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: f.galvanin@ucl.ac.uk 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: f.galvanin@ucl.ac.uk

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