2014 Software Global Client Conference - Invensysiom.invensys.com/EN/SoftwareGCC14Presentations/SimSci/SS OPT-03... · 2014 Software Global Client Conference ... (maximum profit)

2014 Software Global Client Conference

Tips & Tricks from

ROMeo

Implementation

Projects

Robert S. Morrison

Optimization Consultant

September 30, 2014


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Tips & Tricks from ROMeo Implementation Projects

Discussion and Questions

Use of Some Key ROMeo Features

Modeling Considerations

ROMeo Applications

Introduction to ROMeo


What is ROMeo?

• Comparable to offline simulators (e.g. PRO/II) Rigorous

• Connected to process data and process control with full automation Online Modeling

• Model represented by sets of equations and solved simultaneously Equation-based

• Designed for large scale optimization Optimization


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


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


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 Sequential (custom algorithm) Simultaneous (SQP)

Ease of Model Configuration Preferred for Design

Preferred for Data Reconciliation,

Optimization, and Model Adaptation

Scalability to Very Large Applications Limited

(difficulty increases rapidly with scale)

Yes

(well suited for very large models)

Designed for Automated Tuning to Match

Plant Operation (Data Reconciliation) No

Yes

(includes measurement models, supports

Data Reconciliation)

Designed for Profit Optimization Limited

Yes

(built-in profit objective support)

Designed for On-line Automation (sequencing

and interfacing features) Limited

Yes

(includes plant lineup and control status)

Designed for multiple calculation modes

(mode dependent specification) No

Yes

(DataRec, Parameterization, Simulation,

Optimization, User extensible)


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


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


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


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


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


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


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


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


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


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)


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


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

Make sure that function is continuous in value and preferably derivative as well

y = -10x + 1500 y = -0.2x2 + 10x + 1000

0

500

1000

1500

2000

0 20 40 60 80 100 120

Y-c

alc

X

Performance

Linear

Quadratic


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


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


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)


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


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


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

…


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 – email access

Packages not distributed with ROMeo’s TCL 8.4 may be available online and installed by the user


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


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


Remote Monitoring

Notifications using email task in RTS

● Requires access to SMTP mail server

● Automatically send email with attachments from sequence or model app (e.g. reports,

spreadsheets, etc)

● Can be used for text notifications with email to text via phone carriers SMS gateway


ROMeo designed for

Rigorous Online Modeling and equation-based optimization

and

A WHOLE LOT MORE!

Discussion and Questions


Related Support, Services, Training & Expo Demos

> SimSci Portfolio Tech Support 24/7

> Project-specific Tech Support options

Support

Training

> ROMeo Training

> SimSci Reactor Modeling Training

> SimSci APC Training

> Yield Accounting Training

> APC Implementation

> ROMeo Implementation and Consulting

> Yield Accounting Implementation

Services

Expo Demos

> ROMeo Platform Demo

> ROMeo SimSci Reactors Demo

> SimSci APC Demo

> Yield Accounting Demo

> Dynsim Demo

> PRO/II Demo

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2014 Software Global Client Conference - Invensysiom.invensys.com/EN/SoftwareGCC14Presentations/SimSci/SS OPT-03... · 2014 Software Global Client Conference ... (maximum profit)