Hybrid Modeling & Model Predictive Control  

The Potential to Leverage Success in Other Industries

Discussion Overview

• Model Predictive Control – 15 years of Reducing Variability & Improving Quality

• Process Understanding – Leveraging Fundamental AND Empirical Knowledge

• Design Space – ensuring model accuracy with multi-dimensional boundaries

• Lesson’s Learned and Success Achieved in other Industries

• A Practical Risk Based Approach to Implementation

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• Our Mission• Deliver the world’s leading model-based software to

improve our customers’ profitability

• Founded in 1991• Combined intellectual property of DuPont and Eastman

Chemical Company

• Global Presence• Offices in North America, Europe, China, and Pacific Rim

• Financials• Acquired on November 1, 2007 by

Commitment to Innovation• Team of researchers, computer scientists and industry

experts leveraging more than 155 patents in the field of modeling, control and optimization

Pavilion Technologies

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What is a Model?A model explains or emulates the behavior of a process ...

… using a set of computations

•y = a3 u3 + a2 u2 • + a1 u + a0

•monomer

•modifier

•catalyst

•melt index

•density

A model provides predictive capability through “computational experimentation”

• Manufacturing assets can be understood

• Manufacturing assets can be managed

• Note that the "process" need not be physical

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ModelDevelop Statistical ModelsCharacterize ProcessTrace of Outputs vs. InputsTrace of Output SensitivitiesInteractive Set-Point Analysis

Multivariate Data Analysis

Model Development

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What is a Good Model?

Desired Model Features

• Offer prediction accuracy and maintain computational efficiency

• Provide global validity over the entire operation region

• Enable optimal combination of empirical data, first-principles models, and process knowledge

• Remain physically meaningful

• Offer robustness to modeling inaccuracies and disturbances by enabling optimization-based modification of the models

• Enable solution standardization by simplifying template building

Desired Model Features

• Offer prediction accuracy and maintain computational efficiency

• Provide global validity over the entire operation region

• Enable optimal combination of empirical data, first-principles models, and process knowledge

• Remain physically meaningful

• Offer robustness to modeling inaccuracies and disturbances by enabling optimization-based modification of the models

• Enable solution standardization by simplifying template building

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Established Modeling Paradigms aren't Enough

First Principles Modeling• Leverage explicit knowledge

based on scientific principles

•Strengths•Global validity

•Parameters have physical meaning

•Not data dependant

•Weaknesses•Typically incomplete

•Slow evaluation (solver)

•Could contain implicit outputs

First Principles Modeling• Leverage explicit knowledge

based on scientific principles

•Strengths•Global validity

•Parameters have physical meaning

•Not data dependant

•Weaknesses•Typically incomplete

•Slow evaluation (solver)

•Could contain implicit outputs

Empirical Modeling• Leverage implicit knowledge

based on historical/test data

•Strengths• Typically explicit

• Fast evaluation (no solver)

• Wide applicability and quick to develop

•Weaknesses• Valid for observed data

• Often lacks physical meaning

• Requires rich data

Empirical Modeling• Leverage implicit knowledge

based on historical/test data

•Strengths• Typically explicit

• Fast evaluation (no solver)

• Wide applicability and quick to develop

•Weaknesses• Valid for observed data

• Often lacks physical meaning

• Requires rich data

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Building Parametric Hybrid Models

Specify Hybrid Model Structure

•Often a composite model that includes both empirical and FP components

Specify Hybrid Model Structure

•Often a composite model that includes both empirical and FP components

Train the Model using Constrained Optimization

•Often constraints are imposed to ensure model parameters reflect first-principles knowledge

Train the Model using Constrained Optimization

•Often constraints are imposed to ensure model parameters reflect first-principles knowledge

EmpiricalModel

First-Principles

Model

EmpiricalModel

First-Principles

Model

ConstrainedTrainer

increasing

constraint

effects

decreasing

constraint

effects

Flexible Composite Modeling Example

FundamentalModels

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Pavilion8 in Drying Processes – The Primary Objectives

• Plant “Obedience” is an advanced form of “steady State”

• APC manages the dynamics & rapid disturbances that occur

• Consistency of operation• Enables optimal targets to be

achieved with “confidence”• Preserves safety - plant, people &

product

•

•

•

•Capacity Target uplift = Plant •Obedience and improved •steady state will increase •capacity within dryer limits

