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

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• 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