tier 1 steady state simulation piece paprican ecole polytechnique universidad de guanajuato 1...
TRANSCRIPT
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 1
Process Integration for EnvironmentalControl in Engineering Curricula (PIECE)
Program for North American Program for North American MobilityMobility
in Higher Education (NAMP)in Higher Education (NAMP)
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 2
Module
Steady State Process Simulation
2
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 3
Understand and simulate processes in steady state.
Solve technical and economic problems more quickly, efficiently and successfully.
Propose
This module has been developed to help the students:
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 4
Statement of intent
The student will.
Review basic concepts used in steady – state simulation.
Understand the purpose of steady – state simulation.
Develop models of a processes in steady state.
Simulate processes with help of computer simulators.
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 5
Contents
This module is divided in 3 tiers
Tier 1. Introduction to simulation tool.
Tier 2. How to use computer tool.
Tier 3. How to apply in real world.
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 6
Tier
1Introduction to Steady State,
Process Simulation tool
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 7
1. Basic concepts.
2. Steady – state simulation in a process integration context.
3. Steady – state simulation in a broader context.
Tier 1 is divided in 3 sections
Contents
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 8
1
Basic Concepts
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 9
Show the basic concepts of steady – state simulation.
Improve process simulation skills.
Create your own simulation flowsheets.
Recognize why simulation is useful in the process industries.
Basic concepts
Statement of intent
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 10
Basic Concepts
Steady – state. Models and simulation. Creating models. Unit efficiencies. Stream components. Units. Performing a steady – state simulation study.
Contents
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 11
By steady state we mean, in most systems, the conditions when nothing is changing with time.
Mathematically this corresponds to having all time derivatives equal to zero, or to allowing time to become very large (go to infinity).
Steady – State
Steady – state
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 12
Steady – State
The design of process systems requires both: Steady – state model. Dynamic models.
One use for the steady – state models is in determining the possible region of steady – state operation for a process that can be limited by constraints such as safety, product quality, and equipment performance.
Steady – state
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 13
Model
A model is an abstraction of a process operation used to build, change, improve or control a
process.
Uses of a model: Equipment design, sizing and selection. Comparison of possible configurations. Evaluation of process performance against limits
(e.g. Concentrations, effluent discharge rates). De-bottlenecking and optimization. Control strategy development and evaluation.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 14
ModelThe model is an abstraction of the real word
Models vary by: Phenomena represented (energy,
classifications phase change). Level of detail and granularity Assumptions (perfect mixing, zero heat loss). Kind of input required Functions performed (constraint satisfaction,
optimization). Nature of output generated
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 15
Models vary by purpose and category
Purpose Operator training simulator. Control strategy evaluation. Investment justification (e.g. new equipment
purchase). Other…
Category Physical (e.g. mimic panel) vs. Mathematical. Qualitative vs. Quantitative. Empirical vs. First principle based. Steady state vs. Dynamic state.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 16
Physical Model
From a balance:
Mathematical Model
onaccumulatinconsumptioproductionoutin
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 17
Quantitative
Using non – numeric descriptors.
Fuzzy, logic. Expert system. Turn an alarm on.
Qualitative
Using numbers, and quantifying the
magnitude of the response.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 18
Empirical
Derived from observation.
Often simple.May or may not have
theoretical foundation.Valid only within range
of observation.
First – principle based
Derived from fundamental physical laws.
Most reliable, but we often don’t have them.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 19
Steady – State
Snapshot of a unit operation or plant
Movie of plant operation
Balance at equilibrium condition
Time dependent results
Equilibrium results for all unit operations
Equilibrium conditions not assumed for all units
Equipment sizes, in general not needed
Equipment sizes needed
Amount of information required: small to medium
Amount of information required: medium to large
Dynamic
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 20
Requirements of a good model
Accuracy: close enough to the target. It is required in quantitative and qualitative models.
Validity: we must consider the range of the model. The model must have a solid foundation or justification.
