Modeling and Optimization Tools for Solving PdProduc tion Plann ing an dShdlid Scheduling Pro blems
Alkis Vazacopoulos
CAPD Meeting Carnegie Mellon University Pittsburgh PA
March 2011
1 Production Planning and Scheduling Problems
» Strategic » Tactical » Operational
2 Software Companies/Academic Instit/Consulting Companies
» SAP » Oracle » Aspentech » Honeywell » KBC » Consulting companies
» IBM-CPLEX, Gurobi, FICO, GAMS, …..
3 Technologies
» LP » MIP » NLP » CP » Modeling » Platforms » Business Rules
4 LP Performance across releases (FICO Xpress)
LP Performance
25000 800
795
20000 790
785
15000 780 Total Time 775
ber Solved ber Number Solved tal Time(s)tal mm oo
T 10000 770 Nu
765
5000 760
755
0 750 2003B 2004B 2005B 2006B 2007B 2008A FICO 7.0 Re le as e
Internal test set of 796 public and customer models
5 LP Performance across releases
LP Performance
25000 800
795
20000 790
785
15000 780 Primal Dual Barrier Total Time 775
ber Solved ber Number Solved tal Time(s)tal mm oo
T 10000 770 Nu
765
5000 760
755
0 750 2003B 2004B 2005B 2006B 2007B 2008A FICO 7.0 Re le as e
Internal test set of 796 public and customer models
6 LP Performance across releases
LP Performance
25000 800
795
20000 790 Good 785 15000 780 Primal Dual Barrier Total Time 775
ber Solved ber Number Solved tal Time(s)tal mm oo Is important T 10000 770 Nu
765
5000 760
755
0 750 2003B 2004B 2005B 2006B 2007B 2008A FICO 7.0 Re le as e
Internal test set of 796 public and customer models
7 MIP Performance across releases
290 300000
270 250000
250 200000 Time ved ll
230 nn 150000 Numbers Solved 210 Total Time Number So
100000 Solutio Total 190
50000 170
150 0 2003B 2004B 2005B 2006B 2007B 2008A 7.0 7.1 Release
Internal test set of 320 public and customer models
8 MIP Performance across releases
MIP Performance
250000 290
270 200000
250
150000 Heuristics Cuts 230 Branch and General and Total Time Classic Number Solved Bound problem210 specific
Total Time(s) 100000 ppproblem specific Number Solved parallelize 190
50000 170
0 150 2003B 2004B 2005B 2006B 2007B 2008A FICO 7.0 Re le as e
9 Feedback Loop
MIP Performance
250000 290
270 200000
250
150000 Decomposition Perform Reengineer 230 Algorithms – OperationsTotal - Time B&B Number Solved mber Solved
otal Time(s) more flexible Node210 uu
TT 100000 N
190
50000 170
0 150 2003B 2004B 2005B 2006B 2007B 2008A FICO 7.0 Re le as e
10 Comparison GUROBI vs CPLEX
» Gurobi: developpged to take advantage of multi-core machines » Growing very fast (150 commercial licenses, 25 academic sales, in addition to the academic program, source: jtoedm. com » LP 10-30% better than CPLEX (Mittelman website) » MIP 10% - 30% better on optimality » CPLEX better on Feasibility problems » New MIPLIB data soon
11 Open Source
» SKIP – ZIB, Very competitive: used by Dynadec » CBC – used as an entryyyyp by many companies
» Comparison from Hans Mittelman’s website
12 Benchmarking MIP
» Optimality (what about problems won’t solve after 30 minutes, 60 minutes) » Best feasible solutions » Find k best solutions » Size of the tree » Presolve (reduction of the matrix)
» Develop Sets of Problems » Etc…. »
13 Xpress 7 parallel speed ups
Xppress 7 Xppress 7 1 thread 4 thread Improvement “Internal” deterministic 25,724 17,447 -47% “Internal” opportunistic 25,724 13,067 -96% Coral deterministic 24,484 16,129 -52% Coral opportunistic 24,484 11,137 -120%
14 mzzv42z: Single/Two Threads Xpress FICO IVE
»1 thread »2 threads
»15 15 Xpress 7 parallel speed ups
Xppress 7 Xppress 7 1 thread 4 thread Improvement “Internal” 25,724 17,447 -47% deterministicParallel Parallel MIP “Internal” processors (same Deterministic opportunistic on a 25,724computer) 13,067P-MIP -96% Network Coral P-MIP deterministic 24,484 16,129 -52% Coral opportunistic 24,484 11,137 -120%
16 Concurrent LP solver
» Efficient LP method parallelization
Primal Dual Barrier Concurrent Overall Speedup 1 thread 19,940 13,459 - -
2 threads 19,940 13,459 6,193 (B+D) 23% 4 threads 19,940 13,459 7,540 7,491 70%
Time(concurrent) ~ min(Time(dual), Time(primal), Time(barrier))
Controls: LPTHREADS and DETERMINISTIC (concurrent is nondeterministic!)
