Scheduling Technology in Crude Oil-Refining Industries: moving from simulation to optimization
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Transcript of Scheduling Technology in Crude Oil-Refining Industries: moving from simulation to optimization
Scheduling Technology in Crude Oil-Refining Industries: 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. Moro2
Upstream Downstream Distribution Gas & Energy Biofuels
Profit in 2014 = -6,5 billion of U.S dollarsPETROBRAS employees = 86,111Service Provider employees = 360,180
Upstream
Refining Petrochemicals
Distribution
Gas & EnergyBiofuels
(Menezes , Moro, Lin, Medronho & Pessoa, 2014)
Fuel Incomes (%)
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
Summary
5
Scheduling Technology in PETROBRAS
Space
Time
Supply Chain
Refinery
Process Unit
second hour day month year
RTOControlon-line off-line
Scheduling
Operational Planning
Tactical Planning
Strategic Planning
SIPP
PIMS
PLANAB
PLANINV
SimulationPetrobras
LP 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/dietProcess unit operationsBlendingInventoriesDeliveries
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
SIPP and Other Initiatives for Scheduling
SIPP ARAUCARIASMARTCrude Oil
TransferringRefinery Units Fuels
Deliveries
Fuels Blending
Crude Oil Receiving
InventoriesCrude Oil Blending
Crude Oil Transferring
Refinery Units Fuels Deliveries
Product BlendingCrude 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
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.
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
GAMS
Pre-Formatted (Simulation) Modeling Platform (Optimization)
Soteica
IMPL
AIMMSOff-LineOn-Line
Price 10k (dev.) and 20k (dep.) +20% year100 k/year (per tool)
Modeling Built-infacilities
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, SoteicaScheduling: Aspen, Princeps, Soteica, InvensysBlending: 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
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 UniversitiesIT 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)
(1)(2)(3)
(4)(5)(6)(7)
(8)
(9)(10)(11)(12)(13)(14)
(Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE
xX
xX
x
x
Application in Boiler Feed Water Treatment
Crude Tank Assignment + Improved Swing Cut(CTA) (ISW)
Kerosene
Light Diesel
ATR
CDUC1C2C3C4
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
FGLPG
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 crudepr 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
C1C2C3C4
N
K
LD
HD
Naphtha
Heavy DieselCrude
CDU
ASTM D86
TBP
Inter-conversion
Evaporation Curves
Interpolation
Ideal Blending
Evaporation Curve
Multiple Components
Final Product
ASTM D86
Interpolation
Inter-conversion
TBP
New Temperature: NTNew Yield: YNTDifference in Yield: DYNT
Crude Oil Transferring
Refinery Units Fuels Deliveries
Product Blending
Crude Oil Receiving Inventories
Opportunities in CTA+ISW+DBCTO
CTAISW DBCTO
New-SIPP
GOMMCrude Oil Blending
Bottleneck Scheduling
Step 1: Identify Key Bottlenecks (see below)Step 2: Design Optimization StrategyStep 3: Determine Information RequirementsStep 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, ESCAPE25, 2015)
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 Planning/Scheduling/Supply Chain
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