Download as PDF, PPTX
![The new and optimized TBP
curve for DC1 given its front
and back-end shifts is now:
[(1.053%,312.8),
(10.015%,432.9),
(31.188%,521.6),
(52.361%,565.3),
(73.534%,606.4),
(94.707%,668.3),
(98.995%,689.3)]
Temperature (oF)
Yield(%)
Figure. ASTM D86 distillation curves, including the final
blend, which is determined by the blended TBP
interconversion to ASTM D86.
21
Example
Solver: SLPQPE_CPLEX 12.6
Reduction in DC1’s T99 (TPB) from 715.7 to 689.3 oF
630 (ASTM D86)](https://image.slidesharecdn.com/smartprocessoperationsinfuelsindustries-applicationsandopportunities-160204225438/85/Smart-Process-Operations-in-Fuels-Industries-Applications-and-Opportunities-presentation-21-320.jpg)
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Smart Process Operations in Fuels Industries: Applications and Opportunities (presentation)
- 1. Brenno C. Menezes PostdoctoralFellow Technological Research Institute São Paulo, SP, Brazil Jeffrey D. Kelly CTO and Co-Founder IndustrIALgorithms Toronto, ON, Canada - Easy to implement (End-user as implementer: configure not code) - Integrates parts not yet integrated - Uses actual plant data - Reduces optimization search space in further problems (mainly MILP) - Tries to boost the polyhedral space of optimization (mainly NLP) - Automated-execution for faster and better solutions Smart: (six fundamentals) Smart Process Operations in Fuels Industries: Applications and Opportunities ITAM, Mexico City, Feb 5th, 2016. TÉCNICAS AVANZADAS DE OPTIMIZACIÓN PARA EL SECTOR PETROLERO
- 2. Decision-Making Tools inthe oil-refining industry 2 Space Time Supply Chain Refinery Process Unit second hour day month year RTOControl on-line off-line Scheduling Operational Planning Tactical Planning Strategic Planning Operational Corporate week
- 3. Decision-Making Tools inPETROBRAS (in oil-refining) 3 Space Time Supply Chain Refinery Process Unit second hour day month year RTOControl on-line off-line Operational Planning Tactical Planning Strategic Planning Simulation Petrobras NLP Optimization Commercial (Aspentech) LP Optimization Petrobras Operational Corporate week Scheduling
- 4. 4 Space Time Supply Chain Refinery Process Unit second hour daymonth year RTOControl on-line off-line Operational Planning Tactical Planning Strategic Planning Simulation NLP Optimization LP Optimization Operational Corporate week Decision-Making Tools in the oil-refining industry: Challenges Scheduling
- 5. 5 Space Time Supply Chain Refinery Process Unit second hour daymonth 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 Integrations time space Scheduling Decision-Making Tools in the oil-refining industry: Challenges (Menezes, Kelly, Grossmann & Vazacopoulos 2014) (Menezes, Kelly & Grossmann, 2015) (Kelly & Zyngier, 2016)
- 6. 6 Space Time Supply Chain Refinery Process Unit second hour daymonth year RTOControl on-line off-line Operational Planning Operational week Optimization (MILP and NLP) (LP) Integrations time space Scheduling Smart Process Operations: Applications around Scheduling (NLP) Distillation Blending and Cutpoint Optimization (MILP) Reduces optimization search space Boosts polyhedral space of optimization Uses actual plant data Integrates parts not yet integrated Uses actual plant data Integrates parts not yet integrated Data-Driven Real Time Optimization Crude to Tank Assignment Easy to implement Automated-solution
- 7. Crude Transferring Refinery Units Fuel Deliveries Fuel Blending Crude Dieting Crude Receiving HydrocarbonFlow 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 VDU Receiving or Stock Tanks Transferring or Feedstock Tanks Charging Tanks Whole Scheduling: from Crude to Fuels
