piecenamp module 8 – introduction to process integration 1 program for north american mobility in...

Module 8 – Introduction to Process Integration 11

PIECENAMPProgram for North American Mobility in Higher EducationProgram for North American Mobility in Higher Education

NAMNAMPP

Introducing Process integration for Environmental Control in Engineering CurriculaIntroducing Process integration for Environmental Control in Engineering Curricula

Introduction to Introduction to Process IntegrationProcess Integration

Tier IITier II

Module 8Module 8

PIECEPIECE


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How to use this presentationHow to use this presentation

This presentation contains internal links to other slides and This presentation contains internal links to other slides and external links to websites:external links to websites:

Example of a linkExample of a link (text underlined in grey): link to a slide in the (text underlined in grey): link to a slide in the presentation or to a websitepresentation or to a website

: link to the tier table of contents: link to the tier table of contents

: link to the last slide viewed: link to the last slide viewed

: when the user has gone over the whole presentation, : when the user has gone over the whole presentation, some multiple choice questions are given at the end of this tier. some multiple choice questions are given at the end of this tier. This icon takes the user back to the question statement if a This icon takes the user back to the question statement if a wrong answer has been givenwrong answer has been given


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Table of contentsTable of contentsProject SummaryProject Summary

Participating institutionsParticipating institutionsModule creatorsModule creators

Module Structure & PurposeModule Structure & PurposeTier IITier II

Statement of IntentStatement of IntentSectionsSections2.1 Worked example using Data-Driven Modeling, 2.1 Worked example using Data-Driven Modeling, more specifically Multivariate Analysismore specifically Multivariate Analysis2.2 Worked example using Thermal Pinch Analysis2.2 Worked example using Thermal Pinch Analysis2.3 Worked example using Integrated Process 2.3 Worked example using Integrated Process Control and Design, more specifically Controllability Control and Design, more specifically Controllability AnalysisAnalysisQuizQuiz


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ObjectivesObjectives Create web-based modules to assist universities to Create web-based modules to assist universities to address the introduction to Process Integration into address the introduction to Process Integration into engineering curriculaengineering curriculaMake these modules widely available in each of the Make these modules widely available in each of the participating countriesparticipating countries

Participating institutionsParticipating institutions Two universities in each of the three countries Two universities in each of the three countries (Canada, Mexico and the USA)(Canada, Mexico and the USA)Two research institutes in different industry sectors: Two research institutes in different industry sectors: petroleum (Mexico) and pulp and paper (Canada)petroleum (Mexico) and pulp and paper (Canada)Each of the six universities has sponsored 7 exchange Each of the six universities has sponsored 7 exchange students during the period of the grant subsidised in students during the period of the grant subsidised in part by each of the three countries’ governmentspart by each of the three countries’ governments

Project SummaryProject Summary


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Program for North American Mobility in Higher EducationProgram for North American Mobility in Higher Education NAMPNAMP

Process integration for Environmental Control in Engineering CurriculaProcess integration for Environmental Control in Engineering CurriculaPIECEPIECE

University of University of OttawaOttawa

École École Polytechnique Polytechnique de Montréalde Montréal

Instituto Instituto Mexicano del Mexicano del

PetrPetróóleoleo

PapricanPaprican

Universidad Universidad AutAutóónoma de noma de

San Luis PotosSan Luis Potosíí

University of University of Texas A&MTexas A&M

Universidad de Universidad de GuanajuatoGuanajuato North Carolina North Carolina

State State UniversityUniversity


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Module 8Module 8

This module was This module was created by:created by:

Carlos Alberto Miranda Carlos Alberto Miranda AlvarezAlvarez

Jean-Martin Jean-Martin BraultBrault

Host Host InstitutionInstitution

FroFromm

Host Host directordirector

Paul StuartPaul Stuart

Martin Picon-Martin Picon-NuNuññezez


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What is the structure of this module?What is the structure of this module?

All modules are divided into 3 tiers, each with a specific All modules are divided into 3 tiers, each with a specific goal:goal:

Tier I: Background InformationTier I: Background InformationTier II: Case Study ApplicationsTier II: Case Study ApplicationsTier III: Open-Ended Design ProblemTier III: Open-Ended Design Problem

These tiers are intended to be completed in that particular These tiers are intended to be completed in that particular order. Students are quizzed at various points to measure order. Students are quizzed at various points to measure their degree of understanding, before proceeding to the their degree of understanding, before proceeding to the next level. Each tier contains a statement of intent at the next level. Each tier contains a statement of intent at the beginning and a quiz at the end.beginning and a quiz at the end.

Structure of Module 8Structure of Module 8


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What is the purpose of this module?What is the purpose of this module?

It is the intent of this module to cover the basic It is the intent of this module to cover the basic aspects of aspects of Process Integration MethodsProcess Integration Methods and and ToolsTools, and to place , and to place Process IntegrationProcess Integration into a into a broad perspective. It is identified as a pre-broad perspective. It is identified as a pre-requisite for other modules related to the requisite for other modules related to the learning of learning of Process Integration.Process Integration.

Purpose of Module 8Purpose of Module 8


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Tier IIWorked Examples


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Tier II Statement of intentTier II Statement of intent

The goal of this tier is to demonstrate various The goal of this tier is to demonstrate various concepts and tools of Process Integration using concepts and tools of Process Integration using real examples. Three examples will be given, real examples. Three examples will be given, focusing mainly on three Process Integration focusing mainly on three Process Integration tools. At the end of Tier II, the student should tools. At the end of Tier II, the student should have a general idea of what is:have a general idea of what is:

Data-Driven Modeling - Multivariate AnalysisData-Driven Modeling - Multivariate AnalysisThermal Pinch AnalysisThermal Pinch AnalysisIntegrated Process Control and Design – Integrated Process Control and Design – Controllability AnalysisControllability Analysis


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Tier II ContentsTier II Contents

Tier Tier IIII is broken down into three sections is broken down into three sections

2.1 Worked example using Data-Driven 2.1 Worked example using Data-Driven Modeling, more specifically Multivariate Modeling, more specifically Multivariate AnalysisAnalysis2.2 Worked example using Thermal Pinch 2.2 Worked example using Thermal Pinch AnalysisAnalysis2.3 Worked example using Integrated 2.3 Worked example using Integrated Process Control and Design, more Process Control and Design, more specifically Controllability Analysisspecifically Controllability Analysis

A short multiple-choice quiz will follow at the end A short multiple-choice quiz will follow at the end of this tier.of this tier.


