supply chain optimization of petroleum ...focapo.cheme.cmu.edu/2003/pdf/pintofocapo2003.pdf¾general...
TRANSCRIPT
![Page 1: SUPPLY CHAIN OPTIMIZATION OF PETROLEUM ...focapo.cheme.cmu.edu/2003/PDF/PintoFOCAPO2003.pdf¾General Petroleum Supply Chain (PSC) ¾Objectives ¾Representation of proposed elements](https://reader034.vdocument.in/reader034/viewer/2022051810/6016af852e67d00c92184e47/html5/thumbnails/1.jpg)
Sérgio M.S. Neiro and José M. Pinto
FOCAPO 2003, Coral Springs
SUPPLY CHAIN OPTIMIZATION OF PETROLEUM REFINERY COMPLEXES
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General Petroleum Supply Chain (PSC)
Objectives
Representation of proposed elements
Real-world PSC - Petrobras
ResultsSingle refinery PSC
Conclusions
Research needs
Outline
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General Petroleum Supply Chain
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• 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
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InternationalPetroleumCommerce
• Operations
Research.
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• Lee et al. (1996).
• Pinto et al. (2000).
• Más and Pinto (2002).
Crude Oil
Supply
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• Ponnambalam (1992).
• Bok et al. (1998).
• Pinto and Moro (2000).
Refinery - Planning/Scheduling
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• Ross (2000).
• Iakovou (2001).
• Magatão et al. (2002).
• Stebel et al. (2002).
• Rejowski and Pinto (2003).
Distribution
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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
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Refinery - Processing Unit Model
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( ) 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
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Supply, Distribution – Storage Model
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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≤
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( )
( )
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
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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••
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PETROBRAS - SP
Petroleum Supply Chain
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Supply Chain Components
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Petroleum Distribution Overview
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Product Distribution Overview
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RPBC flowsheet
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Intermediate connections
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Modeling Example
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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
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Refinery Multiperiod Planning – REVAP results
DICOPT(NLP CONOPT++)(MIP OSL, CPLEX)
Demand profile - GLN
Steep
Smooth
Steep
Smooth
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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∈
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Planning under Uncertainty - REVAP results
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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
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Supply Chain Example – Petroleum Selection
LarabMarlinRgnCabiunAlbacoBicudoCondosoBonitCabiunaLarabeCoso
Case 1 Case 2
Case 3
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Supply Chain Example – REVAP
10905114047551
169451648911400
176419331001
00
871
600600660
0060
00
6551
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Supply Chain Example – RPBC
138914081389
9824100759924
544951775449
363636
151515
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Supply Chain Example – Oil Supply
542005420054200
360003600025992
902009020080192
850085008500 35500
3550035500
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Supply Chain Example – Product Supply
1764218311362
3345288910800
2454025208
0
44004400
11660
177001770017700
640364036403
6545608911000
200182001920018
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Supply Chain Example – Intermediate Streams
60224666
480
58
PFO1APFO1A
PFO4A0530
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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
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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
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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