bpewo presentation mar-11 v02.pdf

7/27/2019 BPEWO Presentation Mar-11 v02.pdf

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Integration of Nonlinear

Planning Optimization Abdulrahman Alattas,

Chemical Engineering Department

Carnegie Mellon University

EWO Meeting – March 20111



n ro uc on

Refinery production planning modelsOptimizing refinery operation

ru e se ec on

Maximizing profit; minimizing cost

LP-based, linear rocess unit e uations

Current Project Collaboration with BP Refining Technology

Develop a refinery planning model with nonlinear process unit equations inte rate schedulin elements

2



Development

3



Example

x

Complex refineryFixedyield

Swingcut

Crude1 (lighter) 142 0

u

Processing 2 crude

ru e ee stocCrude2 (heavier) 289 469

Other Feedstock Heavy Naphtha 13 9

Fuel Gas 13 17

heavy naphtha

Swing cut modelRefinery

Production

LPG 18 20

Light Naphtha 6 6

Premium Gasoline 20 20

Offers lower net cost

& different feed

Reg. Gasoline 80 92Gas Oil 163 170

Fuel Oil 148 160

Shows benefits of better equations

4



Development

Crude distillation unit (CDU) 5



asca e o umns

Cascaded Columns Representationof a Crude Distillation Column

(Gadalla et al, 2003)

Typical Crude Distillation Column(Gadalla et al, 2003)

6



ggrega e o e

D

top

Ltop

To

D1

FeedF

Vtopfee

d

Ltopfeed

Section

Feed

Top

F

VtopFeed

LtopFeed

Vbotfee

d

Lbotfee

d

Bottom

Section

Bottom

VbotFeed LbotFeed

BVbot

Lbot

B

Steam

n

7

Conventional distillation Steam distillation



e nery ann ng o e s

Aggregate Model Mixed-type distillation cascade

Challenges with full CDU model

4+ cascaded columns

Feed

32+ components Bottom

Section

FeedF

Bottom

Section

8



o e - n ro

CDU is a series of fractionationunits

T

TC4

Dist3

Dist2

Cut point temperature is the

separation temperatureT

C1

TC2

Feed

Prod2

Prod3

Prod4Dist1

Prod1

4

5

6

Component Distribution of A Distillation Column Using FI

-1

0

1

2

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 L o g ( X D i s t / X P r o d

) i

Slope=1.95 ase on e esfractionation index

9-4

-3

-2

Log io

Slope=3.40



o e - qua ons

Mass balance TemperatureTc

TE j TI j1

Com onent

2

Tc j 1 Tc j

distribution apor pressure

5.963461 Tr j, i 1.176391 Tr j, i 1.5

0.559607 1 Tr j, i 31.319 1 Tr j, i 6

/Tr j, i

10

j, i i

i

4.785221 Tr j, i 0.4139991 Tr j, i 1.5

0.8912391 Tr j, i

3

4.986621 Tr j, i

6

/Tr j, i



o e - emar s

FI Model is crude independent FI values are characteristic of the column

FI values are readily calculated and updated from

refinery data vo s more comp ex, non near mo e ng

equations

the CDU

model 11



o e – xamp eGO

FI model example Venezuelan crude TC3

TC4

Dist4

Dist3

Dist2

TC5

N

40 Pseudo-components, 5 cuts

4 cases:

Maximizing naphtha (N), heavy naphtha (HN), light

TC

1

2

Feed

BR

HD

LD

distillate (LD), heavy distillate (HD)

Cut-point temperature and product quantities

reflect the different business objectives

Stats Equations: 562

Variables: 568

Product

Run

Gas

OH Naphtha H NaphthaL Dist. H Dist.

B.

Residue

Max Naphtha 6.2 112.9 35.1 68.6 16.5 60.7

Cut point temperature

Run Naphtha H Naphtha L Dist. H Dist. B. Residue

Max Naphtha 272.7 417.0 426.4 526.8 595.3

o ver:

Time: 0.360 sec

Max H Naph. 6.2 107.4 53.0 56.1 16.6 60.7

Max L Dist. 6.2 111.5 10.7 95.0 16.0 60.5

Max H Dist. 6.2 111.5 10.7 94.0 16.9 60.513

ax ap . . . . . .

