PROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC AND REAL-TIME OPTIMIZATION

Transcription

1 Richard Salliss October 2016 Richard Salliss 25 Oct 2016 PROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC AND REAL-TIME OPTIMIZATION

2 Honeywell s Advanced Control Solutions Profit Controller Profit Sensor Pro CV MV Past Optimal Response Setpoint Future Control Performance Monitor Predicted Unforced Response Control Funnel Assumed Values Profit Stepper Minimum Effort Move Profit Optimizer: Real-time, Dynamic Optimization Optimum *

3 Profit Optimization Suite One Consistent technology platform - MPC and Real Time Optimization - Fleible Modeling environment - Unmatched operational awareness - Lowest lifecycle cost Profit Loop Eperion Profit Controller (C300/ACE) Profit Suite - Profit Controller - Profit Sensor - Profit Stepper Profit Optimizer (DQP Multi-Unit Optimization) Profit Eecutive (SuperDQP Multi-Asset Optimization) Continuum of Control Solutions Single Variable Linear Control Multiple-Variable Non Linear Control and Optimization Seconds Hours/Days Profit CPM (Control Performance Monitor)

4 Profit Suite R440 Final Scope Profit Optimizer Enhancements - Phase 1 APC Vendor Agnostic parameter mapping - Optimization engine interior point enhancements UOP Synergy and toolkits - Hydroprocessing (hydrocracking) and Olefle (C4) toolkits Honeywell User Eperience - PSOS Thin Client and user configurable colors - CAB Prioritized enhancements (Stepper, PSES backbuild) - MV Transformations - Models from historical data in PSES Platform & Infrastructure Compliance - 64 bit only and include support for Windows Server 2012, 2016 Windows 10 - Upgrading PHD to R321, UPS to R321 - Allow for custom install path

5 Traditional Optimization Overview Build SS model for Optimizer - Linear, NL, large scale - Represents plant behavior - Define objective function Calculate SS targets to determine the end goal Realize the operating value by dealing with dynamics - Ensure feasibility - Prevent constraint violations - Push plant to SS Update the model, objective function and dynamic compensator - Compensate for changing conditions - Repeat cycle SS Model Limits Economics and Ranges SS Optimizer Feasibility LP Override Process SS Targets Dynamic MPC Compensator Controller Feedback SS Feasible Controller SS Controller Targets Process Ramped Changes Path Target Ramp Rates Without Profit Optimization, the LP Override must be engineered to provide feasible SS controller targets and a realistic ramp rate to the MPC controller

6 Honeywell Optimization Solutions Profit Optimizer Dynamic Optimization Solution Start Po Dynamic optimization no steady-state detection is needed 3-5 minute eecution frequency Full formulation objective function w/ QP solver Cooperative optimization approach Shares common models with Profit Controller application(s) Calculates optimization speed for each controller depending on underlying process dynamics & settling time Patented bridge model technology: full dynamic relationships across the optimization scope. Gain scheduling for non-linear enhancement (via Profit Controller/Optimizer Gain Mappers) Direct move in the most profitable direction Global optimum when possible

7 Profit Optimizer Concepts Base structure MVs Profit Optimizer (DQP) Profit controller Model matri CVs MVs CVs Optimization Objective Function Can have multiple objective functions but only one eecuting

8 Profit Optimizer Concepts Bridge models add dynamic feed forward dependence MV1 MV2 DV1 MV1 MV2 DV MV1 MV2 DV1 MV1 MV2 MV1 MV2 DV1 DV BM Bridge Model inputs Bridge Model output MV1 MV2 DV

9 Profit Optimizer Concepts Optimization CVs - multi-unit steady state CVs (no feed back). MVs CV CVs Optimization Constraints y y y y y y y

10 Profit Optimizer Concept Multiple applications built for good control and optimization performance Global objective function to fully utilize degrees of freedom Local optimizer Controller App 1 Overall Optimization Horizon Global SS Local optimizer Controller App 2 Local optimizer Controller App 3 Local optimizer Controller App 4 Time

