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Lecture 1_Introduction to Process Modeling.pdf
1. Process Systems Engineering (PSE)
Process Systems Engineering (PSE)
Ph.D. Xiang Zhang 1
Lectures (E-learning enrollment key: pse2223xz):
Ph.D. Xiang Zhang
Team leader at the Department for Process Systems Engineering
Max Planck Institute for Dynamics of Complex Technical Systems
Sandtorstrasse 1, 39106 Magdeburg, Germany
Phone: +49 391 6110 390; Office: S3.07
zhangx@mpi-magdeburg.mpg.de
Tutorials (Friday 5:00-7:00 PM in Building 22A, Lecture hall 2):
Andrea Maggi, maggi@mpi-magdeburg.mpg.de (PhD Student)
Edgar Medina, sanchez@mpi-magdeburg.mpg.de (PhD Student)
Severo Balasbas, sbalasbasiii@mpi-Magdeburg.mpg.de (PhD student)
Examination form: Written Exam
08.02.2023 15-17 Messe1 (further notice if changed)
3. Process Systems Engineering (PSE)
Course Description
3
Process or chemical engineering is all about manufacturing!
To manage manufacturing systems and enable efficient process design, the
behaviour of systems is usually described by mathematical models.
This course will provide a systematic approach to the mathematical development
of process models and highlight how to analyse and solve the models.
Main Contents
Steady-state process modeling & Solution methods
Dynamic lumped-parameter system (LPS) modeling & Solution methods
Dynamic distributed-parameter system (DPS) modeling & Solution methods
Ph.D. Xiang Zhang
4. Process Systems Engineering (PSE) 4
Process simulation (mass
& energy balance)
Sugar drying and control
Example of waste
water treatment facility
Safety and risk assessment
Roles of Process Modeling
Ph.D. Xiang Zhang
Not easy to directly work
with the big processes and
plants
Use mathematical models
to describe how processes
work (i.e., simulate real
processes)
The beauty of PSE
5. Process Systems Engineering (PSE) 5
Models & Equations
Distillation
Adsorption Supply Chain
Ph.D. Xiang Zhang
6. Process Systems Engineering (PSE)
Goal of modeling: to mimic the real system
6
Data
Ph.D. Xiang Zhang
Are the models correct / accurate?
7. Process Systems Engineering (PSE)
Main Steps of Modeling
7
For a given system, generate (create) the
mathematical model; analyze the model; solve the
model; create a model object & finally use
Main steps of (process) modelling
(1) Derive model equations representing the system;
(2) Analyze the model equations;
(3) Develop solution strategies for solving model equations;
(4) Solve and apply the developed model, e.g., for optimizing the system’s
performance; for controlling the system’s dynamic behavior
General models: mathematical representation of a system
Process engineering models: mathematical representation of a process system
Ph.D. Xiang Zhang
8. Process Systems Engineering (PSE) 8
The Process System
Inputs, u
Outputs, y
States, x
Parameters, p
System (S)
u y
x
y = S[u, x, p, t] (dynamic)
y = S[u, x, p] (steady-state)
A process system can be simply a process unit (e.g., reactor, distillation).
It can also be a chemical plant represented by a process flowsheet.
p
Ph.D. Xiang Zhang
9. Process Systems Engineering (PSE)
Tasks for Modeling
Ph.D. Xiang Zhang
and Optimization
Example one: give input I and parameter p,
once state x is fixed, output O can be calculated
10. Process Systems Engineering (PSE)
A Typical Process System: Distillation
Balance Equations
Constitutive Equations
Constraint Equations
Define Boundary Describe System Develop Building Blocks
Mathematical
model
Decomposition
Aggregation
Buildingblock
10
Ph.D. Xiang Zhang
11. Process Systems Engineering (PSE) 11
Problem
definition
Identify
controlling
factors
Analyze
problem data
Construct the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
7-Step Modeling Procedure
Ph.D. Xiang Zhang
13. Process Systems Engineering (PSE)
Problem Definition (Step 1)
Clear description of system
establish underlying assumptions
Statements of modeling
intended goal or use of the model
acceptable error of the model predictions
anticipated inputs and disturbances
type of spatial distribution (lumped or distributed model)
time characteristics (static or dynamic model)
13
Ph.D. Xiang Zhang
14. Process Systems Engineering (PSE)
CSTR Example (Step 1)
Goal
• effect of inlet change
• study dynamic behavior
• +/-10% accuracy
CSTR description
• process/system details
• lumped ? - assumed
• dynamic? - yes
in-flow
out-flow
f, CAi
f, CA
, CB
14
Continuous Stirred Tank Reactor (CSTR)
Ph.D. Xiang Zhang
15. Process Systems Engineering (PSE) 15
Problem
definition
Identify
controlling
factors
Analyze
problem data
Construct the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
Modeling Procedure
Ph.D. Xiang Zhang
16. Process Systems Engineering (PSE)
Controlling Factors (Step 2)
Chemical reaction
Mass transfer (diffusion, evaporation, precipitation, etc.)
