module 8: introduction to process integration â€“ tier 2

Created atCreated atÉÉcolecole PolytechniquePolytechnique de de

MontrMontrééal &al &Universidad de Universidad de GuanajuatoGuanajuato

PIECEPIECEProgram for North American Mobility In Higher EducationProgram for North American Mobility In Higher Education

RevRev:2.2:2.2

Program for North American Mobility in Program for North American Mobility in Higher Education (NAMP)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 Module 8: Introduction to Process Integration Process Integration –– Tier 2Tier 2

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Module 8: Introduction to Process Integration

Project Summary

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

Participating institutionsParticipating institutionsTwo universities in each of the three countries (Canada, Two universities in each of the three countries (Canada, Mexico and the USA)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 partpart by by each of the three countrieseach of the three countries’’ governments governments

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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 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: OpenTier III: Open--Ended Design ProblemEnded Design Problem

These tiers are intended to be completed in that particular ordeThese tiers are intended to be completed in that particular order. r. Students are quizzed at various points to measure their degree oStudents are quizzed at various points to measure their degree of f understanding, before proceeding to the next level.understanding, before proceeding to the next level.

Each tier contains a statement of intent at the beginning and thEach tier contains a statement of intent at the beginning and there ere is a quiz at the end of Tiers I and II.is a quiz at the end of Tiers I and II.

Structure 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 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 Process IntegrationIntegration into a broad perspective. It is identified as a preinto a broad perspective. It is identified as a pre--requisite for other modules related to the learning of requisite for other modules related to the learning of Process Process Integration.Integration.

Purpose of Module 8

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Tier 2 Worked Examples

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Tier II: Objective

Tier II: Statement of intentTier II: Statement of intent

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

DataData--Driven Modeling Driven Modeling -- Multivariate AnalysisMultivariate AnalysisThermal Pinch AnalysisThermal Pinch AnalysisIntegrated Process Control and Design Integrated Process Control and Design –– Controllability Controllability AnalysisAnalysis

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Tier II is broken down into three sectionsTier II is broken down into three sections

2.1 Worked example using Data2.1 Worked example using Data--Driven Modeling, more specifically Driven Modeling, more specifically Multivariate AnalysisMultivariate Analysis

2.2 Worked example using Thermal Pinch Analysis2.2 Worked example using Thermal Pinch Analysis2.3 Worked example using Integrated Process Control and Design, 2.3 Worked example using Integrated Process Control and Design,

more specifically Controllability Analysismore specifically Controllability Analysis

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

Tier II: Contents

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2.1 Worked example 1: Data2.1 Worked example 1: Data--Driven Driven Modeling Modeling –– Multivariate AnalysisMultivariate Analysis

2.2 Worked example 2: Thermal Pinch 2.2 Worked example 2: Thermal Pinch AnalysisAnalysis

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

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


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

Outline

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Worked example 1: Data-Driven Modeling –

Multivariate Analysis

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

2.922.922.272.2733--11110066

2.972.972.222.2222--11110066

3.13.12.442.4411--11110066

3.263.262.522.523300000055

3.323.322.562.562200000055

3.163.162.892.891100000055

2.632.632.972.973311--110044

2.572.572.72.72211--110044

3.223.223.023.021111--110044

2.982.982.532.53330011--1133

2.452.452.62.6220011--1133

2.672.672.452.45110011--1133

2.312.312.652.65331100--1122

2.472.472.552.55221100--1122

3.233.232.632.63111100--1122

2.562.562.452.4533--11--11--1111

3.223.222.362.3622--11--11--1111

2.742.742.512.5111--11--11--1111

Y sansY sansY avecY avecRepRepX5X5X4X4X1X1TmtTmt

Graphical representation of MVAGraphical representation of MVA

Raw Data: Raw Data: impossible to impossible to

interpretinterpret

Statistical ModelStatistical Model(internal (internal

to to software)software)

