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Module 8 Introduction to Process Integration

• Program for North American Mobility in Higher
  Education (NAMP)
• Introducing Process Integration for Environmental
  Control in Engineering Curricula (PIECE)

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Purpose of Module 8

• What is the purpose of this module?
• This module is intended to covey the basic
  aspects of Process Integration Methods and Tools,
  and places Process Integration into a broad
  perspective. It will be identified as a
  pre-requisite for all other modules related to
  the learning of Process Integration.

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Struture of module 8

• What is the structure of this module?
• The Module 8 is divided into 3 tiers, each with
  a specific goal
• Tier 1 Background Information
• Tier 2 Case Study Applications of Process
  Integration
• Tier 3 Open-Ended Design Problem
• These tiers are intended to be completed in
  order. Students are quizzed at various points,
  to measure their degree of understanding, before
  proceeding.
• Each tier contains a statement of intent at the
  beginning, and a quiz at the end.

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Tier 1 Background Information
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Tier 1 Statement of intent

• Tier 1 Statement of intent
• The goal is to provide a general overview of
  process integration tools, with a focus on its
  link with profitability analysis. At the end of
  Tier 1, the student should
• Distinguish the key elements of Process
  Integration.
• Know the scope of each process integration tool.
• Have overview of each process integration tool.

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Tier 1 contents

• The tier 1 is broken down into three sections
• 1.1 Introduction and definition of Process
  integration.
• 1.2 Overview of PI tools
• 1.3 An around-the-world tour of PI
  practitioners focuses of expertise
• At the end of this tier there is a short
  multiple-answer Quiz.

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Outline

• 1.1 Introduction and definition of Process
  integration.
• 1.2 Overview of Process Integration tools
• 1.3 An around-the-world tour of PI
  practitioners focuses of expertise

1.1 Introduction and definition of Process
integration. 1.2 Overview of Process Integration
tools 1.3 An around-the-world tour of PI
practitioners focuses of expertise
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1.1 Introduction and definition of Process
integration.
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introduction

• The president of your company probably does not
  know what process integration can do for the
  company.........
• .......... But he should. Lets look at why?

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A Very Brief History of Process Integration

• Linnhoff started the area of pinch (bottleneck
  identification) at UMIST in the 60s, focusing on
  the area of Heat Integration
• UMIST Dept of Process Integration was created in
  1984, shortly after the consulting firm
  Linnhoff-March Inc. was formed
• PI is not really easy to define

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Definition of process integration

• The International Energy Agency (IEA) definition
  of process integration
• "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"

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Definition of process integration

• Later, this definition was somewhat broadened and
  more explicitly stated in the description of its
  role in the technical sector by this Implementing
  Agreement
• "Process Integration is the common term used for
  the application of methodologies developed for
  System-oriented and Integrated approaches to
  industrial process plant
• design for both new and retrofit applications.
• Such methodologies can be mathematical,
  thermodynamic and economic models, methods and
  techniques. Examples of these methods include
  Artificial Intelligence (AI), Hierarchical
  Analysis, Pinch Analysis and Mathematical
  Programming. Process Integration refers to
  Optimal Design examples of aspects are capital
  investment,energy efficiency, emissions,
  operability, flexibility, controllability, safety
  and yields. Process Integration also refers to
  some aspects of operation and maintenance".
• Later, based on input from the Swiss National
  Team, we have found that Sustainable Development
  should be included in our definition of Process
  Integration.

Truls Gunderson, International Energy Agency
(IEA) Implementing Agreement, A worldwide
catalogue on Process Integration (jun. 2001).
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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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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 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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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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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 modeling
• Process statistics
• Process optimization
• Process economics
• Process control
• Process design

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Modern Process Integration context

• Process integration is primarily regarded as
  process design (both new and retrofits design),
  but also involve planning and operation. The
  methods and systems are applied to continuous,
  semi-batch, and batch process.
• Business objectives currently driving the
  development of PI
• Emphasis is on retrofit projects in the new
  economy driven by Return on Capital Employed
  (ROCE)
• PI is Finding value in data quality
• Corporations wish to make more knowledgeable
  decisions
• For operations,
• During the design process.

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Modern Process Integration context

• Possible Objectives
• Lower capital cost design, for the same design
  objective
• Incremental production increase, from the same
  asset base
• Marginally-reduced unit production costs
• Better energy/environmental performance, without
  compromising competitive position

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Modern Process Integration context

• Among the design activities that these systems
  and methods address today are
• Process Modeling and Simulation, and Validations
  of the results in order to have information
  accurate and reliable of the process.
• 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
• Add to the joint Efforts in the Process
  Industries and Society for a Sustainable
  Development.

