We propose a quantitative analysis of an enterprise-wide optimization for operations of crude-oil refineries considering the integration of planning and scheduling to close the decision-making gap between the procurement of raw materials or feedstocks and the operations of the production scheduling. From a month to an hour, re-planning and re-scheduling iterations can better predict the processed crude-oil basket, diet or final composition, reducing the production costs and impacts in the process and product demands with respect to the quality of the raw materials. The goal is to interface planning and scheduling decisions within a time-window of a week with the support of re-optimization steps. Then, the selection, delivery, storage and mixture of crude-oil feeds from the tactical procurement planning up to the blend scheduling operations are made more appropriately. The up-to-down sequence of solutions are integrated in a feedback iteration to both reduce time-grids and as a key performance indicator.

1. Brenno C. Menezes
PostDoc Research Scholar
Carnegie Mellon University
Pittsburgh, PA, US
Jeffrey D. Kelly
Owner
industri@lgorithms
Toronto, ON, Canada
Enterprise-Wide Optimization for Operations of Crude-Oil
Refineries: closing the procurement and scheduling gap
EWO, Pittsburgh 22nd Sep, Berlin 20th Oct, Barcelona Nov 7th, Salamanca Nov 10th, 2016.
Ignacio E. Grossmann
R. R. Dean Professor of Chemical Engineering
Carnegie Mellon University
Pittsburgh, PA, US
Faramroze Engineer
Senior Consultant
SK-Innovation
Seoul, South Korea
1
- Quantitative Analysis on Opportunities for Planning and Scheduling Integration
- Shows the Scheduling Problem Developed for SK-Energy (3rd biggest refinery among 635)
Procurement
Scheduling
Close the
decision-making GAP
in terms of crude-oil quality
month
week

2. 2
Outline
1- Decision-Making in the Crude-Oil Refining: Identify the Enterprise Optimization
3- Enterprise-Wide Optimization (EWO) for Operations of Crude-Oil Refineries
2- Planning and Scheduling Integration: Opportunities for EWO
Challenges
Commercial and Academic Approaches
4- Whole Crude-Oil Scheduling: from crude unloading to product delivery
Feedstock Storage Assignment (MILP)  FOCAPO
Crude-Oil Blend Scheduling (MILP+NLP)  FOCAPO
Key Aspects in the Modeling and Solution  IMPL
5- Conclusions/Next Steps on Scheduling
MINLP
Relax y [0,1]
as (0,1) in NLP
Re-Planning; Planning + Feedback from Scheduling
Off-Line and On-line Scheduling: Re-Scheduling

3. 3
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
Tactical
Planning
Strategic
Planning
Simulation
LP Optimization
LP Optimization
Operational Corporate
week
Decision-Making in the Oil-Refining Industry: Challenges
Scheduling
NLP
NLP
SQP
SLP
MILP+NLP SLP
LP
PIMS-AO
Spiral Plan
Production Scheduler
MILP+NLP SLPQPE
Structure
(space)
EWO Meeting, Sep 22nd, 2016.
Commercial
Solutions

4. 4
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
Integration
time
space
Scheduling
(Menezes, Kelly, Grossmann & Vazacopoulos 2014)
(Menezes, Kelly & Grossmann, 2015)Simulation
Decision-Making in the Oil-Refining Industry: Challenges
Integration
Structure
(space)
(unmeasured or off-line)
(measured or on-line)
EWO Meeting, Sep 22nd, 2016.
Academic
Approaches

5. Scheduling
5
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Operational
Planning
Tactical
Planning
Operational Corporate
week
1st: Crude Procurement (NLP)
2nd: Modes of Operation (NLP)
Planning and Scheduling Integration: EWO Opportunities
3rd: Ship Scheduling (MILP)
Storage Assignment (MILP)
Crude Blend Scheduling (MILP+NLP)
time
Integration in time
(unmeasured or off-line)
Structure
(space)
Crude Basket
Planned
Operations
Procurement
Scheduling
Close the GAP
EWO Meeting, Sep 22nd, 2016.

