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KBC scheduling hydrocarbon supply chain

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Scheduling in the hydrocarbon supply chain
Enrique Salomone
Software user conference 2018, USA

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KBC scheduling hydrocarbon supply chain

  1. 1. Proprietary Information 1Proprietary Information 1 Scheduling in the Hydrocarbon Supply Chain ChallengesandOpportunitiesforComputerAided DecisionSupport EnriqueSalomone KBC
  2. 2. Proprietary Information 2 Scheduling:fromPlantoOperation
  3. 3. Proprietary Information 3 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
  4. 4. Proprietary Information 4 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
  5. 5. Proprietary Information 5 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?
  6. 6. Proprietary Information 6 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
  7. 7. 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
  8. 8. Proprietary Information 8 Load/unloadinmarineterminals
  9. 9. Proprietary Information 9 Load/unloadinmarineterminals
  10. 10. Proprietary Information 10 Pipelinetransportation
  11. 11. Proprietary Information 11 GasolineBlending
  12. 12. Proprietary Information 12 Lube’sproduction
  13. 13. Proprietary Information 13 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
  14. 14. Proprietary Information 14 Lube’sproductionscheduling Optimal production cycle  Inventory vs. production efficiency trade-off September 12, 2018 time inventory
  15. 15. Proprietary Information 15 Polymer’sproduction ▪ Semi continuous Process ▪ Make-to-stock ▪ Many final products – Different grades y finishing ▪ Transitions and Setups
  16. 16. Proprietary Information 16 CycleoptimizationinPolymer’sproduction Drives to short cycles Customer service Inventory costs Drives to long cycles Production costs
  17. 17. Proprietary Information 17 LNG(LiquefiedNaturalGas)
  18. 18. Proprietary Information 18 LNG(LiquefiedNaturalGas)
  19. 19. Proprietary Information 19 LNGRegasification
  20. 20. Proprietary Information 20 LNGtransportation:InventoryRouting
  21. 21. 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
  22. 22. Proprietary Information 22 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?
  23. 23. Proprietary Information 23 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
  24. 24. Proprietary Information 24 LimitationsofthetraditionalORapproach 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)
  25. 25. Proprietary Information 25 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: LimitationsofthetraditionalORapproach
  26. 26. Proprietary Information 26 example: transfers in tank yards 1 2 3 4 …. Q LimitationsofthetraditionalORapproach
  27. 27. Proprietary Information 27 example: transfers in tank yards LimitationsofthetraditionalORapproach 1 2 3 4 …. Q
  28. 28. Proprietary Information 28 example: transfers in tank yards LimitationsofthetraditionalORapproach 1 2 3 4 …. Q
  29. 29. Proprietary Information 29 example: rotation in feed tanks LimitationsofthetraditionalORapproach A C B
  30. 30. Proprietary Information 30 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 LimitationsofthetraditionalORapproach
  31. 31. Proprietary Information 31 Other issues related with decision problem formulation: Planning constraints are not the same as operational constraints  Example: LimitationsofthetraditionalORapproach
  32. 32. Proprietary Information 32 max min example: Planning constraints vs. Operational constraints LimitationsofthetraditionalORapproach
  33. 33. Proprietary Information 33 max min LimitacionesdelenfoqueORtradicional example: Planning constraints vs. Operational constraints
  34. 34. Proprietary Information 34 max min new max LimitationsofthetraditionalORapproach example: Planning constraints vs. Operational constraints
  35. 35. Proprietary Information 35 max min new max LimitationsofthetraditionalORapproach example: Planning constraints vs. Operational constraints
  36. 36. Proprietary Information 36 LimitationsofthetraditionalORapproach 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
  37. 37. Proprietary Information 37 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 LimitationsofthetraditionalORapproach
  38. 38. Proprietary Information 38 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 LimitationsofthetraditionalORapproach
  39. 39. Proprietary Information 39 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
  40. 40. Proprietary Information 40 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
  41. 41. 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?
  42. 42. Proprietary Information 42 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
  43. 43. 12 September 2018 KBC ADVANCED TECHNOLOGIES Proprietary Information 43 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
  44. 44. 12 September 2018 44 KBC ADVANCED TECHNOLOGIES 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
  45. 45. 12 September 2018 45 KBC ADVANCED TECHNOLOGIES Proprietary Information Integrating optimization models
  46. 46. 12 September 2018 46 KBC ADVANCED TECHNOLOGIES Proprietary Information Integrating optimization models
  47. 47. 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
  48. 48. Proprietary Information 48 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
  49. 49. Proprietary Information 49 CrudeSchedulingOptimization
  50. 50. Proprietary Information 50 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.
  51. 51. Proprietary Information 51 CrudeSchedulingOptimization
  52. 52. Proprietary Information 52 CrudeScheduling
  53. 53. Proprietary Information 53 CrudeSchedulingOptimization
  54. 54. Proprietary Information 54 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
  55. 55. Proprietary Information 55September 12, 2018

Scheduling in the hydrocarbon supply chain Enrique Salomone Software user conference 2018, USA

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