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DRPS : Course Catalogue : School of Engineering : Postgrad (School of Engineering)

Postgraduate Course: Numerical Methods for Chemical Engineers (MSc) (PGEE11169)

Course Outline
SchoolSchool of Engineering CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe problems a chemical engineering faces today range from molecular simulations to computational fluid dynamics and reaction and process modelling. These areas span a wide range of time and length scales and sophisticated numerical methods are required to reach a solution. This course introduces computational and mathematical methods for the solution of multi-scale chemical engineering problems.
Course description Week - Lecture topic (Tutorial/Assignment)

1 Linear equation systems
Linear equation system solvers

2 Nondimensionalisation
Nonlinear algebraic equations

3 Nonlinear systems solvers (Nonlinear systems)
Parameter estimation

4 ODE introduction
Explicit ODE solvers

5 Stiff systems and DAEs (ODE)
Implicit methods 1

6 Optimisation introduction
Linear programming

7 Nonlinear optimisation 1 (Optimisation)
Nonlinear optimisation 2

8 Model vs Data: fitting
Uncertainty in data and models

9 Implicit methods 2
PDE introduction

10 Method of lines with FDM 1 (PDE)
Method of lines with FDM 2
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 20, Feedback/Feedforward Hours 3, Formative Assessment Hours 5, Revision Session Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 146 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam: 0%
Coursework: 100%
Practical Exam: 0%
Feedback During the Q+A sessions after the lectures and during the workshops; feedback on the submitted coursework using details feedback and marking proforma.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the principles of scientific computing with Python, including stability and computational effort;
  2. Understand the most important numerical methods and their working principles, scope and limitations;
  3. Choose, apply and implement the best numerical method to solve multi-scale chemical engineering problems;
  4. Check and interpret the solutions and use them to design and improve chemical engineering processes.
Reading List
Core reading list:

- Steven C. Chapra, Numerical methods for engineers, McGraw-Hill Education

- Svein Linge, Hans Petter Langtangen, Programming for Computations - Python, A Gentle Introduction to Numerical Simulations with Python 3.6, Springer International Publishing
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNumerical methods,computational methods,applied mathematics,chemical engineering
Course organiserDr Daniel Friedrich
Tel: (0131 6)50 5662
Course secretaryMrs Shona Barnet
Tel: (0131 6)51 7715
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