KBC scheduling hydrocarbon supply chain

Proprietary Information 1Proprietary Information 1
Scheduling in the Hydrocarbon
Supply Chain
ChallengesandOpportunitiesforComputerAided
DecisionSupport
EnriqueSalomone
KBC

Proprietary Information 2
Scheduling:fromPlantoOperation

Improvedschedulingbenefits
September 12, 2018
Bridge the gap between the monthly plan
and daily operations
Resolve the upstream/downstream supply
chain integration problems
Reduce the incidence of “crisis” decision
making
Proactively deal with unscheduled events
with operations confidence
Evaluate short term opportunities
$ 2-4 MM
$ 3-6 MM
$ 1-3 MM
$ 1-3 MM
$ 2-4 MM
• Translate Optimized Direction into Detailed Schedule
• Improved Schedule Communication
• Reduce Quality Giveaway
• Detailed batch receipts/shipments versus continuous average
• Inventory “right-sizing” versus simple minimization
• Reduced demurrage
• Shift the focus to making better business decisions
• Improved schedule integrity and certainty of decisions
• Realize increased throughput with greater schedule
acceptance
• Increased range of options for late vessel arrivals,
revised pipeline schedules or operational problems
• Leverage proven model to evaluate opportunity
options through multiple scenarios for broad

Stateoftheart
Planning: based on LP optimization of economic models
 Well stablished technology
 Not many changes in the last 10 years
 There are more sophisticated approaches, they do not become
widely adopted
Scheduling: simulation based, manual decision making
 Optimization in gasoline blending

Stateoftheart
Simulation based, manual decision making…
Is there anything better??
Knowledge and capabilities are available
 Wide technical literature on Operation Research models for scheduling
Strong incentive for automation/optimization
 Important economic penalties for wrong decisions
 Important economic benefits for right decisions
 Hard to find feasible solutions
Why adoption is not widely realized?

Opportunitiesforautomated/optimizeddecisionmakingin
scheduling
Crude supply logistics and crude blending
Load/unload in marine terminals
Pipeline transportation
Gasoline Blending
Lube’s manufacturing
Polymer’s manufacturing
Annual Delivery Program in LNG
September 12, 2018

12 September 2018
Crude Scheduling
Decisions:
• Unloading tanks for
each receipt
• Manage crude
segregation
• Inter-tank transfers and
blends
• Replenish feed tanks
Predicted quality as result of the
detailed logistics
LP feed target
crude
segregation
crude
receipts
crude blends
replenish feed
tanks
Goals:
• Meet volume target
• Obtain quality close to
feed targets
• Smooth out variations
due to logistics

Load/unloadinmarineterminals

Pipelinetransportation

GasolineBlending

Lube’sproduction

Lube’sproductionscheduling
Sequencing in blending operations
 Sequence dependent cleaning and setup
Reduced capacity
Flushing off spec materials
Re-processing
Preferred sequences and undesired transitions
September 12, 2018

Lube’sproductionscheduling
Optimal production cycle
 Inventory vs. production efficiency trade-off
September 12, 2018
time
inventory

Polymer’sproduction
▪ Semi continuous
Process
▪ Make-to-stock
▪ Many final
products
– Different grades y
finishing
▪ Transitions and
Setups

CycleoptimizationinPolymer’sproduction
Drives to short cycles
Customer service
Inventory costs
Drives to long cycles
Production costs

LNG(LiquefiedNaturalGas)

LNGRegasification

LNGtransportation:InventoryRouting

Proprietary Information 21Proprietary Information
Opportunitiesfor
automated/optimized
decisionmakingin
scheduling
Crude supply logistics and
crude blending
Load/unload in marine
terminals
Pipeline transportation
Gasoline Blending
Lube’s manufacturing
Polymer’s manufacturing
Annual Delivery Program
in LNG
September 12, 2018
Hard to find good
feasible solutions
Important economic
penalties for wrong
decisions
Important
economic benefits
for right decisions
Strong incentive for
automation/optimization

Stateoftheart
Simulation based, manual decision making…
Is there anything better??
Knowledge and technical capabilities are available
 Wide technical literature on Operation Research models for scheduling
 Powerful solving techniques
Strong incentive for automation/optimization
 Important economic penalties for wrong decisions
 Important economic benefits for right decisions
 Hard to find feasible solutions
Why adoption is not widely realized?

LimitationsofthetraditionalORapproach
Why the approach that has yield so good results for planning is not
equally fitted for scheduling
Issues for scheduling optimization:
 Model detail level
Time aggregation, logistics constraints
 Formulation of the decision problem
Horizon, variables, constraints and objective function
 Business process fit
re-scheduling, solution assimilation, and dependent sub-tasks

When dealing with scheduling, logistics must be realistically accounted
for
Time aggregated models may not grant operation feasibility
 E.g.: in & out movements to a tank,
 end of period is OK, but depending of the sequence, intra-period capacity
constraints may be violated
Many logistics constraints are frequently not captured
 E.g. aligning tank A with tank X uses a common header that block alignments
from tanks {B,C,D} to {Y,Z}
Accounting for logistics requires modeling with discrete variables
(MINLP, CP)

