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

How to use this presentation This presentation contains internal links to other slides and external links to websites: Example of a link (text underlined in grey): link to a slide in the presentation or to a website : link to the tier table of contents : link to the last slide viewed : when the user has gone over the whole presentation, some multiple choice questions are given at the end of this tier. This icon takes the user back to the question statement if a wrong answer has been given Module 8 – Introduction to Process Integration

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

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

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

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

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

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

Tier II Worked Examples Module 8 – Introduction to Process Integration

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

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

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

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

(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

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

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

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

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 + 0.25 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 75000 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

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 2600.00 200.00 520.00 20.00 150.00 67600.00 170.00 0.00147 (2)Cold 390.00 124800.00 (3)Hot 253.00 657.80 100.00 -32890.00 (4)Hot 23.00 59.80 180.00 30.00 -8970.00 (5)Hot 44.00 114.40 270.00 40.00 -26312.00 (6)Hot 148.00 384.80 290.00 190.00 -38480.00 (7)Hot 13.00 33.80 350.00 -10816.00 (8)Hot 56.00 145.60 380.00 50.00 -48048.00 * Fouling Factor included Table 1 Module 8 – Introduction to Process Integration

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

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

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

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

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

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

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

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 = 57668 kW Cold Utility requirement or target = 30784 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

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

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

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

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

_ + 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

QUIZ Module 8 – Introduction to Process Integration

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

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

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

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

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

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

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

Tier II - Quiz Question 8 In the Relative Gain Array shown in slide 54, what do the values 1.566 and 1.603 for the pairing of F40 and C34, and Pfin and Ret, 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

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

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