22--D Visual OutputsD 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 bIt is assumed that the student is familiar with the following basic statistical asic statistical concepts: mean, median, mode; standard deviation, variance; normconcepts: mean, median, mode; standard deviation, variance; normality, ality, symmetry; degree of association, correlation coefficients; Rsymmetry; degree of association, correlation coefficients; R22, Q, Q22, F, F--test; test; significance of differences, tsignificance of differences, t--test, Chitest, Chi--square; square; eigeneigen values and vectorsvalues and vectors

Statistical tests help characterize an existing dataset. They dStatistical tests help characterize an existing dataset. They do NOT enable you to o NOT enable you to make predictions about future data. For this we must turn to make predictions about future data. For this we must turn to regression regression techniquestechniques……

2.1 Worked example 1: Data-Driven ModelingMultivariate Analysis

Basic StatisticsBasic Statistics

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, , …… xxnn))Find an algebraic equation that Find an algebraic equation that ““best expressesbest expresses”” the relationship between y and the relationship between y and the the xxii’’ss::

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

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

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160180200220240

150 160 170 180 190 200 210 220 230 240

YObs

erve

d

YPredicted

IDEAL MODELIDEAL MODEL

Figure 1


Types of MVATypes of MVA1.1. Principal Component Analysis (PCA)Principal Component Analysis (PCA)

XX’’s onlys onlyIn PCA, we are maximizing the In PCA, we are maximizing the variancevariance that is explained by that is explained by the modelthe model

2.2. Projection to Latent Structures (PLS)Projection to Latent Structures (PLS)a.k.a. a.k.a. ““Partial Least SquaresPartial Least Squares””XX’’s and Ys and Y’’ssIn PLS, we are maximizing the In PLS, we are maximizing the covariancecovariance

X Y

X

MVA software generates two types of outputs: results, and diagnoMVA software generates two types of outputs: results, and diagnostics.stics.Results: Score Plots, Loadings PlotsResults: Score Plots, Loadings PlotsDiagnostics: Plot of Residuals, ObservedDiagnostics: Plot of Residuals, Observedvs. Predicted, and many morevs. Predicted, and many more

Types of MVA outputsTypes of MVA outputs

Q1Q1 Q2Q2

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

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

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

Reference: Manchester Metropolitan University

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

Figure 2

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

In effect the major axis is a new variable, size. At its simplesIn effect the major axis is a new variable, size. At its simplest, t, size = length + breadthsize = length + breadthlinear combination of the two existing variables, which are givlinear combination of the two existing variables, which are given equal weightingen equal weighting

Suppose that we consider length to be more important than breadtSuppose that we consider length to be more important than breadth in the determination h in the determination of size. In this case we could use weights or coefficients to inof size. In this case we could use weights or coefficients to introduce differential troduce differential contributions: 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 axiFor convenience, we would normally plot the graph with the X axis horizontal, this would s horizontal, this would give the appearance of rotating the points rather than the axes.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 asA criterion for the second axis is that it should account for as much of the much of the remaining variation as possible. However, it must also be uncorrremaining variation as possible. However, it must also be uncorrelated elated (orthogonal) with the first. (orthogonal) with the first.

In this example the lengths and orientations of these axes are In this example the lengths and orientations of these axes are given by the given by the eigeneigen values and values and eigeneigen vectors of the correlation matrix. If we retain only the vectors of the correlation matrix. If we retain only the 'size' variable we would retain 1.75/2.00 x 100 (87.5%) of the o'size' variable we would retain 1.75/2.00 x 100 (87.5%) of the original riginal variation. Thus, if we discard the second axis we would lose 12.variation. Thus, if we discard the second axis we would lose 12.5% of the 5% of the original information.original information.

