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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)

2
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.

3
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.

7
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
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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

45
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

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Data Reconciliation
Illustration Of Random Gross Errors

• abnormality

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Data Reconciliation

• Solutions To Problems
• Random errors Data processing
• Based on successive measurement of each
  individual variable Temporal redundancy
• Traditional filtering techniques
• Wavelet Transform techniques
• Inconsistency Data reconciliation
• Based on plant structure Spatial redundancy
• Subject to conservation laws
• Unmeasured data
• Data reconciliation

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Data Reconciliation
Measurement Problem Handling
Processing random errors
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Data Reconciliation

• Data Treatment Typical Strategy
• Establish Plant facilities operating regimes
• Data processing
• Remove random noise
• Detect and correct abnormalities
• Steady state detection
• Identify steady-state duration
• Select data set
• Data reconciliation
• Detect gross errors
• Correct inconsistencies
• Calculate unmeasured parameters

54
Data Reconciliation
METHODOLOGY EMPLOYED
From Plant Facilities
Process data
For simulation and further applications
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Data Reconciliation
                    
                                                
                

• What is data reconciliation?
• Data reconciliation is the validation of process
  data using knowledge of plant structure and the
  plant measurement system

                  
                                                
           
                  
            
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Data Reconciliation

• Objectives of Data Reconciliation
• Optimally adjust measured values within given
  process constraints
• mass, heat, component balances
• Improve consistency of data to calibrate and
  validate process simulation
• Estimate unmeasured process values
• Obtain values not practical to measure directly
• Substitute calculated values for failed instrument

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Data Reconciliation

• Possible Benefits
• More accurate and reliable simulation results
• More reliable data for process analysis and
  decision making by mill manager
• Instrument maintenance and loss detection
• e.g. US3.5MM annually in a refinery by
  decreasing loss by 0.5 of 100K BPD
• Improve measurement layout
• Decrease number of routine analysis
• Improve advanced process control
• Clear picture of plant operating condition
• Early detections of problems
• Quality at process level
• Work Closer to specifications.

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Data Reconciliation

• Data Reconciliation Problem of Process Under
  Different Status
• Steady-state data reconciliation
• based on steady-state model
• Using spatial redundancy
• Dynamic data reconciliation
• based on dynamic models
• Using both spatial temporal redundancy

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Data reconciliation (DR)

• DR Problem Of Process Under Different Status
  (Contd.)
• General expression of conservation law
• input- output generation- consumption-
  accumulation 0
• Steady state case
• no accumulation of any measurement
• Constraints are expressed algebraically
• Dynamic process
• Accumulation cannot be neglected
• Constraints are differential equations

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Data Reconciliation

• Data Reconciliation of Different Constraints
• Linear data reconciliation
• Only mass balance is considered
• flows are reconciled
• Bilinear data reconciliation
• Component balance imposed as well as energy
  balance
• flows composition measurements are reconciled
• Nonlinear data reconciliation
• Mass/energy/component balances are included
• Flow rate, composition, temperature or pressure
  measurements are reconciled

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Data Reconciliation
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Pinch Analysis.
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Pinch Analysis
What is Pinch Analysis?

• The prime objective of Pinch Analysis is to
  achieve financial savings in the process
  industries by optimizing the ways in which
  process utilities (particularly energy, mass,
  water, and hydrogen), are applied for a wide
  variety of purposes.
• The Heat Recovery Pinch (Thermal Pinch Analysis
  now) was discovered indepently by Hohmann (71),
  Umeda et al. (78-79) and Linnhoff et al. (78-79).
  
• Pinch Analysis does this by making an inventory
  of all 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 Pinch analysis is a technique to design
• Recovery Networks (Heat and Mass)
• Utility Networks (so called Total site Analysis)
• The basis of Pinch Analysis
• The use of thermodynamic principles (first and
  second law).
• The use heuristics (insight), about design and
  economy.