Right level of complexity: models can be simple, usually macroscopic, or detailed, usually microscopic. The detail level of phenomena should be considered. Easy to understand.
Computational efficiency: the models should be calculable using reasonable amounts of time and computing resources.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 21
Simulation
Predicts the behavior of a plant by solving the mathematical relationships that describe the behavior of the plant’s constituent components.
Involves performing a series of experiments with a process model.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 22
Importance of steady – state simulation
Better understanding of the process. Consistent set of typical mill data. Objective comparative evaluation of options
for return on investment etc. Identification of bottlenecks, instabilities, etc. Ability to perform many experiments cheaply
once model built. Avoidance of ineffective solutions.
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 23
Constructing a model
When we try to represent a phenomena, to predict future conditions, or to know how the process will behave in certain situation, it is common to use mathematical expressions.
V
VSV
dVBdSnFdVbdt
d
Models & simulation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 24
Constitutive relations
Relate the diffusive flux of a certain quantity with the local properties of the material and with the transport driving force.
Express the movement of a certain quantity in the decreasing gradient direction of the quantity.
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 25
Constitutive relations
Fourier’s law:
Fick’s first law:
)( CpTq
Cpk
AABA CJ D
Thermal diffusion
Mass diffusionABD
Newton’s law: v
v Kinematic viscosity
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 26
Variation EquationsConservation Equations or
Equations of change
Those relate the accumulation of a quantity with the rate of entrance or formation of the same quantity in a specific volume. Those are based in fundamental principles and have universal description.
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 27
Conservation of mass
consumed
mass
ofrate
produced
mass
ofrate
out
mass
ofrate
in
mass
ofrate
onaccumulati
mass
ofrate
In a differential element:
It is common practice to express the balance in a differential element, and convert the equation to a differential form.
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 28
Conservation of mass
)( v t
For a pure component:
RNt
Ci
i
iii JvCN
reaction chemical ain n Consumptioor ProductionR
Conservation of chemical species:
Note: steady – state no change in the time. 0t
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 29
Conservation of energy
Note: steady – state no change in the time.
0t
VP HTvC q
Where HV is rate of heat generated by external source (electricity, compression, chemical
reactions, etc.).
energy kinetic
internal of
onaccumulati
of rate
ssurroudingon
systemby done
workof ratenet
conduction
byaddition
heat of ratenet
convectionby out
energy kinetic and
internal of rate
convectionby in
energy kinetic and
internal of rate
Creating models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 30
Unit efficiencies
An engineer may define energy efficiency in a very restrictive equipment sense. Energy efficiency has been used to describe what actually may be conservation.
Energy efficiency in a more subjective sense may refer to the relative economy with which energy inputs are used to provide services.
QW
Unit efficiencies
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 31
Typical Efficiencies
ValuesCompressors = 0.8
Motor = 0.9
Turbine = 0.8
Pump = 0.5
Unit efficiencies
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 32
Stream Components
Ideal gas law and equations of state. Solubility relations (solid in liquid and gas in liquid). Reaction stoichiometry and equilibrium. Simple vapor/liquid relationships such as Raout’s law.
Overall stream flows and components are calculated based on physical and chemical properties such as:
Stream components
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 33
Conversion of stream components
Mechanical work.
A
BC
AB
A B
Via chemical reaction.
Heat.
Stream components
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 34
Engineering Units
The official international system of units is the SI . But older systems, particularly the centimeter – gram – second (cgs) and foot – pound – second (fps), are still in use.
It was originated in France, in 1790 by the French Academy of Science.
The units should be based on unvarying quantities in nature.
Multiples of units should be decimal. The base units should be used to derive other
units.
Units
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 35
Engineering Units
Units
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 36
Steady state model derivation.
Calculation order.
Recycle streams.
Convergence and iteration.
Recycle convergence methods.
Granularity model.
Performing a Steady – State simulation Study
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 37
Steady state model derivation
1.- Define Goals.a) Specific design decisions.b) Numerical values.c) Functional relationships.d) Required accuracy.
