piece program for north american mobility in higher education rev:2.2 created at École...
TRANSCRIPT
Created atCreated atÉcole Polytechnique de École Polytechnique de
Montréal &Montréal &Universidad de GuanajuatoUniversidad de Guanajuato
PIECEPIECEProgram for North American Mobility In Higher EducationProgram for North American Mobility In Higher Education
Rev:2.2Rev:2.2
Program for North American Mobility Program for North American Mobility in Higher Education (NAMP)in Higher Education (NAMP)
Introducing Process Integration for Introducing Process Integration for Environmental Control in Engineering Environmental Control in Engineering
Curricula (PIECE)Curricula (PIECE)
Module 8: Introduction to Process Module 8: Introduction to Process Integration – Tier 2Integration – Tier 2
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Module 8: Introduction to Process Integration
Project Summary
Objectives Objectives 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 Each of the six universities has sponsored 7 exchange students during the period of the grant exchange students during the period of the grant subsidised in part by each of the three countries’ subsidised in part by each of the three countries’ governments governments
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Module 8: Introduction to Process Integration
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 goal:All Modules are divided into 3 tiers, each with a specific 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 next their degree of understanding, before proceeding to the next level.level.
Each tier contains a statement of intent at the beginning and Each tier contains a statement of intent at the beginning and there is a quiz at the end of Tiers I and II.there is a quiz at the end of Tiers I and II.
Structure of Module 8
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Module 8: Introduction to Process Integration
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 aspects of It is the intent of this module to cover the basic aspects of Process Integration MethodsProcess Integration Methods and and ToolsTools, and to place , and to place Process IntegrationProcess Integration into a broad perspective. It is into a broad perspective. It is identified as a pre-requisite for other modules related to identified as a pre-requisite for other modules related to the learning of the learning of Process Integration.Process Integration.
Purpose of Module 8
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Module 8: Introduction to Process Integration
Tier 2 Worked Examples
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Module 8: Introduction to Process Integration
Tier II: Objective
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 Analysis
Thermal Pinch AnalysisThermal Pinch Analysis
Integrated Process Control and Design – Integrated Process Control and Design – Controllability AnalysisControllability Analysis
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Module 8: Introduction to Process Integration
Tier II is broken down into three sectionsTier II is broken down into three sections
2.1 Worked example using Data-Driven Modeling, more 2.1 Worked example using Data-Driven Modeling, more specifically Multivariate Analysisspecifically Multivariate Analysis
2.2 Worked example using Thermal Pinch Analysis2.2 Worked example using Thermal Pinch Analysis
2.3 Worked example using Integrated Process Control and 2.3 Worked example using Integrated Process Control and Design, more specifically Controllability AnalysisDesign, more specifically Controllability Analysis
A short multiple-choice quiz will follow at the end of this A short multiple-choice quiz will follow at the end of this tier. tier.
Tier II: Contents
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Module 8: Introduction to Process Integration
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
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 2.3 Worked example 3: Integrated Process Worked example 3: Integrated Process Control and Design – Controllability AnalysisControl and Design – Controllability Analysis
Outline
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Module 8: Introduction to Process Integration
Worked example 1: Data-Driven Modeling –
Multivariate Analysis
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Module 8: Introduction to Process Integration
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis – Reminder
TmtTmt X1X1 X4X4 X5X5 RepRep Y avecY avec Y sansY sans
11 -1-1 -1-1 -1-1 11 2.512.51 2.742.74
11 -1-1 -1-1 -1-1 22 2.362.36 3.223.22
11 -1-1 -1-1 -1-1 33 2.452.45 2.562.56
22 -1-1 00 11 11 2.632.63 3.233.23
22 -1-1 00 11 22 2.552.55 2.472.47
22 -1-1 00 11 33 2.652.65 2.312.31
33 -1-1 11 00 11 2.452.45 2.672.67
33 -1-1 11 00 22 2.62.6 2.452.45
33 -1-1 11 00 33 2.532.53 2.982.98
44 00 -1-1 11 11 3.023.02 3.223.22
44 00 -1-1 11 22 2.72.7 2.572.57
44 00 -1-1 11 33 2.972.97 2.632.63
55 00 00 00 11 2.892.89 3.163.16
55 00 00 00 22 2.562.56 3.323.32
55 00 00 00 33 2.522.52 3.263.26
66 00 11 -1-1 11 2.442.44 3.13.1
66 00 11 -1-1 22 2.222.22 2.972.97
66 00 11 -1-1 33 2.272.27 2.922.92
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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Module 8: Introduction to Process Integration
It is assumed that the student is familiar with the following basic It is assumed that the student is familiar with the following basic statistical concepts: mean, median, mode; standard deviation, variance; statistical concepts: mean, median, mode; standard deviation, variance; normality, symmetry; degree of association, correlation coefficients; Rnormality, symmetry; degree of association, correlation coefficients; R22, , QQ22, F-test; significance of differences, t-test, Chi-square; eigen values and , F-test; significance of differences, t-test, Chi-square; eigen values and vectorsvectors
