piece program for north american mobility in higher education rev:1.2 created at: École...

Created at:Created at:École Polytechnique de École Polytechnique de

Montréal &Montréal &Universidad de GuanajuatoUniversidad de Guanajuato

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

Rev:1.2Rev:1.2

Module 8: “Introduction to Module 8: “Introduction to Process IntegrationProcess Integration””

Program for North American Mobility Program for North American Mobility in Higher Education (NAMP)in Higher Education (NAMP)

Introducing Process Integration for Introducing Process Integration for Environmental Control in Engineering Environmental Control in Engineering

Curricula (PIECE)Curricula (PIECE)

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Module 8: introduction to process integration

What is the purpose of this module?What is the purpose of this module?

This module is intended to covey the basic aspects of This module is intended to covey the basic aspects of Process IntegrationProcess Integration MethodsMethods and and ToolsTools, and places , and places Process IntegrationProcess Integration into a broad perspective. It will be into a broad perspective. It will be identified as a pre-requisite for all other modules related to identified as a pre-requisite for all other modules related to the learning of the learning of Process Integration.Process Integration.

Purpose of Module 8

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Struture of module 8

What is the structure of this module?What is the structure of this module?

The Module 8 is divided into 3 “tiers”, each with a specific The Module 8 is divided into 3 “tiers”, each with a specific goal:goal:

Tier 1: Background InformationTier 1: Background InformationTier 2: Case Study Applications of Process IntegrationTier 2: Case Study Applications of Process IntegrationTier 3: Open-Ended Design ProblemTier 3: Open-Ended Design Problem

These tiers are intended to be completed in order. Students These tiers are intended to be completed in order. Students are quizzed at various points, to measure their degree of are quizzed at various points, to measure their degree of understanding, before proceeding.understanding, before proceeding.

Each tier contains a statement of intent at the beginning, and Each tier contains a statement of intent at the beginning, and a quiz at the end.a quiz at the end.

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Tier 1: Background Information

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Tier 1: Statement of intent

Tier 1: Statement of intent:Tier 1: Statement of intent:

The goal is to provide a general overview of The goal is to provide a general overview of process integration tools, process integration tools, withwith a focus on a focus on it’sit’s link link with profitability analysis. At the end of Tier 1, with profitability analysis. At the end of Tier 1, the student should:the student should:

DistingDistinguish uish the key elemthe key elemenents of Process Integration.ts of Process Integration.

KKnow now the scope of the scope of each process integration tooleach process integration tool..

Have overview of each process integration tool.Have overview of each process integration tool.

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Tier 1: contents

The tier 1 is broken down into three sectionsThe tier 1 is broken down into three sections::

1.1 Introduction and definition of Process integration.1.1 Introduction and definition of Process integration.

1.2 Overview of PI tools 1.2 Overview of PI tools

1.3 An “around-the-world tour” of PI practitioners focuses 1.3 An “around-the-world tour” of PI practitioners focuses of expertiseof expertise

At the end of this tier there is a short multiple-answer Quiz. At the end of this tier there is a short multiple-answer Quiz.

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Outline

1.1 Introduction and definition of Process 1.1 Introduction and definition of Process integration.integration.

1.2 Overview of Process Integration tools 1.2 Overview of Process Integration tools

1.3 An “around-the-world tour” of PI practitioners 1.3 An “around-the-world tour” of PI practitioners focuses of expertisefocuses of expertise


1.2 Overview of P1.2 Overview of Process rocess IIntegrationntegration tools tools

1.3 An 1.3 An ““around-the-world tour” of PI practitioneraround-the-world tour” of PI practitionerss focuses of expertisefocuses of expertise

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1.1 Introduction and definition of Process integration.

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introduction

The president of your company probably does The president of your company probably does not know what process integration can do for the not know what process integration can do for the company.........company.........

.......... But he should. Let’s look at why?.......... But he should. Let’s look at why?

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A Very Brief History of Process Integration

Linnhoff started the area of pinch (bottleneck Linnhoff started the area of pinch (bottleneck identification) at UMIST in the 60’s, focusing on identification) at UMIST in the 60’s, focusing on the area of Heat Integration the area of Heat Integration

UMIST Dept of Process Integration was created UMIST Dept of Process Integration was created in 1984, shortly after the consulting firm in 1984, shortly after the consulting firm Linnhoff-March Inc. was formedLinnhoff-March Inc. was formed

PI is not really easy to define…PI is not really easy to define…

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Definition of process integration

The International Energy Agency (IEA) definition of The International Energy Agency (IEA) definition of process integrationprocess 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 explicitly stated in the description of it’s role in the technical sector by this Implementing Agreement:"Process Integration is the common term used for the application of methodologies developed for System-oriented and Integrated approaches to industrial process plant

design 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: capital investment,energy efficiency, emissions, operability, flexibility, controllability, safety and yields. Process Integration also refers to some aspects of operation and maintenance".

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

TrTruuls Gunderson, International Energy Agency (IEA) Implementing Agreement, ls Gunderson, International Energy Agency (IEA) Implementing Agreement, “A worldwide catalogue on Process Integration” (jun. 2001).“A worldwide catalogue on Process Integration” (jun. 2001).

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El-Halwagi, M. M., El-Halwagi, M. M., Pollution Prevention through Process Integration: Systematic Pollution Prevention through Process Integration: Systematic Design ToolsDesign Tools. Academic Press, 1997. . Academic Press, 1997.

““A Chemical Process is an integrated system of A Chemical Process is an integrated system of interconnected units and streams, and it should be treated interconnected units and streams, and it should be treated

as such. Process Integration is a holistic approach to process as such. Process Integration is a holistic approach to process design, retrofitting, and operation which emphasizes the design, retrofitting, and operation which emphasizes the

unity of the process. In light of the strong interaction among unity of the process. In light of the strong interaction among process units, streams, and objectives, process integration process units, streams, and objectives, process integration offers a unique framework for fundamentally understanding offers a unique framework for fundamentally understanding the global insights of the process, methodically determining the global insights of the process, methodically determining

its attainable performance targets, and systematically its attainable performance targets, and systematically making decisions leading to the realization of these targets. making decisions leading to the realization of these targets.

There are three key components in any comprehensive There are three key components in any comprehensive process integration methodology: synthesis, analysis, and process integration methodology: synthesis, analysis, and

optimization.”optimization.”

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Nick Hallale, Aspentech – CEP July 2001 – “Burning Nick Hallale, Aspentech – CEP July 2001 – “Burning Bright Trends in Process Integration”Bright Trends in Process Integration”

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

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

and more sustainable”and more sustainable”

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North American Mobility Program in Higher North American Mobility Program in Higher Education (NAMP)-January 2003Education (NAMP)-January 2003

““Process integration (PI) is the synthesis of process control, Process integration (PI) is the synthesis of process control, process engineering and process modeling and simulation process engineering and process modeling and simulation

into tools that can deal with the large quantities of operating into tools that can deal with the large quantities of operating data now available from process information systems. It is an data now available from process information systems. It is an emerging area, which offers the promise of improved control emerging area, which offers the promise of improved control

and management of operating efficiencies, energy use, and management of operating efficiencies, energy use, environmental impacts, capital effectiveness, process design, environmental impacts, capital effectiveness, process design,

and operations management.” and operations management.”

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

other techniques are being drawn upon for holistic other techniques are being drawn upon for holistic analysis, in particular:analysis, in particular:

Process modelingProcess modeling

Process statisticsProcess statistics

Process optimizationProcess optimization

Process economicsProcess economics

Process controlProcess control

Process designProcess design

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Modern Process Integration context

Process integration is primarily regarded as process Process integration is primarily regarded as process design (both new and retrofits design), but also design (both new and retrofits design), but also involve planning and operation. The methods and involve planning and operation. The methods and systems are applied to continuous, semi-batch, and systems are applied to continuous, semi-batch, and batch process. batch process.

Business objectives currently driving the Business objectives currently driving the development of PI:development of PI:

a)a) Emphasis is on Emphasis is on retrofitretrofit projects in the “ projects in the “new economynew economy” ” driven by Return on Capital Employed (ROCE)driven by Return on Capital Employed (ROCE)

b)b) PI is “PI is “Finding value in data qualityFinding value in data quality””c)c) Corporations wish to make more knowledgeable Corporations wish to make more knowledgeable

decisions: decisions: 1.1. For operations, For operations, 2.2. During the design processDuring the design process..

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Possible ObjectivesPossible Objectives::

Lower capital cost design, for the same Lower capital cost design, for the same design objectivedesign objective

Incremental production increase, from the Incremental production increase, from the same asset basesame asset base

Marginally-reduced unit production costsMarginally-reduced unit production costs

Better energy/environmental performance, Better energy/environmental performance, without compromising competitive positionwithout compromising competitive position

ReducingReducing

COSTSCOSTS

POLLUTIONPOLLUTION

ENERGYENERGY

IncreasingIncreasing

THROUGHPTHROUGHPUTUT

YIELDYIELD

PROFITPROFIT

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Among the design activities that these systems and methods address Among the design activities that these systems and methods address today are:today are:

PProcessrocess ModelingModeling and and SimulationSimulation, and , and ValidationsValidations of the of the results in order to have information accurate and reliable of the results in order to have information accurate and reliable of the process.process.

Minimize Minimize Total Annual CostTotal Annual Cost by optimal Trade-off between by optimal Trade-off between Energy, Equipment and Raw Material Energy, Equipment and Raw Material

Within this trade-off: minimize Within this trade-off: minimize EnergyEnergy, improve , improve Raw MaterialRaw Material usage and minimize usage and minimize CapitalCapital Cost Cost

Increase Increase Production VolumeProduction Volume by Debottlenecking by Debottlenecking

Reduce Reduce OperatingOperating Problems by Problems by correctcorrect (rather than maximum) (rather than maximum) use of Process Integrationuse of Process Integration

Increase Plant Increase Plant ControllabilityControllability and and FlexibilityFlexibility

Minimize undesirable Minimize undesirable EmissionsEmissions

Add to the joint Efforts in the Process Industries and Society for a Add to the joint Efforts in the Process Industries and Society for a SustainableSustainable Development. Development.

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Summary of Process Integration elements

Process knowledgeProcess knowledge

Process DataProcess Data

PI systems PI systems & Tools& Tools

Improving overall plant Improving overall plant facilities energy efficiency facilities energy efficiency and productivity requires a and productivity requires a multi-pronged analysis multi-pronged analysis involving a variety of involving a variety of technical skills and technical skills and expertise, including: expertise, including:

•Knowledge of both Knowledge of both conventional industry conventional industry practice and state-of-the-practice and state-of-the-art technologies available art technologies available commerciallycommercially

•Familiarity with industry Familiarity with industry issues and trendsissues and trends

•Methodology for Methodology for determining correct determining correct marginal costsmarginal costs..

•Procedures and tools for Procedures and tools for Energy, Water, and raw Energy, Water, and raw material Conservation material Conservation auditsaudits

• Process information Process information systemssystems

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In conclusion, process integration has evolved from Heat In conclusion, process integration has evolved from Heat recovery methodology in the 80’s to become what a recovery methodology in the 80’s to become what a number of leading industrial companies and research number of leading industrial companies and research groups in the 20groups in the 20thth century regarding the century regarding the holistic analysis of holistic analysis of processes, involving the following elements:processes, involving the following elements:

Process data – Process data – lots of itlots of it

Systems and tools – Systems and tools – typically computer-orientedtypically computer-oriented

Process engineering principles - Process engineering principles - in-depth process in-depth process sector knowledgesector knowledge

TargetingTargeting - Identification of ideal unit constraints for - Identification of ideal unit constraints for the overall processthe overall process

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Outline


1.2 Overview of Process Integration tools.1.2 Overview of Process Integration tools.

1.3 An “around-the-world tour” of PI practitioners 1.3 An “around-the-world tour” of PI practitioners focuses of expertise.focuses of expertise.




