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Crude-Oil Scheduling Technology: moving from
simulation to optimization
1Refining Optimization, PETROBRAS Headquarters, Rio de Janeiro, Brazil.
2Center for Information, Automation and Mobility, Technological Research Institute, São Paulo, Brazil.
Brenno C. Menezes,1,2 Marcel Joly,1,2 Lincoln F. L. Moro1
Upstream Downstream Distribution Gas & Energy Biofuels
ESCAPE25, Copenhagen, Jun 2nd, 2015
Upstream
Refining
Petrochemicals
Distribution
Gas & EnergyBiofuels
(Menezes , Moro, Lin, Medronho & Pessoa, 2014)
Heavy/Acid Oil
Light Oil
Fuels
Fuel Income (%)
1- Scheduling Technology in PETROBRAS (home-grown solution SIPP)
2- Workshop on Commercial Scheduling Technologies in Oct, 2013
3- Refactoring/Remaking of SIPP: GUI + IT Developments
Modeling + Engineering Advancements
4- Applications of Optimization (CTA+ISW, DBCTO, MOVPath, Demi-Water)
5- Opportunities (CTA+ISW+DBCTO, Bottleneck Scheduling, Smart Operations)
6- Conclusions
Outline
5
Scheduling Technology in PETROBRAS
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Scheduling
Operational
Planning
Tactical
Planning
Strategic
Planning
Simulation
Petrobras
NLP Optimization
Commercial (Aspentech)
LP Optimization
Petrobras
Operational Corporate
SIPP: Integrated System for Production Scheduling
week
6
What to do?
How and When to do?
Crude transf./receiving/diet
Process unit operations
Blending
Inventories
Deliveries
SheWhart or PlanDoCheckAct (PDCA) Management Cycle
Scheduling Technology in PETROBRAS
(Joly et al., 2015)
estimation
7
Operational Planning (MINLP): (Neiro and Pinto, 2005)
Strategic Planning (MILP and MILP+NLP): (Menezes et al., 2015ab)
(Menezes , Kelly & Grossmann, 2015a): Phenomenological Decomposition Heuristic , ESCAPE25
(Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE
Goal: Multi-Site Scheduling
SIPP and Other Initiatives for Scheduling
SIPP
ARAUCARIASMART
Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Fuels
Blending
Inventories
Crude Oil
Blending
SMART:
- Genetic Alg. model
using non-optimized
starting points
ARAUCARIA
- Continuous-time
impossible to be
executed in practice
Crude Oil
Receiving
Initiative Pitfalls:
Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Product
Blending
Crude Oil
Receiving Inventories
Inventory control
Yields updated by hand
Crude heavy/light and sour/sweet
Blending indices from literature
Scheduling is
Worst Case Best Case
Crude, Units, Inventories, Deliveries
Yields updated automatically
Crude in several properties/yields
Blending using daily data/interp.
Crude Oil
Blending
10
Initial Snapshot
Insert / Alter
Scheduling
Execute Simulation
Verify Results
Evaluate / Validate
Results
SIPP’s Workflow
11
As a normal outcome, schedulers abandon these solutions and then
return to their simpler spreadsheet simulators due to:
(i) efforts to model and manage the numerous scheduling scenarios
(ii) requirements of updating premises and situations that are
constantly changing
(iii) manual scheduling is very time-consuming work.
SIPP’s or Simulation-based Solution Problems
“Automation
-of-Things”
(AoT) Automated Data Integration = IT Development
Automated Decision-Making = Optimization
Automated Data Integrity = Data Rec./Par. Est.
Needs of
12
Simulation X Optimization
Simulation
Pros
• Wide-refinery simulation
• Familiar to Scheduler
• Quick solution (can be
rigorous)
Cons
• Trial-and-error
• Only feasible solution
Optimization
Pros
• Automated search for a
feasible solution
• Optimized solution (Local)
Cons
• Optimization of subsystems
• Solution time can explode
• High-skilled schedulers
• Global optimal (dream)
Workshop on Commercial Scheduling
Technologies in Oct, 2013
(Joly et al., 2015) M3Tech
Honeywell
SIMTO
Production Scheduler
Out of the market
GAMS
Pre-Formatted (Simulation) Modeling Platform (Optimization)
Soteica
IMPL
AIMMS
Off-Line
On-Line
Average
Price
10k (dev.) and 20k (dep.) +20% year100 k/year
(per tool)
Modeling Built-in
facilities
Without
facilities
Black
Box
Demanded Tools 1 13
Configuration Coding Configuration
Workshop on Commercial Scheduling
Technologies in Oct, 2013
OPL
- Drawer to generate flowsheet structures (Visual Prog. Lang.)