• The Pinch Analysis makes extensive use of various
  graphical representations

65
Pinch Analysis

• The Pinch Analysis provides insights about the
  process.

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

• The pinch Analysis integrates economic parameters

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Pinch Analysis
The Four phases of pinch analysis in the design
of recovery process
Which involves collecting data for the process
and the utility system
Which establishes figures for the best
performance in various aspects.
Where an initial Heat Exchanger Network is
established by heuristics tools allowing a
minimum target to be reached.
Where an initial design is simplified and
improved economically.
67
Pinch Analysis

• Heat Exchanger Network (HEN)
• HEN design is the classical domain of Pinch
  Analysis. By making proper use of temperature
  driving forces available between process steams,
  the optimum heat exchanger network can be
  designed, taking into account constraints of
  equipment location, materials of construction,
  safety, control, and operating flexibility. This
  then sets the hot and cold utility demand profile
  of the plant.
• When used correctly, Pinch Analysis yields
  optimum HEN designs that one would have been
  unlikely to obtain by experience and intuition
  alone.

68
Pinch Analysis

• Combined Heat and Power (CHP)
• CHP is the terminology used to describe plant
  energy utilities, boilers, steam turbines, gas
  turbines, heat pumps, etc. Traditionally, these
  have been referred to as "plant utilities",
  without distinguishing them from other plant
  utilities such as cooling water and wastewater
  treatment.
• The CHP system supplies the hot utility and power
  requirements of the process. Pinch Analysis
  offers a convenient way to guarantee the optimum
  design, which can include the use of cogeneration
  or three-generation (use of hot utility to
  produce cold utility and power for things like
  refrigeration).

69
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
  to design the processes. The energy target is the
  minimum theoretical energy demand for the plant
  or site.
• Pinch Analysis will therefore quickly identify
  where energy savings are likely to be found.
• Reduction of emissions
• Pinch Analysis enable to the engineer with tool
  to find the best way to change the process, if
  the process let it.

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Pinch Analysis

• In addition, Pinch Analysis allow you to
• Update or Development of Process Flow Diagrams
• Identify the bottleneck in the process
• Departmental Simulations
• Full Plant Facilities Simulation
• Determine Minimal Heating (Steam) and Cooling
  Requirements
• Determine Cogeneration and Three-generation
  Opportunities
• Determine Projects with Cost Estimates to Achieve
  Energy Savings
• Evaluation of New Equipment Configurations for
  the Most Economical Installation
• Pinch Replaces the Old Energy Studies with a Live
  Study that Can Be Easily Updated Using Simulation

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Optimization by Mathematical Programming
72
Optimization by Mathematical Programming
introduction

• A Mathematical Model of a system is a set of
  mathematical relationships (e.g., equalities,
  inequalities, logical conditions) which represent
  an abstraction of the real world system under
  consideration.
• A Mathematical Model can be developed using
• Fundamental approaches ? Accepted theories of
  sciences are used to derive the equations (e.g.,
  Thermodynamics Laws).
• Empirical Methods ? Input-output data are
  employed in tandem with statistical analysis
  principles so as to generate empirical or Black
  box models.
• Methods Based on analogy ? Analogy is employed in
  determining the essential features of the system
  of interest by studying a similar, well
  understood system.

73
Optimization by Mathematical Programming
introduction

• A mathematical Model of a system consists of four
  key elements
• Variables ? The variables can take different
  values and their specifications define different
  states of the systems.
• Continuous,
• Integer,
• Mixed set of continuous and integer.
• Parameters ? The parameters are fixed to one or
  multiple specific values, and each fixation
  defines a different model.
• Constraints ? the constraints are fixed
  quantities by the model statement
• Mathematical Relationships ? The mathematical
  model relations can be classified as
• Equalities ? usually composed of mass balance,
  energy balance, equilibrium relations, physical
  property calculations, and engineering design
  relations which describe the physical phenomena
  of the system.
• Inequalities ? consist of allowable operating
  regimes, specifications on qualities, feasibility
  of heat and mass transfer, performance
  requirements, and bound on availabilities and
  demands.
• Logical conditions ? provide the connection
  between the continuous and integer variables.
• The mathematical relations can be algebraic,
  differential, or a mixed set of both constraints.
  These can be linear or nonlinear.