Figure 6 Figure 7


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Projection to Latent Structures (PLS)Projection to Latent Structures (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 YX and Yprovide a predictive equation for Y in terms of the Xprovide a predictive equation for Y in terms of the X’’ss

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 maximize explanareconciling the two sets of components so as to maximize explanation tion of X and Yof X and Y

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

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LetLet´́ss look at a typical integrated look at a typical integrated thermomechanicalthermomechanical pulp (TMP) newsprint pulp (TMP) newsprint mill in North America. The mill manager of that particular plantmill in North America. The mill manager of that particular plant recognizes recognizes that there is too much data to deal with and that there is a neethat there is too much data to deal with and that there is a need to estimate d to estimate the quality of their final product, i.e. paper. He decides to usthe quality of their final product, i.e. paper. He decides to use Multivariate e Multivariate Analysis to derive as much information as possible from the dataAnalysis to derive as much information as possible from the data set and try set and try to determine the most important variables that could have an impto determine the most important variables that could have an impact on act on paper quality in order to be able to classify final product qualpaper quality in order to be able to classify final product quality. The mill ity. The mill manager decides to first look at the refining portion of the pulmanager decides to first look at the refining portion of the pulping process.ping 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 chest after the latency chest (automated, on(automated, on--line line analysis of grab analysis of grab samples): standard samples): standard industry parameters industry parameters including fibre length including fibre length distribution, freeness, distribution, 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 units temperatures in various units immediately connected to the immediately connected to the refiners; voltage at chip screw refiners; voltage at chip screw conveyors; refiner body conveyors; refiner body temperaturetemperature

Season, represented by the Season, represented by the average monthly temperature average monthly temperature measured at a nearby measured at a nearby meteorological stationmeteorological station

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

ResultsResults 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 WinterSpringSummer


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 direction appears to

occur BETWEEN seasons

(≅ 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 direction appears to

occur WITHIN a given season

(≅ Component 1)

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

occur WITHIN a given occur WITHIN a given seasonseason

((≅≅ Component 1)Component 1)

Interpretation of the results Interpretation of the results –– Score PlotScore Plot

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Interpretation of the results Interpretation of the results –– Loadings plotLoadings plot

-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

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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.AI52LIC106.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.PV53LIC011.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

52PIP143.AI

52PIP193.AI52SI055.AI52SIA110.AI

52TIC102.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 throughputRefining energyDilution flowsSteam generation

Pulp throughputPulp throughputRefining energyRefining energyDilution flowsDilution flowsSteam generationSteam generation

Pulp brightnessSeasonPulp brightnessPulp brightnessSeasonSeason

Bleach 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 variThe first component corresponds to throughput: many process variables are related ables are related either directly or indirectly to throughput. Remember we said theither directly or indirectly to throughput. Remember we said that the 1at the 1stst component component was something that varied within an individual season? 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 ““messiermessier””than the first component, and will be less easy to interpret. Itthan the first component, and will be less easy to interpret. It is also possible to note is also possible to note that the that the three years were separatedthree years were separated with respect to this second componentwith respect to this second componentA major clue occurs in the prominence of two important and relatA major clue occurs in the prominence of two important and related tags: ed tags: bleach bleach consumptionconsumption and and pulp brightnesspulp brightness. This would suggest that perhaps the brightness of . This would suggest that perhaps the brightness of the incoming wood chips was different from year to year, requirithe incoming wood chips was different from year to year, requiring more bleaching to ng more bleaching to get a less white pulpget a less white pulpNote also that Note also that ““SeasonSeason”” is prominent. This can be seen with the obvious separation of is prominent. This can be seen with the obvious separation of the seasons on the score plot. This suggests that winter chips athe seasons on the score plot. This suggests that winter chips are less bright than re less bright than summer chipssummer chips

THIRD COMPONENTTHIRD COMPONENT

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

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

REFINER THROUGHPUTREFINER THROUGHPUTComponent 1Component 1Explains 32%Explains 32%