- Upper and lower bounds for yields (more realistic)
- Pre-Solver to reduce problem size and debug "common" infeas.
- Proprietary SLP to solve large-scale NLPs (called SLPQPE)
- Names-to-numbers to generate large models very quickly
- Ability to add ad-hoc formula (e.g., blending rules)
- Generates analytical quality derivatives using complex numbers
- Initial value randomization to search for better solutions
- Digitization/discretization engine (continuous-time data input)
IMPL Important Techniques/Features
(Industrial Modeling and Programming Language)
Modeling and Programming Languages Aspects
- Same process unit models for planning and scheduling
- Planning & scheduling with data-mining, MPC, data rec., RTO
- CDU(N) and VDU(M) as hypos, pseudo-components or micro-
cuts for any NxM arrangement (towers in cascade)
- Hierarchical Decomposition Heuristics HDH (Kelly & Zyngier, 2008)
- Phenomenological Decomposition Heuristics PDH: the MINLP
model is partitioned in MILP and NLP (Menezes, Kelly & Grossmann,
ESCAPE25, 2015)
1- APS (Advanced Planning and Scheduling):
Planning: Aspen, Soteica
Scheduling: Aspen, Princeps, Soteica, Invensys
Blending: Aspen, Princeps, Invensys
2- APC (Advanced Process Control): Aspen, gProms
3- RTO (Real-Time Optimization): Aspen, Invensys
4- Data Reconciliation and Parameter Estimation: Aspen, KBC, Soteica
5- Hybrid Dynamic Simulation: Aspen, KBC, Invensys
6- Differential Equation Solution (ODE and PDE): gProms
Applications in IMPL
1st STEP: separate (GUI + IT) from (Modeling + Engineering)
2nd STEP: prototype (ModEng) using easy-to-use modeling language
3rd STEP: prototype (GUI+IT) in a reactive iteration with 2nd STEP
30% 30%30%
GUI
(Graphic User Interface)
Interfacing/database Modeling+Engineering
10%
Solver
GUI + IT Modeling + Engineering
Refactoring/Remaking of SIPP
4th STEP: integrate (GUI + IT) and (Modeling + Engineering)
GUI + IT Developments
30%30%
GUI
(Graphic User Interface)
Interfacing/database
GUI + IT
Plant
(Visio)
Database
(Oracle)
Simulation
(Visual C++)
IHM
(Delphi)
Movement and Mixing
Optimization Management
GOMM
New GUI in C#
Modeling + Engineering Advancements
30%
Modeling+Engineering
10%
Solver
Modeling + Engineering
1st: Refinery Teams should be
involved in the modeling
Demand: easy-to-use tools
2nd: Optimize subsystems and
integrate them incrementally
HQ R&D
Center
Refineries
Universities
IT Develp.