17 Concurrent LP solver
» Efficient LP method parallelization
Primal Dual Barrier Concurrent Overall Speedup 1 thread 19,940 13,459 - - P-D-B 2 threadsP-Barrier 19,940 13,459 6,193 (B+D) 23% 4 threads 19,940 13,459Combinations 7,540 7,491 70%
Time(concurrent) ~ min(Time(dual), Time(primal), Time(barrier))
Controls: LPTHREADS and DETERMINISTIC (concurrent is nondeterministic!)
18 Customers want multiple alternative solutions Example: air04
» Set partitioning problem for airline crew scheduling » Available from MIPLIB 2003
Default Solver 5-Best Solution Solver 1st SltiSolution 56137 56137 2nd Solution 56166 56137 3rd Solution 56307 56137 4th Solution 56854 56137 5th Solution 57562 56137 Total B&B Nodes 141 15 981 Total Time 26 sec 29 sec
19 Customers want multiple alternative solutions Example: air04
» Set partitioning problem for airline crew scheduling » Available from MIPLIB 2003
Default Solver 5-Best Solution Solver 1st SltiSolution 56137 56137 Strategies- Strategy Robust - nd Business Refinenement 2 SolutionStrategies 56166 56137 Rules Rebalancing 3rd Solution 56307 56137 4th Solution 56854 56137 5th Solution 57562 56137 Total B&B Nodes 141 15 981 Total Time 26 sec 29 sec
20 Xpress-Tuner: How to Tune (Automatically) an Optimization Problem?
21 Xpress-Tuner: Tuning Process
22 Xpress-Tuner: Detailed Results
23 Xpress-Tuner: Comparing logs
24 Xpress-Tuner: Tuning MIP families
25 Xpress-Tuner: Tuning Methods
26 Benchmarking - Optimality
Problems with less than 60 minutes
Reduce time Problems Problems with less Problems Parallel MIP with less more than 2 than 30 Opportunisti hours seconds than 30 cMIPc MIP Solve in 1/10 mitinutes What is a of time – Reduce time examples good Google Parallel MIP solution? Matrix Gen Deterministic Multiple MIP Solutions Parallel Opportunisti Modeling c Languages Explain Solutions
27 MIP Research
» More research on Deterministic Parallel » Predict Size of BB Tree/ Structure » UblUnbalanced dT Trees/h ow t o B Blalance » Solve Smaller problems faster (E Danna – Google) » New Cu ts /Feasibilit y P ump » More CP in Presolve » HdCtit/SftCtitHard Constraints / Soft Constraints – OtiiBBOperations in BB nodes » Parallelize cuts/Heuristics in root node » How to scale better 4 to 8 cores » Data mining in Parameters – usinggp supervised learning » **** Adaptive search – change the parameters dynamically during the Search 28 Non Linear
» Knitro » CONOPT » Xpress SLP : good for process industry » MINLP CMU-IBM , minlppg.org » BONMIN Basic Open Source Nonlinear Mixed Integer programming
29 Comparisons Mittelman
» Knitro vs IPOPT » 27 better Knitro » IPOPT better in 14 cases
30 Global Optimization other technologies
» Baron » Disjjpggunctive programming » Robust » Stochastic
31 Constraint Programming
» ILOG-CP » CHIP » Artelys – Kalis » COMET – Dynadec » SIMPL (Yunes, Aron, Hooker)
» Benchmarking CP codes????
