1. Introduction to Process Integration
Program for North American Mobility in Higher Education
NAMP
Module 8
Introduction to Process Integration
Tier II
Introducing Process integration for Environmental Control in Engineering Curricula
PIECE
Module 8 – Introduction to Process Integration

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Module 8 – Introduction to Process Integration

3. Table of contents Project Summary Module Structure & Purpose Tier II
Participating institutions
Module creators
Module Structure & Purpose
Tier II
Statement of Intent
Sections
2.1 Worked example using Data-Driven Modeling, more specifically Multivariate Analysis
2.2 Worked example using Thermal Pinch Analysis
2.3 Worked example using Integrated Process Control and Design, more specifically Controllability Analysis
Quiz
Module 8 – Introduction to Process Integration

4. Project Summary Objectives Participating institutions
Create web-based modules to assist universities to address the introduction to Process Integration into engineering curricula
Make these modules widely available in each of the participating countries
Participating institutions
Two universities in each of the three countries (Canada, Mexico and the USA)
Two research institutes in different industry sectors: petroleum (Mexico) and pulp and paper (Canada)
Each of the six universities has sponsored 7 exchange students during the period of the grant subsidised in part by each of the three countries’ governments
Module 8 – Introduction to Process Integration

5. PIECE
Process integration for Environmental Control in Engineering Curricula
Paprican
École Polytechnique de Montréal
Universidad Autónoma de San Luis Potosí
University of Ottawa
Universidad de Guanajuato
North Carolina State University
Instituto Mexicano del Petróleo
University of Texas A&M
NAMP
Program for North American Mobility in Higher Education
Module 8 – Introduction to Process Integration

6. This module was created by:
Carlos Alberto Miranda Alvarez
Paul Stuart
From
Host Institution
Host director
Martin Picon-Nuñez
Jean-Martin Brault
Module 8 – Introduction to Process Integration

7. What is the structure of this module?
Structure of Module 8
What is the structure of this module?
All modules are divided into 3 tiers, each with a specific goal:
Tier I: Background Information
Tier II: Case Study Applications
Tier III: Open-Ended Design Problem
These tiers are intended to be completed in that particular order. Students are quizzed at various points to measure their degree of understanding, before proceeding to the next level. Each tier contains a statement of intent at the beginning and a quiz at the end.
Module 8 – Introduction to Process Integration

8. What is the purpose of this module?
Purpose of Module 8
What is the purpose of this module?
It is the intent of this module to cover the basic aspects of Process Integration Methods and Tools, and to place Process Integration into a broad perspective. It is identified as a pre-requisite for other modules related to the learning of Process Integration.
Module 8 – Introduction to Process Integration

9. Tier II Worked Examples
Module 8 – Introduction to Process Integration

10. Tier II Statement of intent
The goal of this tier is to demonstrate various concepts and tools of Process Integration using real examples. Three examples will be given, focusing mainly on three Process Integration tools. At the end of Tier II, the student should have a general idea of what is:
Data-Driven Modeling - Multivariate Analysis
Thermal Pinch Analysis
Integrated Process Control and Design – Controllability Analysis
Module 8 – Introduction to Process Integration

11. Tier II is broken down into three sections
Tier II Contents
Tier II is broken down into three sections
2.1 Worked example using Data-Driven Modeling, more specifically Multivariate Analysis
2.2 Worked example using Thermal Pinch Analysis
2.3 Worked example using Integrated Process Control and Design, more specifically Controllability Analysis
A short multiple-choice quiz will follow at the end of this tier.
Module 8 – Introduction to Process Integration

12. Tier II Outline
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Module 8 – Introduction to Process Integration

13. 2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
Module 8 – Introduction to Process Integration

14. (internal to software)
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis – Reminder
Graphical representation of MVA
Statistical Model
(internal to software)
Tmt X1 X4 X5
Rep
Y avec
Y sans 1 -1
2.51
2.74 2
2.36
3.22 3
2.45
2.56
2.63
3.23
2.55
2.47
2.65
2.31
2.67
2.6
2.53
2.98 4
3.02
2.7
2.57
2.97 5
2.89
3.16
3.32
2.52
3.26 6
2.44
3.1
2.22
2.27
2.92 . . . . . . . . . . . .
Raw Data: impossible to interpret
2-D Visual Outputs
trends Y X
hundreds of columns
thousands of rows
Module 8 – Introduction to Process Integration