Center
Petrobras case:
- HQ + CMU + São Paulo/Rio
Universities
- R&D
Center
Several Brazilian
Universities
+
Research Phase Development Phase
(5-10 years) (1-3 years)
dataflow or diagrammatic programming
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)
x = continuous variables (flow f)
y = binary variables (setup su)
j
𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (1)
𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (2)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(3)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(4)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭
(5)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭
(6)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭
(7)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (8)
j
Semi-continuous
equations for units
Semi-continuous
equations for streams
Mixer for each i, but
using lo/up bounds
Splitter for each j, but
using lo/up bounds
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(9)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(10)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭
(11)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭
(12)
𝐦(𝐦∈𝐮)
𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≤ 𝟏 ∀ 𝐮, 𝐭
(13)
𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝒎′,𝒕 + 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≥ 𝟐 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕∀ 𝒎′
, 𝒋 , (𝐢, 𝐦) (14)
(Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE
xX
xX
x
x
j
Several unit feeds
(treated as yields
with lower and
upper bounds)
Selection of modes
in one physical unit
Structural
Transitions
Application in Boiler Feed Water Treatment
Crude Tank Assignment + Improved Swing Cut
(CTA) (ISW)
Kerosene
Light Diesel
ATR
CDU
C1C2
C3C4
SW1
SW2
SW3
VR
VDU
N
K
LD
HD
D1HT
Naphtha
Heavy Diesel
LVGO
HVGO HTD2
D2HT
HTD1
to hydrotreating
and/or reforming
(To FCC)
Crude C
Crude D
(To Delayed Coker)
to hydrotreating
to caustic and
amines treating
JET
GLN
FG
LPG
VGO
FO
Final Products
MSD
HSD
LSD
Crude A
Crude B
(Menezes, Kelly & Grossmann, 2013)(IAL, 2015)
Clusters or Crude Tanks
Crude
Min cr,pr(Crude-Cluster)2
cr crude
pr property
pr ou yields: naphtha-yield (NY), diesel-yield (DY), diesel-sulfur (DS) and residue-yield (RY)
Improve the flexibility in the search for
optimized diet/recipe/blend
Distillation Blending and Cutpoint Temperature
Optimization (DBCTO) (Kelly, Menezes & Grossmann, 2014)
From Other
Units
From CDU
Kerosene
Light Diesel
ATR
C1C2
C3C4
N
K
LD
HD
Naphtha
Heavy Diesel
Crude
CDU
ASTM D86
TBP
Inter-conversion
Evaporation
Curves
Interpolation
Ideal Blending
Evaporation
Curve
Multiple
Components
Final
Product
ASTM D86
Interpolation
Inter-conversion
TBP
𝐘𝐍𝐓𝟗𝟗 = 𝟎. 𝟗𝟎 +
𝟎. 𝟗𝟗 − 𝟎. 𝟗𝟎
𝐎𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎
𝐍𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎
𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟏𝟎 −
𝟎. 𝟏𝟎 − 𝟎. 𝟎𝟏
𝐎𝐓𝟏𝟎 − 𝐎𝐓𝟎𝟏
𝐎𝐓𝟏𝟎 − 𝐍𝐓𝟎𝟏
𝐃𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟎𝟏 − 𝐘𝐍𝐓𝟎𝟏
𝐃𝐘𝐍𝐓𝟗𝟗 = 𝐘𝐍𝐓𝟗𝟗 − 𝟎. 𝟗𝟗
𝐎𝐥𝐝 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞: 𝐎𝐓
New Temperature: NT
New Yield: YNT
Difference in Yield: DYNT
Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Product
Blending
Crude Oil
Receiving
Inventories
Opportunities in CTA+ISW+DBCTO
CTA
ISW DBCTO
New-SIPP with optimization
GOMMCrude Oil
Blending
New-SIPPOT
inside GOMM
to register the
execution of
the scheduling
Bottleneck Scheduling
Step 1: Identify Key Bottlenecks (see below)
Step 2: Design Optimization Strategy
Step 3: Determine Information Requirements
Step 4: Prototype and Implement, etc.
Quantity-related:
Inventory containment
Hydraulically constrained
Logic-related (Physics):
Mixing, certification delays, run-lengths, etc.
Sequencing and timing
Quality-related (Chemistry):
Octane limits on gasoline
Freeze and cloud-points on
kerosene and diesels, etc
Step 5: Capture Benefits Immediately
(Harjunkoski, 2015)
Scheduling Solution Development Curves
Smart Operations
(Qin, 2014)(Christofides et al., 2007)
(Davis et al., 2012)
(Huang et al., 2012)
(Chongwatpol and Sharda, 2013)
(Ivanov et al., 2013)
Smart Process Manufacturing Big Data RFID in APS and Supply Chain
Opportunity for Molecular Scheduling for a selected crude feed
Example: when crude is selected for 2-4 days, after the 1st shift of 8h update all
data using Information and Communication Technologies (ICT) integrated with
Data-Mining applications and then use this in the Decision-Making
31
• Partnership Industry-Academia is fundamental for modeling advances.
Our vision it is missing some RPSE section, initiative, journal, meeting, etc.
• Automated DMs (Decision-Making and Data Mining)
• Permit schedulers to model using VPL in diagrammatic programming
• When moving from simulation to optimization:
Conclusions
- Optimize subsystems and then, if necessary, integrate them
incrementally
- Integrate distillates cutpoints and blending using daily data in
today’s operations as well as hydrotreating severity, etc.