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1.2 Overview of Process Integration Tools

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Process Process SimulationSimulation

•Steady stateSteady state

•DynamicDynamic

Pinch AnalysisPinch Analysis

Optimization by Optimization by Mathematical Mathematical ProgrammingProgramming

Stochastic Stochastic Search MethodsSearch Methods

Life Cycle Life Cycle AnalysisAnalysis

DataData--Driven Driven Process Process ModelingModeling

Business Model Business Model And Supply And Supply Chain Chain Modeling.Modeling.

Integrate Integrate Process Design Process Design and Controland Control

Real Time Real Time OptimizationOptimization


DataData ReconciliationReconciliation

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Process Simulation

•Steady state

•Dynamic

Pinch Analysis

Optimization by Mathematical Programming

Stochastic Search Methods

Life Cycle Analysis

Data-Driven Process Modeling

Business Model Business Model •Supply Chain Supply Chain ManagmentManagment..

Integrate Integrate Process Design Process Design and Controland Control

Real Time Real Time OptimizationOptimization


Reconciliation Data

NEXTNEXT

Click hereClick here










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Process Simulation

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Process Simulation

Process modelingProcess modeling

What is a model?What is a model?

““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 activity using models to solve problems in the areas of the process design, control, problems in the areas of the process design, control, optimization, hazards analysis, operation training, risk optimization, hazards analysis, operation training, risk assessment, and software engineering for computerassessment, and software engineering for computer aided aided engineering environmentsengineering environments..

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Process Simulation

Tools of process modelingTools of process modeling

Process modeling is Process modeling is an an understanding of understanding of the the process process phenomena anphenomena andd transform transforming thising this understanding into a understanding into a model.model.

Process ModelingProcess Modeling

SystemSystem

TheoryTheoryPhysics Physics

andand

ChemistryChemistry

ApplicatioApplicationn

ComputeComputess

ScienceScience

StatisticStatisticss

NumericNumericalal

MethodsMethods

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Process Simulation

What is a model used for?What is a model used for?

Nilsson (1995) presents a generalized model, which, as Nilsson (1995) presents a generalized model, which, as depicted in the figure below, can be used for different depicted in the figure below, can be used for different basic problem formulations: Simulation, Identification, basic problem formulations: Simulation, Identification, estimation and design.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 the the model, output is studied model, output is studied (Simulation)(Simulation)

Inverse: Output is applied on Inverse: Output is applied on the the model, Input is studiedmodel, Input is studied

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Process Simulation

If both Input and Output are Known, we have If both Input and Output are Known, we have three formulations (Juha Yaako, 1998):three formulations (Juha Yaako, 1998):Identification:Identification: We can find the structure and parameters We can find the structure and parameters in the model.in the model.

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

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

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Process Simulation

Demands set to models:Demands set to models:

Accuracy Accuracy Requirements placed on quantitative and qualitative Requirements placed on quantitative and qualitative models. models.

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

Complexity Complexity Models can be simple (usually macroscopic) or detailed Models can be simple (usually macroscopic) or detailed (usually microscopic). The detail level of the phenomena should be (usually microscopic). The detail level of the phenomena should be considered.considered.

Computational Computational The models should currently regard computational The models should currently regard computational orientation.orientation.

RobustnessRobustness Models that can be used for multiple processes are Models that can be used for multiple processes are always desired.always desired.

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Process Simulation

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

PROCESPROCESSS

MODELMODELInputInput OutputOutput


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

XX11, ..., , ..., XXmm

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

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

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

A model does A model does not include not include everything.everything.

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:Types of models:IntuitiveIntuitive: the immediate understanding of something without : the immediate understanding of something without conscious reasoning or study. This are seldom used.conscious reasoning or study. This are seldom used.

VerbalVerbal: If an intuitive model can be expressed in words, it becomes a : If an intuitive model can be expressed in words, it becomes a verbal model. First step of model development.verbal model. First step of model development.

CausalCausal: as the name implies, these model are about the causal : as the name implies, these model are about the causal relations of the processes.relations of the processes.

QualitativeQualitative: These models are a step up in model sophistication from : These models are a step up in model sophistication from causal models.causal models.

QuantitativeQuantitative: Mathematical models are an example of quantitative : Mathematical models are an example of quantitative models. These models can be used for (nearly) every application in models. These models can be used for (nearly) every application in process engineering. The problem is that these models are not process engineering. The problem is that these models are not documented or can be too costly to construct when there is not documented or can be too costly to construct when there is not enough knowledge (physical and chemical phenomena are poorly enough knowledge (physical and chemical phenomena are poorly understood). Sometimes the application encountered does not understood). Sometimes the application encountered does not require such model sophistication.require 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 Simulation: “what if” experimentation with a modelmodel

Simulation involves performing a series of experiments Simulation involves performing a series of experiments with a process model.with a process model.

MODELMODELInputInput OutputOutput

XX11, ..., , ..., XXmm

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

MODELMODEL

(t)(t)


XX(t)(t)11, ..., , ..., XX(t)(t)mm

YY(t)(t)11, ..., Y, ..., Y(t)(t)tt

Steady StateSteady State

•Snapshot

•Algebraic equations

DynamicDynamic

•Movie (time functions)

•Time is an explicit variable differential equations

•Certain phenomena require dynamic simulation (e.g. control strategies, real time descition).

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Process Simulation

Illustration:Illustration:

Staedy state simulation of a storage Staedy state simulation of a storage tanktank

Hi-LimitHi-Limit

Lo-LimitLo-Limit

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

Dynamic simulation of a storage tankDynamic simulation of a storage tank

t = time t = time

LevelLevel

000 21 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 The steady-state simulationsteady-state simulation does not solve time-dependent does not solve time-dependent equations. The Subroutines simulate the steady-state operation equations. The Subroutines simulate the steady-state operation of the process units ( operation subroutines) and estimate the of the process units ( operation subroutines) and estimate the sizes and cost the process units ( cost subroutines).sizes and cost the process units ( cost subroutines).

A A simulation flowsheetsimulation flowsheet, on the other hand, is a collection of , on the other hand, is a collection of simulation units(e.g., reactor, distillation columns, splitter, simulation units(e.g., reactor, distillation columns, splitter, mixer, etc.), to represent computer programs (subroutines) to mixer, etc.), to represent computer programs (subroutines) to simulate the process units and areas to represent the flow of simulate the process units and areas to represent the flow of information among the simulation units represented by arrows.information among the simulation units represented by arrows.

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Process Simulation

To convert from a To convert from a process flowsheetprocess flowsheet to a to a simulation flowsheetsimulation flowsheet, , one replaces the process unit with simulation units (Models). For one replaces the process unit with simulation units (Models). For each simulation unit, one assigns a subroutine (or block) to each simulation unit, one assigns a subroutine (or block) to solve its equations. Each of the simulators has a extensive list of solve its equations. Each of the simulators has a extensive list of subroutines to model and solve the equations for many process subroutines to model and solve the equations for many process units.units.

TheThe Dynamic simulation Dynamic simulation enables the process engineer to study enables the process engineer to study the dynamic response of potential process design or the the dynamic response of potential process design or the existent Process to typical disturbances and changes in existent Process to typical disturbances and changes in operating conditions, as well as, strategies for the start up and operating conditions, as well as, strategies for the start up and shut down of the potential process design or existing process.shut down of the potential process design or existing process.

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Process Simulation

Differences between Steady State and Differences between Steady State and Dynamic SimulationDynamic Simulation

Steady-State SimulationSteady-State Simulation Dynamic SimulationDynamic Simulation

Snapshot of a unit operation or Snapshot of a unit operation or plantplant

Mimic of plant operationMimic of plant operation

Balance at equilibrium Balance at equilibrium conditioncondition

Time dependent resultsTime dependent results

Equilibrium results for all unit Equilibrium results for all unit operationsoperations

It doesn’t assume equilibrium It doesn’t assume equilibrium conditions for all unitsconditions for all units

Equipment sizesEquipment sizes in general not in general not neededneeded

Equipment sizes neededEquipment sizes needed

Amount of information Amount of information required: small to mediumrequired: small to medium

Amount of information Amount of information required: medium to largerequired: medium to large

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Process Simulation

Solution StrategiesSolution Strategies

The Sequential Modular StrategyThe Sequential Modular Strategy flowsheet broken into unit operations (modules)flowsheet broken into unit operations (modules) each module is calculated in sequenceeach module is calculated in sequence problems with recycle loopsproblems with recycle loops

The Simultaneous Modular StrategyThe Simultaneous Modular Strategy develops a linear model for each unitdevelops a linear model for each unit modules with local recycle are solved simultaneouslymodules with local recycle are solved simultaneously flowsheet modules are solved sequentiallyflowsheet modules are solved sequentially

The Simultaneous Equation-solving StrategyThe Simultaneous Equation-solving Strategy describe entire flowsheet with a set of equationsdescribe entire flowsheet with a set of equations all equations are sorted and solved togetherall equations are sorted and solved together hard to solve very large equations systemshard to solve very large equations systems

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Process Simulation

Why steady-state simulation is important:Why steady-state simulation is important:

Better understanding of the processBetter understanding of the process

Consistent set of typical plant/facility dataConsistent set of typical plant/facility data

Objective comparative evaluation of options for Return Objective comparative evaluation of options for Return On Investment (ROI) etc.On Investment (ROI) etc.

Identification of bottlenecks, instabilities etc.Identification of bottlenecks, instabilities etc.

Perform many experiments cheaply once the model is Perform many experiments cheaply once the model is builtbuilt

Avoid implementing ineffective solutionsAvoid implementing ineffective solutions

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Process Simulation

Why dynamic simulation is important:Why dynamic simulation is important:

ADVANCEMENT OF PLANT OPERATIONS/OPERATIONAL SUPPORT / OPTIMIZATION

Predictive simulationOptimal conditions

OPTIMIZATION ofplant operationsOnline

system

EDUCATION, TRAINING CONTROL SYSTEM

Operation training simulatorDCS control logic

Plant diagnosis system

Quasi-onlinesystem

PROCESS DESIGN / ANALYSISExamination of operations

Control strategiesAdvanced control systems

Batch scheduling

Off-line system

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Challenges of simulation

Simulation is not the highest priority in the plant Simulation is not the highest priority in the plant facilitiesfacilities

Production or quality issues take precedenceProduction or quality issues take precedence

Hard to get plant facilities resources for simulationHard to get plant facilities resources for simulation

““Up front” time required before results are availableUp front” time required before results are availableModel must be calibrated, and results validated, before they Model must be calibrated, and results validated, before they can be trustedcan be trusted

At odds with “quarterly balance sheet culture”At odds with “quarterly balance sheet culture”

May need to structure project to get some results out earlyMay need to structure project to get some results out early

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Data Reconciliation

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Data Reconciliation

Typical Objectives of Data Treatment.Typical Objectives of Data Treatment.

