MODEL.LA Modeling Laboratory

MODEL.LAModeling Laboratory

This guided introduction (produced from screen captures of the actual program) will provide an overview of the modeling

capabilities of MODEL.LA.

•To advance through this tour, please press the right arrow key () on your keyboard, or click the left mouse button.

•To replay a portion of this tour, please press the left arrow key () on your keyboard until the desired location is reached.

•To quit the tour at any time, press the escape key (Esc) on your keyboard.

Press the key to continue.

MODEL.LAModeling Laboratory

This guided introduction (produced from screen captures of the actual program) will provide an overview of the modeling

capabilities of MODEL.LA.

•To advance through this tour, please press the right arrow key () on your keyboard, or click the left mouse button.

•To replay a portion of this tour, please press the left arrow key () on your keyboard until the desired location is reached.

•To quit the tour at any time, press the escape key (Esc) on your keyboard.

Press the key to continue.

The MODEL.LA Modeling Methodology

MODEL.LA contains no predefined models. Rather it leads students through a structured sequence of modeling

decisions--decisions the student makes based on the context of some engineering problem.

From these decisions, MODEL.LA will automatically derive the necessary modeling equations and guide the student through

their numerical solution.

Thus, the educational focus shifts from textbook equation selection and solution, to the richer task of model formulation

and analysis of process behavior.

Press the key to continue.Press the key to review.

The MODEL.LA Modeling Methodology

MODEL.LA contains no predefined models. Rather it leads students through a structured sequence of modeling

decisions--decisions the student makes based on the context of some engineering problem.

From these decisions, MODEL.LA will automatically derive the necessary modeling equations and guide the student through

their numerical solution.

Thus, the educational focus shifts from textbook equation selection and solution, to the richer task of model formulation

and analysis of process behavior.


Modeling Assistant

A rich body of chemical engineering science exists and is currently taught to students in the context of abstract idealized examples. Modeling unifies these scientific principles in their

application to real engineering problems.

Unfortunately, students are never taught a modeling methodology. Rather they are forced to infer modeling techniques from exposure to example after example.

The Modeling Assistant of MODEL.LA provides the starting point which guides the student in applying classic classroom

concepts in a structured process of model development.


Modeling Assistant

A rich body of chemical engineering science exists and is currently taught to students in the context of abstract idealized examples. Modeling unifies these scientific principles in their

application to real engineering problems.

Unfortunately, students are never taught a modeling methodology. Rather they are forced to infer modeling techniques from exposure to example after example.

The Modeling Assistant of MODEL.LA provides the starting point which guides the student in applying classic classroom

concepts in a structured process of model development.


Modeling Assistant (2)

Models are much more than just equations. They are a concise representation of a real process. The most important step in model development is not the selection of equations

from a textbook, but rather the identification and characterization of the relevant physical and chemical

phenomena assumed to occur in the process.

Model development begins with identification of control volumes of interest.



Models are much more than just equations. They are a concise representation of a real process. The most important step in model development is not the selection of equations

from a textbook, but rather the identification and characterization of the relevant physical and chemical

phenomena assumed to occur in the process.

Model development begins with identification of control volumes of interest.



It continues with perception of how these control volumes interact through transport of material, energy, and chemical

species.



It continues with perception of how these control volumes interact through transport of material, energy, and chemical

species.



The relevant chemical species and reactions must be identified.



The relevant chemical species and reactions must be identified.



The control volumes are further characterized and refined.



The control volumes are further characterized and refined.



The mechanisms which drive the transport of material, energy, and chemical species are characterized.



The mechanisms which drive the transport of material, energy, and chemical species are characterized.



External control actions may be applied.



External control actions may be applied.



When the physico-chemical phenomena-based description of the model is complete, MODEL.LA automatically derives the corresponding mathematical equations based on chemical

engineering first principles.

Solution of these equations allows the student to analyze the resulting behavior of the process, appreciate the relationship

between phenomena and behavior, and gain immediate feedback on the applicability of the model.

