supply chain optimization of petroleum ...focapo.cheme.cmu.edu/2003/pdf/pintofocapo2003.pdf¾general...

Sérgio M.S. Neiro and José M. Pinto

FOCAPO 2003, Coral Springs

SUPPLY CHAIN OPTIMIZATION OF PETROLEUM REFINERY COMPLEXES

General Petroleum Supply Chain (PSC)

Objectives

Representation of proposed elements

Real-world PSC - Petrobras

ResultsSingle refinery PSC

Conclusions

Research needs

Outline

General Petroleum Supply Chain

• Iyer et al. (1998).• Van den Heever and

Grossmann (2000).• Van den Heever et al.(2000).

Ierapetritou et al. (1999).

• Kosmidis (2002).• Barnes et al. (2002).

Oil field Infrastructure

InternationalPetroleumCommerce

• Operations

Research.

• Lee et al. (1996).

• Pinto et al. (2000).

• Más and Pinto (2002).

Crude Oil

Supply

• Ponnambalam (1992).

• Bok et al. (1998).

• Pinto and Moro (2000).

Refinery - Planning/Scheduling

• Ross (2000).

• Iakovou (2001).

• Magatão et al. (2002).

• Stebel et al. (2002).

• Rejowski and Pinto (2003).

Distribution

Development of an optimization model

that is able to represent

a petroleum supply chain to support the

decision making planning process of

supply, production and distribution

Objective

Refinery - Processing Unit Model

( ) u

u ,t u ',s ,u ,tu ',s

QF Q∈

= ∑US

( )u

u ,s ,t u ,t u ,s u ,p ,t u ,s ,v u ,v ,tu

QS QF . f PF QGain .V∈

= + ∑VO

u ,s

u ,s ,t u ,s ,u ',tu '

QS Q∈

= ∑UO

( )

( )

u

u

u',s ,u ,t u ',s , p ,tu ',s

u ,p ,tu ',s ,u ,t

u ',s

Q .PSPF

Q∈

∈

=∑

∑US

US

u ,s ,p ,t u ,s ,p u ,p ,t u u ,t u ,v ,t uPS f ( PF | p ,QF ,V | v )= ∈ ∈PI VO

L Uu u ,t uQF QF QF≤ ≤ L U

u ,v u ,v ,t u ,vV V V≤ ≤

Feed mixing

Unit

Model

Bounds

Product splitting stream

Supply, Distribution – Storage Model

Supply, Distribution - Pipeline Model

( ) u

u ,t u ',s ,u ,tu',s

QF Q∈

= ∑US

u ,s ,t u ',s ,u ,tQS Q=

u ,s ,t u ,s ,u ',tQS Q=

Uu ,t uQF QF≤

( )

( )

u dem SB

u f

p pipe

u ,t u,t u ,t u ,t u,tt u t

u u,v u ,v ,t u,t u u ,tu t v u t

u u ,t u u ,tu t u t

Max Z Cp . QF Vol .Cpet .Lot

[ Cr Cv .V ] .QF Cinv .Vol

Cinv .Vol Ct .QF

∈ ∈ ∈ ∈

∈ ∈ ∈ ∈ ∈

∈ ∈ ∈ ∈

= − −

− + −

− −

∑ ∑ ∑ ∑

∑ ∑ ∑ ∑ ∑

∑ ∑ ∑ ∑

U T U T

U T VO U T

U T U T

PSC

Supply Chain Model

Large Scale MINLP

subject to the models of:

• processing units

• tank

• pipeline

{ }QF ,QS ,Q,Vol ,Lot PF ,PS ,V y 0,1+∈ℜ ∈ℜ ∈

Supply Chain Model – cont. from previous slide

units that compose refinery topologyrefineries that compose the supply chain

••

petroleum and product tanks that compose refineries petroleum and product tanks that compose terminals refineries and terminals that compose the supply chain

•••

pipeline network for petroleum supply pipeline network for product distribution••