Provide reliable information and knowledge of Provide reliable information and knowledge of complete complete data for validation of process simulation and analysisdata for validation of process simulation and analysis

Yield monitoring and accountingYield monitoring and accountingPlant facilities management and decision-makingPlant facilities management and decision-makingOptimization and controlOptimization and control

Perform instrument maintenancePerform instrument maintenanceInstrument monitoringInstrument monitoringMalfunction detectionMalfunction detectioncalibrationcalibration

Detect operating problemsDetect operating problemsProcess leaks or product lossProcess leaks or product loss

Estimate unmeasured valuesEstimate unmeasured valuesReduce random and gross errors in measurements Reduce random and gross errors in measurements Detect steady statesDetect steady states

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Data TreatmentData Treatment

Business Business managementmanagement

Scheduling &Scheduling &optimizationoptimization

Site & plantSite & plantmanagementmanagement

Advanced controlAdvanced control

Basic process controlBasic process control

INFO

RM

ATI

ON

INFO

RM

ATI

ON

Data treatment is Data treatment is critical forcritical for

• Process simulationProcess simulation• Control and optimization Control and optimization • Management planningManagement planning

Data Reconciliation

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Manual data

On-line data

Lab data

Data Treatment

ProductionProduction

Equipment performanceEquipment performance

ModelingModeling and Simulation and Simulation

OptimizationOptimization

Instrumentation designInstrumentation design

Plant shutdownPlant shutdown

Instrument maintenanceInstrument maintenance

Management planningManagement planning

Data Reconciliation

Overview

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Data Reconciliation

Typical Problems With Process Typical Problems With Process MeasurementsMeasurements

Measurements inherently corrupted by errors: Measurements inherently corrupted by errors: measurement faultsmeasurement faultserrors during processing and transmission of the errors during processing and transmission of the measured signalmeasured signal

Random errors Random errors Caused by random or temporal eventsCaused by random or temporal events

Inconsistency (Gross) errorsInconsistency (Gross) errorsCaused by nonrandom events: instrument Caused by nonrandom events: instrument miscalibration or malfunction, process leaksmiscalibration or malfunction, process leaks

Non-measurementsNon-measurementsSampling restriction, measuring technique, Sampling restriction, measuring technique, instrument failureinstrument failure

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Data Reconciliation

Random errorsRandom errors

FeaturesFeatures

High frequencyHigh frequency

Unrepeatable: neither magnitude nor sign can be Unrepeatable: neither magnitude nor sign can be predicted with certitudepredicted with certitude

SourcesSources

Power supply fluctuationPower supply fluctuation

Signal conversion noiseSignal conversion noise

Changes in ambient conditionChanges in ambient condition

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Data Reconciliation

Inconsistency (Gross error)Inconsistency (Gross error)

FeaturesFeaturesLow frequencyLow frequencyPredictable: certain sign and magnitudePredictable: certain sign and magnitude

SourcesSourcesCaused by nonrandom eventsCaused by nonrandom eventsInstrument relatedInstrument related

• Miscalibration or malfunctionMiscalibration or malfunction• Wear or corrosion of the sensorsWear or corrosion of the sensors

Process relatedProcess related• Process leaksProcess leaks• Solid depositsSolid deposits

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Illustration Of Random & Gross Errors:

Gross error

Random errors

abnormality

t

F

Reliable valueReliable value

Data Reconciliation

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Data Reconciliation

Solutions To ProblemsSolutions To Problems

Random errors: Random errors: Data processingData processing

Based on successive measurement of each individual Based on successive measurement of each individual variable: Temporal redundancyvariable: Temporal redundancy

Traditional filtering techniquesTraditional filtering techniques

Wavelet Transform techniquesWavelet Transform techniques

Inconsistency: Inconsistency: Data reconciliationData reconciliation

Based on plant structure: Spatial redundancyBased on plant structure: Spatial redundancy

Subject to conservation lawsSubject to conservation laws

Unmeasured dataUnmeasured data Data reconciliation

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Data Reconciliation

ReconcilingGross errors

t

F

Processingrandom errors

Measurement Problem Handling:Measurement Problem Handling:

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Data Reconciliation

Data Treatment Typical StrategyData Treatment Typical Strategy

1.1. Establish Plant facilities operating regimesEstablish Plant facilities operating regimes

2.2. Data processingData processing

Remove random noiseRemove random noise

Detect and correct abnormalitiesDetect and correct abnormalities

3.3. Steady state detectionSteady state detection

Identify steady-state duration Identify steady-state duration

Select data setSelect data set

4.4. Data reconciliationData reconciliation

Detect gross errorsDetect gross errors

Correct inconsistenciesCorrect inconsistencies

Calculate unmeasured parametersCalculate unmeasured parameters

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Data Reconciliation

Data processing

Steady state detection

Variables classification

Gross error detection

Data reconciliation

Applications

Process dataFrom Plant Facilities

reconciliation

For simulation and further applications

METHODOLOGY EMPLOYEDMETHODOLOGY EMPLOYED

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Data Reconciliation

THERMODYNAMIC PROPERTIES

STATISTICALPRINCIPLES

ACCURATE and RELIABLE INFORMATION










1 + 1 = 3 !!!


STATISTICAL PRINCIPLES

1.3 + 1.3 = 2.6ACCURATE and RELIABLE INFORMATION

1 + 1 = 3 !!!


STATISTICAL PRINCIPLES

1.3 + 1.3 = 2.6ACCURATE and RELIABLE INFORMATION

What is data reconciliation?What is data reconciliation?

Data reconciliation is the validation of process data using Data reconciliation is the validation of process data using knowledge of plant structure and the plant measurement knowledge of plant structure and the plant measurement system”system”

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Data Reconciliation

Objectives of Data ReconciliationObjectives of Data Reconciliation

Optimally adjust measured values within given process Optimally adjust measured values within given process constraintsconstraints

mass, heat, component balancesmass, heat, component balances

Improve consistency of data to calibrate and validate Improve consistency of data to calibrate and validate process simulation process simulation

Estimate unmeasured process valuesEstimate unmeasured process values

Obtain values not practical to measure directlyObtain values not practical to measure directly

Substitute calculated values for failed instrumentSubstitute calculated values for failed instrument

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Data Reconciliation

Possible BenefitsPossible Benefits::

More accurate and reliable simulation resultsMore accurate and reliable simulation results

More reliable data for process analysis and decision More reliable data for process analysis and decision making by mill managermaking by mill manager

Instrument maintenance and loss detection:Instrument maintenance and loss detection:e.g. US$3.5MM annually in a refinery by decreasing loss by e.g. US$3.5MM annually in a refinery by decreasing loss by 0.5% of 100K BPD0.5% of 100K BPD

Improve measurement layoutImprove measurement layout

Decrease number of routine analysisDecrease number of routine analysis

Improve advanced process controlImprove advanced process control

Clear picture of plant operating conditionClear picture of plant operating condition

Early detections of problemsEarly detections of problems

Quality at process levelQuality at process level

Work Closer to specifications.Work Closer to specifications.

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Data Reconciliation

Data Reconciliation Problem of Process Under Data Reconciliation Problem of Process Under Different StatusDifferent Status

Steady-state data reconciliationSteady-state data reconciliation

based on steady-state modelbased on steady-state model

Using spatial redundancyUsing spatial redundancy

Dynamic data reconciliationDynamic data reconciliation

based on dynamic modelsbased on dynamic models

Using both spatial & temporal redundancyUsing both spatial & temporal redundancy

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Data reconciliation (DR)

DR Problem Of Process Under Different Status DR Problem Of Process Under Different Status (Contd.)(Contd.)

General expression of conservation lawGeneral expression of conservation law::

input- output + generation- consumption- input- output + generation- consumption- accumulation= 0accumulation= 0

Steady state case:Steady state case:

no accumulation of any measurementno accumulation of any measurement

Constraints are expressed algebraicallyConstraints are expressed algebraically

Dynamic process:Dynamic process:

Accumulation cannot be neglectedAccumulation cannot be neglected

Constraints are differential equationsConstraints are differential equations

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Data Reconciliation

Data Reconciliation of Different ConstraintsData Reconciliation of Different Constraints

Linear data reconciliationLinear data reconciliation

Only mass balance is consideredOnly mass balance is considered

flows are reconciledflows are reconciled

Bilinear data reconciliationBilinear data reconciliation

Component balance imposed as well as energy Component balance imposed as well as energy balancebalance

flows & composition measurements are reconciledflows & composition measurements are reconciled

Nonlinear data reconciliationNonlinear data reconciliation

Mass/energy/component balances are includedMass/energy/component balances are included

Flow rate, composition, temperature or pressure Flow rate, composition, temperature or pressure measurements are reconciledmeasurements are reconciled

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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 Quantified PerformancePerformance

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Pinch Analysis.

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

The The Heat Recovery PinchHeat Recovery Pinch (Thermal Pinch Analysis now) was discovered (Thermal Pinch Analysis now) was discovered indepently by Hohmann (71), Umeda et al. (78-79) and indepently by Hohmann (71), Umeda et al. (78-79) and LLinnhoff et al. (78-innhoff et al. (78-79). 79).

Pinch Analysis does this by making an inventory of all producers and Pinch Analysis does this by making an inventory of all producers and consumers of these utilities and then systematically designing an optimal consumers of these utilities and then systematically designing an optimal scheme of utility exchange between these producers and consumers. scheme of utility exchange between these producers and consumers. Energy, Mass, Energy, Mass, andand water re-use are at the heart of Pinch Analysis activities. water re-use are at the heart of Pinch Analysis activities.

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

What is Pinch Analysis?What is Pinch Analysis?

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The The PPinch analysis is a technique to 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 second The use of thermodynamic principles (first and second law).law).

The use heuristics (insight), about design and The use heuristics (insight), about design and economy.economy.

•The Pinch Analysis makes extensive use of various The Pinch Analysis makes extensive use of various graphical representationsgraphical representations

FEATURESFEATURES

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•The Pinch Analysis provides insightsThe Pinch Analysis provides insights about the process about the process..

•In Pinch analysis, the design engineering controls the In Pinch analysis, the design engineering controls the design proceduredesign procedure ( (interactive methodinteractive method))..

•The pinch Analysis integrateThe pinch Analysis integratess economic parameters economic parameters

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The Four phases The Four phases of pinch analysis in the design of of pinch analysis in the design of recovery process:recovery process:

TargetingTargeting

DesignDesign

OptimizationOptimization

ProcessProcess

SimulationSimulation

Data ExtractionData Extraction

Which involves collecting Which involves collecting data for the process and data for the process and the utility systemthe utility system

Which establishes figures Which establishes figures for the best performance for the best performance in various aspects.in various aspects.Where an initial Heat Where an initial Heat Exchanger Network is Exchanger Network is established by heuristics established by heuristics tools allowing a minimum tools allowing a minimum target to be reached.target to be reached.Where an initial design is Where an initial design is simplified and improved simplified and improved economically.economically.

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Heat Exchanger NetworkHeat Exchanger Network (HEN) (HEN)

HEN design is the classical domain of Pinch Analysis. By HEN design is the classical domain of Pinch Analysis. By making proper use of temperature driving forces making proper use of temperature driving forces available between process steams, the optimum heat available between process steams, the optimum heat exchanger network can be designed, taking into exchanger network can be designed, taking into account constraints of equipment location, materials of account constraints of equipment location, materials of construction, safety, control, and operating flexibility. construction, safety, control, and operating flexibility. This then sets the hot and cold utility demand profile of This then sets the hot and cold utility demand profile of the plant.the plant.

When used correctly, Pinch Analysis yields optimum When used correctly, Pinch Analysis yields optimum HEN designs that one would have been unlikely to HEN designs that one would have been unlikely to obtain by experience and intuition alone.obtain by experience and intuition alone.

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Combined Heat and PowerCombined Heat and Power (CHP) (CHP)

CHP is the terminology used to describe plant energy CHP is the terminology used to describe plant energy utilities, boilers, steam turbines, gas turbines, heat utilities, boilers, steam turbines, gas turbines, heat pumps, etc. Traditionally, these have been referred to pumps, etc. Traditionally, these have been referred to as "plant utilities", without distinguishing them from as "plant utilities", without distinguishing them from other plant utilities such as cooling water and other plant utilities such as cooling water and wastewater treatment. wastewater treatment.

