CH3133 - Modelling, Simulation and Optimisation for Chemical Engineering

LECTURE 01 – WEEK 01
COURSE OVERVIEW &
AN INTRODUCTION
Ho Chi Minh City University of technology

Lecturer: Aqsha, Khoa Ta Dang

Ho Chi Minh City University of technology
Institut Teknologi Bandung
2024
Image Souurce: Licdn.com
1. LECTURER & COURSE OVERVIEW
Aqsha aqsha@itb.ac.id | +62 813 888 70350 | https://www.linkedin.com/in/aqsha/
Chemical Engineering ITB, Bioenergy Engineering & Chemurgy ITB, PPEBT ITB, PKE ITB, CCS-COE
ITB
Research Area: Waste to Energy, Catalyst, Biofuel, Biobased Product, BECCS
WOS h-index: 17 | SCOPUS h-index: 19
EDUCATION FELLOWSHIPS
2016: PhD - Chemical & Petroleum Engineering, UOFC - Research Fellow / Postdoctoral, Univ. Of Calgary (CANADA)
2008: MSc - Mechanical & Manufacturing Eng., UOFC - Visiting Researcher, Univ. Of Calgary (CANADA)
2004: BEng– Chemical Engineering, ITB - Visiting Researcher, SOJO University, Kumamoto (JAPAN)
- Research Supervisor, Program Master, Swinburne Univ. (MALAYSIA)
WORKING EXPERIENCES
- Lecturer, Chemical Engineering, Bioenergy Eng. ITB REVIEWER, SCIENTIFIC & ADVISORY BOARD
- Expert Staff – BAPPENAS/GIZ – Forest Bioeconomy - Adsorption, SPRINGER
(‘22-’23) - FUEL, ELSEVIER
- Tech. Advisor, CM Technologies, Canada (2017-20) - Energy & Fuels, ACS Publications
-
- Assistant Professor, Univ. Tek. PETRONAS (2017-20) Bioresources, NCSTATE
- R&D Manager, PE Fuel Internasional (2014-17) - The Canadian Journal of Chemical Engineering- WILEY BLACKWELL
- AB Engineering, Consultant (2008-2010) - Academic Appointment Review Committee (AARC), UOFC CANADA
- Field Engineer, PT. Rekayasa Industri (2004-05) - 35th Murata Science Foundation Research Grant - MURATA
ACHIEVEMENTS - ISESCO-TWAS (Kingdom of Morocco) Research Grant, ISESCO
- Teaching Excellence Award, UOFC, 2014 - 8th International Forum on Industrial Bioprocessing, IBA
- WFEO Delegate for UNFCCC-COP21 Paris, 2015 - Bulletin of Chemical Reaction Engineering & Catalysis, UNDIP ICS
- Best MPU4 Community Service Project 2018, 2019 - Int. Journal of Renewable Energy Development, CBIORE UNDIP
- Bronze Award – Invention & Innovation Award, the - World Chemical Engineering Journal, UNTIRTA
19th International Expo, Malaysia, 2020 - Journal of Chemical Process Engineering, UMI
COURSE OVERVIEW
COURSE ID : CH3133
COURSE TITLE : Modelling, Simulation and Optimization for Chemical
Engineering

LECTURE : 30 Hours (2 Credits)

PROJECT : 45 Hours (1 Credit)
SELF STUDY : 60 Hours
OTHERS : 15 Hours
TOTAL HOURS : 150 Hours
TOTAL CREDIT : 3 Credits

Source:
COURSE DESCRIPTION
 The first part will present the basic approach to the problem of mathematical modelling, will
develop determinative and statistical math-models for several important chemical engineering
systems.
 Just remember to always go back to basics: mass, energy, momentum balances in their time-
varying form.
 In addition, experiment mathematical model is discussed in this part.
 Students will study simulation techniques for solving some of the systems of equations.
 A number of useful numerical methods are discussed in this part, including numerical
integration of ordinary differential equations.
 The second part discusses the optimization problem formulation and six steps for optimization
that can serve as general guide for problem solving in design and operations analysis.
 Some most popular optimization methods, covering the following: extremal methods of classic
analysis, linear programming, dynamic programming, nonlinear programming, random search,
experimentally statistical optimization, are presented.

