Building Cogeneration Planning Scheduling Systems using IBM ILOG ODME, CPLEX and impress

Building Cogeneration Planning

and Scheduling

Applications using IBM ODME

and iMPress

DecisionBrain & Industrial Algorithms LLC.

7/19/2013 Copyright, DB & IAL

Agenda

• What is ODME?

• What are Industrial Modeling Frameworks?

• What is iMPress?

• ODME-iMPress Implementation

• Benefits

• Proof of Concept

2

3

Based on IBM ILOG Optimization

Portfolio

Engines and Tools

CPLEX Optimization High-performance mathematical and constraint programming solvers, modeling language, and development environment

Solution Platform

ODM Enterprise Build and deploy analytical decision support applications based on optimization technology

Oil&Gas Production Scheduling

ILOG ODM Enterprise

Architecture

(OR)

(IT)

Embeds all CPLEX Optimization Studio

Reporting

Data Integration

Data Modeling

ODM Enterprise IDE

ODM Enterprise

Optimization Server/Engine

ODM Enterprise

Client & Planner

Optimization Modeling,

Tuning, Debugging

Application UI Configuration (LoB)

Development Deployment

Application UI Customization

Business Use

Custom GUI

Batch process

ODM Enterprise

Data Server

Industrial Modeling Frameworks

(iMF’s)

• Process industry business problems are

complex hence an iMF provides a pre-project

or pre-solution advantage (head-start).

• An iMF embeds intellectual-property and

know-how related to the process’s flowsheet

modeling as well as its problem-solving

methodology.

iMPress

• iMPress stands for “Industrial Modeling &

PRE-Solving System” and is our proprietary

platform for discrete and nonlinear modeling.

• iMPress can “interface”, “interact”, “model”

and “solve” any production-chain, supply-

chain, demand-chain and/or value-chain

optimization problem.

6

Cogenerartion Scheduling

Application Types

• Off-Line Environments:

– Usually « dynamic » optimization with discrete (logic) & linear variables using Mixed Integer Linear Programming not including feedback (feedforward only).

• On-Line Environments:

– Usually « steady-state » optimization with continuous & nonlinear variables using NLP including feedback (and feedforward).

– Usually includes steady-state detection, data reconciliation and regression (« moving horizon estimation ») with diagnostics for monitoring.

Off-Line Optimization

• Sometimes called « load shedding, shifting &

scheduling ».

– Determines steam and power production subject to

supply availability and demand requirements.

– Respects transition (sequence-dependent)

management of producing units such as boilers and

turbogenerators i.e., understands resting (standby),

ramping (startup/shutdown) and running (setup)

which IAL calls « Phasing ».

– Similar to a « product wheel » found in specialty batch

& fast moving consumer goods industries.

Off-Line Optimization –

« Phasing »

• « Phasing » forces a predictable operational

sequence or order for selected units.

Off-Line Optimization –

« Phasing » • REST = min. 3-d, RAMPUP = 1-d, RUN’s =

min. 3 - max. 10-d, RAMPDOWN = 1-d, Past-Horizon = 2-d & Future-Horizon = 60-d.

On-Line Optimization

• Typically assumes discrete/logic variables are fixed – IAL calls this « phenomenological decomposition ».

• If plant is at « steady-state* » then optimize process or operating conditions using NLP (IPOPT, KNITRO, XPRESS-SLP, IAL-SLPQPE).

• Apply nonlinear data reconciliation & parameter estimation to provide gross-error/outlier detection & calibrate model.

* Kelly & Hedengren, « A steady-state detection algorithm to detect non-stationary drifts in processes », Journal of Process Control,

23, 2013.

On-Line Optimization • An important aspect is to callout/callback to

physical/thermodynamic properties such as enthalpies. – STEAM67.DLL is « wrapped » in

STEAM67_H.DLL to compute saturated enthalpy and its first-order derivatives using its saturated temperature.

&sCondition

HOTF

COLDF

WARMF

HOTT

COLDT

WARMT

FBAL

HFBAL

&sCondition

&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sConditi

on_Names

HOTH,dynamic,c:\IndustrialAlgorithms\PhysicalProperties\Debug\,steam67_H,steam67_H,1,1e-6,HOTT

COLDH,dynamic,c:\IndustrialAlgorithms\PhysicalProperties\Debug\,steam67_H,steam67_H,1,1e-6,COLDT

WARMH,dynamic,c:\IndustrialAlgorithms\PhysicalProperties\Debug\,steam67_H,steam67_H,1,1e-6,WARMT

&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sConditi

on_Names

Conditions-&sMacro,@sValue

FBAL,HOTF + COLDF - WARMF

HFBAL,HOTH*HOTF + COLDH*COLDF - WARMH*WARMF

Conditions-&sMacro,@sValue

Cogeneration (Steam/Power) iMf

7/19/2013 Copyright, Industrial Algorithms LLC

• Time Horizon: 168 time-periods w/ hour

periods.

• Continuous Variables = 5,000

• Binary Variables = 1,000

• Constraints = 7,500

• Time to First Good Solution = 5 to 30-

seconds

• Time to Provably Optimal = 5 to 15-minutes

Cogeneration (Steam/Power) iMf

Water

Pump

• Time Horizon: 168 time-periods w/ hour

periods.

• Continuous Variables = 5,000

• Binary Variables = 1,000

• Constraints = 7,500

• Time to First Good Solution = 5 to 30-seconds

• Time to Provably Optimal = 5 to 15-minutes.

• Solver: CPLEX

7/19/2013 Copyright, Industrial Algorithms LLC

Cogeneration (Steam/Power) iMf

ODME-iMPress-CPLEX

System Architecture

ODME-iMPress-CPLEX

System Architecture

• A domain-specific data model was created in

ODME using the usual master-data and

transactional-data partitions.

• A mapping between iMPress’ data model and

ODME’s data model was established.

• Java code was written to export iMPress’ IML

file (Industrial Modeling Language).

• SWIG Java was used to create a Java Native

Inerface (JNI) to iMPress.

ODME-IMPRESS-CPLEX

System Architecture

• Java code was written to call iMPress-CPLEX

using its API’s.

• Java code was written to access the solution(s)

from iMPress-CPLEX using its API’s and to

populate the ODME solution-data partition.

ODME Screen Shots

Data-Model in ODME

Master-Data

Transactional-Data

Gantt Chart for Reference (Base)

Trend Plots for Reference (Base)

Demand Variability Scenario Data w/

Reference in ()

Trend Plots for Demand Variability

Scenario w/ Reference

Benefits • Perfectly fit your business model and decision processes

• Sophisticated optimization capabilities able to tackle complex,

non-linear and large-scale problems

• A solution that can be quickly adapted to new production

processes

• A user-friendly GUI to help planners driving refinery operational

excellence and analyzing refinery behavior

• What-if scenario analysis for confident decision-making

• See all your data and options in one place with drill-downs and

graphics

• Collaborate with other planners

• Powered by IBM ILOG CPLEX Optimizers

Proof-of-Concept (POC)

• Select plant type, size and complexity.

• Determine if off-line or on-line

application.

• Configure plant model.

• Integrate data sources.

• Solve plant model with plant data.

• Tune plant model (for accuracy &

tractability).