2.- Prepare information.a) Sketch process and identify
system.b) Identify variables of interest.c) State assumptions and data.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 38
Steady state model derivation
3.- Formulate model.a) Conservation balances.b) Constitutive equations.c) Rationalize (combine equations and collect
terms).d) Check degrees of freedom.e) Dimensionless groups (Pr, Nu, Re, etc.).
4.- Determine solution.a) Analytical.b) Numerical.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 39
Steady state model derivation
5.- Analyze resultsa) Check results for correctness
Limiting and approximate answersAccuracy of numerical method
b) Interpret resultsPlot solutionRelate results to data and assumptions Evaluate sensitivityAnswer “what if questions”
-1
-0.5
0
0.5
1
0 1 2 3 4 5 6 7 8 9 10
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 40
Steady state model derivation
6.- Validate model.
a) Select key values for validation.b) Compare with experimental results.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 41
Calculation Order
In most process simulators, the units are computed (simulated) one at a time. The calculation order is automatically computed to be consistent with the flow of information in the simulation flowsheet, where the information flow depends on the specifications for the chemical process.
1 2 3 4
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 42
Recycle FlowsA simulation flowsheet usually contains information
recycle loops. That is, cycles for which too few streams variables are known to permit the equation for each unit to be solved independently.
1 2 3 4
For these processes, a solution technique is needed to solve the equations for all the units in the recycle loop.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 43
Solution technique
Consist in guessing a value for the recycle stream. This value is generally not going to equal the calculated value, this represent another problem which is solved by “iteration”.
CalculationInitial guessingvalues
New values fromThe calculation
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 44
Iteration
Convergence units use convergence subroutines to compare the newly computed variables (in the feed stream to the convergence unit) with guessed values (in the product stream from the convergence unit) and to compute new guess values when the two streams are not identical to within convergence tolerances. This procedure is call iteration. It involves re – calculating the flowsheet.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 45
Convergence
Is the process to compare the guessed value with the computed value, until find a value within the tolerance range.
Guess value – calculated value < Tolerance
Guess value
YesNo
Convergence
When the criteria is achieve, the solution is found, and is time to stop the iteration.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 46
Convergence
Initialize each unit
Convergence?
Start
t = 0, k = 0 Guess torn streams
no
Stop
k = k + 1
Xij yij
ex ji ji,,1 y - no
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 47
Recycle convergence methods
Where is the vector of guesses for n recycle (tear) variables and is the vector of the recycle variable computed from the guesses after one pass through the simulation units in the recycle loop. Clearly, the objective of the convergence unit is to adjust so as to drive toward zero.
**)( xxfy
*x)( *xf
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 48
Successive substitution as the basic and obvious
methodAlso call direct iteration. In this method the new
guess for x is simply made equal to f(x*).
Performing a SS simulation study
x0* x1
*
f(x
* )
Locus ofIterates
When the slope of the locus of iterates (f(x),x) is close to unity, a large number of
iterations may be required before convergence occurs
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 49
Other convergence methods
When the method of successive substitutions requires a large number of iterations, another methods are used to accelerate convergence:
Wegstein’s method. Newton – Raphson method. Broyden’s quasi – Newton method. The dominant – eigenvalue method.
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 50
Wegstein’s method
In this method, the two previous iterates of f(x*) and x* are extrapolated linearly
to obtain the next value of x as the point of intersection.
x0* x1
*
f(x
* ) Locus ofIterates
Performing a SS simulation study
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 51
Granularity of modeling
With the advance in technology, it is possible to combine on a single computer the full capability of a high fidelity simulation models.
High fidelity process simulation is commonly used by many industries in the design of a process.
Granularity of modeling
Is the level of detail taken into account in a simulation.
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 52
Comparing Coarse vs. Fine
models
Granularity of modeling
Coarse:
Bleaching tower
Kxzx
Dzx
v
2
2
A coarse model represent the equipment with few detail.
