cpd lecture unit 5

8/3/2019 CPD Lecture Unit 5

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1Module 8: introduction to process integration

Outline

.1 1 Introduction and definition

.of Process integration

.1 2 Overview of P rocess

Integrationtools


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1.1 Introduction and definition ofProcess integration.


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A Very Brief History of Process Integration

Linnhoff started the area of pinch (bottleneckidentification) at UMIST in the 60s, focusing on

the area of Heat IntegrationUMIST Dept of Process Integration was createdin 1984, shortly after the consulting firmLinnhoff-March Inc. was formed

PI is not really easy to define


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Definition of process integration

The International Energy Agency (IEA) definition ofprocess integration

"Systematic and General Methods for DesigningIntegrated Production Systems, ranging from

Individual Processes to Total Sites, with specialemphasis on the Efficient Use of Energy and

reducing Environmental Effects"

From an Expert Meetingin Berlin, October 1993


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Definition of process integrationLater, this definition was somewhat broadened and more explicitlystated in the description of its role in the technical sector by thisImplementing Agreement:"Process Integration is the common term used for the application of methodologiesdeveloped for System-oriented and Integrated approaches to industrial process plantdesign for both new and retrofit applications.Such methodologies can be mathematical, thermodynamic and economic models,methods and techniques. Examples of these methods include: Artificial Intelligence

(AI), Hierarchical Analysis, Pinch Analysis and Mathematical Programming. Process Integration refers to Optimal Design; examples of aspects are: capitalinvestment,energy efficiency, emissions, operability, flexibility, controllability, safetyand yields. Process Integration also refers to some aspects of operation andmaintenance".

Later, based on input from the Swiss National Team, we have found that SustainableDevelopmentshould be included in our definition of Process Integration.

son, International Energy Agency (IEA) Implementing Agreement, A worldwide catalogue on Proson, International Energy Agency (IEA) Implementing Agreement, A worldwide catalogue on Pro

2001).2001).


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El-Halwagi, M. M.,Pollution Prevention through Process Integration: System. Academic Press, 1997.

A Chemical Process is an integrated system ofinterconnected units and streams, and it should be treated

as such. Process Integration is a holistic approach toprocess design, retrofitting, and operation which

emphasizes the unity of the process. In light of the strong

interaction among process units, streams, and objectives,process integration offers a unique framework forfundamentally understanding the global insights of the

process, methodically determining its attainableperformance targets, and systematically making decisionsleading to the realization of these targets. There are threekey components in any comprehensive process integration

methodology: synthesis, analysis, and optimization.
http://www.apcatalog.com/cgi-bin/AP?ISBN=0122368452&LOCATION=US&FORM=FORM2http://www.apcatalog.com/cgi-bin/AP?ISBN=0122368452&LOCATION=US&FORM=FORM2


7/1427Module 8: introduction to process integration


Nick Hallale, Aspentech CEP July 2001 BurningBright Trends in Process Integration

Process Integration is more than just pinch technologyand heat exchanger networks. Today, it has far wider

scope and touches every area of process design.Switched-on industries are making more money from theirraw materials and capital assets while becoming cleaner

and more sustainable




So What Happened?In addition to thermodynamics (the foundation of pinch),

other techniques are being drawn upon for holistic

analysis, in particular:Process modeling

Process statistics

Process optimization

Process economicsProcess control

Process design



Modern Process Integration context

Process integration is primarily regarded asprocess design (both new and retrofits design),but also involve planning and operation. Themethods and systems are applied to continuous,

semi-batch, and batch process.

Business objectives currently driving thedevelopment of PI:

a)Emphasis is on retrofit projects in the neweconomy driven by Return on CapitalEmployed (ROCE)

b)PI is Finding value in data qualityc) Corporations wish to make more knowledgeable

decisions:

1.For operations,2.Durin the desi n rocess.




Possible Objectives:

Lower capital cost design, for the samedesign objective

Incremental production increase, from thesame asset base

Marginally-reduced unit production costsBetter energy/environmental performance,without compromising competitive position

ReducingReducingCOSTSCOSTS

POLLUTIONPOLLUTION

ENERGYENERGY

IncreasingIncreasingTHROUGHPTHROUGHP

UTUT

YIELDYIELD

PROFITPROFIT




Among the design activities that these systems and methodsaddress today are:

ProcessModeling and Simulation, and Validations of theresults in order to have information accurate and reliable of theprocess.

Minimize Total Annual Cost by optimal Trade-off betweenEnergy, Equipment and Raw Material

Within this trade-off: minimize Energy, improve Raw Materialusage and minimize Capital Cost

Increase Production Volume by Debottlenecking

Reduce Operating Problems by correct (rather than maximum)use of Process Integration

Increase Plant Controllability and Flexibility

Minimize undesirable Emissions

Add to the joint Efforts in the Process Industries and Society for

a Sustainable Development.



Summary of Process Integration elements

Process knowledgeProcess knowled ge

Process DataProcess Data

PI syst em sPI syst em s

& Tools& Tools

Improving overall plantImproving overall plant

facilit ies energy efficiencyfacilit ies energy efficiency

and productivity requires aand productivity requires a

multi-pronged analysismulti-pronged analysis

involving a variety ofinvolving a variety of

technical skills andtechnical skills and

expe rt ise, including:exper tise, including:

Knowledge of bothKnowledge of both

conventional industryconventional industrypractice and state-of-practice and state-of-

the-art technologiesthe-art technologies

available commerciallyavailable commercially

Familiarity withFamiliarity withindustry issues andindustry issues and

trendstrendsMethodology forMethodology fordetermining correctdetermining correct

ma rginal costs.ma rginal costs.

Procedures and toolsProcedures and toolsfor Energy, Water, andfor Energy, Water, and

raw mater ia lraw mater ia lConserva t ion a uditsConserva tion au dits




In conclusion, process integration has evolved from Heatrecovery methodology in the 80s to become what anumber of leading industrial companies and research

groups in the 20th century regarding the holistic analysis ofprocesses, involving the following elements:

Process data lots of it

Systems and tools typically computer-oriented

Process engineering principles - in-depth processsector knowledge

Targeting - Identification of ideal unit constraints forthe overall process



Outline

.1 1 In tro d u ctio n a n d d e fin itio n o f P ro ce ss

.in te g ra tio n

. .1 2 Overview of Process Integration tools

. - - 1 3 An around the world tour of PI practitioners.focuses of expertise

.1 1 Introduction and definition of Process.1 1 Introduction and definition of Process.integration.integration

.1 2 Overview of P.1 2 Overview of P rocessrocess IIntegrationntegrationtoolstools

.1 3 An.1 3 An - - around the world tour of PI practitioner- - around the world tour of PI practitionerss

focuses of expertisefocuses of expertise



1.2 Overview of ProcessIntegration Tools



1.2 Overview of Process Integration Tools

ProcessProcess

SimulationSimulation

Steady stateSteady state

DynamicDynamic

PinchPinch

AnalysisAnalysis

at ion by Mathematicalat ion by Mathematical

mingming

Stochastic Searchtochastic Search

Methodsethods

Life CycleLife Cycle

AnalysisAnalysisat aat a --Driven ProcessDriven Process

odelingodeling

Business M ode l And Supply ChainBusiness M ode l And Supply ChainModeling.Modeling.

grate Process Design andgrate Process Design and

troltrol

Real Tim eRea l Tim e

OptimizationOptimization

ProcessProcess

DataData



1.2 Overview of Process Integration Tools

ProcessProcess


Steady stateSteady state

DynamicDynamic

PinchPinch

AnalysisAnalysis

at ion by Mathematicalat ion by Mathematical

mingming

Stochastic Searchtochastic Search

Methodsethods

Life CycleLife Cycle

AnalysisAnalysisat aat a --Driven ProcessDriven Process

odelingodeling

Business ModelBusiness ModelSupply ChainSupply Chain

ManagmentManagment ..

grate Process Design andgrate Process Design and

troltrol

Real Tim eRea l Tim e


ProcessProcess

DataData

Click hereClick here












Process Simulation



Process Simulation

Process modeling

W hat is a m odel?W hat is a m odel?

