Introduction Autoclave experiment 523 IC1..Reactor ...€¦ · Chemical Reaction Engineering II...
Transcript of Introduction Autoclave experiment 523 IC1..Reactor ...€¦ · Chemical Reaction Engineering II...
The role of advanced computational tools in teaching
process systems modelling and design classes at UCL Federico Galvanin Department of Chemical Engineering, University College London (UCL), Torrington Place, WC1E 7JE London, United Kingdom E-mail: [email protected] Galvanin System Identification Group: http://www.homepages.ucl.ac.uk/~ucecfga
Introduction
Process Systems Modelling & Design Module Structure
Interactive Support Sessions in PSMD
[1] Biegler, L. T., Grossman, I. E., Westerberg, A. W. (1997). Systematic methods of chemical process design. Prentice
Hall, New York (U.S.A.).
[2] Process Systems Enterprise, gPROMS, www.psenterprise.com/gproms, 1997-2019.
[3] Woods, D. R. (1996). Problem-based learning for large classes in chemical engineering. New Directions for
Teaching and Learning, 68, 91-99.
[4] Pistikopoulos, E. N. (1995). Uncertainty in Process Design and Operations. Comp. Chem. Eng., 19, S553.
[5] Bard, Y (1977). Nonlinear parameter estimation, Academic Press, New York (U.S.A.).
[6] Available from: https://www.psenterprise.com/products/gproms/technologies/global-system-analysis
[7] Perie, M., Marion, S., Gong, B. (2007). The role of interim assessments in a comprehensive assessment system: A
policy brief. Available from: http://www.nciea.org/publications/PolicyBriefFINAL.pdf
Bibliography
The development of reliable models of in chemical engineering is required for the
simulation, design, optimisation and advanced control of chemical processes [1]. However,
modelling, designing and simulating complex systems in chemical engineering applications
represent a challenging task for students. It requires:
1. Solid analytical skills 2. Maturity on mathematical modelling 3. Essential programming/computational skills
The presentation shows how these challenging aspects are tackled at UCL Chemical
Engineering in a module entitled “Process Systems Modelling & Design”.
Additional elements have been introduced in PSMD to support the students in the intricate
decisions to be made in design projects:
i) Interactive Q&A sessions: to discuss doubts on modelling, to improve critical and
lateral thinking on the modelling assumptions propagating to simulation activities.
ii) Moodle discussion forums: in the platform students can post questions but also reply to
questions posed by their peers, engaging with them and with the lecturer using a virtual
environment.
iii) Project Interim Assessment [7]: students are asked to provide an update of challenges
and issues arising in their modelling and simulation activities in the form of a flash
group presentation, followed by a plenary discussion.
Dr Federico Galvanin
Lecturer in Chemical Engineering
email: [email protected]
WEB: http://www.homepages.ucl.ac.uk/~ucecfga
The UCL Chemical Engineering Programme
continuous stirred tank reactor (CSTR)
Use of Advanced Computational Tools for Process Simulation and Design Under Uncertainty
Goal: to explore the effect of uncertainty on process design decisions [4], exploring
• Model validation and robustness
• Process simulation under uncertainty
• Effect of disturbances on the dynamic behaviour of a system
The level of confidence of students on “sloppy” models is often such that the
optimisation activities are carried out without critical thinking, and without assessing
the robustness of the model itself, inevitably leading to wrong design conclusions.
Model-Based Process
Design Activities
Process Systems Modelling & Design (PSMD) is a 4th year module (cohort ~ 90
students). The course integrates three key elements:
• Mathematical modelling of chemical engineering fundamentals
• Training in the use of equation-oriented process simulation tools (gPROMS ModelBuilder) [2] • Application of gPROMS ModelBuilder to the simulation of complex plant items
Mathematical
modelling
Training in the use of
gPROMS
gPROMS Simulation of
complex plant items
What?