The CHP system supplies the hot utility and power The CHP system supplies the hot utility and power requirements of the process. Pinch Analysis offers a requirements of the process. Pinch Analysis offers a convenient way to guarantee the optimum design, convenient way to guarantee the optimum design, which can include the use of cogeneration or three-which can include the use of cogeneration or three-generation (use of hot utility to produce cold utility and generation (use of hot utility to produce cold utility and power for things like refrigeration).power for things like refrigeration).

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Possible BenefitsPossible Benefits::

One of the main advantages of Pinch Analysis over One of the main advantages of Pinch Analysis over conventional design methods is the ability to set a conventional design methods is the ability to set a target energy consumption for an individual process or target energy consumption for an individual process or for an entire production site before to design the for an entire production site before to design the processes. The energy target is the minimum processes. The energy target is the minimum theoretical energy demand for the plant or site. theoretical energy demand for the plant or site.

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

Reduction of emissionsReduction of emissions

Pinch Analysis enable to the engineer with tool to find Pinch Analysis enable to the engineer with tool to find the best way to change the process, if the process let the best way to change the process, if the process let it.it.

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In addition, Pinch Analysis allow you to:In addition, Pinch Analysis allow you to:

Update or Development of Process Flow DiagramsUpdate or Development of Process Flow Diagrams

Identify the bottleneck in the processIdentify the bottleneck in the process

Departmental SimulationsDepartmental Simulations

Full Plant Facilities SimulationFull Plant Facilities Simulation

Determine Minimal Heating (Steam) and Cooling Determine Minimal Heating (Steam) and Cooling RequirementsRequirements

Determine Cogeneration and Three-generation Determine Cogeneration and Three-generation OpportunitiesOpportunities

Determine Projects with Cost Estimates to Achieve Energy Determine Projects with Cost Estimates to Achieve Energy SavingsSavings

Evaluation of New Equipment Configurations for the Most Evaluation of New Equipment Configurations for the Most Economical InstallationEconomical Installation

Pinch Replaces the Old Energy Studies with a Live Study Pinch Replaces the Old Energy Studies with a Live Study that Can Be Easily Updated Using Simulationthat Can Be Easily Updated Using Simulation

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Optimization by Mathematical Programming: introduction

A Mathematical Model of a system is a set of A Mathematical Model of a system is a set of mathematical relationships (e.g., equalities, inequalities, mathematical relationships (e.g., equalities, inequalities, logical conditions) which represent an abstraction of the logical conditions) which represent an abstraction of the real world system under consideration.real world system under consideration.

A Mathematical Model can be developed using:A Mathematical Model can be developed using:Fundamental approachesFundamental approaches Accepted theories of sciences Accepted theories of sciences are used to derive the equations (e.g., Thermodynamics are used to derive the equations (e.g., Thermodynamics Laws).Laws).

Empirical MethodsEmpirical Methods Input-output data are employed in Input-output data are employed in tandem with statistical analysis principles so as to generate tandem with statistical analysis principles so as to generate empirical or “Black box” models.empirical or “Black box” models.

Methods Based on analogyMethods Based on analogy Analogy is employed in Analogy is employed in determining the essential features of the system of interest determining the essential features of the system of interest by studying a similar, well understood system.by studying a similar, well understood system.

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Optimization by Mathematical Programming: introduction

A mathematical Model of a system consists of four key elements:A mathematical Model of a system consists of four key elements:1.1. VariablesVariables The variables can take different values and their The variables can take different values and their

specifications define different states of the systems.specifications define different states of the systems.1.1. ContinuousContinuous, , 2.2. IntegerInteger,,3.3. Mixed set of continuous and integerMixed set of continuous and integer..

2.2. ParametersParameters The parameters are fixed to one or multiple specific The parameters are fixed to one or multiple specific values, and each fixation defines a different model. values, and each fixation defines a different model.

3.3. ConstraintsConstraints the constraints are fixed quantities by the model the constraints are fixed quantities by the model statementstatement

4.4. Mathematical RelationshipsMathematical Relationships The mathematical model relations can The mathematical model relations can be classified as:be classified as:1.1. Equalities Equalities usually composed of mass balance, energy balance, usually composed of mass balance, energy balance,

equilibrium relations, physical property calculations, and engineering equilibrium relations, physical property calculations, and engineering design relations which describe the physical phenomena of the system.design relations which describe the physical phenomena of the system.

2.2. Inequalities Inequalities consist of allowable operating regimes, specifications on consist of allowable operating regimes, specifications on qualities, feasibility of heat and mass transfer, performance qualities, feasibility of heat and mass transfer, performance requirements, and bound on availabilities and demands.requirements, and bound on availabilities and demands.

3.3. Logical conditions Logical conditions provide the connection between the continuous and provide the connection between the continuous and integer variables.integer variables.

The mathematical relations cThe mathematical relations caan be n be algebraicalgebraic, , ddiifffferentialerential, or a , or a mixedmixed set set of of bothboth constraints constraints. These. These can be can be linearlinear or or nonlinearnonlinear..

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What is Optimization?What is Optimization?A A optimization problemoptimization problem is a mathematical model which in is a mathematical model which in addition to the before mentioned elements contains one or addition to the before mentioned elements contains one or mmoreore performance criteria. performance criteria.

The The performance criteriaperformance criteria is denoted as is denoted as an an objective functionobjective function. . IIt can be t can be minimizationminimization of cost, the of cost, the maximizationmaximization or profit or or profit or yield of a process for instance.yield of a process for instance.

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

A well defined optimization problem features a number of A well defined optimization problem features a number of variables greater than the number of equality constraints, which variables greater than the number of equality constraints, which implies that there exist degrees of freedom upon which we implies that there exist degrees of freedom upon which we optimize.optimize.

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The typical mathematical model structureThe typical mathematical model structure for an optimiztion problem takes the following form: for an optimiztion problem takes the following form:

integer

0),(

0),(..

),(min,

Yy

Xx

yxg

yxhts

yxf

n

yx

Where x is a vector of n continuous variables, y is a vector of Where x is a vector of n continuous variables, y is a vector of integer variables, h(x,y)= 0 are m equality constraints, g(x,y) integer variables, h(x,y)= 0 are m equality constraints, g(x,y) 0 0 are 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)Classes of Optimization Problems (OP)

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

If there exist nonlinear terms in the objective function and/or constraintsIf there exist nonlinear terms in the objective function and/or constraints without the use of integer varialbeswithout the use of integer varialbes, the OP becomes a , the OP becomes a nonlinear nonlinear programming (NLP)programming (NLP) problem. problem.

If integer variables If integer variables are usedare used, , theythey participate linearly and participate linearly and separtlsepartly from the y from the continuouscontinuous variables variables, and the objective function and constraints are linear, , and the objective function and constraints are linear, then OP becomes a then OP becomes a mixed-integer linear programming (MILP)mixed-integer linear programming (MILP) problem. problem.

If integer variables If integer variables are usedare used, and there exist nonlinear terms in the , and there exist nonlinear terms in the objective function andobjective function and/or/or constraints, then constraints, then the the OP becomes a OP becomes a mixed-mixed-integer nonlinear programming (MINLP)integer nonlinear programming (MINLP) problem. problem.

Whenever possible, linear programs (LP or MILP) are used because Whenever possible, linear programs (LP or MILP) are used because they they guaranteeguarantee global solutions. global solutions.

MINLP problems features many applications in engineering.MINLP problems features many applications in engineering.

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Applications:Applications:Process SynthesisProcess Synthesis

Heat Exchanger NetworksHeat Exchanger Networks

Distillation SequencingDistillation Sequencing

Mass Exchanger NetworksMass Exchanger Networks

Reactor-based SystemsReactor-based Systems

Utility SystemsUtility Systems

Total Process SystemsTotal Process Systems

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

Design and Scheduling of Multiproduct PlantsDesign and Scheduling of Multiproduct Plants

Interaction of Design and ControlInteraction of Design and Control

Molecular Product DesignMolecular Product Design

Facility Location and allocationFacility Location and allocation

Facility Planning and SchedulingFacility Planning and Scheduling

Topology of Transport NetworksTopology of Transport Networks NEXTNEXT

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Why stochastic Search MethodsWhy stochastic Search Methods

All of the model formulations that you have encountered thus far All of the model formulations that you have encountered thus far in the Optimization have assumed that the data for the given in the Optimization have assumed that the data for the given problem are known accurately. However, for many actual problem are known accurately. However, for many actual problems, the problem data cannot be known accurately for a problems, the problem data cannot be known accurately for a variety of reasons. The first reason is due to simple measurement variety of reasons. The first reason is due to simple measurement errorerror.. The second and more fundamental reason is that some The second and more fundamental reason is that some data represent information about the future (e.g., product data represent information about the future (e.g., product demand or price for a future time period) and simply cannot be demand or price for a future time period) and simply cannot be known with certainty. known with certainty.

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

Genetic Algorithms (GAs)Genetic Algorithms (GAs)

Tabu searchTabu search

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

GA has the capability of collectively searching for multiple GA has the capability of collectively searching for multiple optimal solutions for the same best cost.optimal solutions for the same best cost.

Such information couldSuch information could be be very useful to a designer, because one very useful to a designer, because one configuration could be much easiconfiguration could be much easieer to build than another.r to build than another.

SA takes one solution and efficiently moves it around in the SA takes one solution and efficiently moves it around in the search space, avoidinsearch space, avoidingg local optima. local optima.

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What is GAs?What is GAs?GAs simulate the survival of the fittest among individuals over GAs simulate the survival of the fittest among individuals over consecutive generation for solving a problem. Each individual consecutive generation for solving a problem. Each individual represents a point in a search space and a possible solution. The represents a point in a search space and a possible solution. The individuals in the population are then made to go through a process individuals in the population are then made to go through a process of evolution.of evolution.

GAs are based on an analogy with the genetic structure and GAs are based on an analogy with the genetic structure and behaviour of chromosomes within a population of individuals using behaviour of chromosomes within a population of individuals using the following foundations:the following foundations:

Individuals in a population compete for resources and mates. Individuals in a population compete for resources and mates.

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

Genes from Genes from ““goodgood”” individuals propagate throughout the population so individuals propagate throughout the population so that two good parents will sometimes produce offspring that are better that two good parents will sometimes produce offspring that are better than either parent. than either parent.

Thus each successive generation will become more suited to their Thus each successive generation will become more suited to their environment. environment.

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A A population of individuals is maintained within population of individuals is maintained within search spacesearch space for a GA, for a GA, each representing a possible solution to a given problem. Each each representing a possible solution to a given problem. Each individual is coded as a finite length vector of components, or variables, individual is coded as a finite length vector of components, or variables, in terms of some alphabet, usually the in terms of some alphabet, usually the binarybinary alphabet alphabet {0,1}{0,1}. .

Gene Chromosome

Population

TheThe chromosome (solution) is composed of several genes chromosome (solution) is composed of several genes (variables). A (variables). A fitness scorefitness score (the best objective funtion) (the best objective funtion) is is assigned to each solution representing the abilities of an assigned to each solution representing the abilities of an individual to individual to “compete”“compete”. The individual with the optimal (or . The individual with the optimal (or generally near optimal) fitness score is sought. The GA aims to generally near optimal) fitness score is sought. The GA aims to use selective use selective ““breedingbreeding”” of the solutions to produce of the solutions to produce ““offspringoffspring”” better than the parents by combining information better than the parents by combining information from the chromosomes.from the chromosomes.

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Stochastic Search MethodsThe general genetic algorithm The general genetic algorithm solution is found bysolution is found by::

1.1. [Start] [Start] Generate random population of Generate random population of n n chromosomes chromosomes (suitable solutions for the problem)(suitable solutions for the problem)

2.2. [Fitness] [Fitness] Evaluate the fitness Evaluate the fitness f(x) f(x) (objective function)(objective function) of of each chromosome each chromosome x x in the population.in the population.