At any point the student is free to revisit the assumptions made, make changes, provide additional details, etc.



When the physico-chemical phenomena-based description of the model is complete, MODEL.LA automatically derives the corresponding mathematical equations based on chemical

engineering first principles.

Solution of these equations allows the student to analyze the resulting behavior of the process, appreciate the relationship

between phenomena and behavior, and gain immediate feedback on the applicability of the model.

At any point the student is free to revisit the assumptions made, make changes, provide additional details, etc.


Jacketed Continuous Stirred Tank Reactor (CSTR) Example

The following will illustrate the use of MODEL.LA in the development of a model of a Jacketed CSTR--a uniform continuous flow reactor surrounded by a cooling jacket.

The modeling activity begins with declaration of a control volume representing the Jacketed CSTR.

The Add New Process Units tab is selected.


Jacketed Continuous Stirred Tank Reactor (CSTR) Example

The following will illustrate the use of MODEL.LA in the development of a model of a Jacketed CSTR--a uniform continuous flow reactor surrounded by a cooling jacket.

The modeling activity begins with declaration of a control volume representing the Jacketed CSTR.

The Add New Process Units tab is selected.


Declaration of CSTR Control Volume

The Blackbox Unit Icon is selected (meaning no internal detail will initially be present in the unit).



The Blackbox Unit Icon is selected (meaning no internal detail will initially be present in the unit).



The modeled unit is added to the flowsheet.

The Hierarchical Tree pane lists all modeled units in the model.

The Properties View lists all assumptions made regarding the currently selected modeled unit



The modeled unit is added to the flowsheet.

The Hierarchical Tree pane lists all modeled units in the model.

The Properties View lists all assumptions made regarding the currently selected modeled unit


Hierarchical Tree

Properties View

Flowsheet

Context-Sensitive Right-Click Menus

A right button mouse click on the modeled unit icon

activates a context- sensitive menu listing all options available for that particular modeled-unit.


Naming of Jacketed_Cstr

The Rename option is selected.


Naming of Jacketed_Cstr

and the default name Unit0 is changed to

Jacketed_CSTR.


Resize Icon

The icon for the Jacketed_CSTR is resized.


Load Icon

and the option to load a new icon is selected.


Load Icon

Graphical icons do not introduce any new assumptions to the

model, but allow the modeler to provide visual clues to the purpose

of a model.


Declaration of Interactions with Surroundings

A feed stream of reactants enters the Jacketed_Cstr from the surroundings.

This is declared by selecting the Add New Fluxes tab of the Modeling Assistant,

selecting the Convective Flux Icon...



and adding it to the flowsheet.


Naming of Feed Stream

The feed stream is renamed reactants_input.

This is accomplished by selecting the Edit Fluxes tab of the Modeling Assistant,

selecting the Rename Flux Icon...


Naming of Feed Stream

and entering the desired name.



The product stream, products_output, transports material from the reactor to

its surroundings.

It is added and named is a similar manner.



Flows of coolant from (coolant_inlet) and to (coolant_outlet) the

surroundings are also declared.



Review of Assumptions

The Properties View is automatically updated after each modeling

declaration.


Refinement of Jacketed_Cstr

At the current level of detail, the student realizes that there is no way to state that

the reaction mixture and cooling jacket fluid are in separate vessels within the

Jacketed_Cstr.

Therefore, the structure of the Jacketed_Cstr must be refined by selecting the Edit Process Units tab and selecting the

Specify Internal Subunits option.



This activates the refinement flowsheet for the Jacketed_Cstr.

The abstract boundary of the Jacketed_Cstr is represented by the dashed line. Fluxes to/from

the unit initially appear terminating at this boundary.

New units added within this boundary are subunits of the abstract unit.



Refined subunits are again added using the Add New Process Units of the Modeling Assistant.

In this case, the first subunit is assumed to contain a single liquid phase.



The subunit is added to the Jacketed_Cstr.


