Process Integration

PIECE NAMP

Module 8

Introduction to Process

Integration

Tier I

NAMP PIECE

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Module 8 – Introduction to Process Integration 2

NAMP PIECE

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


Real Time Optimization

Business Model & Supply Chain Modeling

1.3 Around-the world tour of PI practitioners

Quiz

Institutions – World Map

Institutions – North & South America

Institutions – Europe

Institutions – Asia, Africa & Oceania

Companies


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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


Universidad

Autónoma de San

Luis Potosí

Paprican

Universidad de

Guanajuato

Instituto

Mexicano del

Petróleo

École

Polytechnique de

Montréal

University of

Ottawa

University of

Texas A&M

North Carolina

State University

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Module 8

This module was created by:

Carlos Alberto Miranda Alvarez

Paul Stuart

From

Jean-Martin Brault

Module 8 – Introduction to Process Integration

Host Institution Host director

Martin Picon-Nuñez

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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


PIECE

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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)


1.3 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



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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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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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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 units only, but also at integration

Integration Primer, IEA) within units (Process between


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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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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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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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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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So What Happened?

In addition to thermodynamics (the foundation of Pinch), other techniques are being drawn upon for holistic analysis, in particular:

Process modeling

Process statistics

Process optimization

Process economics

Process control

Process design


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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 r aw 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 e missions and promote pollution prevention

Add to the joint efforts in the process industries and society for a sustainable development


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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

Increasing

THROUGHPU

T

YIELD

PROFIT


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Summary of Process Integration elements

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

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

Familiarity with industry issues and trends

Methodology for determining correct marginal costs

Procedures and tools for energy, water, and raw material conservation audits

Process information systems

Real-Time

Process Data

PI systems

& Tools

Process knowledge

(models)


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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

20 th 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



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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

Life Cycle Analysis

Data-Driven

Process Modeling

Integrated Process

Design and Control


Process Simulation

•Steady-state

•Dynamic

Data Treatment and Reconciliation

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


Methods

Life Cycle Analysis


•Steady state

•Dynamic

Data Treatment and Reconciliation

Data-Driven

Process Modeling


Design and Control


Process Data

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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

PROCESS

Output A model does not include everything:

X

1

, ..., X n

Y

1

, ..., Y k

Output n>m and k>t

Input

MODEL

“All models are wrong, some models are useful”

X

1

, ..., X m

Y

1

, ..., Y t

Figure 1

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.

George Box, PhD,

University of Wisconsin

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

Why is dynamic simulation important?

OPTIMIZATION of plant operations

Online system ADVANCEMENT of plant operations/

OPERATIONAL SUPPORT

OPTIMIZATION

Quasi-online system

Off-line system

EDUCATION, TRAINING

CONTROL SYSTEM

PROCESS DESIGN / ANALYSIS

Input

MODEL

X

1

, ..., X m

Figure 2

Output

Y

1

, ..., Y t


Input

X(t)

1

, ..., X(t) m

MODEL

(t)

Figure 3

Output

Y(t)

1

, ..., Y(t) t

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Data Treatment and

Reconciliation


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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 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?

Unclosed Balances?

Unidentified Losses?

Gross Error Detection

Closed Balances

Identified Losses

Efficiency?

Performance?

Monitored Efficiency

Quantified Performance

DATA RECONCILIATION


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Pinch Analysis


PIECE

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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


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Optimization by

Mathematical

Programming


PIECE

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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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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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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


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Methods


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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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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


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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

Recycling and

Disposal as Waste at the end of its useful life

Use, Reuse and

Maintenance of the product

Extraction and

Processing of Raw

Materials

Manufacturing

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


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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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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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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


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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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Controllability

PIECE


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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Why is Controllability important?

Smoother operation of process closer to operating limits

Flexibility

Stability and better performance of control loops and structures

System relatively insensitive to perturbations

Efficient management of interacting networks

Improvement of current dynamics


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Real-Time Optimization

(RTO)


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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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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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RTO - Schematically

Reconciliation

& gross error detection

Updating process model

(Steady-state  dynamic simulation)

PIECE


Business objectives;

Economic data;

Product specification

Steady-state detection

Optimization

(objective functions)

Cost, process,

Environmental & product Data

Figure 6


Plant facility

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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


Process

Operation

Analysis and

Optimization

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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 set is consistent throughout the plant

Data Validation &

Reconciliation

Process (P) &

Accounting

Environmental

Data

(E) Data

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

Product

Data

Market

Data


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BM-SCM – Integrated Business & Process Model

Model that deals with the classification, recording, allocation, and summarization

Process Data of data for the purpose of management decision making and financial reporting

Product Data

Market Data

Click here

Integrated Business

and

Process Model

Process

Simulation

Models

Principles

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

(Waste)

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



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1.3 Around-the-world tour of PI practitioners which focuses on their expertise


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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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Institutions

PIECE


Europe

North and South America

Africa, Middle-East, Asia and

Oceania

Click on a continent to view institutions from that continent


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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


Back to

World Map

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Institutions-Europe

Belgium (1)

Denmark (1)

Greece (1)

Spain (1)

Hungary (1)

Sweden (1)

Finland (3)

Norway (1)

Switzerland (1)

France (1)

Portugal (2)

UK (5)

Germany (2)

Slovenia (1)

PIECE




Back to

World Map

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Institutions-Africa, Middle-East, Asia and Oceania

South Africa (1)

Israel (1)

India (1)

Australia (3)



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World Map

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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


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

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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/



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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


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



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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


