Smart Process Operations in Fuels Industries: Applications and Opportunities (presentation)

Brenno C. Menezes

Postdoctoral Fellow

Technological Research Institute

São Paulo, SP, Brazil

Jeffrey D. Kelly

CTO and Co-Founder

IndustrIALgorithms

Toronto, ON, Canada

- Easy to implement (End-user as implementer: configure not code)- Integrates parts not yet integrated- Uses actual plant data- Reduces optimization search space in further problems (mainly MILP)- Tries to boost the polyhedral space of optimization (mainly NLP)- Automated-execution for faster and better solutions

Smart: (six fundamentals)

Smart Process Operations in Fuels Industries:

Applications and Opportunities

ITAM, Mexico City, Feb 5th, 2016.

TÉCNICAS AVANZADAS DE OPTIMIZACIÓN PARA EL

SECTOR PETROLERO

Decision-Making Tools in the oil-refining industry

2

Space

Time

Supply Chain

Refinery

Process Unit

second hour day month year

RTOControl

on-line off-line

Scheduling

Operational Planning

Tactical Planning

Strategic Planning

Operational Corporate

week

Decision-Making Tools in PETROBRAS (in oil-refining)

3

Space

Time

Supply Chain

Refinery

Process Unit


RTOControl

on-line off-line


Tactical Planning

Strategic Planning

SimulationPetrobras

NLP Optimization Commercial (Aspentech)

LP Optimization Petrobras


week

Scheduling

4

Space

Time

Supply Chain

Refinery

Process Unit


RTOControl

on-line off-line


Tactical Planning

Strategic Planning

Simulation

NLP Optimization

LP Optimization


week

Decision-Making Tools in the oil-refining industry: Challenges

Scheduling

5

Space

Time

Supply Chain

Refinery

Process Unit


RTOControl

on-line off-line


Tactical Planning

Strategic Planning


week

1st: Logic variables (MILP)

2nd: Nonlinear Models (NLP)Optimization

(MILP and NLP)

ExpansionInstallation

Modes of operation

MaintenanceCleaning (Decoking)Catalyst Change

Integrations

time

space

Scheduling

Decision-Making Tools in the oil-refining industry: Challenges

(Menezes, Kelly, Grossmann & Vazacopoulos 2014)

(Menezes, Kelly & Grossmann, 2015)

(Kelly & Zyngier, 2016)

6

Space

Time

Supply Chain

Refinery

Process Unit


RTOControl

on-line off-line


Operational

week

Optimization(MILP and NLP)

(LP)

Integrations

time

space

Scheduling

Smart Process Operations: Applications around Scheduling

(NLP)Distillation Blending and

Cutpoint Optimization

(MILP)

Reduces optimization search space

Boosts polyhedral space of optimization

Uses actual plant data

Integrates parts not yet integrated

Uses actual plant data

Integrates parts not yet integrated

Data-Driven Real Time Optimization

Crude to Tank Assignment

Easy to implement Automated-solution

Crude Transferring

Refinery Units Fuel Deliveries

Fuel Blending

Crude Dieting

Crude Receiving

Hydrocarbon Flow

FCC

DHT

NHT

KHT

REF

DC

B

L

E

N

SRFCC

Fuel gas

LPG

Naphtha

Gasoline

Kerosene

Diesel

Diluent

Fuel oil

Asphalt

Crude-Oil Management Crude-to-Fuel Transformation Blend-Shop

VDU

Receiving or

Stock Tanks

Transferring or

Feedstock Tanks

Charging

Tanks

Whole Scheduling: from Crude to Fuels

(IAL, 2015)Clusters or Stock Tanks

Crude

Min cr,yield/property(Crude-Cluster)2

cr crudepr property

yields or properties: naphtha-yield (NY), diesel-yield (DY), diesel-sulfur (DS) and residue-yield (RY)

Crude Tank Assignment for Improved Schedulability

Receiving or

Stock Tanks

Transferring or

Feedstock Tanks

Charging

Tanks

Crude-Cluster

Determines Crude-oil Segregation Rules


number of crude 5 10 15

number of equality contraints 146 308 468

number of inequality contraints 1215 2430 3645

number of continuous variables 436 836 1236

number of binary variables 246 451 656





number of binary variables 861 1066 1271





number of binary variables 1476 1681 1886Solver: CPLEX 12.6, 8 threads in Parallel Branch-and-Cut


Cluster 1 Membership (16): 1, 4, 6, 8, 12, 13, 15, 21, 28, 30, 34, 36, 40, 41, 43, 45

Cluster 2 Membership (9): 3, 9, 14, 24, 25, 27, 32, 33, 37

Cluster 3 Membership (18): 2, 5, 10, 11, 16, 17, 18, 19, 20, 22, 26, 29, 31, 35, 38, 39, 42, 44