Naming of Vessel

The subunit is renamed Vessel.


Load Icon

A graphic icon representing the Vessel is selected.



The second subunit is added to the Jacketed_Cstr...



The Hierarchical Tree reflects the refinement of the Jacketed_Cstr into a Vessel and Jacket.


Jacket

the subunit is renamed Jacket, and a new icon loaded.


Declaration of Internal Interaction

The two subunits of the Jacketed_Cstr interact with the transfer of energy from the Vessel to

the Jacket.

This is declared by selecting the Energy Flux icon on the Modeling Assistant...


Declaration of Internal Interaction

and dragging on the refinement flowsheet from the Vessel to the Jacket.


Naming of Flux

The energy flux is renamed q_exchange.


Flux Mechanism

The amount of heat exchanged is not known in advance. It is determined by a temperature

differential between the Vessel and the Jacket.

Flux mechanism are declared using the Edit Flux Properties option on the Edit Fluxes tab of

the Modeling Assistant


Flux Mechanism

A Surface Convection flux mechanism is selected, where the heat exchanged is

proportional to the temperature difference between the Vessel and the Jacket.


Chemical Species

In addition to the structural description of the model, the chemical species and reactions

present must be declared.

First the chemical species are added using the Declare Chemical Species option on the Specify

Species and Reactions tab.


Chemical Species Database

The database contains over 1400 chemical species with data on constant and temperature-

dependant physical and thermodynamic properties.

The species are organized by chemical group, common name, IUPAC name, chemical formula,

and atomic structure.


Chemical Species Database

Four compounds have been selected.


Chemical Reactions

Once chemical species have been declared, chemical reactions may be specified.

The reactions are added using the Declare Chemical Reactions option on the Specify

Species and Reactions tab.


Chemical Reactions

Reactions are characterized by the stoichiometry of the reactant and product

species, any catalyst, and the reversibility of the reaction.


Chemical Reactions

The reversible reaction of Acetic Acid and 1-Butanol to Water and n-Butyl Acetate has been

declared.


Chemical Reactions

Any number of chemical reactions may be specified. In this example, only one reaction is

considered.

The rate law of the reaction will now be characterized.


Chemical Reaction Rate Law

Since the reaction is reversible, rate laws for both the forward and reverse reaction are

specified.


Chemical Reaction Rate Law

The forward reaction rate is assumed to be second order with respect to Acetic Acid and

first order with respect to 1-Butanol.

The reverse reaction rate is assumed to be first order with respect to both water and n-Butyl

Acetate.

More complex rate laws can be specified, along with Arrhenius temperature dependencies for all

rate constants.


Reaction Assignment

The reaction is now assumed to only occur in the Vessel.

The Vessel icon is selected and the Assign Reactions and Species option on the Edit

Process Units tab is activated.


Reaction Assignment

Since the Vessel has a liquid phase, the reaction will be assigned to that phase using the Material

Content dialog.

In this dialog, the species present are also selected, along with equation of state or activity

coefficient models for each phase.


Reaction Assignment

The reaction is assigned to the liquid phase.

The reactants and products of the reaction are also automatically assigned to the material

content of the Vessel.


Model Simulation

When the student feels the physico-chemical description of the model is complete, the Model

Simulation tab is selected, and the Edit Simulation Options option is activated.


Model Simulation

The conditions under which the equations will be generated are specified.


Model Consistency Check

A completeness and consistency check of the model is activated using the Check Model

Consistency option.


Model Consistency Check

The model is analyzed and determined to be inconsistent since the boundary fluxes to the

abstract Jacketed_Cstr have not been allocated to the subunits.

The model cannot be simulated until this inconsistency is remedied.


Flux Allocation

The products_output flux originates from the Vessel. Thus, the products_output flux icon is

selected and dragged to the Vessel Icon.


Flux Allocation

In a similar manner, the reactants_input flux is allocated to the Vessel, and coolant_inlet and

coolant_outlet fluxes are allocated to the Jacket.