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Tier II OutlineTier II Outline

2.1 Worked example 1: Data-Driven Modeling – 2.1 Worked example 1: Data-Driven Modeling – Multivariate AnalysisMultivariate Analysis

2.2 Worked example 2: Thermal Pinch Analysis2.2 Worked example 2: Thermal Pinch Analysis

2.3 Worked example 3: Integrated Process 2.3 Worked example 3: Integrated Process Control and Design – Controllability AnalysisControl and Design – Controllability Analysis





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2.1 Worked example 1: Data-Worked example 1: Data-Driven Modeling – Driven Modeling –

Multivariate AnalysisMultivariate Analysis


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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis – Reminder

Tmt X1 X4 X5 Rep

Y avec

Y sans

1 -1 -1 -1 1 2.51 2.74

1 -1 -1 -1 2 2.36 3.22

1 -1 -1 -1 3 2.45 2.56

2 -1 0 1 1 2.63 3.23

2 -1 0 1 2 2.55 2.47

2 -1 0 1 3 2.65 2.31

3 -1 1 0 1 2.45 2.67

3 -1 1 0 2 2.6 2.45

3 -1 1 0 3 2.53 2.98

4 0 -1 1 1 3.02 3.22

4 0 -1 1 2 2.7 2.57

4 0 -1 1 3 2.97 2.63

5 0 0 0 1 2.89 3.16

5 0 0 0 2 2.56 3.32

5 0 0 0 3 2.52 3.26

6 0 1 -1 1 2.44 3.1

6 0 1 -1 2 2.22 2.97

6 0 1 -1 3 2.27 2.92

Graphical representation of Graphical representation of MVAMVA

Raw Data: Raw Data: impossible impossible to interpretto interpret

Statistical ModelStatistical Model(internal (internal

to to softwaresoftware

))

2-D Visual Outputs2-D Visual Outputs

trends

trendstrends

Y

XX

X

X

thousands of rows

hundreds of columns

..

. ...

. . .

.

. .


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It is assumed that the student is familiar with the following It is assumed that the student is familiar with the following basic statistical concepts: mean, median, mode; standard basic statistical concepts: mean, median, mode; standard deviation, variance; normality, symmetry; degree of association, deviation, variance; normality, symmetry; degree of association, correlation coefficients; Rcorrelation coefficients; R22, Q, Q22, F-test; significance of differences, , F-test; significance of differences, t-test, Chi-square; eigen values and vectorst-test, Chi-square; eigen values and vectors Statistical tests help characterize an existing dataset. They do Statistical tests help characterize an existing dataset. They do

NOT enable you to make predictions about future data. For this NOT enable you to make predictions about future data. For this we must turn to we must turn to regression techniquesregression techniques……

Basic Basic StatisticsStatistics

RegressionRegressionTake a set of data points, each described by a vector of values Take a set of data points, each described by a vector of values (y, x(y, x11, x, x22, … x, … xnn))Find an algebraic equation that “best expresses” the Find an algebraic equation that “best expresses” the relationship between y and the xrelationship between y and the xii’s:’s:

Y =Y = bb11xx11 + b + b22xx22 + … + b + … + bnnxxnn + e + e

Data Requirements:Data Requirements: normalized data, errors normally normalized data, errors normally distributed with mean zero and independent variables distributed with mean zero and independent variables uncorrelateduncorrelated

2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis


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160

180

200

220

240

150 160 170 180 190 200 210 220 230 240

YO

bse

rved

YPredicted

IDEAL MODELIDEAL MODEL

Figure 1

Types of Types of MVAMVA1.1. Principal Component Analysis (PCA)Principal Component Analysis (PCA)

X’s onlyX’s onlyIn PCA, we are maximizing the In PCA, we are maximizing the variancevariance that is that is explained by the modelexplained by the model

2.2. Projection to Latent Structures (PLS)Projection to Latent Structures (PLS)a.k.a. “Partial Least Squares”a.k.a. “Partial Least Squares”X’s and Y’sX’s and Y’sIn PLS, we are maximizing the In PLS, we are maximizing the covariancecovariance

X Y

X

MVA software generates two types of outputs: results, and MVA software generates two types of outputs: results, and diagnostics.diagnostics. Results: Score Plots, Loadings PlotsResults: Score Plots, Loadings Plots Diagnostics: Plot of Residuals, Observed Diagnostics: Plot of Residuals, Observed

vs. Predicted, and many morevs. Predicted, and many more

Types of MVA Types of MVA outputsoutputs

Q1Q1 Q2Q2



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Consider these fish. We Consider these fish. We could measure, for each could measure, for each fish, its length and fish, its length and breadth.breadth.

Suppose that 50 fish were Suppose that 50 fish were measured, a plot like the one shown measured, a plot like the one shown in figure 2 might be obtained. There in figure 2 might be obtained. There is an obvious relationship between is an obvious relationship between length and breadth as longer fish length and breadth as longer fish tend to be broader.tend to be broader.

Reference: Manchester Metropolitan University

Principal Component Analysis Principal Component Analysis (PCA)(PCA)

Figure 2

2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis - PCA


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Move the axes so that their origins are now centered on the cloud of points : Move the axes so that their origins are now centered on the cloud of points : this is a change in the measurement scale. In this case the relevant means were this is a change in the measurement scale. In this case the relevant means were subtracted from each value.subtracted from each value.

In effect the major axis is a new variable, size. At its simplest, In effect the major axis is a new variable, size. At its simplest, size = length + size = length + breadthbreadth linear combination of the two existing variables, which are given equal linear combination of the two existing variables, which are given equal weightingweighting Suppose that we consider length to be more important than breadth in the Suppose that we consider length to be more important than breadth in the

determination of size. In this case we could use weights or coefficients to determination of size. In this case we could use weights or coefficients to introduce differential contributions: introduce differential contributions: size = 0.75 x length + 0.25 x breadthsize = 0.75 x length + 0.25 x breadth For convenience, we would normally plot the graph with the X axis horizontal, For convenience, we would normally plot the graph with the X axis horizontal,

this would give the appearance of rotating the points rather than the axes.this would give the appearance of rotating the points rather than the axes.

Figure 3

Figure 5

Figure 4




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A criterion for the second axis is that it should account for as much of A criterion for the second axis is that it should account for as much of the remaining variation as possible. However, it must also be the remaining variation as possible. However, it must also be uncorrelated (orthogonal) with the first. uncorrelated (orthogonal) with the first.

In this example the lengths and orientations of these axes are given In this example the lengths and orientations of these axes are given by the eigen values and eigen vectors of the correlation matrix. If we by the eigen values and eigen vectors of the correlation matrix. If we retain only the 'size' variable we would retain 1.75/2.00 x 100 retain only the 'size' variable we would retain 1.75/2.00 x 100 (87.5%) of the original variation. Thus, if we discard the second axis (87.5%) of the original variation. Thus, if we discard the second axis we would lose 12.5% of the original information.we would lose 12.5% of the original information.

