2014 Software Global Client Conference

Transcription

1

2 Tips & Tricks from ROMeo Implementation Projects Robert S. Morrison Optimization Consultant September 30, 2014

3 Mobile App: Please take a moment Check into Session by: Select Detailed Schedule Select the specific session Click on Check in Take Session Survey by: Select Detailed Schedule Select the specific session Scroll Down to Survey and Provide Feedback

4 Tips & Tricks from ROMeo Implementation Projects Introduction to ROMeo ROMeo Applications Modeling Considerations Use of Some Key ROMeo Features Discussion and Questions

5 What is ROMeo? Rigorous Online Modeling Equation-based Optimization Comparable to offline simulators (e.g. PRO/II) Connected to process data and process control with full automation Model represented by sets of equations and solved simultaneously Designed for large scale optimization

6 ROMeo Why equation-based? Advantages of Equation Oriented Modeling Speed of solution for large scale applications Flexibility of model specification (swapping inputs and outputs) Ease of adding and solving equations Simultaneous solution of model equations and optimization Data Reconciliation Mode - Optimal model fitting to match plant data Optimization Mode - Optimization of plant operation to maximize profit No custom algorithms required for each model or flowsheet convergence no recycles Easier to modify and maintain

7 ROMeo Why equation-based? Other Equation Oriented Modeling Considerations Every variable needs an estimate Built in Initial Estimate Generation methods All variables accessible to the user Reasonable bounds generally required Default bounds for selected variables Flexible bounding features for automated bound management Derivatives of equation residuals required Automatically calculated, mostly analytical, some numerical Equations should generally be continuous functions between model bounds Discontinuities should be minimized/avoided Design consideration when adding user models and equations

8 Why use ROMeo? ROMeo Versus Offline Steady State Simulators Offline Design Simulators ROMeo Steady State Yes Yes Rigorous Unit Models with broad model library and flowsheet paradigm Yes Yes Rigorous Thermodynamics Yes Yes Solution Approach Ease of Model Configuration Scalability to Very Large Applications Designed for Automated Tuning to Match Plant Operation (Data Reconciliation) Designed for Profit Optimization Designed for On-line Automation (sequencing and interfacing features) Designed for multiple calculation modes (mode dependent specification) Sequential (custom algorithm) Preferred for Design Limited (difficulty increases rapidly with scale) No Limited Limited No Simultaneous (SQP) Preferred for Data Reconciliation, Optimization, and Model Adaptation Yes (well suited for very large models) Yes (includes measurement models, supports Data Reconciliation) Yes (built-in profit objective support) Yes (includes plant lineup and control status) Yes (DataRec, Parameterization, Simulation, Optimization, User extensible)

9 Where is ROMeo typically used? Typical ROMeo Applications Automated closed loop profit optimization of one or more process units Automated equipment performance monitoring and maintenance benefits calculation Material Balance Reconciliation Offline planning tool for feedstock optimization Production allocation Any application requiring automation of a process model Project Expectations Drive Implementation Approaches Reasonably priced Justifiable Worthwhile ROI Efficient to deliver Efficient to run Maintainable Accurate Reliable Versatile Automatic

10 Typical ROMeo On-line Optimization Application Characteristics & Functionalities More than just a model! Refining, petrochemical and gas processing Large scale and complex processes Automatically run when process is near steady state Automatically adapting to process configuration changes Automatically and optimally tuned to current operating and equipment conditions Automatically adapting to control configuration and bounds Automatically determining the optimal operating point (maximum profit) within allowable operating window Automatically returning operating targets to plant APC May involve other automated calculations ( Engineering optimization, automated cleaning cases studies, etc) May involve offline interactive components (simulation and optimization case studies, etc) Other types of applications may involve other functionalities

11 ROMeo Application Design and Implementation Considerations Designed and Scoped to Meet Customer Specific Requirements Suitable model scope and detail/fidelity Model modularity and organization Data interfacing Model validation Use Cases Automation approach User interfaces Solution presentation reports, displays, notifications

12 What should be included in the model? Primary Real Time Optimization (RTO) modeling goals Optimize the process unit operation within the allowable operating limits Accurately compute profit change as a function of plant optimization variable (MVC manipulated variable (MV) changes Reliably predict constraint variable (MVC CVs) values as a function of plant MV changes RTO model scope Include model components that impact: Optimization Variables Those operating variables that can be moved within limits to achieve an improved profit Constraints Those equipment, safety and operating limits that must be honored Objective Function How is the unit s (company s) profit impacted by changes in the units operation Don t include unnecessary model scope as it adversely impacts system efficiency and maintainability as well as required effort and project cost

