Transcript Slide 1

NAMP
Program
for North American Mobility in Higher Education
PIECE
NAMP
Module 8
Introduction to Process
Integration
Tier I
Introducing
Process integration
for Integration
Environmental Control in Engineering Curricula
Module
8 – Introduction
to Process
PIECE
1
NAMP
PIECE
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Table of contents
Project Summary
Participating institutions
Module creators
Module Structure & Purpose
Tier I
Statement of Intent
Sections
1.1 Introduction & Definition of Process Integration (PI)
Brief history of PI
Modern context of PI
IEA definition of PI
M. El-Halwagi definition of PI
Nick Hallale definition of PI
NAMP-PIECE definition of PI
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Table of contents (2)
Tier I
1.1 Introduction & Definition of Process Integration (PI)
Possible objectives of PI
Summary of PI elements
Conclusion
1.2 Overview of PI tools
Overview of PI tools
Process Simulation
Data Treatment & Reconciliation
Pinch Analysis
Optimization by Mathematical Programming
Stochastic Search Methods
Life Cycle Analysis
Data-driven Process Modeling
Integrated Process Design & Control
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Table of contents (3)
Tier I
1.2 Overview of PI tools
Real Time Optimization
Business Model & Supply Chain Modeling
1.3 Around-the world tour of PI practitioners
Institutions
Institutions
Institutions
Institutions
Companies
–
–
–
–
World Map
North & South America
Europe
Asia, Africa & Oceania
Quiz
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Project Summary
Objectives
Create web-based modules to assist universities to address
the introduction to Process Integration into engineering
curricula
Make these modules widely available in each of the
participating countries
Participating institutions
Two universities in each of the three countries (Canada,
Mexico and the USA)
Two research institutes in different industry sectors:
petroleum (Mexico) and pulp and paper (Canada)
Each of the six universities has sponsored 7 exchange
students during the period of the grant subsidised in part by
each of the three countries’ governments
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NAMP integration for Environmental Control in Engineering Curricula
Process
Paprican
PIECE
PIECE
École
Polytechnique de
Montréal
Universidad
Autónoma de San
Luis Potosí
University of
Ottawa
Universidad de
Guanajuato
North Carolina
State University
Instituto
Mexicano del
Petróleo
Program
forIntroduction
North American
Mobility
in Higher Education
Module
8–
to Process
Integration
University of
Texas A&M
NAMP
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Module 8
This module was created by:
Carlos Alberto Miranda Alvarez
Paul Stuart
From
Host Institution
Host director
Martin Picon-Nuñez
Jean-Martin Brault
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Structure of Module 8
What is the structure of this module?
All modules are divided into 3 tiers, each with a specific
goal:
Tier I: Background Information
Tier II: Case Study Applications
Tier III: Open-Ended Design Problem
These tiers are intended to be completed in that particular
order. Students are quizzed at various points to measure
their degree of understanding, before proceeding to the
next level. Each tier contains a statement of intent at the
beginning and a quiz at the end.
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Purpose of Module 8
What is the purpose of this module?
It is the intent of this module to cover the basic aspects
of Process Integration Methods and Tools, and to
place Process Integration into a broad perspective. It
is identified as a pre-requisite for other modules related
to the learning of Process Integration.
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Tier I
Background Information
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Tier I Statement of intent
The goal of this tier is to provide a general overview of
Process Integration tools, with focus on their link with
profitability analysis. At the end of Tier I, the student
should be able to:
Distinguish the key tools of Process Integration
Understand the scope of each Process Integration
tool
Have an overview of each Process Integration tool
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Tier I Contents
Tier I is broken down into three sections
1.1 Introduction and definition of Process
Integration (PI)
1.2 Overview of PI tools
1.3 Around-the-world tour of PI practitioners
which focuses on their expertise
A short multiple-choice quiz will follow at the end of this
tier.
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Tier I Outline
1.1 Introduction and definition of Process Integration
1.2 Overview of Process Integration tools
1.3 Around-the-world tour of PI practitioners which
focuses on their expertise
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1.1 Introduction and definition of
Process Integration
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Introduction and Definition of Process Integration
The president of your company probably does not know
what Process Integration can do for the company.........
.......... but he should. Let’s look at why...
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Introduction and Definition of Process Integration
A brief history of Process Integration
1960’s-1970’s Linnhoff started the area of Pinch
(bottleneck identification) at University of Manchester
Institute of Science and Technology (UMIST),
focusing on the area of Thermal Pinch. At about the
same time, the UMIST Department of Process
Integration was created, shortly after the consulting
firm Linnhoff-March Inc. was formed
1980’s-1990’s Concept expansion from energy to
process design
1990’s-2000’s Analogies used to derive Pinch
concept from heat exchanger networks to mass
transfer, water treatment and hydrogen systems
PI is not really easy to define…
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Introduction and Definition of Process Integration
Modern Process Integration context
1. Process Integration might be regarded as a set of early stage
process techniques for both new and retrofit design
2. Business objectives drive the development of PI:
a) Emphasis is on retrofit projects in the “new economy” driven by
Return on Capital Employed (ROCE)
b) PI is finding value in data, especially as real time data systems
have been implemented
3. Corporations wish to make more knowledgeable decisions:
1. For operations
2. During the design process
4. A strong trend today is to move away from unit operations and
focus on phenomena. We no longer look at integration between
units only, but also at integration within units (Process
Integration Primer, IEA)
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Introduction and Definition of Process Integration
Definition of Process Integration
The International Energy Agency (IEA) definition of Process
Integration (1993):
“Systematic and general methods for designing integrated production systems, ranging
from individual processes to total sites, with special emphasis on the efficient use of
energy and reducing environmental effects”
“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, 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”
 Sustainable Development
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Introduction and Definition of Process Integration
Definition of Process Integration
El-Halwagi, M. M., Pollution Prevention through Process
Integration: Systematic Design Tools. Academic Press, 1997.
“A chemical process is an integrated system of interconnected units
and streams, and it should be treated as such. Process Integration
is a holistic approach to process design, retrofitting, and operation
which emphasizes the unity of the process. In light of the strong
interaction among process units, streams, and objectives, Process
Integration offers a unique framework for fundamentally
understanding the global insights of the process, methodically
determining its attainable performance targets, and systematically
making decisions leading to the realization of these targets. There
are three key components in any comprehensive Process
Integration methodology: synthesis, analysis, and optimization.”