Figure 6 Figure 7




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Projection to Latent Structures Projection to Latent Structures (PLS)(PLS)

PLS finds a set of orthogonal components that :PLS finds a set of orthogonal components that :maximize the level of explanation of maximize the level of explanation of bothboth X and Y X and Yprovide a predictive equation for Y in terms of the X’sprovide a predictive equation for Y in terms of the X’s

This is done by:This is done by:fitting a set of components to X (as in PCA)fitting a set of components to X (as in PCA)similarly fitting a set of components to Ysimilarly fitting a set of components to Yreconciling the two sets of components so as to reconciling the two sets of components so as to maximize explanation of X and Ymaximize explanation of X and Y

Interpretation of the PLS results has all the difficulties of Interpretation of the PLS results has all the difficulties of PCA, plus another one: making sense of the individual PCA, plus another one: making sense of the individual components in both X and Y space. In other words, for components in both X and Y space. In other words, for the results to make sense, the first component in X must the results to make sense, the first component in X must be be related somehowrelated somehow to the first component in Y to the first component in Y



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Let´s look at a typical integrated thermomechanical pulp (TMP) Let´s look at a typical integrated thermomechanical pulp (TMP) newsprint mill in North America. The mill manager of that newsprint mill in North America. The mill manager of that particular plant recognizes that there is too much data to deal with particular plant recognizes that there is too much data to deal with and that there is a need to estimate the quality of their final and that there is a need to estimate the quality of their final product, i.e. paper. He decides to use Multivariate Analysis to product, i.e. paper. He decides to use Multivariate Analysis to derive as much information as possible from the data set and try derive as much information as possible from the data set and try to determine the most important variables that could have an to determine the most important variables that could have an impact on paper quality in order to be able to classify final product impact on paper quality in order to be able to classify final product quality. The mill manager decides to first look at the refining quality. The mill manager decides to first look at the refining portion of the pulping process.portion of the pulping process.

Problem StatementProblem Statement

Figure 8



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X and Y VariablesX and Y Variables

Y variablesY variables Pulp quality data Pulp quality data

after the latency after the latency chest (automated, chest (automated, on-line analysis of on-line analysis of grab samples): grab samples): standard industry standard industry parameters parameters including fibre including fibre length distribution, length distribution, freeness, freeness, consistency, and consistency, and brightnessbrightness

X variablesX variables Incoming chips: size Incoming chips: size

distribution, bulk density, distribution, bulk density, humidityhumidity Refiner operating data: Refiner operating data:

throughput; energy split throughput; energy split between the primary and between the primary and secondary refiner; dilution secondary refiner; dilution rates; levels, pressures and rates; levels, pressures and temperatures in various temperatures in various units immediately units immediately connected to the refiners; connected to the refiners; voltage at chip screw voltage at chip screw conveyors; refiner body conveyors; refiner body temperaturetemperature Season, represented by Season, represented by

the average monthly the average monthly temperature measured at a temperature measured at a nearby meteorological nearby meteorological stationstation

Y

X’s

Figure 9



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This is the R2 and Q2 plot for the model. The R2 values tell us that the first component explains 32% of the variability in the original data, the second another 7% and the third another 6%.

The Q2 values are lower. This means that the predictive power of the model is around 40% when using all three components. This may seem low, but is normal for real process data.

0.00

0.20

0.40

0.60

0.80

1.00

Com

p[1]

Com

p[2]

Com

p[3]

Comp No.

32-months version 2.M2 (PCA-X), Extreme outliers removed R2X(cum)Q2(cum)

Figure 10

ResultResultss

34-months of 1 day rev. 2 (incl. chip data) no. 2.M4 (PCA-X), Bad residuals removedt[1]/t[2]/t[3]Colored according to classes in M4

No ClassClass 1Class 2Class 3Class 4

Autumn Winter Spring Summer


2000

2001

2002 Figure 11



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-5

0

5

-10 0 10 20

t[2]

t[1]

34-months of 1 day rev. 2 (incl. chip data) no. 2.M4 (PCA-X), Untitledt[1]/t[2]Colored according to classes in M4

No ClassClass 1Class 2Class 3Class 4

Figure 12

Variation in this Variation in this direction appears to direction appears to

occur BETWEEN occur BETWEEN seasons seasons

(( Component 2) Component 2)

Variation in this Variation in this direction appears to direction appears to

occur BETWEEN occur BETWEEN seasons seasons

(( Component 2) Component 2)

Variation in this Variation in this direction appears direction appears to occur WITHIN a to occur WITHIN a

given seasongiven season(( Component 1) Component 1)

Variation in this Variation in this direction appears direction appears to occur WITHIN a to occur WITHIN a

given seasongiven season(( Component 1) Component 1)

Interpretation of the results – Score Interpretation of the results – Score PlotPlot



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Interpretation of the results – Loadings Interpretation of the results – Loadings plotplot

-0.20

-0.10

0.00

0.10

0.20

-0.20 -0.10 0.00 0.10

p[2

]

p[1]

34-months of 1 day rev. 2 (incl. chip data) no. 2.M4 (PCA-X), Bad residuals removedp[1]/p[2]

X

SEASON

33LI214.AI52FFC117.PV52FFC166.PV

52FIC104.PV52FIC115.PV

52FIC116.PV 52FIC154.PV

52FIC164.PV

52FIC165.PV

52FIC167.PV

52FIC177.PV

52HIC812.PV

52IIC128.PV

52IIC178.PV

52JCC139.PV

52JI189.AI

52JIC139.AI

52LIC106.PV

52PCA111.PV52PCA161.PV

52PCB111.PV

52PCB161.PV

52PIC105.PV52PIC159.PV

52PIC705.PV52PIC961.PV

52SIC110.PV

52SQI110.AI

52TI011.AI52TI031.AI

52TI118.AI52TI168.AI

52TIC010.CO52TIC793.PV

52XAI130.AI52XIC130.AI52XIC180.AI52XPI130.AI

52XQI195.AI

52ZIC147.PV

52ZIC148.PV52ZIC197.PV

52ZIC198.PV

53AI034.AI

53AI054.AI

53FFC455.PV

53FI012.AI

53HIC762.PV

53LIC011.PV

53LIC301.PV

53NI716.AI53NIC013.PV

53PIC210.PV

53PIC305.PV

53PIC308.PV

53PIC309.PV

53WI012.AI

Pex_L1_Blan

Pex_L1_Cons

Pex_L1_CSF

Pex_L1_LMF

Pex_L1_P200

Pex_L1_PFCPex_L1_PFLPex_L1_PFM

Pex_L1_R100

Pex_L1_R14

Pex_L1_R28Pex_L1_R48

53LIC510.PV

52FR960.AI52FRA703.AI

52KQC139.AI52KQC189.AI

52PI128.AI52PI178.AI

52PI706.AI

52PIA143.AI

52PIA193.AI

52PIB143.AI

52PIB193.AI

52PIP143.AI

52PIP193.AI

52SI055.AI52SIA110.AI52TIC102.PV

52TIC711.PV

52TR964.AI52XIC811.PV

52X_130.AI_split_L1.

52ZI144.AI

52ZI194.AI

53AIC453.PV

53LR405.AI53LV301.AI

53NIC100.PV811FI102.AI

811FI104.AI85FQ101.AI

85LCB320.AI

85LCS320.AI

CopDENS

CopSICC

Cop>9/8

Cop>7/8

Cop>5/8

Cop>3/8Cop>3/16

Cop<3/16

CopECORCopCARCopECLA

Pulp throughputPulp throughputRefining energyRefining energyDilution flowsDilution flowsSteam generationSteam generation

Pulp throughputPulp throughputRefining energyRefining energyDilution flowsDilution flowsSteam generationSteam generation

Pulp brightnessPulp brightnessSeasonSeason

Pulp brightnessPulp brightnessSeasonSeason

Bleach consumptionBleach consumptionBleach consumptionBleach consumption

Figure 13



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Interpretation of the resultsInterpretation of the resultsFirst ComponentFirst Component

The first component corresponds to throughput: many process variables are The first component corresponds to throughput: many process variables are related either directly or indirectly to throughput. Remember we said that the related either directly or indirectly to throughput. Remember we said that the 11stst component was something that varied within an individual season? component was something that varied within an individual season?