15. Basic Statistics Regression 2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Basic Statistics
It is assumed that the student is familiar with the following basic statistical concepts: mean, median, mode; standard deviation, variance; normality, symmetry; degree of association, correlation coefficients; R2, Q2, F-test; significance of differences, t-test, Chi-square; eigen values and vectors
Statistical tests help characterize an existing dataset. They do NOT enable you to make predictions about future data. For this we must turn to regression techniques…
Regression
Take a set of data points, each described by a vector of values (y, x1, x2, … xn)
Find an algebraic equation that “best expresses” the relationship between y and the xi’s:
Y = b1x1 + b2x2 + … + bnxn + e
Data Requirements: normalized data, errors normally distributed with mean zero and independent variables uncorrelated
Module 8 – Introduction to Process Integration

16. X X Y Types of MVA Types of MVA outputs
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Types of MVA
Principal Component Analysis (PCA)
X’s only
In PCA, we are maximizing the variance that is explained by the model
Projection to Latent Structures (PLS)
a.k.a. “Partial Least Squares”
X’s and Y’s
In PLS, we are maximizing the covariance
X Y X
Types of MVA outputs
MVA software generates two types of outputs: results, and diagnostics.
Results: Score Plots, Loadings Plots
Diagnostics: Plot of Residuals, Observed
vs. Predicted, and many more 1 6 8 2 4 5 7 9 3
YObserved
YPredicted
IDEAL MODEL
Figure 1 Q1 Q2
Module 8 – Introduction to Process Integration

17. Principal Component Analysis (PCA)
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis - PCA
Principal Component Analysis (PCA)
Consider these fish. We could measure, for each fish, its length and breadth.
Figure 2
Suppose that 50 fish were measured, a plot like the one shown in figure 2 might be obtained. There is an obvious relationship between length and breadth as longer fish tend to be broader.
Module 8 – Introduction to Process Integration
Reference: Manchester Metropolitan University

18. 2.1 Worked example 1: Data-Driven Modeling Multivariate Analysis - PCA
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 subtracted from each value.
Figure 3
Figure 4
In effect the major axis is a new variable, size. At its simplest, size = length + breadth
 linear combination of the two existing variables, which are given equal weighting
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 introduce differential contributions: size = 0.75 x length x breadth
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.
Figure 5
Module 8 – Introduction to Process Integration
Reference: Manchester Metropolitan University

19. 2.1 Worked example 1: Data-Driven Modeling Multivariate Analysis - PCA
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 uncorrelated (orthogonal) with the first.
Figure 6
Figure 7
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 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 we would lose 12.5% of the original information.
Module 8 – Introduction to Process Integration
Reference: Manchester Metropolitan University

20. Projection to Latent Structures (PLS)
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis - PCA
Projection to Latent Structures (PLS)
PLS finds a set of orthogonal components that :
maximize the level of explanation of both X and Y
provide a predictive equation for Y in terms of the X’s
This is done by:
fitting a set of components to X (as in PCA)
similarly fitting a set of components to Y
reconciling the two sets of components so as to maximize explanation of X and Y
Interpretation of the PLS results has all the difficulties of PCA, plus 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 component in X must be related somehow to the first component in Y
Module 8 – Introduction to Process Integration

21. Problem Statement 2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Problem Statement
Let´s look at a typical integrated thermomechanical pulp (TMP) newsprint mill in North America. The mill manager of that 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 product, i.e. paper. He decides to use Multivariate Analysis to derive as much information as possible from the data set and try to determine the most important variables that could have an impact on paper quality in order to be able to classify final product quality. The mill manager decides to first look at the refining portion of the pulping process.
Figure 8
Module 8 – Introduction to Process Integration

22. X and Y Variables 2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
X and Y Variables
X variables
Incoming chips: size distribution, bulk density, humidity
Refiner operating data: throughput; energy split between the primary and secondary refiner; dilution rates; levels, pressures and temperatures in various units immediately connected to the refiners; voltage at chip screw conveyors; refiner body temperature
Season, represented by the average monthly temperature measured at a nearby meteorological station
Y variables
Pulp quality data after the latency chest (automated, on-line analysis of grab samples): standard industry parameters including fibre length distribution, freeness, consistency, and brightness Y
X’s
Figure 9
Module 8 – Introduction to Process Integration