6. Scheduling
6
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Tactical
Planning
Operational Corporate
week
1st: Crude Procurement (NLP)
2nd: Modes of Operation (NLP)
3rd: Ship Scheduling (MILP)
Storage Assignment (MILP)
Crude Blend Scheduling (MILP+NLP)
time
Integration in time
(unmeasured or off-line)
Structure
(space)
Planned
Operations
Procurement
Scheduling
Close the GAP
Operational
Planning
Crude Basket
EWO Meeting, Sep 22nd, 2016.
Planning and Scheduling Integration: Re-Plan + Feedback
Re-Planning to reduce the crude basket
and operations from 1 month to 4 weeks

7. modes of operation
Campaign (1 month)
EWO for Operations of Crude-Oil Refineries: Old Fashion Planning
Operational Planning
Process
Units
Fuel
Farms
Raw
Material
Crude basket
Instrumentation, Advanced Process Control and RTO
Scheduling
Tactical Planning
on-line
off-line
model data
Crude
Procurement Crude-to-Fuel Transformation
Fuel
Sales
1m 2m 3m
3m
model data
model data
(NLP)
(NLP)
(3 months)
Orders (decisions): Feedforward
EWO Meeting, Sep 22nd, 2016.
Tactical:
1m
Operational:
m = month
Scheduling:

8. collaboration
modes of operation
Campaign (1 month)
EWO for Operations of Crude-Oil Refineries: Proposed Re-Planning
Week for
crude arrival
Operational Planning
Process
Units
Fuel
Farms
Raw
Material
Crude basket
Instrumentation, Advanced Process Control and RTO
Scheduling
Tactical Planning
on-line
off-line
model data
Crude
Procurement Crude-to-Fuel Transformation
Fuel
Sales
1m 2m 3m
1w 2w 3w 4w
blocked production
3m
model data
model data
(NLP)
(NLP)
(3 months)
Crude basket
4 weeks+ 2m
(1st week
in days/shifts
+ 3 weeks)
Ship
Rearrangements
Orders (decisions): Feedforward Key Indicators (performance): Feedback
(4 weeks)(4 weeks)
EWO Meeting, Sep 22nd, 2016.
4w+2m 4w
Tactical: Operational:
7 days
21 shifts
m = month
w = week
4w
Scheduling:

9. collaboration
modes of operation
Campaign (1 month)
EWO: Re-Planning + Feedback from Scheduling
Week for
crude arrival
Operational Planning
Process
Units
Fuel
Farms
Raw
Material
Crude basket
Instrumentation, Advanced Process Control and RTO
Scheduling
Tactical Planning
on-line
off-line
model data
Crude
Procurement Crude-to-Fuel Transformation
Fuel
Sales
1m 2m 3m
1w 2w 3w 4w
2h 4h 166h 168h
blocked production
FSA
CBS
model data
model data
SHS = Ship Scheduling
(NLP)
(NLP)
(3 months)
Crude basket
4 weeks+ 2m
Ship
Rearrangements
Orders (decisions): Feedforward Key Indicators (performance): Feedback
(4 weeks)(4 weeks)
(MILP)
(MILP+
NLP)
SK example : 7 days/2h time-step
EWO Meeting, Sep 22nd, 2016.
Conclusions:
• Reduce the crude-oil procurement decision from a month to a week
• Feedback from Scheduling for detailed Planning within time-steps of days/shifts
(1st week
in days/shifts
+ 3 weeks)

10. FSA = Feedstock Storage Assignment
collaboration
modes of operation
Campaign (1 month)
EWO for Operations of Crude-Oil Refineries: Off-Line Scheduling
Week for
crude arrival
Operational Planning
Process
Units
Fuel
Farms
Raw
Material
Crude basket
Instrumentation, Advanced Process Control and RTO
Scheduling
Tactical Planning
on-line
off-line
model data
Crude
Procurement Crude-to-Fuel Transformation
Fuel
Sales
1m 2m 3m
1w 2w 3w 4w
1d 2d 29d 30d
2h 4h 166h 168h
blocked production
SHS
FSA
CBS
model data
model data
SHS = Ship Scheduling
CBS = Crude-oil Blend Scheduling
(NLP)
(NLP)
(3 months)
Crude basket
4 weeks+ 2m
CDU IBP/FBP
Ship
Rearrangements
Orders (decisions): Feedforward Key Indicators (performance): Feedback
(4 weeks)(4 weeks)
(MILP)
(MILP)
(MILP+
NLP)
SK example : 7 days/2h time-step
EWO Meeting, Sep 22nd, 2016.
(1st week
in days/shifts
+ 3 weeks)
(NLP) = 100 K to 0.5 million
(MILP) = 30 K to 50 K
Number of Variables:

11. Crude Blend Scheduling (MILP+NLP)
13
Scheduling
Structure
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
week
space
Measured updates/feedback:
a) Fill-Draw delays in Storage Tanks
b) Data Rec in feed tank real composition and properties
SK example developed in SK-Innovation Internship: 7 days/2h time-step in Discrete-Time
Integration in space
(measured or on-line)
Re-Scheduling
On-line Scheduling
Real-time Scheduling
Data-Analysis with Feedback
Data-Analytics + Decision-Making:
a) Distillation Blending and Cutpoint Opt. (ASTM D86 => TBP + flows)
b) Data-Driven RTO (LP to relate the majors pairs of
Depend./Independ. Variables): SSD, SSDR, SSGE, SSGO
EWO for Operations of Crude-Oil Refineries: On-Line Scheduling