Cluster 4 Membership (2): 7, 23

Cluster 1 Means: 18.75, 20.84, 0.1594, 8.58

Cluster 2 Means: 32.88, 13.61, 0.0900, 1.53

Cluster 3 Means: 14.08, 17.06, 0.2300, 18.64

Cluster 4 Means: 7.72, 10.31, 1.83, 35.39

NY DY DS RY

CrudeA

Component/Psedocomponent(Micro-cuts)

BoilingPoint(ºC)

Yields(Vol%)

Gravity(Kg/m3)

Sulfur(W%)

Methane (CH4) -161.52 0.0041

Ethane (CH2-CH2) -88.59 0.0081

: : : :

N-pentane 36.09 0.0152

Hypo40 40 1.1427

Hypo50 50 1.4874

: : :

Hypo840 840 0.2544

Hypo850 850 0.0210

PT

CrudeB

CrudeC

CrudeD

K=y/x=Pvap/P

KHypo=f(P,T,Column)

Molar & EnergyBalances

Fixed Yields

Delta Base

Chronology

LN

TFurnace Yields & Properties = base+delta x TFurnace

LN 20

Pinto et al, 2000; Neiro and Pinto, 2004

CDU/VDU Cut and Swing-Cut IBP (ºC) FBP (ºC)

Fuel Gas C1C2 -273 -50

LGP C3C4 -50 20

Light Naphtha LN 20 150

Heavy Naphtha HN 150 190

Kerosene K 190 250

Light Diesel LD 250 390

Heavy Diesel HD 390 420

Atmosferic Residue ATR 420 850

Light Vacuum Gasoil LVGO 420 580

Heavy Vacuum Gasoil HVGO 580 620

Vacuum Residue VR 620 850

150

CDU/VDU Cut and Swing-Cut TIB (ºC) TEB (ºC)

Fuel Gas C1C2 -273 -50

LGP C3C4 -50 20

Light Naphtha LN 20 140

Swing-Cut 1 SW1 140 160

Heavy Naphtha HN 160 180


Kerosene K 210 240


Light Diesel LD 260 360


Heavy Diesel HD 380 420

Atmosferic Residue ATR 420 850

Light Vacuum Gasoil LVGO 420 580

Heavy Vacuum Gasoil HVGO 580 620

Vacuum Residue VR 620 850

SW1

SW2

SW3

SW4CrudeA


BoilingPoint(ºC)

Yields(Vol%)

Gravity(Kg/m3)

Sulfur(W%)

Methane (CH4) -161.52 0.0041

Ethane (CH2-CH2) -88.59 0.0081

: : : :

N-pentane 36.09 0.0152

Hypo40 40 1.1427

Hypo50 50 1.4874

: : :

Hypo840 840 0.2544

Hypo850 850 0.0210

PT

CrudeB

CrudeC

CrudeD

K=y/x=Pvap/P

KHypo=f(P,T,Column)


Fixed Yields

Swing-Cuts

Delta Base

Chronology

LN

HN

SW1160

LN

140

Zhang et al, 2001; Li et al, 2005

20

Menezes, Kelly and Grossmann, 2013

CrudeA


BoilingPoint(ºC)

Yields(Vol%)

Gravity(Kg/m3)

Sulfur(W%)

Methane (CH4) -161.52 0.0041

Ethane (CH2-CH2) -88.59 0.0081

: : : :

N-pentane 36.09 0.0152

Hypo40 40 1.1427

Hypo50 50 1.4874

: : :

Hypo840 840 0.2544

Hypo850 850 0.0210

PT

CrudeB

CrudeC

CrudeD

K=y/x=Pvap/P

KHypo=f(P,T,Column)


Fixed Yields

Swing-Cuts

Fractionation Index (FI)

Delta Base

ChronologyAlattas, Grossmann and Palou-Rivera, 2011, 2012

Defines crude diet based on assay for Tcut=(IBPi+FBPj)/2

Defines new IBPi and FBPi

for a selected crude diet

CrudeA


BoilingPoint(ºC)

Yields(Vol%)

Gravity(Kg/m3)

Sulfur(W%)

Methane (CH4) -161.52 0.0041

Ethane (CH2-CH2) -88.59 0.0081

: : : :

N-pentane 36.09 0.0152

Hypo40 40 1.1427

Hypo50 50 1.4874

: : :

Hypo840 840 0.2544

Hypo850 850 0.0210

PT

CrudeB

CrudeC

CrudeD

K=y/x=Pvap/P

KHypo=f(P,T,Column)


Fixed Yields

Swing-Cuts


Delta Base

Chronology

Hybrid Models

Sanchez and Mahalec, 2012

Defines crude diet based on assay for Tcut=(IBPi+FBPj)/2


BoilingPoint(ºC)

Yields(Vol%)

