Proceedings of the 6th International Conference on Process Systems Engineering (PSE ASIA)

25 - 27 June 2013, Kuala Lumpur.

Simultaneous optimization for heat-integrated

crude oil distillation system

Yiqing LUO, He WANG and Xigang YUAN

State Key Laboratory of Chemical Engineering, School of Chemical Engineering and

Technology, Tianjin University, Tianjin 300072, CHINA

Abstract

Crude oil distillation is a process of major importance in the refining industry. The

operating variables of the distillation process have a critical effect on the process profit

and energy recovery. However, the objective of heat recovery and product profit of the

system does not always coordinate with each other. In this paper, a systematic approach

which has considered the product profit and energy recovery simultaneously during the

optimization of the crude oil distillation systems is presented, while meeting required

product qualities by searching for an optimal operating condition by manipulating the

operating variables. The shortcut model (SCFrac) in ASPEN Plus was used to describe

the crude oil distillation process, and the pinch analysis was adopted to identify the

target of energy recovery without knowing exact heat exchanger network structure. The

optimization problem was solved by applying a particle swarm optimization (PSO)

algorithm to achieve the objective of maximum net profit. Finally, a case study was

used to illustrate the application of the approach for crude oil distillation systems and

the results showed good agreement with rigorous simulation.

Keywords: Crude oil distillation; Shortcut model; Optimization; PSO; Net profit

1. Introduction

Crude oil distillation is the most fundamental and energy-intensive process in the

petroleum refining and petrochemical industry. The operating variables of the

distillation process as well as the heat exchanger network have a critical effect on the

process profit and energy recovery. However, the objective of heat recovery and product

profit of the crude oil distillation system do not always coordinate with each other, more

heat recovery of the system does not mean more profit for the system. For example,

increasing heat duties of the pump-around near to the bottom of the atmospheric column

in the system will enable more heat to be recovered, but the naphtha product yield of the

column will decrease, as a result, the product profit from higher price product will

decrease.

So far, there have been lots of advances in crude oil distillation process, such as

increasing heat integration opportunities and obtaining an optimal heat exchanger

network structure (Friedler, 2010; Promvitak, et al, 2009), decreasing CO2 emission

(Gadalla, et al, 2006), considering the availability of heavy crude oils by the crude oil

blending to increase the economic profit of refinery (Mittal, et al, 2011; More, et al,

2010), optimizing crude oil distillation operation by simulating and analyzing the

operating variables (Zhang, et al, 2013; Gadalla, et al, 2003; Inamdar, et al, 2004), and

so on. However, few researchers considered the product profit and energy recovery

860 Y. Q. Luo, et al

simultaneously during the optimization of the crude oil distillation systems. So in this

paper, based on the SCFrac shortcut model of ASPEN Plus and particle swarm

optimization (PSO) algorithm (Luo, et al, 2007), a systematic approach was presented

that aimed to obtain optimum operating conditions of the existing distillation process

with considering energy recovery and product profit simultaneously in the system. All

operating parameters to be optimized include product yields, stripping steam flow rate

and pump-around duties. Finally, a case study was used to illustrate the application of

the approach for crude oil distillation systems, with respect to energy demand and net

profit improvement.

2. Process Description

A typical process flow sheet for crude oil distillation system includes an atmospheric

distillation unit and a vacuum distillation unit. In this paper, all of the equipment

conditions and the range of the operating variable value are based on an existing crude

oil distillation unit in China.

The crude oil from the storage tank is preheated up to around 300℃ after passing

through the preheat trains and the desalter unit, and then routed to the atmospheric

furnace in which the crude oil is heated to around 370℃ at 145kPa and enters

atmospheric column flash zone at stage 29. The atmospheric column contains 31

theoretical stages with pressure maintained at 131kPa (top) and 147kPa (bottom). The

temperature of the top condenser is 47℃, and a steam stream (STC) feeds to the bottom

of atmospheric column. There are 3 pump-arounds, and each of them is located from

stage 3 to 1, from stage 12 to 10, and from stage 21 to 19 respectively. The top distillate

product is naphtha. The side products from 3 steam strippers (ST1, ST2, ST3) are

kerosene, diesel and atmospheric gas oil (AGO), which draw from stage 10, 13 and 24

respectively.

Figure 1. Simplified process flow sheet

The reduce crude oil from the bottom of the atmospheric column is heated up to about

394℃ in the vacuum furnace and enters the vacuum column flash zone. The vacuum

column has 14 theoretical stages with pressure maintained at 2.7kPa (top) and 3.9kPa

(bottom). The vacuum column is enabled with 3 pump-arounds, with PAV1 located

from stage 2 to 1, PAV2 located from stage 7 to 6 and PAV3 located from stage 9 to 8.