Source:
COURSE GOALS / OBJECTIVES
By the end of the course, students should be able to:

1. Conceptualize and Formulate Models: Understand the key concepts of process modeling and
be able to formulate mathematical models that accurately represent various chemical
engineering systems, including unit operations, chemical reactions, and multi-phase systems.
2. Apply Simulation Tools: Utilize computational tools and software packages to implement
numerical methods for simulating chemical processes. Build, validate, and execute simulation
models to predict the dynamic behavior of chemical systems under different operating
conditions.
3. Analyze and Interpret Simulation Results: Interpret simulation outputs to analyze process
behavior, identify bottlenecks, and make informed decisions for process optimization.
Understand the limitations of simulation models and critically evaluate the results.
4. Optimize Process Performance: Formulate optimization problems for chemical engineering
processes, considering objectives such as maximizing efficiency, and minimizing costs. Apply
optimization techniques, including linear and nonlinear programming, sensitivity analysis, and
heuristic methods, to find optimal solutions.

Source:
LEARNING OUTCOMES
L.O.1 Develop mathematical models for chemical processes
L.O.1.1 Building mathematical models for chemical engineering processes

L.O.2 Design and simulate some basic chemical processes

L.O.2.1 Simulation of some chemical engineering processes

L.O.3 Built and solve optimization models

L.O.3.1 Building optimization problems and determining optimal points in
chemical engineering

L.O.4 Develop an experimental design for a specific study

L.O.4.1 Design experimental and determine experimental regression equations

Source:
COURSE SCHEDULE
TIME : WEDNESDAY, 3.00-5.00 Vietnam Time (GMT +7)
PLATTFORM : GOOGLE MEET (https://meet.google.com/xxe-ngtk-azy)
LECTURE MODE : Online Presentation, Class Activities
# OF MEETING : 16 Times (once a week)
START DATE : AUGUST 28, 2024
FINAL DATE : DECEMBER 11, 2023

Source:
ASSESSMENTS
ASSIGNMENTS : 40%
MIDTERM EXAM : -
FINAL EXAM : 60% (70 Minutes, MCQ TEST, Week 17-18)
QUIZ : -
HOMEWORK : -
TOTAL : 100%

PREREQUISITES
MT1005 : CALCULUS 2 (Recommended)
CH3347 : Reaction Engineering (Co-Req)

Source:
REFERENCES
Process modeling, simulation and Optimization in Chemical
control for chemical engineers Engineering
William L. Luyben Suman Dutta
McGraw Hill Cambridge University Press
1989 2016

Optimization of chemical Processes Chemical Engineering Design:

Thomas F. Edgar, D. M. Himmelblau Principles, Practice, and Economics
of Plant and Process Design
McGraw Hill
Gavin Towler and Ray Sinnott
1987
Butterworth – Heinemann, 2021

Source:
PART 1. PROCESS MODELING
 Introduction to (Mathematical)Modelling (Today!)
 Degrees of freedom, Building a process model, Developing
constitutive relations, Developing constitutive relations,
Testing your model
 Modeling of mechanical processes
 Modeling of heat transfer
 Modeling of mass transfer
 Modeling of chemical reactions
 Experimental statistical model
PART 2: PROCESS SIMULATION
 Introduction to process simulation
 Streams input-output, Equation of states
 Application of numerical methods to solve the differential
equation systems (non-reactive & reactive process)
 Application of softwares for design and simulation of
chemical processes
 Interpreting process simulation results
PART 3: PROCESS OPTIMISATION
 Problem Formulation, Building the optimization problems
in Chemical Engineering
 Methods for solving optimization problems, Theory of
convexity
 Linear Programming & Non-linear Optimisation
WHAT IS MODEL?

Source: ChemistryLearner.com, AnalyticsBuddhu

WHAT IS MODEL?
 Representation of a physical system by mathematical equations

 Models at their best are no more than approximation of the real process

 Equations are based on fundamental laws of physics (conservation principle,

transport phenomena, thermodynamics, and chemical reaction kinetics)

Source: Liu et al 2013

WHAT IS PROCESS MODELLING
 Process modelling in chemical engineering means building mathematical
equations or algorithms that describes the physical reality of a process
 Engineers can then use the mathematical equations to evaluate different
scenarios of process conditions instead of experimenting with the real system
 The model is only an ideal representation of the process, it is not reality.