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Summary of Process Integration elements

• Improving overall plant facilities energy
  efficiency and productivity requires a
  multi-pronged analysis involving a variety of
  technical skills and expertise, including
• Knowledge of both conventional industry practice
  and state-of-the-art technologies available
  commercially
• 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

Process Data
Process knowledge
PI systems Tools
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Definition of process integration

• In conclusion, process integration has evolved
  from Heat recovery methodology in the 80s to
  become what a number of leading industrial
  companies and research groups in the 20th century
  regarding the holistic analysis of processes,
  involving the following elements
• Process data lots of it
• Systems and tools typically computer-oriented
• Process engineering principles - in-depth process
  sector knowledge
• Targeting - Identification of ideal unit
  constraints for the overall process

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Outline

• 1.1 Introduction and definition of Process
  integration.
• 1.2 Overview of Process Integration tools.
• 1.3 An around-the-world tour of PI
  practitioners focuses of expertise.

1.1 Introduction and definition of Process
integration. 1.2 Overview of Process Integration
tools 1.3 An around-the-world tour of PI
practitioners focuses of expertise
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1.2 Overview of Process Integration Tools
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1.2 Overview of Process Integration Tools
Business Model And Supply Chain Modeling.
Real Time Optimization
Pinch Analysis
Data Reconciliation
Optimization by Mathematical Programming
Stochastic Search Methods

• Process Simulation
• Steady state
• Dynamic

Life Cycle Analysis
Data-Driven Process Modeling
Integrate Process Design and Control
Process Data
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1.2 Overview of Process Integration Tools

• Business Model
• Supply Chain Managment.

Real Time Optimization
Pinch Analysis
Reconciliation Data
Optimization by Mathematical Programming
Stochastic Search Methods

• Process Simulation
• Steady state
• Dynamic

Life Cycle Analysis
Data-Driven Process Modeling
Integrate Process Design and Control
Process Data
NEXT
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Process Simulation
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Process Simulation

• Process modeling

What is a model? A model is an abstraction of a
process operation used to build, change, improve,
control, and answer questions about that process
Process modeling is an activity using models
to solve problems in the areas of the process
design, control, optimization, hazards analysis,
operation training, risk assessment, and software
engineering for computer aided engineering
environments.
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Process Simulation

• Tools of process modeling

Process Modeling
System Theory
Physics and Chemistry
Computes Science
Numerical Methods
Application
Statistics
Process modeling is an understanding of the
process phenomena and transforming this
understanding into a model.
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Process Simulation

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

MODEL
Input
Output
I
O
If the model is known, we have two uses for our
model Direct Input is applied on the model,
output is studied (Simulation) Inverse Output is
applied on the model, Input is studied
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Process Simulation

• If both Input and Output are Known, we have
  three formulations (Juha Yaako, 1998)
• Identification We can find the structure and
  parameters in the model.
• Estimation If the internal structure of model is
  known, we can find the internal states in model.
• Design If the structure and internal states of
  model are known, we can study the parameters in
  model.

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Process Simulation

• Demands set to models
• Accuracy ? Requirements placed on quantitative
  and qualitative models.
• Validity ? Consideration of the model
  constraints. A typical model process is
  non-linear, nevertheless, non-linear models are
  linearized when possible, because they are easier
  to use and guarantee global solutions.
• Complexity ? Models can be simple (usually
  macroscopic) or detailed (usually microscopic).
  The detail level of the phenomena should be
  considered.
• Computational ? The models should currently
  regard computational orientation.
• Robustness ? Models that can be used for multiple
  processes are always desired.

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Process Simulation

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

A model does not include everything. ngtm, and
kgtt. All models are wrong, Some models are
useful George Box, PhD University of Wisconsin
Input
Output
PROCESS
X1, ..., Xn
Y1, ..., Yk
Input
Output
MODEL
X1, ..., Xm
Y1, ..., Yt
In the process industry we find, two levels of
models Plant models, and models of unit
operations such as reactor, columns, pumps, heat
exchangers, tanks, etc.
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Process Simulation

• Types of models
• Intuitive the immediate understanding of
  something without conscious reasoning or study.
  This are seldom used.
• Verbal If an intuitive model can be expressed in
  words, it becomes a verbal model. First step of
  model development.
• Causal as the name implies, these model are
  about the causal relations of the processes.
• Qualitative These models are a step up in model
  sophistication from causal models.
• Quantitative Mathematical models are an example
  of quantitative models. These models can be used
  for (nearly) every application in process
  engineering. The problem is that these models are
  not documented or can be too costly to construct
  when there is not enough knowledge (physical and
  chemical phenomena are poorly understood).
  Sometimes the application encountered does not
  require such model sophistication.