And the side products, vacuum diesel (VD), light vacuum gas oil (LVGO) and heavy

vacuum gas oil (HVGO) withdraw from stage 2, 7 and 9 respectively. The residue is the

bottom product. The simplified process flow sheet is shown in Figure 1.

Simultaneous optimization for heat-integrated crude oil distillation system 861

3. Crude oil distillation model

3.1. Shortcut model

In the design and retrofit problem of crude oil distillation system, shortcut models

(Gadalla et al, 2003) are more suitable for optimization and can be solved in a quicker

way, especially when multiple variables are considered simultaneously. In this paper,

the SCFrac shortcut model in ASPEN Plus is used to model and simulate the crude oil

distillation. The SCFrac divides an n-product column into n-1 sections. By specifying

the column pressure, estimated product flow, number of theoretical stage, steam flow

and the value of product property (such as D86 95% point), the SCFrac can give

relatively accurate results of the crude oil distillation system including the product

status and heat duty (heat duty of each pump-around) in each section.

3.2. Heat recovery calculation

For the calculation of heat recovery from the heat exchanger networks, the pinch

analysis (Linnhoff, 1983) is adopted to identify the target of energy recovery without

knowing exact heat exchanger network structure. In crude oil distillation, the crude oil

feed is the only one cold stream, and the products and pump-arounds are all hot stream.

By using pinch analysis, relatively accurate results of theoretical hot (required furnace

heat) and cold (required cooling duty) utility consumption can be obtained in a quicker

way. In this work, the minimum temperature approach is 20℃.

4. Optimization framework

4.1. Variables

For each product of crude oil distillation, the range of D86 95% point is specified as in

Table 1, within which the quality of the product can be regarded as satisfaction

guaranteed. The variables selected to optimization include stripping steam flow rates

(ST1, ST2, ST3, STC), and D86 95% points of naphtha (DN), kerosene (DK), diesel (DD),

AGO (DA) and vacuum diesel (DVD), as shown in Table 1.

Table 1. Bounds of optimization variable value Variable Range (℃) Variable Range (kg/h)

DN 170~185 ST1 0~1000

DK 250~260 ST2 0~1000

DD 310~320 ST3 0~1000

DA 355~365 STC 4000~9000

DVD 360~365

4.2. Objective function

In industrial process, the profit of whole system is concerned about most. So in this

work, the objective function of the optimization is maximizing the annualized net profit

that the product profit reduces crude oil cost, steam cost, and energy cost of furnace fuel

oil and cooling water respectively. Details of product/utility prices (Table 2) and the

objective function are given below. Work hour of refinery per year is 8400h.

Objective function:

8400][ STSTjCCii PFErengyPFPFMaxZ (1)

3600)( CWCWFF QPPQEnergy (2)

Where, Fi is the flow rate of a particular product (m3/h); FC is the flow rate of crude oil

feed (m3/h); Pi is the price of a particular product; QF and QCW are the required furnace

heat and required cooling duty calculated from heat recovery system (MW); FSTj is the

flow rate of a particular steam (kg/h); Energy is the system energy cost. And all these

value are the function of stripping steam flow rates and D86 95% points of each product.

862 Y. Q. Luo, et al

Table 2. Prices of products and utilities (More, et al, 2010) Item Price Unit Item Price Unit

PN (Naphtha) 1003.8 $/m3 PF (Fuel oil) 0.011 $/MJ

PK (Kerosene) 861.1 $/m3 PCW (Cooling

water) 4.74×10-3 $/MJ

PD (Diesel) 810.2 $/m3

PA (AGO and VD) 767.5 $/m3 PST (Steam) 0.0055 $/kg

PV (LVGO and HVGO) 718.2 $/m3 PC 629.0 $/m3

PR (residue) 449.0 $/m3

4.3. Optimization methodology

Figure 2. The methodology framework

The optimization problem of crude oil distillation system can be formulated as a NLP

model, which has been solved by using a particle swarm optimization (PSO) algorithm

in this paper. The PSO is a population based stochastic optimization technique. In PSO,

the potential solutions, named as particles, ‘fly’ through the problem space following

the current optimum particles. Each particle is regarded as a point in a D-dimensional

space that updates according to its own experience and of other particles. The algorithm

is simple in concept and can get better results in a faster and cheaper way. The basic

equations of PSO are given below.

1 1

i i i

k k k x x v (3)

1 1 1 2 2( ) ( )i i i i g i

k k k k k k kc r c r v v p x p x (4)

In each update step k, for each particle i, the position xi is updated as equation (3), and

the step velocity is calculated by equation (4), based on the particle’s best position pki

and group’s best position pkg. c1 and c2 are two positive parameters, in this work,

c1=c2=2; ω= 0.729, is the inert weight; r1 and r2 represent uniform random numbers

between 0 and 1.