Component 2Component 2Explains 7%Explains 7%

Component 3Component 3Explains 6%Explains 6%

BRIGHTNESS

BRIGHTNESS

SUM

MER

/ W

INTE

RSU

MM

ER /

WIN

TER

Summary of the PCA resultsSummary of the PCA results

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Quality Quality ““reference mapreference map””

XX

X

Figure 14

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

Outline

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Worked example 2: Thermal 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 to Costs related to exchange area exchange area

go upgo up

From 100% utility...From 100% utility... ... to 100% internal exchanges... to 100% internal exchanges

$$

TradeTrade--offoffTradeTrade--offoff

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

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Example: Recovery BoilerExample: Recovery Boiler

Obvious solution: preheat Obvious solution: preheat entering fresh water with entering fresh water with hot condensate leaving hot 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 Network Heat Exchanger Network DesignDesign

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

with specific energy with specific energy savings objectives in savings objectives in

mindmind Analysis Analysis Targeting, i.e. Targeting, i.e. energy, design energy, design and economical and economical

targetstargets

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

Exchanger Network Exchanger Network that will reach energy that will reach energy targets at lowest costtargets at lowest cost

Transfer of Transfer of obtained results obtained results to plant realityto plant reality


ΔΔTminTmin

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Cold composit

e

Cold composit

e

curv

ecu

rveHot

com

posit

e

Hot co

mpo

site

curv

ecu

rveΔΔTminTmin

Heating RequirementHeating Requirement

Cooling RequirementCooling Requirement

PinchPinchpointpoint


Composite CurvesComposite Curves

TemperatureTemperature

EnthalpyEnthalpy

Figure 16

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Mass Integration Mass Integration –– Composite Curves for pollution preventionComposite Curves for pollution prevention

Figure 17

Figure 18

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Problem StatementProblem StatementA process engineer in a consulting firm is hired by an oil refinA process engineer in a consulting firm is hired by an oil refinery to ery to design the Conventional Atmospheric Crude Fractionation Units sedesign the Conventional Atmospheric Crude Fractionation Units section ction of the refinery facility, as shown in figure 17. The main objectof the refinery facility, as shown in figure 17. The main objective of this ive of this project is to minimize the energy consumption by using Thermal Pproject is to minimize the energy consumption by using Thermal Pinch inch Analysis. The plant is currently using 75000 kW in hot utilitiesAnalysis. The plant is currently using 75000 kW in hot utilities. In this . In this example, stress will be put on the construction of the compositeexample, stress will be put on the construction of the composite curves 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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33--55ººCCLowLow--temperature temperature processesprocesses

1010--2020ººCCChemicalChemical

1010--2020ººCCPetrochemicalPetrochemical

3030--4040ººCCOil RefiningOil Refining

ΔΤΔΤminminIndustrial SectorIndustrial Sector

Table 2


DesalterDesalter

Crude TowerCrude Tower

NaphthaNaphtha--PAPA

KeroseneKerosene

LL--gasoilgasoil

HH--gasoilgasoil

ATBATB

CrudeCrudeFeedFeed

2020ºº

BPABPA

150150ºº 150150ºº 390390ºº

150150ºº

100100ºº

180180ºº 3030ºº

4040ºº

3030ºº

5050ºº

270270ºº

290290ºº

190190ºº

350350ºº

380380ºº

11 22

33

66

44

55

88

77Crude PreCrude Pre--heat train heat train

ºº ººC ConditionC 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 ExtractionData Extraction

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Table 3

1. Sort in ascending order the hot streams temperatures, omittin1. Sort in ascending order the hot streams temperatures, omitting g common temperaturescommon temperatures

Using the data above, we form temperature intervals for the procUsing the data above, we form temperature intervals for the processess

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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Table 4

stream interval, === ∑−

− jiCPCPstreamj

streamji

2. Sum up the CP of every stream present in each temperature int2. Sum up the CP of every stream present in each temperature intervalerval