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 53
Fine model
Bleaching tower
iio VLL
mi
mii
mo
moo C
CL
CC
L
11
0r
tKK
c
io
ijiijiojo YVXLXL ,,,
Liquors
Fibers
Chromophores
Chemicals
PFR
CSTR
CSTR
The same equipment is divided in 3, and the substances into account are more than just an
approximation.
Granularity of modeling
KxxDxv 2 More than 1 direction
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 54
Benefits
The detail level is low
The time involve is less
The solution effort is few
The solution is approximated
The detail level is big
Time require is big
The solution effort is big
The solution is exact
Granularity of modeling
Coarse Models Fine Models
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 55
2
Steady state simulation in a process integration
context
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 56
Steady state simulation in a
process integration context
Recognize the components in a simulation flowsheet.
Check the procedure to create a process. What is the importance of the computer. What can we obtain as a result of a simulation.
Statement of intent
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 57
Steady – state simulation in a process integration
context Process flowsheets. Simulation flowsheets. Process synthesis methodologies. Minimal time and expense. Computer – based process. Data reconciliation. Process insights resulting from simulation.
Table of content
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 58
Process flowsheets
Process flowsheets are the language of chemical processes. Like a work of art, they describe an existing process or a hypothetical process in sufficient detail to convey the essential features.
Process flowsheets
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 59
Process flowsheetA process flowsheet is a collection of icons to
represent process units and arcs to represent the flow of materials to and from the units.
Fresh Feed
Steam Heater
Reactor
Flash
Distillation
Product
Process flowsheets
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 60
Simulation
The analysis of a simulation, is the tool chemical engineers use to interpret process flowsheets, to locate malfunctions, and to predict the performance of the process.
Simulation flowsheets
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 61
Simulation Flowsheet
A simulation flowsheet, on the other hand, is a collection of simulation units, each representing a computer program (subroutine or model) that simulates a process unit, and arcs to represent the flow of information among the simulation units.
Mixer
Heater
Reactor
Flash
Column
MathematicalConvergence
Unit
Simulation flowsheets
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 62
Process synthesis methodologies Total enumeration of an explicit space: is
the most obvious. Here we generate and evaluate every alternative design. We locate the better alternative by directly comparing the evaluations.
Evolutionary methods: follow from the generation of a good base case design. Designers can then make many small changes, a few at a time, to improve the design incrementally.
Structured Decision Making: following a plan that contains all the alternatives.
Process synthesis methodologies
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 63
Process synthesis methodologies
Design to target: these have been especially useful in designing heat recovery and reactor networks. The utility requirements become the targets for the design.
Problem abstraction: Here the search for better design alternatives begins by formulating a less detailed problem statement and attempting to solve this more abstract problem first.
Process synthesis methodologies
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 64
Minimal time
Fresh Feed
Steam
Heater
Reactor Flash
Distillation
Product
Change inHeat Duty
Change inReactor Properties
Change inColumn Properties
Changecomposition
In feed
With a simulation, you can simulate one day of process operation in just seconds, and make as many changes as you want.
Minimal time and expense
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 65
Minimal expense
Simulated “learning experiences” are much less costly than making real mistakes in the real plant.
Is easy to model the process with different kind of equipment without having to invest in it.
Minimal time and expense
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 66
Computer – Based process representation which can
be re - usedMost of the times, there are already models which
can be adapted to the process under study, with minimal changes. This minimizes the time needed to set up complicated equations.
Re-using models is much easier than building new ones, specially if the process is being modeled for the first time.
Computer based process
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 67
Data reconciliation
Data reconciliation is a technique for improving the quality of measured plant data. These measurements are inherently inaccurate due to instrument failures, limitations of measurement techniques, etc.
As a result, data are obtained that violate mass and energy balance constraints of describe a physically infeasible process.
Data reconciliation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 68
How Data reconciliation
works
t
F
Reconcilingerrors
Find a set of data that:
Constitutes some kind of “best fit” (least squares) to the observed data.
Satisfies mass – energy balance and other criteria.