74
Optimization by Mathematical Programming

• What is Optimization?
• A optimization problem is a mathematical model
  which in addition to the before mentioned
  elements contains one or more performance
  criteria.
• The performance criteria is denoted as an
  objective function. It can be minimization of
  cost, the maximization or profit or yield of a
  process for instance.
• If we have multiple performance criteria then the
  problem is classified as multi-objective
  optimization problem.

A well defined optimization problem features a
number of variables greater than the number of
equality constraints, which implies that there
exist degrees of freedom upon which we optimize.
75
Optimization by Mathematical Programming

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

Where x is a vector of n continuous variables, y
is a vector of integer variables, h(x,y) 0 are m
equality constraints, g(x,y) ? 0 are p inequality
constraints, and f(x,y) is the objective function.
76
Optimization by Mathematical Programming

• Classes of Optimization Problems (OP)
• If the objective function and constraints are
  linear without the use of integer variables, then
  OP becomes a linear programming (LP) problem.
• If there exist nonlinear terms in the objective
  function and/or constraints without the use of
  integer varialbes, the OP becomes a nonlinear
  programming (NLP) problem.
• If integer variables are used, they participate
  linearly and separtly from the continuous
  variables, and the objective function and
  constraints are linear, then OP becomes a
  mixed-integer linear programming (MILP) problem.
• If integer variables are used, and there exist
  nonlinear terms in the objective function and/or
  constraints, then the OP becomes a mixed-integer
  nonlinear programming (MINLP) problem.
• Whenever possible, linear programs (LP or MILP)
  are used because they guarantee global solutions.
• MINLP problems features many applications in
  engineering.

77
Optimization by Mathematical Programming

• Applications
• Process Synthesis
• Heat Exchanger Networks
• Distillation Sequencing
• Mass Exchanger Networks
• 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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Stochastic Search Methods
79
Stochastic Search Methods

• Why stochastic Search Methods
• All of the model formulations that you have
  encountered thus far in the Optimization 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.

80
Stochastic Search Methods

• There are probabilistic algorithms, such as
• Simulated annealing (SA)
• Genetic Algorithms (GAs)
• Tabu search
• These are suitable for problems that deal with
  uncertainty. These computer algorithms or
  procedure models do not guarantee global
  optimally but are successful and widely known to
  come very close to the global optimal solution
  (if not to the global optimal).
• GA has the capability of collectively searching
  for multiple optimal solutions for the same best
  cost.
• Such information could be very useful to a
  designer, because one configuration could be much
  easier to build than another.
• SA takes one solution and efficiently moves it
  around in the search space, avoiding local optima.

81
Stochastic Search Methods

• What is GAs?
• GAs simulate the survival of the fittest among
  individuals over consecutive generation for
  solving a problem. Each individual represents a
  point in a search space and a possible solution.
  The individuals in the population are then made
  to go through a process of evolution.
• GAs are based on an analogy with the genetic
  structure and behaviour of chromosomes within a
  population of individuals using the following
  foundations
• Individuals in a population compete for resources
  and mates.
• Those individuals most successful in each
  'competition' will produce more offspring than
  those individuals that perform poorly.
• Genes from good individuals propagate
  throughout the population so that two good
  parents will sometimes produce offspring that are
  better than either parent.
• Thus each successive generation will become more
  suited to their environment.