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Introduction and Definition of Process Integration
Definition of Process Integration
Nick Hallale, Aspentech – CEP July 2001 – Burning Bright
Trends in Process Integration
“Process Integration is more than just Pinch technology and Heat
Exchanger Networks. Today, it has a far wider scope and touches
every area of process design. Switched-on industries are making
more money from their raw materials and capital assets while
becoming cleaner and more sustainable”
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Introduction and Definition of Process Integration
Definition of Process Integration
North American Mobility Program in Higher Education
(NAMP)-January 2003
“Process Integration (PI) is the synthesis of process control,
process engineering and process modeling and simulation
into tools that can deal with the large quantities of operating
data now available from process information systems. It is an
emerging area, which offers the promise of improved control
and management of operating efficiencies, energy use,
environmental impacts, capital effectiveness, process design,
and operations management.”
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Introduction and Definition of Process Integration
Definition of Process Integration
So What Happened?
In addition to thermodynamics (the foundation of Pinch), other
techniques are being drawn upon for holistic analysis, in particular:
Process
Process
Process
Process
Process
Process
modeling
statistics
optimization
economics
control
design
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Introduction and Definition of Process Integration
Here are some of the design activities that these techniques and methods
address today:
Process modeling and simulation, and validation of the results in
order to have accurate and reliable process information for both new and
retrofit design
Minimize total annual cost by optimal trade-off between energy,
equipment and raw material. Within this trade-off: minimize energy,
improve raw material usage and minimize capital cost
Increase production volume by debottlenecking
Reduce operating problems by correct (rather than maximum) use of
Process Integration
Increase plant controllability and flexibility
Minimize undesirable emissions and promote pollution prevention
Add to the joint efforts in the process industries and society for a
sustainable development
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Introduction and Definition of Process Integration
Possible objectives
– Lower capital cost, for the same design objective
– Incremental production increase, from the same
asset base
– Marginally-reduced unit production costs by process
optimization
– Better energy/environmental performance, without
compromising competitive position
Reducing
COSTS
POLLUTION
ENERGY
Module 8 – Introduction to Process Integration
Increasing
THROUGHPU
T
YIELD
PROFIT
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Introduction and Definition of Process Integration
Summary of Process Integration elements
Improving overall plant facilities
energy efficiency and
productivity requires a multipronged analysis involving a
Real-Time
variety of technical skills and
Process Data
expertise, including:
Knowledge of both
conventional industry practice
and state-of-the-art
technologies commercially
available
PI systems
Familiarity with industry
& Tools
issues and trends
Methodology for determining
correct marginal costs
Procedures and tools for
energy, water, and raw
material conservation audits
Process information systems
Module 8 – Introduction to Process Integration
Process knowledge
(models)
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Introduction and Definition of Process Integration
Conclusion
Process Integration has evolved from heat recovery
methodology in the 80’s to become what a number of
leading industrial companies and research groups in the
20th century regard as the holistic analysis of processes,
involving the following elements:
Process data
Systems and tools
Process engineering principles and in-depth
process sector knowledge
Targeting
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Tier I Outline
1.1 Introduction and definition of Process Integration
1.2 Overview of Process Integration tools
1.3 Around-the-world tour of PI practitioners which
focuses on their expertise
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1.2 Overview of Process
Integration Tools
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Overview of Process Integration Tools
Business Model
and Supply Chain
Management
Real Time Optimization
Pinch Analysis
Optimization by
Mathematical
Programming
Stochastic Search
Methods
Process Simulation
•Steady-state
Life Cycle Analysis
Data-Driven
Process Modeling
•Dynamic
Data Treatment and Reconciliation
Integrated Process
Design and Control
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Process Data
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Overview of Process Integration Tools
Business Model
•Supply
Chain
Management
Real Time Optimization
Pinch Analysis
Optimization by
Mathematical
Programming
Stochastic Search
Methods
Process Simulation
Life Cycle Analysis
•Dynamic
Data-Driven
Process Modeling
•Steady state
Data Treatment and Reconciliation
Integrated Process
Design and Control
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Process Data
NEXT
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Process Simulation
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Process simulation
Simulation: “what if” experimentation with a model
Simulation involves performing a series of experiments with a
process model
Input
Output
PROCESS
X1, ..., Xn
Y1, ..., Yk
Input
Output
MODEL
X1, ..., Xm
Y1, ..., Yt
Figure 1
A model does not
include
everything:
n>m and k>t
“All models are wrong,
some models are
useful”
George Box, PhD,
University of Wisconsin
In the process industry, we find two levels of models: plant models,
and models of unit operations such as reactors, columns, pumps,
heat exchangers, tanks, etc.
There are two types of simulation: steady-state and dynamic
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Process simulation – Process modeling
Process Modeling is an understanding of the phenomena of a
given process and the transformation of this understanding into a
model.
What is a model used for?
A model is an abstraction of a process operation used to
build, change, improve, control, and answer questions about
that process
A model can be used for different basic problem
formulations: simulation, identification, estimation and design
A model can be used to solve problems in the areas of the
process design, control and optimization, risk analysis,
operator training, risk assessment, and software engineering
for computer aided engineering environments
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Process simulation – Steady-state & Dynamic
Why is steady-state simulation
important?
Better understanding of the process
Consistent set of typical plant/facility
data
Objective comparative evaluation of
options for Return On Investment
(ROI) etc.
Identification of bottlenecks,
instabilities, etc.
Performs many experiments cheaply
once the model is built
Avoids implementing ineffective
solutions
Input
Output
MODEL
X1, ..., Xm
Figure 2
Y1, ..., Yt
Module 8 – Introduction to Process Integration
Why is dynamic simulation
important?