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 TTCPQ3. 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 shown iWe obtain the enthalpy for each temperature interval, as shown in n the column the column QQint,hint,h


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Table 6

4. Obtain the accumulated enthalpy for each temperature interval4. Obtain the accumulated enthalpy for each temperature interval

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 on5. Plot temperature on the Y axis versus accumulated enthalpy on the X axisthe X axis

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Cold Composite Curve

250300350400450500550600650700

0 50000 100000 150000 200000 250000H (kW)

T(K

)

Figure 23

293

423

663

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


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Cold composite curveHot composite curve

This representation reduces the entire process into one combineThis representation reduces the entire process into one combined hot and cold streamd hot and cold streamThe heat recovery between the composite curves can be increasedThe heat recovery between the composite curves can be increased until we reach until we reach ΔΔTminTmin. .

Composite curves, just like individual streams can be shifted hoComposite curves, just like individual streams can be shifted horizontally on the Trizontally on the T--H diagram H diagram without causing changes to the process because H is a state funcwithout causing changes to the process because H is a state functiontion

This sets the minimum hot (This sets the minimum hot (QQHminHmin) and cold () and cold (QQCminCmin) utilities requirements for the entire ) utilities requirements for the entire process and the maximum possible processprocess and the maximum possible process--process heat recoveryprocess heat recovery

Internal Heat Recovery QHmin

Minimum Minimum Cooling Cooling

RequirementRequirement

QCminMinimum Minimum Heating Heating

RequirementRequirement

0

Application Composite Curves

100

200

300

400

500

600

700

0 50000 100000 150000 200000 250000H (kW)

T (K

)

Figure 24


ΔTmin= 40K

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As seen in the previous slides, from the temperatureAs seen in the previous slides, from the temperature--enthalpy plot, we enthalpy plot, we can determine three useful pieces of information:can determine three useful pieces of information:

Amount of possible processAmount of possible process--process heat recovery represented by the process heat recovery represented by the area between the two composites curvesarea between the two composites curves

Hot Utility requirement or target = 57668 kWHot Utility requirement or target = 57668 kWCold 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 anComposite curves are excellent tools for learning the methods and d understanding the overall energy situation, but minimum energy understanding the overall energy situation, but minimum energy consumption and the heat recovery Pinch are more often obtained consumption and the heat recovery Pinch are more often obtained by by numerical proceduresnumerical procedures. This method is called the. This method is called the Problem Table Problem Table Algorithm. Algorithm. Typically, it is based on notions of Typically, it is based on notions of Heat CascadeHeat Cascade..

Q5Q5 Q6Q6

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Outline

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Worked example 3: Integrated Process Control

and Design –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 VariablesInput Variables(manipulated)(manipulated)

DisturbancesDisturbances

UncertaintiesUncertainties

Internal interactionsInternal interactions

PROCESS RESILIENCYPROCESS RESILIENCY

PROCESS FLEXIBILITYPROCESS FLEXIBILITY

Control LoopControl Loop

Figure 25

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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 analysis)Controllability analysis)

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

Figure 26

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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

++++

++++

ΔΔuu11ssss

)y- gain, (OL , 11111

1 uKuy =

ΔΔ

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

Main Effect:Main Effect:

Figure 28

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

F11

F21

F12

F22

u1

u2

y1

y2

+

+

++C2

e2y2sp

+ _

Δu1 ss

1r1111 yΔ+= OLCL KKTotal Effect:Total Effect:Interactive EffectInteractive Effect

Main EffectMain EffectFigure 29

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=CLK 11OLK 11 1ryΔ+

Main Effect (1Main Effect (1stst Experiment)Experiment)OLK11=11λ CLK11

Total Effect (2Total Effect (2nd nd Experiment)Experiment)