32 »Example of relaxation for alldifferent
alldiff (x1,, xn ) x j 1,,n »Convex hull relaxation, which is the strongest possible linear relaxation (JNH, Williams & Yan): n x 1 n(n 1) j 2 j1 x 1 | J | (| J | 1), all J 1, ,nwith | J | n j 2 jJ »For n = 4:
x1 x 2 x3 x 4 10 x1 x 2 x3 6, x1 x 2 x 4 6, x1 x3 x 4 6, x 2 x3 x 4 6 x1 x 2 3, x1 x3 3, x1 x 4 3, x 2 x3 3, x 2 x 4 3, x3 x 4 3 x1 , x 2 , x3 , x 4 1
33 »Automatic relaxations
»The following constraints can be relaxed automatically with Xpress‐Kalis : » linear constraints . alldifferent(X) . occurrence(X,v) opY . distribute(X,A,m,M) . X = Min/Max(Y) . absolute value X = | Y | . distance v op |X‐Y| . element(2D) X = A[I] | X = A[I][J] . cycle . logical constraints (,,or,and)
34 An integer knapsack problem with ssdecostaide constrain t
min 5x1 8x2 4x3 subj.to 3x1 5x2 2x3 30 alldiff(x1, x2, x3) x j {1,2,3,4}
»MILP needs additional xi jyij , all i constraints and 00--11 j variables to express the y 1, all i alldiff: ij j
35 Pure CP model in Xpress -KALIS
36 HYBRID Model in Xpress -KALIS
37 Modeling Platforms
38 Modeling Languages
» OPL: Integrates MIP and CP » GAMS: All solvers available, NLP, MIP, Stochastic » Mosel : Programming and Modeling together
39 Modeling Languages
» OPL: Integrates MIP and CP » GAMS: All solvers available, NLP, MIP, Stochastic » Mosel : Programming and Modeling together
40 More
» IBM ODM: Optimization Decision Manager » Next ggpeneration : Eclipse » Simulation » Python link
41 Eclipse – Platform - Future
42 »CP, MIP and their combination
» Oppgtimization Technologies
» Mixed Integer Programming: MIP » Finite Domain Constraint Progggramming: CP
» Planning & Scheduling »CP »CP+MIP» MIP
» Long and mid-term planning: MIP » SSothort-term pl annin g, sch eduling : C P » Time Horizon Supply chain optimization Reqq,,uires MIP, CP, and their combination
43 Solving Planning and Scheduling Models
»INTEGRATED ENV IRONMENT
»Common Planni ng and S ch ed uli ng Model
»LP/MIP »CP
44 »Combining MIP & CP
»Master problem »MIP
»Sub problem »CP
»Feasible »Infeasible »(OK) »Add Cut to MIP
» Mixed l ogica l/ Linea r Pr ogra mming fra me wo rk (Hooker et al)
45 Project
» LISCOS Project » European Union 5th Framework » BASF - Chemi cal s » P&G - Food » PSA - Cars » Barbot - Paint
46 Production Scheduling Problem
» Schedules Crude-Oil Blend-Shops in Oil-Refineries. » In the Categgyory of “Closed-Shop” Scheduling. » Involves Quantity, Logic and Quality Decisions. » Quantity = Flow, Rate, Inventory » Logic = Mixing-Delay, Up-Time, One-Flow-In » Quality = Octane, Sulphur, Yield, Temperature » Intended to Reduce Quality Variation.