Online
system
OPTIMIZATION of
plant operations
ADVANCEMENT of plant operations/
OPERATIONAL SUPPORT
OPTIMIZATION
Quasi-online
system
Off-line
system
EDUCATION, TRAINING
CONTROL SYSTEM
PROCESS DESIGN / ANALYSIS
Input
X(t)1, ..., X(t)m
MODEL
(t)
Figure 3
Output
Y(t)1, ..., Y(t)t
Next Tool
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Data Treatment and
Reconciliation
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Data Treatment & Reconciliation
Objectives of Data Treatment
Provide reliable information and knowledge of complete data for
validation of process simulation and analysis
Perform instrument maintenance
Detect operating problems
Estimate unmeasured values
Reduce random and gross errors in measurements
Detect steady states
Objectives of Data Reconciliation
Optimally adjust measured values within given process constraints
Improve consistency of data to calibrate and validate process
simulation
Estimate unmeasured process values
Detect gross errors to further investigate operation/instrument
problems
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Data Treatment & Reconciliation
Data Reconciliation
Data Reconciliation is the validation of process data using knowledge of
plant structure and of the plant measurement system
Figure 4
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Data Treatment & Reconciliation - Benefits
Measurement Errors?
Gross Error Detection
Unclosed Balances?
Closed Balances
Unidentified Losses?
Identified Losses
Efficiency?
Monitored Efficiency
Performance?
Quantified Performance
DATA RECONCILIATION
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Next Tool
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Pinch Analysis
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Pinch Analysis
What is Pinch Analysis?
In the process industries, the prime objective of Pinch Analysis is
to optimize the ways in which process utilities (particularly energy,
mass, water, and hydrogen) are applied for a wide variety of
purposes
Pinch Analysis does this by creating an inventory of all producers
and consumers of these utilities and then systematically designing
an optimal scheme of utility exchange between these producers
and consumers. Energy, mass, and water re-use are at the heart of
Pinch Analysis activities
With the application of Pinch Analysis, savings can be achieved in
both capital investment and operating cost. Emissions can be
minimized and throughput maximized
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Pinch Analysis
Features
The basis of Pinch Analysis:
The use of thermodynamic principles (first and second law)
The use of design and economy heuristics
Pinch Analysis is a technique to design:
Heat Exchanger Networks (HEN) & Mass Exchange Networks
(MEN)
Utility Networks
Pinch Analysis makes extensive use of various graphical
representations
In Pinch Analysis, the engineer controls the design procedure
(interactive method)
Pinch Analysis integrates economic parameters
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Pinch Analysis
Possible Benefits
One of the main advantages of
Pinch Analysis over
conventional design methods is
the ability to set a target
energy consumption for an
individual process or for an
entire production site before
designing the processes
Pinch Analysis quickly identifies
where energy, water, hydrogen
and other material savings are
likely to be found
Reduction of emissions
Pinch Analysis enables the
engineer to find the best way
to change a process, if the
process allows it
In addition, Pinch Analysis allows you to
Update or develop process flow diagrams
Identify process bottlenecks
Run both departmental and full plant
facilities simulations
Determine minimal heating (steam) and
cooling requirements
Identify cogeneration opportunities
Estimate costs of projects to achieve
energy savings
Evaluate new equipment configurations
for the most economical installation
Substitute past energy studies with a live
study that can be easily updated using
simulation
Next Tool
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Optimization by
Mathematical
Programming
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Optimization of Mathematical Programming
Mathematical Model
A Mathematical Model of a system is a set of mathematical
relationships (e.g., equalities, inequalities, logical conditions)
which represents an abstraction of the real world system under
consideration
A Mathematical Model can be developed using:
Fundamental approaches
Empirical methods
Methods based on analogy
A Mathematical Model of a system consists of four key
elements:
Variables
Parameters
Constraints
Mathematical relations
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Optimization of Mathematical Programming
What is Optimization?
An optimization problem is a mathematical model which in
addition to the key elements stated in the previous slide
contains one or more performance criteria
The performance criteria are represented by an objective
function. This function can be the minimization of costs, the
maximization of profit or yield of a process, for example
If we have multiple performance criteria, the problem is then
classified as a multi-objective optimization problem
There are different classes of optimization problems: linear and
non-linear programming, LP and NLP, mixed-integer linear
programming (MILP) and mixed-integer non-linear programming
(MINLP)
Whenever possible, linear programs (LP or MILP) are used because
they guarantee global solutions. MINLP problems also feature
many applications in engineering.
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Optimization of Mathematical Programming
Applications
Process Synthesis
Heat Exchanger Networks (HEN)
Mass Exchanger Networks (MEN)
Distillation sequencing
Reactor-based systems
Utility systems
Total process systems
Design, scheduling, and planning of process
Design and retrofit of multiproduct plants
Design and scheduling of multiproduct plants
Interaction of design and control
Molecular product design
Facility location and allocation
Facility planning and scheduling
Topology of transport networks
Next Tool
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Stochastic Search
Methods
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Stochastic Search Methods
Why Stochastic Search Methods?
All of the model formulations that you have encountered thus far
in the Optimization section have assumed that the data for the
given problem are known accurately. However, for many actual
problems, the problem data cannot be known accurately for a
variety of reasons. The first reason is due to simple measurement
error. The second and more fundamental reason is that some data
represent information about the future (e.g., product demand or
price for a future time period) and simply cannot be known with
certainty.
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Stochastic Search Methods
There are different types of stochastic algorithms
Simulated Annealing (SA)
Genetic Algorithms (GAs)
Tabu Search
These algorithms are suitable for problems that deal with
uncertainty. These computer algorithms or procedure
models do not guarantee global optima but are successful
and widely known to come very close to the global optimal
solution.
SA takes one solution and efficiently moves it around in
the search space, avoiding local optima
GAs have the capability of collectively searching for
multiple optimal solutions for the same optimal cost
Tabu Search is an iterative procedure that explores the
search space of all feasible solutions by a sequence of
moves
Next Tool
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Life Cycle
Analysis (LCA)
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Life Cycle Analysis
What is Life Cycle Analysis?