Second ComponentSecond ComponentThe 2The 2ndnd component component explains only 7%explains only 7% of the total variability. It is therefore of the total variability. It is therefore “messier” than the first component, and will be less easy to interpret. It is “messier” than the first component, and will be less easy to interpret. It is also possible to note that the also possible to note that the three years were separatedthree years were separated with respect to this with respect to this second componentsecond componentA major clue occurs in the prominence of two important and related tags: A major clue occurs in the prominence of two important and related tags: bleach consumptionbleach consumption and and pulp brightnesspulp brightness. This would suggest that perhaps . This would suggest that perhaps the brightness of the incoming wood chips was different from year to year, the brightness of the incoming wood chips was different from year to year, requiring more bleaching to get a less white pulprequiring more bleaching to get a less white pulpNote also that “Season” is prominent. This can be seen with the obvious Note also that “Season” is prominent. This can be seen with the obvious separation of the seasons on the score plot. This suggests that winter chips separation of the seasons on the score plot. This suggests that winter chips are less bright than summer chipsare less bright than summer chips

Third ComponentThird ComponentThe 3The 3rdrd component component explains only 6%explains only 6% of the total variability of the total variabilityThe 3The 3rdrd component is related to the time of year. A reasonable interpretation component is related to the time of year. A reasonable interpretation would be that summer chips differ from winter chips in some way would be that summer chips differ from winter chips in some way other thanother than brightness, which was already covered by the second component. This could brightness, which was already covered by the second component. This could be, for instance, the ease with which the wood fibres can be separated from be, for instance, the ease with which the wood fibres can be separated from each othereach other



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Using PCA, we have determined that 45% of the variability in the Using PCA, we have determined that 45% of the variability in the original 130 variables can be represented by using just 3 new original 130 variables can be represented by using just 3 new variables or “components”. These three components are variables or “components”. These three components are orthogonal, meaning that the variation within each one occurs orthogonal, meaning that the variation within each one occurs independently of the others. In other words, the new components independently of the others. In other words, the new components are are uncorrelateduncorrelated with each other. with each other.

REFINER REFINER THROUGHPUTTHROUGHPUT Component 1Component 1

Explains 32%Explains 32%Component 2Component 2Explains 7%Explains 7%

Component 3Component 3Explains 6%Explains 6%

BRIGHTNESS

BRIGHTNESS

SU

MM

ER

/ W

INTER

SU

MM

ER

/ W

INTER

Summary of the PCA resultsSummary of the PCA results



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Quality “reference map”Quality “reference map”

XX

X

Figure 14



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2.2 Worked example 2: Worked example 2: Thermal Pinch AnalysisThermal Pinch Analysis


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PROCESSPROCESS

COLDCOLDUtilityUtility

HOTHOTUtilityUtility

2.2 Worked example 2: Thermal Pinch Analysis – Reminder

Utility Utility UsageUsage

Internal Internal ExchangesExchanges

Utility costs Utility costs go downgo down

Costs related Costs related to exchange to exchange area go uparea go up

From 100% From 100% utility...utility...

... to 100% internal ... to 100% internal exchangesexchanges

$$

Trade-offTrade-offTrade-offTrade-off

What is Thermal Pinch Analysis?What is Thermal Pinch Analysis?


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Example: Recovery Example: Recovery BoilerBoiler

Obvious solution: Obvious solution: preheat entering fresh preheat entering fresh water with hot water with hot condensate leaving condensate leaving boilerboiler

Figure 15

At least 40 streams to heat and cool…

What about an entire site ?2.2 Worked example 2: Thermal Pinch Analysis


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SimulationSimulation

ExtractionExtraction

PlantPlant

TargetingTargeting

Heat Exchanger Heat Exchanger Network DesignNetwork Design

Data Extraction Data Extraction (hot and cold (hot and cold streams) with streams) with

specific energy specific energy savings objectives savings objectives

in mindin mind

Analysis Analysis Targeting, i.e. Targeting, i.e. energy, design energy, design

and and economical economical

targetstargetsUse of heuristics to Use of heuristics to design a Heat design a Heat

Exchanger Network Exchanger Network that will reach that will reach

energy targets at energy targets at lowest costlowest cost

Transfer of Transfer of obtained obtained

results to plant results to plant realityreality

TminTmin

2.2 Worked example 2: Thermal Pinch Analysis


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Cold c

ompos

ite

Cold c

ompos

ite

curv

e

curv

e

Hot

com

posi

te

Hot

com

posi

te

curv

e

curv

eTminTmin

Heating RequirementHeating Requirement

Cooling RequirementCooling Requirement

PinchPinchpointpoint

Composite CurvesComposite Curves

TemperatureTemperature

EnthalpyEnthalpy

Figure 16



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Mass Integration – Composite Curves for pollution Mass Integration – Composite Curves for pollution preventionprevention

Figure 17

Figure 18



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Problem Problem StatementStatementA process engineer in a consulting firm is hired by an oil A process engineer in a consulting firm is hired by an oil refinery to design the Conventional Atmospheric Crude refinery to design the Conventional Atmospheric Crude Fractionation Units section of the refinery facility, as shown in Fractionation Units section of the refinery facility, as shown in figure 17. The main objective of this project is to minimize the figure 17. The main objective of this project is to minimize the energy consumption by using Thermal Pinch Analysis. The plant energy consumption by using Thermal Pinch Analysis. The plant is currently using 75000 kW in hot utilities. In this example, is currently using 75000 kW in hot utilities. In this example, stress will be put on the construction of the composite curves stress will be put on the construction of the composite curves with the objective of identifying energy savings opportunities. with the objective of identifying energy savings opportunities.

Furnace

Desalter

Crude Tower

Naphtha-PA

Kerosene

L-gasoil

H-gasoil

ATB

Crude E1

E2E3

E4

E5 E6

E71 2

5

6

7 8

92

10

11

13 14

15 16

BPA12

Furnace

Desalter

Crude Tower

Naphtha-PA

Kerosene

L-gasoil

H-gasoil

ATB

Crude E1

E2E3

E4

E5 E6

E71 2 3 4

5

6

7 8

9 10

11

13 14

15 16

BPA12

Figure 19



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3-5ºCLow-temperature processes

