The smart revolution, the 4th after the mechanical, electrical and digital ones, increasingly becomes a reality inside the oil and gas industries. Based on six smart fundamentals: 1-) easy to implement, 2-) integrates parts not yet integrated, 3-) uses actual plant data, 4-) reduces the optimization search space, 5-) tries to boost the polyhedral space of optimization and finally 6-) automated-execution for faster and better solutions.  We start by identifying the challenges in advanced planning and scheduling inside petroleum refining industries. Specifically, three "smart" process operations around scheduling optimization are explained. The 1st is what we call the crude-oil to tank assignment problem (CTAP) where a mixed-integer linear model determines the design or destination of crude-oils (feedstocks) transferring from terminals to refinery storage tanks in order to minimize the a) deviation of quality of each crude-oil, b) reduce the optimization search space in further crude-oil scheduling optimization and c) boost the polyhedral space of optimization. This is to prepare for the transfers of crude-oil charged to the tanks by considering the most important (key) quality bottleneck constraints that drive the economics, efficiency and emissions of the refinery. The 2nd smart application is a nonlinear optimization integrating the distillation blending and cutpoint temperature optimization using experimental plant data (ASTM or SD) of the atmospheric tower distillation curves to define new initial and final boiling points (cutpoints) of the distillates by considering the market demands of the blend-shops for the final fuel products. The 3rd is a "data-driven" real-time optimization (RTO) implemented using an LP to integrate the multiple continuous-process units in the refinery as a whole using well-known closed-loop parameter estimation techniques in IMPL to estimate steady-state gains or first-order derivatives from routine plant operating data.  IMPL is the modeling and solving platform to be introduced and discussed further given that it provides an integrated environment to develop and deploy industrial applications of this nature.  In addition, it should be emphasized that these types of applications can be applied to other process industries especially in the oil and gas and related industries.

1. 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

2. 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

3. Decision-Making Tools in PETROBRAS (in oil-refining)
3
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
Tactical
Planning
Strategic
Planning
Simulation
Petrobras
NLP Optimization
Commercial (Aspentech)
LP Optimization
Petrobras
Operational Corporate
week
Scheduling

4. 4
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
Tactical
Planning
Strategic
Planning
Simulation
NLP Optimization
LP Optimization
Operational Corporate
week
Decision-Making Tools in the oil-refining industry: Challenges
Scheduling

5. 5
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
Tactical
Planning
Strategic
Planning
Operational Corporate
week
1st: Logic variables (MILP)
2nd: Nonlinear Models (NLP)
Optimization
(MILP and NLP)
Expansion
Installation
Modes of operation
Maintenance
Cleaning (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. 6
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
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

7. 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

8. (IAL, 2015)Clusters or Stock Tanks
Crude
Min cr,yield/property(Crude-Cluster)2
cr crude
pr 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

9. Crude Tank Assignment for Improved Schedulability
r of crude 5 10 15
r of equality contraints 146 308 468
r of inequality contraints 1215 2430 3645
r of continuous variables 436 836 1236
r of binary variables 246 451 656
r of crude 20 25 30
r of equality contraints 628 788 948
r of inequality contraints 4860 6075 7290
r of continuous variables 1636 2036 2436
r of binary variables 861 1066 1271
r of crude 35 40 45
r of equality contraints 1108 1268 1428
r of inequality contraints 8505 9720 10935
r of continuous variables 2836 3236 3636
r of binary variables 1476 1681 1886
Solver: CPLEX 12.6, 8 threads in Parallel Branch-and-Cut

10. Crude Tank Assignment for Improved Schedulability
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

11. CrudeA
Component/
Psedocomponent
(Micro-cuts)
Boiling
Point
(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
P
T
CrudeB
CrudeC
CrudeD
K=y/x=Pvap/P
KHypo=f(P,T,Column)
Molar & Energy
Balances
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

