant operations [11].

Crude oil contains complex mixtures which are very difficult to handle, meter, or transport. In

addition to the difficulty, it is also unsafe and uneconomical to ship or transport these mixtures to

refineries and gas plants for processing. However, environmental constraints exist for the safe and

acceptable handling of hydrocarbon fluids and disposal of produced salt water. It is therefore

necessary to process the produced fluids in the field to yield products that meet the specifications set

by the customer and is safe to handle [1].

International Journal of Recent Trends in Engineering & Research (IJRTER)

Volume 03, Issue 05; May - 2017 [ISSN: 2455-1457]

@IJRTER-2017, All Rights Reserved 325

In order to maximize oil/gas production and increasing its market value, the oil and gas industry has

shown keen interest in the development and optimization of separation efficiency between oil-gas-

water in the crude stabilization process. Crude oil stabilization is a pre-treatment process which

involve the removal of light hydrocarbons along with hydrogen sulphide and also reduces the vapor

pressure. Dissolved gas in the crude oil must be removed to meet pipeline, storage or tanker Reid

Vapor Pressure (RVP) specification. The presence of the most volatile hydrocarbon increases the

RVP [5]. The impact of RVP is often referred to as the gasoline volatility. In this study, RVP has

been set as a criterion for off-spec conditions of the product - that is, a maximum of 83 kPa.

Process simulation software packages are extensively used nowadays to estimate the product

efficiency and enhance the performance of the system by optimizing operating parameters [12].

There have been few simulating software packages such as Aspen Plus, Aspen HYSYS and PRO/II

for use in the oil and gas industries. In this study, Aspen HYSYS has been chosen as the suitable

simulation software, in that it has vast importance for chemical engineers to simulate and optimize a

process, which differs from many of the alternative commercial simulators in two main respects.

First, it has the ability for interactively interpreting commands, as they are entered one at a time.

Second, uses subroutines to model the process units, it has a unique feature that information

propagates in both forward and reverse directions [2].

According to the information obtained from literature, some scientists and engineers have carried out

some researches related to the subject matter of this work. Reference [14] studied process simulation

and operating parameters optimization of minus pressure flash technology for crude stabilization

process. Reference [6] studied process simulation of crude oil stabilization: a case study of

Terengganu crude oil terminal. Reference [10] studied simulation and optimization of H2S expulsion

from crude oil with the use of equilibrium mode. Reference [8] studied process simulation and

assessment of a back-up condensate stabilization Unit. Reference [7] studied modelling and

optimization of fluid catalytic cracking unit using Hysys.

In this study, the model can provide an effective planning and operations tool. In view of the

potential gains suggested by the results, modelling and optimization using computer simulator can

bring new insight in the quest for a better crude oil stabilization system. In addition, it would

significantly enhance recovery of stabilized liquid and reduce greenhouse gas emissions at the

storage tanks and it would also be safe and economical to ship or transport the stabilized crude to

refineries and gas plants. Furthermore, cost of production would be reduced by minimizing process

system requirement due to pumping cavitation.

II. MATERIALS AND METHODS

A. Materials

The simulation software used in this study was Aspentech Hysys version 8.4 developed by Aspen

Technology, Incorporation, Crosby Drive Bedford, Massachusetts, U.S.A. The data used for this

study was obtained from Usan FPSO terminal. They include; detailed Process Flow Diagram of Usan

FPSO terminal of crude oil stabilization system, inlet feed operating parameters, comprehensive

crude oil compositions, equipment summary unit operation conditions and utilities.

B. Methods

The methods adopted in accomplishing this work, which was carried out to obtain the model, are

outlined as follows.

1. Model development and process simulation procedure - Operational data for the simulation and

optimization of the model were obtained from Usan Floating, Production, Storage and Offloading

(FPSO) terminal. The simulation was performed in Aspen HYSYS (version 8.4) simulation

environment. The components involved in the simulation process were characterized into pure and

pseudo component and were selected from the simulator data base. The pseudo components

(hentriacontanes to hexatriacontanes plus) which were not found in the data base of the component


@IJRTER-2017, All Rights Reserved

library window, were created and added to the pure component as shown in Figure 1. Due to the

presence of polar and non-polar hydrocarbons, as well as a three

employed as the fluid package to predict the binary interaction and activit

liquid and vapor phase as shown in Figure 2.

The feed stream was defined by entering the feed conditions (flow rate, temperature, pressure and

stream name) as shown in Figure 3. The streams were then connected to unit operations.

stabilization facilities consist of two three

compressors, two valves, two mixers and a pump. After the input information and operating unit

model were set-up, the process steady state simulatio

shown in Figure 4. The case was saved as “Base case” and a report was created.