Gravity(Kg/m3)

Sulfur(W%)

Methane (CH4) -161.52 0.0041

Ethane (CH2-CH2) -88.59 0.0081

: : : :

N-pentane 36.09 0.0152

Hypo40 40 1.1427

Hypo50 50 1.4874

: : :

Hypo840 840 0.2544

Hypo850 850 0.0210

PT

CrudeB

CrudeC

CrudeD

K=y/x=Pvap/P

KHypo=f(P,T,Column)


Fixed Yields

Swing-Cuts


Delta Base

ChronologyDBCTO

Hybrid Models

Kelly, Menezes and Grossmann, 2014

CrudeA

Defines new IBPi and FBPi

for a selected crude dietDefines crude diet based on assay for Tcut=(IBPi+FBPj)/2

Distillation Blending and Cutpoint Temperature Optimization (DBCTO)

From Other

Units

From CDU

Kerosene

Light Diesel

ATR

C1C2

C3C4

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

(Kelly, Menezes & Grossmann, 2014)

Cutpoint Temperature Optimization

T01 T05 T10 T30 T50 T70 T90 T95 T99

Temperature

Yie

ld (

%) Back-end:

Front-end:

New Temperature: NT

Old Temperature: OTNew Yield: YNT

Cutpoint Temperature Optimization

Temperature (oF)

Yie

ld (

%) Back-end:

Front-end:

T99: 230→214

T01: 91→85

New Temperature: NT

Old Temperature: OTNew Yield: YNT

95.87%

-1.45%

Curves Renormalization

Difference in Yield: DYNT

Temperature (oF)

Yie

ld (

%)

Optimized (renormalized)

DYNT99

DYNT01

• Maximize flow of DC1 and DC2 ($0.9 for DC1 and $1.0 DC2) with lower and upper bounds of 0.0 and 100.0 m3 each (DC3 and DC4 are fixed at 1 m3). The ASTM D86 specifications are D10 ≤ 470, 540 ≤ D90 ≤ 630 and D99 ≤ 680.

DC1 DC2 DC3 DC4

1% 305.2 (353) 322.2 (367) 327.0 (385) 302.4 (368)

10% 432.9 (466) 447.1 (476) 405.2 (435) 369.7 (407)

30% 521.6 (523) 507.1 (509) 457.1 (462) 441.0 (449)

50% 565.3 (551) 549.5 (536) 503.3 (492) 513.8 (502)

70% 606.4 (581) 598.4 (573) 551.1 (528) 574.3 (550)

90% 668.3 (635) 666.1 (634) 605.8 (574) 625.4 (592)

99% 715.7 (672) 757.7 (689) 647.0 (608) 655.2 (620)

Table. Inter-Converted TBP (ASTM D86) Temperatures in Degrees F.

Example

The new and optimized TBP

curve for DC1 given its front

and back-end shifts is now:

[(1.053%,312.8),

(10.015%,432.9),

(31.188%,521.6),

(52.361%,565.3),

(73.534%,606.4),

(94.707%,668.3),

(98.995%,689.3)]

Temperature (oF)

Yie

ld (

%)

Figure. ASTM D86 distillation curves, including the final

blend, which is determined by the blended TBP

interconversion to ASTM D86.21

Example

Solver: SLPQPE_CPLEX 12.6

Reduction in DC1’s T99 (TPB) from 715.7 to 689.3 oF

630 (ASTM D86)

Data-Driven Real-Time Optimization (DDRTO)

• Uses LP coefficients estimated directly from the plant or process using off-line closed-loop data and then we optimize this in real-time using an LP.

• Sits above a MPC layer to reset its targets or setpoints over time.• Optimizes IV’s subject to lower and upper bounds on both the IV’s and DV’s.

Bias Updating

Bounding Rules

max 𝐿𝐼𝑉𝑖; 𝑃𝐼𝑉𝑖 − 𝐷𝐿𝐼𝑉𝑖 ≤ 𝐼𝑉𝑖 ≤ m𝑖𝑛 𝑈𝐼𝑉𝑖; 𝑃𝐼𝑉𝑖 − 𝐷𝑈𝐼𝑉𝑖 ∀ 𝑖

max 𝐿𝐷𝑉𝑗; 𝑃𝐷𝑉𝑗 − 𝐷𝐿𝐷𝑉𝑗 ≤ 𝐷𝑉𝑗 ≤ m𝑖𝑛 𝑈𝐷𝑉𝑗; 𝑃𝐷𝑉𝑗 − 𝐷𝑈𝐷𝑉𝑗 ∀ 𝑗

max

𝑗=1

𝑛𝐷𝑉

𝑤𝐷𝑉𝑗 ∗ 𝐷𝑉𝑗

𝑠𝑡. 𝐷𝑉𝑗 + 𝑏𝐷𝑉𝑗 −

i=1

𝑛𝐼𝑉

𝑆𝑆𝐺𝑗,𝑖 ∗ 𝐼𝑉𝑖 + 𝑏𝐼𝑉𝑖 = 0 ∀ 𝑗

IVi is ith independent variableDVj is jth dependent variablewDVj is jth dependent variable’s profit or economic weight: cost (-) and price (+)bIVi and bDVj are the biases due to measurement feedbackSSGj,i is the steady-state gain elementPIVi and PDVj are the past valuesLIVi,UIVi, LDVj, UDVj are the lower and upper bounds.DLIV,i, DUIV,i and DLDV,j, DUDV,j are the lower and upper delta bounds.