13 Modeling Approaches How should the units/variables be modeled? Modeling approaches First principles (rigorous models) generally used preferentially Empirical (correlated models) Linear constraint projection (gain units) often used to model constraint variables not readily modeled with first principles models Proper modeling of effects on objective prediction most important Reactor yields Product separation Major variable utilities for energy intensive processes

14 Reactor Modeling Built-in and User-added Conversion Model User configured reaction stoichiometry and conversion SIMSCI Reactor Models - Refining SIMSCI - FCC, HFAlky, SFAlky, ISOM, Reformer, HDP (hydrotreating and hydrocracking), VOM (visbreaking), Coker Third Party Reactor Models KBC FCCSim, HCRSim, REFSim, VGOHTRSim, NHTRSim Technip - SPYRO (ethylene furnace) User Added Reactor Models Native Milano (ROMeo s model development language) EEB - External Equation Block, open form external model BBM - Black Box Model closed form

15 Model Specification Techniques Think predictive How does the plant behave? Online model specification (defining model inputs) should reflect how the actual plant equipment and control system operate Different approach from offline design simulations Mode dependent behavior implemented based on non-process units (measurements, tuning parameters, SISO, MVC, etc) Specify models based on process control Regulatory controlled variables or Setpoints MVC MVs and CVs Specify models based on equipment performance Equipment performance parameters Performance variable values or variable offsets determined in Data Reconciliation or Parameterization Example specifications Exchanger heat transfer coefficient better than fixed outlet temperature (if not controlled) Compressor head curve with offset better than just compressor outlet pressure (if not controlled) Specify pumparound flow if it s an MV in the plant MVC

16 Model Modularity and Organization Techniques Subflowsheets and Block Diagrams Model Organization driven by Plant complexity Model development and maintenance considerations Model Use Cases Plant Operating Lineups Block diagrams primarily used for display/graphic purposes Subflowsheets used for functional model management Turn on/off/down a process area without losing state of underlying equipment Solve a plant area separately while maintaining meaningful specification Easily manage parallel model development effort by maintaining flowsheet modularity

17 Model Modularity and Organization Techniques Customizations broadly used in almost every model Customizations Custom Variables Custom Equations Fix and Free Variables (swap model inputs and outputs) Customization Types Flowsheet access variables in current flowsheet units or subflowsheets Unit access variables in unit or unit products Subflowsheet/Boundary variables in subflowsheet including at least one boundary stream variable New user variables can be added to Unit and Flowsheet Customizations, and Process Streams

18 Model organization techniques Use of Customizations broadly used in almost every model Place customizations in the lowest appropriate level - flowsheet/subflowsheet/unit Keeps the custom calculations close to the units/streams affected Allows further customization at higher model levels Higher level customizations can change specifications in lower level customizations Modularize custom calculations Segregate custom calculations from cross unit spec swaps Use separate customizations for unrelated calculations Allows improved control over turning custom calculations on/off Learn use of subflowsheet customization and interactions between them Use to fix/free variables entering/leaving subflowsheet (one or both swapped specs should be a boundary variable) Used for back propagation through subflowsheets (demand driven flow, pressure propagation, etc)

19 Flowsheet and Unit Customizations Little known features - $IF $IF construct allows conditional inclusion of equation terms Used to exclude terms when referenced equipment is out of service Condition is evaluated based on static initial value of a parameter

20 Y-calc Flowsheet and Unit Customizations Little known features - Case Case construct allows conditional inclusion of equation terms Used to select term to be included based on a variable value Condition is evaluated based on current value of a model variable during solution Allows piecewise fit functions to be evaluated during solution Performance y = -10x y = -0.2x x Make sure that function is continuous in value and preferably derivative as well Linear Quadratic X

21 Interfacing Models to Plant Data Interfacing Model to plant data with External Data Interface (EDI) EDI provides convenient access to numerous data sources Process and control data generally accessed through plant data historian Model measurements generally linked to plant average values Lab and analyzer data often linked to most recent value Control status and bounds generally interfaced to current values Model results frequently exported to plant historian

22 Interfacing Models to the Plant Control Adapting models to plant control configuration MVCs (and SISOs) Specify MVC2 Unit MVs and CVs Optimization Variables and Constraints On/Off control Fix/Free independents, ignore/honor bounds Bound values (operator entered, engineering limits, etc) High/Low Limited flags Normal steps rate limit opt moves Most control interfacing all in one place Supports solution validation checks