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

Consortium: CAPD (Centre for

Advanced Process Decision-making, founded 1986, 20 members (2001)) including operating companies, engineering & contracting companies, consulting companies and software vendors

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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


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

NEXT



Institutions

84

NAMP

USA

Auburn University, Auburn

PIECE


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



Institutions

NEXT

85

NAMP PIECE


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/



Institutions

NEXT

86

NAMP PIECE


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



Institutions

87

NAMP PIECE


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/


Back to Europe

Institutions

88

NAMP PIECE


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/


Back to Europe

Institutions

89

NAMP PIECE


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


Back to Europe

Institutions

90

NAMP PIECE


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/


Back to Europe

Institutions

91

NAMP PIECE


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/


Back to Europe

Institutions

92

NAMP

Portugal

Universidade do Porto, Porto

PIECE


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/


Back to Europe

Institutions

93

NAMP

Slovenia

University of Maribor, Maribor

PIECE


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/


Back to Europe

Institutions

94

NAMP PIECE


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


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

Consortium: "Manufacturing Reference

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


Back to Europe

Institutions

95

NAMP PIECE


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


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

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


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


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

Consortium: Process

Systems Engineering (PSE) with 17 members (2003) including operating, engineering & contracting companies, software vendors


Back to Europe

Institutions

NEXT

97

NAMP PIECE


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


Efficient use of raw materials (including water)

Energy efficiency

Emissions reduction

E fficient use of capital

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

NAMP PIECE


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/


Back to Europe

Institutions

99

NAMP PIECE


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/


Back to Asia

Institutions

100

NAMP PIECE


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


Systems involving reaction, mixing and separation

(e.g. reactive distillation)

Non-isothermal chemical reactor systems

Optimization of dynamic systems

Has founded its own consultancy enterprise called Wits

Enterprise http://www.enterprise.wits.

ac.za/


Back to Africa

Institutions

101

NAMP PIECE


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/


Back to Oceania

Institutions

102

NAMP PIECE


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

Back to

Companies

103

NAMP PIECE


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 .”


Integration

Dynamic process modeling

Dynamic optimization

Enterprise modeling

Extensive training for all its products

PI Technologies gPROMS ® , for g eneral PRO cess

M odeling S ystem  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


Back to

Companies

104

NAMP PIECE


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/


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Companies

NEXT

105

NAMP PIECE


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”


Integration

Energy Targeting Using Pinch Analysis

Simulation modeling

Linear optimization

Over 150 studies completed

PI Technologies

PARIS ™ (Production A nalysis for

R ate and I nventories S trategies) 

Decision-Making tool for optimizing pulp and paper mill operations)

O-Pinch ™ ( O perational Pinch )

SPARTA ™  real-time steam and power cost optimizer

Water Close ™  water pinch


Back to

Companies

NEXT

106

NAMP PIECE


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


Back to

Companies

107

NAMP

End of Tier I

PIECE

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.


NAMP

QUIZ


PIECE

NAMP

Question 1

Where was the concept of Process Integration first developed?

Atlanta, USA

Guanajuato, Mexico

Manchester, UK

Montreal, Canada

PIECE

Tier I - Quiz


NAMP PIECE

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


NAMP PIECE

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

1 and 3

2 and 4

1,2 and 3


2 and 3

3 and 4

1,3 and 4

All of the above

112

NAMP PIECE

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

1,2 and 5

2 and 4

1,2,3 and 5


1,3 and 6

2,3,5 and 6

1,2,3 and 4

All of the above

113

NAMP

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

1

2

1,2,3 and 5


PIECE

Tier I - Quiz

3 and 4

3

1,2,3 and 4

All of the above

114

NAMP PIECE

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

1

2

1,2,3 and 5


3 and 4

3

1,2,3 and 4

All of the above

115

NAMP PIECE

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

2

1 and 2

3

1,2 and 3


NAMP PIECE

Tier I - Quiz

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

1,2 and 4

1,2 and 3


4

1,3 and 4

All of the above

117

NAMP 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

2,3 and 4

1,2 and 3


1 and 3

1,3 and 4

All of the above

118

NAMP

Answers

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

Manchester, UK

 1,2,4,9 and 10 ;  3,5,6,7 and 8

1,3 and 4

1,2,3 and 5

3

2

3

1,2 and 4

2,3 and 4

PIECE

Tier I - Quiz


Process Integration - École Polytechnique de Montréal

Introduction to Process

Integration

Tier I

How to use this presentation

Table of contents

Table of contents (2)

Table of contents (3)

Project Summary

Structure of Module 8

What is the structure of this module?

Purpose of Module 8

What is the purpose of this module?

Tier I

Background Information

Tier I Statement of intent

Tier I Contents

Tier I is broken down into three sections

Tier I Outline

1.1 Introduction and definition of

1960’s-1970’s

1980’s-1990’s

1990’s-2000’s

holistic analysis of processes,

Process data

Systems and tools

Process engineering principles and in-depth process sector knowledge

Targeting

Tier I Outline

1.2 Overview of Process

Integration Tools

Process Simulation

Data Treatment and

Reconciliation

Pinch Analysis

Optimization by

Mathematical

Programming

Stochastic Search

Methods

Life Cycle

Analysis (LCA)

Data-Driven Process

Modeling

Integrated Process

Design & Control

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

Flexibility

Improvement of current dynamics

Real-Time Optimization

(RTO)

Business Model and

Supply Chain Modeling

(BM-SCM)

Data Validation &

Reconciliation

Tier I Outline

1.3 Around-the-world tour of PI practitioners which focuses on their expertise

Institutions

Companies

End of Tier I

QUIZ

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