10-20ºCChemical

10-20ºCPetrochemical

30-40ºCOil Refining

minIndustrial Sector

Table 2

DesalterDesalter

Crude TowerCrude Tower

Naphtha-PANaphtha-PA

KeroseneKerosene

L-gasoilL-gasoil

H-gasoilH-gasoil

ATBATB

CrudeCrudeFeedFeed

20º20º

BPABPA

150º150º 150º150º 390º390º

150º150º

100º100º

180º180º 30º30º

40º40º

30º30º

50º50º

270º270º

290º290º

190º190º

350º350º

380º380º

11 22

33

66

44

55

88

77Crude Pre-heat train Crude Pre-heat train

º ºC Conditionº ºC Condition

Stream NumberStream Number

Figure 20

Process Heat Mass Heat Supply Target Stream Heat* Foulingstream capacity flow capacity temperature Temperature Heat Transfernumber rate flowrate duty coefficientand type (J/kgK) (kg/s) (kW/K) (ºC) (ºC) (kW) (W/m2 K) (m2ºC/W)(1)Cold 2600.00 200.00 520.00 20.00 150.00 67600.00 170.00 0.00147(2)Cold 2600.00 200.00 520.00 150.00 390.00 124800.00 170.00 0.00147(3)Hot 2600.00 253.00 657.80 150.00 100.00 -32890.00 170.00 0.00147(4)Hot 2600.00 23.00 59.80 180.00 30.00 -8970.00 170.00 0.00147(5)Hot 2600.00 44.00 114.40 270.00 40.00 -26312.00 170.00 0.00147(6)Hot 2600.00 148.00 384.80 290.00 190.00 -38480.00 170.00 0.00147(7)Hot 2600.00 13.00 33.80 350.00 30.00 -10816.00 170.00 0.00147(8)Hot 2600.00 56.00 145.60 380.00 50.00 -48048.00 170.00 0.00147* Fouling Factor included

Table 1

Data Data ExtractionExtraction



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Table 3

1. Sort in ascending order the hot streams temperatures, 1. Sort in ascending order the hot streams temperatures, omitting common temperaturesomitting common temperatures

Using the data above, we form temperature intervals for the Using the data above, we form temperature intervals for the processprocess

T1T1

T2T2

T3T3

T4T4

IntervalInterval

11

22

33

Temperatures are sorted in ascending

order, omitting common temperatures

TT

HHFigure 21

2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves


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Table 4

stream interval,

jiCPCP

streamj

streamji

2. Sum up the CP of every stream present in each temperature 2. Sum up the CP of every stream present in each temperature intervalinterval

6.938.338.59741 HH CPCPCP

We then obtain the Composite CP for each temperature intervalWe then obtain the Composite CP for each temperature interval



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Table 5

)(* 1 iiii TTCPQ

3. Calculate the net enthalpy for each temperature interval3. Calculate the net enthalpy for each temperature interval

kWTTCPQ 936)303313(*6.93)(* 0111

We obtain the enthalpy for each temperature interval, as We obtain the enthalpy for each temperature interval, as shown in the column Qshown in the column Qint,hint,h



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Table 6

4. Obtain the accumulated enthalpy for each temperature 4. Obtain the accumulated enthalpy for each temperature intervalinterval

iii QSumQSumQ 1

9369360101 QSumQSumQ



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303313323

373

423453463

543563

623653

Hot Composite Curve

300

400

500

600

700

0 50000 100000 150000 200000H (kW)

T (

K)

Figure 22

5. Plot temperature on the Y axis versus accumulated enthalpy on 5. Plot temperature on the Y axis versus accumulated enthalpy on the X axisthe X axis



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Cold Composite Curve

250

300

350

400

450

500

550

600

650

700

0 50000 100000 150000 200000 250000

H (kW)

T(K

)

Figure 23

293

423

663

The construction of the Cold Composite Curve is similar to that of The construction of the Cold Composite Curve is similar to that of the Hot Composite Curve.the Hot Composite Curve. Table 7



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Cold composite curve

Hot composite curve

This representation reduces the entire process into one combined hot and cold This representation reduces the entire process into one combined hot and cold streamstream The heat recovery between the composite curves can be increased until we The heat recovery between the composite curves can be increased until we

reach DTmin. Composite curves, just like individual streams can be shifted reach DTmin. Composite curves, just like individual streams can be shifted horizontally on the T-H diagram without causing changes to the process because H horizontally on the T-H diagram without causing changes to the process because H is a state functionis a state function This sets the minimum hot (QHmin) and cold (QCmin) utilities requirements for This sets the minimum hot (QHmin) and cold (QCmin) utilities requirements for

the entire process and the maximum possible process-process heat recoverythe entire process and the maximum possible process-process heat recovery

Internal Heat Recovery QHmin

Minimum Minimum Cooling Cooling

RequiremenRequirementt

QCminMinimum Minimum Heating Heating

RequiremenRequirementt

0

Application Composite Curves

100

200

300

400

500

600

700

0 50000 100000 150000 200000 250000H (kW)

T (

K)

Figure 24

Tmin= 40K



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As seen in the previous slides, from the temperature-enthalpy As seen in the previous slides, from the temperature-enthalpy plot, we can determine three useful pieces of information:plot, we can determine three useful pieces of information: Amount of possible process-process heat recovery Amount of possible process-process heat recovery

represented by the area between the two composites curvesrepresented by the area between the two composites curves Hot Utility requirement or target = 57668 kWHot Utility requirement or target = 57668 kW Cold Utility requirement or target = 30784 kWCold Utility requirement or target = 30784 kW

Summary of resultsSummary of results

Composite curves are excellent tools for learning the methods Composite curves are excellent tools for learning the methods and understanding the overall energy situation, but minimum and understanding the overall energy situation, but minimum energy consumption and the heat recovery Pinch are more energy consumption and the heat recovery Pinch are more often obtained by often obtained by numerical proceduresnumerical procedures. This method is . This method is called thecalled the Problem Table Algorithm. Problem Table Algorithm. Typically, it is based on Typically, it is based on notions of notions of Heat CascadeHeat Cascade..

Q5Q5 Q6Q6



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2.3 Worked example 3: Worked example 3: Integrated Process Control Integrated Process Control – Controllability Analysis– Controllability Analysis


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2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis – Reminder

Fundamentals

ProcessProcess

sensorsensor

InputInputVariablesVariables

OutputOutputVariablesVariables

(controlled and(controlled andmeasured)measured)

Input Input VariablesVariables(manipulated)(manipulated)

DisturbancesDisturbances

UncertaintiesUncertainties

Internal interactionsInternal interactions

PROCESS RESILIENCYPROCESS RESILIENCY

PROCESS FLEXIBILITYPROCESS FLEXIBILITY

Control LoopControl Loop

Figure 25


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CCCC FCFC

C, FC, F

Water: F1,C1Water: F1,C1

Pulp: F2,C2Pulp: F2,C2

OUTPUTSOUTPUTS(Best Selection by (Best Selection by Controllability Controllability analysis)analysis)

INPUTSINPUTS(manipulated variables (manipulated variables or or disturbances)disturbances)

EFFECTSEFFECTS

Figure 26

2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis


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FF1111

FF2121

FF1212

FF2222

uu11

uu2 2

yy11

yy22

++

++

++++

yy11

yy22

CC11

CC22

yy1sp1sp

yy2sp2sp

++

++ __

__

Figure 27



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FF1111

FF2121

FF1212

FF2222

uu11

uu2 2

yy11

yy22

++++

++++

uu11ssss

)y- gain, (OL , 11111

1 uKuy

Experiment 1Experiment 1: Step Change in u1 with all loops : Step Change in u1 with all loops openopen

Main Effect:Main Effect:

Figure 28



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Experiment 2Experiment 2: Step Change in u1 with all loops : Step Change in u1 with all loops closedclosed

F11

F21

F12

F22

u1

u2

y1

y2

+

+

++C2

e2y2sp

+ _

u1 ss

1r1111 y OLCL KKTotal Effect:Total Effect:Interactive EffectInteractive Effect

Main EffectMain EffectFigure 29



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CLK11OLK11 1ry

Main Effect (1Main Effect (1stst Experiment) Experiment)OLK1111 CLK11

Total Effect (2Total Effect (2nd nd

Experiment)Experiment)

Relative Gain and Relative Gain Array Relative Gain and Relative Gain Array (RGA)(RGA)

1111 : measure of the : measure of the extent of extent of steady state steady state interactioninteraction in using u in using u11

to control yto control y11, , whilewhile using uusing u22 to control y to control y22

2221

1211

11Relative GainRelative Gainyy11 uu11

CL

OL

j

i

j

i

ij

u

y

u

y

ij

Relative Gain ArrayRelative Gain Arrayyyii uujj



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Selection of Loops using RGA – Selection of Loops using RGA – How to select the How to select the configuration with minimum interactionconfiguration with minimum interaction

yyii : Controlled : Controlled variablevariableuujj : Manipulated : Manipulated variablevariable

1ij

0ij

10 ij

1ij

0ij

ImplicationImplication RecommendationRecommendation

Loop Loop ii not subject to interactive not subject to interactive action from other loopsaction from other loops ji uy :Pair

uujj has no direct influence on has no direct influence on yyii ji uy :pairnot Do

- - Loops are interactingLoops are interacting- below 0.5, interactive effect > main effect- below 0.5, interactive effect > main effect

ji uy :Avoid

- - Loops are interactingLoops are interacting- interactive effect acts in opposition to the - interactive effect acts in opposition to the main effectmain effect ji uy :high at Avoid ij

- - Loops are interactingLoops are interacting- interactive effect not only acts in - interactive effect not only acts in opposition to the main effect, it is also more opposition to the main effect, it is also more dominantdominant

ji uy :pairnot Do

Table 8



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NiederlinskiNiederlinski (NI) (NI) : system stability index: system stability indexCondition NumberCondition Number (CN)(CN) and and Disturbance Disturbance Condition Number (DCN) Condition Number (DCN) : sensibility measure: sensibility measureRelative Disturbance Gain (RDG)Relative Disturbance Gain (RDG) : index that : index that gives an idea of the influence of internal gives an idea of the influence of internal interactions on the effect of disturbancesinteractions on the effect of disturbancesOthers: Others: Singular Value DecompositionSingular Value Decomposition (SVD)(SVD)

Other Controllability IndexesOther Controllability Indexes



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Problem Problem StatementStatement

S

S

32 31

24

23

22

21

20

16

15

14

1312

11

10 6

5

CUVP AT ECUVP AT E 1

4

3

2

1

2.94705 %

2264.4 lt/min

13924 lt/min1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt/min2.79214 %

1195

8.7

lt/m

in

2.96551 %

1114

4.5

lt/m

in

3.51

707

%

595.592 lt/min

3.02375 %

48686 lt/min2.19041 % 2.03148 %

4749

4 lt/

min

1.81

%

3.78

427

%

5961

.63

lt/m

in 0.4

%15

786

lt/m

in

3157.18 lt/min

12628.8 lt/min

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt/min11565.2 lt/min

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt/min

10299.6 lt/min2.99513 %

6300 lt/min

4000 lt/min

Base Case: TMP Newsprint MillSteady State Simulation

401.885 l/min18 %

Wet web

Fresh water

Fresh Pulp (7 %)

Broke (18 %)

WWTank

Machine Chest

MixingChest

BrokeTank

PulpTank

F5F5

F8F8

F7F7

F2F2

F6F6

F3F3

F4F4

F1F1

Figure 30

In this case-study, a process control engineer is asked to create a In this case-study, a process control engineer is asked to create a model of the thermomechanical pulping process to find the best model of the thermomechanical pulping process to find the best process control selection and variable pairing for a plant that has not process control selection and variable pairing for a plant that has not been built yet. been built yet. Consider the simplified newsprint paper machine Consider the simplified newsprint paper machine short loop configuration shown in figure 30. Variable pairing short loop configuration shown in figure 30. Variable pairing techniques will be applied as well as the use of controllability techniques will be applied as well as the use of controllability indexes.indexes.



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INPUTSName ID stream Flow(lt/min) Cons. (%) Temp (°C) Fines (%) TDS (ppm) Flow(TN/d)Fresh Pulp 1 4000.0 7.0 67.0 20.7 6049 5791.3Broke 3 100.0 18.0 54.0 29.0 4063 151.3Fresh water 63 2264.4 0.0 55.0 0.0 0 3214.1

OUTPUTSName ID stream Flow(lt/min) Cons. (%) Temp (°C) Fines (%) TDS (ppm) Flow(TN/d)Wet Web 62 401.9 18.00 61.5 30.06 4063 605.8Dilution 1 32 6300.0 0.40 61.5 98.80 3270 8937.2Dilution 2 6 495.6 0.40 61.5 98.80 3270 703.0Dilution 3 22 249.4 0.40 61.5 98.80 3270 353.7Dilution 4 16 814.2 0.40 61.5 98.80 3270 1155.1Dilution of Rejects Screen 41 4769.6 0.40 61.5 98.80 3270 6766.2Ww drained from forming zone 61 15786.0 0.40 61.5 98.80 3270 22394.1Ww Short Loop 40 3157.2 0.40 61.5 98.80 3270 4478.8Pulp to Headbox 34 13924.0 1.00 62.6 61.06 3826 19786.0Pulp to Screen 25 62610.0 1.93 62.6 10.07 3826 89243.4Diluted Broke entering Mixing Chest 30 595.6 3.52 60.3 35.53 3389 854.4Diluted Pulp entering Mixing Chest 33 10299.6 3.00 63.6 27.03 4317 14728.5Pulp leaving Mixing Chest 12 10895.2 3.02 63.4 27.57 4267 15582.9Pulp leaving Machine Chest 24 12473.3 2.95 63.4 27.85 4237 17835.7Rejects (Screening system) 52 5961.6 3.78 62.5 18.24 3776 8551.0Accepts (Hydrocyclone) 36 47493.9 1.81 62.5 1.61 3776 67672.6Pulp entering Machine Chest 23 11144.5 2.97 63.4 27.78 4244 15936.6Pulp entering Cuvier de pâte 43 13287.5 2.79 63.3 28.47 4176 18990.7Ww Long Loop 15 12628.8 0.40 61.5 98.80 3270 17915.2Ww Short Loop after accepts 46 50651.1 1.72 62.4 3.01 3744 72151.4Broke Ratio, % 5.5Retention, % 54.9

Stock Chest

Table 9

controlledcontrolled

manipulatedmanipulated disturbancesdisturbances

Pfin = % Fines retained

Problem StatementProblem Statement



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S

S

32 31

24

23

22

21

20

1 6

1 5

1 4

1 31 2

1 1

1 0 6

5


4

3

2

1

2.94705 %

2264.4 lt/min

13924 lt/min1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt/min2.79214 %

1195

8.7

lt/m

in

2.96551 %11

144.