Modelling of
reactors, separators,
heat exchangers
gPROMS Model
Builder environment
and syntax
Simulation and
optimisation of unit
operations using
gPROMS
How? Lectures Lectures Computer tutorials
When? 2h per week 2h per week 4h per week, two groups
Assessed? Yes, through in-
class tests (20%)
Yes, through online
Quizzes (10%)
Yes, through
• gPROMS Exam (5%)
• Courseworks (15%)
Final Design Project (50%) Simulation, Design and Optimisation of a Full
Chemical Process [3]
Week Tuesday 9-11 Friday 9-11 Tuesday 14-18 Assessment
2 (1-Oct) Intro & gPROMS I Modelling I Tutorial 0
3 (8-Oct) gPROMS II Modelling II Tutorial 1 Quiz 1
4 (15-Oct) gPROMS III CW1 - part 1 Tutorial 2
5 (22-Oct) gPROMS IV Remarks on CW1 Tutorial 3 CW1-part 2
6 (29-Oct) gPROMS V Modelling III gPROMS exam
7 (5-Nov) Reading week (Project Launch)
8 (12-Nov) gPROMS VI CW2 – part 1 Tutorial 4
9 (19-Nov) gPROMS VII Modelling IV Tutorial 5 CW2 - part 2
10 (26-Nov)
gPROMS VIII Modelling V Tutorial 6 Quiz 2
11 (3-Dec) gPROMS in industry
Project Interim Assessment
Tutorial 7
12 (10-Dec) Q&A Q&A Q&A
13 (17-Dec) - - - Project
Typical
Lecture Plan
B. Global Systems Analysis (GSA) [6] and Sensitivity Analysis
A. Parameter Estimation and Model Validation [5]
C. Disturbance Analysis and Perturbation Studies in Process Design Activities
CSTR
REACTOR
A (m2)
h (m)
Qin2 (m3/s) CB,in2 (mol/m3)
CA (mol/m3), MA (mol) CB (mol/m3), MB (mol) CC (mol/m3), MC (mol) Qout (m3/s)
CA,out (mol/m3) CB,out (mol/m3) CC,out (mol/m3)
Qin1 (m3/s) CA,in1 (mol/m3)
GOAL: to explore the
effect of parametric
uncertainty on
performance stability
of a unit operation
(Reactor, Separator).
GOAL: to learn how to
properly validate a
kinetic model from
experimental data by
using nonlinear
parameter estimation.
Example: Kinetics of phenol
hydrodeoxidation from batch reactor data
Estimation of kinetic parameters
• Parameter value
• Parametric uncertainty
• Fitting performance (LOF)
Students evaluate the precision and the
accuracy of parameter estimation
GOAL: to explore the
effect of disturbances on
system dynamics in unit
operations.
FOCUS ON:
• Effect of parametric
uncertainty in nonlinear
dynamic systems.
• Model robustness.
FOCUS ON:
• Uncertainty analysis
• Stochastic simulation
• Simulation and design
under uncertainty
Example: GSA in a dynamic CSTR reactor
?
?
FOCUS ON:
• Process dynamics under
uncertainty
• Implementation/study
of perturbations
Transport Phenomena I
Thermodynamics
Physical Chemistry
Computational Modelling & Analysis
Engineering Challenges
Design & Professional Skills I
Mathematical Modelling & Analysis
Introduction to Chemical Engineering
Y
E
A
R
1
Process Design Principles
Mathematical Modelling & Analysis II Mathematical Modelling & Analysis II
Particulate Systems & Sep. Processes II
Chemical Reaction Engineering I
Minor I
Design & Professional Skills II Design & Professional Skills II
Engineering Experimentation
Process Heat Transfer
Separation Processes I
Y
E
A
R
2
Process Design Project
Transport Phenomena II
Adv. Safety & Loss Prevention
Minor III
Process Design Project
Process Dynamics & Control
Chemical Reaction Engineering II
Minor II
Y
E
A
R
3
Scenario 1
Scenario 2
Scenario 5
Scenario 6
Scenario 3
Scenario 4
YEAR 1
YEAR 2
YEAR 3
Research Project
Elective
Elective
Elective
Research Project
Process Systems Modelling. & Design
Elective
Elective
Y
E
A
R
4
YEAR 4 IEPCore
Design
Lab
Research
Elective
KEY:
Core w/Lab
TERM 1 TERM 2
How to Change
BEng
MEng
Year 1-3: most of
the background on
CE Fundamentals
Process Design Modules:
• Process design Principles: “Douglas approach”
• 3rd year Process Design Project: 3rd year (capstone) design project
• Process Systems Modelling & Design: 4th year design project
Example: Effect of feed flowrate
variation in a tubular reactor.
Reactor is modelled using a
distributed model (i.e. PDEs)
Effect on the dynamic profile of
• Selectivity/Yield
• Outlet Composition
Students asked to select:
• Responses to be characterised
• Type and nature of perturbation
A + B → C
u, θ y
A1 effect
A2 effect
Selectivity
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0 1000 2000 3000 4000 5000 6000
Measurement Time
Absolute residual for variable:Autoclave_experiment_523_IC1..Reactor -> molar_conc
("CYCLOHEXANE")
118000
119000
120000
121000
122000
123000
124000
125000
0.0000107 1.072E-05 1.074E-05 1.076E-05 1.078E-05 0.0000108 1.082E-05 1.084E-05 1.086E-05 1.088E-05 0.0000109 1.092E-05
Re
acto
r ->
Arr
he
niu
s ac
tiva
tio
n e
ne
rgy
("1
")
Reactor -> Arrhenius constant ("1")
95% Confidence Ellipsoid
Optimal point