Statistical tests help characterize an existing dataset. They do NOT Statistical tests help characterize an existing dataset. They do NOT enable you to make predictions about future data. For this we must turn enable you to make predictions about future data. For this we must turn to to regression techniquesregression techniques……
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
Basic Basic StatisticsStatistics
RegressionRegressionTake a set of data points, each described by a vector of values (y, xTake a set of data points, each described by a vector of values (y, x11, , xx22, … x, … xnn))
Find an algebraic equation that “best expresses” the relationship Find an algebraic equation that “best expresses” the relationship between y and the xbetween y and the xii’s:’s:
Y =Y = bb11xx11 + b + b22xx22 + … + b + … + bnnxxnn + e + e
Data Requirements:Data Requirements: normalized data, errors normally distributed with normalized data, errors normally distributed with mean zero and independent variables uncorrelatedmean zero and independent variables uncorrelated
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Module 8: Introduction to Process Integration
160
180
200
220
240
150 160 170 180 190 200 210 220 230 240
YO
bser
ved
YPredicted
IDEAL MODELIDEAL MODEL
Figure 1
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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, ObservedDiagnostics: 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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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis – PCA
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
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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis – PCA
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
Reference: Manchester Metropolitan University
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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis – PCA
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
Reference: Manchester Metropolitan University
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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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 Y
provide 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 Y
reconciling the two sets of components so as to maximize reconciling the two sets of components so as to maximize explanation of X and Yexplanation of X and Y
Interpretation of the PLS results has all the difficulties of PCA, plus Interpretation of the PLS results has all the difficulties of PCA, plus another one: making sense of the individual components in both X another one: making sense of the individual components in both X and Y space. In other words, for the results to make sense, the first and Y space. In other words, for the results to make sense, the first component in X must be component in X must be related somehowrelated somehow to the first component to the first component in Yin Y
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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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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Module 8: Introduction to Process Integration
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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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Module 8: Introduction to Process Integration
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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
Autumn Winter Spring Summer
2000
2001
2002 Figure 11
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Module 8: Introduction to Process Integration
2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
Autumn Winter Spring Summer
Autumn Winter Spring Summer
-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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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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
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52PIB143.AI
52PIB193.AI
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52PIP193.AI
52SI055.AI52SIA110.AI52TIC102.PV
52TIC711.PV
52TR964.AI52XIC811.PV
52X_130.AI_split_L1.
52ZI144.AI
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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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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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 Component
The 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 component
A 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 pulp
Note 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 Component
The 3The 3rdrd component component explains only 6%explains only 6% of the total variability of the total variability
The 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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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
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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2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis
Quality “reference map”Quality “reference map”
XX
X
Figure 14
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Module 8: Introduction to Process Integration
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
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 2.3 Worked example 3: Integrated Process Worked example 3: Integrated Process Control and Design – Controllability AnalysisControl and Design – Controllability Analysis
Outline
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Module 8: Introduction to Process Integration
Worked example 2: Thermal Pinch Analysis
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Module 8: Introduction to Process Integration
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
2.2 Worked example 2: Thermal Pinch Analysis
Figure 15
At least 40 streams to heat and cool…
What about an entire site ?