Figure 1. Choosing system components from data bank


Volume 03, Issue 05; May

2017, All Rights Reserved

ndow, were created and added to the pure component as shown in Figure 1. Due to the

polar hydrocarbons, as well as a three-phase system, Peng

employed as the fluid package to predict the binary interaction and activity of the components in the

liquid and vapor phase as shown in Figure 2.


stream name) as shown in Figure 3. The streams were then connected to unit operations.

stabilization facilities consist of two three-phase separators, a flash drum, three heaters, two


up, the process steady state simulation was successfully executed by the simulator as





326

ndow, were created and added to the pure component as shown in Figure 1. Due to the

phase system, Peng-Robinson was

y of the components in the


stream name) as shown in Figure 3. The streams were then connected to unit operations. The crude

phase separators, a flash drum, three heaters, two


n was successfully executed by the simulator as





Figure 2. Selecting Peng

Figure 3. Schematic of feed stream conditions




Figure 2. Selecting Peng-Robinson as the fluid Package

Figure 3. Schematic of feed stream conditions



327

Package



Figure 4. Process flow diagram of the modeled crude oil stabilization scheme.

2. Process optimization - After the model of crude oil stabilization process had been simulated, its

optimization was carried out with the aid of the optimizer tool of the same Aspen HYSYS version

8.4 used for the model development and simulation, which was accessed upon the additi

“Optimizer Spreadsheet” unto the developed model. The low and high bound for the primary

(manipulated) variables were selected and set as shown in Figure 5. The criterion for the

optimization was to maximize net profit subjected to a Reid vapor p

constraint as shown in Figure 6. The optimization equation is presented in equation (1). Sequential

quadratic programming was the optimizer algorithm employed due to its ability in handling linear

and non-linear algebraic functions

unconstraint optimization problems. The optimization was then executed. The case was saved as

“optimized case” and a report was created.

�� ⁄ � ��where;

i denotes the gas and liquid product,

involves the steam, compression and pump cost respectively.





After the model of crude oil stabilization process had been simulated, its


8.4 used for the model development and simulation, which was accessed upon the additi



optimization was to maximize net profit subjected to a Reid vapor pressure of 83 kPa as the



linear algebraic functions as well as equality and inequality functions of constraint and


“optimized case” and a report was created.

��

denotes the gas and liquid product, j denotes the unit cost of the gas and liquid product and utilities

involves the steam, compression and pump cost respectively.



328


After the model of crude oil stabilization process had been simulated, its


8.4 used for the model development and simulation, which was accessed upon the addition of the



ressure of 83 kPa as the



as well as equality and inequality functions of constraint and


1

denotes the unit cost of the gas and liquid product and utilities



Figure 5. Setting up primary (manipulated) variables

Figure 6. Profit maximization and constraint window

III. RESULTS AND DISCUSSION

The summary of optimum results comparing the base and optimized cases of the model is presented

in Table 1.




Figure 5. Setting up primary (manipulated) variables

Figure 6. Profit maximization and constraint window

III. RESULTS AND DISCUSSION




329





Table 1. Summary of optimum results of the base and optimized cases

Parameters

Base case

Optimized case

Difference

between base and

optimized cases

Fuel gas product flow

(m3/yr_gas)

2.83 million 3.89 million 1.06 million

Gasoline product flow

(bbl/yr)

9.87 million 13.00 million 3.13 million

Heater 1 heat flow (kJ/yr) 3.8 x 109 1.8 x 10

9 2.0 x 10

9

Heater 2 heat flow (kJ/yr) 4.0 x109 3.0 x 10

9 1.0 x 10

9

Heater 3 heat flow (kJ/yr) 3.3 x 109 2.3 x 10

9 1.0 x 10

9

Stage 2 Feed pressure (kPa) 680 680 0.00

Stage 3 Feed pressure (kPa) 338 324 14

RVP (kPa) 84.07 82.54 1.53

Net profit (N /yr) 135 billion 177 billion 42 billion

Table 1 presents summary of optimum results comparing the base and optimized cases of the model.

The optimized case showed improvements in production of 37.46% (i.e. 1.06 million m3/yr) fuel gas

product and 31.71% (i.e.3.13 million bbl/yr) gasoline product over the base case. These can be

attributed to decreased in heat flows of heaters 1, 2 and 3 from a high bound of 3.8 × 109, 4.0 × 10

9

and 3.3 × 109 kJ/yr to a low bound of 1.8 × 10

9, 3.0 × 10

9and 2.3 × 10

9 kJ/yr respectively. This

observation is in conformity with the findings of [4], who observed that increased heat supply breaks

emulsions and resulted into excessive vaporization and loss of liquid product. Hence, it is pertinent

to ensure that heat supplied at intervals does not exceed the high bound in order to ascertain good

performance output. It can also be seen that the Reid Vapor Pressure (RVP) decreased from a high

bound nof 84.07 kPa to a low bound of 82.54 kPa. These can be attributed to decrease in operating

pressure at the final stage. This implied that operating pressure and temperature of the final stage

dictates the vapor pressure of the liquid product, because at low operating pressure, the final stage

heavy gas component will flash out from the liquid [3]. The net profit showed tremendous

improvement from N 135 billion to N 177 billion, about 31.11% increase. The increased in the net

profit can be attributed to high production rate of the desired products and this observation is in

agreement with the finding of [7].

The effects of heaters 1, 2 and 3 heat flows on net profit is shown in Figures 7 – 9.

