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A Nonlinear Integrated Model for Operational Planning
of Multi-Site Refineries
Brenno C. Menezes, Lincoln F. Moro Refining Optimization PETROBRAS Petroleo S.A. Rio de Janeiro, RJ
Ignacio E. Grossmann Department of Chemical Engineering Carnegie Mellon University Pittsburgh, PA
1
Jeffrey D. Kelly Toronto, ON
industrIALgorithms
Thesis Overview
Summary
2
1st- Operational Planning of Sao Paulo Refineries
2nd- Swing-Cuts Improvements
Why Nonlinear?
Why Integrated?
Why Operational Planning?
Why Multi-Site Refineries?
A Nonlinear Integrated Model for Operational Planning
of Multi-Site Refineries
5 min
5 min
5 min
Quantitative Methods for Investment and Strategic
Planning in the Oil-Refining Industry
Brenno C. Menezes, Lincoln F. Moro Refining Optimization PETROBRAS Petroleo S.A. Rio de Janeiro, RJ
Ignacio E. Grossmann Department of Chemical Engineering Carnegie Mellon University Pittsburgh, PA
3
Jeffrey D. Kelly Toronto, ON
Fernando Pellegrini, Ricardo Medronho Department of Chemical Engineering Federal University of Rio de Janeiro Rio de Janeiro, RJ
industrIALgorithms
The thesis aims to develop a quantitative method to predict necessaries
structural modifications in the Brazilian refining and logistics assets through time
PETROBRAS Current Tool for Strategic Planning (PLANINV) – LP Tool
No Framework Synthesis
Optimize only the streams transfers (fuel and petroleum import/export, fuel local market supply)
PLANINV Framework OT
4
The necessity to develop a strategic level supply chain planning models in order to address issues in a quantitative manner rather than the qualitative approaches used till now is acknowledge by the industry and still remains an active research area. (Shapiro, 2004; Papageorgiou, 2008)
Still need modification on the figure. I want to show a scheme with current Investment Methodology
Jul-13
GAMS Distillation Models (CDU/VDU) Swing-Cuts Improvements
Sep-12 Nov-12
IMPRESS
PIMS
Jan-13 May-13 Aug-12
Nonlinear Operational Planning of Sao Paulo Refineries
EWO
Multi-plant Operational Planning of Sao Paulo Refineries
REPLAN Investments Cases for 2020-2030
Multi-period Investment Planning with NPV as Goal (refining only) MINLP
CAPD & EWO
Multi-period Strategic and Logistics Planning with NPV as Goal (refining, transportation and terminals) MILP
Distillation Models CDF least-square Interpolation
Mar-13
Binary variables
Blending
Refining Processes
fixed recipes
variable recipes
fixed yields
swing cuts (fixed properties)
Which Surface?
GAMS
IMPRESS
Planning the Future & Existing Refining Units (MINLP; DICOPT++)
Planning the Future & Existing Refining Units and Logistics (MILP; UOSS/QLQP) 8
Basic Equations for Modeling Process Unit in a Refinery
Mixer:
u u',s,u
(u',s)
QF = Q
uUS
Feed Properties:
u,p u,p u',s,u u',s,p(u',s) (u',s) ,pPF =f Q ,PF
u u u,sUS US PO
Products from Units:
u,s u,s u u,p u,v vpQS =f QF ,PF ,V
uu VOPI
Products Properties from Units:
u,s,p u,s,p u,p u,v vpPS =f PF ,V
uu VOPI
Splitter:
u u,s,u'
(u',s)
QS = Q
uUS
Mixer Unit Splitter
Splitter
Splitter
9
Qu’,s,u
Qu’,s,u
Qu’,s,u
Qu,s,u’
Qu,s,u’
Qu,s,u’
Qu,s,u’
Qu,s,u’
Qu,s,u’
Qu,s,u’
QSu,s
QSu,s
QSu,s
QFu
QFu Feed Flow
QSu,s Product Flow
Qu,s,u’ Transfer Stream Flow
PFu,p Feed Property
PSu,s,p Product Property PFu,p
PSu,s,p
PSu,s,p
PSu,s,p u units
s streams
p properties
NLP Monoperiod (Operational Planning)
Find the best quantity of Kero and VGO from REPLAN to: Kero => REVAP VGO => REVAP/RBPC - Today maximum permitted for Kero is 1500 m3/d - From the NLP Model the best value is 2300 m3/d.
