EWO Mee'ng – September 2012
Petrobras Refining Decision-Making Design
Thesis Formulation & Algorithm
Thesis Prospectus
Mathematical Modeling for Strategic and Investment Planning in the Oil-Refining Industry
Brenno C. Menezes, Lincoln F. Moro Refining Op7miza7on PETROBRAS Petróleo S.A. Rio de Janeiro, RJ
Ignacio E. Grossmann Department of Chemical Engineering Carnegie Mellon University PiKsburgh, PA 15213
September 26th, 2012
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Space
Supply Chain
Refinery
Unit
second day month year 'me
RTO
Scheduling
Opera'onal Planning
Tac'cal Planning
Strategic Planning
PETROBRAS in use
PETROBRAS in P&D
Commercial
opera'onal corpora've
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this work aims to develop a quantitative method to predict necessaries structural modifications in the Brazilian refining assets through time
PETROBRAS Current Tool for Strategic Planning (PLANINV) – LP Tool
No Synthesis of the Framework
OpCmize only the streams transfer (fuel and petroleum import/export, fuel local market supply)
PLANINV Framework OT
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PETROBRAS INVESTMENTS
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Oil and Natural Gas Produc'on Oil and Natural Gas Processing Fuel Demand A and B Scenarios
PETROBRAS PRODUCTION, PROCESSING AND DEMAND BALANCE
Pre-‐Salt Discovery
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PETROBRAS CURRENT SITUATION
• HUGE INVESTMENTS
• HUGE FUEL DEMAND
• NEW PETROLEUM FIELDS
• NEW FUEL QUALITY SPECIFICATIONS
• BRAZILIAN UNUSUAL MARKET
Demand a new method to plan all future investments
More efficient and precise than the current one
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Mixer:
Feed ProprieCes:
Outputs from Units
Output from Units ProprieCes:
SpliUer:
Mixer Unit SpliKer
SpliKer
SpliKer
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the problem will determine frameworks modificaCons in order to supply all fuels quanCty and quality demands, maximizing the NPV over a period (25 years, discreCzed in 1 y):
• Existence of new process (yu,t); • Expansion of exitent Unit (yeu,t) ; • ProducCon level;
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REVAP Example: Y(refinery, unit, number of unit, 'me)
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GAMS
IMPRESS
Planning the Future Refining Units (MINLP; DICOPT++)
Planning the Future Refining Units and LogisCcs (MILP; UOSS/QLQP) 14
Proposed Algorithm
1st: Solve NLP with fix Y(R,U,N,T) at T=0
2st: Solve mulCperiod MINLP
Max mul'period NPV = Sales – Oil Purchases – Costs opera'onal/investment
X
Y and YE [0,1]
X= streams flows and proprieCes, process variables
Y= process unit existence , YE=expansion
If penalCes=0 => no investments If penalCes>0 => investments
NLP with penalCes in:
-‐ Fuel demands -‐ Diesel and gasoline Sulfur content -‐ Gasoline octane Number
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GAMS NLP Model
REVAP Opera'onal Planning: preparing for mul'period with binary y(R,U,N,T), T=1
Framework: generic {DESING(R,U,N,T)}
CDU fixed yields and simplified changes (NL) FCC base models for Y (NL) PDA extracCon factor model (NL) HDT sulfur reducCon (NL) Rigorous Blending Rules (NL)
EquaCons:
Volume balance Units TransformaCons (NL) Blending
Solver CONOPT
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Sahinidis, N. V., Grossmann, I. E., Fornari, R. E., Chathrathi, M. (1989). OpCmizaCon 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 produc4on. Computers and Chemical Engineering, 22 (1), 1039-‐1042.
Li, W., Hui, C.W. e Li, A. (2005). IntegraCng CDU, FCC and blending models into a refinery planning. Computers and Chemical Engineering, 29, 2010-‐2028.
AlaUas, A. M., Grossmann, I. E., Paulo-‐Rivera, I. (2011). IntegraCon of nonlinear crude disCllaCon unit models in refinery planning opCmizaCon. Industrial and Engineering Chemistry Research, 50, 6860-‐6870.
AlaUas, A. M., Grossmann, I. E., Paulo-‐Rivera, I. (2012). Refinery producCon planning: mulCperiod 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.
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