From Stochastic knowledge
From first Principles
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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
MODEL

• Steady State
• Snapshot
• Algebraic equations

X1, ..., Xm
Y1, ..., Yt
Input
Output
MODEL (t)

• Dynamic
• Movie (time functions)
• Time is an explicit variable ? differential
  equations
• Certain phenomena require dynamic simulation
  (e.g. control strategies, real time descition).

X(t)1, ..., X(t)m
Y(t)1, ..., Y(t)t
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Process Simulation

• Illustration

Staedy state simulation of a storage tank
Dynamic simulation of a storage tank t time
m1
m1
Simulation unit
Hi-Limit
Level
Mconstant
Lo-Limit
Mf(t)
m2
m2(t)
Acumulation In - Out Production - Consumption
0In - Out Production - Consumption
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Process Simulation

• The steady-state simulation does not solve
  time-dependent equations. The Subroutines
  simulate the steady-state operation of the
  process units ( operation subroutines) and
  estimate the sizes and cost the process units (
  cost subroutines).
• A simulation flowsheet, on the other hand, is a
  collection of simulation units(e.g., reactor,
  distillation columns, splitter, mixer, etc.), to
  represent computer programs (subroutines) to
  simulate the process units and areas to represent
  the flow of information among the simulation
  units represented by arrows.

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Process Simulation

• To convert from a process flowsheet to a
  simulation flowsheet, one replaces the process
  unit with simulation units (Models). For each
  simulation unit, one assigns a subroutine (or
  block) to solve its equations. Each of the
  simulators has a extensive list of subroutines to
  model and solve the equations for many process
  units.
• The Dynamic simulation enables the process
  engineer to study the dynamic response of
  potential process design or the existent Process
  to typical disturbances and changes in operating
  conditions, as well as, strategies for the start
  up and shut down of the potential process design
  or existing process.

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Process Simulation

• Differences between Steady State and Dynamic
  Simulation

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Process Simulation

• Solution Strategies

• The Sequential Modular Strategy
• flowsheet broken into unit operations (modules)
• each module is calculated in sequence
• problems with recycle loops
• The Simultaneous Modular Strategy
• develops a linear model for each unit
• modules with local recycle are solved
  simultaneously
• flowsheet modules are solved sequentially
• The Simultaneous Equation-solving Strategy
• describe entire flowsheet with a set of equations
• all equations are sorted and solved together
• hard to solve very large equations systems

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Process Simulation

• Why steady-state simulation is 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.
• Perform many experiments cheaply once the model
  is built
• Avoid implementing ineffective solutions

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Process Simulation

• Why dynamic simulation is important

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Challenges of simulation

• Simulation is not the highest priority in the
  plant facilities
• Production or quality issues take precedence
• Hard to get plant facilities resources for
  simulation
• Up front time required before results are
  available
• Model must be calibrated, and results validated,
  before they can be trusted
• At odds with quarterly balance sheet culture
• May need to structure project to get some results
  out early

NEXT
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Data Reconciliation
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Data Reconciliation

• Typical Objectives of Data Treatment.
• Provide reliable information and knowledge of
  complete data for validation of process
  simulation and analysis
• Yield monitoring and accounting
• Plant facilities management and decision-making
• Optimization and control
• Perform instrument maintenance
• Instrument monitoring
• Malfunction detection
• calibration
• Detect operating problems
• Process leaks or product loss
• Estimate unmeasured values
• Reduce random and gross errors in measurements
• Detect steady states

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Data Reconciliation

• Data treatment is critical for
• Process simulation
• Control and optimization
• Management planning

Business management
INFORMATION
Site plant management
Scheduling optimization
Advanced control
Basic process control
Data Treatment
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Data Reconciliation
Overview
Manual data
On-line data
Data Treatment
Lab data
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Data Reconciliation

• Typical Problems With Process Measurements
• Measurements inherently corrupted by errors
• measurement faults
• errors during processing and transmission of the
  measured signal
• Random errors
• Caused by random or temporal events
• Inconsistency (Gross) errors
• Caused by nonrandom events instrument
  miscalibration or malfunction, process leaks
• Non-measurements
• Sampling restriction, measuring technique,
  instrument failure

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Data Reconciliation

• Random errors
• Features
• High frequency
• Unrepeatable neither magnitude nor sign can be
  predicted with certitude
• Sources
• Power supply fluctuation
• Signal conversion noise
• Changes in ambient condition

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Data Reconciliation

• Inconsistency (Gross error)
• Features
• Low frequency
• Predictable certain sign and magnitude
• Sources
• Caused by nonrandom events
• Instrument related
• Miscalibration or malfunction
• Wear or corrosion of the sensors
• Process related
• Process leaks
• Solid deposits