82
Stochastic Search Methods

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

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

83
Stochastic Search Methods

• The general genetic algorithm solution is found
  by

• Start Generate random population of n
  chromosomes (suitable solutions for the problem)
• Fitness Evaluate the fitness f(x) (objective
  function) of each chromosome x in the population.
• New population Create a new population by
  repeating following steps until the new
  populationis complete
• Selection Select two parent chromosomes from a
  population according to their fitness (the better
  fitness, the bigger chance to be selected)
• Crossover With a crossover probability cross
  over the parents to form a new offspring
  (children). If no crossover was performed,
  offspring is an exact copy of parents..
• Mutation With a mutation probability mutate new
  offspring at each locus (position in chromosome).
• Accepting Place new offspring in a new
  population 4.
• Replace Use new generated population for a
  further run of algorithm 4.
• Test If the end condition is satisfied, stop,
  and return the best solution in current
  population 5.
• Loop Go to step 2

84
Stochastic Search Methods

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

Each chromosome has one binary string. Each bit
in this string can represent some characteristic
of the solution. Or the whole string can
represent a number Of course, there are many
other ways of encoding. This depends mainly on
the solved problem. For example, one can encode
directly integer or real numbers. Sometimes it is
also useful to encode some permutations.
85
Stochastic Search Methods

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

There are other ways how to make crossovers, and
we can choose multiple crossover points.
Crossovers can be rather complicated and vary
depending on the encoding of chromosome. Specific
crossovers made for a specific problem can
improve performance of the genetic algorithm.
86
Stochastic Search Methods

• Mutation
• After a crossover is performed, mutation takes
  place. This is to prevent the falling of all
  solutions in the population into a local optimum.
  Mutation changes the new offspring randomly. For
  binary encoding we can switch a few randomly
  chosen bits from 1 to 0 or from 0 to 1. Mutation
  can then be shown as

The mutation depends on the encoding as well as
the crossover. For example when we are encoding
permutations, mutation could be exchanging two
genes.
87
Stochastic Search Methods

• GAs Characteristics
• A GA makes no assumptions about the function to
  be optimized (Levine, 1997) and thus can also be
  used for nonconvex objective functions
• A GA optimizes the tradeoff between exporting new
  points in the search space and exploiting the
  information discovered thus far
• A GA operates on several solutions
  simultaneously, gathering information from
  current search points and using it to direct
  subsequent searches which makes a GA less
  susceptible to the problems of local optima and
  noise
• A GA only uses the objective function or fitness
  information, instead of using derivatives or
  other auxiliary knowledge, as are needed by
  traditional optimization methods.

88
Stochastic Search Methods
GA Solution Procedure
Start
Initial Population
1st Generation
Get Objective Function Value for Whole
Population (Internal optimization)
Nth Generation
Yes
Optimum?
Stop
No

• Generate New Population
• GA parameters
• GA strategies

(N1)th Generation
89
SA and GA comparation In theory and Practice
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90
Life Cycle Analysis.
91
Life Cycle Analysis

• What is Life Cycle Analysis?
• Technique for assessing the environmental aspects
  and potential impacts associated with a product
  by
• 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 to the objectives of
  the study heading
• Evaluation of some aspects of a product system
  through all stages of its life cycle

92
Life Cycle Analysis

• Why LCA is important
• Tool for improvement of environmental performance
• Systematic way of managing an organizations
  environmental affairs
• Way to address immediate and long-term impacts of
  products, services and processes on the
  environment
• Focus on continual improvement of the system

93
Life Cycle Analysis
LCA methodology
LIFE-CYCLE ASSESSMENT
Goal and Scope definition
94
Life Cycle Analysis

• Goal and scope definitions
• goal ? application, use and users
• scope ? borders of the assessment
• functional unit ? scale for comparison
• efficiency
• durability
• performance quality standard
• system boundaries ? process, inputs and outputs
  defined
• data quality ? reflected in the end results
• critical review process ? verification of validity

95
Life Cycle Analysis

• Inventory analysis
• data collection ? qualitative or quantitative,
  most work intensive
• refining system boundaries ? after initial data
  collection
• calculation ? no formal description, software
• validation of data ? assessment of data quality
• relating data to the specific system ? data must
  be ralted to the functional unit
• allocation ? done when not all impacts and
  outputs are within the system boundaries