Data reconciliation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 69
Opportunity to do data reconciliation
This amounts to validation of the process data using knowledge of the plant structure and the plant measurement system
Data reconciliation
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 70
Process insights resulting from modeling
1.1. Identification:Identification: We can find the We can find the structure and parameters in the structure and parameters in the modelmodel..
2.2. Estimation:Estimation: If the internal structure of If the internal structure of model is known, we can find the model is known, we can find the internal states in model.internal states in model.
3.3. Design:Design: If the structure and internal If the structure and internal states of model are known, we can states of model are known, we can study the parameters in the model.study the parameters in the model.
MODELMODEL
Process insights resulting from modeling
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 71
Process insights resulting from modeling
If the model is known, we have two uses for our If the model is known, we have two uses for our model:model:
1.1. Direct:Direct: input is specified, output is studied input is specified, output is studied (simulation).(simulation).
2.2. Inverse:Inverse: output is specified, input is studied. output is specified, input is studied. Used when an objective must be met Used when an objective must be met (production, composition). (production, composition).
Process insights resulting from modeling
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 72
3
Steady – State Simulation in a
Broader Context
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 73
Steady – State Simulation in a Broader
Context
Show how to take a decision to create a process. Know if the process is viable, in terms of stability
and economic. Taking in count security aspects.
Statement of intent
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 74
Aspects of Process Design
Process design.
Stability and sensitivity.
Process optimization.
Economic evaluation of alternatives.
Operator training.
Table of content
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 75
Process design
The design of chemical products begins with the identification and creation of potential opportunities to satisfy societal needs and to generate profit. The scope of chemical product is extremely broad. They can be roughly classified as:
1. basic chemical products.2. Industrial products.3. Consumer products.
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 76
Process designManufacturing
ProcessNatural
ResourcesBasic chemical
Products
ManufacturingProcess
Basic ChemicalProcess
IndustrialProducts
ManufacturingProcess
Basic ChemicalIndustrial Products
ConsumerProducts
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 77
Motivation for design projects
1. Desires of customers for chemicals with improved properties for many applications.
2. New inexpensive source of a raw material with new reaction paths and methods of separation.
3. New markets are discovered.
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 78
Steps in a Process Design
1. Process Design – Questions to Answer
Is the chemical structure known? Is a process required to produce the
chemicals? Is the gross profit favorable? Is the process still promising after further
elaboration? Is the process and/or product feasible?
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 79
Steps in a Process Design
Create and assess primitive problem.
Find chemicals or chemical mixtures that have the desired properties and performance.
Process creation. Development of base
case.
Detailed design, equipment sizing, and optimization.
Startup assessment. Reliability and safety
analysis. Written design report
and oral presentation. Plant design,
construction, startup and operation.
2. Process Design – Steps
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 80
3. Process Design – Procedure
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 81
Detailed Process Synthesis Using Algorithmic Methods Create and evaluate chemical reactor networks
for conversion of feed to product chemicals.• Separation trains for recovering species in multi-
component mixture.• Reactor separator recycle networks.
Locate and reduce energy usage.• Create and evaluate efficient networks of heat
exchangers with turbines for power recovery.• Networks of mass exchangers to reduce waste.
Process design
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 82
Feasible Region
The region within which the process can be operated is called the operating window or feasible operating region.
nRxxgxcxFR ,0)(,0)(
Feasible region
g3=0
g2=0
g1=0
Stability and Sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 83
Feasible Region
One can not in general say a priori how a thermodynamic model will behave when extrapolated beyond the region in which data were available for determining its empirical parameters.
Stability and Sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 84
Stability of the processWhen a process is disturbed from an initial steady
state, it will, in general, respond in one of 3 ways.
a) Proceed to a steady state and remain there.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 85
Stability of the process
b) Fail to attain to steady – state conditions because its output grows indefinitely.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 86
Stability of the process
c) Fail to attain steady – state conditions because the process oscillates indefinitely with a constant amplitude.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 87
Stability of the process
A steady state system xs is said to be stable if for each possible region of radios >0 around the steady state, there is an initial state x0 at t=t0 falling within a radius >0 around the steady state that causes the dynamic trajectory to stay within the region (x-xs)< for all times t>t0.