3.3. [New population] [New population] Create a new population by repeating Create a new population by repeating following steps until the new populationis completefollowing steps until the new populationis complete

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

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

3.3. [Mutation] [Mutation] With a mutation probability mutate new offspring at With a mutation probability mutate new offspring at each locus (position ineach locus (position in chromosome).chromosome).

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

4.4. [Replace] [Replace] Use new generated population for a further run of Use new generated population for a further run of algorithm 4.algorithm 4.

5.5. [Test] [Test] If the end condition is satisfied, If the end condition is satisfied, stopstop, and return the , and return the best solution in current population 5.best solution in current population 5.

6.6. [Loop] [Loop] Go to step Go to step 22

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Encoding of a ChromosomeEncoding of a Chromosome

The chromosome should in some way contain information about The chromosome should in some way contain information about the the solution which it represents. The most used way of encoding solution which it represents. The most used way of encoding is a binary string. The chromosome then could look like this:is a binary string. The chromosome then could look like this:

Each chromosome has one binary string. Each bit in this string Each chromosome has one binary string. Each bit in this string can represent some characteristic of thecan represent some characteristic of the solution. Or the whole solution. Or the whole string can represent a number string can represent a number

Of course, there are many other ways of encoding. This depends Of course, there are many other ways of encoding. This depends mainly on the solved problem. Formainly on the solved problem. For example, one can encode example, one can encode directly integer or real numbersdirectly integer or real numbers.. SSometimes it is ometimes it is also also useful to useful to encode someencode some permutations.permutations.

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Stochastic Search MethodsStochastic Search Methods

CrossoverCrossover

After we have decided what encoding we will use, we can make a After we have decided what encoding we will use, we can make a step to crossover. Crossover selectsstep to crossover. Crossover selects genes from parent genes from parent chromosomes and creates a new offspring. The simplest way how chromosomes and creates a new offspring. The simplest way how to do this is toto do this is to choose randomly some crossover point and choose randomly some crossover point and everything before this point copy from a first parenteverything before this point copy from a first parent and thenand then everything after a crossover point copy from the second parent.everything after a crossover point copy from the second parent.

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

There are other ways how to make crossoverThere are other ways how to make crossoverss, , and and we can we can choose mchoose multipleultiple crossover points. crossover points. CrossoverCrossoverss can be rather can be rather complicated and vary dependcomplicated and vary depending oning on the encoding of the encoding of chromosome.chromosome. Specific crossoverSpecific crossoverss made for a specific problem can made for a specific problem can improve performance of the genetic algorithm.improve performance of the genetic algorithm.

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Stochastic Search MethodsStochastic Search Methods

MutationMutation

After a crossover is performed, mutation takeAfter a crossover is performed, mutation takess place. This is to place. This is to prevent prevent the the falling falling of of all solutions in all solutions in the the populationpopulation into a local into a local optimum. Mutation changes the new offspringoptimum. Mutation changes the new offspring randomly randomly. For . For binarybinary encoding we can switch a few randomly chosen bits from encoding we can switch a few randomly chosen bits from 1 to 0 or from 0 to 1. Mutation can then be1 to 0 or from 0 to 1. Mutation can then be shown as 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 could permutations, mutation could be exchanging two genes.be exchanging two genes.

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GAs Characteristics:GAs Characteristics:A GA makes no assumptions about the function to be optimized (A GA makes no assumptions about the function to be optimized (LLevine, evine, 1997) and thus can also be used for nonconvex objective functions1997) and thus can also be used for nonconvex objective functions

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

A GA operates on several solutions simultaneously, gathering information A GA operates on several solutions simultaneously, gathering information from current search points and using it to direct subsequent searches from current search points and using it to direct subsequent searches which makes a GA less susceptible to the problems of local optima and which makes a GA less susceptible to the problems of local optima and noisenoise

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

Optimum?Optimum?

Generate New PopulationGenerate New Population•GA parametersGA parameters•GA strategiesGA strategies

StopStop

11stst Generation Generation

NNthth Generation Generation

(N+1)(N+1)thth Generation Generation

YesYes

NoNo

GA Solution ProcedureGA Solution Procedure

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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?What is Life Cycle Analysis?

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

An inventory of relevant inputs and outputs of a systemAn inventory of relevant inputs and outputs of a system

Evaluating the potential environmental impacts Evaluating the potential environmental impacts associated with those inputs and outputsassociated with those inputs and outputs

Interpreting the results of the inventory and impact Interpreting the results of the inventory and impact phases in relation to the objectives of the study headingphases in relation to the objectives of the study heading

Evaluation of some aspects of a product system through all Evaluation of some aspects of a product system through all stages of its life cyclestages of its life cycle

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Life Cycle Analysis

Why LCA is important:Why LCA is important:

Tool for improvement of environmental Tool for improvement of environmental performanceperformance

Systematic way of managing an organization’s Systematic way of managing an organization’s environmental affairsenvironmental affairs

Way to address immediate and long-term Way to address immediate and long-term impacts of products, services and processes on impacts of products, services and processes on the environmentthe environment

Focus on continual improvement of the systemFocus on continual improvement of the system

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DIRECT APPLICATIONS•Product development and improvement

•Strategic planification

•Public policy

•Marketing

•Etc.

Goal and

Scope

definition

Inventory

analysis

Impact

assessment

Interpretation

LIFE-CYCLE ASSESSMENT

OTHER ASPECTS•Technical•Economic•Market•Social etc.

Life Cycle Analysis

LCA methodology:LCA methodology:

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Life Cycle Analysis

Goal and scope definitionsGoal and scope definitions

goal goal application, use and users application, use and users

scope scope borders of the assessment borders of the assessment

functional unit functional unit scale for comparison scale for comparison• efficiencyefficiency• durabilitydurability• performance quality standardperformance quality standard

system boundaries system boundaries process, inputs and process, inputs and outputs definedoutputs defined

data quality data quality reflected in the end results reflected in the end results

critical review process critical review process verification of validity verification of validity

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Life Cycle Analysis

Inventory analysisInventory analysis

data collection data collection qualitative or quantitative, most qualitative or quantitative, most work intensivework intensive

refining system boundaries refining system boundaries after initial data after initial data collectioncollection

calculation calculation no formal description, software no formal description, software

validation of data validation of data assessment of data quality assessment of data quality

relating data to the specific system relating data to the specific system data must data must be ralted to the functional unit be ralted to the functional unit

allocation allocation done when not all impacts and done when not all impacts and outputs are within the system boundariesoutputs are within the system boundaries

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Life Cycle Analysis

Impact assessmentImpact assessment

category definition category definition impact categories defined impact categories defined

classification classification inventory input and output inventory input and output appointed to impact categoriesappointed to impact categories

characterization characterization assign relative contribution assign relative contribution

weighting weighting when comparison of the impact when comparison of the impact categories is not possiblecategories is not possible

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Life Cycle Analysis

Interpretation/improvement assessmentInterpretation/improvement assessment

identification of significant environmental identification of significant environmental issues issues information structured in order to information structured in order to get a clear view on key environmental get a clear view on key environmental issuesissues

evaluation evaluation completeness analysis, completeness analysis, sensitivity analysis, consistency analysissensitivity analysis, consistency analysis

conclusions and recommendations conclusions and recommendations improve reporting of the LCAimprove reporting of the LCA

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Life Cycle Analysis

Possible Benefits:Possible Benefits:Improvements in overall environmental Improvements in overall environmental performance and complianceperformance and compliance

Provides a framework for using pollution Provides a framework for using pollution prevention practices to meet LCA objectivesprevention practices to meet LCA objectives

Increased efficiency and potential cost savings Increased efficiency and potential cost savings when managing environmental obligationswhen managing environmental obligations

Promotes predictability and consistency in Promotes predictability and consistency in managing environmental obligationsmanaging environmental obligations

More effective measurement of scarce More effective measurement of scarce environmentalenvironmental

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Data-Driven Process Modeling

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Process IntProcess Integration Challengeegration Challenge::Make sense of Make sense of masses ofmasses of data data

Many organisations today are faced with the same challenge: TOO MUCH DATA

It is the last item that is of interest to us as chemical engineers

Drowning in data!Drowning in data!

Data-Driven Process Modelling

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Data-Rich but Knowledge-PoorData-Rich but Knowledge-Poor

Far too much data for a human brainFar too much data for a human brain

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

BigBig Problem: Problem: Interesting, useful patterns and Interesting, useful patterns and relationships relationships not intuitively obviousnot intuitively obvious lie hidden inside lie hidden inside enormous, unwieldy databasesenormous, 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, to This approach uses the plant process data directly, to establish mathematic correlations.establish mathematic correlations.

Unlike the theoretical models, empirical models do NOT Unlike the theoretical models, empirical models do NOT take the process fundamentals into account. They only take the process fundamentals into account. They only use pure mathematical and statistical techniques. Multi-use pure mathematical and statistical techniques. Multi-Variable Analysis (MVA) is one such method, because it Variable Analysis (MVA) is one such method, because it reveals patterns and correlations independently of any reveals patterns and correlations independently of any pre-conceived notions.pre-conceived notions.

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

OUTSIDE INOUTSIDE INEmpirical Model

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With MVA With MVA you moveyou move

From From DataData to to InformationInformation..

From From InformationInformation to to KnowledgeKnowledge..

– From From KnowledgeKnowledge to to ActionAction..

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What is MVA?What is MVA?

MMulti-ulti-VVariate ariate AAnalysis” (nalysis” (> 5 variables)> 5 variables)MVA uses ALL available data to capture the most MVA uses ALL available data to capture the most information possibleinformation possible

Principle: boil down hundreds of variables down to aPrinciple: boil down hundreds of variables down to a mere mere handfulhandful

MVA


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MVA Example: Apples and OrangesMeasurable differencesMeasurable differences

Colour, 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 only However, always only oneone latentlatent attributeattribute

Apple or orange?Apple or orange?

+1 -1


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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 to

interpret

Statistical Model

2-D Visual Outputs

(internal to

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 about What about an an extreme extreme outlier?outlier?


Effect of Outliers on MVAEffect of Outliers on MVA

OUTLINEROUTLINER

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1 component

Extreme outliers very detrimental to MVA

New New (wrong) (wrong) componentcomponent!!

Linear regressionby Least squares !Linear regressionby Least squares !

Real component has Real component has become mere noisebecome mere noise

Effect of Outliers on MVA


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Benefits:Benefits:

Explore Inter-RelationshipsExplore Inter-RelationshipsCreate and Learn by modellingCreate and Learn by modelling

« What-if » Exercises« What-if » ExercisesLow-cost investigation of optionsLow-cost investigation of options

Soft Sensor (Inferential Control)Soft Sensor (Inferential Control)for parameters we can’t measure directlyfor parameters we can’t measure directly

Feed-Forward (Model-Based) ControlFeed-Forward (Model-Based) Control

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Integrate Process Design and Control

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Control Objectives:Control Objectives:

Product specifications variability should be kept to a Product specifications variability should be kept to a minimum minimum -->--> process variability (To Control Product process variability (To Control Product quality).quality).

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

Plants become highly integrated in terms of mass and Plants become highly integrated in terms of mass and energy and therefore, process dynamics are often difficult energy and therefore, process dynamics are often difficult to control. The Control is permanently necessary to do for to control. The Control is permanently necessary to do for allowing the process to operate in the best conditions.allowing the process to operate in the best conditions.

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it is a it is a property of a process property of a process that accounts for the that accounts for the easeease with which a with which a continuous plantcontinuous plant can be can be held at a specified operating policyheld at a specified operating policy, , despite despite external disturbancesexternal disturbances (resiliency) and (resiliency) and uncertaintiesuncertainties (flexibility) and (flexibility) and regardless of the control system imposed on such a plant.regardless of the control system imposed on such a plant.