96
Life Cycle Analysis

• Impact assessment
• category definition ? impact categories defined
• classification ? inventory input and output
  appointed to impact categories
• characterization ? assign relative contribution
• weighting ? when comparison of the impact
  categories is not possible

97
Life Cycle Analysis

• Interpretation/improvement assessment
• identification of significant environmental
  issues ? information structured in order to get a
  clear view on key environmental issues
• evaluation ? completeness analysis, sensitivity
  analysis, consistency analysis
• conclusions and recommendations ? improve
  reporting of the LCA

98
Life Cycle Analysis

• Possible Benefits
• Improvements in overall environmental performance
  and compliance
• Provides a framework for using pollution
  prevention practices to meet LCA objectives
• Increased efficiency and potential cost savings
  when managing environmental obligations
• Promotes predictability and consistency in
  managing environmental obligations
• More effective measurement of scarce environmental

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Data-Driven Process Modeling
100
Data-Driven Process Modelling
Process Integration ChallengeMake sense of
masses of data
Drowning in data!

• Many organisations today are faced with the same
  challenge TOO MUCH DATA
• It is the last item that is of interest to us as
  chemical engineers

101
Data-Driven Process Modelling

• Data-Rich but Knowledge-Poor
• Far too much data for a human brain
• Limited to looking at one or two variables at a
  time
• Big Problem Interesting, useful patterns and
  relationships not intuitively obvious lie hidden
  inside enormous, unwieldy databases

102
Data-Driven Process Modelling
Empirical Model

• This approach uses the plant process data
  directly, to establish mathematic correlations.
• Unlike the theoretical models, empirical models
  do NOT take the process fundamentals into
  account. They only use pure mathematical and
  statistical techniques. Multi-Variable Analysis
  (MVA) is one such method, because it reveals
  patterns and correlations independently of any
  pre-conceived notions.
• Obviously this approach is very sensitive to
  Garbage-in, garbage-out which is why validation
  of the model is so important.

103
Data-Driven Process Modelling

• With MVA you move
• From Data to Information.
• From Information to Knowledge.
• From Knowledge to Action.

104
Data-Driven Process Modelling

• What is MVA?
• Multi-Variate Analysis (gt 5 variables)
• MVA uses ALL available data to capture the most
  information possible
• Principle boil down hundreds of variables down
  to a mere handful

MVA
?
105
Data-Driven Process Modelling

• MVA Example Apples and Oranges
• Measurable differences
• Colour, shape, firmness, reflectivity,
• Skin smoothness, thickness, morphology,
• Juice water content, pH, composition,
• Seeds colour, weight, size distribution,
• et cetera
• However, always only one latent attribute
• Apple or orange?

-1
1
106
Data-Driven Process Modelling
How MVA Works
Statistical Model
(internal to software)
.
.
.
.
.
.
.
.
.
.
.
.
Raw Data impossible to interpret
Y
trends
trends
X
X
trends
X
X
700 columns
9,000 rows
2-D Visual Outputs
107
Data-Driven Process Modelling
Effect of Outliers on MVA
1 component
?
What about an extreme outlier?
OUTLINER
108
Data-Driven Process Modelling
Effect of Outliers on MVA
1 component
?
Linear regression by Least squares !
New (wrong) component!
?
Extreme outliers very detrimental to MVA
Real component has become mere noise
109
Data-Driven Process Modelling

• Benefits
• Explore Inter-Relationships
• Create and Learn by modelling
•  What-if  Exercises
• Low-cost investigation of options
• Soft Sensor (Inferential Control)
• for parameters we cant measure directly
• Feed-Forward (Model-Based) Control

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Integrate Process Design and Control
111
Integrate Process Design and Control

• Control Objectives
• Product specifications variability should be kept
  to a minimum --gt process variability (To Control
  Product quality).
• Safety issues(separate equipments), 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. The Control is
  permanently necessary to do for allowing the
  process to operate in the best conditions.