In this work, PSO algorithm has connected with the SCFrac model by using the Visual

Basic and ASPEN Plus ActiveX automation sever. The framework of the optimization

process is shown in Figure 2.

5. Case study and results

A case study has carried out based on an existing crude oil distillation system and the

conditions of the unit have been mentioned above. The feed oil of the unit is DAGANG

crude oil, and the throughput is 5208000m3/a (4.5Mt/a). The optimization approach

Build SCFrac simulation flow sheet in ASPEN

Set k=1, randomly initialize particle positions of variable xi

Write the values of variable xi into ASPEN and run simulation

Get results from ASPEN and calculate the objective function

Update particle and group best position pki and pk

g

Update velocity Vki for all particles

Update particles’ positions

Stopping criterion satisfied?

k=k+1

Output results and stop

YES

NO

Simultaneous optimization for heat-integrated crude oil distillation system 863

presented in this paper was applied to achieve the maximum annualized profit by

optimizing the variables in Table 1. And the results of the optimization are shown below.

Table 3. Results of optimization variables Variable Value (℃) Variable Value (kg/h)

DN 184.0 ST1 503.6

DK 254.6 ST2 594.5

DD 310.2 ST3 501.2

DA 362.1 STC 5860.1

DVD 363.6

Table 4. Results of optimal operating parameters Parameter Value Parameter Value

FN (m3/h) 58.5 FVD (m3/h) 14.8

FK (m3/h) 42.5 FLVGO (m3/h) 120.5

FD (m3/h) 48.4 FHVGO (m3/h) 84.2

FA (m3/h) 39.7 FR (m

3/h) 184.0

Duty(PAC1) (kW) 4212 Duty(PAV1) (kW) 3919

Duty(PAC2) (kW) 5499 Duty(PAV2) (kW) 7676

Duty(PAC3) (kW) 9826 Duty(PAV3) (kW) 17094

Condenser duty (kW) 10231 Energy cost (M$/a) 14.28

Energy cost (M$/a) 14.28 Annualized profit (M$/a) 284.45

Table 5. Comparison of product flow rate with rigorous simulation Parameter Optimal

result

Rigorous

simulation result

Parameter Optimal

result

Rigorous

simulation result

FN (m3/h) 58.5 56.6 FVD (m3/h) 14.8 12.8

FK (m3/h) 42.5 41.7 FLVGO (m3/h) 120.5 125.6

FD (m3/h) 48.4 48.5 FHVGO (m3/h) 84.2 82.1

FA (m3/h) 39.7 41.4 FR (m

3/h) 184.0 180.9

(a) (b)

Figure 3. Results of the optimization. (a) Maximum annualized profit in each searching

step k; (b) Optimized Energy cost in each searching step k

The results of optimization variables and optimal operating parameters are shown in

Table 3 and Table 4. Figure 3 (a) and Figure 3 (b) show the results of optimization

problem, which are the optimized annualized profit result and the value of energy cost

in each searching step k. It is observed that the objective of maximum profit and

minimum energy cost of the crude oil distillation system does not coordinate with each

other. The maximum annualized profit is 284.45M$/a and the corresponding energy

cost is 14.28M$/a. But when the energy cost is minimum (13.91M$/a), the annualized

profit is only 240.97M$/a. So less energy cost represents more heat recovery, but it

doesn’t mean more net profit. And Figure 3 also shows that the PSO algorithm got the

maximum annualized profit during less than 50 times optimization (k=46). It proved

that the PSO algorithm presented in this work can solve this problem in a fast way. By

864 Y. Q. Luo, et al

specifying the product D86 95% points and pump-around heat duty of the optimization

results as the design specification, a rigorous simulation in ASPEN Plus was carried out.

Compared with the results of product flow rate in Table 5, it showed good agreement

between the optimization approach and rigorous simulation. It proved that the

optimization approach could provide relatively accurate and feasible results.

6. Conclusions

In this paper, a systematic approach which has considered the product profit and energy

recovery simultaneously during the optimization of the crude oil distillation systems

was presented, while meeting required product qualities through searching for an

optimal operating condition by manipulating the operating variables. The shortcut

model (SCFrac) in ASPEN Plus was used to describe the crude oil distillation process,

and the pinch analysis was adopted to identify the target of energy recovery without

knowing exact heat exchanger network structure. The result of optimization shows that

the operating variables of the crude distillation process such as product yields, pump-

around duties, stripping steam flow rates, etc. have a critical effect on the process profit

and energy recovery. And the objective of maximum profit and minimum energy cost

does not coordinate with each other, they should be optimized simultaneously. The

methodology developed in this paper can be solved with PSO algorithm in a fast way

and provide a quantitative support for the optimization of the crude oil distillation

systems. Compared with the results of rigorous simulation, it can be proved that the

optimization approach can give relatively accurate results.

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