Steady state xsRegion >0
Radius State x
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 88
Sensitivity analysis
In many cases, it is useful to know how a chemical process respond when a equipment parameter or stream variable is varied, rather than running simulation only in few parameters.
The sensitivity analysis permits the tabulation of output variables at equal increments over a specified range of parameter or variable values.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 89
Sensitivity analysis
Example: Carbon monoxide and hydrogen are reacted to form methanol.
OHCHHCO 322
The reaction is exothermic; consider an adiabatic reactor. 100% of the carbon monoxide is converted. For a fixed flow rate of carbon monoxide, it is desired to know how the outlet temperature varies with respect to the flow rate of hydrogen in the feed stream.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 90
Sensitivity analysis
The temperature decreases as the mole flow in feed increases.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 91
Sensitivity analysis
One of the most important contributions of sensitivity analysis is that it allows one to identify those variables which, when changed, have the greatest impact on the process output.
Stability and sensitivity
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 92
Optimization
Completely specified case.
Over-specified case.
Under-specified.
From a Mathematical point of view, chemical engineers encounter 3 situations when solving equations.
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 93
Completely specified case
Nequations = Nvariables
When the number of equations is equal to the number of variables, then we can proceed to solve the problem.
3x – 2y + 9z = 3
6x – 11y + z = 7
x – 15y + 4z = 25
In this case, we have 3 equations with 3
unknowns.
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 94
Over – specified case
Nvariables < Nequations
which is commonly referred to as the reconciliation (data reconciliation and rectification) problem.
Many variables are determined in >1 way – values must be reconciled
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 95
Under – specified case
Nvariables > Nequations
Also called optimization problems.
The optimization is used to maximize or minimize a specified objective function by manipulating decision variables (feed stream, block input, or other input variables).
Some variables are undetermined – can be manipulated to optimize the process.
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 96
OptimizationNvariables – Nequations= ND
The decision variable, d, is iteratively adjusted to achieve the optimal solution to a specified objective. Some methods commonly used are:
Successive linear programming (SLP).
Successive quadratic programming (SQP). (used by Aspen plus, Hysys.plant)
Generalized reduced gradient (GRG).
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 97
OptimizationAny optimization problem can be represented as:
)(min xf 0)(.. xcts 0)( xg nRx
)(xf Is the objective function.
Is the set of m equations in n variables x. The equality constraints
Is the set of r inequality constraints. Those bound the feasible region of operation.
0)( xc
0)( xg
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 98
Optimize on multiple criteria
Some common objectives in optimization of an industrial process are:
Achieve lower capital cost design. Increase production. Reduce unit operation cost. Reduce environmental impact. Reduce energy consumption.
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 99
Degrees of freedom
A degree of freedom analysis is incorporated in the development of each subroutine that simulate a process unit. These subroutines solve sets of Nequations involving Nvariables.
ND = Nequations – Nvariables.
Degrees of freedom are the number of input variables you need to specify.
Process optimization
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 100
Quantitative comparison of alternatives
In almost every case encountered by chemical engineer, there are several alternative methods which can be used for any given process or operation.
Formaldehyde production:
1. By catalytic dehydrogenation of methanol. (By controlled oxidation of natural gas)
2. By direct reaction between CO and H2(under special conditions of catalyst, temperature, and pressure)
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 101
Optimum Economic Design
If there are two or more methods for obtaining exactly equivalent final results, the preferred method would be the one involving the least total cost.
Alternative designs do not give final products or results that are exactly equivalent. It then becomes necessary to consider the quality of the product or the operation as well as the total cost.
$
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 102
Economic evaluation of alternatives
Throughout the design process, estimates of the cost of equipment and other costs related to the capital investment play a crucial role in selecting from among the design alternatives.
The total capital investment (TCI).
The annual cost of manufacture (COM).