112
Integrate Process Design and Control
CONTROLLABILITY
it is a property of a process that accounts for
the ease with which a continuous plant can be
held at a specified operating policy, despite
external disturbances (resiliency) and
uncertainties (flexibility) and regardless of the
control system imposed on such a plant.
Process Variability
Sources
MIN
DESIGN
CONTROL

-Dynamics -Tunings - Control configurations
Changes in Process
Steady State Dynamic Simulations
113
Integrate Process Design Control
Fundamentals
Input Variables
PROCESS RESILIENCY
Control Loop
Disturbances
Process
Internal interactions
Output Variables (controlled and Measured)
Input Variables (Manipulated)
Uncertainties
PROCESS FLEXIBILITY
114
Integrate Process Design and Control
e.g. Controllability analysis for control
structures design
Water, F1
CC
FC
C, F
Pulp, F2
Interactions
INPUTS (process variables or disturbances)
OUTPUTS
EFFECTS
(Best Selection by Controllability analysis)
115
Integrate Process Design and Control
Why Controllability is important

• The process will be more capable to move smoothly
  around the possible operating edge
• Stability and better performance of control
  loops and structures
• System relatively insensitive to perturbations
• Efficient management of interacting networks

Flexibility
Improvement of current dynamics
116
Integrate Process Design and Control
The Top level of the process control, Strategic
control level is thus concerned with achieving
the appropriate values principally of

• Production rate (time)
• Product quality, and
• Energy economy.

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117
Real Time Optimizations (RTO)
118
Real Time Optimizations

• The Process Industries are increasingly compelled
  to operate profitably in 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 adopt to the changing market conditions and to
  reduce the operating cost. Hence, the importance
  of real-time or on-line optimization of an entire
  plant is rapidly increasing.

119
Real Time Optimizations

• What is RTO?
• Real-time Optimization is a model-based
  steady-state technology that determines the
  economically optimal operating policy for a
  process in the near term
• The system optimizes a process simulation and not
  the process directly
• Performance measured in terms of economic benefit
• Is an active field of research
• Model accuracy, error transmission, performance
  evaluation

120
RTO Schematically
Reconciliation And gross Error Detection
Updating Process Model (Steady State?Dynamic Simul
ation)
Business Objectives Economic Data Product
Specification
Optimization (Objectives Functions)
Steady State Detection
Cost, Process, Environmental, Product Data
Plant Facility
121
Direct Search Method Schematically
Dynamic Simulation (Model)
RTO Algorithm (Objective Fct, Constraints)
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122
Business Model And Supply Chain Modeling
123
Business Model And Supply Chain Modeling
Cost, Process, Environmental Product Outcomes
Cost, Process, Environmental Product Outcomes
Process Design Analysis And Synthesis
Process Operation Analysis and Optimization
Integrated Business Process Model
Cost, Process, Environmental Product Data
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124
Cost, Process, Environmental Product Data
Integrated Business Process Model
Cost, Process, Environmental and Product Data
Reconciled PE Data
Data Reconciliation
Processed PE Data
Data Processing
Process (P) Environmental (E) Data
Accounting Data
Product Data
Market Data
Once the model is built it can be used to
validate and reconcile data
Plant Facilities
125
Integrated Business and Process Model
Data Driven Models
Process Simulation Models
1st Principles Models
126
Supply Chain and 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

• 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 recycling, re-use and collection
  of used material (Beamon 1999)

127
Supply Chain and Environmental Supply Chain

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

128
Process Design Analysis and Synthesis
Process Design Analysis and Synthesis
Process Design Analysis Design Objectives

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

Process Design Analysis and Synthesis Loop
Process Integration Tools
Integrated Business Process Model
Capital Effectiveness Analysis
129
Process Operation Analysis and Optimization
Process Design Analysis and Optimization
Detailed Process Investigation to Validate
Recommendations

• Data reconciliation for instrument validation
• Dynamic simulation
• Process control strategies
• MVA (Soft sensor dev.)
• Real-time optimi