Technique for assessing the environmental aspects
and potential impacts associated with a product
by:
–
–
–
–
Establishing an inventory of relevant inputs and outputs of a
system
Evaluating the potential environmental impacts associated with
those inputs and outputs
Interpreting the results of the inventory and impact phases in
relation with the objectives of the study
Evaluation of some aspects of a product system
through all stages of its life cycle
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Life Cycle Analysis
Extraction and
Processing of Raw
Materials
Recycling and
Disposal as Waste
at the end of its
useful life
Manufacturing
Use, Reuse and
Maintenance of the
product
Packaging
Marketing
Figure 5
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Life Cycle Analysis
Possible Benefits
Improves overall environmental performance and
compliance
Provides a framework for using pollution
prevention practices to meet LCA objectives
Increases efficiency and potential cost savings
when managing environmental obligations
Promotes predictability and consistency in
managing environmental obligations
Measures scarce environmental resources more
effectively
Next Tool
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Data-Driven Process
Modeling
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Data-Driven Process Modeling
Process Integration challenge
Make sense of masses of data
Necessity to work on bigger samples if full advantage is to
be taken of all accessible information
Drowning in data!
Data-Rich but Knowledge-Poor
Interesting, useful patterns and relationships not intuitively
obvious lie hidden inside enormous, unwieldy databases.
Also, many variables are correlated
Data mining techniques: Neural Networks, Multiple
Regression, Decision Trees, Genetic Algorithms, Clustering,
MVA, etc.
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Data-Driven Process Modeling
Theoretical vs. Empirical Model
Theoretical model  uses First Principles to mimic the inner
workings of a process
Empirical model  uses the plant process data directly to
establish mathematical correlations
Unlike the theoretical models, empirical models do NOT take the
process fundamentals into account. They only use pure
mathematical and statistical techniques. Multivariate Analysis
(MVA) is one such method, because it reveals patterns and
correlations between variables independently of any preconceived notions
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Data-Driven Process Modeling
What is MVA?
Multivariate Analysis” (> 5 variables)
MVA uses ALL available data to capture information as much
as possible
Principle: boil down hundreds of variables down to a mere
handful
MVA

Benefits
Explore inter-relationships
« What-if » exercises
Software sensors
Feed-forward control
Next Tool
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Integrated Process
Design & Control
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Integrated Process Control & Control
Context
Safety issues, energy costs, environmental concerns have
increased complexity and sensitivity of processes
Plants become highly integrated in terms of mass and energy
and therefore, process dynamics are often difficult to control
Objectives
Product specifications variability should be kept at a minimum
 process variability (to control product quality)
Control is essential to operate a process in the best conditions
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Integrated Process Control & Control
Controllability
Controllability is the property of a process that
accounts for the ease with which a continuous
plant can be held at a specified operating
regime despite bounded external disturbances
and uncertainties and regardless of the control
system imposed on such a process
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Integrated Process Control & Control
Why is Controllability important?
Smoother operation of process
closer to operating limits
Stability and better
performance of control loops
and structures
System relatively insensitive to
perturbations
Efficient management of
interacting networks
Flexibility
Improvement
of current
dynamics
Next Tool
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Real-Time Optimization
(RTO)
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Real Time Optimization
Context
The process industries are increasingly compelled to
operate profitably in a very dynamic and global market.
The increasing competition in the international area and
stringent product requirements mean decreasing profit
margins unless plant operations are optimized
dynamically to adapt to the changing market conditions
and to reduce the operating cost.
Importance of real-time or on-line optimization!
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Real Time Optimization
What is Real-Time Optimization (RTO)?
Real-Time Optimization is a model-based steadystate technology that determines the economically
optimal operating regime for a process in the near
term
The system optimizes a process simulation, not the
process directly
Performance measured in terms of economic benefit
Is an active field of research  model accuracy,
error transmission, performance evaluation
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Real Time Optimization
Business objectives;
RTO - Schematically
Economic data;
Reconciliation
Updating process model
& gross error
(Steady-statedynamic
detection
simulation)
Steady-state detection
Product specification
Optimization
(objective functions)
Cost, process,
Plant facility
Environmental &
product Data
Figure 6
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Next Tool
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Business Model and
Supply Chain Modeling
(BM-SCM)
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Business Model and Supply Chain Modeling (BM-SCM)
Cost, Process,
Environmental &
Product Outcomes
Process
Design
Analysis
And
Synthesis
Integrated Business &
Process Model
Cost, Process,
Environmental &
Product Data
Module 8 – Introduction to Process Integration
Process
Operation
Analysis
and
Optimization
Back to PI
Tools
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BM-SCM – Cost, Process, Environmental & Product Data
Integrated Business & Process Model
Reconciled
P&E Data
The double arrows
mean
that the data
Data
Reconciliation
set is consistent throughout the plant
facilities
Processed
Data
Validation &
Cost,
Process,
P&E Data Environmental
andProcessing
Product
Data
Data
Reconciliation
Accounting
Data
Process (P) &
Environmental
(E) Data
Product
Data
Market
Data
Once the model is built, it
can be used to validate and
reconcile
dataFacilities
Plant
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BM-SCM – Integrated Business & Process Model
Model that deals with the Accounting
classification,
Data
recording, allocation, and summarization
Data
of data for the purpose ofProcess
management
decision making and financial
reporting Data
Environmental
Product Data
Market Data
Click here
Integrated Business
and
Process
Model
Process
Data
1st Principles
Driven Models
Simulation
Models
Models
Cost, Process,
E & P Data
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BM-SCM – Supply Chain & Environmental Supply Chain
Supply Chain (SC) is a network of organizations that are involved,
through upstream and downstream linkages, in the different
processes and activities that produce value in the form of