A model is an abstraction of a process operation used to build,change, improve, control, and answer questions about that process

Process modeling is aProcess modeling is ann activity using models to solve problems inactivity using models to solve problems inthe areas of the process design, control, optimization, hazardsthe areas of the process design, control, optimization, hazards

analysis, operation training, risk assessment, and softwareanalysis, operation training, risk assessment, and softwareengineering for computerengineering for computeraided engineering environmentsaided engineering environments..



Process Simulation

Tools of process modeling

Process modeling isProcess modeling is anan understanding ofunderstanding ofthethe processprocessphenomena anphenomena andd transformtransform ing thising this understanding into aunderstanding into amodel.model.

Process ModelingProcess Modeling

SystemSystemTheoryTheory

PhysicsPhysicsandand

ChemistryChemistry

ApplicatioApplicationn

ComputeComputess

ScienceScience

StatisticStatisticss

NumericNumericalal

MethodsMethods


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Process Simulation

What is a model used for?

Nilsson (1995) presents a generalized model, which, asdepicted in the figure below, can be used for differentbasic problem formulations: Simulation, Identification,estimation and design.

MODELMODEL

InputInput OutputOutput

II OO

If the model is known, we have two uses for our model:If the model is known, we have two uses for our model:

Direct: Input is applied onDirect: Input is applied on thethe model, output is studied (Simulation)model, output is studied (Simulation)

Inverse: Output is applied onInverse: Output is applied on thethe model, Input is studiedmodel, Input is studied


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Process Simulation

If both Input and Output are Known, wehave three formulations (Juha Yaako, 1998):

Identification:We can find the structure and parametersin the model.

Estimation: If the internal structure of model is known,we can find the internal states in model.

Design: If the structure and internal states of model areknown, we can study the parameters in model.


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Process Simulation

Demands set to models:

Accuracy Requirements placed on quantitative andqualitative models.

Validity Consideration of the model constraints. Atypical model process is non-linear, nevertheless, non-linear models are linearized when possible, becausethey are easier to use and guarantee global solutions.

Complexity Models can be simple (usuallymacroscopic) or detailed (usually microscopic). The

detail level of the phenomena should be considered.Computational The models should currently regardcomputational orientation.

Robustness

.


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Process Simulation

The figure below shows a comparison of input and outputfor a process and its model. Note that always n > m and k> t.

PROCESPROCESSS

MODELMODEL



XX11, ..., X, ..., Xnn

XX11, ..., X, ..., Xmm

YY11, ..., Y, ..., Ykk

YY11, ..., Y, ..., Ytt

In the process industrIn the process industryy wewe findfind, two levels of models; Plant models,, two levels of models; Plant models,and models of unit operations such as reactor, columns, pumps,and models of unit operations such as reactor, columns, pumps,heat exchangers, tanks, etc.heat exchangers, tanks, etc.

A model does not include everA model does not include ever

n>m, and k>t.n>m, and k>t.

All models are wrong,

Some models are useful

George Box, PhD

University of Wisconsin


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Process Simulation

Types of models:Intuitive: the immediate understanding of something without

conscious reasoning or study. This are seldom used.Verbal: If an intuitive model can be expressed in words, it becomes averbal model. First step of model development.

Causal: as the name implies, these model are about the causalrelations of the processes.

Qualitative: These models are a step up in model sophistication fromcausal models.

Quantitative: Mathematical models are an example of quantitativemodels. These models can be used for (nearly) every application inprocess engineering. The problem is that these models are notdocumented or can be too costly to construct when there is not

enough knowledge (physical and chemical phenomena are poorlyunderstood). Sometimes the application encountered does notrequire such model sophistication.

From first PrinciplesFrom first Principles From Stochastic knowledgeFrom Stochastic knowledge


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Process Simulation

Simulation: what if experimentation with a

modelSimulation involves performing a series of experiments

with a process model.

MODELMODEL


XX11, ..., X, ..., Xmm YY11, ..., Y, ..., Ytt

MODELMODEL(t)(t)


XX(t)(t)11, ..., X, ..., X(t)(t)mm YY(t)(t)11, ..., Y, ..., Y(t)(t)tt

Steady StateSteady State

SnapshotAlgebraic equations

DynamicDynamic

Movie (time functions)

Time is an explicit variable differentialequations

Certain phenomena require dynamicsimulation (e.g. control strategies, real time

descition).


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Process Simulation

Illustration:

Staedy state simulation of a storage tankStaedy state simulation of a storage tank

Hi-LimitHi-Limit

Lo-LimitLo-Limit

0=In - Out + Production - Consumption0=In - Out + Production - Consumption Acumulation = In - Out + Production - ConsumptionAcumulation = In - Out + Production - Consumption

Dynamic simulation of a storage tankDynamic simulation of a storage tank

t = timet = time

LevelLevel

00021

+= mm ( ) 0021 += tmmdt

dM

MM=f(t)=f(t)M=constantM=constant

mm11

mm22 mm22(t)(t)

mm11

mm22

tt

mm22

tt

Simulation unitSimulation unit


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Process Simulation

The steady-state simulation does not solve time-dependentequations. The Subroutines simulate the steady-state operation

of the process units ( operation subroutines) and estimate thesizes and cost the process units ( cost subroutines).

A simulation flowsheet, on the other hand, is a collection ofsimulation units(e.g., reactor, distillation columns, splitter,mixer, etc.), to represent computer programs (subroutines) to

simulate the process units and areas to represent the flow ofinformation among the simulation units represented by arrows.


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Process Simulation

To convert from a process flowsheet to a simulation flowsheet,one replaces the process unit with simulation units (Models).For each simulation unit, one assigns a subroutine (or block)

to solve its equations. Each of the simulators has a extensivelist of subroutines to model and solve the equations for manyprocess units.

The Dynamic simulation enables the process engineer to studythe dynamic response of potential process design or theexistent Process to typical disturbances and changes inoperating conditions, as well as, strategies for the start up and

shut down of the potential process design or existing process.


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Process Simulation

Differences between Steady State and

Dynamic Simulation

Steady-State SimulationSteady-State Simulation Dynamic SimulationDynamic Simulation

Snapshot of a unit operation or plantSnapshot of a unit operation or plant Mimic of plant operationMimic of plant operation

Balance at equilibrium conditionBalance at equilibrium condition Time dependent resultsTime dependent resultsEquilibrium results for all unitEquilibrium results for all unitoperationsoperations

It doesnt assume equilibriumIt doesnt assume equilibriumconditions for all unitsconditions for all units

Equipment sizesEquipment sizes in general notin general notneededneeded

Equipment sizes neededEquipment sizes needed

Amount of information required:Amount of information required:

small to mediumsmall to mediumAmount of information required:Amount of information required:

medium to largemedium to large


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Process Simulation

Solution Strategies

The Sequential Modular StrategyThe Sequential Modular Strategyflowsheet broken into unit operat ions (modules)flowsheet broken into unit operat ions (modules)each module is calculated in sequenceeach module is calculated in sequenceproblems with recycle loopsproblems with recycle loops

The Simultaneous Modular St rategyThe Simultaneous Modular St rategydevelops a linear m odel for each unitdevelops a linear model for each unitmodules with local recycle are solvedmodules with local recycle are solved

simultaneouslysimultaneouslyflowsheet m odules are solved sequent iallyflowsheet m odules are solved sequent ially

The Simultaneous Equation-solving StrategyThe Simultaneous Equation-solving Strategy describe ent ire flowsheet w ith a set of equat ionsdescribe ent ire flowsheet w ith a set of equat ionsall equations are sorted and solved togetherall equations are sorted and solved t ogetherhard to solve very large equations system shard to solve very large equations system s


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Process Simulation

Why steady-state simulation is important:

Better understanding of the process

Consistent set of typical plant/facility dataObjective comparative evaluation of options for ReturnOn Investment (ROI) etc.