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

λλ1111 : measure of the : measure of the extent of extent of steady state steady state

interactioninteraction in using uin using u11 to to control ycontrol y11, , whilewhile using uusing u22

to control yto control y22

⎥⎦

⎤⎢⎣

⎡=Λ

2221

1211

λλλλ

11λRelative GainRelative Gain

yy11 uu11

CL

OL

⎟⎟⎠

⎞⎜⎜⎝

⎛

∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛

∂∂

=

j

i

j

i

ij

uy

uy

λijλ

Relative Gain ArrayRelative Gain Arrayyyii uujj

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

yyii : Controlled variable: Controlled variableuujj : Manipulated variable: Manipulated variable

1=ijλ

0=ijλ

10 << ijλ

1>ijλ

0<ijλ

ImplicationImplication RecommendationRecommendationLoop Loop ii not subject to interactive action not subject to interactive action from other loopsfrom 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 effectbelow 0.5, interactive effect > main effect

ji uy − :Avoid

-- Loops are interactingLoops are interacting-- interactive effect acts in opposition to the main interactive effect acts in opposition to the main effecteffect ji uy − :high at Avoid ijλ

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

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 Condition Disturbance Condition Number (DCN) Number (DCN) : sensibility measure: sensibility measureRelative Disturbance Gain (RDG)Relative Disturbance Gain (RDG) : index that gives an : index that gives an idea of the influence of internal interactions on the idea of the influence of internal interactions on the effect of disturbanceseffect of disturbancesOthers: Others: Singular Value DecompositionSingular Value Decomposition (SVD)(SVD)

Other Controllability IndexesOther Controllability Indexes

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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

C U V P A T EC U V P A T E 1

4

3

2

1

2.94705 %

2264.4 lt /m in

13924 lt /m in1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt /m in2.79214 %

1195

8.7

lt/m

in

2.96551 %

1114

4.5

lt/m

in

3.51

707

%

595.592 lt /m in

3.02375 %

48686 lt /m in2.19041 % 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 /m in

12628.8 lt /m in

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt /m in11565.2 lt /m in

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt /m in

10299.6 lt /m in2.99513 %

6300 lt /m in

4000 lt /m in

Base C ase: TMP Newsprint MillSteady State Sim ulation

401.885 l /min18 %

W et web

Fresh water

F resh Pulp (7 %)

Broke (18 %)

W WTank

Machine Chest

Mix ingChest

BrokeTank

PulpTank

F5F5

F8F8

F7F7

F2F2

F6F6

F3F3

F4F4

F1F1

Figure 30

In this caseIn this case--study, a process control engineer is asked to create a model of study, a process control engineer is asked to create a model of the the thermomechanicalthermomechanical pulping process to find the best process control selection pulping process to find the best process control selection and variable pairing for a plant that has not been built yet. and variable pairing for a plant that has not been built yet. Consider the Consider the simplified newsprint paper machine short loop configuration showsimplified newsprint paper machine short loop configuration shown in figure n in figure 30. Variable pairing techniques will be applied as well as the u30. Variable pairing techniques will be applied as well as the use of se of controllability indexes.controllability 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


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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 /m in

13924 lt /m in1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt /m in2.79214 %

1195

8.7

lt/m

in

2.96551 %11

144.

5 lt/

min

3.51

707

%

595.592 lt /m in

3.02375 %

48686 lt /m in2.19041 % 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 /m in

12628.8 lt /m in

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt /m in11565.2 lt /m in

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt /m in

10299.6 lt /m in2.99513 %

6300 lt /m in

4000 lt /m in

Base Case: TMP N ewsprint MillSteady State Sim ulat ion

401.885 l /min18 %

W et web

Fresh water

F resh Pulp (7 %)

Broke (18 %)