The model consistency is then rechecked.


Consistency Check

The model is still incomplete because no species have been declared to be present in the

Jacket.


Species Assignment

Water is assigned to the Jacket.


Consistency Check

The model is now complete.


Model Simulation

Steady-state equations are now automatically generated from the phenomena-based model

description provided by the student.


Numerical Engine

The numerical engine toolbar guides the student through a consistent numerical specification for

solution of the model equations.

The first task is to select the design (or known) variables and provide numerical values.


Model Equations

The mathematical model consists of 55 equations with 67 variables.

The model includes mass balances, energy balances, physical and thermodynamic property correlations, reaction rate expressions--all based

fully on the modeling assumptions of the student.


Design Variable Specification

In this model, 12 variables must be selected and their values specified.

All variables appear grouped by their corresponding modeled unit, flux, or chemical

reaction.


Design Variable Specification

The student uses available data to decide which variables he/she feels are appropriate.

MODEL.LA ensures the selection is structurally consistent, and provides feedback if any subset

of equations would be overspecified by a variable selection.


Model Simulation

The model is now ready for numerical simulation.


Model Results

The model is solved numerically, and the results plotted versus the varied volume variable.


Model Simulation

The student decides to observe the effect of varying the volume of the Vessel on the model

behavior.


Model Simulation

The student decides to observe the behavior of the model under dynamic conditions.

A new mathematical model is generated, with 75 equations and 90 variables.


Design Variables

Without the steady-state assumption, additional design variables must be specified by the

student.


Initial Conditions

The initial condition variables are specified by the student in a manner analogous to that of the

design variable specification.


Initial Conditions

Since the model is dynamic, initial conditions must also be specified.


Model Simulation

The dynamic model is now ready for simulation.


Model Simulation

The student decides to simulate for 5 minutes of simulation time.


Model Results

The dynamic behavior of the model is plotted versus time.

The results may be printed in graphical form, or exported to any spreadsheet in tabular form.


External Control Action

The student can enhance the study of the Jacketed Cstr by imposing external controllers

on the process...


External Control Action

and observing the effect of control on its dynamic behavior.


Jacketed CSTR Model Summary

The Jacketed CSTR example illustrates how MODEL.LA shifts the focus of modeling from equation selection and

manipulation to the deeper task of articulating the physical and chemical phenomena which characterize the behavior of a

process.

Examples such as these enforce the understanding of abstract concepts taught in the classroom. By making students active participants in model development, they become aware of the

assumptions, limitations, and applicability of such models. This is much deeper than the superficial understanding they

gain as passive onlookers when a model is derived on a blackboard or in a textbook.


Jacketed CSTR Model Summary

The Jacketed CSTR example illustrates how MODEL.LA shifts the focus of modeling from equation selection and

manipulation to the deeper task of articulating the physical and chemical phenomena which characterize the behavior of a

process.

Examples such as these enforce the understanding of abstract concepts taught in the classroom. By making students active participants in model development, they become aware of the

assumptions, limitations, and applicability of such models. This is much deeper than the superficial understanding they

gain as passive onlookers when a model is derived on a blackboard or in a textbook.


Model Summary

All assumptions behind the completed model may be easily reviewed by the student or the instructor

using the Project Data dialog.

The Project Data organizes the assumptions using a hierarchical tree of all modeled units, materials, and

phases in the model.

Selecting an item in the tree produces a list of all associated assumptions, with hypertext to navigate

through the model.


Hierachical Tree

Summary of Assumptions with

Hypertext

Model Summary

chemical reactions and rate laws...


Model Summary

All assumptions regarding process fluxes...


Model Summary

relevant chemical species...


Model Summary

process controllers...


Model Summary

and process transmission lines are displayed.


Model Summary

The student has complete flexibility in revisiting any assumptions made, making revisions, adding detail, etc.

After any changes, the consistency and completeness of the modified model is verified, new equations are generated, and

the model is again solved numerically.