- 8. (IAL, 2015)Clusters orStock Tanks Crude Min cr,yield/property(Crude-Cluster)2 cr crude pr property yields or properties: naphtha-yield (NY), diesel-yield (DY), diesel-sulfur (DS) and residue-yield (RY) Crude Tank Assignment for Improved Schedulability Receiving or Stock Tanks Transferring or Feedstock Tanks Charging Tanks Crude-Cluster Determines Crude-oil Segregation Rules
- 9. Crude Tank Assignmentfor Improved Schedulability r of crude 5 10 15 r of equality contraints 146 308 468 r of inequality contraints 1215 2430 3645 r of continuous variables 436 836 1236 r of binary variables 246 451 656 r of crude 20 25 30 r of equality contraints 628 788 948 r of inequality contraints 4860 6075 7290 r of continuous variables 1636 2036 2436 r of binary variables 861 1066 1271 r of crude 35 40 45 r of equality contraints 1108 1268 1428 r of inequality contraints 8505 9720 10935 r of continuous variables 2836 3236 3636 r of binary variables 1476 1681 1886 Solver: CPLEX 12.6, 8 threads in Parallel Branch-and-Cut
- 10. Crude Tank Assignmentfor Improved Schedulability Cluster 1 Membership (16): 1, 4, 6, 8, 12, 13, 15, 21, 28, 30, 34, 36, 40, 41, 43, 45 Cluster 2 Membership (9): 3, 9, 14, 24, 25, 27, 32, 33, 37 Cluster 3 Membership (18): 2, 5, 10, 11, 16, 17, 18, 19, 20, 22, 26, 29, 31, 35, 38, 39, 42, 44 Cluster 4 Membership (2): 7, 23 Cluster 1 Means: 18.75, 20.84, 0.1594, 8.58 Cluster 2 Means: 32.88, 13.61, 0.0900, 1.53 Cluster 3 Means: 14.08, 17.06, 0.2300, 18.64 Cluster 4 Means: 7.72, 10.31, 1.83, 35.39 NY DY DS RY
- 11. CrudeA Component/ Psedocomponent (Micro-cuts) Boiling Point (ºC) Yields (Vol%) Gravity (Kg/m3) Sulfur (W%) Methane (CH4) -161.520.0041 Ethane (CH2-CH2) -88.59 0.0081 : : : : N-pentane 36.09 0.0152 Hypo40 40 1.1427 Hypo50 50 1.4874 : : : Hypo840 840 0.2544 Hypo850 850 0.0210 P T CrudeB CrudeC CrudeD K=y/x=Pvap/P KHypo=f(P,T,Column) Molar & Energy Balances Fixed Yields Delta Base Chronology LN TFurnace Yields & Properties = base+delta x TFurnace LN 20 Pinto et al, 2000; Neiro and Pinto, 2004 CDU/VDU Cut and Swing-Cut IBP (ºC) FBP (ºC) Fuel Gas C1C2 -273 -50 LGP C3C4 -50 20 Light Naphtha LN 20 150 Heavy Naphtha HN 150 190 Kerosene K 190 250 Light Diesel LD 250 390 Heavy Diesel HD 390 420 Atmosferic Residue ATR 420 850 Light Vacuum Gasoil LVGO 420 580 Heavy Vacuum Gasoil HVGO 580 620 Vacuum Residue VR 620 850 150
- 12. CDU/VDU Cut andSwing-Cut TIB (ºC) TEB (ºC) Fuel Gas C1C2 -273 -50 LGP C3C4 -50 20 Light Naphtha LN 20 140 Swing-Cut 1 SW1 140 160 Heavy Naphtha HN 160 180 Swing-Cut 2 SW2 180 210 Kerosene K 210 240 Swing-Cut 3 SW3 240 260 Light Diesel LD 260 360 Swing-Cut 4 SW4 360 380 Heavy Diesel HD 380 420 Atmosferic Residue ATR 420 850 Light Vacuum Gasoil LVGO 420 580 Heavy Vacuum Gasoil HVGO 580 620 Vacuum Residue VR 620 850 SW1 SW2 SW3 SW4CrudeA Component/ Psedocomponent (Micro-cuts) Boiling Point (ºC) Yields (Vol%) Gravity (Kg/m3) Sulfur (W%) Methane (CH4) -161.52 0.0041 Ethane (CH2-CH2) -88.59 0.0081 : : : : N-pentane 36.09 0.0152 Hypo40 40 1.1427 Hypo50 50 1.4874 : : : Hypo840 840 0.2544 Hypo850 850 0.0210 P T CrudeB CrudeC CrudeD K=y/x=Pvap/P KHypo=f(P,T,Column) Molar & Energy Balances Fixed Yields Swing-Cuts Delta Base Chronology LN HN SW1 160 LN 140 Zhang et al, 2001; Li et al, 2005 20 Menezes, Kelly and Grossmann, 2013
- 13. CrudeA Component/ Psedocomponent (Micro-cuts) Boiling Point (ºC) Yields (Vol%) Gravity (Kg/m3) Sulfur (W%) Methane (CH4) -161.520.0041 Ethane (CH2-CH2) -88.59 0.0081 : : : : N-pentane 36.09 0.0152 Hypo40 40 1.1427 Hypo50 50 1.4874 : : : Hypo840 840 0.2544 Hypo850 850 0.0210 P T CrudeB CrudeC CrudeD K=y/x=Pvap/P KHypo=f(P,T,Column) Molar & Energy Balances Fixed Yields Swing-Cuts Fractionation Index (FI) Delta Base Chronology Alattas, Grossmann and Palou-Rivera, 2011, 2012 Defines crude diet based on assay for Tcut=(IBPi+FBPj)/2