• Steady-State Detection (SSD)- Determines if unit-operation is stationary (no accumulations) or at steady-state.

• Steady-State Data Reconciliation (SSDR)- Determines if unit-operation’s measurement system is statistically free of gross-errors and that there are no detectable losses/leaks.

• Steady-State Gain Estimation (SSGE)- Determines steady-state gains (actual first-order partial derivatives) using open- or closed-loop routine operating data though other hybrid methods such as rigorous models can be combined.

• Steady-State Gain Optimization (SSGO)- Determines new setpoints using quantity optimization and on-line measurement feedback to help “incrementally” situate the plant/sub-plant to a more profitable operating or processing space.


Steady-State Gains (SSG’s)

Independent Variables (IV’s)

Dependent Variables (DV’s)


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_𝒙𝒎𝒇𝐦,𝒕 ≥ 𝑳𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ∀ 𝐦, 𝐭 (1)

𝐯𝟐𝐫_𝒙𝒎𝒇𝐦,𝒕 ≤ 𝑼𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ∀ 𝐦, 𝐭 (2)

𝐣∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ∀ (𝐢,𝐦), 𝐭(3)

𝐣∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ∀ (𝐢,𝐦), 𝐭(4)

𝐢∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭(5)

𝐢∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,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_𝒙𝒎𝒇𝐦,𝒕 ∀ (𝐢,𝐦), 𝐭(9)

𝐣∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇𝐦,𝒕 ∀ (𝐢,𝐦), 𝐭(10)

𝐢∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇𝐦,𝒕 ∀ (𝐣,𝐦), 𝐭(11)

𝐢∈(𝐣,𝐢)

𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇𝐦,𝒕 ∀ (𝐣,𝐦), 𝐭(12)

𝐦(𝐦∈𝐮)

𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ≤ 𝟏 ∀ 𝐮, 𝐭(13)

𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝒎′,𝒕 + 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖𝐦,𝒕 ≤ 𝟐 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕∀ 𝒎′, 𝒋 , (𝐢,𝐦) (14)

xX

xX

x

x

Several unit feeds(treated as yieldswith lower andupper bounds)

Selection of modesin one physical unit

StructuralTransitions

j

28

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.

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

29

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 (Smart user)

• Global optimal (dream)

(Joly et al., 2015) M3Tech

Honeywell

SIMTO

Production Scheduler

Out of the market

Workshop on Commercial Scheduling Technologies in Oct, 2013

GAMS

Pre-Formatted (Simulation) Modeling Platform (Optimization)

Soteica

IMPL

AIMMSOff-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)

- Generates analytical quality derivatives using complex numbers

- Ability to add ad-hoc formula (e.g., blending rules)

- Digitization/discretization engine (continuous-time data input)

- Names-to-numbers to generate large models very quickly

- Initial value randomization to search for better solutions

IMPL Important Techniques/Features(Industrial Modeling and Programming Language)

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

Smart Operations: Opportunities in “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 Process Operations: Opportunities in ICT

(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

Example: when crude is selected for 2-4 days, after the 1st shift of 8h update all data usingInformation and Communication Technologies (ICT) integrated with Data-Mining applicationsand then use this in the Decision-Making.

36

Integration Strategies for Multi-Scale Optimization in the Oil-Refining

Industry (Multi-Layer and Multi-Entity)Brenno C. Menezes, Ignacio E. Grossmann and Jeffrey D. Kelly

reduce bottleneck and idling of equipment

maintenance of equipment


Strategic Planning

Plants Terminals Fuels

Processing Distribution Marketing & Sales

Raw Material

Procurement

expansion, installation, extension of equipment

production level, supply chain service

Instrumentation, Advanced Process Control and RTO

Scheduling

Tactical Planning

model data

model data

model data

model data

cycle data orders (feedforward)

key indicators (feedback)

coordination and collaboration

modes of operation,campaign

on-line

off-line

Gracias

It seems a paradox, but I have been saying that the biggest human fear is not the fear of the darkness, but the fear of the light.

(Sri Prem Baba)

37

Thank You

Engineering

Smart Process Operations in Fuels Industries: Applications and Opportunities (presentation)