DESIGNDESIGN CONTROLCONTROL++

Changes inChanges inProcessProcess

-Dynamics-Dynamics-Tunings-Tunings- Control - Control

configurationsconfigurations

Process VariabilitySources MIN

Steady State & Dynamic Simulations


CONTROLLABILITYCONTROLLABILITY

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Integrate Process Design & Control

Process

sensor

InputVariables

OutputVariables

(controlled andMeasured)

Input Variables(Manipulated)

Disturbances

Uncertainties

Internal interactionsInternal interactions

PROCESS RESILIENCY

PROCESS FLEXIBILITY

Control Loop

Fundamentals:Fundamentals:

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CC FC

C, F

Water, F1

Pulp, F2

OUTPUTSINPUTS(process variables or disturbances)

EFFECTS(Best Selection by Controllability analysis)


e.g. Controllability analysis for control structures design

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The process will be more The process will be more capable to move smoothly capable to move smoothly around the possible around the possible operating edgeoperating edge

Stability and better Stability and better performance of control performance of control loops and structures loops and structures

System relatively insensitive System relatively insensitive to perturbationsto perturbations

Efficient management of Efficient management of interacting networksinteracting networks

Improvement of current dynamics

Flexibility

Why Controllability Why Controllability is important:is important:

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Production rate Production rate (time)(time)

Product quality, andProduct quality, and

Energy economy.Energy economy.

The Top level of the process control,

“Strategic control level is thus concerned with

achieving the appropriate values principally of:

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Real Time Optimizations (RTO)

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Real Time Optimizations

The Process Industries are increasingly compelled to The Process Industries are increasingly compelled to operate profitably in very dynamic and global operate profitably in very dynamic and global market. The increasing competition in the market. The increasing competition in the international area and stringent product international area and stringent product requirements mean decreasing profit margins unless requirements mean decreasing profit margins unless plant operations are optimized dynamically to adopt plant operations are optimized dynamically to adopt to the changing market conditions and to reduce the to the changing market conditions and to reduce the operating cost. Hence, the importance of operating cost. Hence, the importance of real-time or real-time or on-line optimizationon-line optimization of an entire plant is rapidly of an entire plant is rapidly increasing.increasing.

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Real Time Optimizations

What is RTO?What is RTO?

Real-time Optimization is a model-based Real-time Optimization is a model-based steady-state technology that determines the steady-state technology that determines the economically optimal operating policy for a economically optimal operating policy for a process in the near termprocess in the near term

The system optimizes a process The system optimizes a process simulationsimulation and not the process directlyand not the process directly

Performance measured in terms of Performance measured in terms of economic benefiteconomic benefit

Is an active field of research:Is an active field of research:• Model accuracy, error transmission, performance Model accuracy, error transmission, performance

evaluationevaluation

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RTO – Schematically

ReconciliationReconciliation

And gross ErrorAnd gross Error

DetectionDetection

Updating Process ModelUpdating Process Model

(Steady State(Steady StateDynamicDynamic

Simulation)Simulation)

Steady State DetectionSteady State DetectionOptimizationOptimization

(Objectives Functions(Objectives Functions))

Bu

sin

ess O

bje

ctiv

es;

Bu

sin

ess O

bje

ctiv

es;

Econ

om

ic D

ata

;Econ

om

ic D

ata

;

Pro

du

ct S

pecifi

catio

nP

rod

uct S

pecifi

catio

n

Cost, Process,Cost, Process,

Environmental,Environmental,

Product DataProduct Data

Plant FacilityPlant Facility

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Direct Search Method Schematically

DynamicSimulation

(Model)

RTO Algorithm(Objective Fct,Constraints)

SETPOINTS(DOFs)

Selected Ouputs

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Business Model And Supply Chain Modeling

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Integrated Business & Process Model

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

Product DataProduct Data

Cost, Process,Cost, Process,Environmental & Environmental & Product OutcomesProduct Outcomes

ProcessProcessDesignDesign

AnalysisAnalysisAnd And

SynthesisSynthesis

ProcessProcessOperationOperationAnalysisAnalysis

and and OptimizatioOptimizatio

nn

Business Model And Supply Chain Modeling

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

Product Data Product Data


ProcessProcessDesignDesign

AnalysisAnalysis and and

SynthesiSynthesiss

Click Click HereHere ProcessProcess

OperationOperationAnalysisAnalysis

and and OptimizatiOptimizati

onon


Integrated Business &

Process ModelClick HereClick Here

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

Product Product OutcomesOutcomes

NEXTNEXT

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Plant Facilities

Integrated Business & Process Model

Process (P) & Environmental

(E) Data

Accounting Data

Product Data

MarketData

Data Processing

ProcessedP&E Data

Data Reconciliation

ReconciledP&E Data

Cost, Process, Environmental & Product Data

The double arrows mean all the The double arrows mean all the data are consistent together data are consistent together

throughout all the plant facilitiesthroughout all the plant facilitiesData Validation & Data Validation &

ReconciliationReconciliation

Once the model is built it Once the model is built it can be used to validate can be used to validate

and reconcile dataand reconcile data

Cost, Process, Environmental Cost, Process, Environmental

and Product Dataand Product Data

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1st PrinciplesModels

Integrated Business and Process Model

Cos

t Acc

ount

ing

Mod

elS

upply Chain(S

C) and

Env. S

C M

odels

Cos

t Acc

ount

ing

Mod

el

Model that deals with the classification, recording, allocation, and summarization for the purpose of management decision making and financial reporting

Data Driven Models

ProcessedP&E data


Environmental Environmental DataData

Market DataMarket Data

Accounting DataAccounting Data


Product DataProduct DataSupply C

hain(SC

) and

Env. S

C M

odels


Data Driven Models

ProcessSimulation

Models

Integrated Business Integrated Business

and and

Process ModelProcess Model

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Supply Manufacturing Retail

DistributionConsumer

Supply ManufacturingManufacturing Retail

Distribution

Retail

DistributionConsumerConsumer

Supply Chain and Environmental Supply Chain

Supply Chain (SC)Supply Chain (SC) is a network of organizations that are is a network of organizations that are involved, through upstream and downstream linkages, in the involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form different processes and activities that produce value in the form of products and services in the hands of the ultimate customerof products and services in the hands of the ultimate customer

Supply Manufacturing Retail

DistributionConsumer

W

Recycling

W

Collection

RemanufacturingReuse

W

W W

WW

W

Supply ManufacturingManufacturing Retail

Distribution

Retail

DistributionConsumerConsumer

W

Recycling

W

Collection


W

W W

WW

W

WW

RecyclingRecycling

WWW

CollectionCollection



WW

WW WW

WWWW

WW

(Waste)(Waste)

Environmental Supply Chain (ESC)Environmental Supply Chain (ESC) holds all the elements a holds all the elements a traditional supply chain has but is extended to a semi-closed traditional supply chain has but is extended to a semi-closed loop in order to also account for the environmental impact of loop in order to also account for the environmental impact of the supply chain and recycling, re-use and collection of used the supply chain and recycling, re-use and collection of used material (Beamon 1999) material (Beamon 1999)

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Supply Chain and Environmental Supply Chain

The objective of the SC and ESC models are:The objective of the SC and ESC models are:To integrate inter-organizational units along a SC and To integrate inter-organizational units along a SC and coordinate materials, information and financial flows coordinate materials, information and financial flows in order to fulfill customer demands with the aim of in order to fulfill customer demands with the aim of improving SC profitability and responsivenessimproving SC profitability and responsiveness

To gain insight in the total environmental impact of To gain insight in the total environmental impact of the production process (from supplier to customer the production process (from supplier to customer and back to the facility by recycling) and all the and back to the facility by recycling) and all the products that are manufactured. (closely linked to products that are manufactured. (closely linked to LCA)LCA)

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Inte

gra

ted

Bu

sin

ess

& P

roc

ess

Mo

del

Capital EffectivenessAnalysis

Process Integration

Tools

Process DesignAnalysis – Design

Objectives

Process Design

Analysis and Synthesis

Loop

•Process simulation•Data Reconciliation•MVA using relationaldatabase•Pinch analysis• LCA•SC and ESC model analysis•Controllability Analysis•Optimization (Deterministic and/or Stochastic)

Process Design Analysis and Synthesis

Process Design Analysis and SynthesisProcess Design Analysis and Synthesis

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Inte

gra

ted

Bu

sin

ess

& P

roc

ess

Mo

del

Objective Functionfor

Process Optimization

Process Integration

Tools

Detailed ProcessInvestigation to

Validate Recommendations

Process Operation

Analysis and Optimization

Loop

•Data reconciliation for instrument validation•Dynamic simulation•Process control strategies •MVA (Soft sensor dev.)•Real-time optimization•Optimizated supply chain Model

Process Operation Analysis and Optimization

Process Design Analysis and OptimizationProcess Design Analysis and Optimization

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Outline


1.2 Overview of Process Integration tools 1.2 Overview of Process Integration tools

1.3 An “around-the-world tour” of PI practitioners 1.3 An “around-the-world tour” of PI practitioners focuses of expertisefocuses of expertise




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1.3 An “around-the-world tour” of PI practitioners focuses of

expertise (May 2003).

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Around the World tour of PI practitioners focuses of experience

Courtesy mainly of the www – to capture the Courtesy mainly of the www – to capture the flavor of the evolution of Process Integrationflavor of the evolution of Process Integration

PI is relatively new:PI is relatively new:

Researchers build on their strengthsResearchers build on their strengths

Many of the ground-breaking techniques are Many of the ground-breaking techniques are coming from universitiescoming from universities

When techniques become practical, the When techniques become practical, the private sector generally capitalizes and private sector generally capitalizes and techniques advance more rapidlytechniques advance more rapidly

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Carnegie Mellon University, Department of Chemical Engineering, Pittsburgh, Carnegie Mellon University, Department of Chemical Engineering, Pittsburgh, USAUSAMajor Contact:Major Contact: Professor Ignacio E. Grossmann, head of department Professor Ignacio E. Grossmann, head of departmentWeb:Web: http://www.cheme.cmu.edu/research/capd/http://www.cheme.cmu.edu/research/capd/

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

Current research in Process Integration includes:Current research in Process Integration includes:1) Insights to Aid and Automate Synthesis (Invention)1) Insights to Aid and Automate Synthesis (Invention)2) Structural Optimization of Process Flowsheets2) Structural Optimization of Process Flowsheets3) Synthesis of Reactor Systems and Separation Systems3) Synthesis of Reactor Systems and Separation Systems4) Synthesis of Heat Exchanger Networks4) Synthesis of Heat Exchanger Networks5) Global Optimization techniques relevant to Process Integration5) Global Optimization techniques relevant to Process Integration6) Integrated Design and Scheduling of Batch plants6) Integrated Design and Scheduling of Batch plants7) Supply chain dynamics and optimization7) Supply chain dynamics and optimization