•

•Yield Target uplift = Plant •Obedience and VOA control •will allow moisture to increase. • •Plant Obedience = APC

on when the model is in the valid zone approx @ 30% solids

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Fermentation Control ApplicationCapabilities

• 50% reduction in batch EtOH and Dextrose/residual sugars variability

• Continuously manage enzymes to maximize throughput and ethanol yields

• Optimal target on temperature and pH for fermentation

• Manage fermentations to match production targets

Benefits- Increase in batch drop ethanol yield

(MMGPY) by 0.5-1.0%

- Increase in fermentation capacity by 5-12% (MMGPY)

- Increase in batch yields (gal/bu) by 2-5%

- Reduce enzymes/gal ($$ enzymes/gal) by 5-10%

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Slurry Tank

Liquefaction

Backset

Beer Feed

FermenterYeast Propagation

Enzymes

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Slurry Tank

Liquefaction

Backset

Beer Feed

FermenterYeast Propagation

Enzymes

Bio-Fermentation Batch A-1022Bio-Fermentation Batch A-1022

Predicted vs ActualPredicted vs Actual

Bio-Fermentation Batch A-1145Bio-Fermentation Batch A-1145

Predicted vs ActualPredicted vs Actual

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Reactor Control ApplicationCapabilities

• Adjust reactor conditions to maintain at-grade quality control of the resin properties

• Control concentrations within the reactor for improved reactor stability

• Control and/or maximizes production rates within the reactor

• Optimize transitions

• Standardize reactor best practices for complex procedures

Benefits

• Faster transition times

• Reduce off-spec

• Increase production

• Reduce variability

• Improve catalyst and monomer efficiency

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4 Steps of PAT Implementation by John R. Davis, PE and John Wasynczuk, PhD

DiscoverAnalyze Existing Processes

DevelopDevelop Statistical Models

Characterize Process

DemonstrateSubstantiate Statistical

Correlations

DeployPerform Continuous Monitoring &

Analysis

Better Process Knowledge

Through Analytics

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DiscoverAnalyze existing processes

Data Consolidation & VisualizationDCS/SCADA Historical Data

PAT Sensor Array Data

Quality

LIMS

MES/ERP

Data PreprocessingRemove Outliers

Signal Conditioning

Begin analysis in hours not days & weeks

•4 Steps of PAT Implementation by John R. Davis, PE and John Wasynczuk, PhD

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DevelopDevelop Statistical Models

Characterize Process

Trace of Outputs vs. Inputs

Trace of Output Sensitivities

Interactive Set-Point Analysis

Multivariate Data Analysis

Pavilion’s modeling technology can incorporate fundamental models into empirical models, leveraging the benefits both provide for Linear, Non-Linear, Monotonic and Non-monotonic processes

•4 Steps of PAT Implementation by John R. Davis, PE and John Wasynczuk, PhD

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DemonstrateSubstantiate Statistical

Correlations

Predicted vs. Actual Scatter-Plots

Interactive What-Ifs Tools

Provide different input values to tool predicts output values (Prediction)

Provide desired output values to tool determines best input values (Optimization)

•4 Steps of PAT Implementation by John R. Davis, PE and John Wasynczuk, PhD

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Powder Moisture Inferential Accuracy

Te Rapa D1 Moisture Prediction

2.5

2.6

2.7

2.8

2.9

3

3.1

3.2

3.3

3.4

3.5

Time (h)

Mo

istu

re (

%)

IPT Moisture (%) Raw VOA Moisture (%) Biased VOA Moisture (%)

•22/4/03• 5pm

•22/4/03• 11pm

•23/4/03•5am

•23/4/03•11am

•23/4/03•5pm

Moisture Inferential Accuracy

DeployPerform continuous monitoring &

analysis

•BENEFIT

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•104

•102

•100

•98

•96

•94

•92

•90

•SPECIFICATION OR LIMIT

•BEFORE ADVANCED•PROCESS CONTROL

•WITH ADVANCED PROCESS CONTROL & OPTIMIZATION

•Standard•Control

•Advanced•Control

•Optimization

•4 Steps of PAT Implementation by John R. Davis, PE and John Wasynczuk, PhD

Lessons Learned• A systematic modeling approach enables

cross-divisional collaboration (research, operations, quality, engineering)

• There are a number of unit processes (fermentation, drying, mixing, etc.) that have proven results improvements leveraging this approach and technology in other industries