5 lt/

min

3.51

707

%

595.592 lt/min

3.02375 %

48686 lt/min2.19041 % 2.03148 %

4749

4 lt/

min

1.81

%

3.78

427

%

5961

.63

lt/m

in 0.4

%15

786

lt/m

in

3157.18 lt/min

12628.8 lt/min

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt/min11565.2 lt/min

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt/min

10299.6 lt/min2.99513 %

6300 lt/min

4000 lt/min


401.885 l/min18 %

Wet web

Fresh water

Fresh Pulp (7 %)

Broke (18 %)

WWTank

Machine Chest

MixingChest

BrokeTank

PulpTank

BR

Ret

Pfin

CC

FinesFines


ManipulatedManipulated

ControlledControlled

Figure 31



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t

C

BR

C

C

C

C

Re34

43

23

30

33

020.4265.0608.0068.0042.0077.0114.0

025.0004.0049.0001.0001.0001.0002.0

000.0000.0340.3000.0000.0775.0065.0

030.0004.0036.0016.0010.0018.0027.0

029.0004.0036.0001.0011.0020.0029.0

038.0005.0024.0001.0001.0404.0002.0

028.0004.0018.0001.0001.0001.0031.0

finP

F

F

F

F

F

F

40

3

16

22

6

32

597.4075.0

079.0164.0

000.0000.0

060.0455.0

058.0483.0

076.0052.0

056.0518.0

1

1

f

C== ++

GGpp GGdd

Process Gain Matrices and Steady-State Process Gain Matrices and Steady-State ControllabilityControllability


t

C

BR

C

C

C

C

Re34

43

23

30

33

finPFFFFFF 4031622632

603.1615.0000.0001.0000.0001.0010.0608.0566.1006.0005.0001.0003.0039.0000.0000.0003.1000.0000.0013.0010.0

001.0058.0000.0941.0000.0000.0000.0000.0000.0000.0053.0947.0000.0001.0020.0047.0014.0000.0004.0009.1001.0016.0038.0011.0000.0047.0000.0942.0

RGARGA

ControlledControlled ManipulatedManipulated

==



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S

S

32 31

24

23

22

21

20

1 6

1 5

1 4

1 31 2

1 1

1 0 6

5


4

3

2

1

2.94705 %

2264.4 lt/min

13924 lt/min1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt/min2.79214 %

1195

8.7

lt/m

in

2.96551 %11

144.

5 lt/

min

3.51

707

%

595.592 lt/min

3.02375 %

48686 lt/min2.19041 % 2.03148 %

4749

4 lt/

min

1.81

%

3.78

427

%

5961

.63

lt/m

in 0.4

%15

786

lt/m

in

3157.18 lt/min

12628.8 lt/min

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt/min11565.2 lt/min

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt/min

10299.6 lt/min2.99513 %

6300 lt/min

4000 lt/min


401.885 l/min18 %

Wet web

Fresh water

Fresh Pulp (7 %)

Broke (18 %)

WWTank

Machine Chest

MixingChest

BrokeTank

PulpTank

BR

Ret

Pfin

Figure 32



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Niederlinski Index (NI) Niederlinski Index (NI) Stability considerationsStability considerationsNI < 0. System will be unstable under closed-loop NI < 0. System will be unstable under closed-loop conditionsconditionsNI > 0. System is stabilizable (function of controller NI > 0. System is stabilizable (function of controller parameters)parameters)

Condition number (CN)Condition number (CN) Sensitivity to model uncertaintySensitivity to model uncertaintyCN CN ~<~< 2. Multivariable 2. Multivariable effects of uncertainty are not effects of uncertainty are not likely to be seriouslikely to be seriousCN CN ~>~> 10. ILL-CONDITIONED process 10. ILL-CONDITIONED processCN=713CN=713

NI=0.73NI=0.73

Controllability Indexes (1)Controllability Indexes (1)



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Disturbance Condition Number (DCN) Disturbance Condition Number (DCN) Is the action taken Is the action taken by the manipulated variable large or small?by the manipulated variable large or small?

11≤ DCN ≤ CN≤ DCN ≤ CN

Relative Disturbance Gain (RDG) Relative Disturbance Gain (RDG) Internal interaction Internal interaction among the loops is favorable or unfavorable to reject among the loops is favorable or unfavorable to reject disturbances?disturbances?

RDG ~<2 .RDG ~<2 . Internal interactions reduce the effect of the Internal interactions reduce the effect of the disturbancedisturbance

The effect of both disturbances, %C and %fines in The effect of both disturbances, %C and %fines in FRESH PULP, is reduced by internal interactions. FRESH PULP, is reduced by internal interactions. All All

RDG’s are ~<2RDG’s are ~<2

Controllability Indexes Controllability Indexes (2)(2)

DCN for %CDCN for %Cfresh pulpfresh pulp = 9.2 = 9.2DCN for %finesDCN for %finesfresh pulpfresh pulp = 4.6 = 4.6

It is harder to reject a sudden change in fresh pulp It is harder to reject a sudden change in fresh pulp consistencyconsistency



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ConclusioConclusionn

Control structure configuration: RGA results Control structure configuration: RGA results confirmed current implementation in confirmed current implementation in newsprint millsnewsprint mills Internal interactions of the aforementioned Internal interactions of the aforementioned

configuration reduce the effect of disturbances configuration reduce the effect of disturbances on output variableson output variables The process is ill-conditioned. Model The process is ill-conditioned. Model

uncertainty may be highly amplifieduncertainty may be highly amplified Resiliency Indexes, DCN and RDG, can be Resiliency Indexes, DCN and RDG, can be

used to account for disturbance rejection in used to account for disturbance rejection in newsprint processesnewsprint processes



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This is the end of Tier II. At this point, we assume that you have done all the reading. You should have a pretty good idea of what Process Integration is as well as basic knowledge in regards to Multivariate Analysis, Thermal Pinch Analysis and Controllability Analysis. For further information on the tools presented in Tier II as well as on other Process Integration tools introduced in Tier I, please consult the references slides in Tiers I and II.

Prior to advancing to Tier III, a short multiple choice quiz will follow.

End of Tier II


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QUIZ


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Question 1Question 1What is Principal Components Analysis used for?What is Principal Components Analysis used for?

1.1. Understand relations between the variables of a systemUnderstand relations between the variables of a system

2.2. Identify the components having an influence on one or many Identify the components having an influence on one or many outputsoutputs

3.3. Predict certain outputsPredict certain outputs

4.4. Maximize the covariance of a set of variablesMaximize the covariance of a set of variables

2 and 2 and 33

1,2 and 31,2 and 3

11

1 and 1 and 22

1 and 1 and 3333

Tier II - Quiz


PIECENAMP

Question 2Question 2Associate each Multivariate Analysis output with the kind of Associate each Multivariate Analysis output with the kind of information it provides the user with.information it provides the user with.