Heat transfer (conductive, convective, radiative)
Fluid flow
Mixing
…
16
Controlling factors or mechanisms: physico-chemical processes and
phenomena taking place in the system relevant to the modeling goal
Solid Background in Chemical Engineering
Ph.D. Xiang Zhang
17. Process Systems Engineering (PSE)
CSTR Example (Step 2)
Irreversible reaction A B
Perfect mixing
No heat loss or supplied
(adiabatic)
in-flow
out-flow
f, CAi
f, CA
, CB
17
Ph.D. Xiang Zhang
18. Process Systems Engineering (PSE) 18
Problem
definition
Identify
controlling
factors
Analyze
problem data
Construct the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
Modeling Procedure
Ph.D. Xiang Zhang
19. Process Systems Engineering (PSE)
Physico-chemical data
Heat capacities, viscosity, …
Kinetic parameters:
k0, E, ΔHR, …
Equipment data
volume V, …
Plant (process) data
in-flow
out-flow
f, CAi
f, CA
, CB
Problem Data (Step 3) – CSTR example
19
Ph.D. Xiang Zhang
20. Process Systems Engineering (PSE) 20
Problem
definition
Identify
controlling
factors
Analyze
problem data
Build the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
Modeling Procedure
Ph.D. Xiang Zhang
21. Process Systems Engineering (PSE)
Model Development (Step 4)
Assumptions
Boundaries and balance volumes (How big the system is?)
Conservation equations
mass balance
energy balance
momentum balance
Constitutive equations
reaction rates, transfer rates
property relations, balance volume relations
equipment & control constraints
Initial and boundary conditions
Classification of variables
known parameters
user-defined variables
unknown variables (degrees of freedom, DOF) and DOF-
dependent variables
21
Ph.D. Xiang Zhang
22. Process Systems Engineering (PSE)
Model Development: Balance Equations
Input Output
Accumulation
Generation
Control shell (control volume)
Infinitesimal control shell (control volume)
Normal to the
control shell
Normal to the
control shell
n
consumptio
generation
system
of
out
flow
system
into
flow
quantity
of
change
net
No quantity distribution inside the control shell
Net change of quantity (also known as accumulation)
Accumulation can be applied for mass, energy (heat), and momentum
When accumulation is zero, steady-state; Otherwise, dynamic process
22
Ph.D. Xiang Zhang
23. Process Systems Engineering (PSE)
Model Development and Analysis (Step 4)
– CSTR Example
23
illustrated on the blackboard …
in-flow
out-flow
f, CAi
f, CA
, CB
Ph.D. Xiang Zhang
24. Process Systems Engineering (PSE) 24
Problem
definition
Identify
controlling
factors
Analyze
problem data
Construct the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
Modeling Procedure
Ph.D. Xiang Zhang
25. Process Systems Engineering (PSE)
The selection of model solution methods fully
depends on the mathematical forms of the model
Algebraic equations (AE)
Ordinary differential equations (ODE)
Partial differential equations (PDE)
Differential-algebraic equations (DAE)
Solve the Model (Step 5)
25
Ph.D. Xiang Zhang
26. Process Systems Engineering (PSE) 26
• Mechanistic vs. Empirical
• Stochastic vs. Deterministic
• Linear vs. Nonlinear
• Steady-state vs. Dynamic
• Lumped vs. Distributed
• Continuous vs. Discrete
Model Classification
Ph.D. Xiang Zhang
27. Process Systems Engineering (PSE) 27
Model Type & Mathematical Form
Steady state Dynamic
Lumped AE ODE
Distributed Elliptic PDE Parabolic PDE
Ph.D. Xiang Zhang
28. Process Systems Engineering (PSE) 28
Problem
definition
Identify
controlling
factors
Analyze
problem data
Construct the
model
Solve the
model
Verify the
model solution
Validate the
Model
1
2
4 7
5
3 6
Modeling Procedure
Ph.D. Xiang Zhang
29. Process Systems Engineering (PSE)
Model Verification and Validation
• Generate plant data
• Analyze plant data for quality
• Independent hypothesis (assumption) testing
• Parameter or model structure re-estimation
• Revise the model until suitable for purpose
29
Ph.D. Xiang Zhang