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

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Imperial College, Centre for Process Systems Engineering, London, UKImperial College, Centre for Process Systems Engineering, London, UKMajor Contact:Major Contact: Prof. Efstratios N Pistikopoulos Prof. Efstratios N PistikopoulosWeb:Web: http://www.ps.ic.ac.uk/http://www.ps.ic.ac.uk/ and and http://www.psenterprise.comhttp://www.psenterprise.comResearch Area:Research Area: Recognized as the largest research group in the area of Recognized as the largest research group in the area of Process Systems Engineering (PSE), which includes Synthesis/Design, Process Systems Engineering (PSE), which includes Synthesis/Design, Operations, Control and Modeling. The group is recognized as a world-wide Operations, Control and Modeling. The group is recognized as a world-wide center of excellence in Process Modeling, Numerical Techniques/Optimization center of excellence in Process Modeling, Numerical Techniques/Optimization and Integrated Process Design (includes simultaneous consideration of Process and Integrated Process Design (includes simultaneous consideration of Process Integration and Control). The Centre is also an important contributor in the Integration and Control). The Centre is also an important contributor in the area of Integration and Operation of Batch Processes.area of Integration and Operation of Batch Processes.Current research in Process Integration includes:Current research in Process Integration includes:1) Integrated Batch Processing1) Integrated Batch Processing2) Design and Management of Integrated Supply Chain Processes2) Design and Management of Integrated Supply Chain Processes3) Uncertainty and Operability in Process Design3) Uncertainty and Operability in Process Design4) Formulation of Mathematical Programming Models to address problems in 4) Formulation of Mathematical Programming Models to address problems in Process Synthesis and IntegrationProcess Synthesis and Integration

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

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UMIST, Department of Process Integration, Manchester, UKUMIST, Department of Process Integration, Manchester, UKMajor Contact:Major Contact: Professor Robin Smith, head of department Professor Robin Smith, head of departmentWebWeb: : http://www.cpi.umist.ac.uk/http://www.cpi.umist.ac.uk/Research AreaResearch Area: Recognized as the pioneering and major research group : Recognized as the pioneering and major research group in the area of Pinch Analysis. Previous research includes targets and design in the area of Pinch Analysis. Previous research includes targets and design methods for Heat Exchanger Networks (grassroots and retrofits), Heat and methods for Heat Exchanger Networks (grassroots and retrofits), Heat and Power systems, Heat driven Separation Systems, Flexibility, Total Sites, Power systems, Heat driven Separation Systems, Flexibility, Total Sites, Pressure Drop considerations, Batch Process Integration, Water and Waste Pressure Drop considerations, Batch Process Integration, Water and Waste Minimization and Distributed Effluent Treatment.Minimization and Distributed Effluent Treatment.

Current research is organized in three major areas:Current research is organized in three major areas:1) Efficient Use of Raw Materials (including Water)1) Efficient Use of Raw Materials (including Water)2) Energy Efficiency2) Energy Efficiency3) Emissions Reduction3) Emissions Reduction4) E4) Eefficient use of capital.efficient use of capital.

Consortium:Consortium: "Process Integration Research Consortium" with 27 members "Process Integration Research Consortium" with 27 members (2003) including operating companies, engineering & contracting (2003) including operating companies, engineering & contracting companies, consulting companies and software vendors. The consortium companies, consulting companies and software vendors. The consortium was founded in 1984 by six multinational companies.was founded in 1984 by six multinational companies.

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

Major Contact:Major Contact: Professor Thore Berntsson, head of department Professor Thore Berntsson, head of department

Web:Web: http://www.hpt.chalmers.se/http://www.hpt.chalmers.se/

Research Area:Research Area: Methodology development and applied research based on Pinch Methodology development and applied research based on Pinch Technology. Emphasis on new Retrofit methods including realistic treatment of Technology. Emphasis on new Retrofit methods including realistic treatment of geographical distances, pressure drops, varying fixed costs, etc. Important new geographical distances, pressure drops, varying fixed costs, etc. Important new Concepts include the Cost Matrix for Retrofit Screening and new Grand Composite Concepts include the Cost Matrix for Retrofit Screening and new Grand Composite type Thermodynamic Diagrams for Heat and Power applications (including Gas type Thermodynamic Diagrams for Heat and Power applications (including Gas Turbines and Heat Pumps). Research towards pulp and paper with focus on energy Turbines and Heat Pumps). Research towards pulp and paper with focus on energy and environment.and environment.

Research areas are:Research areas are:

1) Retrofit Design of Heat Exchanger Networks1) Retrofit Design of Heat Exchanger Networks

2) Process Integration of Heat Pumps in Grassroots and Retrofits2) Process Integration of Heat Pumps in Grassroots and Retrofits

3) Gas Turbine based CHP plants in Retrofit Situations3) Gas Turbine based CHP plants in Retrofit Situations

4) Applied research in Pulp and Paper industry, such as black liquor gasification, 4) Applied research in Pulp and Paper industry, such as black liquor gasification, closing the bleaching plant, etc.closing the bleaching plant, etc.

5) Environmental aspects of Process Integration, especially greenhouse gas emissions)5) Environmental aspects of Process Integration, especially greenhouse gas emissions)

IndustryIndustry: Close co-operation with some of the major pulp and paper industry groups, : Close co-operation with some of the major pulp and paper industry groups, including training courses, consulting, etc.including training courses, consulting, etc.

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École Polytechnique de Montréal, Chemical engineering Department, École Polytechnique de Montréal, Chemical engineering Department, Quebec, CanadaQuebec, Canada

Major Contact:Major Contact: Dr. Paul Stuart, Chair holder Dr. Paul Stuart, Chair holder

WebWeb: : http://www.pulp-paper.cahttp://www.pulp-paper.ca

Research AreaResearch Area: the application of Process Integration in the pulp and paper : the application of Process Integration in the pulp and paper industry, with emphasis on pollution prevention techniques and profitability analysis, industry, with emphasis on pollution prevention techniques and profitability analysis, the Efficiency use of energy and Raw Materials (including Water), process control, the Efficiency use of energy and Raw Materials (including Water), process control, and plant sustainability.and plant sustainability.

Research areas are::Research areas are::

1)1)process simulation,process simulation,

2)2)Data reconciliation,Data reconciliation,

3)3)Process Control,Process Control,

4)4)Networks Analysis HEN and MEN, Networks Analysis HEN and MEN,

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

6)6)Business Model.Business Model.

7)7)Data Driving Modeling.Data Driving Modeling.

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

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Universitat Politècnica de Catalunya, Chemical Engng. Department, Universitat Politècnica de Catalunya, Chemical Engng. Department, Barcelona, SpainBarcelona, SpainMajor Contact:Major Contact: Professor Luis Puigjaner, Director LCMA Professor Luis Puigjaner, Director LCMAWeb: Web: http://tqg.upc.es/http://tqg.upc.es/Research Area:Research Area: Pioneering work on Computer Aided Process Operations. Pioneering work on Computer Aided Process Operations. Within Process Integration, the group is recognized for its contributions in Within Process Integration, the group is recognized for its contributions in Time-Dependent Processes, such as Combined Heat and Power, Combined Time-Dependent Processes, such as Combined Heat and Power, Combined Energy-Waste and Waste Minimization, Integrated Process Monitoring, Energy-Waste and Waste Minimization, Integrated Process Monitoring, Diagnosis and Control and finally Process Uncertainty. Diagnosis and Control and finally Process Uncertainty. Current research in the area of Process Integration includes:Current research in the area of Process Integration includes:1) Evolutionary Modeling and Optimization1) Evolutionary Modeling and Optimization2) Multi-objective Optimization in time-dependent systems2) Multi-objective Optimization in time-dependent systems3) Combined Energy and Water Use Minimization3) Combined Energy and Water Use Minimization4) Integration of Thermally Coupled Distillation Columns4) Integration of Thermally Coupled Distillation Columns5) Hot-gas Recovery and Cleaning Systems5) Hot-gas Recovery and Cleaning SystemsConsortium:Consortium: "Manufacturing Reference Centre" with 12 members (1966) "Manufacturing Reference Centre" with 12 members (1966) including Conselleria d'Indústria and associated operating companies, including Conselleria d'Indústria and associated operating companies, engineering and contracting companies, consultants and software vendors.engineering and contracting companies, consultants and software vendors.

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Texas A&M University, Chemical Engineering Department, Texas, Texas A&M University, Chemical Engineering Department, Texas, USAUSAMajor ContactMajor Contact: Professor Mahmoud M. El-Halwagi: Professor Mahmoud M. El-HalwagiWebWeb:: http://process-integration.tamu.edu/ http://process-integration.tamu.edu/ andand http://www-che.tamu.edu/cpipe/ http://www-che.tamu.edu/cpipe/Research AreaResearch Area: Recognized as a leading research group in the areas of : Recognized as a leading research group in the areas of Mass Integration and Pollution Prevention through Process Integration.Mass Integration and Pollution Prevention through Process Integration.Research areas are:Research areas are:

1) Global allocation of Mass and Energy1) Global allocation of Mass and Energy2) Synthesis of Waste Allocation and Species Interception Networks2) Synthesis of Waste Allocation and Species Interception Networks3) Physical and Reactive Mass Pinch Analysis3) Physical and Reactive Mass Pinch Analysis4) Synthesis of Heat-Induced Networks4) Synthesis of Heat-Induced Networks5) Design of Membrane-Hybrid Systems5) Design of Membrane-Hybrid Systems6) Design of Environmentally acceptable Reactions6) Design of Environmentally acceptable Reactions7) Integration of Reaction and Separation Systems7) Integration of Reaction and Separation Systems8) Flexibility and Scheduling Systems8) Flexibility and Scheduling Systems9) Simultaneous Design and Control9) Simultaneous Design and Control10) Global Optimization via Interval Analysis10) Global Optimization via Interval Analysis

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University of Guanajuato, University of Guanajuato, Faculty of Chemistry, Guanajuato, Faculty of Chemistry, Guanajuato, México México

Major contact: Major contact: Dr. Martin-Picon-Nunez, DirectorDr. Martin-Picon-Nunez, Director

Web: Web: http://www.ugto.mx

Research Area:Research Area: Hosts the only course Masters Program in process Hosts the only course Masters Program in process integration in North America, they are developing in the next areasintegration in North America, they are developing in the next areas Analysis of Processes, Power Systems, and to develop of technology Analysis of Processes, Power Systems, and to develop of technology benign Environmental. benign Environmental.


1) Synthesis of Processes; Modeling, Simulation, Control and 1) Synthesis of Processes; Modeling, Simulation, Control and Optimization of Processes; New Processes and Materials.Optimization of Processes; New Processes and Materials.

2) Recovery systems of Heat; Renewable sources of Energy; 2) Recovery systems of Heat; Renewable sources of Energy; Thermodynamic Optimization.Thermodynamic Optimization.

3) Contaminated Atmosphere rehabilitation; Treatment of Effluents; 3) Contaminated Atmosphere rehabilitation; Treatment of Effluents; Environmental Processes.Environmental Processes.

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University of the Witwatersrand, Process & Materials Eng., Johannesburg, University of the Witwatersrand, Process & Materials Eng., Johannesburg, South AfricaSouth Africa

Major Contact:Major Contact: Professor David Glasser, AECI Professor Professor David Glasser, AECI Professor

WebWeb: : http://www.wits.ac.za/fac/engineering/procmat/homepage.htmlhttp://www.wits.ac.za/fac/engineering/procmat/homepage.html

Research AreaResearch Area: Recognized as the major research group in the development of : Recognized as the major research group in the development of the Attainable Region (AR) method for Reactor and Process Synthesis. The the Attainable Region (AR) method for Reactor and Process Synthesis. The Attainable Region concept has been expanded to systems where mass transfer, Attainable Region concept has been expanded to systems where mass transfer, heat transfer and separation take place. In its generalized form (reaction, mixing, heat transfer and separation take place. In its generalized form (reaction, mixing, separation, heat transfer and mass transfer), the Attainable Region concept separation, heat transfer and mass transfer), the Attainable Region concept provides a Synthesis tool that will provide targets for "optimal" designs against provides a Synthesis tool that will provide targets for "optimal" designs against which more practical solutions can be judged.which more practical solutions can be judged.