Identification of bottlenecks, instabilities etc.

Perform many experiments cheaply once the model is

builtAvoid implementing ineffective solutions


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Process Simulation

Why dynamic simulation is important:

ADVANCEMENT OF PLANT OPERATIONS/OPERATIONAL SUPPORT / OPTIMIZATION

Predictive simulationOptimal conditions

OPTIMIZATION of

plant operationsOnlinesystem

EDUCATION, TRAININGCONTROL SYSTEM

Operation training simulatorDCS control logic

Plant diagnosis system

Quasi-onlinesystem

PROCESS DESIGN / ANALYSISExamination of operations

Control strategiesAdvanced control systems

Batch scheduling

Off-linesystem


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Challenges of simulation

Simulation is not the highest priority in the plantfacilities

Production or quality issues take precedence

Hard to get plant facilities resources for simulationUp front time required before results areavailable

Model must be calibrated, and results validated, beforethey can be trusted

At odds with quarterly balance sheet cultureMay need to structure project to get some results outearly


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Data Reconciliation


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Data Reconciliation

Typical Objectives of Data Treatment.

Provide reliable information and knowledge of completedata for validation of process simulation and analysis

Yield monitoring and accountingPlant facilities management and decision-making

Optimization and controlPerform instrument maintenance

Instrument monitoringMalfunction detectioncalibration

Detect operating problemsProcess leaks or product loss

Estimate unmeasured valuesReduce random and gross errors in measurementsDetect steady states


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BusiBusinesnesss

manmanageage

menmentt

INFORM

ATION

INFORM

ATION

Data treatment isData treatment iscritical forcritical for

Process simulationProcess simulationControl and optimizationControl and optimizationManagement planningManagement planning

Data Reconciliation


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Manualdata

On-linedata

Labdata

DataTreatment

ProductionProduction

Equipment performanceEquipment performance

ModelingModeling and Simulationand Simulation


Instrumentation designInstrumentation design

Plant shutdownPlant shutdown

Instrument maintenanceInstrument maintenance

Management planningManagement planning

Data Reconciliation

Overview


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Data Reconciliation

Typical Problems With ProcessMeasurements

Measurements inherently corrupted by errors:measurement faults

errors during processing and transmission of themeasured signal

Random errorsCaused by random or temporal events

Inconsistency (Gross) errorsCaused by nonrandom events: instrumentmiscalibration or malfunction, process leaks

Non-measurementsSampling restriction, measuring technique,instrument failure


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Module : introduction to process integration

Data Reconciliation

Random errors

Features

High frequency

Unrepeatable: neither magnitude nor sign can be

predicted with certitude

Sources

Power supply fluctuation

Signal conversion noise

Changes in ambient condition


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Data Reconciliation

Inconsistency (Gross error)

FeaturesLow frequencyPredictable: certain sign and magnitude

SourcesCaused by nonrandom eventsInstrument related

Miscalibration or malfunction

Wear or corrosion of the sensors

Process related Process leaks Solid deposits


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Illustration Of Random & Gross Errors:

Grosserror

Random

erro

rs

na

bnormality

t

F

Reliable valueReliable value

Data Reconciliation


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Data Reconciliation

Solutions To Problems

Random errors: Data processing

Based on successive measurement of each individualvariable: Temporal redundancy

Traditional filtering techniquesWavelet Transform techniques

Inconsistency: Data reconciliation

Based on plant structure: Spatial redundancy

Subject to conservation laws

Unmeasured data

Data reconciliation


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Data Reconciliation

Reconciling

Grosserro

rs

t

F

Processing

randomerrors

M easurem ent Problem Handling:M easurement Problem Ha ndling:


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Data Reconciliation

Data Treatment Typical Strategy

1. Establish Plant facilities operating regimes

2. Data processing

Remove random noise

Detect and correct abnormalities3. Steady state detection

Identify steady-state duration

Select data set

4. Data reconciliation

Detect gross errors

Correct inconsistencies

Calculate unmeasured parameters

D t R ili ti


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Data Reconciliation

Data processing

Steady state detection

Variables classification

Gross error detection

Data reconciliation

Applications

Process data

METHODOLOGY EMPLOYEDMETHODOLOGY EMPLOYED

D t R ili ti


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Data Reconciliation

THERMODYNAMIC

PROPERTIES

STATISTICAL

PRINCIPLES

ACCURATE and RELIABLE INFORMATION

THERMODYNAMIC

PROPERTIES

STATISTICAL

PRINCIPLES


THERMODYNAMIC

PROPERTIES

STATISTICAL

PRINCIPLES


THERMODYNAMIC

PROPERTIES

STATISTICAL

PRINCIPLES


1 + 1 = 3 !!!

THERMODYNAMICPROPERTIES

STATISTICALPRINCIPLES

1.3 + 1.3 = 2.6


1 + 1 = 3 !!!

THERMODYNAMICPROPERTIES

STATISTICALPRINCIPLES

1.3 + 1.3 = 2.6


What is data reconciliation?

Data reconciliation is the validation of process data usingknowledge of plant structure and the plant measurementsystem


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Data Reconciliation

Objectives of Data Reconciliation

Optimally adjust measured values within given processconstraints

mass, heat, component balancesImprove consistency of data to calibrate and validateprocess simulation

Estimate unmeasured process values

Obtain values not practical to measure directly

Substitute calculated values for failed instrument

D t R ili ti


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Data Reconciliation

Possible Benefits:

More accurate and reliable simulation results

More reliable data for process analysis and decisionmaking by mill manager

Instrument maintenance and loss detection:

e.g. US$3.5MM annually in a refinery by decreasing loss by0.5% of 100K BPD

Improve measurement layout

Decrease number of routine analysis

Improve advanced process control

Clear picture of plant operating conditionEarly detections of problems

Quality at process level

Work Closer to specifications.


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Data Reconciliation

Data Reconciliation Problem of Process UnderDifferent Status

Steady-state data reconciliation

based on steady-state model

Using spatial redundancy

Dynamic data reconciliationbased on dynamic models

Using both spatial & temporal redundancy


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Data reconciliation (DR)

DR Problem Of Process Under Different Status(Contd.)

General expression of conservation law:

input- output + generation- consumption-accumulation= 0

Steady state case:

no accumulation of any measurementConstraints are expressed algebraically

Dynamic process:

Accumulation cannot be neglected

Constraints are differential equations

Data Reconciliation


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Data Reconciliation

Data Reconciliation of Different Constraints

Linear data reconciliation

Only mass balance is considered

flows are reconciled

Bilinear data reconciliation

Component balance imposed as well as energybalance

flows & composition measurements are reconciled

Nonlinear data reconciliationMass/energy/component balances are included

Flow rate, composition, temperature or pressuremeasurements are reconciled

Data Reconciliation


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DATA RECONCILIATIONDATA RECONCILIATION

Data Reconciliation

Measurement Errors?Measurement Errors? Gross Error DetectionGross Error Detection

Unclosed Balances?Unclosed Balances? Closed BalancesClosed Balances

Unidentified Losses?Unidentified Losses? Identified LossesIdentified Losses

Efficiency?Efficiency? Monitored EfficiencyMonitored Efficiency

Performance?Performance? Quantified PerformanceQuantified Performance


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Pinch Analysis.