23. Results 2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Autumn Winter Spring Summer
2000
2001
2002
Figure 11
Results
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.
Figure 10
Module 8 – Introduction to Process Integration

24. Interpretation of the results – Score Plot
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Interpretation of the results – Score Plot
Variation in this direction appears to occur BETWEEN seasons
( Component 2)
Figure 12
Autumn Winter Spring Summer
Variation in this direction appears to occur WITHIN a given season
( Component 1)
Module 8 – Introduction to Process Integration

25. Interpretation of the results – Loadings plot
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Interpretation of the results – Loadings plot
Bleach consumption
Pulp throughput
Refining energy
Dilution flows
Steam generation
Pulp brightness
Season
Figure 13
Module 8 – Introduction to Process Integration

26. Interpretation of the results
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Interpretation of the results
First Component
The first component corresponds to throughput: many process variables are related either directly or indirectly to throughput. Remember we said that the 1st component was something that varied within an individual season?
Second Component
The 2nd component explains only 7% of the total variability. It is therefore “messier” than the first component, and will be less easy to interpret. It is also possible to note that the three years were separated with respect to this second component
A major clue occurs in the prominence of two important and related tags: bleach consumption and pulp brightness. This would suggest that perhaps the brightness of the incoming wood chips was different from year to year, requiring more bleaching to get a less white pulp
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 are less bright than summer chips
Third Component
The 3rd component explains only 6% of the total variability
The 3rd component is related to the time of year. A reasonable interpretation would be that summer chips differ from winter chips in some way other than 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 each other
Module 8 – Introduction to Process Integration

27. Summary of the PCA results
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Summary of the PCA results
Using PCA, we have determined that 45% of the variability in the original 130 variables can be represented by using just 3 new variables or “components”. These three components are orthogonal, meaning that the variation within each one occurs independently of the others. In other words, the new components are uncorrelated with each other.
REFINER THROUGHPUT
Component 1
Explains 32%
Component 2
Explains 7%
Component 3
Explains 6%
BRIGHTNESS
SUMMER / WINTER
Module 8 – Introduction to Process Integration

28. Quality “reference map”
2.1 Worked example 1: Data-Driven Modeling
Multivariate Analysis
Quality “reference map” X X X
Figure 14
Module 8 – Introduction to Process Integration

29. Tier II Outline
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Module 8 – Introduction to Process Integration

30. 2.2 Worked example 2: Thermal Pinch Analysis
Module 8 – Introduction to Process Integration

31. PROCESS HOT COLD Utility From 100% utility...
2.2 Worked example 2: Thermal Pinch Analysis – Reminder
What is Thermal Pinch Analysis?
PROCESS
COLD
Utility
HOT
Utility Usage
Utility costs go down
Trade-off $
Trade-off
Costs related to exchange area go up
Internal Exchanges
From 100% utility...
... to 100% internal exchanges
Module 8 – Introduction to Process Integration

32. At least 40 streams to heat and cool…
2.2 Worked example 2: Thermal Pinch Analysis
What about an entire site ?
At least 40 streams to heat and cool…
Example: Recovery Boiler
Obvious solution: preheat entering fresh water with hot condensate leaving boiler
Figure 15
Module 8 – Introduction to Process Integration

33. Heat Exchanger Network Design
2.2 Worked example 2: Thermal Pinch Analysis
Simulation
Extraction
Data Extraction (hot and cold streams) with specific energy savings objectives in mind
Targeting
Analysis Targeting, i.e. energy, design and economical targets
Tmin
Heat Exchanger Network Design
Use of heuristics to design a Heat Exchanger Network that will reach energy targets at lowest cost
Plant
Transfer of obtained results to plant reality
Module 8 – Introduction to Process Integration

34. 2.2 Worked example 2: Thermal Pinch Analysis
Composite Curves
Temperature
Enthalpy
Figure 16
Heating Requirement
Hot composite
curve
Tmin
Cold composite
curve
Pinch
point
Cooling Requirement
Module 8 – Introduction to Process Integration

35. 2.2 Worked example 2: Thermal Pinch Analysis
Mass Integration – Composite Curves for pollution prevention
Figure 18
Figure 17
Module 8 – Introduction to Process Integration