11 Refinery PO Structure Eample PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC 14 PC 15 CC / au Legend: Bridge model Source / clone Optimization constraints

12 Profit Optimizer SS Model Profit Bridge Gains Global Economics and Ranges Profit Optimizer Profit Optimizer coordinates the economics of multiple Profit Controllers for multi-unit plant-wide optimization Limits SS MV Targets Gains MV Economics QP Override 1 QP Override 2 QP Override 3 Local Feasible SS MV targets Local Feasible SS MV targets Local Feasible SS MV targets Profit Controller 1 Profit Optimizer Link Other Profit Controller 2 Profit Controller 3 Feedback Optimal Dynamic Path Optimal Dynamic Path Optimal Dynamic Path Process 1 Process 2 Process 3 Ensures Optimal Dynamic Global Path

13 R440 Optimization Enhancements Profit Optimizer will be enhanced to include Hessian Updating which will: - Allow optimizer to use curvature-of-the-surface information compared to simple gain updating Optimum * - Allow superior handling of unconstrained (interior point) solutions - Improve ability to deal with process non-linearities - Faster convergence on the optimum solution - Provide additional benefits of up to 30% (depending on process non-linearities) Solution Start P

14 Non Linear Optimization Background The low hanging fruits have already been taken - Embedded optimization (e.g., PVOs) within PC and PO Remaining opportunities may require nonlinear optimization - SS RTO is an option, but Initial and maintenance costs are typically high It requires a higher level of epertise to implement/maintain - Honeywell offers a more cost-effective approach Similar to the eisting PO/PC optimization scheme It allows nonlinear objective functions Nonlinear constraints are handled at controller level via gain-updating Honeywell nonlinear dynamic optimization approach: - Use nonlinear models where they make the difference and are costeffective - Epand frequent gain updating to frequent Hessian updating - Use a Time-Sequenced QP algorithm to provide SS solution projections

15 Nonlinear Optimization in a Nutshell 99+% of nonlinear optimization problems in process industries are solved by sequential quadratic programing (SQP or sequential QP) How does sequential QP work? - Here is an animated illustration: 2 P-Optimal Point Rosenbrock's Banana Function: 2 2 f() 100( 2-1 ) (1-1 ) 2 Starting Point 1

16 HW Approach: modify SQP for real-time use Change Sequential QP (SQP) to Time-Sequenced QP (TSQP) Update the Hessian matri and model-predicted constraints along the sequence Solve the time-sequenced QP successively inside the control feedback loop - If the model error or disturbance is negligible, the TSQP gets the same solution as SQP (or SS RTO) - If the model error or disturbance is modest, the TSQP gets feedback corrections along the way - If the model error or disturbance is/becomes large (over time), the TSQP should do better. - We all know: Disturbances are part of life in process industries! 2 P-Optimal Point Actual Optimal Rosenbrock's Banana Function: f() 100( 2-1 ) (1-1 ) Starting Point 1 Update the objective function, model gains, future predictions and constraints at a userspecified frequency (or condition)