W WTank

Machine C hest

Mix ingC hest

BrokeTank

PulpTank

BR

Ret

Pfin

CC

FinesFines


ManipulatedManipulated

ControlledControlled

Figure 31

55

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⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

tCBRCCCC

Re34

43

23

30

33

⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

−−−−−−−

−−−−−−−−−−−−−−

020.4265.0608.0068.0042.0077.0114.0025.0004.0049.0001.0001.0001.0002.0

000.0000.0340.3000.0000.0775.0065.0030.0004.0036.0016.0010.0018.0027.0029.0004.0036.0001.0011.0020.0029.0038.0005.0024.0001.0001.0404.0002.0028.0004.0018.0001.0001.0001.0031.0

⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

finPFFFFFF

40

3

16

22

6

32

⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

− 597.4075.0079.0164.0000.0000.0060.0455.0058.0483.0076.0052.0056.0518.0

⎥⎦

⎤⎢⎣

⎡

1

1

fC

== ++

GGpp GGdd

Process Gain Matrices and SteadyProcess Gain Matrices and Steady--State ControllabilityState Controllability


⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

tCBRCCCC

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

ΛΛ ==

56

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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 /m in

13924 lt /m in1.00382 %

6261

0 lt/

min

1.92

733

%

13287.5 lt /m in2.79214 %

1195

8.7

lt/m

in

2.96551 %11

144.

5 lt/

min

3.51

707

%

595.592 lt /m in

3.02375 %

48686 lt /m in2.19041 % 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 /m in

12628.8 lt /m in

814.

218

lt/m

in

249.

355

lt/m

in

11814.6 lt /m in11565.2 lt /m in

495.

588

lt/m

in

1106

9.6

lt/m

in47

69.6

lt/m

in

100 lt /m in

10299.6 lt /m in2.99513 %

6300 lt /m in

4000 lt /m in

Base Case: TMP N ewsprint MillSteady State Sim ulat ion

401.885 l /min18 %

W et web

Fresh water

F resh Pulp (7 %)

Broke (18 %)

W WTank

Machine C hest

Mix ingC hest

BrokeTank

PulpTank

BR

Ret

Pfin

Figure 32

57

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NiederlinskiNiederlinski Index (NI) Index (NI) Stability considerationsStability considerations

NI < 0. System will be unstable under closedNI < 0. System will be unstable under closed--loop conditionsloop conditions

NI > 0. System is NI > 0. System is stabilizablestabilizable (function of controller parameters)(function of controller parameters)

Condition number (CN)Condition number (CN) Sensitivity to model uncertaintySensitivity to model uncertainty

CN CN ~<~< 2. Multivariable2. Multivariable effects of uncertainty are not likely to be effects of uncertainty are not likely to be seriousserious

CN CN ~>~> 10. ILL10. ILL--CONDITIONED processCONDITIONED process

CN=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 by the Is the action taken by the manipulated variable large or small?manipulated variable large or small?

11≤≤ DCN DCN ≤≤ CNCN

Relative Disturbance Gain (RDG) Relative Disturbance Gain (RDG) Internal interaction among the Internal interaction among the loops is loops is favorablefavorable or or unfavorableunfavorable to reject disturbances?to reject 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 FRESH The effect of both disturbances, %C and %fines in FRESH PULP, is reduced by internal interactions. PULP, is reduced by internal interactions. All All RDGRDG’’ss are ~<2are ~<2

Controllability Indexes (2)Controllability Indexes (2)

DCN for %DCN for %CCfreshfresh pulppulp = 9.2= 9.2DCN for %DCN for %finesfinesfreshfresh pulppulp = 4.6= 4.6

It is harder to reject a sudden change in fresh pulp consistencyIt is harder to reject a sudden change in fresh pulp consistency

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ConclusionConclusion

Control structure configuration: RGA results Control structure configuration: RGA results confirmed current implementation in newsprint millsconfirmed current implementation in newsprint mills

Internal interactions of the aforementioned Internal interactions of the aforementioned configuration reduce the effect of disturbances on configuration reduce the effect of disturbances on output variablesoutput variables