36. 2.2 Worked example 2: Thermal Pinch Analysis
Problem Statement
A process engineer in a consulting firm is hired by an oil refinery to design the Conventional Atmospheric Crude Fractionation Units section of the refinery facility, as shown in figure 17. The main objective of this project is to minimize the energy consumption by using Thermal Pinch Analysis. The plant is currently using kW in hot utilities. In this example, stress will be put on the construction of the composite curves with the objective of identifying energy savings opportunities.
Furnace
Desalter
Crude Tower
Naphtha - PA
Kerosene L
gasoil H
ATB
Crude E1 E2 E3 E4 E5 E6 E7 1 2 5 6 7 8 9 10 11 13 14 15 16
BPA 12 3 4
Figure 19
Module 8 – Introduction to Process Integration

37. Data Extraction 2.2 Worked example 2: Thermal Pinch Analysis
Desalter
Crude Tower
Naphtha-PA
Kerosene
L-gasoil
H-gasoil
ATB
Crude
Feed
20º
BPA
150º
390º
100º
180º
30º
40º
50º
270º
290º
190º
350º
380º 1 2 3 6 4 5 8 7
Crude Pre-heat train
º ºC Condition
Stream Number
Figure 20
3-5ºC
Low-temperature processes
10-20ºC
Chemical
Petrochemical
30-40ºC
Oil Refining
DTmin
Industrial Sector
Table 2
Data Extraction
Process
Heat
Mass
Supply
Target
Stream
Heat*
Fouling
stream
capacity
flow
temperature
Temperature
Transfer
number
rate
flowrate
duty
coefficient
and type
(J/kgK)
(kg/s)
(kW/K)
(ºC)
(kW)
(W/m 2 K) (m
ºC/W)
(1)Cold
200.00
520.00
20.00
150.00
170.00
(2)Cold
390.00
(3)Hot
253.00
657.80
100.00
(4)Hot
23.00
59.80
180.00
30.00
(5)Hot
44.00
114.40
270.00
40.00
(6)Hot
148.00
384.80
290.00
190.00
(7)Hot
13.00
33.80
350.00
(8)Hot
56.00
145.60
380.00
50.00
* Fouling Factor included
Table 1
Module 8 – Introduction to Process Integration

38. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
1. Sort in ascending order the hot streams temperatures, omitting common temperatures
Temperatures are sorted in ascending order, omitting common temperatures
Table 3
Using the data above, we form temperature intervals for the process T1 T2 T3 T4
Interval 1 2 3 T H
Figure 21
Module 8 – Introduction to Process Integration

39. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
2. Sum up the CP of every stream present in each temperature interval
Table 4
We then obtain the Composite CP for each temperature interval
Module 8 – Introduction to Process Integration

40. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
3. Calculate the net enthalpy for each temperature interval
Table 5
We obtain the enthalpy for each temperature interval, as shown in the column Qint,h
Module 8 – Introduction to Process Integration

41. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
4. Obtain the accumulated enthalpy for each temperature interval
Table 6
Composite curves consist of temperature (T) – enthalpy (H) profiles of heat availability in the process (the hot composite curve)
Module 8 – Introduction to Process Integration

42. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
5. Plot temperature on the Y axis versus accumulated enthalpy on the X axis
Hot Composite Curve
300
400
500
600
700
50000
100000
150000
200000
H (kW)
T (K)
Figure 22
653
623
563
543
463
453
423
373
323
313
303
Module 8 – Introduction to Process Integration

43. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
The construction of the Cold Composite Curve is similar to that of the Hot Composite Curve.
Table 7
Cold Composite Curve
250
300
350
400
450
500
550
600
650
700
50000
100000
150000
200000
250000
H (kW)
T(K)
Figure 23
663
Composite curves consist of temperature (T) – enthalpy (H) profiles of heat availability in the process (the hot composite curve) and heat demands in the process (the cold composite curve) together in a graphical representation.
423
293
Module 8 – Introduction to Process Integration