- 14. Defines new IBPiand FBPi for a selected crude diet CrudeA Component/ Psedocomponent (Micro-cuts) Boiling Point (ºC) Yields (Vol%) Gravity (Kg/m3) Sulfur (W%) Methane (CH4) -161.52 0.0041 Ethane (CH2-CH2) -88.59 0.0081 : : : : N-pentane 36.09 0.0152 Hypo40 40 1.1427 Hypo50 50 1.4874 : : : Hypo840 840 0.2544 Hypo850 850 0.0210 P T CrudeB CrudeC CrudeD K=y/x=Pvap/P KHypo=f(P,T,Column) Molar & Energy Balances Fixed Yields Swing-Cuts Fractionation Index (FI) Delta Base Chronology Hybrid Models Sanchez and Mahalec, 2012 Defines crude diet based on assay for Tcut=(IBPi+FBPj)/2
- 15. Component/ Psedocomponent (Micro-cuts) Boiling Point (ºC) Yields (Vol%) Gravity (Kg/m3) Sulfur (W%) Methane (CH4) -161.520.0041 Ethane (CH2-CH2) -88.59 0.0081 : : : : N-pentane 36.09 0.0152 Hypo40 40 1.1427 Hypo50 50 1.4874 : : : Hypo840 840 0.2544 Hypo850 850 0.0210 P T CrudeB CrudeC CrudeD K=y/x=Pvap/P KHypo=f(P,T,Column) Molar & Energy Balances Fixed Yields Swing-Cuts Fractionation Index (FI) Delta Base ChronologyDBCTO Hybrid Models Kelly, Menezes and Grossmann, 2014 CrudeA Defines new IBPi and FBPi for a selected crude diet Defines crude diet based on assay for Tcut=(IBPi+FBPj)/2
- 16. Distillation Blending andCutpoint Temperature Optimization (DBCTO) From Other Units From CDU Kerosene Light Diesel ATR C1C2 C3C4 N K LD HD Naphtha Heavy Diesel Crude CDU ASTM D86 TBP Inter-conversion Evaporation Curves Interpolation Ideal Blending Evaporation Curve Multiple Components Final Product ASTM D86 Interpolation Inter-conversion TBP (Kelly, Menezes & Grossmann, 2014)
- 17. Cutpoint Temperature Optimization T01T05 T10 T30 T50 T70 T90 T95 T99 Temperature Yield(%) Back-end: Front-end: New Temperature: NT Old Temperature: OT New Yield: YNT
- 18. Cutpoint Temperature Optimization Temperature(oF) Yield(%) Back-end: Front-end: T99: 230→214 T01: 91→85 New Temperature: NT Old Temperature: OT New Yield: YNT 95.87% -1.45%
- 19. Curves Renormalization Difference inYield: DYNT Temperature (oF) Yield(%) Optimized (renormalized) DYNT99 DYNT01
- 20. • Maximize flowof DC1 and DC2 ($0.9 for DC1 and $1.0 DC2) with lower and upper bounds of 0.0 and 100.0 m3 each (DC3 and DC4 are fixed at 1 m3). The ASTM D86 specifications are D10 ≤ 470, 540 ≤ D90 ≤ 630 and D99 ≤ 680. DC1 DC2 DC3 DC4 1% 305.2 (353) 322.2 (367) 327.0 (385) 302.4 (368) 10% 432.9 (466) 447.1 (476) 405.2 (435) 369.7 (407) 30% 521.6 (523) 507.1 (509) 457.1 (462) 441.0 (449) 50% 565.3 (551) 549.5 (536) 503.3 (492) 513.8 (502) 70% 606.4 (581) 598.4 (573) 551.1 (528) 574.3 (550) 90% 668.3 (635) 666.1 (634) 605.8 (574) 625.4 (592) 99% 715.7 (672) 757.7 (689) 647.0 (608) 655.2 (620) Table. Inter-Converted TBP (ASTM D86) Temperatures in Degrees F. Example
- 21. The new andoptimized TBP curve for DC1 given its front and back-end shifts is now: [(1.053%,312.8), (10.015%,432.9), (31.188%,521.6), (52.361%,565.3), (73.534%,606.4), (94.707%,668.3), (98.995%,689.3)] Temperature (oF) Yield(%) Figure. ASTM D86 distillation curves, including the final blend, which is determined by the blended TBP interconversion to ASTM D86. 21 Example Solver: SLPQPE_CPLEX 12.6 Reduction in DC1’s T99 (TPB) from 715.7 to 689.3 oF 630 (ASTM D86)
- 22. Data-Driven Real-Time Optimization(DDRTO) • Uses LP coefficients estimated directly from the plant or process using off-line closed- loop data and then we optimize this in real-time using an LP. • Sits above a MPC layer to reset its targets or setpoints over time. • Optimizes IV’s subject to lower and upper bounds on both the IV’s and DV’s. Bias Updating Bounding Rules max 𝐿𝐼𝑉𝑖; 𝑃𝐼𝑉𝑖 − 𝐷𝐿𝐼𝑉𝑖 ≤ 𝐼𝑉𝑖 ≤ m𝑖𝑛 𝑈𝐼𝑉𝑖; 𝑃𝐼𝑉𝑖 − 𝐷𝑈𝐼𝑉𝑖 ∀ 𝑖 max 𝐿𝐷𝑉𝑗; 𝑃𝐷𝑉𝑗 − 𝐷𝐿𝐷𝑉𝑗 ≤ 𝐷𝑉𝑗 ≤ m𝑖𝑛 𝑈𝐷𝑉𝑗; 𝑃𝐷𝑉𝑗 − 𝐷𝑈𝐷𝑉𝑗 ∀ 𝑗 max 𝑗=1 𝑛𝐷𝑉 𝑤𝐷𝑉𝑗 ∗ 𝐷𝑉𝑗 𝑠𝑡. 𝐷𝑉𝑗 + 𝑏𝐷𝑉𝑗 − i=1 𝑛𝐼𝑉 𝑆𝑆𝐺𝑗,𝑖 ∗ 𝐼𝑉𝑖 + 𝑏𝐼𝑉𝑖 = 0 ∀ 𝑗 IVi is ith independent variable DVj is jth dependent variable wDVj is jth dependent variable’s profit or economic weight: cost (-) and price (+) bIVi and bDVj are the biases due to measurement feedback SSGj,i is the steady-state gain element PIVi and PDVj are the past values LIVi,UIVi, LDVj, UDVj are the lower and upper bounds. DLIV,i, DUIV,i and DLDV,j, DUDV,j are the lower and upper delta bounds.
