Brenno C. Menezes Postdoctoral Fellow Technological Research Institute São Paulo, SP, Brazil Jeffrey D. Kelly CTO and Co-Founder IndustrIALgorithms Toronto, ON, Canada Ignacio E. Grossmann R. R. Dean Professor of Chemical Engineering Carnegie Mellon University Pittsburgh, PA, US Lincoln F. L. Moro Senior Consultant PETROBRAS São Paulo, SP, Brazil IAL Quantitative Methods for Strategic Investment Planning in the Oil-Refining Industry CMU, Pittsburgh, Oct 2 nd , 2015.
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Quantitative Methods for Strategic Investment Planning in the Oil-Refining Industry
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Brenno C. Menezes
Postdoctoral Fellow
Technological Research Institute
São Paulo, SP, Brazil
Jeffrey D. Kelly
CTO and Co-Founder
IndustrIALgorithms
Toronto, ON, Canada
Ignacio E. Grossmann
R. R. Dean Professor of Chemical Engineering
Carnegie Mellon University
Pittsburgh, PA, US
Lincoln F. L. Moro
Senior Consultant
PETROBRAS
São Paulo, SP, Brazil
IAL
Quantitative Methods for Strategic Investment
Planning in the Oil-Refining Industry
CMU, Pittsburgh, Oct 2nd, 2015.
Strategic Planning in PETROBRAS: PLANINV (LP)
No Process Design Synthesis Quantitative Methods
Process Design Optimization (MILP)
2
Delayed Coker
AtmosphericDistillation
CMU, Pittsburgh, Oct 2nd, 2015.
What, Where, When to Invest?
Simplified Process Models +NLP
ProcessingBlending
Quantitative Methods for Strategic Investment
Planning in the Oil-Refining IndustryBrenno C. Menezes, Ignacio E. Grossmann, Lincoln F. L. Moro and Jeffrey D. Kelly
3
Space
Time
Supply Chain
Refinery
Process Unit
second hour day month year
RTOControl
on-line off-line
Scheduling
Operational Planning
Tactical Planning
Strategic Planning
SimulationPetrobras
NLP Optimization Commercial (Aspentech)
LP Optimization Petrobras
Operational Corporate
week
Decision-Making Tools in PETROBRAS (in oil-refining)
Current Strategic Planning Methodology in PETROBRAS
Strategy
- Increase the supply by one refinery
Refinery Operational Planning
- Simulate the Refinery Process Design
Supply Chain Strategic Planning
- Test the refinery best scenarios in the home-grown global investment tool (LP)
Financial Strategy
- Find the best set of competing investments regarding their capital flow
Strategy Decision
- Invest or not in the supply increase
NPV
Net Present Value
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Refinery Operational Planning
- Simulate the Refinery Process Design
Financial Strategy
- Find the best set of competing investments regarding their capital flow
Capital resource constraints and
uncertainties in product demands
Find the Process Design (MILP)
Max NPVNPV=Sales-Costs
Invest. Costs:*QF+*Y
Strategy
- Increase the supply by one refinery
Supply Chain Strategic Planning
- Test the refinery best scenarios in the home-grown global investment tool (LP)
Strategy Decision
- Invest or not in the supply increase
MINLP
MILP+
NLP
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Proposed Strategic Planning Methodology in PETROBRAS
1st- NLP Operational ProblemZ=profit ($/d) and QFu=unit throughputs to control capacity expansion
2nd- MINLP Strategic Problem (NLP Operational Problem Embedded)Z=NPV ($) and QEu,t and QCu,t to control capacity expansion
3rd- MILP Strategic Problem + NLP Operational Problem (Phenomenological Decomposition Heuristics)Z=NPV ($) and QEu,t, QIu,t and QCu,t to control capacity expansion and installation
Crude Diet
Processing
Blending
- Crude
- Cuts/Final Cuts
- Final Products
NLP
Strategic
Operational
MILP
QFu,t ≤ QCu,t link constraint
Full Space Problem MINLP
Aggregated Approach (single refinery)
Multi-Site Approach
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REDUC
RPCC
REPLAN
REPARRPBC
REGAP
REVAP
RLAM
LUBNOR
REMAN
RECAP
PREMIUM I
PREMIUM II
RNEST
REFAP
2,013
2,408
3,380
-972
2013 2016 2020 2020
Crude Distillation
CapacityOil Products
Demands
Deficit
RNEST:Train 1 - 115 kbpd - Nov/14
Train 2 - 115 kbpd - May/15
COMPERJ-1:
Train 1 - 165 kbpd - Apr/15
PREMIUM I:Train 1 - 300 kbpd - Oct/17
Train 2 - 300 kbpd - Oct/20
PREMIUM II:
300 kbpd - Dec/17
COMPERJ-2:
Train 2 - 300 kbpd - Jan/18
Refineries in Construction: Refineries in Conceptual Project:
Capacity Unit Results(Capacities in Thousands of m3/day)
Project Staging
Three types of capital investment planning (CIP) problems
Project Staging
Motivating example 1: small GCIP flowsheet for expansion
IMPL’s UOPSS Visual Programming Language using DIA
Variable Names:
v2r_xmfm,t: unit-operation m flow variable
v3r_xjifj,i,t: unit-operation-port-state-unit-operation-port-state ji flow variable
v2r_ymsum,t: unit-operation m setup variable
v3r_yjisuj,i,t: unit-operation-port-state-unit-operation-port-state ji setup variable
VPLs (known as dataflow or diagrammatic programming) are based on the idea of "boxes and arrows", where boxes or other screen objects are treated as entities, connected by arrows, lines or arcs which represent relations (node-port constructs). (Bragg et al., 2004)