products and services in the hands of the ultimate customer
W
(Waste)
Recycling
W
W
W
W
Distribution
Distribution
Manufacturing
Supply Manufacturing
Supply
Consumer
Consumer
Retail
Retail
W
Remanufacturing
Reuse
W
W
Collection
Environmental Supply Chain (ESC) holds all the elements a
traditional Supply Chain has, but is extended to a semi-closed
loop in order to also account for the environmental impact of
the Supply Chain and for recycling, re-use and collection of used
material (Beamon 1999)
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BM-SCM – Supply Chain & Environmental Supply Chain
Objectives of the SC and ESC models
To integrate inter-organizational units along a SC and coordinate
materials, information and financial flows in order to fulfill
customer demands and to improve SC profitability and
responsiveness
To gain insight on the total environmental impact of the
production process (from supplier to customer and back to the
facility by recycling) and all the products that are manufactured
(closely linked to LCA)
Back to PI
Tools
BM-SCM
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Tier I Outline
1.1 Introduction and definition of Process Integration
1.2 Overview of Process Integration tools
1.3 Around-the-world tour of PI practitioners which
focuses on their expertise
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1.3 Around-the-world tour of PI
practitioners which focuses on
their expertise
Module 8 – Introduction to Process Integration
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Around-the-world tour of PI Practitioners
Courtesy mainly of the World Wide Web  to
capture the flavour of the evolution of Process
Integration
PI is relatively new
Researchers build on their strengths
Many of the ground-breaking techniques are
coming from universities
When techniques become practical, the private
sector generally capitalizes and techniques
advance more rapidly
Institutions
Companies
END
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Around-the-world tour of PI Practitioners
Institutions
Europe
North and South America
Africa, Middle-East, Asia and
Oceania
Click on a continent to view institutions from that continent
Module 8 – Introduction to Process Integration
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Around-the-world tour of PI Practitioners
Institutions-North and South America
Canada (2)
Mexico (1)
USA (8)
Brazil (1)
To view institutions from a
particular country, click on the flag
of the country of choice
Module 8 – Introduction to Process Integration
Back to
World Map
77
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PIECE
Around-the-world tour of PI Practitioners
Institutions-Europe
Belgium (1)
Greece (1)
Spain (1)
Denmark (1)
Hungary (1)
Sweden (1)
Finland (3)
Norway (1)
Switzerland (1)
France (1)
Portugal (2)
UK (5)
Germany (2)
Slovenia (1)
To view institutions from a
particular country, click on the flag
of the country of choice
Module 8 – Introduction to Process Integration
Back to
World Map
78
NAMP
PIECE
Around-the-world tour of PI Practitioners
Institutions-Africa, Middle-East, Asia and Oceania
South Africa (1)
Israel (1)
India (1)
Australia (3)
To view institutions from a
particular country, click on the flag
of the country of choice
Module 8 – Introduction to Process Integration
Back to
World Map
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PIECE
Around-the-world tour of PI Practitioners
Canada
École Polytechnique de Montréal, Department of Chemical
Engineering, Montréal
Major Contact: Professor Paul Stuart
Web: http://www.pulp-paper.ca
Research areas: the application of Process Integration in the pulp
and paper industry, with emphasis on pollution prevention techniques
and profitability analysis, the efficient use of energy and raw materials
(including water), process control, and plant sustainability
Current research in Process Integration
Process Simulation
Data Reconciliation
Process Control
Networks Analysis (HEN and MEN)
Environmental technologies (e.g. LCA)
Business model
Date-Driven Modeling
Module 8 – Introduction to Process Integration
Consortium: "Process
Integration in the Pulp and
Paper Industry Research
Consortium" with 13
members (2003) including
operating companies,
engineering & contracting
companies, consulting
companies and software
vendors in pulp and paper
industry
Back to Americas
Institutions
NEXT
80
NAMP
PIECE
Around-the-world tour of PI Practitioners
Canada
University of Ottawa, Department of Chemical
Engineering, Ottawa
Major Contact: Professor Jules Thibault
Web: http://www.genie.uottawa.ca/chg/eng/
Brazil
Universidade Federal do Rio de Janeiro, Rio de Janeiro
Major Contact: Professor Eduardo Mach Queiroz
Web: http://www.poli.ufrj.br/
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
81
NAMP
PIECE
Around-the-world tour of PI Practitioners
Mexico
Universidad de Guanajuato, Department of Chemical
Engineering, Guanajuato
Major Contact: Dr Martín-Picón-Núñez
Web: http://www.ugto.mx
Research areas: hosts the only course
Masters Program in Process Integration in North
America. Analysis of processes, Power Systems,
and development of environmentally benign
technology
Current research in Process Integration
Synthesis of processes; modeling, simulation, control
and optimization of processes; new processes and
materials
Heat recovery systems; renewable sources of energy;
thermodynamic optimization
Contaminated atmosphere rehabilitation; treatment
of effluents; environmental processes
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
82
NAMP
PIECE
Around-the-world tour of PI Practitioners
USA
Carnegie Mellon University, Department of Chemical
Engineering, Pittsburgh
Major Contact: Professor Ignacio E. Grossmann
Web: http://capd.cheme.cmu.edu/
Research areas: 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 work in Mathematical
Programming, Optimization, reactor systems, separation systems (especially distillation), Heat Exchanger
Networks, operability and the synthesis of operating procedures
Current research in Process Integration
Insights to aid and automate synthesis (invention)
Structural optimization of process flowsheets
Synthesis of reactor systems and separation systems
Synthesis of Heat Exchanger Networks
Global optimization techniques relevant to Process
Integration
Integrated Design and Scheduling of batch plants
Supply chain dynamics and optimization
Module 8 – Introduction to Process Integration
Consortium: CAPD (Centre for
Advanced Process Decision-making,
founded 1986, 20 members (2001))
including operating companies,
engineering & contracting
companies, consulting companies
and software vendors
NEXT
Back to Americas
Institutions
83
NAMP
PIECE
Around-the-world tour of PI Practitioners
USA
Texas A&M University, Department of Chemical Engineering,
College Station
Major Contact: Professor Mahmoud M. El-Halwagi
Web: http://process-integration.tamu.edu/ and http://www-che.tamu.edu/cpipe/
Research areas:
Recognized as a leading
research group in the
areas of Mass
Integration and Pollution
Prevention through
Process Integration
Current research in Process Integration
Global allocation of mass and energy
Synthesis of waste allocation and species interception
networks
Physical and reactive Mass Pinch Analysis
Synthesis of Heat-Induced Networks
Design of membrane-hybrid systems
Design of environmentally acceptable reactions
Integration of reaction and separation systems
Flexibility and scheduling systems