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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
2.2 Worked example 2: Thermal Pinch Analysis
TminTmin
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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
2.2 Worked example 2: Thermal Pinch Analysis
Composite CurvesComposite Curves
TemperatureTemperature
EnthalpyEnthalpy
Figure 16
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Module 8: Introduction to Process Integration
2.2 Worked example 2: Thermal Pinch Analysis
Mass Integration – Composite Curves for pollution Mass Integration – Composite Curves for pollution preventionprevention
Figure 17
Figure 18
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Module 8: Introduction to Process Integration
2.2 Worked example 2: Thermal Pinch Analysis
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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Module 8: Introduction to Process Integration
3-5ºC3-5ºCLow-temperature Low-temperature processesprocesses
10-20ºC10-20ºCChemicalChemical
10-20ºC10-20ºCPetrochemicalPetrochemical
30-40ºC30-40ºCOil RefiningOil Refining
minminIndustrial SectorIndustrial Sector
Table 2
2.2 Worked example 2: Thermal Pinch Analysis
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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Module 8: Introduction to Process Integration
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
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
Temperatures are sorted in ascending
order, omitting common temperatures
TT
HHFigure 21
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PIECENAMP
Module 8: Introduction to Process Integration
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
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
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Module 8: Introduction to Process Integration
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
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
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Module 8: Introduction to Process Integration
Table 6
4. Obtain the accumulated enthalpy for each temperature 4. Obtain the accumulated enthalpy for each temperature intervalinterval
iii QSumQSumQ 1
9369360101 QSumQSumQ
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
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PIECENAMP
Module 8: Introduction to Process Integration
303313323
373
423453463
543563
623653
Hot Composite Curve
300
400
500
600
700
0 50000 100000 150000 200000H (kW)
T (
K)
Figure 22
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
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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Module 8: Introduction to Process Integration
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
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
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Module 8: Introduction to Process Integration
Cold composite curve
Hot composite curve
This representation reduces the entire process into one combined hot and cold streamThis representation reduces the entire process into one combined hot and cold stream 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 reach Tmin. Composite curves, just like individual streams can be shifted Tmin. Composite curves, just like individual streams can be shifted horizontally on the T-H diagram without causing changes to the process because horizontally on the T-H diagram without causing changes to the process because H is a state functionH is a state function
This sets the minimum hot (QThis sets the minimum hot (QHminHmin) and cold (Q) and cold (QCminCmin) utilities requirements for the ) utilities requirements for the entire process and the maximum possible process-process heat recoveryentire 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
2.2 Worked example 2: Thermal Pinch AnalysisComposite Curves
Tmin= 40K
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Module 8: Introduction to Process Integration
2.2 Worked example 2: Thermal Pinch Analysis
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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Module 8: Introduction to Process Integration
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
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
Outline
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Module 8: Introduction to Process Integration
Worked example 3: Integrated Process Control and Design – Controllability Analysis
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Module 8: Introduction to Process Integration
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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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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
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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
FF1111
FF2121
FF1212
FF2222
uu11
uu2 2
yy11
yy22
++
++
++++
yy11
yy22
CC11
CC22
yy1sp1sp
yy2sp2sp
++
++ __
__
Figure 27
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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
NiederlinskiNiederlinski (NI) (NI) : system stability index: system stability index
Condition NumberCondition Number (CN)(CN) and and Disturbance Disturbance Condition Number (DCN) Condition Number (DCN) : sensibility measure: sensibility measure
Relative 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 disturbances
Others: Others: Singular Value DecompositionSingular Value Decomposition (SVD)(SVD)
Other Controllability IndexesOther Controllability Indexes
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Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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Module 8: Introduction to Process Integration
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
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
controlledcontrolled
manipulatedmanipulated disturbancesdisturbances
Pfin = % Fines retained
Problem StatementProblem Statement
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Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
S
S
32 31
24
23
22
21
20
1 6
1 5
1 4
1 31 2
1 1
1 0 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 %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
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
BR
Ret
Pfin
CC
FinesFines
DisturbancesDisturbances
ManipulatedManipulated
ControlledControlled
Figure 31
55
PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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
DisturbancesDisturbances
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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Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
S
S
32 31
24
23
22
21
20
1 6
1 5
1 4
1 31 2
1 1
1 0 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 %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
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
BR
Ret
Pfin
Figure 32
57
PIECENAMP
Module 8: Introduction to Process Integration
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Niederlinski Index (NI) Niederlinski Index (NI) Stability considerationsStability considerations
NI < 0. System will be unstable under closed-loop NI < 0. System will be unstable under closed-loop conditionsconditions
NI > 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 uncertainty
CN CN ~<~< 2. Multivariable 2. Multivariable effects of uncertainty are not effects of uncertainty are not likely to be seriouslikely to be serious
CN 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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PIECENAMP
Module 8: Introduction to Process Integration
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
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
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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End of Tier II
This is the end of Tier II. At this point, we assume that you have done all 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 the reading. You should have a pretty good idea of what Process Integration is as well as basic knowledge in regards to Multivariate Integration is as well as basic knowledge in regards to Multivariate Analysis, Thermal Pinch Analysis and Controllability Analysis. For further Analysis, Thermal Pinch Analysis and Controllability Analysis. For further information on the tools presented in Tier II as well as on other Process 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 Integration tools introduced in Tier I, please consult the references slides in Tiers I and II.in Tiers I and II.
Prior to advancing to Tier III, a short multiple choice quiz will follow.Prior to advancing to Tier III, a short multiple choice quiz will follow.
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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
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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
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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
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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
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Question 7Question 7
TIER II - QUIZ
Associate 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
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Question 8Question 8
TIER II - QUIZ
1 and 1 and 55
4 and 64 and 6
3 and 63 and 6
2 and 2 and 66
4 and 4 and 552 and 52 and 5
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
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Question 9Question 9
TIER II - QUIZ
Which 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
2 and 2 and 443 and 3 and 44
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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