23 Validating the Model Does the model match the data AND predict the plant behavior? Measurement reconciliation use weights of 1/0 and use realistic expected accuracies Make sure that DataRec objective is reasonable at solution (target < 4 * Number of Measurements) Use DataRec reports and ObjFctContrib macro to quickly evaluate DataRec results Avoid over parameterized model risk when plant is under-instrumented and/or model has excess tuning parameters Don t over focus on absolute model fit while neglecting gains. Sensitivity is more important to optimization model results. Objective and constraint moves in optimization are entirely dependent on model gain predictions (not absolute model fit). Compare model gains (sensitivity analysis) to plant gains (MVC test results, other plant gain tests, and engineering reason)

24 Sensitivity Analysis How are model outputs affected by inputs? Generate Sensitivity Results Solve Simulation or Optimization Model Configure Cause and Response variables in Sensitivity Analysis Save sensitivity case definition for future use Calculate and generate sensitivity report Sensitivity cases can also be run via macro Compare model gains to APC and plant test gains Generate simplified local linear models Linear quality inferentials from rigorous model Update LP models Compute yields and profit terms with respect to various operating variables

25 Extended Model Use Cases (Automated and On Demand) ROMeo is not just for Closed Loop Optimization DataRec and Optimization standard in RTO applications Engineering Optimization Determine potential benefits and plant operating point with broadened bounds Automated case studies Determine equipment maintenance benefits Compute normalized unit performance Generation of linear models (sensitivity analysis) LP updating Results analysis

26 Use of Macros (TCL scripting) A multitude of applications Model building/configuration Add, configure, and interface model units automatically (data driven) Measurements and Tuning Parameters Gain Units MVCs Adapting model to current process configuration lineup macros Turn equipment on or down Custom interfacing to external files Import/export data not suitable for EDI (e.g. load crude assays) Custom reporting Custom automation Custom pre/post solution calculations

27 Use of Macros (TCL scripting) A multitude of features Learn TCL it will be worthwhile Full featured extensible open source programming language with a broad user community Well documented on the web, in reference and training manuals, and in ROMeo online help Extensive ROMeo TCL extensions (see ROMeo online macro help) Milano methods provide access to methods within models, useful for actions not directly supported in macro calls source command allows macros to be stored in external files Packages (math, tcom, tdom, http, pop3, etc) TCL function libraries math extensive library of math functions tcom automation of programs external to ROMeo (e.g. Excel, other COM enabled tools) tdom reading/writing/parsing XML and HTML documents http web access pop3 access Packages not distributed with ROMeo s TCL 8.4 may be available online and installed by the user

28 Automation of ROMeo models Scheduled, Triggered Unattended Automation and HMI Options Automate and Interact with models in various ways Different methods for running models ROMeo client GUI Manual triggering of model solve or macro ROMeo RTS Sequences Scheduled or triggered model tasks (solve or macro) Model macros TCL scripted solving of model Excel OPS Add-in Triggering of model solve from Excel (via OPSServer) OPSServer execution of model solve, macro, RTS sequence, etc compatible with various programming languages/environments Excel ROMeo Portal Alternate Excel Interface

29 ROMeo Application Report Options Built-in text reports (customizable) Built-in html reports (customizable) Custom Excel reports populated directly from model macros or via csv or tab delimited text files Custom macro generated text, html, xml reports Custom XSL may be used to create custom views of standard and custom XML output Custom html and Excel reports based on historian data access tools

30 Remote Monitoring Notifications using task in RTS Requires access to SMTP mail server Automatically send with attachments from sequence or model app (e.g. reports, spreadsheets, etc) Can be used for text notifications with to text via phone carriers SMS gateway

31 ROMeo designed for Rigorous Online Modeling and equation-based optimization and A WHOLE LOT MORE! Discussion and Questions

32 Related Support, Services, Training & Expo Demos Support > SimSci Portfolio Tech Support 24/7 > Project-specific Tech Support options Services > APC Implementation > ROMeo Implementation and Consulting > Yield Accounting Implementation > ROMeo Training Training > SimSci Reactor Modeling Training > SimSci APC Training > Yield Accounting Training Expo Demos > ROMeo Platform Demo > ROMeo SimSci Reactors Demo > SimSci APC Demo > Yield Accounting Demo > Dynsim Demo > PRO/II Demo

33 2014 Schneider Electric. All Rights Reserved. All trademarks are owned by Schneider Electric Industries SAS or its affiliated companies or their respective owners. 33