- Be sure the data is accurate otherwise the decision is bad despite
the modeling

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Crude-Oil Scheduling Technology: moving from simulation to optimization

  • 1. Crude-Oil Scheduling Technology: moving from simulation to optimization 1Refining Optimization, PETROBRAS Headquarters, Rio de Janeiro, Brazil. 2Center for Information, Automation and Mobility, Technological Research Institute, São Paulo, Brazil. Brenno C. Menezes,1,2 Marcel Joly,1,2 Lincoln F. L. Moro1 Upstream Downstream Distribution Gas & Energy Biofuels ESCAPE25, Copenhagen, Jun 2nd, 2015
  • 2. Upstream Refining Petrochemicals Distribution Gas & EnergyBiofuels (Menezes , Moro, Lin, Medronho & Pessoa, 2014) Heavy/Acid Oil Light Oil Fuels
  • 4. 1- Scheduling Technology in PETROBRAS (home-grown solution SIPP) 2- Workshop on Commercial Scheduling Technologies in Oct, 2013 3- Refactoring/Remaking of SIPP: GUI + IT Developments Modeling + Engineering Advancements 4- Applications of Optimization (CTA+ISW, DBCTO, MOVPath, Demi-Water) 5- Opportunities (CTA+ISW+DBCTO, Bottleneck Scheduling, Smart Operations) 6- Conclusions Outline
  • 5. 5 Scheduling Technology in PETROBRAS Space Time Supply Chain Refinery Process Unit second hour day month year RTOControl on-line off-line Scheduling Operational Planning Tactical Planning Strategic Planning Simulation Petrobras NLP Optimization Commercial (Aspentech) LP Optimization Petrobras Operational Corporate SIPP: Integrated System for Production Scheduling week
  • 6. 6 What to do? How and When to do? Crude transf./receiving/diet Process unit operations Blending Inventories Deliveries SheWhart or PlanDoCheckAct (PDCA) Management Cycle Scheduling Technology in PETROBRAS (Joly et al., 2015) estimation
  • 7. 7 Operational Planning (MINLP): (Neiro and Pinto, 2005) Strategic Planning (MILP and MILP+NLP): (Menezes et al., 2015ab) (Menezes , Kelly & Grossmann, 2015a): Phenomenological Decomposition Heuristic , ESCAPE25 (Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE Goal: Multi-Site Scheduling
  • 8. SIPP and Other Initiatives for Scheduling SIPP ARAUCARIASMART Crude Oil Transferring Refinery Units Fuels Deliveries Fuels Blending Inventories Crude Oil Blending SMART: - Genetic Alg. model using non-optimized starting points ARAUCARIA - Continuous-time impossible to be executed in practice Crude Oil Receiving Initiative Pitfalls:
  • 9. Crude Oil Transferring Refinery Units Fuels Deliveries Product Blending Crude Oil Receiving Inventories Inventory control Yields updated by hand Crude heavy/light and sour/sweet Blending indices from literature Scheduling is Worst Case Best Case Crude, Units, Inventories, Deliveries Yields updated automatically Crude in several properties/yields Blending using daily data/interp. Crude Oil Blending
  • 10. 10 Initial Snapshot Insert / Alter Scheduling Execute Simulation Verify Results Evaluate / Validate Results SIPP’s Workflow
  • 11. 11 As a normal outcome, schedulers abandon these solutions and then return to their simpler spreadsheet simulators due to: (i) efforts to model and manage the numerous scheduling scenarios (ii) requirements of updating premises and situations that are constantly changing (iii) manual scheduling is very time-consuming work. SIPP’s or Simulation-based Solution Problems “Automation -of-Things” (AoT) Automated Data Integration = IT Development Automated Decision-Making = Optimization Automated Data Integrity = Data Rec./Par. Est. Needs of
  • 12. 12 Simulation X Optimization Simulation Pros • Wide-refinery simulation • Familiar to Scheduler • Quick solution (can be rigorous) Cons • Trial-and-error • Only feasible solution Optimization Pros • Automated search for a feasible solution • Optimized solution (Local) Cons • Optimization of subsystems • Solution time can explode • High-skilled schedulers • Global optimal (dream)
  • 13. Workshop on Commercial Scheduling Technologies in Oct, 2013 (Joly et al., 2015) M3Tech Honeywell SIMTO Production Scheduler Out of the market
  • 14. GAMS Pre-Formatted (Simulation) Modeling Platform (Optimization) Soteica IMPL AIMMS Off-Line On-Line Average Price 10k (dev.) and 20k (dep.) +20% year100 k/year (per tool) Modeling Built-in facilities Without facilities Black Box Demanded Tools 1 13 Configuration Coding Configuration Workshop on Commercial Scheduling Technologies in Oct, 2013 OPL