- 23. • Steady-State Detection(SSD) - Determines if unit-operation is stationary (no accumulations) or at steady-state. • Steady-State Data Reconciliation (SSDR) - Determines if unit-operation’s measurement system is statistically free of gross- errors and that there are no detectable losses/leaks. • Steady-State Gain Estimation (SSGE) - Determines steady-state gains (actual first-order partial derivatives) using open- or closed-loop routine operating data though other hybrid methods such as rigorous models can be combined. • Steady-State Gain Optimization (SSGO) - Determines new setpoints using quantity optimization and on-line measurement feedback to help “incrementally” situate the plant/sub-plant to a more profitable operating or processing space. Data-Driven Real-Time Optimization (DDRTO)
- 24. Steady-State Gains (SSG’s) Independent Variables (IV’s) Dependent Variables(DV’s) Data-Driven Real-Time Optimization (DDRTO)
- 25. IMPL’s UOPSS VisualProgramming Language using DIA Variable Names: v2r_xmfm,t: unit-operation m flow variable v3r_xjifj,i,t: unit-operation-port-state-unit-operation-port-state ji flow variable v2r_ymsum,t: unit-operation m setup variable v3r_yjisuj,i,t: unit-operation-port-state-unit-operation-port-state ji setup variable VPLs (known as dataflow or diagrammatic programming) are based on the idea of "boxes and arrows", where boxes or other screen objects are treated as entities, connected by arrows, lines or arcs which represent relations (node-port constructs). (Bragg et al., 2004) x = continuous variables (flow f) y = binary variables (setup su) j
- 26. 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≥𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (1) 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (2) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (3) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (4) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭 (5) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭 (6) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (7) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (8) j Semi-continuous equations for units Semi-continuous equations for streams Mixer for each i, but using lo/up bounds Splitter for each j, but using lo/up bounds
- 27. 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚𝐢,𝒎𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (9) 𝐣∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭 (10) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭 (11) 𝐢∈(𝐣,𝐢) 𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭 (12) 𝐦(𝐦∈𝐮) 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≤ 𝟏 ∀ 𝐮, 𝐭 (13) 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝒎′,𝒕 + 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≤ 𝟐 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕∀ 𝒎′ , 𝒋 , (𝐢, 𝐦) (14) xX xX x x Several unit feeds (treated as yields with lower and upper bounds) Selection of modes in one physical unit Structural Transitions j
- 28. 28 As a normaloutcome, schedulers abandon these solutions and then return to their simpler spreadsheet simulators due to: (i) efforts to model and manage the numerous scheduling scenarios (ii) requirements of updating premises and situations that are constantly changing (iii) manual scheduling is very time-consuming work. Simulation-based Solution Problems “Automation -of-Things” (AoT) Automated Data Integration = IT Development Automated Decision-Making = Optimization Automated Data Integrity = Data Rec./Par. Est. Needs of