Source: PSEnterprise
WHAT IS SIMULATION?
 Solving the model equations analytically or numerically

 Modelling and Simulation are valuable tools; safer and cheaper to perform tests
on the model using computer simulations rather than carrying repetitive
experimentations and observations on the real systems

Simple Gas Turbine CHP

Plant Simulation using
ASPENPlus
Source: H. Magnusson, 2006
A SYSTEM

Classification based on thermodynamics

Isolated system – no transfer
Closed system – energy only
Open system – energy and matter

Classification based on number of phases

Homogeneous system
Heterogeneous system

Source: H. Magnusson, 2006

MODEL
 Theoretical based on fundamental principles
 Empirical based on experimental plant data
 Semi-empirical

Types of Model:
1. Steady-state vs. dynamic
2. Lumped vs. distributed parameters
3. Linear vs. non-linear
4. Continuous vs. discrete
5. Deterministic vs. probabilistic models

Source: Syscad
MODEL
1. Steady-state vs. dynamic

Steady-state – no change in the process variables with time

Dynamic – the process variables (e.g., temperature, pressure, and

composition) vary with time

Source: Syscad
MODEL
2. Lumped vs. distributed parameters

A lumped system is one in which the dependent variables of interest are a

function of time alone. Need solving a set of ODEs
A distributed system is one in which all dependent variables are functions
of time and one or more spatial variables. Need solving PDEs
Source: Springer, Satndford CCRMA
MODEL
3. Linear vs. non-linear

Linear and nonlinear models are two types of mathematical functions that
can be used to describe the relationships between variables.

Source: Enterfea
MODEL
4. Continuous vs. discrete

Continuous: the state variables change in a continuous way, and not

abruptly from one state to another (infinite number of states).
Discrete model: the state variables change only at a countable number of
points in time.
Source: StatisticFromAtoZ, Univ. of Albany
MODEL
5. Deterministic vs. probabilistic models

In deterministic models, the output of the model is fully determined by the

parameter values and the initial values, whereas probabilistic (or
stochastic) models incorporate randomness in their approach.

Source: Rezaee et al 2015, preventionWeb

DYNAMIC BEHAVIOUR
Why it happened?

1. Study the operability and controllability of

continuous processes subject to small disturbances
2. Development of start-up and shut-down procedures
3. Study of switching continuous processes from one
steady-state to another
4. Analysis of the safety of processes subject to large
disturbances
5. Study of the design and operation procedures for
intrinsically dynamic processes
(batch/periodic/separation)

Source:
HOW TO BUILD A MODEL

How to calculate the volume of the air inside this

house? Maybe we can use a model.

Source: WikiHow
INGREDIENTS OF PROCESS MODEL
1. Assumptions
• Time, spatial characteristics
• Flow conditions
2. Model equations and characterizing variables
• Mass, energy, and momentum
3. Initial conditions
4. Boundary conditions
5. Parameters

Source: UniversalDanker
PROCESS CLASSIFICATION
Batch vs. Continuous

Batch
o Feedstocks for each processing step (i.e., reaction,
distillation) are charged into the operation unit at the
start of the processing; products are removed at the
end of processing
o Transfer of material from one item of the operation
unit to the next occurs discontinuously – often via
intermediate storage tanks
o Batch processes are intrinsically dynamic –
conditions within the equipment vary over the
duration of the batch

Source: Wikimedia
PROCESS CLASSIFICATION
Example: Batch Reaction Kinetics

Variation
o Semi-batch (fed-batch)
One or more feedstocks to a batch unit
operation to be added during the batch
o Semi-continuous
Some products are removed during the batch

Source: Wikimedia
PROCESS CLASSIFICATION
Example: Continuous Reaction

o Involve continuous flows of material from

one processing unit to the next
o Usually designed to operate at steady-state;
due to external disturbances, even
continuous processes operate dynamically

Source: Wikimedia
PROCESS CLASSIFICATION
Variation in Continuous Process

o Periodic
Continuous processes subjected to a periodic (e.g., sinusoidal or square wave)
variation of one or more of the material/energy input streams

o Industrially important examples

 Periodic adsorption – periodic conditions (pressure/temperature) regulates
preferential adsorption and desorption of different species over different parts
of the cycle
 Periodic catalytic reaction – involves variation of feed composition; under
certain conditions, the average performance of the reactor is improved

Source: Integrated process design instruction, D.R. Lewin, W.D. Seider, J.D. Seader, Comp. Chem. Eng. 26 (2002) 295-306
PROCESS SIMULATOR / TOOLS
 Assist to formulate and solve the material and energy balances with phase and chemical
equilibrium, chemical kinetics, etc. and to size process equipment for cost estimation

 Enable quick development of a base-case design which is verified against process and
thermodynamic data

 Allow rapid assessment of the economic potential for alternative designs as well as derivation of
the optimal operating conditions using optimization methods that incorporate economics

 Allow process evaluation to go beyond economics alone; controllability and operability can be
assessed using dynamic simulation while some simulators automatically provide information to
help determine the environmental impact of each of the product streams.