  • 15. - Drawer to generate flowsheet structures (Visual Prog. Lang.) - Upper and lower bounds for yields (more realistic) - Pre-Solver to reduce problem size and debug "common" infeas. - Proprietary SLP to solve large-scale NLPs (called SLPQPE) - Names-to-numbers to generate large models very quickly - Ability to add ad-hoc formula (e.g., blending rules) - Generates analytical quality derivatives using complex numbers - Initial value randomization to search for better solutions - Digitization/discretization engine (continuous-time data input) IMPL Important Techniques/Features (Industrial Modeling and Programming Language)
  • 16. Modeling and Programming Languages Aspects - Same process unit models for planning and scheduling - Planning & scheduling with data-mining, MPC, data rec., RTO - CDU(N) and VDU(M) as hypos, pseudo-components or micro- cuts for any NxM arrangement (towers in cascade) - Hierarchical Decomposition Heuristics HDH (Kelly & Zyngier, 2008) - Phenomenological Decomposition Heuristics PDH: the MINLP model is partitioned in MILP and NLP (Menezes, Kelly & Grossmann, ESCAPE25, 2015)
  • 17. 1- APS (Advanced Planning and Scheduling): Planning: Aspen, Soteica Scheduling: Aspen, Princeps, Soteica, Invensys Blending: Aspen, Princeps, Invensys 2- APC (Advanced Process Control): Aspen, gProms 3- RTO (Real-Time Optimization): Aspen, Invensys 4- Data Reconciliation and Parameter Estimation: Aspen, KBC, Soteica 5- Hybrid Dynamic Simulation: Aspen, KBC, Invensys 6- Differential Equation Solution (ODE and PDE): gProms Applications in IMPL
  • 18. 1st STEP: separate (GUI + IT) from (Modeling + Engineering) 2nd STEP: prototype (ModEng) using easy-to-use modeling language 3rd STEP: prototype (GUI+IT) in a reactive iteration with 2nd STEP 30% 30%30% GUI (Graphic User Interface) Interfacing/database Modeling+Engineering 10% Solver GUI + IT Modeling + Engineering Refactoring/Remaking of SIPP 4th STEP: integrate (GUI + IT) and (Modeling + Engineering)
  • 19. GUI + IT Developments 30%30% GUI (Graphic User Interface) Interfacing/database GUI + IT Plant (Visio) Database (Oracle) Simulation (Visual C++) IHM (Delphi) Movement and Mixing Optimization Management GOMM New GUI in C#
  • 20. Modeling + Engineering Advancements 30% Modeling+Engineering 10% Solver Modeling + Engineering 1st: Refinery Teams should be involved in the modeling Demand: easy-to-use tools 2nd: Optimize subsystems and integrate them incrementally HQ R&D Center Refineries Universities IT Develp. Center Petrobras case: - HQ + CMU + São Paulo/Rio Universities - R&D Center Several Brazilian Universities + Research Phase Development Phase (5-10 years) (1-3 years) dataflow or diagrammatic programming
  • 21. 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) x = continuous variables (flow f) y = binary variables (setup su) j
  • 22. 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (1) 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (2) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (3) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (4) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭 (5) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭 (6) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (7) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (8) j Semi-continuous equations for units Semi-continuous equations for streams Mixer for each i, but using lo/up bounds Splitter for each j, but using lo/up bounds
  • 23. 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (9) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (10) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭 (11) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭 (12) 𝐦(𝐦∈𝐮) 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≤ 𝟏 ∀ 𝐮, 𝐭 (13) 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝒎′,𝒕 + 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≥ 𝟐 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕∀ 𝒎′ , 𝒋 , (𝐢, 𝐦) (14) (Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE xX xX x x j Several unit feeds (treated as yields with lower and upper bounds) Selection of modes in one physical unit Structural Transitions
  • 24. Application in Boiler Feed Water Treatment
  • 25.