Simultaneous design and control
Global optimization via interval analysis
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
NEXT
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Around-the-world tour of PI Practitioners
USA
Auburn University, Auburn
Major Contact: Professor Christopher Roberts
Web: http://www.eng.auburn.edu/department/che/
Massachusetts Institute of Technology (MIT),
Department of Chemical Engineering, Cambridge
Major Contact: Professor George Stephanopoulos
Web: http://web.mit.edu/cheme/index.html
NEXT
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
85
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Around-the-world tour of PI Practitioners
USA
Princeton University, Princeton
Major Contact: Professor Christodoulos A. Floudas
Web: http://chemeng.princeton.edu/html/home.shtml
Purdue University, West Lafayette
Major Contact: Professor G.V. Rex Reklaitis
Web: https://engineering.purdue.edu/ChE/index.html and
https://engineering.purdue.edu/ECN/
NEXT
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
86
NAMP
PIECE
Around-the-world tour of PI Practitioners
USA
University of Massachusetts, Amherst
Major Contact: Professor J. M. Douglas
Web: http://www.ecs.umass.edu/che/
University of Pennsylvania, Philadelphia
Major Contact: Professor Warren D. Seider
Web: http://www.seas.upenn.edu/cbe/chehome.html
Module 8 – Introduction to Process Integration
Back to Americas
Institutions
87
NAMP
PIECE
Around-the-world tour of PI Practitioners
Belgium
Université de Liège, Laboratory for Analysis and
Synthesis of Chemical Systems (LASSC), Liège
Major Contact: Professor Boris Kalitventzeff
Web: http://www.ulg.ac.be/lassc/
Denmark
Technical University of Denmark, Lyngby
Major Contact: Professor Bjørn Qvale
Web: http://www.et.dtu.dk/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
88
NAMP
PIECE
Around-the-world tour of PI Practitioners
Finland
Åbo Akademi University, Process Design Laboratory, Åbo
Major Contact: Professor Tapio Westerlund
Web: http://www.abo.fi/fak/ktf/at/
Lappeenranta University of Technology, Lappeenranta
Major Contact: Professor Lars Nyström
Web: http://www2.lut.fi/kete/laboratories/Process_Engineering/mainpage.htm
Helsinki University of Technology, Laboratory of Energy
Engineering and Environmental Protection, Helsinki
Major Contact: Professor Carl-Johan Fogelholm
Web: http://eny.hut.fi/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
89
NAMP
PIECE
Around-the-world tour of PI Practitioners
France
INPT-ENSIGC, Chemical Engineering Laboratory,
Toulouse
Major Contact: Professor Xavier Joulia
Web: http://www.ensiacet.fr/ENSIA7_FR/FORMATION/INGENIEUR/GPI/gpi.shtml
Greece
Chemical Process Engineering Research Institute, Hellas
Major Contact: Professor I. Vasalos
Web: http://www.cperi.forth.gr
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
90
NAMP
PIECE
Around-the-world tour of PI Practitioners
Germany
Universität Dortmund, Dortmund
Major Contact: Professor A. Behr
Web: http://www.bci.uni-dortmund.de/tca/web/en/index.html
Technische Universität Hamburg, Harburg
Major Contact: Professor Günter Gruhn
Web: http://www.tu-harburg.de/vt3/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
91
NAMP
PIECE
Around-the-world tour of PI Practitioners
Hungary
Budapest University of Technology and Economics,
Budapest
Major Contact: Professor Zsolt Fonyo
Web: http://www.bme.hu/en/organization/faculties/chemical/
Norway
Norwegian University of Science and Technology, Process
Systems Engineering in Trondheim (PROST), Trondheim
Major Contact: Professor Sigurd Skogestad
Web: http://kikp.chembio.ntnu.no/research/PROST/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
92
NAMP
PIECE
Around-the-world tour of PI Practitioners
Portugal
Universidade do Porto, Porto
Major Contact: Professor Manuel A.N. Coelho
Web: http://www.fe.up.pt/
Instituto Superior Técnico, Lisboa
Major Contact: Professor Clemente Pedro Nunes
Web: http://dequim.ist.utl.pt/english/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
93
NAMP
PIECE
Around-the-world tour of PI Practitioners
Slovenia
University of Maribor, Maribor
Major Contact: Professor Peter Glavič
Web: http://www.uni-mb.si/
Switzerland
Swiss Federal Institute of Technology, Lausanne
Major Contact: Professor Daniel Favrat
Web: http://leniwww.epfl.ch/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
94
NAMP
PIECE
Around-the-world tour of PI Practitioners
Spain
Universitat Politècnica de Catalunya, Chemical Engineering
Department, Barcelona
Major Contact: Professor Luis Puigjaner
Web: http://tqg.upc.es/
Research areas: pioneering work in Computer Aided Process Operations. In Process Integration, the
group is recognized for its contributions in time-dependent processes, such as Combined Heat and
Power, Combined Energy-Waste and Waste Minimization, Integrated Process Monitoring, Diagnosis and
Control and Process Uncertainty
Current research in Process Integration
Consortium: "Manufacturing Reference
Evolutionary modeling and optimization
Multi-objective optimization in time-dependent
systems
Combined energy and water use minimization
Integration of thermally coupled distillation columns
Hot-gas recovery and cleaning systems
Centre" with 12 members (1966) including
Conselleria d'Indústria and associated
operating companies, engineering and
contracting companies, consultants and
software vendors. Also the TQG (General
Chemical Technology) research group has
grown steadily with research related to
kinetics, process design and operation
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
95
NAMP
PIECE
Around-the-world tour of PI Practitioners
Sweden
Chalmers University of Technology, Department of Heat
and Power, Göteborg
Major Contact: Thore Berntsson
Web: http://www.hpt.chalmers.se/
Research areas: methodology development and applied research based on Pinch
Technology. Emphasis on new retrofit methods including realistic treatment of
geographical distances, pressure drops, varying fixed costs, etc. Important new
concepts include the Cost Matrix for Retrofit Screening and new Grand Composite
thermodynamic diagrams for heat and power applications (including gas turbines and
heat pumps). Research in pulp and paper with focus on energy and environment
Current research in Process Integration
Retrofit design of Heat Exchanger Networks
Process Integration of heat pumps in grassroots and retrofits
Gas turbine based CHP plants in retrofit situations
Applied research in pulp and paper industry, such as black liquor
gasification and closing the bleaching plant
Environmental aspects of Process Integration, especially
greenhouse gas emissions
Module 8 – Introduction to Process Integration
Industry: Close
cooperation with
some of the major
pulp and paper
industry groups,
including training
courses and
consulting
Back to Europe
Institutions
96
NAMP
PIECE
Around-the-world tour of PI Practitioners
UK
Imperial College, Centre for Process Systems Engineering,
London
Major Contact: Professor Efstratios N. Pistikopoulos
Web: http://www.ps.ic.ac.uk/ and http://www.psenterprise.com
Research areas: recognized as the largest research group in the area of Process Systems
Engineering (PSE), which includes Synthesis/Design, Operations, Control and Modeling. The group is
recognized as a world-wide centre of excellence in Process Modeling, Numerical Techniques/Optimization
and Integrated Process Design (includes simultaneous consideration of Process Integration and Control).