The process is illThe process is ill--conditioned. Model uncertainty conditioned. Model uncertainty may be highly amplifiedmay be highly amplified

Resiliency Indexes, DCN and RDG, can be used to Resiliency Indexes, DCN and RDG, can be used to account for disturbance rejection in newsprint account for disturbance rejection in newsprint processesprocesses

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End of Tier II

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

Prior to advancing to Tier III, a short multiple choice quiz wilPrior to advancing to Tier III, a short multiple choice quiz will follow.l 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 outpuIdentify the components having an influence on one or many outputsts

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 32 and 3

1,2 and 31,2 and 3

11

1 and 21 and 2

1 and 31 and 3

33

TIER II - QUIZ

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

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

2. Score plot2. Score plot B.B. Shows the distance between each real Shows the distance between each real observation in the initial dataset and the observation in the initial dataset and 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 variable is Shows how strongly each variable is associated with each new componentassociated with each new component

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

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

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

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

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

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

TIER II - QUIZ

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Question 3Question 3The lengths and orientations of the axes obtained with a PCA areThe lengths and orientations of the axes obtained with a PCA are given by given by the the eigeneigen values and values and eigeneigen vectors of the correlation matrix. Let's say the vectors of the correlation matrix. Let's say the length and breadth variables have a lower correlation coefficienlength and breadth variables have a lower correlation coefficient than in t than in the example given in slide 13 and that we obtain the the example given in slide 13 and that we obtain the eigeneigen values shown in values shown in the figure below. If we discard the second axis, what percentagethe figure below. If we discard the second axis, what percentage of the of the original information would we lose?original information would we lose?

12,5%12,5%

0%0%

25%25%

37,5%37,5%

75%75%

62,5%62,5%

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

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Question 5Question 5

HigherHigher

LowerLower

Would stay the sameWould stay the same

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

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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 are more Minimum energy consumption and the heat recovery Pinch are more often obtained by Composite Curvesoften 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 Thorizontally on the T--H diagram without causing changes to the H diagram without causing changes to the processprocess

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 ΔΔTminTmin and the thermal data, Pinch Analysis provides a and the thermal data, Pinch Analysis provides a target for the minimum energy consumptiontarget for the minimum energy consumption

2 and 32 and 3

All of the aboveAll of the above

1 and 31 and 3

1 and 21 and 2

2 and 42 and 4

3 and 43 and 4

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Associate each controllability tool or index with the kind of inAssociate each controllability tool or index with the kind of information it formation it provides the user with.provides the user with.

1. 1. NiederlinskiNiederlinski IndexIndex A.A. Shows the importance of interactions in Shows the importance of interactions in a systema system

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

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

4. Relative Gain Array4. Relative Gain Array D. D. Discusses the stability of a closedDiscusses the stability of a closed--loop loop control configuration control configuration

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

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

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

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

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

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

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1 and 51 and 5

4 and 64 and 6

3 and 63 and 6

2 and 62 and 6

4 and 54 and 5

2 and 52 and 5

In the Relative Gain Array shown in slide 54, what do the valuesIn the Relative Gain Array shown in slide 54, what do the values 1.566 and 1.566 and 1.603 for the pairing of F40 and C34, and 1.603 for the pairing of F40 and C34, and PfinPfin and Ret, tell you?and Ret, 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 but smaller in magnitude than the effect of the main loopsmaller 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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Which of the following statements are false?Which of the following statements are false?

1.1. FeedforwardFeedforward control compensates for immeasurable disturbancescontrol compensates for immeasurable disturbances

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 meet iResiliency is the degree to which a processing system can meet its ts design objectives despite uncertainties in its design parametersdesign objectives despite uncertainties in its design parameters

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

2 and 32 and 3

All of the aboveAll of the above

1 and 31 and 3

1 and 21 and 2

2 and 42 and 4

3 and 43 and 4

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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

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module 8: introduction to process integration â€“ tier 2

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