12. 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
Charging or
Feed Tanks
Whole Scheduling: from Crude to Fuels
Crude-Oil Scheduling Problem
Receiving or
Storage Tanks
Transferring or
Feedstock Tanks
VDU
1996: Lee, Pinto, Grossmann and Park (MILP), discrete-time
2004: Randy, Karimi and Srinivasan (MILP), continuous-time
2009: Mouret, Grossmann and Pestiaux: MILP+NLP continuous-time
2014: Castro and Grossmann: MINLP ; MILP+NLP, continuous-time
2015: Cerda, Pautasso and Cafaro: MILP+NLP, continuous-time
(336h: 14 days; binary ≈ 4,000; continuous ≈ 6,000; constraints ≈ 100K; CPU(s) ≈ 500)
14
2016 Goal: solve the SK Ulsan refinery scheduling for a week
(38 crude, 2 pipelines, 23 storage tanks, 11 feed tanks, 5 CDUs)
1. JD Kelly, BC Menezes, IE Grossmann, F Engineer, 2017, FOCAPO.
2. JD Kelly, BC Menezes, F Engineer, IE Grossmann, 2017, FOCAPO.EWO Meeting, Sep 22nd, 2016.
MINLP -> MILP + NLP
MINLP Relax y [0,1]
as (0,1) in NLP
Current Benchmark
DICOPT (5,000 binary variables)

13. 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
Charging or
Feed Tanks
Whole Scheduling: from Crude-Oils to Fuels
Crude-Oil Blend Scheduling Problem
Receiving or
Storage Tanks
Transferring or
Feedstock Tanks
FSA
VDU
(MILP+NLP)
PDH Decomposition (logistics + quality problems)
Includes logistics details
1996: Lee, Pinto, Grossmann and Park (MILP), discrete-time
2004: Randy, Karimi and Srinivasan (MILP), continuous-time
2009: Mouret, Grossmann and Pestiaux: MILP+NLP continuous-time
2014: Castro and Grossmann: MINLP ; MILP+NLP, continuous-time
2015: Cerda, Pautasso and Cafaro: MILP+NLP, continuous-time
(336h: 14 days; binary ≈ 4,000; continuous ≈ 6,000; constraints ≈ 100K; CPU(s) ≈ 500)
15
(MILP)
2016 Goal: solve the SK Ulsan refinery scheduling for a week
(38 crude, 2 pipelines, 23 storage tanks, 11 feed tanks, 5 CDUs)
Minimize the Quality Variation Feedstocks -> Storage Tanks
Reduces optimization search space for further scheduling
2nd Crude Blend Scheduling
Optimization (CSBO)
Yields
Rates (crude diet, fuel recipes, conversion)
(Menezes, Kelly & Grossmann, 2015)
1. JD Kelly, BC Menezes, IE Grossmann, F Engineer, 2017, FOCAPO.
2. JD Kelly, BC Menezes, F Engineer, IE Grossmann, 2017, FOCAPO.EWO Meeting, Sep 22nd, 2016.
MINLP -> MILP + NLP
1st Feedstock Storage
Assignment (FSA)
FSA
CBSO

14. 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
Charging or
Feed Tanks
Whole Scheduling: from Crude-Oils to Fuels
Crude-Oil Blend Scheduling Problem
Receiving or
Storage Tanks
Transferring or
Feedstock Tanks
FSA
VDU
(MILP+NLP)
PDH Decomposition (logistics + quality problems)
Includes logistics details
17
(MILP)1st Feedstock Storage
Assignment (FSA)
Minimize the Quality Variation Feedstocks -> Storage Tanks
Reduces optimization search space for further scheduling
2nd Crude Blend Scheduling
Optimization (CSBO)
Yields
Rates (crude diet, fuel recipes, conversion)
(Menezes, Kelly & Grossmann, 2015)
EWO Meeting, Sep 22nd, 2016.
1. JD Kelly, BC Menezes, IE Grossmann, F Engineer, 2017, FOCAPO.
2. JD Kelly, BC Menezes, F Engineer, IE Grossmann, 2017, FOCAPO.
CBSO