Key Absolute Maximum Limit Planning Proxy
Target Set Closer to Limit Target Low
Variability Reduced
Manual Crude-Oil Blend Time Scheduling Scheduling Installed » 47 Production Scheduling Problem
» Finite-Capacity Scheduling. » Supports Marine and Pip eline Access Blend-Shops.
Tank1 SourCBH1
PipelineNorth SourCBH2 Tank2 Pipestill1 DayTank1
SourCBH3 Tank3
Tank4
PipelineSouth
Tank5 SweetCBH DayTank2 Pipestill2
Crude-Oil Blend-Header
Tank6
48 Production Scheduling Technology
» Involves the following OR Problems: » Fixed-Charge Network Flow » Lot-Sizing and Scheduling (Small Time-Bucket) » Facility-Location » Multi-Linear Pooling » Uses Discrete-Time Formulation. » Allows for all Logic Constraints to be Configurable. » Creates Elastic/Penalty Variables for all Constraints. » Modeled using Xpress-Mosel.
» 49 Production Scheduling Technology
» Involves the following Primal Heuristics programmed using Xpress-Mosel: » RlRelax-and-Fix » Dive-and-Fix » Fixed-Charge Adjustment » Chronological Decomposition » Smooth-and-Dive » Incumbent Elimination » Disaggregation and Warm-Start » Parallel Diving (Sorted One-Point Crossover) » Local Search Repair of Infeasible Solutions » Embeds Xpress-Optimizer and Xpress-SLP.
» 50 Production Scheduling Approach
» Finds Optimized or Feasible Schedules. » Maximizes Profit and Performance.
» At the core is Bender’s Decomposition. » Uses Intelligent Problem Solving structure: » Solve Quantity-Logic (Q-L) first using MILP » Fix Logic, then solve for Quantity-Quality (Q-Q) using SLP » If Q-L infeasible then Q-Q will be infeasible » If Q-L feasible then Q-Q may or may not be feasible » (If Q-Q feasible then Q-L feasible)
» 51 Production Scheduler Demo
52 Existing Products with Xpress-MP
» Finished Product Blend Planning (BLEND) uses Xpress- Optimizer. » Refinery and Petrochemical Planning (RPMS) uses Xpress- Optimizer. » Supply and Distribution Planning (SAND) to use Xpress- Optimizer.
» 53 Examples of Production Systems
54 Petroleum Refining
» Includes many tanks, atmospheric & vacuum fractionators, blenders, reactors, separators, etc. as well as 1100 components and 50 properties . » Logistics has 160K rows, 95K columns, 11K binaries: 12- seconds.
Gas
LPG AU I AU I Streams Naphtha ATF » Quality has 250K L rows, 25K NLIn rows, 100K columns: 900- In Out Gas I ATF LABFS SG Group In LPG