Max L Dist. 272.7 386.2 398.3 606.0 631.1

Max H Dist. 272.7 386.2 398.3 526.8 650.5



ann ng o e xamp eTypical Refinery Configuration ( Adapted from Aronofsky, 1978)

butane

Fuel gas

Prem.

SR Fuel gas

Cat Ref

Crude1,…

Gasoline

Reg.

Gasoline

SR Naphtha

SR Gasoline

Distillate

CDU

Distillate

SR DistillateProduct

Blending

Gas oil

Cat Crack

Crude2,

…. Fuel OilSR GO

Hydrotreatment

blending

Treated Residuum

SR Residuum14



Problem Statement

Information Given Refinery configuration: Process units

ee s oc na ro uc

Crude1 Louisiana Sweet Lightest

Crude2 Texas SweetCrude3 Louisiana Sour

Cases: Processing 2,3 & 4 crude oils

Crude4 Texas Sour Heaviest

Case 1 Crude1 Crude2

Objective

Case 2 Crude1 Crude2 Crude3Case 3 Crude1 Crude2 Crude3 Crude4

Select crude oils and quantities to process

Maximize profit single period time horizon 15



ann ng o e xamp e esu s

Comparison with the fixed yield and swing cut

models

conom cs

FI calculates the maximum profit scenario

Model Case1 Case2 Case3

FI 245 249 247

SC 195 195 191

FY 51 62 59

16



(cont.)

ee s oc resu s:

Different crude purchase option

Case 1

FY 54 46

SC 90 10

FI 72 2870%80%

90%

100%

e d

ru e ee on r u ons or ase

Case 1

FY 54 46

SC 90 10

FI 72 28

Case 2 SC 80 10 10

FI 10 30 60

FY 10 31 49 1020%

30%

40%

50%

60%

C r u d e O i l F

Crude3

Crude2

Crude1

Case 2 SC 80 10 10

FI 10 30 60

FY 10 31 49 10

ase

FI 10 19 61 100%

FY SC FI

Model Type

ase

FI 10 19 61 10

17



Results (cont.)

ro uc s

Increased reg. gasoline

Different fuel oil rates and treated residue

Cases Product FY SC FI

Fuel Gas 12.7 9.5 16.3

Premium Gasoline 20.0 20.0 20.0

Cases Product FY SC FI

Fuel Gas 12.7 9.5 16.3


90%

100%

Refinery Products Slate for Case2

. . .

Fuel Oil 29.5 48.4 25.7

HT Residuum 19.0

Fuel Gas 12.4 9.5 15.9

. . .

Fuel Oil 29.5 48.4 25.7

HT Residuum 19.0

Fuel Gas 12.4 9.5 15.9

60%

70%

80%

P r o d u c t s

HTR

Case 2

. . .

Regular Gasoline 20.1 23.4 39.0

Fuel Oil 32.1 48.5 26.2

HT Residuum 17.0

Case 2

. . .


Fuel Oil 32.1 48.5 26.2

HT Residuum 17.0

20%

30%

40%

R e f i n e r

y FO

RG

PG

FG

18

Case 3



Fuel Oil 32.1 52.0 26.6

HT Residuum 17.4

Case 3



Fuel Oil 32.1 52.0 26.6

HT Residuum 17.4

0%

10%

FY SC FI

Model Type



Results (cont.)

Model statistics

variables

~30% nonlinear variables

Model Variables Equations

Nonlinear

Variables

CPU

Time Solver

FY 14 130 6.7

Case 1SC 163 140 7.3FI 1225 1204 348 7.3 CONOPT

Case 2

FY 185 161 8.4CPLEX

SC 215 176 9.0

19

.

Case 3

FY 231 194 9.8CPLEX

SC 271 214 10.3FI 2395 2342 696 10.8 CONOPT



onc us on

Crude independent Calculates cut point temperature settings uccess u n ca cu a ons FI-based planning model calculates higher profits

using different crude oil purchase decision

Aggregate model Successful models for Conventional distillation and

Resolving modeling full CDU with mixed typecolumns

NLP models Assess the benefit of the different modeling approaches in

terms of accuracy, robustness & simplicity 20



21

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