- UCAS course code
- H801
- UCAS institution code
- M20
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Course unit details:
Chemical Engineering Optimisation
| Unit code | CHEN20051 |
|---|---|
| Credit rating | 10 |
| Unit level | Level 2 |
| Teaching period(s) | Semester 1 |
| Offered by | Department of Chemical Engineering & Analytical Science |
| Available as a free choice unit? | No |
Overview
Chapter 1: Introduction to Chemical Engineering Optimisation
- Scope and hierarchy of engineering optimisation
- Types of mathematical models in chemical engineering
- Types of optimisation (programming) problems
Chapter 2: Construction of Mathematical Models
- Formulation of general optimisation problems
- Process models and constraints
Chapter 3: Fundamentals of Optimisation Theory
- Degrees of freedom
- Unimodality vs. Multimodality
- Review of matrix algebra
Chapter 4: Convexity and Optimality
- Convex functions and regions
- Necessary & sufficient conditions for convexity
- Necessary & sufficient conditions for an optimal solution
Chapter 5: Nonlinear Programming
- Lagrange function for constrained optimisation
- Necessary & sufficient conditions for constrained optimisation problems
Chapter 6: Nonlinear Programming Algorithms
- General algorithms to solve an unconstrained optimisation problem
- General algorithms to solve a constrained optimisation problem
Chapter 7: Linear Programming and Mixed-integer Programming
- Introduction to linear programming
- Graphical solution for two variable problems
- Introduction to mixed-integer programming
Aims
The unit aims to:
This course introduces the main concepts of engineering optimisation theories (e.g. convexity, optimality) and general optimisation algorithms that are predominantly used in the chemical and biochemical industry. Its main aim is to equip the students with the essential mathematical skills for analysing, optimising, and designing (bio)chemical processes.
The course also provides a range of case studies during the class and coursework sessions to enable students to practise optimisation techniques and apply them to real chemical engineering problems. The students will learn about fundamental optimisation theories, how to formulate optimisation problems (both linear and nonlinear), select appropriate mathematical algorithms, implement curve fitting and data analysis, and calculate a high-quality numerical solution.
Learning outcomes
Students will be able to:
- Demonstrate fundamental knowledge of optimisation theory.
- Build mathematical models for engineering optimisation problems.
- Choose appropriate optimisation algorithms to calculate a high-quality optimal solution.
- Extend knowledge to the concept of complexity and optimality.
- Apply classic methods to solve unconstrained and constrained optimisation problems.
- Describe the general procedure to solve linear programming, nonlinear programming, and mixed-integer programming problems.
Teaching and learning methods
- Lectures
- Tutorials
- Coursework sessions
- Drop-in sessions
Assessment methods
| Assessment task | Weighting |
|---|---|
| Continuous assessment | 30% |
| Exam style assessment | 70% |
Recommended reading
- T. Edgar, D. Himmelblau, and L. Lasdon. Optimization of Chemical Processes (2nd edition). McGrawHill.
- R. Smith. Chemical Process Design and Integration, Wiley.
- S. Boyd & L. Vandenberghe. Convex Optimization, Cambridge University Press.
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 16 |
| Seminars | 8 |
| Tutorials | 8 |
| Independent study hours | |
|---|---|
| Independent study | 68 |
Teaching staff
| Staff member | Role |
|---|---|
| Dongda Zhang | Unit coordinator |
Additional notes
This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.