12. 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
Swing-Cut 2 SW2 180 210
Kerosene K 210 240
Swing-Cut 3 SW3 240 260
Light Diesel LD 260 360
Swing-Cut 4 SW4 360 380
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
Component/
Psedocomponent
(Micro-cuts)
Boiling
Point
(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
P
T
CrudeB
CrudeC
CrudeD
K=y/x=Pvap/P
KHypo=f(P,T,Column)
Molar & Energy
Balances
Fixed
Yields
Swing-Cuts
Delta
Base
Chronology
LN
HN
SW1
160
LN
140
Zhang et al, 2001; Li et al, 2005
20
Menezes, Kelly and Grossmann, 2013

13. CrudeA
Component/
Psedocomponent
(Micro-cuts)
Boiling
Point
(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
P
T
CrudeB
CrudeC
CrudeD
K=y/x=Pvap/P
KHypo=f(P,T,Column)
Molar & Energy
Balances
Fixed
Yields
Swing-Cuts
Fractionation
Index (FI)
Delta
Base
Chronology
Alattas, Grossmann and Palou-Rivera, 2011, 2012
Defines crude diet based on
assay for Tcut=(IBPi+FBPj)/2

14. Defines new IBPi and FBPi
for a selected crude diet
CrudeA
Component/
Psedocomponent
(Micro-cuts)
Boiling
Point
(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
P
T
CrudeB
CrudeC
CrudeD
K=y/x=Pvap/P
KHypo=f(P,T,Column)
Molar & Energy
Balances
Fixed
Yields
Swing-Cuts
Fractionation
Index (FI)
Delta
Base
Chronology
Hybrid
Models
Sanchez and Mahalec, 2012
Defines crude diet based on
assay for Tcut=(IBPi+FBPj)/2

15. Component/
Psedocomponent
(Micro-cuts)
Boiling
Point
(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
P
T
CrudeB
CrudeC
CrudeD
K=y/x=Pvap/P
KHypo=f(P,T,Column)
Molar & Energy
Balances
Fixed
Yields
Swing-Cuts
Fractionation
Index (FI)
Delta
Base
ChronologyDBCTO
Hybrid
Models
Kelly, Menezes and Grossmann, 2014
CrudeA
Defines new IBPi and FBPi
for a selected crude diet
Defines crude diet based on
assay for Tcut=(IBPi+FBPj)/2

16. Distillation Blending and Cutpoint Temperature Optimization
(DBCTO)
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
(Kelly, Menezes & Grossmann, 2014)

17. Cutpoint Temperature Optimization
T01 T05 T10 T30 T50 T70 T90 T95 T99
Temperature
Yield(%)
Back-end:
Front-end:
New Temperature: NT
Old Temperature: OT
New Yield: YNT

18. Cutpoint Temperature Optimization
Temperature (oF)
Yield(%)
Back-end:
Front-end:
T99: 230→214
T01: 91→85
New Temperature: NT
Old Temperature: OT
New Yield: YNT
95.87%
-1.45%

19. Curves Renormalization
Difference in Yield: DYNT
Temperature (oF)
Yield(%)
Optimized (renormalized)
DYNT99
DYNT01

20. • 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

21. 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)
Yield(%)
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)

22. 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 variable
DVj is jth dependent variable
wDVj is jth dependent variable’s profit or
economic weight: cost (-) and price (+)
bIVi and bDVj are the biases due to
measurement feedback
SSGj,i is the steady-state gain element
PIVi and PDVj are the past values
LIVi,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.

23. • 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.
Data-Driven Real-Time Optimization (DDRTO)

24. Steady-State
Gains (SSG’s)
Independent
Variables (IV’s)
Dependent
Variables (DV’s)
Data-Driven Real-Time Optimization (DDRTO)

25. 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

26. 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

27. 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)
xX
xX
x
x
Several unit feeds
(treated as yields
with lower and
upper bounds)
Selection of modes
in one physical unit
Structural
Transitions
j

28. 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. 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)

30. (Joly et al., 2015) M3Tech
Honeywell
SIMTO
Production Scheduler
Out of the market
Workshop on Commercial Scheduling Technologies in Oct, 2013

31. 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

32. - 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)

33. 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

34. 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

35. 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 using
Information and Communication Technologies (ICT) integrated with Data-Mining applications
and then use this in the Decision-Making.

36. 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
Operational Planning
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

37. 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