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Pinch AnalysisPinch Analysis

The prime objective of Pinch Analysis is to achieve financialsavings in the process industries by optimizing the ways in whichprocess utilities (particularly energy, mass, water, and hydrogen),are applied for a wide variety of purposes.

The Heat Recovery Pinch(Thermal Pinch Analysis now) was

discovered indepently by Hohmann (71), Umeda et al. (78-79)and Linnhoff et al. (78-79).

Pinch Analysis does this by making an inventory of all producersand consumers of these utilities and then systematicallydesigning an optimal scheme of utility exchange between these

producers and consumers. Energy, Mass, and water re-use are atthe heart of Pinch Analysis activities.

With the application of Pinch Analysis, savings can be achieved inboth capital investment and operating cost. Emissions can beminimized and throughput maximized.

W hat is Pinch Analysis?W hat is Pinch Analysis?



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TheThe PPinch analysis is a technique t o design:inch analysis is a technique to design:

Recovery NetworkRecovery Networks (Heat and Mass)s (Heat and Mass)

Utility Networks (so called Total site Analysis)Utility Networks (so called Total site Analysis)

The basis of Pinch Analysis:The basis of Pinch Analysis:

The use of thermodynamic principles (first and secondThe use of thermodynamic principles (first and secondlaw).law).

The use heuristics (insight), about design and economy.The use heuristics (insight), about design and economy.The Pinch Analysis makes extensive use of various graphicalThe Pinch Analysis makes extensive use of various graphicalrepresentationsrepresentations

FEATURESFEATURES


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The Pinch Analysis prov ides insight sThe Pinch Analysis provides insights about t he processabout t he process..

In Pinch analysis, the design engineering controls theIn Pinch analysis, the design engineering controls thedesign proceduredesign procedure (( interactive m ethodinteractive m ethod)) ..

The pinch Analysis integrateThe p inch Analysis integratess economic parameterseconomic paramet ers



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The Four phasesThe Four phases of pinch analysis in the design of recoveryof pinch analysis in the design of recoveryprocess:process:

TargetingTargeting

DesignDesign


ProcessProcess


Data Ext ractionData Ext raction

Which involves collecting data for the process and theWhich involves collecting data for the process and the

Which establishes figures for the best performance inWhich establishes figures for the best performance in

Where an initial Heat Exchanger Network is established by heuristics tools alWhere an initial Heat Exchanger Network is established by heuristics tools all

Where an initial design is simplified and improvedWhere an initial design is simplified and improved


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Heat Exchanger Network(HEN)

HEN design is the classical domain of Pinch Analysis. By

making proper use of temperature driving forcesavailable between process steams, the optimum heatexchanger network can be designed, taking intoaccount constraints of equipment location, materials ofconstruction, safety, control, and operating flexibility.

This then sets the hot and cold utility demand profile ofthe plant.

When used correctly, Pinch Analysis yields optimum HENdesigns that one would have been unlikely to obtain byexperience and intuition alone.


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Combined Heat and Power (CHP)

CHP is the terminology used to describe plant energyutilities, boilers, steam turbines, gas turbines, heat

pumps, etc. Traditionally, these have been referred toas "plant utilities", without distinguishing them fromother plant utilities such as cooling water andwastewater treatment.

The CHP system supplies the hot utility and power

requirements of the process. Pinch Analysis offers aconvenient way to guarantee the optimum design,which can include the use of cogeneration or three-generation (use of hot utility to produce cold utility andpower for things like refrigeration).


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Possible Benefits:

One of the main advantages of Pinch Analysis overconventional design methods is the ability to set a

target energy consumption for an individual process orfor an entire production site before to design theprocesses. The energy target is the minimumtheoretical energy demand for the plant or site.

Pinch Analysis will therefore quickly identify whereenergy savings are likely to be found.

Reduction of emissions

Pinch Analysis enable to the engineer with tool to findthe best way to change the process, if the process letit.


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In addition, Pinch Analysis allow you to:

Update or Development of Process Flow Diagrams

Identify the bottleneck in the process

Departmental Simulations

Full Plant Facilities SimulationDetermine Minimal Heating (Steam) and CoolingRequirements

Determine Cogeneration and Three-generationOpportunities

Determine Projects with Cost Estimates to AchieveEnergy Savings

Evaluation of New Equipment Configurations for the MostEconomical Installation

Pinch Replaces the Old Energy Studies with a Live Study

that Can Be Easily Updated Using Simulation


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Optimization by MathematicalProgramming


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Optimization by Mathematical Programming:introduction

A Mathematical Model of a system is a set ofmathematical relationships (e.g., equalities, inequalities,logical conditions) which represent an abstraction of

the real world system under consideration.A Mathematical Model can be developed using:

Fundamental approaches Accepted theories of sciencesare used to derive the equations (e.g., ThermodynamicsLaws).

Empirical Methods Input-output data are employed intandem with statistical analysis principles so as togenerate empirical or Black box models.

Methods Based on analogy Analogy is employed indetermining the essential features of the system of interestby studying a similar, well understood system.

Optimization by Mathematical Programming:


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Optimization by Mathematical Programming:introduction

A mathematical Model of a system consists of four key elements:1. Variables The variables can take different values and their

specifications define different states of the systems.1. Continuous,2. Integer,3. Mixed set of continuous and integer.

2. Parameters The parameters are fixed to one or multiplespecific values, and each fixation defines a different model.

3. Constraints the constraints are fixed quantities by the modelstatement

4. Mathematical Relationships The mathematical model relationscan be classified as:

1. Equalities usually composed of mass balance, energy balance,equilibrium relations, physical property calculations, andengineering design relations which describe the physicalphenomena of the system.

2. Inequalities consist of allowable operating regimes,specifications on qualities, feasibility of heat and masstransfer, performance requirements, and bound onavailabilities and demands.

3. Logical conditions provide the connection between thecontinuous and integer variables.

The mathematical relations can be algebraic, differential, or a mixed set


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Optimization by Mathematical Programming

What is Optimization?A optimization problem is a mathematical model which inaddition to the before mentioned elements contains one or

more performance criteria.The performance criteria is denoted as an objective function. It

can be minimization of cost, the maximization or profit or yieldof a process for instance.

If we have multiple performance criteria then the problem isclassified as multi-objective optimization problem.

A well defined opt im ization problem features a number of variablesA well defined opt im ization problem features a number of variablesgreater than the number of equality constraints, which implies thatgreater than the number of equality constraints, which implies thatthere exist degrees of freedom upon which we opt im ize.there exist degrees of freedom upon which we opt im ize.


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The typical mathematical model structure for an optimiztionproblem takes the following form:

integer

0),(

0),(..

),(min,

Yy

Xx

yxg

yxhts

yxf

n

yx

=

Where x is a vector of n cont inuous variables, y is a vector ofWhere x is a vector of n cont inuous variables, y is a vector ofinteger variables, h(x,y)= 0 are m equality constraints, g(x,y) 0integer variables, h(x,y)= 0 are m equality constraints, g(x,y) 0are p inequality constraints, and f(x,y) is the objective function.are p inequality constraints, and f(x,y) is the objective function.


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Classes of Optimization Problems (OP)

If the objective function and constraints are linear without the useof integer variables, then OP becomes a linear programming (LP)problem.