  • 26. Crude Tank Assignment + Improved Swing Cut (CTA) (ISW) Kerosene Light Diesel ATR CDU C1C2 C3C4 SW1 SW2 SW3 VR VDU N K LD HD D1HT Naphtha Heavy Diesel LVGO HVGO HTD2 D2HT HTD1 to hydrotreating and/or reforming (To FCC) Crude C Crude D (To Delayed Coker) to hydrotreating to caustic and amines treating JET GLN FG LPG VGO FO Final Products MSD HSD LSD Crude A Crude B (Menezes, Kelly & Grossmann, 2013)(IAL, 2015) Clusters or Crude Tanks Crude Min cr,pr(Crude-Cluster)2 cr crude pr property pr ou yields: naphtha-yield (NY), diesel-yield (DY), diesel-sulfur (DS) and residue-yield (RY) Improve the flexibility in the search for optimized diet/recipe/blend
  • 27. Distillation Blending and Cutpoint Temperature Optimization (DBCTO) (Kelly, Menezes & Grossmann, 2014) From Other Units From CDU Kerosene Light Diesel ATR C1C2 C3C4 N K LD HD Naphtha Heavy Diesel Crude CDU ASTM D86 TBP Inter-conversion Evaporation Curves Interpolation Ideal Blending Evaporation Curve Multiple Components Final Product ASTM D86 Interpolation Inter-conversion TBP 𝐘𝐍𝐓𝟗𝟗 = 𝟎. 𝟗𝟎 + 𝟎. 𝟗𝟗 − 𝟎. 𝟗𝟎 𝐎𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎 𝐍𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎 𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟏𝟎 − 𝟎. 𝟏𝟎 − 𝟎. 𝟎𝟏 𝐎𝐓𝟏𝟎 − 𝐎𝐓𝟎𝟏 𝐎𝐓𝟏𝟎 − 𝐍𝐓𝟎𝟏 𝐃𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟎𝟏 − 𝐘𝐍𝐓𝟎𝟏 𝐃𝐘𝐍𝐓𝟗𝟗 = 𝐘𝐍𝐓𝟗𝟗 − 𝟎. 𝟗𝟗 𝐎𝐥𝐝 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞: 𝐎𝐓 New Temperature: NT New Yield: YNT Difference in Yield: DYNT
  • 28. Crude Oil Transferring Refinery Units Fuels Deliveries Product Blending Crude Oil Receiving Inventories Opportunities in CTA+ISW+DBCTO CTA ISW DBCTO New-SIPP with optimization GOMMCrude Oil Blending New-SIPPOT inside GOMM to register the execution of the scheduling
  • 29. Bottleneck Scheduling Step 1: Identify Key Bottlenecks (see below) Step 2: Design Optimization Strategy Step 3: Determine Information Requirements Step 4: Prototype and Implement, etc. Quantity-related: Inventory containment Hydraulically constrained Logic-related (Physics): Mixing, certification delays, run-lengths, etc. Sequencing and timing Quality-related (Chemistry): Octane limits on gasoline Freeze and cloud-points on kerosene and diesels, etc Step 5: Capture Benefits Immediately (Harjunkoski, 2015) Scheduling Solution Development Curves
  • 30. Smart Operations (Qin, 2014)(Christofides et al., 2007) (Davis et al., 2012) (Huang et al., 2012) (Chongwatpol and Sharda, 2013) (Ivanov et al., 2013) Smart Process Manufacturing Big Data RFID in APS and Supply Chain Opportunity for Molecular Scheduling for a selected crude feed Example: when crude is selected for 2-4 days, after the 1st shift of 8h update all data using Information and Communication Technologies (ICT) integrated with Data-Mining applications and then use this in the Decision-Making
  • 31. 31 • Partnership Industry-Academia is fundamental for modeling advances. Our vision it is missing some RPSE section, initiative, journal, meeting, etc. • Automated DMs (Decision-Making and Data Mining) • Permit schedulers to model using VPL in diagrammatic programming • When moving from simulation to optimization: Conclusions - Optimize subsystems and then, if necessary, integrate them incrementally - Integrate distillates cutpoints and blending using daily data in today’s operations as well as hydrotreating severity, etc. - Be sure the data is accurate otherwise the decision is bad despite the modeling

Hinweis der Redaktion

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