process integration for environmental control in engineering curricula (piece)

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)



Module

Steady State Process Simulation

2



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:



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.



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

1Introduction to Steady State,

Process Simulation tool



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



1

Basic Concepts



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



Basic Concepts

Steady – state. Models and simulation. Creating models. Unit efficiencies. Stream components. Units. Performing a steady – state simulation study.

Contents



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



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



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



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



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



Physical Model

From a balance:

Mathematical Model

onaccumulatinconsumptioproductionoutin

Models & simulation



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



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



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



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



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



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



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



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



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



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



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



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



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



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



Typical Efficiencies

ValuesCompressors = 0.8

Motor = 0.9

Turbine = 0.8

Pump = 0.5

Unit efficiencies



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



Conversion of stream components

Mechanical work.

A

BC

AB

A B

Via chemical reaction.

Heat.

Stream components



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



Engineering Units

Units



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



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.





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.





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





6.- Validate model.

a) Select key values for validation.b) Compare with experimental results.




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




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.




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




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.




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.




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




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




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*).


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



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.




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




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.


Is the level of detail taken into account in a simulation.



Comparing Coarse vs. Fine

models


Coarse:

Bleaching tower

Kxzx

Dzx

v

2

2

A coarse model represent the equipment with few detail.



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.


KxxDxv 2 More than 1 direction



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


Coarse Models Fine Models



2

Steady state simulation in a process integration

context



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



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



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



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



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



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



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




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.




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



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



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



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



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



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



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





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).




3

Steady – State Simulation in a

Broader Context



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



Aspects of Process Design

Process design.

Stability and sensitivity.

Process optimization.

Economic evaluation of alternatives.

Operator training.

Table of content



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



Process designManufacturing

ProcessNatural

ResourcesBasic chemical

Products

ManufacturingProcess

Basic ChemicalProcess

IndustrialProducts

ManufacturingProcess

Basic ChemicalIndustrial Products

ConsumerProducts

Process design



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



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



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



3. Process Design – Procedure



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



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



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



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



Stability of the process

b) Fail to attain to steady – state conditions because its output grows indefinitely.





c) Fail to attain steady – state conditions because the process oscillates indefinitely with a constant amplitude.





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




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.





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.





The temperature decreases as the mole flow in feed increases.





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.




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



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.




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




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.




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).




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




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.




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.




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



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.

$





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).




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.




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”.




Direct cost

Indirect cost




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.




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.




Cost sensitivity analysis

“Planning should stimulate thinking, not overwhelm it”




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



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



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



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



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



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



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



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



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.



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?

process integration for environmental control in engineering curricula (piece)

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