FCC FCC,RCRFCC FCC FCC,s,RCR FCC,RCR
FCC,s,TRX FCC,s,TCC
QS QF .[Y Y .(PF PF )
Y .TRX Y .TCC] s
FCCSO
CDi,sCDi,s CDi CDi,s i iQS QF . (Y Y . HOT ) s , CD CDiSO CD
PDA,ASFR PDAQS QF .(1 EXT)
k k kHT ,HTs,S HT ,S HT kPF =PF 1 SEV HT HT
Crude recipe: Yields Sulfur Gravity Acidity
Swing Cuts Fractionation-Index Interpolation Regressed CDF
(Moro, Zanin & Pinto, 1998)
Refining Framework Modeling
11
CDU Models Yields Properties Modeling Reference
Fixed yields Fixed Fixed LP conventional approach
Delta Base Base+Delta Base+Delta NLP Moro, Zanin & Pinto (1998)
Swing-Cuts Pre-Cut Fixed LP Zhang et. Al. (2001)
Swing Cuts Modificated Pre-Cut with
operational modes
LS, Prop=f(Cum. Yie) NLP Li, Hui & Li (2005)
Fractionation-Index
(Heaviside function to
control FIR and FIS)
Geddes eq.
K=y/x
K=f(T,FIR,FIS)
Non-distribution in T
FIR=FI rectifying section
FIS=FI stripping section
NLP Alattas, Grossmann & Rivera (2011)
Fractionation-Index
(Binary logic to control FIR
and FIS)
Geddes eq.
K=y/x
K=f(T,FIR,FIS)
Non-distribution in T
FIR=FI rectifying section
FIS=FI stripping section
MINLP Alattas, Grossmann & Rivera (2012)
Hybrid
(mass/energy equations +
empirical PLS relations)
Mass/Energy
balance
Tray Temperature
Measurements
PLS only for TBP SLP Mahalec & Sanchez (2012)
Swing-Cuts Improvement Cutting & Blending
Hypos-Swing
Ordination
Volume-Mass weighted
interpolation
NLP Menezes, Kelly & Grossmann (2013)
Linear & Monotonic Spline
Interpolation
Yield=f(T) Prop=f(T) NLP Menezes, Kelly & Grossmann (2013)
Least-Squares Fit of CDF Yield=CDF(T) Prop=CDF(T) NLP Menezes, Kelly & Grossmann (2013)
Fractionation-Index
(Heaviside function to
control FIR and FIS)
Same as above Prop=f(T,FIR,FIS) NLP Menezes & Grossmann (2013)
Fractionation-Index
(Binary logic to control FIR
and FIS)
Same as above Prop=f(T,FIR,FIS) MINLP Menezes & Grossmann (2013)
A
B
C
Cut-to-Mix Mix-to-Cut
Assay
CDF/Interpolation
Hypos Cutting and blending
Hypos Tcut Functions
Mass Bal + Constrains (Corrections)
Volume/Mass weighted Interpolation
Conventional Approach
Cut-to-Mix Hypos-Swing Ordination
Mix-to-Cut Hypos-Swing Ordination
Hypos -> Cuts -> FCuts Swing-Cuts flows as variables
Hypos -> FCuts (need Hypos-Swing-Cuts Ordination) Hypos flows as variables
Big CDU Hypo
Hypos -> FCuts Tcut as variables [TISW,TESW]
CDF (Weibull Extreme)
Linear Interp.
Monotonic Spline
CDU Hypos per crude
Fraction Index
Yields Distribution (K=y/x)
Properties Distribution
Hypos -> FCuts Tcut as variables [TISW,TESW]
GAMS is not supporting monotonic splines (piecewise Hermite polynomial)
Motivating Example: Swing-Cuts Model This example is the well-known Swing-Cuts model applied in commercial tools for operational planning like as PIMS. The Swing-Cuts (SW1, SW2, SW3, SW4) are treated as a normal Cuts (LN,HN,K,LD,HD) with constants properties, so if the SWi is going to the upper or lower adjacent cut they will affect the Final Cuts properties (LN,HN,K,LD,HD) equally.