44. 2.2 Worked example 2: Thermal Pinch Analysis – Composite Curves
Application Composite Curves
100
200
300
400
500
600
700
50000
100000
150000
200000
250000
H (kW)
T (K)
Figure 24
QHmin
Minimum Cooling Requirement
QCmin
Minimum Heating Requirement
Internal Heat Recovery
DTmin= 40K
Cold composite curve
Hot composite curve
The Composite curves provide valuable information about maximum heat recovery (Qrecovery), minimum External heating (QH,min), minimum external cooling (QC,min) and localitation of the heat recovery Pinch for a given value of Dtmin.
The hot end and cold end overshoots indicate minimum hot utility requirement (QHmin) and minimum cold utility requirement (QCmin), of the process for the chosen DTmin.
The energy requirement for a process is supplied via process to process heat exchange and/or exchange with several utility levels (steam levels, refrigeration levels, hot oil circuit, furnace flue gas, etc.).
This representation reduces the entire process into one combined hot and cold stream
The heat recovery between the composite curves can be increased until we reach DTmin. Composite curves, just like individual streams can be shifted horizontally on the T-H diagram without causing changes to the process because H is a state function
This sets the minimum hot (QHmin) and cold (QCmin) utilities requirements for the entire process and the maximum possible process-process heat recovery
Module 8 – Introduction to Process Integration

45. 2.2 Worked example 2: Thermal Pinch Analysis
Summary of results
As seen in the previous slides, from the temperature-enthalpy plot, we can determine three useful pieces of information:
Amount of possible process-process heat recovery represented by the area between the two composites curves
Hot Utility requirement or target = kW
Cold Utility requirement or target = kW
Composite curves are excellent tools for learning the methods and understanding the overall energy situation, but minimum energy consumption and the heat recovery Pinch are more often obtained by numerical procedures. This method is called the Problem Table Algorithm. Typically, it is based on notions of Heat Cascade. Q5 Q6
Module 8 – Introduction to Process Integration

46. Tier II Outline
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
2.1 Worked example 1: Data-Driven Modeling – Multivariate Analysis
2.2 Worked example 2: Thermal Pinch Analysis
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Module 8 – Introduction to Process Integration

47. 2.3 Worked example 3: Integrated Process Control – Controllability Analysis
Module 8 – Introduction to Process Integration

48. 2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis – Reminder
Fundamentals
PROCESS RESILIENCY
Input
Variables
Disturbances
Control Loop
sensor
Input Variables
(manipulated)
Process
Internal interactions
Output
Variables
(controlled and
measured)
Uncertainties
PROCESS FLEXIBILITY
Figure 25
Module 8 – Introduction to Process Integration

49. Water: F1,C1 C, F Pulp: F2,C2 CC FC EFFECTS
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Water: F1,C1 CC FC
Pulp: F2,C2
C, F
Figure 26
INPUTS
(manipulated variables or
disturbances)
EFFECTS
OUTPUTS
(Best Selection by Controllability analysis)
Module 8 – Introduction to Process Integration

50. _ + u1 y1 + C1 F11 y1 y1sp + F21 F12 + y2sp y2 u2 y2 + C2 F22 + _
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis _ + u1 y1 + C1
F11 y1
y1sp +
F21
F12 +
y2sp y2 u2 y2 + C2
F22 + _
Figure 27
Module 8 – Introduction to Process Integration

51. Main Effect: Experiment 1: Step Change in u1 with all loops open ss
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Experiment 1: Step Change in u1 with all loops open ss
Du1
F11
F21
F12
F22 u1 u2 y1 y2 +
Figure 28
Main Effect:
Module 8 – Introduction to Process Integration

52. Total Effect: Experiment 2: Step Change in u1 with all loops closed
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Experiment 2: Step Change in u1 with all loops closed
Du1 ss
F11
F21
F12
F22 u1 u2 y1 y2 + C2 e2
y2sp _
Figure 29
Main Effect
Total Effect:
Interactive Effect
Module 8 – Introduction to Process Integration

53. Relative Gain and Relative Gain Array (RGA)
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Relative Gain and Relative Gain Array (RGA)
11 : measure of the extent of steady state interaction in using u1 to control y1, while using u2 to control y2
Main Effect (1st Experiment)
Total Effect (2nd Experiment)
Relative Gain
Relative Gain Array y1 u1 yi uj
Module 8 – Introduction to Process Integration

54. uj has no direct influence on yi
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Selection of Loops using RGA – How to select the configuration with minimum interaction
Implication
Recommendation
Loop i not subject to interactive action from other loops
uj has no direct influence on yi
- Loops are interacting
- below 0.5, interactive effect > main effect
- interactive effect acts in opposition to the main effect
- interactive effect not only acts in opposition to the main effect, it is also more dominant
Table 8
yi : Controlled variable
uj : Manipulated variable
Module 8 – Introduction to Process Integration