PETROBRAS - SP

Petroleum Supply Chain

Supply Chain Components

Petroleum Distribution Overview

Product Distribution Overview

RPBC flowsheet

Intermediate connections

Modeling Example

Tanks and CD1 Model

tan k1, petroleum,t tan k1, petroleum,CD1,tQS Q , t= ∀

tan k1,petroleum,CD1,t500 Q 20000, t≤ ≤ ∀

{ }

{ }

{ }

u', petroleum,CD1,p ,t u', petroleum,pu' tan k1,2 ,3

CD1,p ,tu', petroleum,CD1,t

u' tan k1,2 ,3

Q .Pr opPF , t

Q

p YC3C4,YLN ,YHN ,YK ,YLD,YHD,YATR

∈

∈

= ∀

∈

∑

∑

{ }CD1,t u ', petroleum,CD1,t

u' tan k1,2 ,3QF Q , t

∈= ∀∑

1 1CD1,s ,t CD1,t CD1,p ,t CD1,s ,V CD1,V ,tQS QF .PF QGain .V , t

s pC3C4 YC3C4

LN YLNHN YHNK YK

LD YLDHD YHDATR YATR

= + ∀

→ →

→ → →

→ →

CD1,s ,p ,t CD1,s ,p CD1,s,p CD1,V1,t CD1 CD1,sPS Prop +PGain .V ,s , p ,t= ∀ ∈ ∈SO PO

CD1,s

CD1,s ,t CD1,s ,u ',tu '

QS Q∈

= ∑UO

Refinery Multiperiod Planning – REVAP results

DICOPT(NLP CONOPT++)(MIP OSL, CPLEX)

Demand profile - GLN

Steep

Smooth

Steep

Smooth

Refinery Planning – Model with Uncertainty

( )u p f u

c ,t u ,t ,c u,t,c u ,t ,c c ,t u ,t ,c u,s,t,cc t c t u s

Max Z prob Cp QF Vol prob Cf QS∈ ∈ ∈ ∈ ∈ ∈ ∈

= − −

∑ ∑ ∑ ∑ ∑ ∑ ∑

C T U C T U SO

{ }f uf p

u u ,t ,c u u,v u ,v ,t u,tc t u c t vu ,

Cb y [ Cr ( Cv .V )] QF∈ ∈ ∈ ∈ ∈ ∈∈

− − + ∑ ∑ ∑ ∑ ∑ ∑ ∑

C T U C T VOU \ U U

p

u ,t ,c u ,t ,ct u

Cinv Vol∈ ∈

−

∑ ∑

T U s.t. refinery constraints

c1 c2 cN

Discrete Scenarios

1

P1% P2% PN%c,tprob =∑

Cc∈

Planning under Uncertainty - REVAP results

Cases:1: Complete model

2: Pre-selection of some suppliers

3: Interruption of pipeline segment SG-RV

General constraints:Planning horizon: one / two time periods

Supply of 20 oil types

Generation of 32 products (6 transported with pipelines)

Supply Chain Example

Supply Chain Example – Petroleum Selection

LarabMarlinRgnCabiunAlbacoBicudoCondosoBonitCabiunaLarabeCoso

Case 1 Case 2

Case 3

Supply Chain Example – REVAP

10905114047551

169451648911400

176419331001

00

871

600600660

0060

00

6551

Supply Chain Example – RPBC

138914081389

9824100759924

544951775449

363636

151515

Supply Chain Example – Oil Supply

542005420054200

360003600025992

902009020080192

850085008500 35500

3550035500

Supply Chain Example – Product Supply

1764218311362

3345288910800

2454025208

0

44004400

11660

177001770017700

640364036403

6545608911000

200182001920018

Supply Chain Example – Intermediate Streams

60224666

480

58

PFO1APFO1A

PFO4A0530

Case Case 1 Case 2 Case 3

Number of time periods 1 2 1 2 1 2

Constraints 2304 4607 2306 4611 2304 4607

Variables 2544 5087 2544 5087 2544 5087

Discrete variables 195 390 195 390 195 390

Solution time (CPU s) 116.8 656.2 152 915.6 157.8 2301

Objective Value ($ x106) 20.4 41.3 20.3 41.1 18.0 36.3

Supply Chain Example – Computational Results

Mathematical programming

-General refinery topology

-General petroleum supply chain representation

-Representation of nonlinear properties and multiple periods

-Non-convex Large-Scale MINLP

Real-world applications

-General planning trends along multiple periods

-Analysis of scenarios (discrete probabilities)

-Intensive computational effort

Conclusions

Modeling

Upstream-Downstream Integration

Multi country supply chains (royalties, tariffs)

Modeling of uncertainties

Efficient solution methods

Decomposition (spatial, temporal, functional)

Approaches (Lagrangian Relaxation, Cross Decomposition)

Research needs