Even with much more detailed models (MINLP or CP) there
are issues related with the decision problem formulation
 Dealing with resource’s redundancy and operating
alternatives
 Alternative solutions without a significant impact in
business objectives
 Examples:

example: transfers in tank yards
1 2 3 4 ….
Q

1 2 3 4 ….
Q

example: rotation in feed tanks
A
C
B

Alternative solutions without significant impact
in business objectives
 Introduction of artificial penalties
Automatic decision (optimization) on variables
related to process flexibility may reduce the
response capabilities to cope unforeseen
events

Other issues related with decision problem
formulation:
Planning constraints are not the same as
operational constraints
 Example:

max
min
example: Planning constraints vs. Operational constraints

max
min
LimitacionesdelenfoqueORtradicional

max
min
new max

Planning constraints are not the same as
operational constraints
 More artificial penalties...
Automatic decision (optimization) on variables
related to process flexibility may reduce the
response capabilities to cope unforeseen events

Other issues related with decision problem formulation
:
When shortening the horizon of analysis, many
important economic trade-off are not captured
Degrees of freedom should be used to:
 Conduct operation close to planning targets
 Preserve flexibility for unexpected situations
Automatic decision (optimization) on short term objectives
(other tan minimize deviation with planning targets) may
result uneconomical on the longer run

Business process issues:
Operation managers need to develop an understanding of
every new solution
Every new schedule requires time to assimilate and triggers
additional adjustments on dependent sub-tasks
Poor support for infeasibilities analysis
Automatic re-scheduling, without supervision hard to be
accepted as a practice
Using optimization model for what-if analysis may be very
frustrating experience

KeyaspectsinschedulingtheHCSupplyChain:
Material movement logistics
 Distinguishing significant decisions from redundant degrees of freedom
Inventory management
 Liquid containment
 Quality control
 Absorbing disruptions
Predicting yields and qualities from process units
 Operating beyond the “expected” region captured by the LP model
 Managing Assay deviations from actual cargo qualities

KeyaspectsinschedulingtheHCSupplyChain:
Feasibility Management
 Ensuring operational feasibility short term
 Managing medium term uncertainty
 Being able to work with infeasible projections
Objective for automated decisions
 Planned target adherence vs economics
 Preserving operation flexibility/robustness
“Surgical” re-scheduling

Proprietary Information 41Proprietary Information
Simulationwith
automated/optimized
decisionmaking
Simulation cannot be replaced by
pure automated or optimization
approaches
How to enrich simulation
approaches with automation or
optimization?

Simulationwithautomated/optimizeddecisionmaking
Supply Chain Simulation Model
Task Simulation Model
Task Definition Model
Manual Definition
(as operating)
Automated Definition
(Target Oriented Tasks and
Business rules)
Optimization Based Definition
(MILP and CP models)
Max
Process Simulation Model

12 September 2018
KBC ADVANCED TECHNOLOGIES
Improving SC models from operation & rigorous simulation
Production Accounting
SC Scheduling
Process
Simulation Unit Monitoring
Plant Information
SC Planning
plan targets
sch. vs actual
biases
operational dataoperational data
reconciled operational data
and yield account
Parametric sets unit
envelope data
LP vectors
reconciled operational data
and yield accountVM-PA
VM-SCS
PETROSIM

12 September 2018 44
Proprietary Information
Integrating optimization models
SC Simulation
Models
User interfaces for
interacting with
optimization
Solvers
Initialize
optimization
case with
simulated data
Task
definitions
(schedule)
Mathematical
Program
Models

12 September 2018
Crude Scheduling Optimization
Decisions:
• Unloading tanks for each
receipt
• Manage crude segregation
• Inter-tank transfers and
blends
• Replenish feed tanks
Predicted quality as result of
the detailed logistics
LP feed
target
crude
segregation
crude
receipts
crude
blends replenish
feed tanks
Goals:
• Meet volume target
• Obtain quality close to feed
targets
• Smooth out variations due to
logistics

CrudeScheduling
MHT Dock TRN Barge Dock TRN Ship Dock
TK353
TK354
TK355
TK356
TK352
TK163
TK164
TK162
TK166
TK178
TK165
TK185
TK181
TK174
TK186
TK182
TK175
TK94
TK93
TK96
TK95
543 Crude
544 Crude
East Field (543) West Field (544)
Marcus Hook

CrudeSchedulingOptimization

Case scope:
 3 berths, 21 receiving tanks, 2 Blend tanks, 2 feed tanks, 2 CDUs
10 crudes
60 days horizon
Solving logistics for volume and smoothing 1 property in CDU stream
(CCR on VTB)
The logistics optimization took about 4-minute to solve using CPLEX
MILP and the quality optimization took the same amount of time using
the CPLEX QP.

CrudeScheduling

FinalRemarks
We expect to see increased adoption of optimization and automation
on top of simulation as commercial products evolve to:
 Integrate hard operation research methods into flexible modeling
 Practical user interfaces with enhanced support for feasibility handling
 Seamless integration with rolling horizon, detailed simulation
We expect to see a tighter integration of rigorous process model and
actual operation data
 Improve prediction of yields and qualities
 Reduce the effort to maintain SC models
September 12, 2018

Proprietary Information 55September 12, 2018

KBC scheduling hydrocarbon supply chain

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