If there exist nonlinear terms in the objective function and/orconstraints without the use of integer varialbes, the OP becomes anonlinear programming (NLP) problem.

If integer variables are used, they participate linearly and separtlyfrom the continuous variables, and the objective function andconstraints are linear, then OP becomes a mixed-integer linearprogramming (MILP) problem.If integer variables are used, and there exist nonlinear terms in theobjective function and/or constraints, then the OP becomes a mixed-integer nonlinear programming (MINLP) problem.

Whenever possible, linear programs (LP or MILP) are used because

they guarantee global solutions.MINLP roblems features man a lications in en ineerin .


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Applications:Process Synthesis

Heat Exchanger Networks

Distillation Sequencing

Mass Exchanger Networks

Reactor-based Systems

Utility SystemsTotal Process Systems

Design, Scheduling, and Planning of ProcessDesign and Retrofit of Multiproduct Plants

Design and Scheduling of Multiproduct Plants

Interaction of Design and ControlMolecular Product Design

Facility Location and allocation

Facility Planning and Scheduling

Topology of Transport Networks


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70Module : introduction to process integration

Stochastic SearchMethods


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Stochastic Search Methods

Why stochastic Search Methods

All of the model formulations that you have encountered thus farin the Optimization have assumed that the data for the givenproblem are known accurately. However, for many actualproblems, the problem data cannot be known accurately for avariety of reasons. The first reason is due to simple measurementerror. The second and more fundamental reason is that somedata represent information about the future (e.g., product

demand or price for a future time period) and simply cannot beknown with certainty.


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There are probabilistic algorithms, such as:Simulated annealing (SA)

Genetic Algorithms (GAs)

Tabu search

These are suitable for problems that deal with uncertainty. These computer algorithms or procedure models do notguarantee global optimally but are successful and widely knownto come very close to the global optimal solution (if not to theglobal optimal).

GA has the capability of collectively searching for multiple

optimal solutions for the same best cost.Such information could be very useful to a designer, because oneconfiguration could be much easier to build than another.

SA takes one solution and efficiently moves it around in thesearch space, avoiding local optima.


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What is GAs?GAs simulate the survival of the fittest among individuals overconsecutive generation for solving a problem. Each individualrepresents a point in a search space and a possible solution. The

individuals in the population are then made to go through aprocess of evolution.GAs are based on an analogy with the genetic structure andbehaviour of chromosomes within a population of individualsusing the following foundations:

Individuals in a population compete for resources and mates.

Those individuals most successful in each 'competition' will producemore offspring than those individuals that perform poorly.

Genes from good individuals propagate throughout the populationso that two good parents will sometimes produce offspring that arebetter than either parent.

Thus each successive generation will become more suited to their

environment.


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A population of individuals is maintained within search spacefor a GA, each representing a possible solution to a givenproblem. Each individual is coded as a finite length vector ofcomponents, or variables, in terms of some alphabet, usuallythe binary alphabet {0,1}.

TheThe chromosome (solution) is composed of several geneschromosome (solution) is composed of several genes(variables). A(variables). A fitness scorefitness score (the best objective funtion)(the best objective funtion) isisassigned to each solution representing the abilities of anassigned to each solution representing the abilities of anindividual toindividual to competecompete . The individual with the optimal (or. The individual with the optimal (orgenerally near opt imal) fitness score is sought. The GA aims togenerally near opt imal) fitness score is sought. The GA aims touse selectiveuse selective breedingbreeding of the solutions to produceof the solutions to produce

offspringoffspring better than the parents by combining informationbetter than the parents by combining informationfrom t he chromosomes.from t he chromosomes.



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The general genetic algorithm solution is foundby:1.1. [Star t ][Star t ] Generate random population ofGenerate random population ofnn chromosomeschromosomes(suitable solutions for the problem)(suitable solutions for the problem)

2.2.[Fitness][Fitness] Evaluate the fitnessEvaluate the fitness f(x)f(x) (objective function)(objective function)of eachof eachchromosomechromosomexxin the population.in the population.

5.5.[New population][New population] Create a new population by repeating following stepsCreate a new population by repeating following stepsuntil the new populationis completeuntil the new populationis complete

1.1.[[Selection]Selection] Select two parent chromosomes from a population accordingSelect two parent chromosomes from a population accordingto their fitness (theto their fitness (the better fitness, the bigger chance to be selected)better fitness, the bigger chance to be selected)

2.2.[Crossover][Crossover] With a crossover probability cross over the parents to formWith a crossover probability cross over the parents to forma newa new offspringoffspring (children). If no crossover was performed, offspring is(children). If no crossover was performed, offspring isan exact copy of parents..an exact copy of parents..

3.3.[Mutation][Mutation] With a mutation probability mutate new offspring at eachWith a mutation probability mutate new offspring at eachlocus (position inlocus (position in chromosome).chromosome).

4.4.[Accepting][Accepting] Place new offspring in a new population 4.Place new offspring in a new population 4.

7.7.[Replace][Replace] Use new generated population for a further run of algorithm 4.Use new generated population for a further run of algorithm 4.8.8.[Test][Test] If the end condition is satisfied,If the end condition is satisfied, stopstop, and return the best solution, and return the best solution

in current population 5.in current population 5.

9.9.[Loop][Loop] Go to stepGo to step 22


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Encoding of a ChromosomeThe chromosome should in some way contain information aboutthe solution which it represents. The most used way of encodingis a binary string. The chromosome then could look like this:

Each chromosome has one binary string. Each bit in this stringEach chromosome has one binary string. Each bit in this string

can represent some characteristic of thecan represent some characteristic of the solution. Or the wholesolution. Or the wholestring can represent a num berstring can represent a num ber

Of course, there are many other ways of encoding. This dependsOf course, there are many other ways of encoding. This dependsmainly on the solved problem. Formainly on the solved problem. For example, one can encodeexample, one can encodedirectly integer or real numbersdirectly integer or real numbers.. SSometimes it isometimes it is alsoalso useful touseful toencode someencode some permutations.permutations.

Stochastic Search MethodsStochastic Search Methods


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Crossover

After we have decided what encoding we will use, we can make astep to crossover. Crossover selects genes from parentchromosomes and creates a new offspring. The simplest way howto do this is to choose randomly some crossover point andeverything before this point copy from a first parent and theneverything after a crossover point copy from the second parent.

Crossover can then look like this ( | is the crossover point):

There are other ways how to make crossoverThere are other ways how to make crossoverss,, andand we canwe canchoose mchoose m ultipleultiple crossover points.crossover points. CrossoverCrossoverss can be rathercan be rathercomplicated and vary dependcomplicated and vary depend ing oning on the encoding ofthe encoding ofchromosome.chromosome. Specific crossoverSpecific crossoverss made for a specific problem canmade for a specific problem canimprove performance of the genetic algorithm .improve performance of the genetic algorithm .

Stochastic Search MethodsStochastic Search Methods


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MutationAfter a crossover is performed, mutation takes place. This is toprevent the falling of all solutions in the population into a localoptimum. Mutation changes the new offspring randomly. Forbinary encoding we can switch a few randomly chosen bits from1 to 0 or from 0 to 1. Mutation can then be shown as:

The mutation depends on the encoding as well as the crossover.The mutation depends on the encoding as well as the crossover.For example when we are encodingFor example when we are encoding permutations, mutation couldpermutations, mutation couldbe exchanging tw o genes.be exchanging tw o genes.