The Centre is also an important contributor in the area of integration and operation of batch processes
Current research in Process Integration
Consortium: Process
Integrated batch processing
Design and management of integrated Supply Chain
processes
Uncertainty and operability in process design
Formulation of mathematical programming models to
address problems in process synthesis and integration
Systems Engineering (PSE)
with 17 members (2003)
including operating,
engineering & contracting
companies, software vendors
Module 8 – Introduction to Process Integration
NEXT
Back to Europe
Institutions
97
NAMP
PIECE
Around-the-world tour of PI Practitioners
UK
UMIST, Department of Process Integration, Manchester
Major Contact: Professor Robin Smith
Web: http://www.cpi.umist.ac.uk/
Research areas: recognized as the pioneering and major research
group in the area of Pinch Analysis. Previous research includes targets and
design methods for Heat Exchanger Networks (grassroots and retrofits),
Heat and Power systems, Heat driven Separation Systems, Flexibility, Total
Sites, Pressure Drop considerations, Batch Process Integration, Water and
Waste Minimization and Distributed Effluent Treatment
Current research in Process Integration
Efficient use of raw materials (including water)
Energy efficiency
Emissions reduction
Efficient use of capital
Module 8 – Introduction to Process Integration
Industry: Research
Consortium in Process
Integration created in
1984 and now formed
by 26 major companies
representing different
aspects of the process
and utility industries
Back to
Europe
Institutions
NEXT
98
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Around-the-world tour of PI Practitioners
UK
University of Edinburgh, Edinburgh
Major Contact: Professor Jack W. Ponton
Web: http://www.chemeng.ed.ac.uk/
University College, London
Major Contact: Dr. David Bogle
Web: http://www.chemeng.ucl.ac.uk/
University of Ulster, Coleraine
Major Contact: Professor J.T. McMullan
Web: http://www.engineering.ulster.ac.uk/
Module 8 – Introduction to Process Integration
Back to Europe
Institutions
99
NAMP
PIECE
Around-the-world tour of PI Practitioners
Israel
Technion, Israel Institute of Technology, Haifa
Major Contact: Professor Daniel R. Lewin
Web: http://www.technion.ac.il/technion/chem-eng/index_explorer.htm
India
Indian Institute of Technology, Bombay
Major Contact: Dr. Uday V. Shenoy
Web: http://www.che.iitb.ac.in/
Module 8 – Introduction to Process Integration
Back to Asia
Institutions
100
NAMP
PIECE
Around-the-world tour of PI Practitioners
South Africa
University of the Witwatersrand, Process & Materials
Engineering, Johannesburg
Major Contact: Professor David Glasser
Web: http://www.procmat.wits.ac.za/
Research areas: recognized as the major research group in the development of the Attainable
Region (AR) method for Reactor and Process Synthesis. The Attainable Region concept has been
expanded to systems where mass transfer, heat transfer and separation take place. In its generalized
form (reaction, mixing, separation, heat transfer and mass transfer), the Attainable Region concept
provides a Synthesis tool that will provide targets for "optimal" designs against which more practical
solutions can be judged
Current research in Process Integration
Systems involving reaction, mixing and separation
(e.g. reactive distillation)
Non-isothermal chemical reactor systems
Optimization of dynamic systems
Module 8 – Introduction to Process Integration
Has founded its own consultancy
enterprise called Wits
Enterprise
http://www.enterprise.wits.
ac.za/
Back to Africa
Institutions
101
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PIECE
Around-the-world tour of PI Practitioners
Australia
University of Adelaide, Adelaide
Major Contact: Dr. B.K. O'Neill
Web: http://www.chemeng.adelaide.edu.au/
Murdoch University, Rockingham
Major Contact: Professor Peter Lee
Web: http://wwweng.murdoch.edu.au/engindex.html
University of Queensland, Brisbane
Major Contact: Professor Ian Cameron
Web: http://www.cheque.uq.edu.au/
Module 8 – Introduction to Process Integration
Back to Oceania
Institutions
102
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PIECE
Around-the-world tour of PI Practitioners
Companies
Linnhoff March Limited, Northwich, Cheshire, UK
Web: http://www.linnhoffmarch.com/ and http://www.kbcat.com/
Linnhoff March is the pioneering company of Pinch Technology and is now a
division of KBC Process Technology Limited since 2002. KBC Advanced Technologies
is the leading independent process engineering consultancy, improving operational
efficiency and profitability in the hydrocarbon processing industry worldwide
List of Services in the area of Process
Integration
Project execution and consulting
Software development and support
Training assistance
Typical Projects: 1200 assignments over 18 years
PI Technologies
Pinch Technology (analysis and HEN
Design,Total Site Analysis)
Water Pinch™ for wastewater
minimization
Combined thermal and hydraulic
analysis of distillation columns PI
software: extensively proven state-ofthe-art software including
SuperTarget, PinchExpress,
WaterTarget and Steam97
NEXT
Module 8 – Introduction to Process Integration
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Companies
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Around-the-world tour of PI Practitioners
Companies
Process Systems Enterprise Limited, London, UK
Web: http://www.psenterprise.com
“Process Systems Enterprise Limited (PSE) is a provider of advanced model-based
technology and services to the process industries. These technologies address
pressing needs in fast-growing engineering and automation market segments of the
chemicals, petrochemicals, oil & gas, pulp & paper, power, fine chemicals, food,
pharmaceuticals and biotech industries.”