This provides immediate feedback to the student on the applicability of the model, and enforces the direct cause-effect

relationship of phenomena and process behavior.


Acetic Anhydride Plant Model

Students become confident with the chemical engineering concepts and a methodology of modeling though examples

such as the Jacketed CSTR.

Moreover, the concepts they learn from these examples scale tremendously as they are integrated into models of complete

chemical plants. The following example illustrates the development of a model of a plant for the production of Acetic

Anhydride from Acetone and Acetic Acid.



Students become confident with the chemical engineering concepts and a methodology of modeling though examples

such as the Jacketed CSTR.

Moreover, the concepts they learn from these examples scale tremendously as they are integrated into models of complete

chemical plants. The following example illustrates the development of a model of a plant for the production of Acetic

Anhydride from Acetone and Acetic Acid.



The plant model starts as a simple blackbox, with two input streams, two

output streams, seven chemical species, and three reactions.



Even at this abstract level of detail, there is much to learn. The student determines

from simulation that the yield of the intermediate product Ketene must be at

least 0.85 for the plant to make any profit.


Acetic Anhydride Plant Refinement

The student uses a hierarchical approach to design and refines the plant into a reaction section and a separation section. At this level of detail, the

student learns from simulation that Acetic Acid and Acetone must be recycled from the separation section back to the reaction section for the plant to

be profitable.


Acetic Anhydride Reaction Section

Synthesis of the reaction section requires the student to make decisions regarding the routing of feed, product, recycle, and cooling streams among

the reactors...


Acetic Anhydride Reaction Section

and selection of a vapor phase equation of state and liquid phase activity coefficient model in the 2

phase reactor where the final product, Acetic Anhydride, is formed.


Acetic Anhydride Separation Section

Synthesis of the separation section involves the use of absorption for recovery of organics from a

gaseous waste stream, and distillation for purification of the recycled raw materials and final

product.


Acetic Anhydride Plant Refinement

The process of refinement, simulation and refinement continues, where simulation at each level of detail determines decisions made at the

subsequent level.

This continues until the plant is modeled down to the level of vapor liquid equilibria on each

distillation column tray.


Acetic Anhydride Plant Summary

The final mathematical model, derived automatically from the most detailed model description the student provides, has 3894

equations and 4116 variables.

Without the high-level modeling assistance that MODEL.LA provides, it would be infeasible for students to derive and solve

such real-world engineering problems themselves.

MODEL.LA removes the tedium and frustration associated with mathematical derivations and manipulations and allows students to

concentrate on the real engineering decisions behind model development--regarding physical and chemical phenomena,

topological and hierarchical structure, cause-effect relationships, and behavior characterization.


2-D Distributed Tubular Reactor

The final example illustrates the capability of MODEL.LA to model spatially distributed systems.

The model portrays a tubular reactor with axial and radial spatially distributed properties.



The concepts required for this model are the same as those for models of lumped (non-distributed) systems.

The differential element approach is used. Here the student assumes axial convective flux, axial and radial

energy flux, and axial and radial diffusive flux of two chemical species. There is also energy flux to a

surrounding cooling jacket at the outer radial boundary.



The dynamic mathematical model automatically derived from this phenomena-based description

consists of 91 partial differential, integral and algebraic equations.



The results for the tubular reactor are plotted using animated surface plots in Excel. The student observes that a “hot spot” develops at the center of the reactor

near the reactor entrance.


2-D Distributed Tubular Reactor Summary

The modeling of spatially distributed systems fills most students with dread, and leaves even the best students unsure about the

formulation and solution of the resulting partial differential equations. As a result, many schools do not even include the

modeling of such systems in their curriculum.

Real engineering problem solving requires such models. MODEL.LA not only makes it possible for students to solve such

problems, but to do so with confidence, and with a deep understanding of the chemical engineering principles involved.

Press the key to conclude this tutorial.Press the key to review.

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MODEL.LA Modeling Laboratory