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 103
Is a one – time expense for the design, construction, and start – up of a new plant or a revamp of an existing plant.
Total capital investment
Estimation of the total capital investment1. Order – of magnitude estimate based on bench –
scale laboratory experiments.
2. Study estimate based on a preliminary process design.
3. Preliminary estimate based on detailed process design studies lading to an optimized process design.
4. Definitive estimate based on a detailed plant design.
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 104
Investment justification
Objective: to evaluate the costs and benefits of investment in process modifications.
Inputs and outputs with costs attached must be accurately represented.
Differences between candidate solutions must be accurately modelled.
Level of detail just enough to enable cost-benefit calculations.
Other parts of the process can be a “black box”.
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 105
Direct cost
Indirect cost
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 106
Quantify cost – benefit of various possibilities
When designing a greenfield plant, there are many possibilities which can be evaluated to get the best cost – benefit ratio.
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 107
Cost sensitivity analysis
Sensitivity analysis is important in order to avoid information overload:
It usually is best to do an initial analysis using only the data you have, being careful about indicating where the data is weak or you are using best guesses.
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 108
Cost sensitivity analysis
“Planning should stimulate thinking, not overwhelm it”
Economic evaluation of alternatives
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 109
Operator training
Today, cheap computer power allows virtually any operator to have enough capability to simulate large flowsheets with considerable detail on the desktop. Process flowsheet simulators now have a sophisticated user interface, large physical properties databanks, and many thermodynamic models.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 110
Running sophisticated process simulations does not guarantee correct results. You need to understand the thermodynamic assumptions underlying the program and how to ensure proper application.
Operator training
The personnel using the simulators, should be trained beforehand, and be aware of problems that may appear.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 111
Operator Training simulation
Objective: to mimic response of displays to simulate process excursion and operator inputs.
integration with physical operator console. simplified process representation, just enough
to generate appropriate responses. progressive series of exercises as part of
system. trainee evaluation as part of system.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 112
Opportunity to increase process and systems
awareness in operating personnelA simulated process can be easily executed in a
computer, without the expense of real equipment and without the risk of disrupting the real plant’s production.
In this virtual world, computer simulations allow all manner of extreme conditions and “what – if” scenarios to be tested safely.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 113
Develop competence in unusual, undesirable, or
dangerous process operation conditions
The only certain way to test how a proposed control system will handle every conceivable situation is to design it, install it, and try it out.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 114
Simulators avoid dangerous process operation
A simulated control system can be installed in a simulated plant without the expense of real equipment and without the risk of disrupting the real plant’s production.
Computer simulation allow all manner of extreme conditions and “what if” scenarios to be tested safely.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 115
Increase comfort level with advanced technology
A simulator trainer substitutes for the real plant and the real control system. If the simulation is realistic, the trainees don’t know the difference.
Operator training
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 116
Simulation in Process Design and Operation
Before constructing a plant, or making any changes to it, it is always desirable to know how it is going to behave. Steady – state simulation, is the tool one use.
In this tier, the basics tools to understand a process design, construct it and simulate it in a computer, were shown.
Summary
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 117
Quiz
Quiz
A first pass simulation using mill data indicates that the boiler is generating more steam than the heating value of the fuel will provide (i.e. efficiency greater than 100 %). An appropriate response would be:
a) Ignore the problem as insignificant.b) Replace the boiler simulation model with one that will
give you realistic results.c) Recommend a certificate of appreciation for outstanding
performance be presented to the boiler operating crew.d) Double check the accuracy of the measurements and
arrange for test to be performed on the boiler fuel.
TIER 1 STEADY STATE SIMULATION PIECE
PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 118
a) Inaccuracies in the process data.
b) Incompatible process specifications in different parts of the sequential flowsheet that are “fighting” each other.
c) The actual process in fact never balances.
d) Unrealistic assumptions about unit efficiencies.
e) To many recycle loops.
Your simulation flowsheet is failing to converge. What would be the most likely cause of this problem?