List of Services in the area of Process
Integration
Dynamic process modeling
Dynamic optimization
Enterprise modeling
Extensive training for all its products
PI Technologies
gPROMS®, for general PROcess
Modeling System  Steady-state and
dynamic process simulation,
optimization (MINLP) and parameter
estimation software, packaged for
different users
Model Enterprise®  supply chain
modeling and execution environment
Model Care®  business model
NEXT
Module 8 – Introduction to Process Integration
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Companies
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Around-the-world tour of PI Practitioners
Companies
Industrial and Power Association-National Engineering
Laboratory (NEL), UK
Web: http://www.ipa-scotland.org.uk/home.asp
QuantiSci Limited, UK
Web: http://www.quantisci.co.uk/
NEXT
Module 8 – Introduction to Process Integration
Back to
Companies
105
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PIECE
Around-the-world tour of PI Practitioners
Companies
American Process Inc., Atlanta, USA
Web: http://www.americanprocess.com
“Founded in 1994, American Process Inc is the premier consulting engineering
specialist firm dedicated to energy cost minimization in pulp and paper and other
industries. Our success is largely due to offering custom tailored solutions for our
customers, understanding that each mill is a unique operation, thereby optimizing
the potential for savings”
List of Services in the area of Process
Integration
Energy Targeting Using Pinch Analysis
Simulation modeling
Linear optimization
Over 150 studies completed
PI Technologies
PARIS™ (Production Analysis for
Rate and Inventories Strategies) 
Decision-Making tool for optimizing pulp
and paper mill operations)
O-Pinch™ (Operational Pinch)
SPARTA™  real-time steam and
power cost optimizer
Water Close™  water pinch
NEXT
Module 8 – Introduction to Process Integration
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Companies
106
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PIECE
Around-the-world tour of PI Practitioners
Companies
Advanced Process Combinatorics (APC), USA
Web: http://www.combination.com
Aspen Technology Inc. (AspenTech), USA
Web: http://www.aspentech.com and http://www.hyprotech.com
Module 8 – Introduction to Process Integration
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Companies
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End of Tier I
This is the end of Tier I. At this point, we assume that you have
done all the reading. Some of this information might still seem
confusing but remember that we are still trying to set all the pieces
in the Process Integration puzzle.
Prior to advancing to Tier II, a short multiple choice quiz will
follow.
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QUIZ
Module 8 – Introduction to Process Integration
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Tier I - Quiz
Question 1
Where was the concept of Process Integration first developed?
Atlanta, USA
Guanajuato, Mexico
Manchester, UK
Montreal, Canada
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Tier I - Quiz
Question 2
Using PI techniques and methods allows you to observe different variations
in a process, a plant or a company. Use each one of the following and
indicate if they would be reduced or increased in a Process Integration
context.
1. Costs
2. Pollution
3. Throughput
4. Energy Use
5. Yield
6. Profit
7. Data Use
8. Production Volume
9. Water Use
10. Operating Problems
 1,4,6,7 and 9 ;  2,3,5,8 and 10
 2,3,6,8 and 10 ;  1,4,5,7 and 9
 1,2,4,9 and 10 ;  3,5,6,7 and 8
 3,4,5,7 and 8 ;  1,2,6,9 and 10
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Tier I - Quiz
Question 3
Which of the following statements are false?
1. Steady-state simulations enable the process engineer to study
strategies for start-up and shut down
2. In the process industry, we find two levels of models: models of unit
operations and plant models
3. A model can represent exactly what goes on in a process
4. Generally, dynamic simulations are used to estimate the sizes and costs
of process units
1 and 2
2 and 3
1 and 3
3 and 4
2 and 4
1,3 and 4
1,2 and 3
All of the above
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Tier I - Quiz
Question 4
What are plant measurements usually corrupted by?
1. Random power supply fluctuations
2. Ambient conditions
3. Sensor miscalibration
4. Computer calculation capacity and speed
5. Hostile process environment
6. Sampling frequency
1,2 and 3
1,3 and 6
1,2 and 5
2,3,5 and 6
2 and 4
1,2,3 and 4
1,2,3 and 5
All of the above
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Tier I - Quiz
Question 5
What was Pinch Analysis originally conceived for?
1. Oil refinery emissions reduction
2. Capital investment and operating costs savings
3. Heat Exchanger Network design
4. Better use of hydrogen in refineries
5. Utility Network design
2 and 3
3 and 4
1
3
2
1,2,3 and 4
1,2,3 and 5
All of the above
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Tier I - Quiz
Question 6
What does an objective function represent in an optimization problem?
1. Interactions among variables
2. Performance criteria
3. Parameters
4. Mass and energy balances
5. Equalities or inequalities
2 and 3
3 and 4
1
3
2
1,2,3 and 4
1,2,3 and 5
All of the above
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Tier I - Quiz
Question 7
The entire research area of Genetic Algorithms was inspired by Darwin's
theory of natural selection and survival of the fittest. Unlike natural
evolution, a Genetic Algorithm program is usually able to do what?
1. Solve problems over a long period of time, through processes such as
reproduction, mutation, and natural selection
2. Each generation of the program improves upon the quality of the
solution (each new generation is better than the previous one)
3. Generate and evaluate thousands of generations in seconds
2 and 3
1 and 2
1
3
2
1,2 and 3
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Question 8
Which of the following statements are false?
1. The need for capital investment savings has led to the creation of datamining techniques
2. A “black-box” model using the plant process data directly takes the
process fundamentals into account
3. Multivariate analysis is defined as the simultaneous analysis of more
than five variables
4. Multivariate Analysis methods are used to replace the physical analysis
of a process
1
4
1,2 and 4
1,3 and 4
1,2 and 3
All of the above
Module 8 – Introduction to Process Integration
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PIECE
Tier I - Quiz
Question 9
With a Real-Time Optimization system:
1. The process is optimized directly
2. Up-to date decisions on plant operations and maintenance to maximize
plant profitability can be made
3. Decisions can be made before complete information about the data is
available
4. It is possible to determine the economically optimal operating regime
for a process in the near term
1
1 and 3
2,3 and 4
1,3 and 4
1,2 and 3
All of the above
Module 8 – Introduction to Process Integration
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Tier I - Quiz
Answers
Question 1
Manchester, UK
Question 2
 1,2,4,9 and 10 ;  3,5,6,7 and 8
Question 3
1,3 and 4
Question 4
1,2,3 and 5
Question 5
3
Question 6
2
Question 7
3
Question 8
1,2 and 4
Question 9
2,3 and 4
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