Figure 7. Effect of heater 1 heat flow on net profit

1.766E+11

1.767E+11

1.767E+11

1.768E+11

1.768E+11

1.769E+11

1.769E+11

1.770E+11

1.770E+11

1.771E+11

0.00E+00 2.00E+09 4.00E+09 6.00E+09 8.00E+09

Net

pro

fit

(N/y

r)

Heat flow (kJ/yr)




Figure 7 shows the effect of heat flow for heater 1 on net profit. It can be seen that the net profit

decreased from 176 994 855 496.79 to N176 661 516 403.04 as the heat flow increased from 7.63E

+07 to 2.90E +08 kJ/yr. Thus, in order to maintain an optimum net profit at the specified sales

specification of product RVP of 83 kPa, the heat flow should be kept at 7.63 E + 07 kJ/yr. This

implied that the decrease in the net profit was attributed to an increase in heat flow which reduced

the volume of the fuel gas and gasoline product flow and thus, the net profit. According to [13],

adding heat can cause a significant loss of the lower-boiling point hydrocarbons (light ends). This

causes “shrinkage” of the oil, or loss of volume of the output. As a result, will lead to reduction of

the net profit due to reduction in the output. Similarly, Figures 8 and 9 followed the same trend as

above.



The effects of feed conditions on the product Reid vapor pressure for the simulated crude oil

stabilization scheme is presented in Figures 10 – 12.

1.7680E+11

1.7685E+11

1.7690E+11

1.7695E+11

1.7700E+11

1.7705E+11

0.00E+00 2.00E+09 4.00E+09 6.00E+09 8.00E+09

Net

pro

fit

(N

/yr)

Heat flow (kJ/yr)

1.7670E+11

1.7675E+11

1.7680E+11

1.7685E+11

1.7690E+11

1.7695E+11

1.7700E+11

1.7705E+11

1.7710E+11

0.00E+00 2.00E+09 4.00E+09 6.00E+09 8.00E+09

Net

pro

fit

(N/y

r)

Heat flow (kJ/yr)




Figure 10. Effect of inlet feed temperature on product RVP

Figure 10 presents the effect of inlet feed temperature on product RVP. It can be seen that the

product RVP decreased from 90.13 to 51.33 kPa as the inlet feed temperature increased from 10 to

100oC. This implied that, increase in the inlet temperature caused more portion of the light

components to flash off from the crude and hence reduced the RVP of the product. Thus, the

optimum temperature that the crude stabilization plant can tolerate in order to achieve the specified

RVP (83 kPa) was between 26oC and 29

oC. Hence, any temperature lower than 26

oC or higher than

29oC would cause the stabilized crude to become off specification as it will require a higher heat

duty to attain the required temperature before entering the pressure vessel. This observation is in

agreement with the finding of [8] that RVP decreased as the feed temperature increased.

Figure 11. Effect of inlet molar flow rate on product RVP

Figure 11 shows the effect of feed molar flow rate on product RVP. It can be seen that the product

RVP increased from 78.95 to 82.89 kPa as the molar flow rate increased from 10 000 to 80 000

m3/h_gas. This implied that increase in the molar flowrate would require more heat to flash off the

volatile components. As a result, the RVP increased because of insufficient heat to maintain the RVP




of the product. Therefore, for an acceptable RVP of 83 kPa, the optimum molar flow rate that can be

processed by the crude stabilization plant was found to be in the range of 60 000 and 80 000

m3/h_gas. This observation is in conformity with the finding of [6] who observed that increase in

feed flow rate increased the product RVP.

Figure 12. Effect of inlet feed pressure on product RVP

Figure 12 presents the effect of inlet feed pressure on product RVP. It can be seen that the product

RVP decreased from 82.96 to 82.27 kPa as the inlet feed pressure increased from and 1000 to 9000

kPa. This implied that the high pressure drop in the pressure vessel led to high amount of volatile

component flashed off to the stabilization gas header. Thus, the stabilized crude contained traces of

light components which gave rise to lower RVP conducive for safe storage at atmospheric condition.

Therefore, the optimum inlet feed pressure that can meet the required product specification was

found to be in the range of 1 650 and 1 800 kPa. The above assertion is backed upon by the fact that,

at high feed pressure the feed tends to change to the liquid phase while in the three-phase separator,

the pressure should be as low as possible to flash-off the light ends [9].

IV. CONCLUSIONS

The process simulation and optimization of crude oil stabilization scheme of Usan FPSO terminal

has been successfully performed and developed. The optimized case showed improvements in

production of 1.06 million m3/yr fuel gas product and 3.13 million bbl/yr gasoline product over the

base case. Economic consideration showed an improvement of 31.11% (i.e. N 42 billion) in net

profit for the optimized case over the base case at optimal operating conditions. In addition, the

optimum feed flow rate, temperature and pressure were between the ranges of 6.0 e+03 to 8.0e+03

m3/h, 26 to 29

oC, and 1.65 to 1.8 MPa respectively. Therefore, it has been shown that the developed

Aspen HYSYS model of this research work can be used to represent, simulate and optimize a crude

oil stabilization system successfully.

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Process Simulation and Optimization of Crude Oil ... · Process Simulation and Optimization of Crude ... 1,2,3 Department of Chemical ... process modeling, simulation and optimization