Source: AspenTech, Matlab

EXAMPLE : REACTION RATE
A  Produk
dN A V  V0
 rA V
dt
dCA
rA  kC A    kCA
dt

Zero order First Order Second Order

dCA dCA dCA
 rA  k  rA  kCA  rA  kCA2
dt dt dt

at t  0, CA  CA 0 at t  0, CA  CA 0 at t  0, CA  CA 0
CA 0  1 1
 CA  CA 0  kt  ln  kt    kt
CA  CA CA 0
INTEGRAL MODEL/METHOD
Guess and check for α = 0, 1, 2 and check against experimental plot

  0  1   2
C  1 1
C A  C A 0  kt ln  A 0   kt   kt
 CA  CA C A0

CA ln(CA0/CA) 1/CA

t t t
INTEGRAL MODEL/METHOD
The liquid phase reaction

Trityl (A) + Methanol (B)  Products

was carried out in a batch reactor at 25°C in a solution of benzene and pyridine in an excess
of methanol (CB0 = 0.5 mol/dm3). Pyridine reacts with HCl, which then precipitates as
pyridine hydro-chloride thereby making the reaction irreversible. The reaction is first order in
methanol (B). The concentration of triphenyl methyl chloride (A) was measured as a
function of time and is shown below.

t (min) 0 50 100 150 200 250 300

CA (mol/dm³) 0.05 0.038 0.0306 0.0256 0.0222 0.0195 0.0174

Use the integral method to confirm that the reaction is second order with regard to triphenyl
methyl chloride.
INTEGRAL MODEL/METHOD
Trityl (A) + Methanol (B)  Products

t (min) 0 50 100 150 200 250 300

CA (mol/dm³) 0.05 0.038 0.0306 0.0256 0.0222 0.0195 0.0174
1/CA (dm³/mol) 20 26.3 32.7 39.1 45 51.3 57.5
Formulating Process Simulation
Synthesis of the toluene hydrodealkylation process

Principal reaction:
C7H8 + H2 → C6H6 + CH4 (1)
toluene benzene

Side reaction:
2 C6H6 → C12H10 + H2 (2)
biphenyl

mp = 69.2 oC

Source: Integrated process design instruction, D.R. Lewin, W.D. Seider, J.D. Seader, Comp. Chem. Eng. 26 (2002) 295-306
Formulating Process Simulation
Laboratory Data

1. Irreversible reaction w/o catalyst between 1200-1270 oF

2. ~ 75 mole % toluene converted to benzene
3. ~ 2 mole % benzene from reaction (1) converted to biphenyl
4. Plant capacity – conversion of 274.2 lbmol hr-1 of toluene or ~ 200 MMlb yr-1 assuming 330 days
of operation per year

Figure 1. Reactants, products and reactions details

Source: Integrated process design instruction, D.R. Lewin, W.D. Seider, J.D. Seader, Comp. Chem. Eng. 26 (2002) 295-306
Formulating Process Simulation
Additional Information

 Exothermic reaction releases large amount of heat  Toluene recycle stream can be calculated
 Possible carbon deposition given 75 mole % conversion and complete
 hydrogen gas at the outlet is returned to the inlet recovery
 Expensive separation of methane from hydrogen  Heat of reaction at 1268 oF and 494 psia can
 Methane exists together with hydrogen be calculated using Aspen PLUS  5.84 x
 Unreacted toluene should not be wasted 106 Btu hr-1 using RSTOIC subroutine
 Remaining toluene is returned to the inlet

Source: Integrated process design instruction, D.R. Lewin, W.D. Seider, J.D. Seader, Comp. Chem. Eng. 26 (2002) 295-306
PLEASE CONTACT ME!

AQSHA
Assistant (Research) Professor
Dept. of Bioenergy Engineering & Chemurgy
Dept. of Chemical Engineering
Institut Teknologi Bandung, Indonesia

Waste to Energy Conversion | Biofuel & Bio-based Product Development |

CCS/BECCS | Biomimicry Catalyst | Thermochemical Conversion

Cell/WA: +62 813 888 70350 | aqsha@itb.ac.id | aqsha.edu@gmail.com

Program Studi Teknik Kimia, Teknik Pangan, Teknik Bioenergi dan Kemurgi, Institut Teknologi Bandung 40