55. Other Controllability Indexes
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Other Controllability Indexes
Niederlinski (NI) : system stability index
Condition Number (CN) and Disturbance Condition Number (DCN) : sensibility measure
Relative Disturbance Gain (RDG) : index that gives an idea of the influence of internal interactions on the effect of disturbances
Others: Singular Value Decomposition (SVD)
Module 8 – Introduction to Process Integration

56. 2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Problem Statement
In this case-study, a process control engineer is asked to create a model of the thermomechanical pulping process to find the best process control selection and variable pairing for a plant that has not been built yet. Consider the simplified newsprint paper machine short loop configuration shown in figure 30. Variable pairing techniques will be applied as well as the use of controllability indexes. F5 F8 F7 F2 F6 F3 F4 F1
Figure 30
Module 8 – Introduction to Process Integration

57. 2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
controlled
Problem Statement
Stock Chest
Table 9
manipulated
disturbances
Pfin = % Fines retained
Module 8 – Introduction to Process Integration

58. 2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Pfin
Disturbances C
Ret
Fines
Controlled BR
Manipulated
Figure 31
Module 8 – Introduction to Process Integration

59. Process Gain Matrices and Steady-State Controllability
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Process Gain Matrices and Steady-State Controllability = + Gp Gd
Controlled
Manipulated
Disturbances ú û ù ê ë é -
603 . 1
615
000
001
010
608
566
006
005
003
039
013
058
941
053
947
020
047
014
004
009
016
038
011
942
RGA
 =
Module 8 – Introduction to Process Integration

60. 2.3 Worked example 3: Integrated Process Control and Design – Controllability AnalysisBR
Ret
Pfin
Figure 32
Module 8 – Introduction to Process Integration

61. Controllability Indexes (1)
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Controllability Indexes (1)
Niederlinski Index (NI)  Stability considerations
NI < 0. System will be unstable under closed-loop conditions
NI > 0. System is stabilizable (function of controller parameters)
Condition number (CN)  Sensitivity to model uncertainty
CN ~< 2. Multivariable effects of uncertainty are not likely to be serious
CN ~> 10. ILL-CONDITIONED process
NI=0.73
CN=713
Module 8 – Introduction to Process Integration

62. DCN for %finesfresh pulp = 4.6
2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Controllability Indexes (2)
Disturbance Condition Number (DCN)  Is the action taken by the manipulated variable large or small?
1≤ DCN ≤ CN
Relative Disturbance Gain (RDG)  Internal interaction among the loops is favorable or unfavorable to reject disturbances?
RDG ~<2 . Internal interactions reduce the effect of the disturbance
DCN for %Cfresh pulp = 9.2
DCN for %finesfresh pulp = 4.6
 It is harder to reject a sudden change in fresh pulp consistency
The effect of both disturbances, %C and %fines in FRESH PULP, is reduced by internal interactions. All RDG’s are ~<2
Module 8 – Introduction to Process Integration

63. 2.3 Worked example 3: Integrated Process Control and Design – Controllability Analysis
Conclusion
Control structure configuration: RGA results confirmed current implementation in newsprint mills
Internal interactions of the aforementioned configuration reduce the effect of disturbances on output variables
The process is ill-conditioned. Model uncertainty may be highly amplified
Resiliency Indexes, DCN and RDG, can be used to account for disturbance rejection in newsprint processes
Module 8 – Introduction to Process Integration

64. End of Tier II
This is the end of Tier II. At this point, we assume that you have done all the reading. You should have a pretty good idea of what Process Integration is as well as basic knowledge in regards to Multivariate Analysis, Thermal Pinch Analysis and Controllability Analysis. For further information on the tools presented in Tier II as well as on other Process Integration tools introduced in Tier I, please consult the references slides in Tiers I and II.
Prior to advancing to Tier III, a short multiple choice quiz will follow.
Module 8 – Introduction to Process Integration

65. QUIZ
Module 8 – Introduction to Process Integration

66. Tier II - Quiz
Question 1
What is Principal Components Analysis used for?
Understand relations between the variables of a system
Identify the components having an influence on one or many outputs
Predict certain outputs
Maximize the covariance of a set of variables
2 and 3
1 and 3 1 3
1 and 2
1,2 and 3
Module 8 – Introduction to Process Integration