1. Residuals plot1. Residuals plot A.A. SShows all the original data points hows all the original data points in a in a new set of coordinates or new set of coordinates or componentscomponents

2. Score plot2. Score plot B.B. Shows the distance between each Shows the distance between each real real observation in the initial dataset and observation in the initial dataset and the the predicted value based on the modelpredicted value based on the model

3. Observed vs. Predicted3. Observed vs. Predicted C. Shows the accuracy of predictionC. Shows the accuracy of prediction

4. Loadings plot4. Loadings plot D. D. Shows how strongly each Shows how strongly each variable is variable is associated with each associated with each new componentnew component

11BB, 2, 2AA, 3, 3CC, , 44DD

11BB, 2, 2DD, 3, 3CC, , 44AA

11CC, 2, 2DD, 3, 3AA, , 44BB11AA, 2, 2DD, 3, 3BB, , 44CC

11DD, 2, 2BB, 3, 3AA, 4, 4CC

11BB, 2, 2CC, 3, 3DD, , 44AA

Tier II - Quiz


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Question 3Question 3The lengths and orientations of the axes obtained with a PCA are The lengths and orientations of the axes obtained with a PCA are given by the eigen values and eigen vectors of the correlation given by the eigen values and eigen vectors of the correlation matrix. Let's say the length and breadth variables have a lower matrix. Let's say the length and breadth variables have a lower correlation coefficient than in the example given in slide 13 and correlation coefficient than in the example given in slide 13 and that we obtain the eigen values shown in the figure below. If we that we obtain the eigen values shown in the figure below. If we discard the second axis, what percentage of the original discard the second axis, what percentage of the original information would we lose?information would we lose?

12,5%12,5%

0%0%

25%25%

37,5%37,5%

75%75%

62,5%62,5%

Tier II - Quiz


PIECENAMP

Question 4Question 4In the context of a Thermal Pinch Analysis, what is a hot stream? In the context of a Thermal Pinch Analysis, what is a hot stream?

1. A process stream that needs to be heated1. A process stream that needs to be heated

2. A process stream with a very high temperature2. A process stream with a very high temperature

3. A process stream that is used to generate steam3. A process stream that is used to generate steam

4. A process stream that needs to be cooled4. A process stream that needs to be cooled

11

22

33

44

Tier II - Quiz


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Question 5Question 5

HigherHigher

LowerLower

Would stay the Would stay the samesame

A Thermal Pinch Analysis has been performed at a plant and the A Thermal Pinch Analysis has been performed at a plant and the TTminmin was set at 40ºC. If another plant was to be built with a lower was set at 40ºC. If another plant was to be built with a lower TTminmin, how would the corresponding energy costs be in , how would the corresponding energy costs be in comparison to the first plant?comparison to the first plant?

Tier II - Quiz


PIECENAMP

Question 6Question 6Which of the following statements are true?Which of the following statements are true?

1.1. Minimum energy consumption and the heat recovery Pinch Minimum energy consumption and the heat recovery Pinch are more often obtained by Composite Curvesare more often obtained by Composite Curves

2.2. Composite curves, just like individual streams, can be shifted Composite curves, just like individual streams, can be shifted horizontally on the T-H diagram without causing changes to horizontally on the T-H diagram without causing changes to the processthe process

3.3. Heat can sometimes be transferred across the PinchHeat can sometimes be transferred across the Pinch

4.4. With the help of With the help of Tmin and the thermal data, Pinch Analysis Tmin and the thermal data, Pinch Analysis provides a target for the minimum energy consumptionprovides a target for the minimum energy consumption

2 and 2 and 33

All of the All of the aboveabove

1 and 31 and 3

1 and 1 and 22

2 and 2 and 443 and 3 and 44

Tier II - Quiz


PIECENAMP

Question 7Question 7Associate each controllability tool or index with the kind of Associate each controllability tool or index with the kind of information it provides the user with.information it provides the user with.

1. Niederlinski Index1. Niederlinski Index A.A. Shows the importance of Shows the importance of interactions in interactions in a systema system

2. Relative Disturbance Gain2. Relative Disturbance GainB.B. EstimEstimates the sensitivity of the ates the sensitivity of the problem's answer to error in the problem's answer to error in the

input input

3. Condition Number3. Condition Number C. Includes disturbances in C. Includes disturbances in interactions interactions analysisanalysis

4. Relative Gain Array4. Relative Gain Array D. D. Discusses the stability of a Discusses the stability of a closed-loop closed-loop control configuration control configuration 11BB, 2, 2AA, 3, 3CC, ,

44DD

11DD, 2, 2CC, 3, 3BB, , 44AA

11CC, 2, 2DD, 3, 3AA, , 44BB11AA, 2, 2DD, 3, 3BB, , 44CC

11DD, 2, 2BB, 3, 3AA, 4, 4CC

11BB, 2, 2CC, 3, 3DD, , 44AA

Tier II - Quiz


PIECENAMP

Question 8Question 8

1 and 1 and 55

4 and 64 and 6

3 and 63 and 6

2 and 2 and 66


In the Relative Gain Array shown in slide 54, what do the values In the Relative Gain Array shown in slide 54, what do the values 1.566 and 1.603 for the pairing of F40 and C34, and Pfin and Ret, 1.566 and 1.603 for the pairing of F40 and C34, and Pfin and Ret, tell you?tell you?

1. T1. There is no interaction with other control loopshere is no interaction with other control loops

2. The interactive effect is more important than the main effect2. The interactive effect is more important than the main effect

3. 3. The manipulated input has no effect on outputThe manipulated input has no effect on output

4. 4. The interactions from the other loops are opposite in direction The interactions from the other loops are opposite in direction but smaller in magnitude than the effect of the main loopbut smaller in magnitude than the effect of the main loop

5. Pairing is recommended5. Pairing is recommended

6. Pairing is not recommended6. Pairing is not recommended

Tier II - Quiz


PIECENAMP

Question 9Question 9Which of the following statements are false?Which of the following statements are false?

1.1. Feedforward control compensates for immeasurable Feedforward control compensates for immeasurable disturbancesdisturbances

2.2. Feedback control compensates for measurable disturbancesFeedback control compensates for measurable disturbances

3.3. Resiliency is the degree to which a processing system can Resiliency is the degree to which a processing system can meet its design objectives despite uncertainties in its design meet its design objectives despite uncertainties in its design parametersparameters

4.4. Flexibility is the degree to which a processing system can Flexibility is the degree to which a processing system can meet its design objectives despite external disturbancesmeet its design objectives despite external disturbances

2 and 2 and 33

All of the All of the aboveabove

1 and 31 and 3

1 and 1 and 22


Tier II - Quiz


PIECENAMP

AnswersAnswersQuestion 1Question 1 1 and 21 and 2

Question 2Question 2 11BB, 2, 2AA, 3, 3CC, 4, 4DD

Question 3Question 3 37,5%37,5%

Question 4Question 4 44

Question 5Question 5 LowerLower

Question 6Question 6 2 and 42 and 4

Question 7Question 7 11DD, 2, 2CC, 3, 3BB, 4, 4AA

Question 8Question 8 4 and 54 and 5

Question 9Question 9 All of the aboveAll of the above

Tier II - Quiz

piecenamp module 8 – introduction to process integration 1 program for north american mobility in...

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