In Out I LABFS Naphtha Gas oil In TK 517 Gas SG In Out TT Out LPG I GO Out In Out In Out AU I Streams In Out TK 92 NG Naphtha ATF ATF AU I ATF In ATF TK 518 AU I In TK 85 SG Block seconds. Rail LS_BH II ATF LABFS In Out Out SG Crude In Out LABFS In AU II ATF TK 86 AU I Mix II LABFS Gas oil TK 519 Gas oil In Out Gas In TT HCU Kero MTO In Out TK 90 LPG II GO ATF Blend In Out RCO AU II Streams HN TK 520 Unit LN KAPL Header In TK 91 In Tank ATF III HN Kero Blending In In AU I RCO SKO KAPL AU I ATF III Kero Gas oil VB Nap Out Out Out AU II In In HSD BS II VB GO AU II ATF TK 108,109 III GO HN HSD Euro III Gas AU IV LGO In Out In DHDS Feed In Naphtha DHDS Naphtha MTO Blend AU IV HGO NG Group LPG IV HN KNPL Out SKO_LABFS In In AU V GO SKO SG Naphtha Gas FO Naphtha Blend In Out HSD HSD BS II LPG IV Kero FPU I HD NG TK 781 Out AU IV Streams DHDS Diesel LN Army_Navy_FO HSD Euro III Out ATF FPU II HD LS_BH In LGO IV In Out UU Out In AU II In DHDS_HSD AU IV HGO A Army Diesel KSPL In Out IV LGO VDU HD DHDS In Out Army or LSHF Blend HS LABFS Army_Navy_FO In VDU HD SRGO TK 782 Gas FCC LCO In Out NG Block DHDS_HSD AU V GO IV HGO TK 823, 824 Gas oil LPG Out PL AU V Streams FPU I HD TK 727,728,729 KDPL NG Crude In Out LN AU II Mix HN FPU II HD In Out KSPL Header In TK 783 RCO LAB RB TK 730 PL Unit V HN SKO_LABFS In VB GO KRPL In Out In Tank FPU I HSD V Kero Gas oil Blending FPU I feed FCC LCO TK 911 In AU II RCO AU II RCO HDiesel Rail V GO Army or LSHF Diesel AU III AU III RCO In Out VGO In Out
Out Unit FPU I HD GO III AU DieselDHDS
AU I AU GO In AU I ATF AU II GO AU III HN AU II ATF AU IV HN AU V Kero
GRSPF Area AU III Kero AU IV Kero AU I LABFS AU V HN AU IV RCO In In AU II LABFS Out Out Rail-BS II Gas Vac Slop Tank TK 723 In HCB HCU Streams Cold RCO ex GRSPF VGO I In Out I HD 700 TK Rail header FCC In In Out TK 726 Out Feed FPU I VR In LPG FO_LSHS Rail-Euro III
VR FCC feed AU V GO FCC Feed blend FCC HN HCU HN FPU HD I In IIFPU HD AU IV LGO HSD BS II Header AU IV HGO HCU Kero
TK 803,804 In HCU Diesel FCC LCO K9 Bottom BIT Hot feed TK 724 Out NG LN HN In In Out FPU II VBF 900 TK Rail header HSD FPU II feed Out In I VR FPU I VR Cold VGO ex GHC Out Out TK 731 IOTL AU I RCO In In TK 703 Out HDiesel LS_BH LCO HN AU II RCO GHC Area Cold VGO ex GRSPF FPU II HD FCC Streams TK 725 HPCL_BPCL PL In DHDS Diesel Out
VGO Unit AU I GO AU II GO AU V HN
In ATF I AU IIIAU HN AU II ATF AU IV HN In AU V KeroAU V AU IIIAU GO AU III Kero
AU IV Kero IOTL_KSPL Header AU I LABFS AU III In Out CLO IIAU LABFS AU III RCO In Out Kero Out Tank In Out In Vac Slop In Out HCU In II HD TK 732 AU IV RCO Feed In Out SRGO Rail HS VGO II Hot feed HCU feed FPU II VR SRGO or LDO TK 704 Cold RCO ex GHC TK 453,454 Out In FO_LSHS
HN HCU HN LDO Rail AU V GO FCC HN HD I FPU
VR IVAU GO FCC LCO
Gas oil HD II FPU TK 907
In AU IV LGO Out
HCU Kero In