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GAs Characteristics:A GA makes no assumptions about the function to be optimized(Levine, 1997) and thus can also be used for nonconvex objectivefunctions

A GA optimizes the tradeoff between exporting new points in thesearch space and exploiting the information discovered thus far

A GA operates on several solutions simultaneously, gatheringinformation from current search points and using it to directsubsequent searches which makes a GA less susceptible to theproblems of local optima and noise

A GA only uses the objective function or fitness information,instead of using derivatives or other auxiliary knowledge, as areneeded by traditional optimization methods.



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StartStart

Initial PopulationInitial Population

Get Objective FunctionGet Objective FunctionValue for Whole PopulationValue for Whole Population

(Internal optimization)(Internal optimization)

OptimumOptimum

??

Generate New PopulationGenerate New PopulationGA parametersGA parametersGA strategiesGA strategies

StoSto

pp

11stst GenerationGeneration

NNthth GenerationGeneration

(N+1)(N+1)thth GenerationGeneration

YesYes

NoNo

GA Solution ProcedureGA Solution Procedure

SA and GA comparation: In theory and Practice


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Life Cycle Analysis.


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Life Cycle Analysis

What is Life Cycle Analysis?

Technique for assessing the environmental aspects andpotential impacts associated with a product by:

An inventory of relevant inputs and outputs of asystem

Evaluating the potential environmental impactsassociated with those inputs and outputs

Interpreting the results of the inventory and impactphases in relation to the objectives of the studyheading

Evaluation of some aspects of a product system throughall stages of its life cycle


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Life Cycle Analysis

Why LCA is important:

Tool for improvement of environmentalperformance

Systematic way of managing an organizationsenvironmental affairs

Way to address immediate and long-termimpacts of products, services and processes onthe environment

Focus on continual improvement of the system


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DIRECT APPLICATIONS

Product developmentand improvement

Strategic planificationPublic policy

Marketing

Etc.

Goaland

Scope

definition

Inventory

analysis

Impact

assessment

Interpretation

LIFE-CYCLE ASSESSMENT

OTHER ASPECTS

Technical

Economic

Market

Social etc.

Life Cycle Analysis

LCA met hodology:LCA met hodology:


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Life Cycle Analysis

Goal and scope definitions

goal ,

scope

functional unit

efficiency

durability performance quality standard

system boundaries ,


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Life Cycle Analysis

Inventory analysis

data collection ,

refining system boundaries

calculation no formal description, software

validation of data

relating data to the specific system

allocation


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Life Cycle Analysis

Impact assessment

category definition

classification

characterization

weighting


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Life Cycle Analysis

Interpretation/improvement assessment

identification of significant environmentalissues

evaluation , ,

conclusions and recommendations


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Life Cycle Analysis

Possible Benefits:Improvements in overall environmentalperformance and compliance

Provides a framework for using pollution

prevention practices to meet LCA objectivesIncreased efficiency and potential cost savingswhen managing environmental obligations

Promotes predictability and consistency in

managing environmental obligationsMore effective measurement of scarceenvironmental


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Data-Driven ProcessModeling


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Process IntProcess Int egr at ion Challengeegr at ion Challenge ::Make sense ofMake sense ofm asses ofm asses ofdatadata

Many organisations today arefaced with the samechallenge: TOO MUCH DATA

It is the last it em that isof interest to us as chemical

engineers

Drowning in data!Drowning in data!

Data-Driven Process Modelling


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Data-Rich but Knowledge-Poor

Far too much data for a human brain

Limited to looking at one or two variables at a time:

Big Problem: Interesting, useful patterns andrelationships not intuitively obvious lie hidden insideenormous, unwieldy databases

0

2

4

6

8

10

12

1 2 3 4 5 6 7

BrainBrain


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This approach uses the plant process data directly, toestablish mathematic correlations.

Unlike the theoretical models, empirical models do NOTtake the process fundamentals into account. They onlyuse pure mathematical and statistical techniques.Multi-Variable Analysis (MVA) is one such method,

because it reveals patterns and correlationsindependently of any pre-conceived notions.

Obviously this approach is very sensitive to Garbage-in,garbage-out which is why validation of the model is so

important.

OUTSIDE INOUTSIDE INEmpirical Model


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With MVA you move

From Data to Information.

From Information to Knowledge.

From Knowledge toAction.


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W hat is M VA?W hat is M VA?

MM ulti-ult i-VV ariatear iate AAnalysis (> 5 variables)nalysis (> 5 variables)

MVA uses ALL available data to capture the most inform ationMVA uses ALL available data to capture the most inform at ionpossiblepossible

Principle: boil down hundreds of var iables down to aPrinciple: boil down hundreds of var iables down to a meremerehandfulhandful

MVA


D D i P M d lli


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MVA Example: Apples and Oranges

Measurable differencesMeasurable differencesColour, shape, firmness, reflectivity,Colour, shape, firmness, reflectivity,

Skin: smoothness, thickness, morphology,Skin: smoothness, thickness, morphology,

Juice: water content, pH, composition,Juice: water content, pH, composition,

Seeds: colour, weight, size distribution,Seeds: colour, weight, size distribution,

et ceteraet cetera

However, always onlyHowever, always only oneone latentlatent attributeattribute

Apple or orange?Apple or orange?

+1 -1



How MVA Works:How MVA Works:
http://members.fortunecity.com/arthurreeve/fruitcol/APPLE1.gifhttp://members.fortunecity.com/arthurreeve/fruitcol/ORANGE2.gif


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TmtTmt X1X1 X4X4 X5X5 RepRep Y avecY avec Y sansY sans

11 -1-1 -1-1 -1-1 11 2.512.51 2.742.74

11 -1-1 -1-1 -1-1 22 2.362.36 3.223.22

11 -1-1 -1-1 -1-1 33 2.452.45 2.562.56

22 -1-1 00 11 11 2.632.63 3.233.23

22 -1-1 00 11 22 2.552.55 2.472.47

22 -1-1 00 11 33 2.652.65 2.312.31

33 -1-1 11 00 11 2.452.45 2.672.67

33 -1-1 11 00 22 2.62.6 2.452.45

33 -1-1 11 00 33 2.532.53 2.982.98

44 00 -1-1 11 11 3.023.02 3.223.22

44 00 -1-1 11 22 2.72.7 2.572.57

44 00 -1-1 11 33 2.972.97 2.632.63

55 00 00 00 11 2.892.89 3.163.16

55 00 00 00 22 2.562.56 3.323.32

55 00 00 00 33 2.522.52 3.263.26

66 00 11 -1-1 11 2.442.44 3.13.1

66 00 11 -1-1 22 2.222.22 2.972.97

66 00 11 -1-1 33 2.272.27 2.922.92

Raw Data:

impossible tointerpret

Statistical Model

2-D Visual Outputs

(internalto

software)

trends

trendstrends

Y

XX

X

X

9,000 rows9,000 rows

700 columns700 columns

... ..

.. .

.

.. .

How MVA Works:How MVA Works:



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1 component1 component

What aboutWhat aboutanan extremeextremeoutlier?outlier?

Effect of Outliers on MVAEffect of Outliers on MVA

OUTLINEROUTLINER



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1 component

Extreme outliersvery detrimentalto MVA

New (wrong)New (wrong)component!component!Linear regressionby Least squares !

Real component hasReal component hasbecome mere noisebecome mere noise

Effect of Outliers on MVA


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Benefits:

Explore Inter-RelationshipsCreate and Learn by modelling

What-if ExercisesLow-cost investigation of options

Soft Sensor (Inferential Control)for parameters we cant measure directly

Feed-Forward (Model-Based) Control


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Integrate Process Designand Control


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Integrate Process Design and Control

Control Objectives:

Product specifications variability should be kept to aminimum --> process variability (To Control Product

quality).