1) Systems involving Reaction, Mixing and Separation (e.g. Reactive Distillation)1) Systems involving Reaction, Mixing and Separation (e.g. Reactive Distillation)

2) Non-isothermal Chemical Reactor Systems2) Non-isothermal Chemical Reactor Systems

3) Optimization of Dynamic Systems3) Optimization of Dynamic Systems

Clients:Clients: they have founded your own consultancy enterprise the name they have founded your own consultancy enterprise the name ““Wits EnterpriseWits Enterprise””..

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Linnhoff March Ltd., Northwich, Cheshire, UKLinnhoff March Ltd., Northwich, Cheshire, UK

Web:Web: http://www.linnhoffmarch.com/http://www.linnhoffmarch.com/

List of Services in the area of Process Integration:List of Services in the area of Process Integration:

Linnhoff March is the pioneering company of Pinch Technology and has Linnhoff March is the pioneering company of Pinch Technology and has built a reputation for being the "Pinch Company", encompassing:built a reputation for being the "Pinch Company", encompassing:

• • Project execution and consultingProject execution and consulting

• • Software development and supportSoftware development and support

• • Training assistanceTraining assistance

PI Technologies: PI Technologies:

• • Pinch Technology (Analysis and HEN DesignTotal Site Analysis)Pinch Technology (Analysis and HEN DesignTotal Site Analysis)

• • Water Pinch™ for Wastewater minimizationWater Pinch™ for Wastewater minimization

• • Combined Thermal and Hydraulic Analysis of Distillation Columns PI Combined Thermal and Hydraulic Analysis of Distillation Columns PI Software: Extensively proven state-of-the-art software including Software: Extensively proven state-of-the-art software including SuperTarget, PinchExpress, WaterTarget and Steam97.SuperTarget, PinchExpress, WaterTarget and Steam97.

Typical Projects: Typical Projects: 1200 assignments over 18 years - or over 50 studies per 1200 assignments over 18 years - or over 50 studies per year in PI, making them the unquestionable world leaderyear in PI, making them the unquestionable world leader((27th February 2002)27th February 2002)Was acquired last year by KBC process Was acquired last year by KBC process technology…technology…

« KBC Advanced Technologies is the leading independent process engineering consultancy, improving operational efficiency and

profitability in the hydrocarbon processing industry worldwide. KBC analyses plant operations and management systems, recommends changes

that deliver material and measurable improvements in profitability, and offers Implementation Services to assist clients in realising measurable

financial improvements »

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American Process Inc., AtlantaAmerican Process Inc., Atlanta, , USAUSA..Web:Web:http://www.americanprocess.comhttp://www.americanprocess.com


““We are the premier consulting engineering specialists dedicated to the pulp We are the premier consulting engineering specialists dedicated to the pulp and paper industry. Prom. energy and water reduction to planning new power and paper industry. Prom. energy and water reduction to planning new power islands. American Process can provide solutions through practical experience, islands. American Process can provide solutions through practical experience, process integration, troubleshooting, and project implementation.” process integration, troubleshooting, and project implementation.”

““Founded in 1994, with offices in Atlanta, GA, Athens, Greece, and Cluj-Founded in 1994, with offices in Atlanta, GA, Athens, Greece, and Cluj-Napoca, Romania, American Process is the premier specialist firm dedicated Napoca, Romania, American Process is the premier specialist firm dedicated to reducing energy, water, and other operating costs for the pulp and paper to reducing energy, water, and other operating costs for the pulp and paper industry.”industry.”

•Energy Targeting Using Pinch Analysis,Energy Targeting Using Pinch Analysis,•PARIS™ (Decision-Making Tool for Optimizing Pulp and Paper Mill PARIS™ (Decision-Making Tool for Optimizing Pulp and Paper Mill Operations)Operations)

• PProductionroduction AAnalysis fornalysis for RRate andate and IInventoriesnventories SStrategies.trategies.•Simulation modeling, Simulation modeling, •linear optimization.linear optimization.

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Process Systems Enterprise Ltd., london, UK.Process Systems Enterprise Ltd., london, UK.

Web: Web: http://www.psenterprise.comhttp://www.psenterprise.com


““Process Systems Enterprise Limited (PSE) is a provider of advanced Process Systems Enterprise Limited (PSE) is a provider of advanced model-based technology and services to the process industries. model-based technology and services to the process industries. These technologies address pressing needs in fast-growing These technologies address pressing needs in fast-growing engineering and automation market segments of the chemicals, engineering and automation market segments of the chemicals, petrochemicals, oil & gas, pulp & paper, power, fine chemicals, food, petrochemicals, oil & gas, pulp & paper, power, fine chemicals, food, pharmaceuticals and biotech industriespharmaceuticals and biotech industries.” .”

•gPROMSgPROMS, for , for ggeneral eneral PROPROcess cess MModelling odelling SSystemystem• Steady-state and dynamic process simulation, optimization (MINLP) Steady-state and dynamic process simulation, optimization (MINLP)

and parameter estimation software, packaged for different users.and parameter estimation software, packaged for different users.•Model Enterprise - Model Enterprise - Supply chain modeling and execution Supply chain modeling and execution environment.environment.•Model Care - Model Care - Business modelBusiness model•PSE provides expert, extensive PSE provides expert, extensive trainingtraining for all its products for all its products

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.........and Many Many others.........and Many Many othersInstitutionInstitution Major ContactMajor Contact Web Web

Åbo Akademi UniversityÅbo Akademi University Professor Tapio WesterlundProfessor Tapio Westerlund http://www.abo.fi/fak/ktf/at/http://www.abo.fi/fak/ktf/at/

Auburn UniversityAuburn University Professor Christopher Professor Christopher Roberts Roberts

http://www.eng.auburn.edu/http://www.eng.auburn.edu/department/che/department/che/

Technical Univ. of Technical Univ. of BudapestBudapest Professor Zsolt FonyoProfessor Zsolt Fonyo http://www.bme.hu/en/http://www.bme.hu/en/

organization/faculties/chemical/organization/faculties/chemical/

Lehrstuhi für Technische Lehrstuhi für Technische Chemie AChemie A

Prof. Dr. A. BehrProf. Dr. A. Behr http://www.chemietechnik.uni-http://www.chemietechnik.uni-dortmund.de/tca/dortmund.de/tca/

Universty of EdinburghUniversty of Edinburgh Professor Jack W. PontonProfessor Jack W. Ponton http://www.chemeng.ed.ac.uk/http://www.chemeng.ed.ac.uk/ecpsse/ecpsse/

INPT-ENSIGC, Chemical INPT-ENSIGC, Chemical Engng. Lab.Engng. Lab. Professor Xavier JouliaProfessor Xavier Joulia

http://excalibur.univ-inpt.fr/~lgc/elgcpa6.html

Swiss Federal Inst. of Swiss Federal Inst. of TechnologyTechnology Professor Daniel FavratProfessor Daniel Favrat http://leniwww.epfl.ch/

University of LiègeUniversity of Liège Professor Boris Professor Boris KalitventzeffKalitventzeff

http://www.ulg.ac.be/lassc/

University of MariborUniversity of Maribor Professor Peter GlavicProfessor Peter Glavic http://www.uni-mb.si/

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InstitutionInstitution Major ContactMajor Contact Web Web

Massachusetts Institute Massachusetts Institute of Technology,of Technology,

Professor George Professor George StephanopoulosStephanopoulos

http://web.mit.edu/cheme/index.html

Norw. Univ. of Sci. and Norw. Univ. of Sci. and Technol.Technol. Professor Sigurd SkogestadProfessor Sigurd Skogestad

http://kikp.chembio.ntnu.no/

research/PROST/

Princeton UniversityPrinceton University Professor Christodoulos A. Professor Christodoulos A. FloudasFloudas

http://titan.princeton.edu/

Purdue UniversityPurdue University Professor G.V. Rex Professor G.V. Rex ReklaitisReklaitis

http://che.www.ecn.purdue.edu/

University of University of MassachusettsMassachusetts Professor J. M. DouglasProfessor J. M. Douglas

http://www.ecs.umass.edu/che/

University CollegeUniversity College Dr. David BogleDr. David Boglehttp://

www.chemeng.ucl.ac.uk/

University of AdelaideUniversity of Adelaide Dr. B.K. O'NeillDr. B.K. O'Neillhttp://

www.chemeng.adelaide.edu.au/

Indian Institute of Indian Institute of TechnologyTechnology Dr. Uday V. ShenoyDr. Uday V. Shenoy

http://www.che.iitb.ernet.in/

Chemical Process Chemical Process Engineering Research Engineering Research InstituteInstitute

Professor I. VasalosProfessor I. Vasalos http://www.cperi.forth.gr

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Technical University of Technical University of DenmarkDenmark Professor Bjørn QvaleProfessor Bjørn Qvale http://www.et.dtu.dk/

TU of Hamburg-Harburg,TU of Hamburg-Harburg, Professor Günter GruhnProfessor Günter Gruhnhttp://www.tu-harburg.de/

vt3/

Helsinki University of Helsinki University of Technology,Technology,

Professor Carl-Johan Professor Carl-Johan Fogelholm, head of Fogelholm, head of

laboratorylaboratory

http://www.hut.fi/Units/Mechanic/

Instituto Superior Instituto Superior Técnico,Técnico,

Professor Clemente Pedro Professor Clemente Pedro NunesNunes

http://dequim.ist.utl.pt/english/

Lappeenranta University Lappeenranta University of Technol.of Technol. Professor Lars NystroemProfessor Lars Nystroem

http://www.lut.fi/kete/laboratories/

Process_Engineering/mainpage.htm

Murdoch UniversityMurdoch UniversityProfessor Peter LeeProfessor Peter Lee http://

wwweng.murdoch.edu.au/engindex.html

University of University of PennsylvaniaPennsylvania Professor Warren D. SeiderProfessor Warren D. Seider

http://www.seas.upenn.edu/cheme/chehome.html

University of PortoUniversity of Porto Professor Manuel A.N. Professor Manuel A.N. CoelhoCoelho

http://www.up.pt/

Universidade Federal do Universidade Federal do Rio de Janeiro.Rio de Janeiro.

Professor Eduardo Mach Professor Eduardo Mach QueirozQueiroz

http://www.ufrj.br/home.php

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University of QueenslandUniversity of Queensland Professor Ian CameronProfessor Ian Cameronhttp://www.cheque.uq.edu.au/

Technion-Israel Institute Technion-Israel Institute of Technologyof Technology Professor Daniel R. LewinProfessor Daniel R. Lewin

http://www.technion.ac.il/technion/chem-eng/index_explorer.htm

University of UlsterUniversity of Ulster Professor J.T. McMullanProfessor J.T. McMullanhttp://www.ulst.ac.uk/faculty/science/energy/index.html

COMPANIESCOMPANIES

Advanced Process Advanced Process Combinatorics (APC)Combinatorics (APC)

http://www.combination.com

Aspen Technology Inc. Aspen Technology Inc. (AspenTech) (AspenTech)

http://www.aspentech.com and http://www.hyprotech.com

National Engineering National Engineering Laboratory (NEL)Laboratory (NEL)

http://www.ipa-scotland.org.uk/members/nel.htm

QuantiSci LimitedQuantiSci Limited http://www.quantisci.co.uk/

...... ......

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End of Tier 1

At the moment we are assuming that you have done all the At the moment we are assuming that you have done all the reading, this is the end of Tier 1. We do not have doubt reading, this is the end of Tier 1. We do not have doubt that much of this information seems fuzzy, but we are only that much of this information seems fuzzy, but we are only trying to set all the pieces in the Process Integration scope.trying to set all the pieces in the Process Integration scope.

Before to pass to tier 2 lefts to answer a short QuizBefore to pass to tier 2 lefts to answer a short Quiz

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QUIZ

piece program for north american mobility in higher education rev:1.2 created at: École...

Documents

process integration

places process integration

piecenamp module

purpose of module

guanajuato module

struture of module

end of tier

expertise slide