BH Block K9 Bottom Euro III HSD Header HCU Diesel BIT In II VR TT AU III Mix VBF Cold VGO ex GHC VDU TK 909 LDO RCO FPU II VR VBU Feed Kero In Out GRSPF Area In Out Unit VDU feed HCU Feed blend HCU VDU HD In In Out LMW In HDiesel Diesel AU I GO TK 910 TK 771 AU IV RCO VR ex 821 Tank In Out VBU VB Naphtha Out HD Blending VGO HD TK 906 Out In VDU Slop Out AU V RCO Out VR ex 822 Out VB Gas oil AU II GO In LAB AU III RCO In VGO TK 821 In In Out Vac Slop VGO III VB Tar AU IV LGO HMW Cold RCO ex GRE In Out TK 97 In Out AU IV Others In Slop Army HSD Header Grouped BH VDU VR AU IV HGO Out TK 822 RCO TT VR BIT FO In In TK 99 Out VBF In PFRCB Gas VDU VR VRT VR AU III Kero AU V GO VDU VR Split Ou t TK 100 In Out TT FO AU IV Kero LPG SRGO Header Heavy Alkylate In Out In TK 701 Out SG LN TK 741 AU IV Kero header In In Out 30-40 GRE RCO Bitumen Blend AU I ATF HN BH TK 746 60-70 TK 207, 208 PL FPU I VR In Out Out BBU Bitumen AU I LABFS In Out TK 742 Out Army or Navy Diesel LS Kero In In Hot VR FPU II VR AU IV In Out DHDS_HSD AU II ATF LGO In In TK 747 80-100 Out TK 702 Out GRE Area Out FO LABFS-NIRMA Out In Out VDU VR LCO AU II LABFS Out TK 743 In Out HS LGO-HGO Swing LGO HN In Out In AU III and AU IV Kero In TK 30-40 AU IV Mix Out Out In Out HGO Out Out Out In LS Block In Out TK 708,709,710,774 Cold VR ex GRE In Out FCC HN In In Out In TK 775 Out CLO In BBU Prdt In AU V Kero Vadinar Crudes In TK 770 HGO TK 205, 206 LAB Section TK 773 In TK 748 TK 60-70 TK 914 RCO GRE Area Cold VR ex GRE Out LABFS Blend Out Unit TK 744 In FCC CLO NIRMA RS LABFS WN In Out Dumad PL HN In Front end (Rerun and Molex) In TK 80-100 In In Out Out TK 749 In Out AU I ATF In Out LAB RS Dumad PL Tank In Out TK 745 AU I LABFS In Out HCU HN AU I RCO TK 89,98(LAB HMW) TK 772 AU IV RCO ATF Kero LAB WN IFO Blend AU II ATF TK 705 GRE RCO router In TK 715 AU II RCO N-P In Out Benzene LAB In Out SKO_HSD AU II LABFS Rail AU V In HCU Kero SKO In Out AU III RCO Out TK 767,87,88(LAB LMW) TK 716 IFO N-P AU III and AU IV Kero Out HN LAB RB In Out Vac Slop In In Out Gas TK 706 AU V Kero LS RCO ex GRE In Out HAB TK 738 Out HCU Diesel TK 557,558(HAB) In BPCL_HPCL PL LABRS VB Tar FO Cutter Stock Blender In Out In Out TK 514 Back end (LAB Unit) TK 717 LPG In Out K9 Bottom In Out LAB RB Out AU IV LGO In LDO TK 901 TK 734 In Out TK 401,402,406 (PFRCB) TK 707 SG HCU Kero TK 302 AU IV HGO TK 739 LN FPU I VGO Out In Out FO ASOJ PL BH In Naphtha re-run Streams VB Nap Out GRSPF Area AU III RCO LAB RB SKO Blend HN FPU HD In TK 718 In Out LS TK 101 Vac Slop AU V In VB Gas oil Kero In Out In Out VB Nap In Out TK 902 Out VB Gas oil LSHS-Others Blend Out VB Gas oil In Out In TT HS AU IV LGO In FCC LCO PL In TK 801 TK 102 Tk 95 AU IV HGO Out In Out RIL
In LSHS Group CLO
AU IV Kero VDU Slop Out RCO GRE
VB Gas Oil VR VDU
Gas oil FPU I VR AU V Mix In Out FPU I_II VR LSHS-Others VR II FPU In Out HS Block VB Tar TK 103 Rail Tk 96 FCC LCO In In Out FPU I HD TK 802 In TK 903 RCO FCC CLO OtOut FO Bl end Out VB Tar VB Tar Tk 220 In Out In Out Unit TK 218 In Out HAB GHC Area TT