15. 18
Crude-Oil Blend Scheduling: Illustrative Example
336h: 14 days discretized into 2-hour time-period durations (168 time-periods)
The logistics problem (MILP): ZMILP=695.6
8,333 continuous + 3,508 binary variables
3,957 equality and 15,810 inequality constraints
Non-Zeros: 59,225 ; Degrees-of-freedom: 7,884
CPU(s): 176.0 seconds / 8 threads in CPLEX 12.6.
The quality problem (NLP): ZNLP=701.9
19,400 continuous variables
14,862 equality and 696 inequality constraint
Non-Zeros: 26,430 ; Degrees-of-freedom: 4,538
CPU(s): 16.8 seconds in the IMPL’ SLP
engine linked to CPLEX 12.6.
Crude blend scheduling
(MILP+NLP)
Clustering
(MILP)
MILP-NLP gap: 0.09% with only one PDH iteration.
EWO Meeting, Sep 22nd, 2016.
IMPL (Industrial Modeling and Programming Language) using Intel Core i7 machine at 2.7 Hz with 16GB of RAM

16. SK Refinery Example
19
The logistics problem (MILP):
33,937 continuous + 29,490 binary variables
8,612 equality and 72,368 inequality constraints
Non-Zeros: 531,204; Degrees-of-freedom: 63,427
CPU(s): 215 seconds (3.58 min) in 8 threads CPLEX 12.6.
The quality problem (NLP):
143,316 continuous variables
88,539 equality and 516 inequality constraint
Non-Zeros: 138,812; Degrees-of-freedom: 54,788
CPU(s): 539 seconds (8.98 min) in the IMPL’ SLP
engine linked to CPLEX 12.6.
MILP-NLP gap :12% to 10% with two PDH iteration.
Units: 5 CDUs in 9 modes of operation + 4 Blenders
Tanks: 35 among storage and feed tanks.
7 days: 168-hours discretized into 2-hour time-period durations (84 time-periods).
EWO Meeting, Sep 22nd, 2016.
IMPL (Industrial Modeling and Programming Language) using Intel Core i7 machine at 2.7 Hz with 16GB of RAM
IMPL’s Pre-solver reduces
non-zeros from 2 to 0.5
million

17. 20
Key Aspects in the Modeling and Solution
1. JD Kelly, BC Menezes, F Engineer, IE Grossmann, 2017, FOCAPO.
3. Quality model1
a. Reformulation to reduce %NLP
and non-zeros (reduce number of
local solutions and their variance)
1. UOPSS
a. flows and setups   with LO and UP
b. LO and UP for flows and yields
c. (Unit+Mode)=Mode m
d. Valid Cut: m-arrow-m’
2. Logistics model1
a. Fill-draw delay
b. Uptime for blenders and arrows
c. Multi-use
Flow
Volume-Based
Properties
Weight-Based
Properties

18. 21
Key Aspects in IMPL’s computing skills
5. Complex Number to find derivatives and derivatives by groups of
variables with the same sparsity pattern.
4. Reverse Polish Notation (symbolic representation in the NLP problems
instead of sparse)
6. Computer memory configuration and pre-solving before starting the
solution. (save memory in the CPU)

19. Conclusion on the Scheduling Problem
22
EWO Meeting, Sep 22nd, 2016.
Novelty:
• Segregates crude management in storage assignment1 and crude blend
scheduling.2
• Phenomenological decomposition in logistics (MILP) and quality (NLP)
problems applied in a scheduling problem.
Impact for industrial applications:
• UOPSS modeling, pre-solving, and parallel processing permitted to solve an
2h time-step discrete-time formulation for a highly complex refinery (34
crude-oils, 24 storage tanks, 9 feed tanks, 5 CDUs): for 7 days (84 time-periods)
1. JD Kelly, BC Menezes, IE Grossmann, F Engineer, 2017, FOCAPO.
2. JD Kelly, BC Menezes, F Engineer, IE Grossmann, 2017, FOCAPO.

20. 23
Next Steps:
• Add upgrading units and their tanks (RFCC hydrotreaters, RFCC, VDU)
• Add cutpoint optimization instead of modes of operation in CDUs
• Crude-Oil Blender x Sequential Blending to prepare the Feed Tanks.
• Factors using bulk qualities as an LP between the Storage and Feed Tanks to
be used in the logistics (MILP): to have a more robust PDH decomposition
• Whole Scheduling: from Crude-Oils to Fuel Deliveries
• Initialization, Synchronization, Real-time Scheduling with feedback
EWO Meeting, Sep 22nd, 2016.
Conclusion on the Scheduling Problem

21. Thank You
24
Q?&A!
www.industrialapplications.club
www.induapps.club
brenno@induapps.club
Gracias