Brenno C. Menezes PostDoc Research Scholar Carnegie Mellon University Pittsburgh, PA, US Jeffrey D. Kelly CTO and Co-Founder IndustrIALgorithms Toronto, ON, Canada Industrial View of Crude-oil Scheduling Problems EWO Meeting, CMU, Pittsburgh, Mar 9 th , 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 st : Crude to Tank Assignment (CTA) for Improved Scheduling: MILP 2 nd : Crude Blend Scheduling Optimization (CBSO): MILP+NLP 1 Remark: Continuous-time model cannot be easily implemented by plant operators Objective: Explore to the limit discrete-time model - 8h-step (shift) for 2-4 weeks (42-84 periods) - 2h-step for 7 days (84 periods) - 1h-step for 4 days (48 periods) Motivation: Replace Full Space MINLP by MILP + NLP decompositions for large problems
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Brenno C. MenezesPostDoc Research ScholarCarnegie Mellon UniversityPittsburgh, PA, US
Jeffrey D. KellyCTO and Co-FounderIndustrIALgorithmsToronto, ON, Canada
Industrial View of Crude-oil Scheduling Problems
EWO Meeting, CMU, Pittsburgh, Mar 9th, 2016.
Ignacio E. GrossmannR. R. Dean Professor of Chemical EngineeringCarnegie Mellon UniversityPittsburgh, PA, US
Faramroze EngineerSenior Consultant SK-InnovationSeoul, South Korea
1st: Crude to Tank Assignment (CTA) for Improved Scheduling: MILP2nd: Crude Blend Scheduling Optimization (CBSO): MILP+NLP
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Remark: Continuous-time model cannot be easily implemented by plant operators
Objective: Explore to the limit discrete-time model - 8h-step (shift) for 2-4 weeks (42-84 periods) - 2h-step for 7 days (84 periods)- 1h-step for 4 days (48 periods)
Motivation: Replace Full Space MINLP by MILP + NLP decompositions for large problems
• Segregates crude management in storage assignment and crude blendscheduling.
• Phenomenological decomposition in logistics (MILP) and quality (NLP)problems applied in a scheduling problem.
• Details all logistics relationships from practiced industrial operations.
11EWO Meeting, Mar 9th, 2016.
ConclusionImpact for industrial applications:
• Quickly reproduced (2weeks) using the structural programming language IMPL
• UOPSS modeling, pre-solving, and parallel processing permitted to solve an 2htime-step discrete-time formulation for a highly complex refinery in Ulsan (38crude, 23 storage tanks, 11 feed tanks, 5 CDUs): for 6-7 days (72-80 time-periods)considering included after the pre-solving
14,753 continuous and 8,481 binary variables;
5,029 equality and 32,852 inequality constraints (DoF=18,205)
CPU: 10.8 min (Cplex 12.6) and 3.6 min (Gurobi 6.5.0) (both in 8 threads)8.4 min (Gurobi 6.5.0) (1 thread)
14,239 continuous and 9,937 binary variables
5,869 equality and 38,517 inequality constraints (DoF=21,307)
CPU: 50.8 min (Cplex 12.6) (in 8 threads)
6 days 7 days
No results (Gurobi 6.5.0)@ 0.0% GAP @ 0.2% GAP
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Sets (.usp)
Configuration (.iml)
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6 days
Included after pre-solving
Statistics (.sdt) Configuration (.iml), cont…..
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7 days
Included after pre-solvingStatistics (.sdt)Configuration (.iml), cont…..