Safety issues(separate equipments), energy costs,environmental concerns have increased complexity andsensitivity of processes

Plants become highly integrated in terms of mass andenergy and therefore, process dynamics are oftendifficult to control. The Control is permanentlynecessary to do for allowing the process to operate in

the best conditions.


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it is ait is aproperty of a processproperty of a process that accounts for thethat accounts for the easeease with whichwith whichaa continuous plantcontinuous plant can becan be held at a specified operating policyheld at a specified operating policy,,despite external disturbancesdespite external disturbances (resiliency) and(resiliency) and uncertaintiesuncertainties(flexibility) and regardless of the control system imposed on such(flexibility) and regardless of the control system imposed on sucha plant.a plant.

DESIGNDESIGN CONTROLCONTROL++

Changes inChanges inProcessProcess

-Dynamics-Dynamics

-Tunings-Tunings

- Control- Controlconfigurationsconfigurations

Process VariabilitySources MIN

Steady State & Dynamic Simulations


CONTROLLABILITYCONTROLLABILITY

Integrate Process Design & Control


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Integrate Process Design & Control

Process

sensor

Input

Variables

OutputVariables

(controlled andMeasured)

Input Variables(Manipulated)

Disturbances

Uncertainties

Internal interactionsInternal interactions

PROCESS RESILIENCY

PROCESS FLEXIBILITY

Control Loop

Fundamentals:Fundamentals:


e.g. Controllability analysis for control structures design


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CC

FC

C, F

Water, F1

Pulp, F2

OUTPUTSINPUTS(process variables ordisturbances)

EFFECTS(Best Selection byControllabilityanalysis)

e.g. Controllability analysis for control structures design

Interactions


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The process will be morecapable to move smoothlyaround the possible

operating edgeStability and betterperformance of controlloops and structures

System relatively insensitiveto perturbations

Efficient management ofinteracting networks

Improvementof current

dynamics

Flexibility

Why ControllabilityWhy Controllability is important:is important :


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Production rate(time)

Product quality, and

Energy economy.

The Top level of theprocess control,

Strategic control level isthus concerned with

achieving the appropriatevalues principally of:


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Real Time Optimizations(RTO)


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Real Time Optimizations

The Process Industries are increasinglycompelled to operate profitably in very dynamic

and global market. The increasing competition inthe international area and stringent productrequirements mean decreasing profit marginsunless plant operations are optimizeddynamically to adopt to the changing marketconditions and to reduce the operating cost.Hence, the importance of real-time or on-lineoptimization of an entire plant is rapidlyincreasing.


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Real Time Optimizations

What is RTO?

Real-time Optimization is a model-basedsteady-state technology that determines the

economically optimal operating policy for aprocess in the near term

The system optimizes a process simulationand not the process directly

Performance measured in terms of economicbenefit

Is an active field of research:

Model accuracy, error transmission,performance evaluation


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RTO Schematically

ReconciliationReconciliation

And gross ErrorAnd gross Error

DetectionDetection

Updating Process ModelUpdating Process Model

(Steady State Dynamic(Steady State Dynamic

Simulation)Simulation)

Steady State DetectionSteady State DetectionOptimizationOptimization

(Objectives Functions(Objectives Functions))

Busine

ssObjectiv

es;

BusinessOb je

ctives;

Econo

m

icData;

Econo

m

icD

ata;

ProductSpe c

ificat io

n

Produ

ctSpecificat io

n

Cost, Process,Cost, Process,

Environmental,Environmental,

Product DataProduct Data

PlantPlantFacilityFacility


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Direct Search Method Schematically

DynamicSimulation(Model)

RTO

Algorithm(ObjectiveFct,

Constraints)

SETPOINTS(DOFs)

SelectedOuputs


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Business Model And SupplyChain Modeling

Business Model And Supply Chain Modeling


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Integrated Business &Process Model

Cost, Process,Cost, Process,Environmental &Environmental &



Environmental &Environmental &Product OutcomesProduct Outcomes

ProcessProcessDesignDesign

AnalysisAnalysisAndAnd

SynthesisSynthesis

ProcessProcessOperationOperationAnalysisAnalysis

andandOptimizationOptimization

Cost, Process,Cost, Process,Environmental &Environmental &



ProcessProcess

DesignDesignAnalysisAnalysis

andandSynthesisSynthesis

Click HereClick Here ProcessProcess

OperationOperationAnalysisAnalysisand Optimizaand Optimiza


Integrated Business&Process Model

Click HereClick Here


Environmental &Environmental &Product OutcomesProduct Outcomes

Integrated Business & Process Model

Cost, Process, Environmental & Product Data


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Plant Facilities

Integrated Business & Process Model

Process (P) &Environmental

(E) Data

Accounting

Data

Product

Data

Market

Data

Data Processing

Processed

P&E Data

Data Reconciliation

Reconciled

P&E Data

ean all the data are consistent together throughout all the plant facilitiesean all the data are consistent together throughout all the plant facilities

Data Validation &Data Validation &

ReconciliationReconciliation

model is built it can be used to validate and reconcile datamodel is built it can be used to validate and reconcile data

Cost, Process, EnvironmentalCost, Process, Environmentaland Product Dataand Product Data

Integrated Business and Process Modelith the classification, recording, allocation, and summarization for the purpose ofAccounting DataAccounting Data


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1st

PrinciplesModels

Cost

Accounti

ng

Model

Supp

lyChain

(SC)an

d

Env.S

CM

odels

CostA

ccounting

Model

DataDrivenModel

sProcessedP&E data


Environmental DataEnvironmental Data

Market DataMarket Data

Process DataProcess Data

Product DataProduct DataSupplyC

hain

(SC)an

d

Env.S

CM

odels


DataDrivenModel

s

ProcessSimulation

Models

Integrated BusinessIntegrated Business

andand

Process ModelProcess Model


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Supply Chain and Environmental Supply Chain

The objective of the SC and ESC models are:To integrate inter-organizational units along a SC andcoordinate materials, information and financial flows

in order to fulfill customer demands with the aim ofimproving SC profitability and responsiveness

To gain insight in the total environmental impact ofthe production process (from supplier to customerand back to the facility by recycling) and all the

products that are manufactured. (closely linked toLCA)

Process Design Analysis and Synthesis


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Integra t

edBusines

s&

ProcessMo

de

l

Capital EffectivenessAnalysis

ProcessIntegration

Tools

Process DesignAnalysis Design

Objectives

Process

DesignAnalysis andSynthesis

Loop

Process simulationData ReconciliationMVA using relationaldatabase

Pinch analysisLCASC and ESC modelanalysisControllability AnalysisOptimization (Deterministic and/or

Stochastic)

Process Design Analysis and SynthesisProcess Design Analysis and Synthesis

Process Operation Analysis and Optimization


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Integra

te

dB

usiness&

Process

Mod

el

Objective Functionfor

Process Optimization

ProcessIntegration

Tools

Detailed Process

Investigation toValidate Recommendations

ProcessOperationAnalysis andOptimization

Loop

Data reconciliation

for instrument validationDynamic simulationProcess control strategiesMVA (Soft sensor dev.)Real-time optimizationOptimizated supply chainModel

Process Design Analysis and OptimizationProcess Design Analysis and Optimization


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Outline

.1 1 In tro d u ctio n a n d d e fin itio n o f P ro ce ss

.in te g ra tio n

.1 2 O v e rvie w o f P ro ce ss In te g ra tio n to o ls

. - - 1 3 An around the world tour of PI practitioners

focuses of expertise

.1 1 Introduction and definition of Process.1 1 Introduction and definition of Process.integration.integration

.1 2 Overview of P.1 2 Overview of P rocessrocess IIntegrationntegrationtoolstools

.1 3 An.1 3 An - - around the world tour of PI practitioner- - around the world tour of PI practitionerssfocuses of expertisefocuses of expertise


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1.3 An around-the-world tour ofPI practitioners focuses of

expertise (May 2003).