- 29. 29 Simulation X Optimization Simulation Pros •Wide-refinery simulation • Familiar to Scheduler • Quick solution (can be rigorous) Cons • Trial-and-error • Only feasible solution Optimization Pros • Automated search for a feasible solution • Optimized solution (Local) Cons • Optimization of subsystems • Solution time can explode • High-skilled schedulers (Smart user) • Global optimal (dream)
- 30. (Joly et al.,2015) M3Tech Honeywell SIMTO Production Scheduler Out of the market Workshop on Commercial Scheduling Technologies in Oct, 2013
- 31. GAMS Pre-Formatted (Simulation) ModelingPlatform (Optimization) Soteica IMPL AIMMS Off-Line On-Line Average Price 10k (dev.) and 20k (dep.) +20% year100 k/year (per tool) Modeling Built-in facilities Without facilities Black Box Demanded Tools 1 13 Configuration Coding Configuration Workshop on Commercial Scheduling Technologies in Oct, 2013 OPL
- 32. - Drawer togenerate flowsheet structures (Visual Prog. Lang.) - Upper and lower bounds for yields (more realistic) - Pre-Solver to reduce problem size and debug "common" infeas. - Proprietary SLP to solve large-scale NLPs (called SLPQPE) - Generates analytical quality derivatives using complex numbers - Ability to add ad-hoc formula (e.g., blending rules) - Digitization/discretization engine (continuous-time data input) - Names-to-numbers to generate large models very quickly - Initial value randomization to search for better solutions IMPL Important Techniques/Features (Industrial Modeling and Programming Language)
- 33. 1- APS (AdvancedPlanning and Scheduling): Planning: Aspen, Soteica Scheduling: Aspen, Princeps, Soteica, Invensys Blending: Aspen, Princeps, Invensys 2- APC (Advanced Process Control): Aspen, gProms 3- RTO (Real-Time Optimization): Aspen, Invensys 4- Data Reconciliation and Parameter Estimation: Aspen, KBC, Soteica 5- Hybrid Dynamic Simulation: Aspen, KBC, Invensys 6- Differential Equation Solution (ODE and PDE): gProms Applications in IMPL
- 34. Smart Operations: Opportunitiesin “Bottleneck” Scheduling Step 1: Identify Key Bottlenecks (see below) Step 2: Design Optimization Strategy Step 3: Determine Information Requirements Step 4: Prototype and Implement, etc. Quantity-related: Inventory containment Hydraulically constrained Logic-related (Physics): Mixing, certification delays, run-lengths, etc. Sequencing and timing Quality-related (Chemistry): Octane limits on gasoline Freeze and cloud-points on kerosene and diesels, etc Step 5: Capture Benefits Immediately (Harjunkoski, 2015) Scheduling Solution Development Curves
- 35. Smart Process Operations:Opportunities in ICT (Qin, 2014)(Christofides et al., 2007) (Davis et al., 2012) (Huang et al., 2012) (Chongwatpol and Sharda, 2013) (Ivanov et al., 2013) Smart Process Manufacturing Big Data RFID in APS and Supply Chain Example: when crude is selected for 2-4 days, after the 1st shift of 8h update all data using Information and Communication Technologies (ICT) integrated with Data-Mining applications and then use this in the Decision-Making.
- 36. 36 Integration Strategies forMulti-Scale Optimization in the Oil-Refining Industry (Multi-Layer and Multi-Entity) Brenno C. Menezes, Ignacio E. Grossmann and Jeffrey D. Kelly reduce bottleneck and idling of equipment maintenance of equipment Operational Planning Strategic Planning Plants Terminals Fuels Processing Distribution Marketing & Sales Raw Material Procurement expansion, installation, extension of equipment production level, supply chain service Instrumentation, Advanced Process Control and RTO Scheduling Tactical Planning model data model data model data model data cycle data orders (feedforward) key indicators (feedback) coordination and collaboration modes of operation, campaign on-line off-line
- 37. Gracias It seems aparadox, but I have been saying that the biggest human fear is not the fear of the darkness, but the fear of the light. (Sri Prem Baba) 37 Thank You