LN
K
LD
HN
HD
ATR
CDU C1C2
C3C4
AGBAMI
BARRACUDA
LULA
MARLIM
PCONCHAS
RONCADOR
SW1
SW2
SW3
SW4
VR
HVGO
VDU
LVGO
PFO
PVGO
PHDS
PLDS
PJFUEL
PGLNLN
K
LD
HN
HD
C3C4
C1C2
VR
HVGO
LVGOCUTS=LN,SW1,HN,SW2,K,SW3,LD,SW4,HD
FCUTS=LN,HN,K,LD,HD
SWINGS=SW1,SW2,SW3,SW4
Using this example as a baseline, different approaches are proposed to improve the properties accuracy of the final cuts:
•Swing-Cuts: Cut-to-Mix with Corrections •Swing-Cuts: Cut-to-Mix with Corrections + Volume/Mass Weighted Interpolation •Swing-Cuts: Cut-to-Mix with Hypo-Swing-Cuts Ordination
•Swing-Cuts: Mix-to-Cut with Hypo-Swing-Cuts Ordination
20 140 160
LN
SW1 HN
180
SW2
LN
HN
Vol
T(ºC) 210
C1C2
C3C4
LN
HN
SW1 SW2
K
SW3
LD
TI TE
C1C2 -273 -50
C3C4 -50 20
LN 20 140
SW1 140 160
HN 160 180
SW2 180 210
K 210 240
SW3 240 260
LD 260 360
SW4 360 380
HD 380 420
ATR 440 850
LVGO 440 580
HVGO 580 620
VR 620 850
CUTS Final Pools
Tcuts Assay
Cutting
blending
FCUTS HYPOS
Big HYPOS FCUTS Final Pools
Hypos-Averaged inside the CDU
Trange
Cutting
blending
HYPOS
Hypos-Swing Ordination
1- Cut-to-Mix with Corrections (Constrains and Mass Balance)
This approach is just a numerical correction once the properties are averaged values. Appling a mass balance and a sulfur mass balance and a set of constrains in the SWis :
(0.778*463+0.796*1999)/(463+1999)=0.793
(0.870*488+0.913*764)/(488+764)=0.896
Conventional Aproacch
2- Cut-to-Mix Correction and Interfacial Interpolation Interpolating the SW1L between the layers (LN,HN).
QSW1UP
(0.778*463+0.796*1999)/(463+1999)=0.793
(0.878*488+0.907*764)/(488+764)=0.896
(0.778*463+0.796*1999)/(463+1999)=0.793
(0.870*488+0.913*764)/(488+764)=0.896
normal approach SWis
properties constants
hypos->Cuts>Final Cuts Ready
1 Cut-to-Mix Mass bal and constrains hypos->Cuts>Final Cuts Ready
2 Cut-to-Mix Mass bal and constrains +
Interfacial Interpolation
hypos->Cuts>Final Cuts Ready
3 Cut-to-Mix Swing-Hypos ordination hypos->Final Cuts Need Ordination
4 Mix-to-Cut hypos->Final Cuts Ready
5 Mix-to-Cut Swing-Hypos ordination hypos->Final Cuts Ready
6 Mix-to-Cut Swing-Hypos ordination,
Mass bal and constrains +
Interfacial Interpolation
hypos->Final Cuts Need Ordination
+ interfacial Interpolation
7 CDF Weibull Extreme Tcuts [TISW,TESW] Ready
8 CDF with sin/cos
Weibull Correction
Weibull Extreme
+correction
Tcuts [TISW,TESW] Need Interpolation in GAMS
9 Linear Interpolation Tcuts [TISW,TESW] Need Interpolation in GAMS
10 Spline Tcuts [TISW,TESW] Not started yet
Fraction Index 11 Fraction Index Tcuts [TISW,TESW] Need properties correction
baseline
Swing Cuts
Regressed
Models
Sahinidis, N. V., Grossmann, I. E., Fornari, R. E., Chathrathi, M. (1989). Optimization model for long range planning in the chemical industry. Computers and Chemical Engineering, 13(9), 1049-1063. Moro, L.F.L., Zanin, A.C. e Pinto, J.M. (1998). A planning model for refinery diesel production. Computers and Chemical Engineering, 22 (1), 1039-1042. Li, W., Hui, C.W. e Li, A. (2005). Integrating CDU, FCC and blending models into a refinery planning. Computers and Chemical Engineering, 29, 2010-2028. Alattas, A. M., Grossmann, I. E., Paulo-Rivera, I. (2011). Integration of nonlinear crude distillation unit models in refinery planning optimization. Industrial and Engineering Chemistry Research, 50, 6860-6870. Alattas, A. M., Grossmann, I. E., Paulo-Rivera, I. (2012). Refinery production planning: multiperiod MINLP with nonlinear CDU model. Industrial and Engineering Chemistry Research (Accepted Aug 23rd). Zyngier, D., Kelly, J. D. (2012). UOPSS: A new paradigm for modeling planning and sheduling systems. ESCAPE 22, June 17-20, London.
References
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