To LAB VBU Tar In FO FPU II HD Tk 733 TK 303,304 In Out Tank2 TK 777 In Out Cutter Stock In Out Rail Tank In Out TK 904 AU V RCO LSHS-TEC Blend TK 301 Tk 219 TK 451 Benzene AU I RCO
AU II RCO In Out In Out Rail FPU II VR Out TK 452 TK 93,94 AU III RCO
FPU II HD LSHS-TEC
TK 823,824 ATF Tanks MTO Tanks LABFS Nirma
NG crude tanks LS Crude tanks LSHS-Other grades LSHS-TEC grade Bitumen 30-40
SRGO or LDO Tanks HSD Euro III HSD BS II SKO Tanks FO Tanks BH Crude tanks RCO tanks VR tanks VGO tanks
SG Crude tanks HS Crude tanks Bitumen - Total Bitumen 60-70 Bitumen 80-100
55 Fast-Moving Consumer Goods
» Includes an highly sequence-dependent edible oil-deodorizer (prize- collecting traveling salesman problem) and many types of edible oil customer demands. » Logistics-only has 130K rows, 57K columns, 8K binaries: 400-seconds.
56 Oil & Gas Processing
» Includes spheroids, spheres, flash drums, stabilizers, vaporizers, splitters, etc. » Logistics has 60K rows , 30K columns, 9K binaries: 15-seconds. » Quality has : 70K L rows, 5K NL rows, 24K columns: 100-seconds.
57 Metals Casting
» Includes arcing-furnaces, decarburizers, cranes, ladles, batch and continuous casters – similar to open-shop/job-shop scheduling. » Logistics-only has 75K rows, 24K columns, 3. 5K binaries: 200-seconds.
58 Petrochemical Cracking & Separating
» Includes cracking furnaces, compressors, distillation towers, cold-box, tanks, bullets, etc. » Logistics has 21K rows , 10K columns, 2. 5K binaries: 50-seconds. » Quality has 21K L rows, 2K NL rows, 8K columns: 5-seconds.
Hydrogen 70FC808 H2 Pipeline
70FC809 Feed Cold Box Gas Tail Gas Discharge TG Pipeline
Feed
70FC805 70FC817 Suction Demethanizer Ethylene Cracked Gas Compressor Charge Eth Pipeline
C2 70FI818 C2 Splitter
70FC805 70FI001 Offsites [2] C3plus Propylene C2 Recycle 70FC827 Deethanizer Prop Pipeline 70FI500 C3 Recycle RX Effluent RX Effluent Feed Charge Charge 70FI501 70FI828 C3 C3 Splitter Furnace Fd Header Primary Fractionator Furnaces
Charge
C4plus
Depropanizer Offsites [1] 70FC830
Charge Offsites [3] 70FC831 Debutanizer
Et hyl ene Plant (3)
Ethylene Inlet TK121 Outl et Loading Jetty Plant (4) Pyrolysis Gasoline Offsites [4]
Inlet TK105 Outl et
Inlet Out let Ethylene Plant (2) Supply Inlet TK106 Outl et Naphtha Header Naphtha
Inlet TK107 Outl et
Feed Naphtha
05F118 TK-118 05218
05F119 TK-119 O5F219 Ethylene Inlet Outl et Loading Plant (1) Rail C4 Header 05F120 TK-120 05F220
05F120 TK-122 05F222
05F123 TK-123 05F223 Mi xed But ane Supply Fuel Oil 59 Steam & Power Generating
» Includes boilers, turbo-generators, steam headers, turbines, relief- values, etc. » Logistics-only has 24K rows, 13K columns, 2K binaries: 30-seconds.
60