Around the World tour of PI practitioners focuses of


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Around the World tour of PI practitioners focuses ofexperience

Courtesy mainly of the www to capture theflavor of the evolution of Process Integration

PI is relatively new:

Researchers build on their strengths

Many of the ground-breaking techniques arecoming from universities

When techniques become practical, the

private sector generally capitalizes andtechniques advance more rapidly


Carnegie Mellon University Department of Chemical Engineering


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Carnegie Mellon University, Department of Chemical Engineering,Pittsburgh, USAMajor Contact: Professor Ignacio E. Grossmann, head of department

Web:http://www.cheme.cmu.edu/research/capd/

Research Area: Recognized as one of the major research groups in the area ofComputer Aided Process Design. In Process Integration, the group is recognized forits work in Mathematical Programming, Optimization, Reactor Systems, SeparationSystems (especially Distillation), Heat Exchanger Networks, Operability and thesynthesis of Operating Procedures.

Current research in Process Integration includes:1) Insights to Aid and Automate Synthesis (Invention)2) Structural Optimization of Process Flowsheets3) Synthesis of Reactor Systems and Separation Systems4) Synthesis of Heat Exchanger Networks

5) Global Optimization techniques relevant to Process Integration6) Integrated Design and Scheduling of Batch plants7) Supply chain dynamics and optimization

Consortium: "Center for Advanced Process Decision-making" with 20 members(2001) including operating companies, engineering & contracting companies,consulting companies and software vendors. The consortium was founded 1986.


Imperial College Centre for Process Systems Engineering London


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Imperial College, Centre for Process Systems Engineering, London,UKMajor Contact:Prof. Efstratios N PistikopoulosWeb:http://www.ps.ic.ac.uk/and http://www.psenterprise.comResearch Area: Recognized as the largest research group in the area ofProcess Systems Engineering (PSE), which includes Synthesis/Design,Operations, Control and Modeling. The group is recognized as a world-widecenter of excellence in Process Modeling, Numerical

Techniques/Optimization and Integrated Process Design (includes

simultaneous consideration of Process Integration and Control). The Centreis also an important contributor in the area of Integration and Operation ofBatch Processes.Current research in Process Integration includes:1) Integrated Batch Processing2) Design and Management of Integrated Supply Chain Processes

3) Uncertainty and Operability in Process Design4) Formulation of Mathematical Programming Models to address problemsin Process Synthesis and Integration

Consortium: "Process Systems Engineering" with 17 members (2003)including operating, engineering & contracting companies, software

vendors.


UMIST Department of Process Integration Manchester UK


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UMIST, Department of Process Integration, Manchester, UKMajor Contact: Professor Robin Smith, head of departmentWeb: http://www.cpi.umist.ac.uk/Research Area: Recognized as the pioneering and major research groupin the area of Pinch Analysis. Previous research includes targets and designmethods for Heat Exchanger Networks (grassroots and retrofits), Heat andPower systems, Heat driven Separation Systems, Flexibility, Total Sites,Pressure Drop considerations, Batch Process Integration, Water and WasteMinimization and Distributed Effluent Treatment.

Current research is organized in three major areas:1) Efficient Use of Raw Materials (including Water)2) Energy Efficiency3) Emissions Reduction

4) Eefficient use of capital.Consortium: "Process Integration Research Consortium" with 27 members(2003) including operating companies, engineering & contractingcompanies, consulting companies and software vendors. The consortiumwas founded in 1984 by six multinational companies.



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Chalmers Univ. of Technol., Department of Heat and Power, Gothenburg,Sweden

Major Contact: Professor Thore Berntsson, head of departmentWeb:http://www.hpt.chalmers.se/Research Area: Methodology development and applied research based on Pinch

Technology. Emphasis on new Retrofit methods including realistic treatment ofgeographical distances, pressure drops, varying fixed costs, etc. Important newConcepts include the Cost Matrix for Retrofit Screening and new Grand Compositetype Thermodynamic Diagrams for Heat and Power applications (including Gas

Turbines and Heat Pumps). Research towards pulp and paper with focus on energyand environment.Research areas are:1) Retrofit Design of Heat Exchanger Networks2) Process Integration of Heat Pumps in Grassroots and Retrofits3) Gas Turbine based CHP plants in Retrofit Situations4) Applied research in Pulp and Paper industry, such as black liquor gasification,closing the bleaching plant, etc.5) Environmental aspects of Process Integration, especially greenhouse gasemissions)

Industry: Close co-operation with some of the major pulp and paper industry

groups, including training courses, consulting, etc.



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cole Polytechnique de Montral, Chemical engineering Department,Quebec, CanadaMajor Contact:

Dr. Paul Stuart, Chair holderWeb: http://www.pulp-paper.caResearch Area: the application of Process Integration in the pulp and paperindustry, with emphasis on pollution prevention techniques and profitability analysis,the Efficiency use of energy and Raw Materials (including Water), process control,and plant sustainability.Research areas are::

1) process simulation,

2) Data reconciliation,

3) Process Control,

4) Networks Analysis HEN and MEN,

5) Environmental technologies (e.g., LCA),

6) Business Model.7) Data Driving Modeling.

Consortium: "Process Integration Research Consortium" with 13 members (2003)including operating companies, engineering & contracting companies, consultingcompanies and software vendors in pulp and paper industry.



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Universitat Politcnica de Catalunya, Chemical Engng.Department, Barcelona, Spain

Major Contact: Professor Luis Puigjaner, Director LCMAWeb: http://tqg.upc.es/Research Area: Pioneering work on Computer Aided Process Operations.Within Process Integration, the group is recognized for its contributions in

Time-Dependent Processes, such as Combined Heat and Power, CombinedEnergy-Waste and Waste Minimization, Integrated Process Monitoring,Diagnosis and Control and finally Process Uncertainty.Current research in the area of Process Integration includes:1) Evolutionary Modeling and Optimization2) Multi-objective Optimization in time-dependent systems3) Combined Energy and Water Use Minimization4) Integration of Thermally Coupled Distillation Columns5) Hot-gas Recovery and Cleaning SystemsConsortium: "Manufacturing Reference Centre" with 12 members (1966)including Conselleria d'Indstria and associated operating companies,engineering and contracting companies, consultants and software vendors.



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p

Texas A&M University, Chemical Engineering Department, Texas,USAMajor Contact: Professor Mahmoud M. El-HalwagiWeb:http://process-integration.tamu.edu/ and http://www-che.tamu.edu/cpipe/Research Area: Recognized as a leading research group in the areas ofMass Integration and Pollution Prevention through Process Integration.Research areas are:1) Global allocation of Mass and Energy2) Synthesis of Waste Allocation and Species Interception Networks3) Physical and Reactive Mass Pinch Analysis4) Synthesis of Heat-Induced Networks5) Design of Membrane-Hybrid Systems6) Design of Environmentally acceptable Reactions7) Integration of Reaction and Separation Systems8) Flexibility and Scheduling Systems9) Simultaneous Design and Control10) Global Optimization via Interval Analysis

Around the World tour of PI practitioners focuses of


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University of Guanajuato, Faculty of Chemistry, Guanajuato,MxicoMajor contact: Dr. Martin-Picon-Nunez, DirectorWeb: http

cpd lecture unit 5

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