17 TSQP Versus Gain Updating Solution Start Po Starting Point 2 0 1

18 TSQP Versus SSRTO Ideal World Solution Starting Point Start Po

19 TSQP Versus SSRTO Real World Optimum Operation NLDO TSQP Path Profit $ Benefits $ Current Operation SSRTO Path Time

20 Opportunities for nonlinear optimization (1) Multiple feed choices for a unit Naphtha Purchased VGO Crude Light Distillate VGO Purchase - Column V Unit C3/C4 to Poly/Alky Crude Blending A Vacuum LVGOl Gasoline Light Gasoil to FCC Tower FCC HVGO LCO Heavy Gasoil to FCC A Vacuum Bottoms to FCC VolumetricHCO Atmos Bottoms Gain ~111% Atmos Bottoms to FCC Slurry Crude Blending Crude Blending Naphtha LCO from FCC Crude Unit Heavy Distillate Fd tank 2 Naphtha Diesel B HTR Diesel Atmos Bottoms Volumetric Cloud = -5 ~-25C Blending Gain Sulfur = 0~10ppm Feed 104% Final Product Tank 2 Naphtha Component Naphtha Tank B Crude Light Distillate Diesel Cloud = -45 ~ -48C Unit Unifiner Sulfur = 4-6ppm Heavy Distillate C Volumetric Atmos Gasoil Swing Cut Gain 101% Debut Overhead Light Naphtha Hydro Component Tank A Heavy Naphtha Atmos Bottoms Vacuum Light Vacuum Gasoil Cracker Tower Light Distillate C Heavy Vacuum Gasoil Heavy Distillate (to FCC) Volumetric Vacuum Tower Bottoms Gain Bottoms Feed HCO from FCC 120% Tanks 3 Debutanizer

21 Multiple feed choices for a unit A unit (or a pool of units) Feed Ratio (A/B) A B Products Profit Curve Shape Categories: Profit Profit Profit Profit A/B Ratio A/B Ratio A/B Ratio A/B Ratio 1) linearly increasing 2) humpback 3) camelback n) linearly decreasing Nonlinear

22 Opportunities for nonlinear optimization (2) Pooled product streams nonlinear property blending Naphtha Crude Light Distillate VGO Purchase - Column V Unit C3/C4 to Poly/Alky A Vacuum LVGOl Gasoline Light Gasoil to FCC Tower FCC HVGO LCO Heavy Gasoil to FCC A Vacuum Bottoms to FCC VolumetricHCO Atmos Bottoms Gain ~111% Atmos Bottoms to FCC Slurry Naphtha LCO from FCC Crude Unit Heavy Distillate Fd tank 2 Naphtha Diesel B HTR Diesel Component Tank A Atmos Bottoms Volumetric Cloud = -5 ~-25C Blending Gain Sulfur = 0~10ppm Feed 104% Final Product Tank 2 Naphtha Component Naphtha Tank B Crude Light Distillate Diesel Cloud = -45 ~ -48C Unit Unifiner Sulfur = 4-6ppm Heavy Distillate C Volumetric Atmos Gasoil Gain 101% Debut Overhead Light Naphtha Debutanizer Hydro Heavy Naphtha Atmos Bottoms Vacuum Light Vacuum Gasoil Cracker Tower Light Distillate C Heavy Vacuum Gasoil Heavy Distillate (to FCC) Volumetric Vacuum Tower Bottoms Gain Bottoms Feed HCO from FCC 120% Tanks 3

23 Opportunities for nonlinear optimization Nonlinear process yield relationship A reactive unit Feed Products Product Value Optimization: ObjFun price i product i cos t i feed i appears linear However, product i f ( conv,..., etc ) nonlinear function Thus, ObjFun NonlinearFun( operating variables)

24 Future development: Profit Eecutive Key Challenge: Business Planning Plantwide economics Production Planning How to get the solution layers to stay consistent and reach the global optimum jointly? Planning (Months) Schedule & Optimization Scheduling (Day/Weeks) Optimal feasible Profit Eecutive Manage intermediate and final: Inventory (volumes) Properties (quality) Timeline (just in time) Control & Optimization Real Time Dynamic Optimization - Profit Optimizer (hr) Local economics App -1 App -2 App -3 App -n

25 Pursue Profit with Honeywell s Profit Suite Layered Optimization Honeywell s unique solution for leveraging eisting models and increasing operator effectiveness while driving ma benefits through large-scale optimization Fleible Licensing Honeywell s fleible approach to software licensing ensures maimum returns and protection of your investment Unified Technology Single platform from Eperion-embedded to plant-wide control & optimization More Benefits Over Time The most comprehensive offering to transform business needs and objectives into real-time operations Faster Realization of Benefits Designed for minimal effort to achieve the first Euro of benefits and build to larger benefits as ROI is justified