67. Tier II - Quiz
Question 2
Associate each Multivariate Analysis output with the kind of information it provides the user with.
1. Residuals plot A. Shows all the original data points in a new set of coordinates or components
2. Score plot B. Shows the distance between each real observation in the initial dataset and the predicted value based on the model
3. Observed vs. Predicted C. Shows the accuracy of prediction
4. Loadings plot D. Shows how strongly each variable is associated with each new component
1B, 2A, 3C, 4D
1D, 2B, 3A, 4C
1C, 2D, 3A, 4B
1B, 2C, 3D, 4A
1A, 2D, 3B, 4C
1B, 2D, 3C, 4A
Module 8 – Introduction to Process Integration

68. Tier II - Quiz
Question 3
The lengths and orientations of the axes obtained with a PCA are given by the eigen values and eigen vectors of the correlation matrix. Let's say the length and breadth variables have a lower correlation coefficient than in the example given in slide 13 and that we obtain the eigen values shown in the figure below. If we discard the second axis, what percentage of the original information would we lose?
12,5%
75%
25%
62,5%
37,5% 0%
Module 8 – Introduction to Process Integration

69. Tier II - Quiz
Question 4
In the context of a Thermal Pinch Analysis, what is a hot stream?
1. A process stream that needs to be heated
2. A process stream with a very high temperature
3. A process stream that is used to generate steam
4. A process stream that needs to be cooled 1 3 2 4
Module 8 – Introduction to Process Integration

70. Tier II - Quiz
Question 5
A Thermal Pinch Analysis has been performed at a plant and the DTmin was set at 40ºC. If another plant was to be built with a lower DTmin, how would the corresponding energy costs be in comparison to the first plant?
Higher
Lower
Would stay the same
Module 8 – Introduction to Process Integration

71. Question 6 Tier II - Quiz Which of the following statements are true?
Minimum energy consumption and the heat recovery Pinch are more often obtained by Composite Curves
Composite curves, just like individual streams, can be shifted horizontally on the T-H diagram without causing changes to the process
Heat can sometimes be transferred across the Pinch
With the help of Tmin and the thermal data, Pinch Analysis provides a target for the minimum energy consumption
2 and 3
2 and 4
1 and 3
3 and 4
1 and 2
All of the above
Module 8 – Introduction to Process Integration

72. Tier II - Quiz
Question 7
Associate each controllability tool or index with the kind of information it provides the user with.
1. Niederlinski Index A. Shows the importance of interactions in a system
2. Relative Disturbance Gain B. Estimates the sensitivity of the problem's answer to error in the input
3. Condition Number C. Includes disturbances in interactions analysis
4. Relative Gain Array D. Discusses the stability of a closed-loop control configuration
1B, 2A, 3C, 4D
1D, 2B, 3A, 4C
1C, 2D, 3A, 4B
1B, 2C, 3D, 4A
1A, 2D, 3B, 4C
1D, 2C, 3B, 4A
Module 8 – Introduction to Process Integration

73. Tier II - Quiz
Question 8
In the Relative Gain Array shown in slide 54, what do the values and for the pairing of F40 and C34, and Pfin and Ret, tell you?
1. There is no interaction with other control loops
2. The interactive effect is more important than the main effect
3. The manipulated input has no effect on output
4. The interactions from the other loops are opposite in direction but smaller in magnitude than the effect of the main loop
5. Pairing is recommended
6. Pairing is not recommended
1 and 5
4 and 5
3 and 6
2 and 5
2 and 6
4 and 6
Module 8 – Introduction to Process Integration

74. Question 9 Tier II - Quiz Which of the following statements are false?
Feedforward control compensates for immeasurable disturbances
Feedback control compensates for measurable disturbances
Resiliency is the degree to which a processing system can meet its design objectives despite uncertainties in its design parameters
Flexibility is the degree to which a processing system can meet its design objectives despite external disturbances
2 and 3
2 and 4
1 and 3
3 and 4
1 and 2
All of the above
Module 8 – Introduction to Process Integration

75. Answers Tier II - Quiz Question 1 1 and 2 Question 2 1B, 2A, 3C, 4D
Question 5 Lower
Question 6 2 and 4
Question 7 1D, 2C, 3B, 4A
Question 8 4 and 5
Question 9 All of the above
Module 8 – Introduction to Process Integration